Every Friday, Brooks sets a 15-minute timer and processes whatever paper is sitting in his inbox. He scans, shreds, files, or routes it. When the timer ends, he stops. Even if the inbox isn’t spotless.

He stops because protecting the repeatable habit matters more than finishing every last page. Wait until paperwork becomes a dreaded chore and the pile grows. Then you skip it again.

I’d rather do a B-level weekly review every week than an A-plus review once a month. Consistent review keeps priorities aligned, tasks current, and decisions timely. Repeatability beats perfection for staying organized.

Does this sound familiar to you? You wait for a free Saturday to overhaul the clothes, the downloads folder, the meeting notes, and the photos. The weekend fills. Monday you’re searching for the same PDF.

Here’s my core thesis. Organized people are not necessarily born organized. They build enough small habits and systems to cross a tipping point. Like a clean house, the hard part is the initial reset. Once everything has a place, staying organized can take just 10-15 minutes a day.

Organization is a learnable skill. You build it from routines, systems, planning, review, and maintenance.

Start with a home for the stuff you always lose

Keys, earbuds, meeting notes, PDFs, photos, clothes, project files. Organized people give all of it a home.

The value is reducing search time and decision fatigue, because you always know where something belongs. You stop reconstructing where last week’s notes went every time you need them.

Choose five high-friction items or file types and assign each a permanent home this week. Physical and digital. The ones you search for. The ones you never put away because they don’t have a spot.

Treat your work area like it deserves respect

Designing an organized environment is mostly about removing friction. Keep frequently used tools within reach. Label what would otherwise require searching. Clear your workspace before the next session. Then treat that work area as a place that deserves respect.

Brooks notes that most people benefit from clarity in their environment, even if a few famous creative figures worked in messy spaces.

Take 10 minutes for a workspace friction audit. What do you reach for every day? What do you search for? What do you avoid? Start with the friction you actually feel.

Put buffer on the calendar

Buffer time is an organization tool. I recommend leaving 10-30 minutes between meetings when possible, and reserving the last 15-30 minutes of the day to catch up, process inboxes, and reset systems so tomorrow starts cleaner.

Brooks adds that people often plan for best-case scenarios, but margin keeps normal surprises from derailing the day. Without that gap, you walk into the next meeting still carrying the last one.

Add one buffer block to tomorrow’s calendar and protect it.

Look at the day before it starts

I see planning as a core skill of organized people. Check your calendar and task list the night before or first thing in the morning so you know what is coming and can prepare before the scramble begins. A prepared person enters meetings already knowing the agenda, context, and next action.

Do you walk in already knowing those three things, or are you figuring them out in the first five minutes of the call?

Brooks described planning as his gateway habit into becoming more organized after years of operating more spontaneously. If you’ve been living in reaction mode, start here.

Use a simple daily planning ritual: calendar review, task review, meeting prep, and top priorities.

Leave things Neutral for your future self

Clear to Neutral is the habit of resetting a space or system after you use it so your future self can start fresh. I compare it to putting your toothbrush back in the bathroom after brushing. Closing browser tabs at the end of a work session. A restaurant kitchen getting cleaned before the next day’s service.

If the kitchen isn’t reset, the next service starts behind. Your desk, your email, and your meeting notes will do the same thing to you.

Pick one recurring activity and define what “neutral” looks like when you are done. Desk work, email, cooking, meeting prep. Choose one. Reset to that every time you finish.

Subtract what no longer serves you

I recommend regularly deleting, pruning, and removing what no longer serves you. Unused apps, clothes you don’t wear, books you won’t reference, gear that no longer fits your life. Organization is maintained by subtraction as much as by better systems. Your space stays easier to navigate when there’s less in it.

Brooks shared that a household garage cleanout led to a commitment that anything new must earn its place. For a cluttered area, use a “one in, one out” rule.

This week, remove 10 items or digital files.

Keep the weekly review small enough to repeat

The weekly review is a built-in reset that keeps disorganization from creeping in. Ask what worked, what did not, where energy dipped, and what needs to change. Then make one small improvement.

That’s the “one tweak a week” philosophy at Asian Efficiency. Small adjustments become transformative over a year. Schedule a 20-minute review this week. Treat it as a minimum viable habit. If you’re a perfectionist, consistency is the goal.

Create one short recurring processing block for paper, email, downloads, receipts, or notes. Set a timer. Stop when it ends, the way Brooks does on Fridays.

Don’t try to overhaul your life. Choose one small organizing habit and run it for the next 7 days.

Just one. Protect it. That’s how you cross the tipping point.

I can block the time. I can know exactly what to do. And the work still will not happen if I don’t have the energy for it.

I frame productivity as three currencies: time, energy, and attention. You need all of them. If you’ve got time blocked and you know exactly what to do, but you lack the energy to do it, the work still will not happen. Energy belongs in the productivity system.

Which currency is actually blocking you this week? Most people would probably say time. I used to say time too. Now I check energy first.

Protect your peak, not someone else’s morning

Brooks has a simple rule. Match your most important work to your personal peak energy window, not someone else’s morning-routine advice.

“Do your important thing first” only works if first thing is when your brain is actually online.

Brooks is a strong morning person. Other people do their best work late at night. If first thing is not when your brain is online, you are doing the important work in the wrong window.

When is your 2-hour peak? Protect it for deep work.

Start with the thing that will energize you

Before I start my task list, I ask, “What is one thing on this list that will energize me today?”

Then I do that early. It creates momentum for the harder or less exciting work that follows.

Brooks added the flip side. Avoid energy-draining tasks right before important meetings. Does that sound familiar? You burn through a pile of admin, then you walk into the meeting already spent.

Tonight, label one item on tomorrow’s list as the energy winner. Do that one early.

After about 90 minutes, move for 60 seconds

I sit at a computer all day. When I skip movement for too long, I notice lower energy.

After about 90 minutes of work, I do 60 seconds of simple exercise. Squats. Hip openers. A physical reboot for people who sit at a computer all day.

I’ve been using this to rebuild my ability to hold a deep squat. One minute. Then I go back.

After your next deep-work block, do 60 seconds of movement before you do anything else.

Batch the draining work

Email checks, meeting prep, note review, other low-energy admin. Sprinkle those through the day and they leak fatigue across the whole schedule.

Put them in one contained block so they can’t follow you around.

Brooks mentioned some listeners tag tasks by energy level. That can be useful. It still has to flex around real-world meetings and priorities.

Create one draining-admin block on your calendar this week.

Plan the break before you start

If you wait for a natural pause, the pause may never come.

Plan your first break before you start working. A pre-planned break becomes an energy reset instead of an afterthought.

Brooks uses a consistent 10 a.m. break with a snack as his orange chime. I often reset after 60 to 90 minutes of focused work. Decide the break in advance. Then take it.

Schedule one 10-minute break tomorrow before you open email.

Lunch will take the afternoon if you let it. I noticed that lunches above roughly 800 calories regularly made me sleepy. A lighter meal with protein kept me focused for the afternoon. Brooks and I have both had pizza-heavy lunches that wiped out the rest of the day. Predictable, once you look.

Run a one-week lunch experiment. Track meal heaviness and afternoon focus.

Make water impossible to miss

I’ve kept water by my bed for more than a decade.

Put it beside your bed, near your computer, or in a marked bottle. Then use an empty or low bottle as the trigger to stand up, move, and refill.

Brooks emphasizes availability and reminders. I’ve used visual, gamified bottles because I respond to seeing the thing in front of me.

One environmental cue. Put the bottle where you can’t miss it.

Squash the mosquitoes (or eat the frog)

You’ve heard eat the frog. Do the hardest task first.

Sometimes I do. Especially when a task keeps rolling over. That rolled-over task becomes tomorrow’s frog.

Other days I squash the mosquitoes. Knock out a few tiny tasks, build momentum, then tackle the big task before the mosquitoes take over the day.

I use both depending on the day. Frog first when a task keeps rolling over. Mosquitoes first when I need activation energy.

Close the day on purpose

A work shutdown ritual preserves tomorrow’s energy.

Spend 5 to 10 minutes closing tabs, brain-dumping loose ends, moving unfinished tasks to tomorrow, and clearing your workspace. Brooks connects this to “clear to neutral.” I care about the closure, the transition from work to home life. The next workday should start at neutral, not in yesterday’s mess.

Tonight, run this:

1. Close the tabs you don’t need.
2. Brain-dump the loose ends.
3. Move unfinished tasks to tomorrow.
4. Clear your workspace.
5. Stop and transition out of work mode.

Five to ten minutes. Do that tonight. Tomorrow starts at neutral, and you get to spend the energy you just protected.

I was testing multiple AI meeting assistants at once. Other people on the call had their own bots joining too. At one point there were more AI notetakers than humans on the call.

Funny. And a little ridiculous. Everyone wanted the meeting captured. Nobody seemed to notice how distracting it was, or how much resistance that many visible bots can create.

That’s where a lot of this is headed. People keep adding another assistant by default. I think you should pick your meeting tools on purpose instead.

The graveyard in your inbox

Brooks described something I recognized immediately. Transcripts and summaries pile up in your inbox. They’re technically searchable. You almost never go back and do anything with them.

He uses meeting recordings mostly to find information later. Searchability is useful. It’s also a reactive, level-one use case.

My inbox fills with summaries I often skim or ignore. Does this sound familiar to you?

Capture without a next step is just a nicer filing cabinet. The whole point of having an assistant in the meeting is to move work forward after the call ends.

Ask first. Every time.

Brooks has a simple etiquette rule for AI meeting assistants: ask first.

A line like this is enough:

“Are you okay if I use an AI notetaker for this call?”

Copy that. Paste it into the invite, or say it out loud at the start.

Brooks doesn’t mind being recorded. He still appreciates being asked. I noticed that people may behave differently when an assistant is present. That’s even more true in sensitive one-on-ones, medical or legal conversations, coaching, or anything confidential.

You can have the best workflow in the world. If you skip consent, you’ve already created friction.

Bot, local recorder, or the meeting platform itself

There’s a real tradeoff here.

Visible meeting bots join the call. Everyone can see them. That’s part of why my “more bots than humans” call felt so weird. Invisible local recording tools capture computer audio instead, so no bot shows up. That can reduce meeting friction.

Brooks noted that local transcription can be better for privacy. It may also have weaker speaker identification. You still have to think about legal and ethical consent either way.

I looked at tools that don’t join meetings at all. Built-in meeting-platform recording is the third option.

Before you default to another bot, decide:

1. Bot-based assistant

Use this when you want the assistant visible, with speaker labels and the usual meeting-bot workflow. Ask first. Don’t stack five of them on the same call.

2. Local recorder

Use this when you want less friction and more privacy. Accept that speaker identification may be weaker. Consent still applies.

3. Built-in meeting platform

Use this when the call is already being recorded by Zoom or whatever you’re in, and you don’t need a third party sitting in the participant list.

Pick one on purpose. Don’t collect assistants like souvenirs.

Put the output where work already happens

A strong AI meeting assistant should update the systems where work actually happens.

I used to treat these tools as summary machines. Recap lands in email. Maybe I skim it. Then the action items die inside the transcript.

Now I look for assistants that can trigger automations through built-in integrations or an automation platform. The output should land in a task manager, a CRM, a project management app, or team chat. Those are the places people already execute.

Here’s the checklist I want you to run after every important call:

Transcript → summary → actions → system update → human review

Don’t stop at the summary. Extract who promised what by when. Push those tasks into the project management system. Then a human looks at it before it becomes “official.”

Brooks connected this to a pattern you’ve probably lived through at work. The same issue gets discussed again two months later because nothing left the last meeting in an actionable format.

The leak isn’t bad discussion. The leak is discussion that never becomes accountable action. Decisions, owners, deadlines, automatic task capture. That’s the meeting system.

Stop relying on memory after client calls

CRM updates are a high-ROI use case, especially if you talk to clients or sell for a living.

Sales and client-facing people often avoid admin work even when it helps them. Brooks said CRM hygiene has been a long-running pain point in software and sales teams. No surprise there.

An assistant can detect new contact details, meeting notes, buying timelines, and follow-up commitments. Then it updates the CRM or creates a reminder automatically.

I’ve used this in a few simple ways: update a contact’s location, add call notes, create a 30-day follow-up task. None of that should depend on you remembering it in the parking lot after the call.

If you lead a team, this is the one to install first. Stop relying on memory after client calls.

Build a content bank from conversations you’re already having

There’s a second use I care about as a creator.

I use transcripts to automatically capture testimonials, customer pain points, research insights, and personal stories. They go into a searchable content bank. Later, that bank supports newsletters, podcast outlines, and posts.

Someone tells an interesting story on a call. Or a customer casually describes a pain point. You don’t need a separate note-taking session. The assistant catches it, you store it, and you can find it when you’re writing.

If you already have a lot of conversations, you already have material. You’re just letting it evaporate.

What to do today

Don’t add another bot to your next call.

Pick one meeting this week. Ask for consent with the script above. After the call, run the checklist all the way through: transcript, summary, actions, system update, human review. Put at least one commitment into your task manager or CRM before you close the laptop.

That’s the jump from passive capture to a workflow you’ll actually use.

There’s a major annual conference in Austin. I host people, attend events, and socialize heavily. I have a great time. Then I end up completely exhausted. Every year. I notice the same drain after the same kind of week, and it is not a mystery anymore.

Hosting takes energy. Events take energy. Heavy socializing takes energy. I have a great time, and I still finish the week with an empty tank. Does this sound familiar to you? A stretch you enjoy, then a crash?

Enjoyable weeks can still drain you. Recurring drains need boundaries and recovery plans. If you can see the week coming, plan the recovery before you are in it.

Your productivity system is a supercar, but energy is the gas in the tank. You can have the perfect calendar, workflows, apps, and focus blocks. If your energy tank is empty, none of it goes anywhere. Time-blocking alone is not enough.

Time management fails when it ignores energy. A perfectly blocked calendar does not help if your body and mind do not have the capacity to do the work. Energy is the fuel that makes productivity systems actually move. Get decent at managing time and this is the next hurdle. It sits in AE’s TEA framework: Time, Energy, Attention.

So before you change tools, calendars, or routines, find where the vitality is leaking.

The Energy Audit is a simple coaching tool for that. This is not a formal scientific protocol. I’m no medical doctor. It’s a practical AE coaching framework. Most people would probably say they need more discipline. Start by naming the drains that make good work harder than it needs to be.

The four drains

“I’m tired” is too vague to act on. Energy drains usually fall into four categories.

Physical drains

Poor sleep. Sitting all day. These show up in your body, and they still get skipped because the calendar looks full. You cannot discipline your way around a body that has been sitting and under-slept.

Mental drains

Nonstop context switching. You blocked the time. Your attention never stayed with the work. The calendar said focus. The day said otherwise.

Emotional drains

Difficult projects. Difficult relationships. Hours can look fine while the cost shows up later. That’s the drain people miss because it doesn’t look like being tired.

Environmental drains

Clutter. Noisy workspaces. The room around you is either helping or draining you. If the space is noisy or cluttered, you are paying for it in energy whether you notice or not.

Stress tolerance works like a battery. When your battery is low, small frustrations feel bigger, focus drops, and burnout risk rises. When your wellness baseline is higher, you can handle intense work periods without paying as high a health cost.

Sleep and regular physical activity are the foundation of that battery. Productivity infrastructure.

Skipping sleep to get more done is like draining your battery to 10% and then wondering why you cannot focus. You may create more hours on paper. You reduce the capacity needed to use those hours well.

Are you protecting sleep, or cutting it to squeeze in more hours?

Put your best hours on the real work

Protect your peak energy hours for your most important work, not low-value meetings. If you are sharpest around mid-morning, reserve that window for deep work. Push admin, batching, or lighter tasks into lower-energy periods.

Have you looked at tomorrow’s calendar against your energy, or only against the clock?

Peak hours and low-energy hours are different resources. Energy-based scheduling is more useful than treating every hour as the same. Open tomorrow’s calendar and move one low-value commitment out of your peak energy window.

After a focus block, use the Clear to Neutral ritual. Close your tabs. Reset your desk. Step away from the screen. Hydrate, move, or simply look out a window.

You want real recovery. Finish a focus block and jump to social media, and you switched from work information to social media information. Your brain did not reset. You fragmented what attention you had left.

Do this between deep work sessions this week. A small habit. Actual recovery.

Run this for 24 hours

Don’t overhaul your life. Run a 24-hour energy audit.

Every two hours, rate your energy from 1 to 10. Note what you were doing, who you were with, and what environment you were in. Keep the log simple. The number. The activity. The people. The room.

At the end of the day, look for patterns. What pushed you toward a 10? What dropped you toward a 1? Look for what repeats. Then choose one drain to remove, or one recovery ritual to add. One. Not a new calendar and a new set of apps in the same weekend.

If you have a recurring seasonal or social energy drain, name it before it happens again. Put a boundary on it. Put recovery on the calendar. Protect sleep during that week like it is part of the work.

I still go to Austin. I still host, attend events, and socialize. I have a great time. I also treat the yearly crash as a pattern that needs a recovery plan.

Set a reminder for two hours from now. When it fires, rate your energy from 1 to 10 and write down what you were doing, who you were with, and where you were. Keep going until this time tomorrow. Then pick one drain to remove or one recovery ritual to add.

One change. Run the audit first.

Masterclass.com/TPS for at least 15% off any annual membership.

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See what ElevenAgents can do for your specific workflows. Start with a demo at elevenlabs.io/tps.

Listen to this episode (MP3)

Cheat Sheet

• Top 3 Productivity Resources [2:33]
• Why teaching someone your workflow exposes what you’ve been tolerating [7:19]
• The three-times rule and the “where am I waiting?” test [11:02]
• Ask AI to comb your past sessions and hand you the fixes [20:20]
• The one book idea that changes how you attack any bottleneck [26:56]
• How half a day of “donkey work” shrank to 15 minutes [32:07]
• The counterintuitive handoff fix: more meetings, not fewer [45:31]
• Why the debt-snowball approach beats a fancy priority matrix [49:36]
• The one friction point to kill this week [55:18]

Links

• MasterClass
• Upwork
• Eleven Agents (ElevenLabs)
• 25X Productivity Coaching
• Codex Micro (Work Louder × OpenAI)
• Stream Deck (Elgato)
• Productivity Academy
• Apple Refurbished Store
• Blueair Blue Pure 511
• Granola
• Wispr Flow
• The Goal by Eliyahu Goldratt
• Atomic Habits by James Clear
• MindNode
• David Sparks (MacSparky)

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I host people. I attend events. I socialize heavily. I have a great time. Then I end up completely exhausted.

Does this sound familiar to you? A stretch that looks like a win from the outside, and a body that feels emptied out when it’s over.

Enjoyable things can still drain you. If the drain is recurring, it needs boundaries and a recovery plan. Leave those out, and the same week will keep taking the same bite.

Here’s the thing. You can have the perfect calendar. The workflows. The apps. The focus blocks. Your productivity system can look like a supercar. Energy is the gas in the tank. If that tank is empty, none of it goes anywhere.

Time-blocking alone will not get you down the road.

At Asian Efficiency, this sits inside TEA: Time, Energy, and Attention. You probably optimized Time first. You blocked the hours, cleaned up the workflows, protected the focus blocks. Then you sat down to do the work and the capacity was not there. Time management fails when it ignores energy. A perfectly blocked calendar does not help if your body and mind do not have the capacity to do the work. Energy is the fuel that makes productivity systems actually move.

The usual reflex is more discipline. Work later. Skip sleep. Skipping sleep to get more done is like draining your battery to 10% and then wondering why you cannot focus. You may create more hours on paper. You reduce the capacity needed to use those hours well.

Sleep and movement are productivity basics. They are not optional wellness extras.

Your battery is doing more work than your to-do list

Stress tolerance works like a battery.

When your battery is low, small frustrations feel bigger. Focus drops. Burnout risk rises. When your wellness baseline is higher, you can handle intense work periods without paying as high a health cost.

Protect sleep. Get regular physical activity. That baseline is what lets you take a hard stretch of work without paying as high a health cost.

Before you change tools, calendars, or routines, find where your vitality is leaking.

The Energy Audit is a simple coaching tool for that. I’ll be straight with you. This is not a formal scientific protocol. It’s a practical AE coaching framework. You stop assuming you need more discipline, and you identify the physical, mental, emotional, and environmental drains that make good work harder than it needs to be.

Energy drains usually fall into four categories.

Physical drains

Poor sleep. Sitting all day. Your body never gets a reset, so even easy work feels steep.

Mental drains

Nonstop context switching. You hop from task to task and call it progress. Your brain never lands.

Emotional drains

Difficult projects. Difficult relationships. The hours can look full and still leave you wiped.

Environmental drains

Clutter. A noisy workspace. The room itself keeps pulling at you.

“I feel tired” does not tell you what to change. A category does.

Give it 24 hours

This is a low-friction experiment. You are not overhauling your life.

For the next 24 hours, every two hours, rate your energy from 1 to 10. Note what you were doing, who you were with, and what environment you were in.

Set a repeating reminder. When it fires, write the score, the activity, the people, the place.

At the end of the day, look for patterns. What pushed you toward a 10? What dropped you toward a 1?

Then choose one drain to remove, or one recovery ritual to add.

One. If you try to fix sleep, the clutter, the meeting load, and your evenings in the same weekend, you will drop the whole thing.

Do you already know your seasonal drain? I have that Austin week every year. Fun can stay. The recovery plan has to exist too. If a social stretch or a yearly event always flattens you, name it now and put boundaries in place before it happens again.

After a focus block, Clear to Neutral

Want one recovery ritual you can add this week?

After a focus block, run Clear to Neutral. Close your tabs. Reset your desk. Step away from the screen. Hydrate. Move. Or simply look out a window.

Create real recovery.

If you bounce from a focus block into social media, you switched from work information to social media information. Your brain is still chewing. That is information fragmentation, and it does not refill the tank.

A clear desk. Water. A little movement. A window. Then start the next block.

Stop treating every hour as equal

If you are sharpest around mid-morning, reserve that window for deep work. Push admin, batching, or lighter tasks into lower-energy periods.

Protect your peak energy hours for your most important work, not low-value meetings.

You already feel the difference in your body. The calendar still pretends your peak hours and your low-energy hours are the same raw material.

Look at tomorrow’s calendar tonight. Move one low-value commitment out of your peak energy window.

Energy-based scheduling is more useful than treating every hour as equal.

Set the 24-hour audit. Every two hours: a number from 1 to 10, what you were doing, who you were with, the environment you were in. When the day is over, pick one drain to remove or one recovery ritual to add.

Just one. Start there.

I was testing multiple AI meeting assistants at once. Other people on the same call had their own assistants joining too. There were more AI notetakers than humans.

It was funny. It was also a snapshot of where work is headed. Everyone wants the meeting captured. The visible bots can be distracting, and they can create resistance from attendees. Choose the tool on purpose. Don’t add another bot by default.

Does this sound familiar to you?

Brooks described the common meeting notes graveyard. Transcripts and summaries pile up in your inbox. They’re technically searchable. You rarely review them or act on them. Brooks uses meeting recordings mostly to find information later. Search is useful. My own inbox fills with summaries I often skim or ignore.

Searchability is a reactive, level-one use case. You captured the meeting so you can go looking if you need something. I stopped there for a while too.

The real productivity leak is the failure to convert discussion into accountable action. You can have a decent meeting. Someone promises a follow-up. Then two months later you’re on another call discussing the same issue, because nothing left the room as a task with an owner and a deadline. Brooks connected this to a common corporate pattern: people discuss problems repeatedly because nothing leaves the meeting in an actionable format.

AI meeting assistants can extract who promised what by when and push those tasks into a project management system. Decisions. Owners. Deadlines. Automatic task capture. That’s the meeting system I want you to build.

Ask first

Brooks has a simple etiquette rule.

Ask first.

A simple “Are you okay if I use an AI notetaker for this call?” can reduce discomfort, especially in a sensitive one-on-one, medical, legal, coaching, or confidential conversation.

Brooks does not mind being recorded. He still appreciates being asked. I’ve noticed that people may behave differently when an assistant is present. A bot that shows up unannounced is a rough way to start a call.

Copy that sentence. Ask it before the assistant joins. If they say no, leave it out.

Bot, local recorder, or built-in platform

There’s a tradeoff between invisible local recording tools and visible meeting bots.

Visible bots join the call. That meeting with more notetakers than humans is what you get when everyone adds a bot by default.

Tools that record computer audio without joining can reduce meeting friction because no bot enters the call. Brooks noted that local transcription can be better for privacy. The limitation is weaker speaker identification. You may not get clean labels for who said what. You still need legal and ethical consent. Quiet recording is still recording.

The third option is the built-in meeting platform. If the tool you already meet in can capture the conversation, you may not need another assistant at all.

So which one do you actually need for this meeting?

Bot-based assistant

Use this when you want speaker identification and automations, and everyone on the call is okay with a bot joining.

Local recorder

Use this when you want less friction and more privacy, and you can live with weaker speaker labels.

Built-in meeting platform

Use this when capture is already sitting inside the tool you meet in, and another assistant would just add noise.

Get the work out of the transcript

A strong AI meeting assistant should update the systems where work actually happens. Instead of leaving action items inside a transcript, connect the assistant to a task manager, a CRM, a project management app, or team chat so the output lands where people already execute.

I used to treat these as summary tools. Hit record, get a recap, maybe skim it. Now I look for assistants that can trigger automations through built-in integrations or an automation platform. The summary is a stop along the way. The work has to arrive in the system you already open.

After every important meeting, run this checklist:

1. Transcript

You have a record of what was said.

2. Summary

Skim this so you don’t have to replay the whole call.

3. Actions

Who promised what, and by when.

4. System update

Push those actions into the task manager, CRM, project tool, or team chat.

5. Human review

Read what the assistant created. Confirm the owners, the dates, and the wording. Then let the tasks live in the tool, not in the notes.

Two setups worth doing first

CRM after client calls

Sales and client-facing professionals often avoid admin work even when it helps them. Brooks said CRM hygiene has been a long-running pain point in software and sales teams. How many follow-ups are sitting in your head instead of in the CRM?

An assistant can detect new contact details, meeting notes, buying timelines, and follow-up commitments, then update the CRM or create a reminder automatically. I’ve used this to update a contact’s location, add call notes, and create a 30-day follow-up task.

Stop relying on memory after client calls. Let the assistant catch the details. You review them. The CRM stays current.

A content bank from conversations you already have

AI meeting assistants can become a content and customer-insight engine. I use transcripts to automatically capture testimonials, customer pain points, research insights, and personal stories into a searchable content bank. Later, that bank supports newsletters, podcast outlines, and posts.

Someone mentions an interesting story or a testimonial casually, in the middle of a meeting. You don’t scramble for a notebook. The assistant captured it. You store it. If you create content or talk to customers all week, you’re sitting on this material already.

Your next meeting

Pick one recurring meeting this week. Before it starts, ask: “Are you okay if I use an AI notetaker for this call?” After it ends, run the checklist. Get the owners and deadlines out of the notes and into the tool you actually work from.

One meeting. One pipeline. Start there.

I’ve been doing a weekly review for more than a decade. That’s a long time to keep a habit alive, especially one that’s easy to skip the moment the week gets messy.

The reason it survived is that I don’t rely on memory. I use a checklist. I won’t bore you with a complicated system. I answer the prompts, clear the inboxes, extract the lessons, and move on. A template makes the review a repeatable process. It also cuts decision fatigue, which is what makes it possible to restart after a missed week or two. You just open the checklist and begin.

Brooks has a strong take on where AI belongs in this. The future should not be AI silently generating a report while you stay passive. The best version keeps you actively reflecting, while AI helps you notice patterns, connect dots, and summarize next steps.

That’s how I use it.

Let AI ask the questions

Here’s the prompt I use:

“Act like my productivity coach. Ask me weekly review questions one at a time. After we finish, summarize everything in a bulleted list.”

I answer verbally while looking at my calendar. Then I use the summary to identify next actions and plan the week. The AI interviews me, pulls the pieces together, and helps me prioritize. I still have to think.

Copy that prompt into ChatGPT or your preferred AI assistant. Sit with your calendar and answer. Then take the bulleted summary and turn it into next actions.

Have you been waiting for a free 60- or 90-minute block before you’ll do a review again? Brooks recommends setting a hard stop before you begin. Even 10 focused minutes is better than skipping it entirely. Constraints create focus. They also make the habit easier to restart, which is the whole point if you’ve fallen off.

Try a 10-minute weekly review this Friday. Set the hard stop first.

Go past “what did I do?”

A recap is comfortable. It also doesn’t change next week.

A strong weekly review asks better retrospective questions, then uses last week’s lessons to shape the next one. I treat this like an agile retrospective. Look back on purpose. Then adjust.

What went well?

Name it. If something worked, you want a chance to repeat it.

What could I improve next week?

One improvement. Make it specific enough to try.

What did I learn?

Capture the lesson while it’s still in reach.

What recurring frustration or annoyance showed up again?

This is the question that catches the stuff you keep stepping over.

Recurring annoyances are productivity signals. If the same friction shows up two or three weekly reviews in a row (poor sleep, a messy inbox, delayed admin work), it is no longer a one-off. It is a system problem asking for a small fix.

Identify one recurring annoyance from the last month. Design one tweak to reduce it.

Give the 20% a home first

Weekly planning works better when you identify the 20% of tasks likely to create 80% of the impact, then protect those first. Brooks emphasized that the 80/20 filter is about intentionality. You still handle the rest of the work. You just give the high-impact tasks calendar priority before the small stuff fills the week.

Choose your top 20% tasks before opening email on Monday.

The Rule of Three keeps that realistic. Choose up to three major outcomes for the week, put them on the calendar first, and treat everything else as secondary. Some weeks may only have one or two big rocks, and that is still a win. Brooks and I both clarified the same point: protect the most important outcomes. Plenty of other tasks still get done.

What are your three big rocks this week? Calendar those first.

Batch the maintenance

Does your week get eaten by little digital tasks that never quite finish? Batching small tasks prevents that clutter from taking over.

Instead of reacting to email, texts, admin, and inbox cleanup one at a time, schedule buffer blocks where similar maintenance tasks are processed together. You get speed and focus from doing them in one sitting.

Brooks shared a story about delayed expense claims. They became a bigger problem because they were never batched.

Add one 30-minute buffer block to next week’s calendar. Pick one category and only do that.

Build a maybe list

You know those tasks you keep dragging from week to week? They create guilt, and they compete with real priorities even when they have no shot at getting done.

The maybe list is a pressure-release valve. Move those tasks to a someday/maybe list so they stay captured and stop clogging the week. Brooks used an optional course as an example: something you keep meaning to take, keep postponing, and keep carrying forward. I frame the list as a safety blanket. The idea stays. The week stays clean.

During this week’s review, move any task you’ve postponed three times to a Maybe list.

Do this Friday

Set a 10-minute hard stop. If you’ve fallen off, this is the restart: don’t wait for the perfect hour. Paste the prompt into your AI assistant. Answer the four questions while you look at your calendar. When you’re done, put next week’s 20% on the calendar before Monday’s email fills the week for you.

Ten minutes. A checklist. You doing the thinking.

I once sat on a call where there were more AI notetakers than humans. I was testing a few assistants at the same time, and other people on the call had brought their own. Bots everywhere. Funny, in a slightly uncomfortable way. Everyone wanted the meeting captured. Nobody seemed to have asked whether another bot in the room was actually useful.

That’s the snapshot I keep coming back to. We got really good at recording meetings. We did not get better at doing anything with what was said.

Brooks has a name for what happens next: the meeting notes graveyard. Transcripts and summaries land in your inbox. They’re technically searchable. You almost never go back and act on them. He mostly uses recordings to find something later, which is a perfectly reasonable habit. I get the same pile. My inbox fills with summaries I skim or ignore.

Searchability is useful. It is also a reactive, level-one use of these tools. Capture without a workflow is just a nicer archive.

Ask before the bot joins

Here’s the thing. People behave differently when an assistant is sitting in the room. I’ve noticed that. A visible bot can make a one-on-one feel less like a conversation and more like a deposition, especially if the call is sensitive.

Brooks’s etiquette rule is simple: ask first. He doesn’t mind being recorded. He still appreciates being asked. That’s the bar.

Copy this and use it at the start of the next call:

“Are you okay if I use an AI notetaker for this call?”

Do that before you drop a bot into a medical, legal, coaching, or confidential conversation. Do it in regular one-on-ones too. Consent is not a feature setting. It’s a sentence you say out loud.

Pick the tool on purpose

There’s a real tradeoff here, and I wish more people treated it like a decision instead of a default.

Visible meeting bots. They join the call. Everyone can see them. Speaker identification is often stronger. They can also distract people, and they can create resistance. I’ve seen that firsthand when the participant list starts looking like a robot convention.

Local recorders. Some tools record computer audio and never join the meeting as a bot. Less friction on the call. Brooks’s take is that local transcription can be better for privacy. The catch is weaker speaker identification, and you still need legal and ethical consent. Invisible does not mean you get to skip the ask.

Built-in meeting platform tools. If the platform already records and transcribes, adding a third-party bot on top may be redundant. And distracting.

Before your next meeting, pick one. Don’t stack them because everyone else did.

The leak is the action, not the discussion

Brooks pointed out a corporate pattern you’ve probably lived through. A team talks about the same problem again two months later because nothing left the first meeting in an accountable format.

The productivity leak is the failure to convert discussion into who promised what by when.

A strong AI meeting assistant should update the systems where work already happens. Don’t leave action items trapped inside a transcript. Connect the assistant to a task manager, a CRM, a project management app, or team chat so the output lands where people execute.

Simple summary tools stop at a recap. The useful ones can trigger automations through built-in integrations or an automation platform.

Run this sequence after every meeting that matters:

1. Transcript. The raw record exists.
2. Summary. You get the recap, but you don’t stop there.
3. Actions. Extract the commitments: owner, task, deadline.
4. System update. Push those into the tool people already use.
5. Human review. Someone still checks the output before it becomes “official.”

That last step matters. Assistants draft. You confirm.

If you want an AE-style meeting system, keep it boring on purpose: decisions, owners, deadlines, and automatic task capture. The same issue should not need a reunion tour.

Stop relying on memory after client calls

CRM updates are a high-ROI use case, and I mean that in the unglamorous way. Sales and client-facing people avoid admin work even when it helps them. Brooks has watched CRM hygiene stay a pain point in software and sales teams for a long time. Memory is a terrible CRM.

An assistant can detect new contact details, meeting notes, buying timelines, and follow-up commitments, then update the CRM or create a reminder automatically.

What that looks like in practice: update a contact’s location, add call notes, create a 30-day follow-up task. Nobody has to remember to do the admin at 6pm after a full day of calls.

If you lead a team that talks to customers, this is the first workflow I’d wire up. Capture is cheap. Follow-through is where deals and relationships actually live.

Turn conversations into a content bank

There’s a second use I care about as a creator. Transcripts can feed a content and customer-insight engine.

I use them to capture testimonials, customer pain points, research insights, and personal stories into a searchable content bank. Later, that bank supports newsletters, podcast outlines, and posts. A story or testimonial that comes up casually in a meeting gets stored without me sitting there taking notes like a court reporter.

You’re already having the conversations. The missed step is filing the good stuff somewhere you can find it on purpose.

Build a simple bank. Tag what you save as testimonial, pain point, insight, or story. When you sit down to write, search the bank first. Don’t start from a blank page when last week’s calls already handed you the material.

Do this on your next call

Don’t add another bot by default. Don’t keep collecting summaries you’ll ignore.

On the next meeting that actually matters, do one thing: ask for consent, then send the output into a real system. Task manager, CRM, project tool, or your content bank. Pick the destination before the call starts, and review what the assistant captured before you trust it.

The graveyard is already full. Stop adding to it.

I host people. I attend events. I socialize heavily. I have a great time.

Then I end up completely exhausted.

The lesson I keep relearning is simple. Recurring drains need boundaries and a recovery plan. I can enjoy the week and still empty the tank. If I treat that crash as a surprise, I spend the next stretch trying to force good work out of a body that has nothing left.

Does this sound familiar to you?

You finish a busy stretch, maybe even a fun one, and then you sit down to do the work that actually matters. The calendar is full. The system looks solid. You still cannot think.

Your productivity system is a supercar. Energy is the gas in the tank. You can have the perfect calendar, workflows, apps, and focus blocks. If the tank is empty, none of it goes anywhere.

Time-blocking alone is not enough. Time management fails when it ignores energy. A perfectly blocked calendar does not help if your body and mind do not have the capacity to do the work. Energy is the fuel that makes productivity systems actually move.

At Asian Efficiency we talk about this as TEA: Time, Energy, Attention. After time management, energy is the next hurdle. Most people keep tuning the calendar. The leak is somewhere else.

Stop assuming you need more discipline

The Energy Audit is a simple coaching tool for finding where your vitality is leaking. Instead of assuming you need more discipline, you identify the physical, mental, emotional, and environmental drains that make good work harder than it needs to be.

This is not a formal scientific protocol. I’m no scientist. It’s a practical coaching framework. Use it before you change tools, calendars, or routines. If you skip this step, you will keep buying systems for a tank that is already empty.

“I’m tired” does not tell you what to fix. Energy drains usually fall into four categories.

Physical drains

Poor sleep. Sitting all day. The body stuff you try to outwork, then wonder why focus will not show up.

Mental drains

Nonstop context switching. One tab, then another, then a message, then back. Your brain never stays with one thing long enough to do it well.

Emotional drains

Difficult projects. Difficult relationships. The work that costs you even when the hours look light on paper.

Environmental drains

Clutter. A noisy workspace. You are trying to concentrate in a room that keeps tapping you on the shoulder.

Look at those four and get specific. Which one is leaking the most this week?

Your battery decides how loud small problems get

Stress tolerance works like a battery. When your battery is low, small frustrations feel bigger, focus drops, and burnout risk rises. When your wellness baseline is higher, you can handle intense work periods without paying as high a health cost.

Sleep and regular physical activity sit under that baseline. They are productivity basics, not optional extras you get to after the real work is done.

Skipping sleep to get more done is like draining your battery to 10% and then wondering why you cannot focus. It may create more hours on paper. It reduces the capacity needed to use those hours well.

That Austin conference is my annual reminder. Hosting, events, heavy socializing, great time, then nothing left. I do not need a new task manager for that. I need a boundary and a recovery plan before it happens again.

What is your version of that week? A seasonal stretch. A social stretch you already know is coming. Put the boundary in now, while you still have a full tank.

Do not spend peak hours on low-value meetings

Are you giving your sharpest hours to the least important work on the calendar?

If you are sharpest around mid-morning, reserve that window for deep work. Push admin, batching, or lighter tasks into lower-energy periods. Protect your peak energy hours for your most important work.

Treating every hour as equal is convenient. Energy-based scheduling is more useful. Some hours can hold deep work. Some hours can hold email-shaped tasks and not much else.

Open tomorrow’s calendar. Find one low-value commitment sitting in your peak energy window. Move it.

Clear to Neutral after a focus block

After a focus block, use the Clear to Neutral ritual.

Close your tabs. Reset your desk. Step away from the screen. Hydrate. Move. Or simply look out a window.

You want real recovery. If you leave a work tab and open social media, you just switched from work information to more information. That is fragmentation. The input never stops, so the battery never comes back.

Use this between deep work sessions. Keep it small. A weekly habit beats a fantasy overhaul of your entire day.

Run a 24-hour Energy Audit

This is the move. Keep it low-friction.

For 24 hours, every two hours, rate your energy from 1 to 10. Note what you were doing, who you were with, and what environment you were in.

At the end of the day, look for patterns. What pushed you toward a 10? What dropped you toward a 1?

Then choose one drain to remove, or one recovery ritual to add.

One leak. One ritual. You do not need a new app for this. You need a day of honest numbers.

If you only optimize your calendar, you still have a supercar with no gas. Run the audit first.

Pick a 24-hour window this week. Every two hours, rate 1 to 10 and write down what you were doing, who you were with, and where you were. Then remove one drain or add one recovery ritual. Just one.

They’d already been through a stretch of tightening how the business ran. They’d been through the hard part. Now we were automating a few back-office things that were eating their time.

I asked how things were going.

They said the only issues left were honestly team related. A lot of those remaining glitches were either a process that could be automated, or a personality issue.

Then they said something that’s stuck with me since: the more they automated, the easier it was to see where the personality issues actually were. That had been so helpful.

That’s not what most people expect from automation.

The Two Things That Look the Same

When you run a business with a team, you’re constantly dealing with friction. Someone drops a ball. The inbox is a disaster. Follow-ups don’t happen. Clients slip through cracks.

The instinct is to look at the person responsible and assume it’s a performance issue.

Sometimes it is. But a lot of the time, what looks like a people problem is actually a process problem with a human face on it.

Before you automate anything, these two things are almost impossible to separate. The chaos looks the same from the outside. Someone didn’t do the thing. Was it because they’re bad at their job, or because there was no clear system for how the thing was supposed to happen?

You can’t always tell.

What the Automation Filter Does

Here’s what I’ve noticed after working through this with a bunch of business owners: automation acts as a diagnostic tool.

When you formalize a process and hand it to an AI agent or an automated system, one of two things happens.

Either the friction disappears. The thing gets done correctly, on time, every time. That tells you the problem was never about the person. The system was broken, and they were just doing their best to navigate it.

Or the friction stays. Maybe the automation works fine, but there’s still a bottleneck. Someone isn’t providing the input the system needs. Someone is overriding the process. The problem is still there even after the process is clean.

That remainder is your actual people problem.

Now you know.

What This Changes About Hiring Decisions

For this business owner, this completely reframed how they were thinking about their team.

They mentioned that some parts of the work never created chaos. Those processes just worked. When I asked what was different, they said those people simply didn’t do the things that caused the problem in the first place.

Where friction remained? The automation made it clear: it wasn’t the process.

They told me that once they knew what could be automated, it would have a huge impact on how they thought about roles and hiring. Some work might not need a person at all. Some roles might need to change.

That’s a big decision. And now they’re making it with real information instead of a gut feeling mixed with frustration.

Before You Have the Hard Conversation

If you’re managing a team and you have someone who keeps frustrating you, here’s what I’d suggest before you do anything else:

Map the process they’re responsible for. Every step, every handoff, every trigger.

Ask: is this actually written down anywhere, or is it in someone’s head?

Ask: if I automated this process completely, would the problem disappear?

If the answer is yes, you don’t have a people problem. You have a design problem.

If the answer is no… now you have real clarity.

A lot of hard team conversations happen too early, before the process has been cleaned up. The person gets blamed for operating inside a broken system. That’s not fair to them, and it doesn’t fix anything.

Automate first. Then see what’s left.

Whatever’s left is the real problem.

If you’re a business owner trying to figure out where to start with automation, check out the 4-Day AI Sprint. It’s designed to help you identify and automate the processes eating most of your time.

Every year, September 15 is her most stressful day. It’s the IRS extension deadline — the date that comes after the April 15 main deadline for clients who filed for extensions. By mid-September, everything that can go wrong has already been going wrong, and the final push to get everything filed correctly is exhausting.

Before we worked together, her routine on deadline days was simple: wake up at 4:30 AM.

Not because she’s a morning person. Because by 4:30, the inbox was already full enough that sleeping later meant spending the rest of the day in catch-up mode. Client emails. Her team copying her on every little thing. Questions that could have been handled by someone else. Urgent things buried under dozens of things that weren’t urgent at all.

Getting up at 4:30 was how she stayed ahead of it.

Six Weeks of Automation Work

When we started working together, the email inbox was one of the first things we addressed. The volume wasn’t the real problem — it was the indiscriminate nature of it. Everything landed in the same place: client requests, team status updates, vendor newsletters, webinar invitations, copies of emails she technically didn’t need to see.

Here’s what we built:

Auto-unsubscribe agent. Any newsletter or promo that came in got added to an unsubscribe queue. The agent handled it. She stopped seeing them.

Routing rules. Client emails from known contacts went to one folder. Team communication went to another. Internal threads got their own space so she could clear them in batches rather than as they arrived.

One daily recap from the team. Her team had a habit of copying her on everything. We changed that: at the end of each day, they sent one recap email with a bulleted list of anything that needed her attention. Everything else, they handled themselves.

Lindy running in the background. The AI agent handled sorting, tagging, and summarizing. It worked overnight. It worked on weekends. It didn’t care that it was deadline season.

The Result

Six weeks after we deployed this system, she checked in around the September 15 deadline.

She mentioned she’d woken up at 6:30 that morning.

I knew what that number meant.

She’d gone from 4:30 to 6:30.

On a deadline. And she didn’t feel a sense of panic.

That’s a two-hour shift. On the single most stressful day of her year.

The weekly report showed a few hours saved in week one, then well into double digits by week two.

And this likely understates the real number. The savings report values each agent step at a fraction of a minute. But some steps — like summarizing a long document or pulling out action items from a thread — would take a human an hour or two. The math doesn’t fully capture the depth of what’s being automated, only the breadth.

Why This Matters More Than Percentages

Most people talk about AI automation ROI in terms of efficiency percentages or time-to-complete metrics. Those numbers are real but they don’t communicate much.

Two extra hours of sleep on the worst morning of the year communicates everything.

The email inbox was a source of constant low-grade stress for her. Not because anything catastrophic was in there on any given day — but because the volume meant she could never be sure. Getting up at 4:30 was a coping strategy for anxiety about what might be lurking.

When the inbox gets handled by an agent that runs overnight, that anxiety goes away. Not because the emails stop coming. Because the signal-to-noise ratio improves enough that you can trust the system.

She now knows that if something urgent landed overnight, the agent has surfaced it. She doesn’t have to dig. She checks at 6:30, sees what matters, and gets to work.

The Practical Lesson

If your morning routine involves starting early to get ahead of your inbox, the inbox is probably your biggest automation target.

The goal isn’t to eliminate email — it’s to eliminate the indiscriminate nature of it. Once every email is routed to the right place and the noise is stripped out, you can engage with what’s left much more efficiently.

Start with the sources of volume. What’s creating the most clutter? Newsletters, vendor updates, CC chains from your team, client emails that don’t need immediate response? Each category has a different solution, but most of them can be addressed without touching the emails you actually care about.

The second lever is your team’s behavior. If your team copies you on everything, that’s not a technology problem — it’s a communication policy problem. A simple change (one recap email daily, CC only when authorization is needed) can cut inbox volume dramatically without any automation at all.

Once you’ve dealt with the obvious sources of noise, add the agent layer on top of what’s left.

The combination is what gets you from 4:30 to 6:30.

If inbox overwhelm is a consistent part of your day, this is one of the first workflows we address in the 4-Day AI Sprint. The results compound quickly once the basic routing is in place.

She told me she’d met her co-founder at one of my events the previous year. They’d connected at a networking dinner I hosted, kept in touch, and eventually decided to build something together. At the time I spoke with her, they were already helping hundreds of people.

I had nothing to do with that outcome. I just put a bunch of interesting people in the same room.

But here’s what struck me: that connection almost didn’t happen. And for most people running organizations, companies, or communities, those connections are sitting dormant in their contact lists right now — uncrossable, unmatchable — because they’ve been sorted into the wrong bucket.

The Two-List Problem

Most people — and most organizations — maintain two separate networks.

There’s the personal list: friends, people from past jobs, people you’ve met at events, neighbors, the person you grabbed coffee with three years ago.

And then there’s the business list: clients, prospects, investors, partners, collaborators. The “professional” contacts.

These two lists usually live in different places. Different apps, different spreadsheets, different mental models. And they rarely talk to each other.

The problem with this setup: some of your best business connections are sitting in the personal list. And they’ll stay there, invisible to your business thinking, because you’ve drawn a line that doesn’t actually reflect how relationships work.

Why the Lines Are Artificial

I was working through the CRM strategy for a members community I’m helping build. The instinct on the team was to keep personal contacts separate from the community contact database.

I pushed back pretty hard.

A cofounder’s social graph is the community graph. To me, they’re not different.

Here’s why that matters: a well-connected cofounder knows people everywhere. Entrepreneurs, investors, creatives, executives — people scattered across contexts and industries. Some of them would be perfect members. Some would be perfect LPs. Some would be perfect introductions to people we haven’t met yet.

But only if we can see them. And the moment you put people in a separate “personal” database that doesn’t connect to the “business” one, you lose the ability to make those matches.

The categories are still useful. Someone who’s a close friend, someone who’s a client, someone who’s a casual acquaintance — those are different relationship types and worth noting. But they should all be in the same searchable, connectable database.

The Human Router Problem

Part of what I do — and what I love doing — is matchmaking. I get excited when I can connect the right two people at the right moment.

But I can only do that if I can see the whole graph.

If someone mentions they’re looking for a placement agent for a fundraise under $10 million, I need to be able to scan my entire network — personal and professional — to think about who’s the right fit. Not just the people in my “investor CRM” or my “business contacts” folder.

The people who are best at this kind of matchmaking share one thing: they don’t artificially separate their networks. They have one view of everyone they know, organized by categories and relationship types, but always searchable together.

What Centralized Contact Data Actually Enables

When you merge your networks into one place, a few things become possible that weren’t before:

Pattern matching across contexts. You might notice that two people from completely different parts of your life share a specific interest or problem — and that they should meet. That observation requires seeing both at once.

Warm introductions on demand. If someone needs to be introduced to a specific type of person, you can scan your whole graph rather than just one slice of it. The probability of having the right connection goes up dramatically.

Proactive outreach timing. When you hear about something relevant to a contact — a funding round, a job change, a project they’d care about — you need to find that person quickly. A fragmented system means you might remember they exist but can’t find them, or worse, don’t think of them at all.

Continuity across relationship stages. Someone who was a personal contact three years ago might be a business partner today. If they’re in a separate database, you’ve lost the history of how the relationship developed.

The Practical Step

If you’ve got a personal address book, a LinkedIn connections export, a business CRM, and a conference contacts spreadsheet — those should all be one database.

Not one app, necessarily. That’s a tools problem, and there are many ways to solve it. The point is that your contact data should be unified enough that you can search across all of it and identify the right person regardless of which context they came from.

The minimum viable version: one spreadsheet or Airtable with every meaningful contact, tagged with how you know them, what categories they fall into, and any relevant notes. From there, you can search across the whole graph.

The value of a large network isn’t the raw size. It’s the ability to see the whole thing at once and make the right connection at the right moment.

Most people have the contacts. They just can’t see them all at the same time.

If you’re thinking about how AI can help manage and activate your network, that’s one of the core things we work through in the 4-Day AI Sprint. The “proactive matching agent” pattern is particularly useful here.

“We can’t automate that. There’s no API.“

It comes up with LinkedIn outreach. MLS listings. Certain healthcare portals and proprietary CRMs. Platforms that are valuable for workflow automation purposes but that don’t expose their data through an API.

The assumption is reasonable: if a platform doesn’t offer programmatic access, you can’t build an agent that interacts with it. So people either do the task manually, hire someone to do it manually, or accept the bottleneck.

Here’s what most people don’t know yet: that assumption stopped being true a few months ago.

The Technology That Changed the Picture

What you can now do is spin up a virtual machine — think of it as a mini computer living in the cloud — and point an AI agent at it with instructions.

The agent opens a browser. Navigates to a website. Logs in with credentials you provide. Clicks around, fills in forms, extracts data, and passes the results to whatever comes next in your workflow.

It operates exactly as a human would. Because from the website’s perspective, it is a human: a browser session, moving through pages, interacting with elements.

This isn’t a brand-new concept — browser automation and “headless browsers” have existed for years. What’s new is how accessible this has become for non-technical users through tools like Lindy, and how well it now integrates with larger AI workflows. The pace of change has been striking — capabilities that felt out of reach not long ago are now a practical option.

The MLS Problem

I worked with a real estate team whose pipeline depends on evaluating properties quickly and getting offers out.

Their whole pipeline runs on speed. The faster they can send an offer after a qualified lead comes in, the more deals they close. Simple as that.

But there was a bottleneck. To prepare an accurate offer, someone has to pull comparable sales from MLS — recent sold properties in the same area, same rough condition. Depending on experience, that research can take a while. At volume, that manual step was strangling their capacity.

They’d already looked into automating it. The answer they kept getting back: MLS doesn’t have an API. No API, no automation.

The virtual machine approach sidesteps that. You give the agent login credentials for the platform. You describe the search process you’d run manually — what to look for, what data to extract, how to structure it. The agent logs in, runs the search, pulls the numbers, and passes them into the rest of the offer-prep workflow.

When people see it work, the usual reaction is some version of: the API limitation just stopped mattering.

That reaction is pretty common. The thing that had been a hard wall turns out to be nothing more than an assumption that nobody questioned.

The LinkedIn Problem

The same pattern shows up in outbound. I’ve seen teams whose business development depends on reaching out to prospects on LinkedIn.

LinkedIn, as most people know, aggressively limits API access. You can’t use their official API to automate outreach. Developers have tried to build workarounds and gotten their accounts banned.

Same solution. A virtual machine logs into LinkedIn the way a person would. From a list of target leads, the agent finds each profile, drafts a personalized outreach message based on public research, and queues it for review.

It ran in the background. The team focused on responding to the conversations that came back.

What This Means in Practice

The broader principle here is worth sitting with: if a human can log into something and do it, an agent can do it too.

That sounds obvious when you say it out loud. But it changes the list of “automatable” tasks significantly.

A lot of the processes people have mentally written off — because the platforms don’t expose APIs, or because the interaction is too nuanced for a simple integration — are actually fair game now.

Some things to check on your “can’t automate” list:

• Industry-specific platforms with no API (legal, healthcare, real estate databases)
• Platforms where the automation you need is technically possible but policy-restricted (LinkedIn outreach, review sites)
• Portals that require login and navigation rather than a direct data feed
• Legacy systems with no modern integration layer

In each case, the question isn’t “does this have an API?” anymore. It’s “can a person log in and do this with a browser?”

If yes, a computer-use agent probably can too.

A Note on Reliability and Risk

Computer-use agents are not as clean as API integrations. They can break when a website updates its layout. They require monitoring. And there are legitimate questions about terms of service on some platforms — it’s worth checking whether automated access is permitted before deploying.

But for internal use cases, industry-specific databases, and platforms where no one has restricted automation per se (they just never built an API), this is a practical and increasingly stable option.

The “no API” wall has a door in it. Most people just haven’t found it yet.

If you’re working on AI automation for your business and have hit the “no API” wall, that’s worth a conversation. This is one of several patterns I work through with clients in the 4-Day AI Sprint program.

They build slide decks. They screenshot dashboards. They create product walkthroughs with voiceovers and screen recordings. They talk about capabilities, use cases, potential ROI.

And most of that explaining… does very little.

Here’s the approach that actually works: just use the tools. Let people experience them in the normal course of working with you. Don’t announce it. Don’t frame it. Just let it happen.

Then, at some point, mention it.

That moment of noticing — the “wait, that was AI?” moment — is worth more than any prepared demo.

The Scheduling Story

A while back I was on a discovery call with someone who was curious about using AI. We’d been corresponding by email, and we set up a short call.

What they didn’t know: I hadn’t scheduled that meeting myself.

My AI scheduling agent — built on Lindy — handled the whole thing. Every email reply came within 30 seconds. It worked through the weekend. Coordinated the back-and-forth on times and details.

Midway through our call, I mentioned it.

They paused. Then said the responses had felt natural. Not robotic. Just… helpful and responsive.

They had interacted with the agent for days — responding to emails, confirming times, asking follow-up questions — without realizing it wasn’t a person.

That moment opened more conversation than anything I could have prepared in advance. They had a lived experience to reference. It wasn’t hypothetical. It was already done.

The Note-Taking Story

A few months before that, I was on a planning call with a client. We were working through the details of an upcoming project.

About 20 minutes into the conversation, they reached for a notepad and started to write something down.

Then they stopped. Laughed.

They asked if I already had something taking notes.

Yep. The whole conversation had been transcribed and organized since the first minute of the call.

They hadn’t needed to write anything down. But they hadn’t known that — until the moment they went to reach for the pen.

That laugh, that small moment of recognition, communicated something that would have taken me five minutes to explain.

Why “Demo by Doing” Works

There’s a reason these moments land harder than prepared demos.

When someone watches a demo, they’re evaluating something from the outside. They’re asking: does this seem real? Could this work for me? Is this too good to be true?

When someone experiences AI in the course of normal work — and then realizes it was AI — they’ve already answered all those questions for themselves. The evidence came from their own lived experience, not from a salesperson.

The scheduling agent became the pitch for the scheduling agent. The note-taker became the pitch for the note-taker. No separate demonstration needed.

There’s also something about the low-key delivery that helps. When you use your tools without announcing them, you’re signaling that this isn’t special or unusual for you. It’s just how you work. That normalcy is reassuring — and hard to fake in a formal demo.

How to Apply This

If you use AI tools professionally, here’s the shift: stop leading with the tools. Start leading with the work.

Use your scheduling agent to book client meetings. Use your transcription tool on every call. Use AI to draft follow-ups, run research, pull together summaries. Let the outputs speak for themselves.

Then, when the moment is right, mention what’s behind the work. Not as a pitch. Just as context.

“That recap you got after our call — that was mostly AI. Took about three minutes. I can show you how to set that up if it’d be useful.”

The conversation that follows is almost always more open than any cold demo would produce. They’ve already seen the quality. They’ve already felt the experience. Now they’re just learning the mechanics.

The Underlying Principle

Show, don’t tell is one of those pieces of advice that gets applied everywhere except the places where it matters most.

Most AI consultants explain capabilities. The ones who get hired demonstrate them — not in staged environments, but in the actual work.

The best demo you can give someone is the one where they don’t realize they’re being demoed until it’s already over.

At that point, you don’t need a pitch. They’ve already made up their mind.

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Cheat Sheet

• Top 3 Productivity Resources [1:42]
• Why the “productivity valley” is so fun to get stuck in [4:43]
• The “would you also like to…” trap that eats your afternoon [6:58]
• 3⃣ The rule that stops you building systems you don’t need [8:32]
• Four ways optimizing turns into procrastination [11:13]
• The search-first notes setup that skips the Obsidian rabbit hole [14:29]
• Why Thanh ditched markdown files for HTML pages [16:14]
• The automation everyone’s sleeping on right now [21:41]
• How to tell you’re consuming instead of doing [28:23]
• The $600 gadget that fixed his “knowledge debt” [31:01]
• The consume-versus-do ratio worth aiming for [37:05]
• Turning saved bookmarks into a weekly experiment [40:49]
• Want to build your own digest? Start here [46:30]

Links

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• 25X Productivity Coaching
• Rework
• Basecamp
• Bird by Bird
• Marked
• Setapp
• Claude
• ChatGPT
• OmniFocus
• Obsidian
• Drafts
• Evernote
• Bending Spoons
• Airtable
• Atomic Habits
• MacSparky
• Keyboard Maestro
• Codex
• Nano Banana (Gemini Image)
• Laura Roeder on X

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It had some rough edges. The interface was a bit clunky. A few things didn’t work perfectly.

I looked around the room and saw the face. You know the face. The one that says, “eh, I’ll wait until this is actually ready.”

I’ve been in enough of these rooms to know what that face costs people.

The Most Expensive Mistake in AI Adoption

Here’s the rule I’ve landed on after watching hundreds of people try to get into AI tools:

If something feels rough right now, that’s the worst version it will ever be.

These tools are improving so fast that a product that feels half-baked today is often genuinely impressive in 60 days. Not “a little better.” Sometimes completely different.

The problem is that most people use current roughness as a signal to wait. But the window to get ahead of a tool is exactly when it’s imperfect. That’s when the early adopters are learning it, when the competition hasn’t touched it yet, when the advantage is still available.

By the time it’s polished and covered on TechCrunch, everybody’s on it.

The Imperfect Demo Moment

A few weeks later I was presenting at a conference. I showed them how AI can respond to customer texts, answer phone calls, and manage appointment bookings.

It wasn’t magic. Some of the demos had rough moments. And I realized partway through that I’d been living in a bubble. I was so used to working with these tools daily that I forgot what they look like to someone seeing them for the first time.

The room was skeptical. But the people willing to sit with imperfect were the ones who leaned in.

That’s what separates early adopters from everyone else. Not technical skill. Tolerance for messy.

The First 30 Days Are Always Messy

The first stretch with any new AI setup is going to be messy.

You’re learning tools from scratch. Building workflows you haven’t built before. Making mistakes. Things aren’t running smoothly.

That’s not a bug. That’s the deal.

Push through it, and you end up with automations that make life significantly easier, the kind of stuff that wouldn’t exist if you waited for perfect. Because you were willing to sit with rough and keep going.

This is what I call the one-tweak-a-week approach to AI adoption. You don’t try to overhaul everything at once. You do one more thing with AI this week than you did last week. And you let that compound.

That’s the pace that actually works. Not the sprint where you try three new tools in a week and burn out. One thing. Learn it. Let it get better. Add the next thing.

Where Most People Are in Their AI Journey

I teach a simple three-level progression for AI:

Level 1: AI Assisted. You’re using AI for individual tasks. Asking ChatGPT to rewrite an email, using Midjourney for images. Occasional use, mostly prompting.

Level 2: AI Workflows. You’re connecting tools together. AI that does multiple steps automatically. Things that run on their own.

Level 3: Building Agents. AI that acts like a teammate. Handles ongoing responsibilities. Knows your context. Proactively does things on your behalf.

Most people are stuck between Level 1 and Level 2. And the reason they’re stuck is usually that they tried something, it was rough, they backed off.

The tools are not the bottleneck. Tolerance for messy is.

Do This This Week

Open the last AI tool you closed because it felt janky. Give it 20 minutes. Not a rebuild. A retry.

Write down one thing it can do today that it couldn’t do last time. Then use it on a real workflow you already run by hand. If it saves you one step, keep it. If it adds steps, drop it.

Put the retry on the calendar so it doesn’t become another tab you forget. One block is enough. And if you’re already wrecked by Friday, don’t stack a second experiment on top.

What to Actually Do

Go back to one tool you tried before and dismissed.

Doesn’t matter if it was three months ago or six months ago. Try it again this week. I’ll bet it’s measurably better.

And if it’s still rough, that’s fine. Spend 20 minutes with it anyway. Let yourself get a little familiar. Check it again in 30 days.

There’s no clean entry point with AI tools. The people who are ahead right now got ahead during the janky phase. They didn’t wait for perfect.

You don’t have to either. Twenty minutes is enough. You don’t need a perfect setup to learn the tool.

If you want a simple first filter so the new tools don’t eat the week, start with Focus Filter.

I pasted it into Gemini and typed one instruction: “Reverse-engineer this photo into a JSON prompt that I could use to recreate this image with a different product.”

Gemini gave me the full breakdown. Composition style. Lighting setup. Lens approximation. Color grading. Mood reference. The framing choices that made the shot feel expensive.

Then I changed one word in the prompt. Swapped “biker jackets” for a different product. Ran it through an image model.

Same aesthetic. Different product.

The room went quiet for a second. Then someone said, “So the best ads in the world are basically a free prompt library.”

Yeah. Exactly.

Why Great Ads Look Great

Most people assume expensive-looking product photography comes down to budget. A great photographer, a good set, professional lighting equipment.

Budget helps. But what actually makes a great shot is a set of composition decisions: the kind of shot, the kind of light, the angle, the negative space, the mood. These decisions are learnable. And more importantly, they’re extractable.

When you look at a Louis Vuitton ad, you’re looking at decades of accumulated visual intelligence. Every element of that photo was a deliberate choice made by someone who spent years studying what works.

AI can read that photo and tell you what those choices were.

Camera Language: What Most People Miss

There’s a concept I teach called camera language. It comes from the idea that when you’re prompting for images, the language of photography (not the language of marketing) is what drives quality.

Generic prompt: “A woman wearing a jacket, professional look, nice lighting.”

Camera language prompt: “85mm portrait lens, soft directional key light from upper-left, slight underexposure for richness, shallow depth of field, editorial fashion framing, cool-muted color grade.”

The second prompt tells the AI what a camera operator would know. Focal length, light source, exposure choice, depth. The output looks completely different: more intentional, more commercial, less like generic AI output.

Most people don’t know camera language. And that’s fine, because you don’t have to learn it yourself. You can ask the AI to extract it from a photo you already love.

Reverse Prompting: Working Backwards

I started teaching this as “reverse prompting.” Instead of trying to describe what you want from scratch, you start with a result you already like and work backwards.

The process:

1. Find an ad or product photo that has the look you want.
2. Paste it into Gemini (or any vision-capable AI).
3. Ask: “Reverse-engineer this image into a detailed prompt I could use to recreate this style with a different subject.”
4. Take the JSON or text prompt it gives you.
5. Change one element (your product, your model, your setting, your season).
6. Run it through an image model.

What you end up with is a style-matched image for your product that would have cost a real photoshoot to produce.

Building a Prompt Library

The real power isn’t the one-off technique. It’s what happens when you start doing this systematically.

Say you’re selling luxury goods and you have five or six visual styles you want to rotate through: editorial fashion, lifestyle, product close-up, campaign, seasonal. Find the best reference ad for each style. Reverse-engineer each one into a prompt. Store them.

Now you have a prompt library. Every time you need a new product image, you pull the style prompt, swap in your product, and generate.

Your visual brand stays consistent. Your production time drops. And every style in your library was originally designed by some of the best creative teams in the world.

What This Looks Like in Practice

The same approach works beyond product ads. Say you need space or interior visuals that match a specific editorial look.

Find a few photography references that have the mood you want. Reverse-engineer each into a camera language prompt. Then adapt each for different spaces or products.

Instead of paying for a full professional photoshoot, you can build a library of photorealistic images in the right style.

That’s what this technique can do for any product or space business.

Do This This Week

Pick one ad you already saved because it looks expensive. Not 20. One.

Paste it into Gemini. Ask for the camera language. Change one noun. Generate 3 versions. Keep the one that still looks like the reference.

Then store the prompt in a note you can reuse. If this turns into a rabbit hole, stop before it becomes busywork. One style is enough for week one.

If you want the rest of the week to stay clean while you test this, keep the plan simple and protect one block for the experiment.

Try It Today

Find an ad or product photo you love. Paste it into Gemini. Ask it to reverse-engineer the image into a reusable prompt.

Take the prompt. Change one element. See what comes out.

It takes about 10 minutes. And the first time you do it, it’s a little bit mind-bending.

If you want a simple first system for keeping experiments like this from taking over the week, start with Focus Filter.

Except the team isn’t there anymore.

They used to have a full production crew. Writers, editors, formatters, someone handling repurposing across platforms. The whole content production line you’d expect at that size.

Now it’s often just them and an AI coding assistant. A cheap monthly subscription.

It Didn’t Happen All at Once

This is the part people miss. They didn’t sit down one day and decide to replace their entire team with AI. There was no grand plan. No company memo.

They started with one task. Repurposing blog posts into social media content. It was tedious, one of those things teams spend hours on every week but nobody loved doing. They tried an AI coding assistant on it. Worked pretty well. Actually more consistent than the human output because it followed the same formatting rules every time.

So they gave it another task. Then another. Each time the tool handled it, that particular role had less to do. After a while, the staffing math shifted. It wasn’t that a cheap monthly tool replaced the team overnight. Workloads just shrank until the old headcount no longer made sense.

I Keep Seeing This Pattern

This isn’t a one-off. I’ve seen operations teams drowning in repetitive work. When you map a typical workflow step by step, a large share of what happens every day doesn’t require a human at all. Copying data between systems. Routing tickets. Formatting reports.

Once the repetitive stuff is handled by AI workflows, those teams can shift from reactive firefighting to actual strategic work. Nobody gets replaced overnight. But the need for a large team just evaporates.

I’ve also seen people skip the usual accounting dashboards for day-to-day questions. They point AI at their own transaction data and ask things like “What was our ad spend last month and how much revenue did it generate?” Instant answer. Try doing that quickly in a typical bookkeeping tool.

The Three Levels

When I teach this in workshops, I break AI adoption into three stages. The first is using AI as an assistant: you ask it questions, it helps you think. The second is building workflows: you connect tools together so things happen automatically. The third is building agents that run on their own.

Most people are stuck at level one. They use ChatGPT to write emails or brainstorm ideas. That’s fine, but the real savings come at levels two and three. That’s where you go from “AI helps me work faster” to “AI does the work while I do something else.”

The operators I mentioned? They’re solidly at level three. Their agents handle the entire content pipeline. They review the output, make tweaks, and move on.

Why Starting Small Actually Works

The biggest mistake I see is people trying to jump straight to full automation. They hear a story like this and think they need to rip out their entire workflow and rebuild it with AI.

That almost never works. The systems are too complex, the stakes feel too high, and one bad output kills their confidence.

What works is starting the way these operators did. Pick one task. The most boring, repetitive thing on your plate. Automate that. Live with it for a week. When you trust it, pick the next one.

It sounds slow. But it compounds faster than you’d expect. One task a month means 12 automations by the end of the year. That’s usually enough to change the shape of your team or your workload entirely.

Do This This Week

Write down the last 10 things you or your team did that felt like copy-paste work. Circle one. Just one.

Then run that workflow by hand once and write the steps in plain English. If you can’t write the steps, you’re not ready to automate it yet.

After you have the steps, try the cheapest tool that can do that one job. Don’t add a second project on top. Live with it for 7 days. If you trust the output, keep it. If you don’t, fix the steps, not the org chart.

Put the review on next week’s plan the same way you’d plan any other real work.

The Real Question

The question isn’t whether AI can do what your team does. For a lot of repetitive knowledge work, it already can. The question is whether you’re willing to start with one task and let the process play out.

A cheap monthly subscription. One tool. One task at a time. Start this week, not after you redesign the whole company.

That’s how a full production team becomes unnecessary.

If you want help turning that one-task habit into a full operating system, look at 25X.

He spent time reading through it. The document described how he makes decisions, how he batches things, when he does his best work. His reaction: it probably knew him better than most people do.

Here’s what happened. He had taken something I show people early on and ran with it. He gathered his own work data and asked the AI one simple question: “Based on all of this, write a detailed profile of who I am, how I work, and how I make decisions.”

The result shook him.

The Mirror Effect

Most people use AI like a search engine. You ask it a question, and it gives you an answer. There’s a clear boundary between you and the tool. You’re the one with all the context.

But when you feed AI your actual data (months of emails, meeting transcripts, calendar patterns), something different happens. It doesn’t just know facts about you. It starts to see patterns. Patterns you might not even see yourself.

The profile nailed things people closest to him probably couldn’t have articulated. Not because the AI is smarter than a human. But because it processed all the data without the filters we put on ourselves. We tell ourselves stories about how we work. The data tells a different story sometimes.

That’s what caught him off guard. Not the speed. Not the writing quality. The accuracy.

Why This Matters for Agents

This kind of profile is the single most important thing you can build if you want AI agents that actually work for you.

Think about it this way. When you hire an executive assistant on their first day, they’re basically useless. Not because they’re incompetent. Because they don’t know you. They don’t know your priorities, your communication style, which meetings matter and which ones are a waste of time. They don’t know that you hate mornings or that you make your worst decisions after 4 PM.

By month six, that same EA is running your life. The only thing that changed is context.

AI agents work the same way. A generic chatbot gives you generic answers. An agent with your context profile gives you answers that fit your actual life.

I have a dozen or so agents running now. My morning briefing agent, my meeting prep agent, my email drafter, my scheduling assistant. All of them read from the same shared context. Same priorities. Same communication preferences. Same list of people I know.

That shared context is what makes the whole system feel like one teammate instead of a pile of separate tools.

The Pilot Analogy

One of my clients put it in a way I keep coming back to. He said most people’s days are like a pilot constantly taking off and landing. That’s the stressful part of flying. You take off for deep work, land for a meeting, take off again for admin, land for a phone call. By 3 PM your brain is fried.

With agents handling the prep work, follow-ups, and admin, you take off once at the beginning of the day and you cruise. You land once at the end. That single shift from constant context switching to sustained focus is where the real value lives.

But it only works if the agents have enough context to handle things without you. Otherwise you’re still the one doing the mental takeoffs and landings. You’re just doing them through a chatbot instead of your inbox.

How to Build Your Profile

The simplest version takes about an hour:

1. Export your data. Grab a few months of sent emails, your calendar for the last quarter, and any meeting transcripts you have.
2. Upload to ChatGPT or Claude. Use a long-context model so it can process everything at once.
3. Ask the right question. “Based on all of this data, write a detailed profile of me. How do I make decisions? When am I most productive? What are my communication patterns? What drains me? What energizes me? What are my blind spots?”
4. Read it and refine. Some things will be off. Fix them. Add things it missed. The first version is about 80% right. You make it better over time.
5. Share it with your agents. This becomes the context file every agent reads before doing anything.

The key is that this document isn’t static. I update mine every few weeks as things change. New projects, shifted priorities, lessons learned. It’s a living document that keeps getting more accurate.

Do This This Week

You don’t need a year of data. Export 30 days of sent email and last month’s calendar. That’s enough to get a first mirror.

When you read the draft, mark 3 lines that feel true and 3 that feel off. Fix the off ones yourself. Then use that profile on one weekly plan, not on every tool you own.

If the profile says you do your best work in a certain window, protect that window. If it says you crash after back-to-back calls, put recovery on the calendar instead of pretending you don’t need it.

It Gets Better Over Time

My Digital Chief of Staff system didn’t happen overnight. It was an accumulation of small improvements over about a year. I’d add one agent, let it run, tune it, then add the next one.

But the context profile was the foundation. Everything else was built on top of it. Without it, every agent is just guessing. With it, they actually know what you care about.

His reaction was the same one I see with almost everyone who tries this. A mix of “this is incredible” and “this is kind of weird.” The weirdness fades. The usefulness doesn’t.

Build the profile. Read it. Fix it. Share it. Then watch everything else start working better.

If you want a lighter starting point, try Focus Filter. Get the noise down first so the profile has room to work.

That works fine for basic stuff. But the real power comes when you connect two agents together so one’s output triggers the other’s input. No human in between.

The Journalist Outreach Example

Here’s a pattern I teach in my workshops. Say you want to reach out to journalists about a product launch.

The old way: you manually research journalists, find their emails, write personalized pitches, and send them one by one. Takes hours. Maybe days.

The two-agent way:

Agent 1 searches for relevant journalists. It looks at their beat, their recent articles, the outlets they write for. Then it populates a Google Sheet with one row per journalist. Name, outlet, email address, recent topics they’ve covered.

Agent 2 monitors that sheet. The moment a new row appears, it wakes up. It reads the journalist’s information, researches their recent work, writes a personalized pitch based on their actual coverage, and sends the email.

You set it up once. Agent 1 feeds Agent 2. The spreadsheet is the handoff point.

The whole thing runs without you touching it after the initial setup. Agent 1 finds the people. Agent 2 sends the messages. The only human decision was choosing what kind of journalist to target.

A Sales Outreach Example

The same pattern shows up in outbound sales. A team is working through prospects from a Google Sheet, then researching each person and writing personalized outreach messages. One at a time.

It can take a long time to work through a list that way.

An agent can do all of it. It takes the prospect data, researches their background, then sends a highly personalized message. Cost: well under a dollar per outbound message.

When a single deal is high-value, the ROI on cheap AI outreach is obvious. You don’t need a spreadsheet for that part.

And prospecting capacity goes from “however many calls we can make in a day” to a much larger volume of personalized touches running in the background while the team does other work.

Why the Spreadsheet Matters

The spreadsheet between the two agents isn’t just a convenience. It’s the architecture that makes the whole thing work.

Think about it like a relay race. Runner 1 doesn’t need to know where Runner 2 is going. They just need to pass the baton cleanly. The spreadsheet is the baton.

Agent 1 writes to it. Agent 2 reads from it. Neither agent needs to know about the other one. They just need to know their piece.

This is important because it means you can swap out either agent without breaking the system. Want to change how you find journalists? Update Agent 1. Want to change the email template? Update Agent 2. The spreadsheet in the middle stays the same.

It also means you can add a third agent. Maybe Agent 3 monitors for replies and categorizes them as “interested,” “not now,” or “unsubscribe.” Now you have a three-agent pipeline that handles outreach from first touch to response handling.

Where This Pattern Works

The two-agent chain works anywhere the process has two distinct phases:

1. Find or collect data (research, scraping, monitoring, intake)
2. Do something with it (send emails, update CRM, create content, file reports)

Here are some real applications:

• Lead generation: Agent 1 scrapes prospects from LinkedIn. Agent 2 sends personalized outreach when new prospects appear.
• Hiring: Agent 1 monitors job boards and filters candidates by criteria. Agent 2 sends screening questions to qualified applicants.
• Customer follow-ups: Agent 1 monitors CRM for deals that haven’t been touched in 7 days. Agent 2 drafts and sends a follow-up email.
• Content monitoring: Agent 1 tracks competitor blogs or social feeds. Agent 2 summarizes new posts and sends a weekly digest.
• Onboarding: Agent 1 collects new employee information from HR forms. Agent 2 generates their onboarding checklist and sends welcome materials.

The common thread: one agent gathers, the other acts. The spreadsheet connects them.

How to Build Your First Chain

Start simple. Pick a two-step process you already do manually.

1. Identify the handoff. Where does phase 1 end and phase 2 begin? That’s where your spreadsheet goes.
2. Build Agent 1. Its only job is to populate the sheet. Test it until the data is clean and consistent.
3. Build Agent 2. Its trigger is “new row in sheet.” Test it with manually entered rows first before connecting it to Agent 1.
4. Connect them. Agent 1 writes, Agent 2 reads. Run the chain end to end with a small batch to verify.

The mistake most people make is trying to build one giant agent that does everything. That’s brittle. If any step fails, the whole thing breaks. Two smaller agents connected by a spreadsheet are more reliable because each one has a simple, focused job.

Start with two. Once that works, add a third.

Do This This Week

Don’t start with journalists unless that’s already your job. Pick a two-step process you already repeat: collect, then act.

Open a blank sheet. Make 5 columns you actually use. Then run the workflow by hand for 5 rows so you know what “clean” looks like.

Only after that should you let an agent write the next 5. Keep AI off the busywork of rebuilding the whole stack. One handoff. One trigger.

If this turns into another project that never ships, put one build block on the week and stop there.

If you want the system for turning chains like this into a real weekly operating rhythm, look at 25X.

That sentence still sounds weird to say. But it’s been my reality for over a year, and I’m starting to see the same shift happening with my clients and workshop attendees.

The Hybrid Org Chart

Right now I have about a dozen AI agents running. A morning briefing agent. A meeting prep agent. An email drafter. A scheduling assistant. A content summarizer. Each one has a specific job, just like a person on a team would.

And the skill that makes me effective at managing these agents is the same skill that makes me effective at managing people: giving clear feedback and iterating.

If an agent isn’t performing well, I don’t throw it away. I adjust the prompt. Give it more context. Set clearer constraints. It’s the exact same feedback loop I’d use with a team member who’s delivering work that’s close but not quite right. “Here’s what I need differently. Here’s more context on why. Try again.”

The feedback loop is the same. The medium is different.

What’s Coming

Here’s what I told a room full of investors in January: the future of work is going to change in a way most people aren’t ready for.

Most of us grew up learning how to manage people. How to hire. How to delegate. How to give feedback in a way that motivates and course-corrects.

Soon you’ll need those same skills, but applied to a mix of humans and digital agents. Your team meeting might include three people and five agents. Your weekly review might cover what your marketing person accomplished AND what your AI content agent produced.

And some of you, probably sooner than you think, will manage a workforce that’s entirely digital. No human employees at all. Just you directing a team of agents.

A Full Team’s Worth of Hours

In a typical week, my agents save the equivalent of several full-time employees putting in a full week of work.

They cost a fraction of one person’s salary. They work 24/7. They don’t call in sick. They don’t need onboarding.

But here’s the thing that matters more than the numbers: what it does to your role.

When your agents handle the repetitive work, you stop being the person who does things. You become the person who reviews things. You stop writing the email. You start approving the email the agent drafted.

That shift from doing to reviewing changes the entire shape of your day. Suddenly you have time for the work that actually requires your judgment. Strategy. Relationships. Decisions that only you can make.

It’s Not About Replacing People

I want to be clear about something, because this conversation gets derailed fast. I’m not talking about firing your team and replacing them with bots.

The best version of this is when your people evolve into higher-value roles. I’ve seen this with teams I’ve worked with. Someone who used to spend most of their day on repetitive questions and routine requests. Once those get automated, they spend their time on the human side of the work: greeting people, building relationships, solving real problems.

Their role didn’t disappear. It got better. The AI took the boring parts. They kept the parts that actually need a human.

Same thing with operators I’ve worked with. They used to spend hours on first drafts of client-facing documents. Now an AI agent drafts them and they review. The time they save goes straight into conversations and the work they’re actually good at.

The New Core Skill

If managing a hybrid team of people and agents is the future, then there’s a new core skill that most people don’t have yet: prompting.

Not prompting in the “write me a poem” sense. Prompting as management. The ability to clearly articulate what you want, provide the right context, set constraints, and iterate on the output until it’s right.

It’s the same thing as being a good delegator. The people who are great at explaining tasks to their team will be great at prompting agents. The people who dump vague instructions on their team and wonder why the output is bad, they’ll have the same problem with AI.

The medium changes. The skill doesn’t.

Do This This Week

Pick one repetitive task you still do yourself. Scheduling. A first draft. A status recap. Write the brief you’d give a new hire, then run that workflow by hand once so you know the steps.

Then try one agent on just that task. Don’t rebuild your company. Use AI without adding busywork. If the output is close, give it the same feedback you’d give a person.

Put the review on your calendar the way you’d put a 1:1. If your week is already packed, keep the plan simple: one task, one agent, one review block.

Where to Start

If you’re managing people now and want to start building toward a hybrid team, here’s the simplest path:

1. Pick one repetitive task that someone on your team does every day. Scheduling. Data entry. Report formatting. Follow-up emails.
2. Build one agent to handle that task. Just one. Get it working reliably.
3. Let the person whose task you automated spend that freed-up time on something higher value. Something that requires judgment, creativity, or relationships.
4. Repeat. One agent at a time. One task at a time. One person evolving at a time.

Within a few months, your org chart starts looking different. Some boxes are people. Some boxes are agents. And you’re managing both.

That’s the future. It’s already here for some of us. The question is just timing.

If you want the flagship system for building that hybrid week, look at 25X.

“There’s a lot of money sitting in my inbox I haven’t followed up on.”

He wasn’t being dramatic. This is a pattern I see all the time. Warm introductions from mutual friends. Proposals that went quiet and never got a follow-up. People who said “let’s circle back in Q2” and Q2 came and went. Conference contacts never emailed after swapping cards.

He knew the money was there. That was the painful part. He just never had time to go back.

The Follow-Up Problem

This comes up in almost every client conversation I have. People spend thousands on marketing, ads, networking events, conferences. They generate leads. And then, nothing.

Not because they’re lazy. Because life happens. New emails push old ones down. Meetings fill the calendar. The follow-up that seemed urgent on Tuesday is invisible by Friday.

I’ve seen this pattern across industries. Real estate agents with 200 contacts they met at open houses and never emailed. Consultants with proposal recipients who went silent. Coaches with people who said “I’m interested” and never heard back.

The leads are already there. Sitting in inboxes, calendars, and CRM databases collecting dust.

What We Built

So we started building AI agents for this. The concept is pretty straightforward.

The agent connects to email and scans for conversations that went cold. It looks for signals: someone expressed interest, a follow-up was promised, there was a proposal sent with no reply, or a warm intro that never got a second message.

For each one, it drafts a re-engagement email in the sender’s voice. Not a generic “just checking in” template. An actual personalized message that references the original conversation.

The agent learns writing patterns from sent email. How they sign off. Their tone. The phrases they use. When the agent starts drafting emails in their voice, they can approve them in seconds instead of writing from scratch.

Early on, these agents typically surface a surprising number of warm leads people had completely forgotten about. People who had already expressed interest in working with them.

Why Follow-Up Beats Lead Gen

There’s a concept I keep coming back to in my workshops: the fastest path to revenue is almost never acquiring new leads. It’s following up with the ones you already have.

Think about it this way. A cold lead requires trust-building. Multiple touchpoints. Proving you’re credible. That takes time and money.

A warm lead already knows you. Already expressed interest. Already had a conversation. The only thing between you and that revenue is a single follow-up email.

And yet most businesses spend 90% of their energy on lead gen and maybe 10% on follow-up. I’ve watched businesses pour money into ads while sitting on inboxes full of people who already want to buy.

The Agent Doesn’t Forget

Here’s what makes AI agents different from a reminder system or a CRM task.

A reminder tells you to follow up. You still have to write the email. You still have to remember the context. You still have to carve out time to do it. And if you’re busy, you snooze the reminder. We all do it.

An AI agent does the work. It reads the original conversation. It knows your voice. It drafts the email. All you do is review and hit send.

I have a similar system for my own meetings. The moment a call ends, my agent reads the transcript and drafts follow-up emails for anyone I mentioned I’d get back to. Within two minutes of hanging up, there’s a perfectly crafted follow-up in my outbox referencing what we discussed and including any resources I mentioned. Prospects are genuinely surprised by how fast I reply.

That speed matters. The faster you follow up, the more business you close. Everyone says they’ll “send that over” after a meeting and then two weeks go by. If you’re the person who follows up within minutes, you stand out.

Do This This Week

You don’t need a full agent to start. Pick the last 90 days of sent mail. Search for “circle back,” “send over,” and “let’s reconnect.”

Make a list of 10 people you already know. Write one follow-up for the warmest one today. Then run that same workflow by hand on the next two. Once the pattern is boring, that’s when an agent is worth building.

If follow-up keeps losing to a packed calendar, protect one block for it. 30 minutes. Same day each week. Treat it like a sales meeting with yourself.

Where to Start

If you think you might have money sitting in your inbox (you probably do), here’s the simple version:

1. Pick one channel. Email is the easiest starting point. But this works with LinkedIn messages, CRM contacts, even text threads.
2. Define “warm.” What counts as a warm lead for your business? Someone who replied positively? Someone who was introduced by a friend? Someone who requested a proposal? Give the agent clear criteria.
3. Let the agent scan. Even a basic search of your last 6-12 months of email will surface forgotten opportunities.
4. Review the drafts. Don’t auto-send. Read each email the agent drafts. Approve, tweak, or skip. You’re still in control.
5. Track what comes back. You’ll be surprised how many people respond with “Oh yeah, I meant to get back to you about that.”

The hardest part isn’t the tech. It’s accepting that you’ve been sitting on revenue for months. Once you see the list, you’ll wonder why you didn’t do this sooner.

If you want a simple first filter for the inbox noise so the follow-ups actually get seen, start with Focus Filter.

“What would have to be true in order for you to 10x your business?”

I first started using it almost by accident. I was wrapping up a session with a client who had gotten buried in day-to-day decisions. We’d covered a lot of ground. I could tell they were getting lost in the weeds.

So I just asked: what would actually have to be true for your business to be 10 times bigger than it is now?

They paused for a long time. Then they said they’d need systems that run without them. And that reframed everything.

Why Tactics Come Second

Most business conversations go straight to tactics. What should I post? Should I run ads? What’s the best email sequence? How do I close more deals?

These aren’t bad questions. But they’re answers to the wrong problem.

If you skip straight to tactics, you end up optimizing for what you’re already doing. You make your current process a little faster, a little cheaper, a little better. And if your current process can’t 10x, you’ve just gotten really good at something that can’t take you where you want to go.

The 10x question forces you to think about your business at a completely different scale before you get tactical.

At 10x, your referral-only pipeline breaks. Your manual onboarding breaks. Your “I handle everything” approach breaks. Most of the systems that work at your current size stop working when you multiply by 10.

And that’s useful information. It tells you what needs to change before you can grow.

How to Actually Use the Question

Here’s how I walk clients through it:

Start big, then drill down.

First, just sit with the question. What would your business look like at 10x? What would marketing look like? Client delivery? Your team? Your day-to-day?

Don’t try to answer it all at once. Let it surface two or three real constraints, the things that would genuinely have to change for 10x to happen.

Use the constraints as a starting point for tactics.

Once you have a real 10x picture, the daily stuff becomes obvious. Here’s an example from my own business: one of the things that would have to be true to 10x is showing up consistently on LinkedIn. That’s a 10x requirement. From there, I get: what do I write, how often, what does the routine look like, what tools do I build to make it easier?

Now those tactics are in service of something. They’re not just busy work.

Revisit the question every quarter.

Things change. Your business changes. What “10x” looks like changes too. I treat this as a quarterly reset, not a one-time exercise.

What Usually Comes Up

When I ask this question with clients, a few things reliably surface.

Process. Almost every owner realizes that their current operations don’t scale. The thing that runs on tribal knowledge, the thing only one person knows how to do, the thing that requires your personal attention, those show up immediately when you try to imagine 10x.

Marketing. Most small businesses underinvest in marketing because they don’t need to at their current size. At 10x, you can’t grow from referrals alone. You need a real engine.

Their own role. This one takes the longest to surface, but it’s often the most important. At 10x, are you doing the same things you’re doing now? Almost certainly not. Figuring out what those things are is one of the most valuable exercises a business owner can do.

Do This This Week

Block 30 minutes. Write the 10x question at the top of a page. Don’t open a tool. Don’t start a deck. Just sit with it.

Write down 3 things that would have to be true. Not 10. Three. Then pick the one that, if it stayed broken, would make the other two irrelevant.

That’s your constraint. Now plan your week around one action that moves that constraint, not around the pile of tactics that showed up in your inbox.

If you need help keeping that action from getting swallowed by a full calendar, protect the block. Treat it like a client meeting.

The Point Isn’t to Actually 10x Tomorrow

Some people hear “10x your business” and immediately get anxious. That’s not the goal here.

The goal is to use 10x as a lens. It’s a forcing function that makes you think clearly about what needs to change, and it surfaces constraints you might not have noticed while you’re busy managing the day-to-day.

Start with the question. Let the tactics follow from there.

If you want help turning those constraints into a week you can actually run, look at 25X. That’s the system I use with clients when the goal is less busywork and more of the work that actually moves the number.

Quick Verdict

If you’re looking for a quick, cheap, and flexible planner, start with the Notion template I share below. If you need a more structured, automated system, consider a paid tool like Todoist or Asana. The key is to pick a tool that fits your workflow, not the other way around.

Quick Summary Box

How I Built the Weekly Planner

Step 1: Define Your Core Tasks

I started by listing the three core tasks I wanted to track each week:

1. Planning – set goals for the week.
2. Execution – track tasks as they happen.
3. Review – reflect on what worked.

I wrote these tasks in a simple Notion page, using a toggle list for each week.

How I Used Notion for a Few Weeks

I tested the planner for four weeks. I used it daily, but I found the review step needed more structure. I added a rating column (1‑5) for each task. This made the review more actionable.

How we evaluated this

This recommendation is based on Thanh’s real experience where the brief explicitly supports it, current pricing and product positioning, and the practical tradeoffs each tool creates once you try to use it in a real workflow. When the evidence is stronger for one option than another, the article says that plainly instead of pretending every tool was used in the exact same way.

What the real decision comes down to

The mistake most people make with AI Workflows vs AI Agents: What Non-Technical Teams Should Build First is treating it like a pure feature comparison. In practice, the better choice usually comes down to workflow shape, switching cost, and how much manual cleanup you can tolerate once the shiny demo is over.

That matters because a tool like ChatGPT can look impressive in a demo and still create drag if it does not match the way your team actually works. The same is true for Lindy. A stronger feature set on paper does not help much if the workflow is harder to maintain, harder to explain to other people, or more expensive once usage scales up.

So the useful question is not which option has the longest checklist. The useful question is which tool removes the most friction for the job you repeat every week. If your bottleneck is fast execution, the answer may be different than if your bottleneck is reasoning quality, system flexibility, or how much babysitting the workflow needs after launch.

Where people usually get this wrong

A lot of buyers over-focus on novelty. They assume the newer or more technical option must be the better long-term bet. But the better long-term bet is usually the tool that your team will actually keep using after the first week. If setup is fragile, handoffs are confusing, or pricing gets weird once usage increases, the tool stops feeling powerful and starts feeling expensive.

That is why articles like this need to talk about operating reality, not just capability. The real test is whether ChatGPT, Lindy, or Claude helps you move faster with less cleanup, fewer weird edge cases, and a clearer reason to keep paying for it a month from now.

FAQ

What is the main thing to know about AI Workflows vs AI Agents: What Non-Technical Teams Should Build First?

People searching for “best AI writing tools” are comparing features, pricing, and real-world performance. They want to know which tool saves the most time for their specific use case (e.g., blog posts, marketing copy, academic writing). The intent is transactional but research-heavy—they’ll read reviews before deciding.

Who should skip ChatGPT?

If your workflow does not match its strengths, the better move is to pick the tool that fits your actual use case instead of the one with the loudest marketing.

Do you need to pay for this right away?

Not always. Start with the workflow you actually need, then upgrade when the time saved or output quality clearly justifies the cost.

A better way to make the choice

Start by naming the one workflow that matters most right now. Then ask which tool makes that workflow easier to set up, easier to trust, and easier to maintain. If you cannot answer those three questions clearly, you probably are not choosing a tool yet. You are still reacting to marketing.

That framing leads to better decisions because it forces you to think about buyer fit. It also makes the recommendation easier to defend. Instead of saying one option is simply best, you can say why it wins for a certain type of operator, founder, or team… and where another option still makes more sense.

Most of us aren’t working under anything close to that constraint. But almost everyone I talk to feels stretched the same way, just for a different reason. You’re a parent. And an employee. Maybe a caregiver too, or running something on the side, or volunteering on weekends. Five hats, sometimes more.

Here’s the mistake nearly everyone makes when life gets that full: they go build a bigger system.

A complicated life doesn’t need a complicated system

The logic feels right. My life has more moving parts now, so my system should have more parts to match. More tags. More projects. More views. A Notion setup with a dashboard for every role.

It’s backwards. The more hats you’re wearing, the more you should push the other way… toward one list, a few priorities, a rhythm you can actually repeat. Complexity is the thing that makes a full life feel unmanageable. Simplicity is what lets you execute when you’ve only got scraps of time.

And you do only have scraps. Let’s do the uncomfortable math.

You have about five hours a week. Really.

At AE we talk about KLO… Keeping the Lights On. It’s all the stuff that isn’t growth but keeps your world running. A CEO’s daily obligations. A parent’s daily obligations. The bills, the emails, the pickups, the meetings you can’t skip. You usually can’t negotiate your way out of KLO.

So look at your week and be honest about what’s left after KLO is done. For most people it’s five hours. Sometimes less than an hour a day. That leftover slice is your project time… the only place real progress lives.

Now here’s what people do with those five hours: they split them across five different projects. An hour here, an hour there. Nothing ever builds momentum, so nothing ever ships. Five stalled starts.

Take the same five hours and pour all of them into the one thing that moves the needle most, and you get visible progress instead. Same hours. Wildly different result.

Pick three. Then pick one hat.

This is where our Rule of Three comes in, and it still works even when you’re juggling five roles. Pick three things to move forward this week. Only three. If saying yes to three means saying no to the other twelve on the list… good. That’s the whole point. Three things done well beats eight left half-finished.

One reframe that takes the fear out of it: three isn’t a cage, it’s a floor. Finish your three by Wednesday? Add more. You’re not under-committing, you’re starting from something you can actually hit.

If even three feels like too much across all your roles, do this instead: pick one hat for the week. Just one. “This week is about my business.” Apply the Rule of Three to that hat, block the time, protect it. Next week you rotate to a different one. Trying to serve every role every single week is exactly what leaves multi-hat people spread thin and worn out.

Not all hours are the same hour

One more piece, and honestly it’s the one most people skip. Don’t just plan for time… plan for energy.

You can block 2pm to write the report. But if 2pm-you is fried, that block is a wish, not a plan. An hour isn’t an hour. For a morning person, 9am can produce deep focused work and 8pm is mush. Same 60 minutes on the clock, totally different value.

So put your most important work in your best window, and shove admin and most meetings into the low-energy times. I keep meetings out of my mornings on purpose, because that’s when I do my best thinking. When you catch yourself putting something off even though you had the time… that’s usually not a discipline problem. It’s an energy problem. Bad sleep, skipped meals, or a lunch so big your body’s busy digesting instead of focusing.

What to actually do this week

Don’t overhaul anything. Do a short weekly review on a quiet Friday or Sunday and try this:

1. Write down your KLO for each role… the non-negotiables. See what’s actually left.
2. Pick your three for the week. Or if you’re slammed, pick one hat and three things inside it.
3. Block those on the calendar first, before anything else fills the space. Rocks in the jar first.
4. Drop the important blocks into your peak-energy hours. Admin goes in the leftovers.

That’s it. No new app. People with far fewer hours still outproduce people with a full workweek. You’ve got more than that. So pick one hat this week, give it your best two hours, and let the other roles wait. They’ll still be there Monday, same as always.

This Wisdom Wednesday, we lay out a different way to think about it — one that changes where AI actually belongs in your day, and where it never will. Ten minutes. One shift. Give it a listen.

Visit www.asianefficiency.com for more productivity tips and tactics.

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Maybe you do this too. A meeting ends 10 minutes early, so you answer a few emails. You finish a project and immediately start the next one. Lunch becomes a browser tab, a protein bar, and Slack.

Then, around 3:30 p.m., you are technically still working but nothing is really moving.

The problem usually is not that you need a better color-coded calendar. It is that you planned your week as if your energy has no limits.

Work has a rhythm

A guest on our podcast described work less like an ultra-marathon and more like a set of focused sprints followed by intentional recovery. That framing stuck with me because it is obvious once you notice it.

You can have a strong 60- or 90-minute stretch. You can write, solve a messy problem, prepare for a difficult conversation, or get through a pile of decisions. But eventually your attention starts slipping. You reread the same sentence. You open an app without remembering why. The work takes longer and gets a little worse.

That is not a character flaw. It is a signal.

A calendar can tell you where your time went. It cannot guarantee you will have the energy for the work you put inside those blocks.

Recovery is part of the work

Most of us treat recovery as something that happens after a productive day. Finish everything, then rest. The issue is that “everything” keeps moving.

There is always another email. Another meeting. Another thing that feels too small to schedule but somehow eats 25 minutes.

So try flipping the order. Put recovery inside the workday before you need it.

This does not need to mean a two-hour spa appointment between calls. A useful recovery block can be very ordinary:

• A 15-minute walk without a podcast.
• Ten minutes to make tea, stretch, and look away from a screen.
• A real lunch away from your desk.
• A buffer after a demanding meeting before you reply to messages.

The point is to give your brain a chance to come down before asking it to perform again.

The empty space is doing something

White space can feel suspicious when your task list is full. You may worry that a blank block means you are falling behind.

But a blank block often prevents the kind of day where you are busy from 8 a.m. to 6 p.m. and still carry five loose ends into dinner.

It gives you room to process what happened in the last meeting. To write down the decision before it disappears. To notice that the task you planned for this afternoon actually needs an hour tomorrow morning.

That little bit of room also makes your system more honest. If every minute is booked, the first surprise of the day breaks the plan. And there is always a surprise. A coworker needs help. A client changes direction. Your kid gets sick. A file will not open. Normal life shows up.

Space is where normal life goes.

Try a sprint-and-recovery week

You do not need to rebuild your whole calendar. Start with one part of your week that regularly leaves you wiped out.

Maybe it is the morning full of calls. Maybe it is the afternoon you reserve for deep work, then ruin by checking email every 12 minutes.

For the next five workdays, try this:

1. Choose one focused sprint. Protect 60 to 90 minutes for work that needs your full attention.
2. Decide what “done enough” looks like before you begin. A rough first draft, a reviewed proposal, a plan for the next step. Keep it concrete.
3. Schedule 15 to 20 minutes after it. No inbox. No catch-up task. Just step away.
4. At the end of the week, notice when your best work happened and what happened right before your energy dropped.

You are not trying to create the perfect rhythm. You are collecting a little evidence from your own week.

Purpose changes the feel of hard work

One story from the episode has stayed with me. At a team meeting, someone shared how the group’s work had mattered far beyond the office. They thanked the people who made it possible.

Afterwards, people said they had been reminded why they were doing the work.

That does not mean purpose removes fatigue. It does not make a hard project easy. But knowing what the work is for can change what deserves your best hours.

When you plan next week, do not only ask, “Where can I fit this?” Ask, “What needs me at my best?”

Then leave enough room to actually show up that way.

I was testing multiple assistants at once. Other people on the call had their own bots joining too. Funny, in a slightly uncomfortable way. Everyone wanted the meeting captured. The visible bots can be distracting. They can create resistance from the people on the call.

Everyone wants the recording. Almost nobody wants another bot sitting in the participant list.

Does this sound familiar?

Brooks has a name for what happens after those calls: the meeting notes graveyard. Transcripts and summaries pile up in your inbox. They’re technically searchable. You almost never go back and actually do anything with them.

I know because my inbox fills with those summaries. I skim them. Sometimes I ignore them. Brooks uses meeting recordings mostly to find information later.

Searchability is useful. It’s also a reactive, level-one use of these tools. You captured the meeting. Then you treated the capture like the work was done.

Here’s the thing. The productivity leak in meetings is the failure to convert discussion into accountable action.

Brooks connected this to a common corporate pattern. People talk through a problem. Then they talk through the same problem again 2 months later. Nothing left the meeting in an actionable format. Nobody extracted who promised what by when.

A strong AI meeting assistant should update the systems where work actually happens. Task manager. CRM. Project management app. Team chat. The output has to land where people already execute.

I used to treat these tools as summary machines. Now I look for assistants that can trigger automations through built-in integrations or an automation platform. The summary is the starting point. The system update is the job.

If you want a sequence you can actually run, use this.

Transcript

The raw conversation gets captured. That’s table stakes.

Summary

A readable recap so you don’t have to rewatch the whole thing.

Actions

Who promised what by when. Names. Deadlines. Specific next steps.

System update

Push those actions into the tool where the work lives. Don’t leave them sitting inside the transcript.

Human review

You still look at it. The assistant drafts. You approve.

That last step matters. People may behave differently when an assistant is present. You want a human checking the output before it becomes official.

There’s a real tradeoff between invisible local recording tools and visible meeting bots. Tools that record computer audio can reduce meeting friction because no bot joins the call. They can also be weaker at speaker identification. You still have legal and ethical consent to think about.

Brooks noted that local transcription can be better for privacy. He also flagged the limitations. I’ve been looking at tools that don’t join the meeting at all.

So which one do you pick?

1. A bot-based assistant

Something joins the call. Everyone can see it. Friction is higher. I’ve seen that friction turn into a pile of bots.

2. A local recorder

Nothing joins the participant list. Less awkward. Speaker ID may be weaker. Privacy can be better. Consent still applies.

3. The built-in meeting platform

Use what the call tool already offers so you don’t add another bot by default.

Pick on purpose. Don’t drop another assistant into the participant list just because everyone else did.

Ask first.

Brooks has a simple etiquette rule for this. Ask. A line like this is enough:

“Are you okay if I use an AI notetaker for this call?”

Copy that. Paste it into the invite or say it at the top of the call.

Brooks doesn’t mind being recorded. He still appreciates being asked. That’s especially true in sensitive one-on-ones, medical conversations, legal calls, coaching sessions, or anything confidential.

That matches something I’ve noticed. People may behave differently when an assistant is present. Asking is the polite version of that fact.

If you want the highest-ROI place to send the output, start with your CRM.

Have you ever walked out of a client call and trusted your memory to update the record later?

Salespeople and client-facing folks avoid admin work even when it helps them. Brooks has watched CRM hygiene stay a pain point in software and sales teams for a long time.

An assistant can pick up new contact details, meeting notes, buying timelines, and follow-up commitments. Then it can update the CRM or create a reminder automatically.

I’ve used this for simple things. Update a contact’s location. Add the call notes. Create a 30-day follow-up task. None of that should depend on you remembering it after the call.

Stop relying on memory after client calls.

There’s a second use I care about as someone who makes content.

AI meeting assistants can become a content and customer-insight engine. I use transcripts to automatically capture testimonials, customer pain points, research insights, and personal stories. They go into a searchable content bank. Later those notes support newsletters, podcast outlines, and posts.

Someone mentions an interesting story or a testimonial casually on a call. I don’t have to take manual notes. The assistant catches it. I file it.

Open the most recent meeting summary sitting in your inbox. The one you skimmed or ignored. Turn 1 action item into a task with an owner and a date.

If that task never leaves the transcript, you still have a graveyard.

I’d look back at the week, note what went well, plan the next one. Standard stuff. The problem was I kept making the same mistakes week after week. Meetings would pile up. Deep work would get squeezed out. I’d hit Sunday and realize I’d been running reactive all week — again.

The review was fine. The timing was the problem. By Sunday, there was nothing I could do about it.

So I built an AI that catches me on Wednesday instead.

The 5 Rules

My perfect week is defined by 5 rules. Not goals, not aspirations — rules. Binary. Either I hit them or I don’t.

1. Fewer than 10 meetings total, with no more than 3 in a single day.
Meetings are not work. They’re coordination. Too many of them and I lose the week.

2. At least 5 hours of deep work.
Real focused work. Not email, not calls. Something that requires me to think. Five hours sounds low, but when you track it honestly you realize how often it’s actually closer to zero.

3. Exercise at least twice.
Non-negotiable health marker. If this drops, everything else drops with it.

4. Some form of recovery at least twice.
Walk, sauna, massage, sitting in a coffee shop without my phone. Doesn’t matter what it is. Active rest is part of the system.

5. No more than 4 social events.
This one surprised me. But too many social obligations — even good ones — chew through energy I need elsewhere.

Simple rules. But putting them in writing forced me to see the shape of a week I actually wanted, not just a week I survived.

Why Wednesday

Every Wednesday, Lindy pulls my calendar, checks the past few days against my 5 rules, and sends a Slack message with a score.

Meetings so far: 6 of 10. Deep work hours: 1.5 of 5. Exercise: 1 of 2. Recovery: 0 of 2.

Three days left. Time to actually do something about it.

That’s the key. The AE weekly review is a proven framework — it’s one of the most important things I do every week. But a Sunday-only review is a feedback loop with a one-week lag. The mid-week check tightens that loop. It turns the weekly review from a post-mortem into a live adjustment.

Wednesday is the inflection point of the week. Still enough runway to change the outcome.

The Streak

Lindy also keeps a streak count. Consecutive weeks where I hit all 5 rules.

I didn’t expect this to matter much when I set it up. It matters a lot. There’s something about not wanting to break a streak that creates accountability the way a Sunday review never quite did. It’s a small thing. But small things compound.

The Question That Does the Most Work

Every Sunday, as part of my full weekly review, Lindy asks me one question:

What are you avoiding that you must do?

Not “what went well this week.” Not “what are your priorities for next week.” Those are fine questions. But this one is different.

It goes straight to the uncomfortable thing. The call you’ve been putting off. The project you keep moving to next week. The conversation you know you need to have.

That question has surfaced more useful information than most planning sessions I’ve ever run. It takes about 30 seconds to answer. And it almost always produces something I need to act on.

If you do any kind of weekly review — even a loose one — add this question. Just that. Don’t overhaul your whole system.

How to Build This

If you want a version of this for yourself, the pieces are simpler than they sound:

1. Define your 5 rules. What does a good week actually look like in concrete, measurable terms?
2. Pick your mid-week check day. Wednesday works for me. Thursday might work better for you.
3. Build the agent. If you use Lindy, you can connect it to your Google Calendar and have it pull events automatically. Claude or any AI with calendar access can do the same thing.
4. Add the “what are you avoiding” question to your Sunday review.

The whole system runs on its own once it’s set up. That’s the point — the Ideal Week framework only works if you actually look at it. The agent makes sure you do.

Want to build your own weekly review agent? The 4-Day AI Sprint walks through exactly this kind of setup — practical agents for your real life, not toy demos.

Not emails. Not texts. Actual cards, with a pen, to people I care about. I’ve been doing it for years. It’s one of those things I look forward to.

Last year I tried something different for one of them. I had a friend I hadn’t connected with in a while — months had passed, wasn’t sure what to say, didn’t want to write something forgettable. So I opened Claude and typed something like: “Here’s a friend. We haven’t talked in months. Here’s what I know about what’s going on in his life. Give me some ideas of things I could write to him.”

It gave me a short list. One of the ideas was good — not perfect, but it pointed me somewhere I hadn’t thought to go. I took that angle, wrote the note completely in my own words, put it in the card, and mailed it.

He texted me a few weeks later. Said it was one of the most thoughtful things he’d gotten all year.

AI didn’t write the card. But it made the card better.

The Fear Underneath the Argument

When I shared this on a podcast, the host mentioned someone who’d said AI was “taking away their zest for life” because of writing. That’s the version of the argument I hear most often: AI is making creative work feel hollow, or replacing the thing that made it meaningful.

I understand where that fear comes from. If writing is yours — if it’s the way you process and connect and express yourself — the idea of a machine doing it instead feels like a loss.

But that’s not what happened with the Christmas card. I wrote it. I chose the words. I decided what to say. The card was mine in every meaningful sense.

What AI did was act as a thought partner. It surfaced an angle I hadn’t considered and gave me something to react to. That reaction — my version of the idea — is what went into the card.

The Jumpstart Problem

There’s a songwriter and content creator I heard talk about this once. She said she initially resisted AI completely — she’s a writer, she creates her own stuff, she didn’t want a machine anywhere near her creative process.

Then a colleague suggested she try using AI just to get started when she was stuck. Not to write for her — just to give her a paragraph to react to, an idea to push against, something to break the silence of a blank page.

She found it changed her relationship with creative blocks entirely. The AI gave her something to work with, and working with it — agreeing, disagreeing, rewriting, throwing it out — was exactly the creative act she’d been struggling to start.

That’s the version I’m interested in too.

Using AI to Think, Not to Produce

The distinction I keep coming back to is: using AI to produce output versus using AI to sharpen your thinking.

Producing output means handing the task over — describe what you want, get the result, ship it. That’s useful for plenty of things. Research summaries. First drafts of repetitive documents. Reformatting content across platforms.

Sharpening thinking is different. You bring your own knowledge and judgment to the problem, use AI to surface angles you might have missed, and then make your own decision. The AI is more like a smart colleague you can think out loud with.

The Christmas card was the second kind. I knew my friend. I knew our history. I knew what I wanted to express. I just needed help finding the angle.

The people who thrive with AI long-term seem to understand this distinction intuitively. They don’t hand things over wholesale — they use AI the way you’d use a good brainstorm partner. You still have to be in the room. You still have to do the thinking. The AI just makes the thinking better.

When AI Makes You More Human

Here’s what struck me about the card: it worked because I brought context that AI couldn’t have.

I knew this friend. I knew what we’d talked about last time we saw each other. I knew what he cared about and what would land versus what would feel generic. When AI gave me the idea, I could immediately evaluate it through all of that. “Yes, that’s the right angle for him specifically.” That judgment was mine.

The same pattern shows up in how I use AI for other things. I’ll ask it to help me think through a workshop structure — but I know what resonates with my audience from doing this in person. I’ll ask it to suggest angles for a piece of content — but I know which stories are mine to tell. The AI isn’t replacing the knowledge I’ve built. It’s helping me deploy it better.

That’s a very different thing from the fear that AI is taking creativity away.

The Actual Threat

If there’s a real threat in this space, I think it’s not that AI replaces your thinking. It’s that people let it.

If you use AI to produce something you didn’t actually think through — a card that sounds nice but has no real connection to the person, a piece of writing that hits the expected points without any original observation — then yes, something is lost. The result is hollow because you weren’t in it.

But that’s a choice about how to use the tool, not a property of the tool itself.

A pen doesn’t write a thoughtful card by itself either. The pen is just how thought becomes readable.

AI, used well, is the same thing — a medium for sharper thinking, not a replacement for it. The handwriting is still yours.

If you’re exploring how to build AI into your work in a way that actually fits your thinking style, the 4-Day AI Sprint covers practical frameworks for exactly that.

There’s this annual conference I go to in Austin. I host people. I attend events. I socialize heavily. I have a great time.

Then I come home completely exhausted.

Same thing. Every year. I used to treat it like I just had a busy stretch and needed more discipline to bounce back. Now I plan for it. Recurring drains need boundaries and a recovery plan. You set those up before the week hits, not after you’re already empty.

Enjoyable things still cost energy. That’s the part I had to learn the hard way. The conference is fun. The people are fun. My tank still hits zero.

Maybe yours is not a conference in Austin. Maybe it’s a seasonal crunch you can already circle on the calendar. Or a social stretch you say yes to every time, then spend days wondering why you cannot think straight.

Do you know that drop? The one that shows up on schedule?

A supercar with no gas

Here’s how I explain this when someone is about to rebuild their whole system.

Your productivity system is a supercar. Calendar, workflows, apps, focus blocks. Energy is the gas in the tank.

You can have the perfect calendar. Clean workflows. All the apps. Beautiful focus blocks. If the tank is empty, none of it goes anywhere.

I used to assume the gap was discipline. Better time blocking. A tighter routine. I’d look at a perfectly blocked day and still feel like I was pushing the car.

Time management fails when it ignores energy. A perfectly blocked calendar does not help if your body and mind do not have the capacity to do the work. Energy is the fuel that makes a productivity system actually move.

This is the next hurdle after time management. At Asian Efficiency the framework is TEA: Time, Energy, Attention. Most people keep polishing Time. They never check the tank.

Treat your battery like infrastructure

Stress tolerance works like a battery.

When your battery is low, small frustrations feel bigger. Focus drops. Burnout risk rises. When your wellness baseline is higher, you can handle intense work periods without paying as high a health cost.

Sleep and regular physical activity sit under that baseline. They are productivity basics. I treat them as infrastructure now, the same way I used to treat my calendar.

Skipping sleep to get more done is like draining your battery to 10% and then wondering why you cannot focus. You create more hours on paper. You reduce the capacity you need to use those hours well.

I know the reflex. Just work later. Just squeeze another block in. I’ve had that reflex after Austin, when I come home wrecked and still try to jump back into important work like the tank magically refilled.

Has that ever happened to you? More hours. Worse work.

4 places the leak actually is

The Energy Audit is a simple coaching tool for finding where your vitality is leaking. Before you change tools, calendars, or routines, find the drain.

Energy drains usually fall into 4 categories.

Physical

Poor sleep. Sitting all day. The body stuff that makes good work harder than it needs to be.

Mental

Nonstop context switching. You never finish one thing before the next thing grabs you.

Emotional

Difficult projects. Hard relationships. Work that costs more than the time block on your calendar.

Environmental

Clutter. A noisy workspace. The room itself is fighting you while you blame your focus.

I’m no scientist. This is not a formal scientific protocol. It’s a practical AE coaching framework I use when “I feel tired” is too vague to act on.

Name the category. Then you can do something about it.

One day. That’s the experiment.

Run a 24-hour Energy Audit.

Every 2 hours, rate your energy from 1 to 10. Note what you were doing, who you were with, and what environment you were in.

At the end of the day, look for patterns. What pushed you toward a 10? What dropped you toward a 1?

Then choose one drain to remove, or one recovery ritual to add. One. This is a low-friction experiment. Not a full life overhaul.

If you want a recovery ritual to use after a focus block, try Clear to Neutral.

Close your tabs. Reset your desk. Step away from the screen. Hydrate. Move. Or simply look out a window.

Create real recovery. A lot of people leave a focus block and open social media. You just switched from work information to social media information. Your brain never got a landing.

Clear to Neutral is a small habit. Use it between deep work sessions this week and see what your next block feels like.

Stop treating every hour as equal

Protect your peak energy hours for your most important work. Not low-value meetings.

If you are sharpest around mid-morning, reserve that window for deep work. Push admin, batching, or lighter tasks into lower-energy periods.

Energy-based scheduling beats treating every hour as equal. You already know this if you’ve ever tried to do your most important work after a day of context switching.

Open tomorrow’s calendar. Find one low-value commitment sitting in your peak window. Move it.

That’s the whole move.

Start with the audit, not a new system

Before you touch your tools, run the 24-hour Energy Audit.

Set a reminder every 2 hours. Score 1 to 10. Write the activity, the people, the environment.

When the day is over, pick one drain to cut or one recovery ritual to add. If you already know a recurring seasonal or social drain is coming, put a boundary on it now. For me, that’s the stretch after Austin. I enjoy the hosting, the events, the socializing. I also plan the recovery.

Do the audit. One day. Then change one thing.

It sounded useful. It looked cool in demos. Then I would try it for a real email and spend the next five minutes fixing weird punctuation, missing words, and sentences that sounded like I had never met the recipient before.

So I went back to typing.

Lately, that has changed.

The newer AI voice tools are much better at understanding how we actually talk. You can pause. You can say um. You can restart a sentence halfway through. The AI layer can clean up the false starts, format the reply, and turn your rambling into something you can review.

That last word matters: review.

I am not talking about handing your inbox to a robot and hoping for the best. I am talking about using your voice to get the first 90 percent down faster, then doing the final pass yourself.

Why voice is worth trying again

There is a simple numbers reason.

Many professionals type around 40 words per minute. Speaking is closer to 100 to 130 words per minute. That gap is big enough to matter, especially if you write a lot of emails, notes, summaries, or plans during the week.

But speed was never the only problem.

Old dictation tools made you talk like a GPS system from 2008. Period. New paragraph. Delete that. No, not that. Go back. It was awkward enough that typing felt calmer.

The new version is different because you can speak more naturally. You can say something like, I want to thank them for sending this over, tell them I agree with the second option, and ask if Thursday afternoon works for the next step.

The AI can turn that into a decent reply. Not perfect. Decent. And decent is enough if you are going to read it before sending.

This is where voice becomes useful for real work. Not as a stunt. Not as a full inbox takeover. Just as a faster way to get your thoughts out of your head.

Workflow 1: reply to emails out loud

Here is the email workflow I have been using.

1. Read the email first.
2. Click into the reply field.
3. Hold the voice shortcut.
4. Speak the reply naturally.
5. Let the AI clean it up.
6. Review it, fix anything weird, then send.

That is it.

The trick is to avoid performing for the tool. Do not try to sound like a lawyer unless you are actually writing legal language. Talk like you would talk to the person if they were sitting across from you with coffee.

For example, instead of thinking through the perfect written sentence, I might say:

Hey, thanks for sending this over. I like the second option better because it gives us more room next week. Can you check whether Tuesday morning or Thursday afternoon works on their side? Also tell them we will send the revised file by Friday.

Then I stop talking.

The draft appears. I read it. Sometimes I change a phrase because it sounds too stiff. Sometimes I add one sentence because I forgot something while talking. Sometimes I delete a little flourish that the AI added because I would never say it that way.

That final pass is where trust is protected.

If a message sounds generic, people feel it. They may not say anything. They may not even notice consciously. But if your email suddenly sounds like a press release when you normally write short, friendly replies, something feels off.

Use voice to go faster. Still make it sound like you.

Workflow 2: do your weekly review by talking

This one surprised me more.

I have been experimenting with doing my weekly review as a voice conversation with an AI assistant. I keep my calendar open, start a voice chat, and ask it to act like a productivity coach.

The instruction is simple:

Ask me one question at a time as I review last week. Start with Monday. After we go through the week, give me a short summary I can paste into my notes.

Then I talk through the week day by day.

Monday had too many meetings. Tuesday had a good focus block in the morning. Wednesday I missed a follow-up. Thursday was fine except I let one call run long. Friday had a bunch of tiny admin stuff that should have been batched.

This works because a weekly review is often easier to talk through than type from scratch. You already know what happened. The hard part is slowing down long enough to notice it.

Having the AI ask one question at a time helps. If it asks five questions at once, I ignore half of them. One question is better.

At the end, I ask for a written summary. Then I paste that into my notes app and clean it up if needed.

I still own the review. The AI is just holding the clipboard.

Where this fits in a calm productivity system

Voice is not for every task.

If I am editing a detailed proposal, I still want my hands on the keyboard. If I need to compare numbers, I want the spreadsheet in front of me. If the email is sensitive, I slow down and write more carefully.

But a lot of daily work is getting thoughts out of your head and into a place where they can be used.

Email replies. Meeting notes. A quick plan for tomorrow. A brain dump after a call. A weekly review summary.

Those are good places to test voice because the cost of a rough first draft is low. You are going to review it anyway.

And please start small.

Do not rebuild your whole system around voice this week. That is how new tools become another project. Pick one workflow you already do every day or every week.

For seven days, try one of these:

• Dictate one batch of email replies each afternoon.
• Do your weekly review as a spoken conversation.

That is enough data.

You will know pretty quickly if the tool fits. Your shoulders will drop a little because you are not typing every thought. Or you will get annoyed because the cleanup takes too long. Both answers are useful.

A few setup tips

If you try this, make the setup boring.

Use a shortcut you can remember. Test it in one app first. Keep your microphone close enough that the transcript is clean. Do not start with your most sensitive client email or the giant weekly report that already makes you procrastinate.

Start with a normal reply.

Something like: thanks, here is my answer, here is the next step.

That is where the habit begins.

One more thing: if the AI writes a sentence that sounds too perfect, cut it. Especially if it adds big words you would never use. Your voice is the asset here. The tool should help you get more of it onto the page, not sand it down until you sound like everyone else.

Try it with one email today. If it saves you fifteen minutes, keep going. If it annoys you, go back to typing for that workflow and test it somewhere else.

Visit www.asianefficiency.com for more productivity tips and tactics.

Make the switch! MINTMOBILE.com/PRODUCTIVITY.

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Listen to this episode (MP3)

Cheat Sheet

• Top 3 Productivity Resources [2:18]
• Why almost nobody talks about productivity systems anymore [6:27]
• How AI quietly collapsed GTD’s five steps into one [9:34]
• Why your step-by-step instructions are secretly slowing the AI down [17:35]
• The reason someone else’s “perfect” system falls apart the moment you copy it [26:35]
• The 10/80/10 split that redraws where your real work happens [31:36]
• The one framework that survives even when all your tools change [36:10]
• The “skinnier me” trap that quietly wrecks the system you just built [41:04]
• The single lever to move today before you close the episode [48:42]

Links

• Mint Mobile
• Upwork
• 25X Productivity Coaching
• The Art of Possibility (Benjamin Zander & Rosamund Stone Zander)
• Anker Laptop Power Bank, 25,000mAh 165W
• Midwit Meme Generator
• Getting Things Done (GTD)
• Getting Results the Agile Way (Agile Results)
• The 12 Week Year
• Wispr Flow
• OmniFocus
• Todoist

If you enjoyed this episode, follow the podcast on Apple Podcasts, Spotify, Stitcher, Overcast, Pocket Casts or your favorite podcast player. It’s easy, you’ll get new episodes automatically, and it also helps the show. You can also leave a review!

Nice idea. It also almost never happened.

My calendar filled first. Meetings expanded. A few optional calls looked harmless. Then there were the little bits of work around the meetings: prep, follow-up, checking what changed while I was away. By Friday, there might have been plenty of activity. There was very little work I could point to and say, “That moved the important thing forward.”

So I stopped treating deep work as something I would get to when the week calmed down. I put it on the calendar first.

For me, the floor is three 90-minute sessions each week. That is 4.5 hours. It is not an ambitious target for every person or every season. It is simply the number I use to make sure focus has a place in my week.

Three sessions sounds small. But when they are protected, they change the shape of a week.

Start with subtraction

People often hear “schedule deep work” and immediately look for another blank spot. If your calendar is already packed, that is the wrong first move.

Before adding anything, look for something to remove.

A client of ours had a calendar full of recurring meetings. Some needed her judgment. Some were meetings where she mostly needed to know what was decided afterward. Those are different jobs.

For the second kind, a transcript and a short AI summary can be enough. You can still see decisions, owners, and dates. You can ask a follow-up question if something affects your work. But you do not have to spend the whole hour in the room.

This is not a case for skipping meetings because meetings are annoying. Some conversations need your presence. A sensitive conversation does. A decision that needs your judgment does. A first meeting with someone you are building trust with probably does too.

But a recurring status meeting may be a different story. Pull up one on your calendar. Ask: if I received the transcript, a summary, and a list of decisions, would I have enough to do my job?

If the answer is yes, you may have just found 30 or 60 minutes for work that has been waiting all month.

Make the block specific enough to defend

A calendar event called “Focus Time” is better than nothing. It is also easy to move when something else comes up.

Give the session a job.

Write “DW: finish first draft of proposal” or “DW: prepare workshop outline.” The label tells you what you agreed to do before the week got noisy. It also makes the tradeoff visible when someone asks for that time.

I have learned that a protected block without a clear target can turn into admin work with a nicer name. You open your laptop, see a message, answer it quickly, then look up and half the session is gone.

Before the block begins, decide what would count as done. Maybe it is a rough outline. Maybe it is a decision memo. Maybe it is the first pass through a report, with ugly notes in the margins.

Done does not need to mean perfect. It needs to mean you can tell whether the session was spent on the work you intended to do.

Why I use 90 minutes

I structure many of my sessions around 90 minutes because it gives me time to get past the restless first few minutes. A 20-minute block can be useful. It can also end right when you are finally inside the problem.

I used to assume longer was always better. Then I started paying attention to what happened near the end of a long stretch. The quality dropped. I reread the same thing. I began reaching for easier work.

Ninety minutes has been a useful container for me. I can settle in, make progress, then stop before I turn the whole day into a willpower contest.

Your number may be 45 minutes right now. Fine. Put 45 minutes on the calendar. Keep the promise. The goal is not to copy someone else’s ideal work block. The goal is to stop leaving your best work to chance.

Track the promise, not your mood

I track my deep work during my weekly review. Calendar events that begin with “DW:” get counted, logged, and compared with the prior week.

You can do this manually in two minutes. You can also automate it with your calendar, a spreadsheet, and an AI assistant. The automation is handy, but it is not the point.

The point is seeing the number.

When deep work is only an intention, it is easy to remember the one session that went well and forget the two you moved. When it is a weekly number, you have something honest to look at.

Did you protect the time? Did meetings crowd it out? Did you schedule blocks at an hour when you were usually tired? Did the work itself need a clearer definition of done?

You do not need a dramatic answer. Sometimes the fix is moving one block from Thursday afternoon to Tuesday morning. Sometimes it is saying no to a meeting you have attended out of habit.

A simple plan for next week

Open next week’s calendar before it fills up.

1. Choose one piece of work that deserves your full attention.
2. Schedule two or three sessions for it, using a duration you can actually protect.
3. Name each block with the work you intend to do.
4. Look at one recurring meeting and decide whether you need to attend live or whether a transcript and summary would do.
5. At the end of the week, count the sessions you kept.

That last step matters. You are building a calendar you can trust.

Put the first block on the calendar now, while the week still has some empty space.

That does not mean you should immediately build an automation.

It means you should get curious about the task.

Where does it start? What information does it need? What would a useful result actually look like? Where do you make a judgment call that a new teammate would miss?

Those questions matter even more with AI. AI can produce a lot of work very quickly. If the brief is fuzzy, it can produce a lot of work that creates a new mess for you to clean up.

Start with the annoying repeat

Think about a task that has shown up a few times lately. Maybe it is turning a client call into a follow-up email. Maybe it is sorting meeting notes. Maybe it is preparing the same report every Friday.

The task does not need to be huge. In fact, smaller tasks are often better places to start.

A founder we coach had a workflow in mind for turning meeting transcripts into organized notes. The temptation was to build the whole thing immediately. Instead, I suggested a slower first pass: run the transcript through an AI chat, inspect the result, adjust the instructions, then repeat it with another meeting.

That manual run reveals the stuff you cannot see from a blank automation canvas. One meeting may have a promised introduction. Another may have a decision with no owner. A third may contain a detail that belongs in a customer record rather than the project notes.

If you automate before seeing those differences, you end up designing for an imaginary version of the work.

Write down what “done” means

The first part of AI delegation is often the most valuable part. You are defining the finish line.

For a post-meeting follow-up, “done” could mean:

• A draft email names the decisions from the call.
• It lists every promised action with an owner.
• It includes dates only when the conversation actually included dates.
• It leaves uncertain details clearly marked for review.

Now the AI has something useful to work toward. You have also made your own expectations visible.

This is the difference between asking, “Summarize this meeting,” and asking for an email draft that helps the conversation move forward. The first request may give you tidy notes. The second one can save you from reopening the transcript two days later, trying to remember what you said you would send.

When I delegate work to AI, I think about it as 80/10/10. The opening 10 percent is the brief: context, resources, constraints, and what a good result looks like. The middle is execution. The last 10 percent is review.

That final pass is where your voice, relationships, and judgment stay in the picture.

Let the AI ask for missing context

Many people get stuck trying to write the perfect prompt. I would flip that around.

Tell the AI what you want to accomplish, then ask: “What do you need from me before you begin?”

For a recurring meeting workflow, it may ask who should receive the follow-up, how direct the tone should be, where tasks belong, or whether introductions need approval before they are drafted. Those are good questions. Answer them once. Save the answers somewhere you can reuse.

You are building a small operating manual for the task.

And you do not need to get every detail right on day one. Run the workflow. Look at what it produced. Add one instruction after a miss. Remove one instruction that did not matter. The workflow gets better because it is based on actual work, not a theory of the work.

Document the judgment before the automation

Some recurring tasks have a hidden decision inside them.

Take a client update. A good update does more than repeat what happened. It decides what belongs in the message, what can wait, what needs a clear owner, and what would create unnecessary noise. If you want AI help with that, document those choices first.

You might write a few simple rules:

1. Include decisions and commitments from the conversation.
2. Do not invent deadlines or certainty.
3. Keep the email short enough that someone will read it.
4. Flag anything sensitive for me rather than sending it onward.

Those rules give the system boundaries. They also make review faster because you know what you are checking for.

This is a useful test for any proposed automation. Can you explain how a skilled person decides whether the output is good? If the answer is no, stay manual for another few rounds. The task may still be worth doing. It just is not ready to run unattended.

Keep the human pass

Automation is great at getting a first draft onto the screen. It is less reliable at knowing when a sentence sounds off, when a detail will land badly, or when the right move is to say less.

That is why I like draft-first workflows. Let AI prepare the follow-up email, pull action items from a transcript, or organize a rough report. Then read it before it leaves your hands.

The review is not busywork. It protects trust. A generic email with your name on it can cost more than the five minutes you saved.

So this week, skip the giant automation project. Pick one task that has annoyed you three times. Run it manually with AI beside you. Write down what good looks like. See where your judgment enters the process.

You may end up with an automation. You may discover the task only needed a template. Either way, you will have a system that matches the work you actually do.

Then I asked him which items supported his organization’s goal for the quarter.

One did.

The other tasks were not bad tasks. Some were useful. A few were personal priorities. Yet the list had given him a false sense of progress. He had been busy. The outcome that mattered had barely moved.

AI makes this easier to do at a much larger scale. You can draft reports, summarize calls, sort research, write follow-ups, build spreadsheets, and create ten versions of something before lunch. That is useful. It also means you need to decide what deserves that speed.

I use a simple model for this: 80-10-10.

The first 10% is deciding what good looks like

Before you open ChatGPT, Claude, or any other tool, pause for a minute.

What are you trying to get done? Who is this for? What will tell you the work is finished?

That is the first 10%. It is often the part people skip because it feels slower than typing a prompt. Then the AI gives them something polished that answers a slightly different question.

Say you need to prepare for a client meeting. “Make me a meeting brief” is a start. It leaves a lot of room for guessing.

A clearer version might be: “Create a one-page meeting brief for a client renewal conversation. Include the last two meeting commitments, open issues, current usage concerns, and five questions I should ask. Flag anything that needs my judgment before the call.”

The AI now has a job. More importantly, you do too. You have defined what you are looking for.

If you are unsure what details to provide, flip the script. Ask the AI to interview you first: “What do you need to know from me before you create this?”

That one sentence changes the interaction. You are no longer trying to guess the perfect prompt from a blank page. You are giving the tool a way to collect the context it needs.

The middle 80% is where AI can do the heavy lifting

Once the outcome is clear, hand over the work that is repetitive, time-consuming, or easy to review.

This could include:

• Turning rough meeting notes into a first draft of follow-up tasks
• Comparing a pile of customer comments for recurring themes
• Creating a first pass at an agenda from a project update
• Organizing a research document into sections you can verify

Notice what these have in common. The work has a shape. You can explain what a finished version should contain. You can check it without starting from scratch.

That is a good fit for AI.

Some work still belongs with you from start to finish. Booking the familiar flight route you take every few months may be faster manually. Choosing groceries can depend on what sounds good that day, what is already in the fridge, or who is coming over. A personal message may need your actual words instead of a clean draft that sounds like no one in particular.

You do not need to force every task through AI. The point is to use it where it removes effort without creating a new review project.

The final 10% protects your standards

The last 10% is where you read the output like the person whose name will be attached to it.

Is it true? Does it sound like you? Did it miss context only you would know? Is the recommendation actually useful, or is it simply well formatted?

This final pass matters more now because generic AI work creates a credibility tax. A report can be accurate and still feel off. An email can be grammatically perfect and still sound unlike the sender. People may not say why they trust it less. They just do.

You do not need to rewrite every sentence. You need to add the human part: judgment, specificity, voice, and the details that tell the reader a real person paid attention.

That is why the student’s task list mattered. He did not need more completed tasks. He needed a clearer result to aim at, then a way to tell whether the work was helping.

Try this with one recurring task

Pick one task you do every week. Maybe it is preparing for a meeting, writing a project update, reviewing customer feedback, or building a plan for the week.

1. Write one sentence describing the outcome.
2. Add a definition of done. Be specific about what the finished work needs to include.
3. Give AI the middle portion of the task.
4. Review the output before it goes anywhere.

Keep the first attempt small. You are not rebuilding your workday. You are testing whether one task becomes easier to finish without losing the part that needs you.

At the end of the week, look at the result rather than the number of prompts you used. Did the task move something forward? Did you trust the final output enough to send it? That is the score worth keeping.

I host people. I attend events. I socialize heavily. I have a great time. Then I end up completely exhausted.

The hosting is enjoyable. The events are enjoyable. The socializing is enjoyable. And I still finish that stretch completely wiped. Recurring drains need boundaries and a recovery plan. I have to put those in place before the next one, not after I am already empty.

Does this sound familiar to you?

Most people in that spot reach for time. New focus blocks. A tighter calendar. Another app. Time is the part of productivity you can see, so it feels like the part you should fix.

Your productivity system is a supercar. The calendar, the workflows, the apps, the focus blocks: that’s the car. Energy is the gas in the tank. You can have the perfect setup and still go nowhere if the tank is empty.

Time-blocking alone is not enough. Time management fails when it ignores energy. A perfectly blocked calendar does not help if your body and mind do not have the capacity to do the work. Energy is the fuel that makes productivity systems actually move.

At Asian Efficiency, this lives inside TEA: Time, Energy, Attention. Once you get decent at managing time, energy is the next hurdle. Treat every hour as equal and you will keep dropping important work into windows where you have nothing left to give.

Your battery is doing more work than your calendar

I think about stress tolerance like a battery.

When your battery is low, small frustrations feel bigger. Focus drops. Burnout risk rises. When your wellness baseline is higher, you can handle intense work periods without paying as high a health cost.

Skipping sleep to get more done is like draining that battery to 10% and then wondering why you cannot focus. You create more hours on paper. You also reduce the capacity you need to use those hours well.

Sleep and movement are productivity basics, not optional extras you get to after the real work. They are the foundation the battery sits on. Protect them the same way you protect a deadline.

The Energy Audit is a practical AE coaching framework. It’s not a formal scientific protocol. Run it as a first step, before you change tools, calendars, or routines.

Four places vitality actually leaks

“I feel tired” does not tell you what to change. Energy drains usually fall into four categories.

Physical drains

Poor sleep. Sitting all day. Your body is already leaking, and then you sit down and ask it for a deep work block anyway.

Mental drains

Nonstop context switching. You bounce from one half-finished thing to the next and never give your brain a clean runway.

Emotional drains

A difficult project. A hard relationship. The work that stays on your chest even after you close the laptop.

Environmental drains

Clutter. A noisy workspace. A room that keeps pulling your attention sideways while you are trying to think.

When you feel depleted, start here. Name the drain. Physical, mental, emotional, environmental. More discipline is usually the wrong first move. Find the leak that makes good work harder than it needs to be.

Are you blaming a discipline problem for an energy leak?

Run a 24-hour Energy Audit

This is a low-friction experiment. One day. You are looking for where vitality leaks, not overhauling your life.

For the next 24 hours, every two hours, rate your energy from 1 to 10. Write down what you were doing, who you were with, and what environment you were in.

That’s the whole log.

At the end of the day, look for patterns. What pushed you toward a 10? What dropped you toward a 1? The same drain usually repeats. A person. A room. A type of meeting. A long stretch of sitting. A stretch of switching.

Then choose one drain to remove or one recovery ritual to add. One. That’s the assignment.

If you already know when you are sharpest, protect that window for your most important work. If mid-morning is your peak, reserve it for deep work. Push admin, batching, and lighter tasks into lower-energy periods. Open tomorrow’s calendar and move one low-value meeting or commitment out of that peak window.

Have you treated every hour tomorrow as if it had the same fuel?

Recover for real between focus blocks

After a focus block, use Clear to Neutral.

Close your tabs. Reset your desk. Step away from the screen. Hydrate. Move. Or simply look out a window.

Create real recovery. If you leave a focus block and go straight into social media, you just swapped work information for more information. Your attention stays fragmented. You feel like you took a break. The tank never filled.

Use Clear to Neutral between deep work sessions this week. Small habit. Real reset.

Do this today

Find one recurring drain. Seasonal or social. The same crash that shows up after the same kind of week. Put a boundary on it, and put a recovery plan next to it, before it happens again.

Then start the 24-hour audit. Every two hours, score yourself from 1 to 10. Write down the work, the people, and the room. Tonight, pick one drain to remove or one recovery ritual to add.

Start there.

Someone decides they want to automate their workflow. They start designing the full system — AI that handles emails, schedules meetings, updates the CRM, preps briefings, drafts proposals, follows up with leads. All of it, connected, running automatically.

They spend weeks drawing it out. Then either never build it, or build something so complex that it breaks on day three.

I’ve watched this happen enough times that I’ve started giving everyone the same first piece of advice: start with a voice note.

What “start with a voice note” actually means

After every meeting, drop a two-minute voice note. What was discussed. What you committed to. Anything important about the person that you want to remember.

That voice note gets automatically transcribed and parsed, and the relevant details get pushed to update the CRM record for that contact.

On the surface, this sounds simple to the point of being obvious. But the impact of this one workflow is larger than it looks.

Most CRM data is stale, incomplete, or wrong. Not because people don’t care about accuracy — they do. It’s because nobody wants to sit down after a call and type notes. It’s tedious, it breaks the flow of the day, and it always gets deprioritized.

Voice is different. Two minutes while you’re still in the mental context of the meeting. The AI handles the transcription and the data entry. The result is accurate CRM records — maybe for the first time.

Accurate records mean better handoffs between teammates. Fewer things falling through the cracks. A real history of every relationship in one place.

That’s already a meaningful win.

How one workflow becomes two

Once the data is accurate and you trust it, something interesting happens: the next step practically builds itself.

The day before any meeting, the system can pull notes from your last interaction and send you a briefing. You walk into every call knowing exactly where you left off — what was discussed, what you promised, what the person is working on.

That’s not a separate second workflow. It’s the same workflow extended. The voice note created the data; the briefing is just reading it back to you at the right time.

This is the pattern I see with every good automation system: what starts as one simple step reveals the next step. The system designs itself once you’re in motion.

What this looks like in practice

I have an agent called Teddy that manages my CRM backlog overnight. It enriches contact information, drafts follow-up emails, updates records based on recent activity. By morning, it’s completed a batch of work that would have taken an hour of manual data entry.

My job is quality control. I review what Teddy handled, approve or adjust anything that needs a human touch, and move on.

That shift — from data entry to quality control — only became possible because I started with a simple foundation. Accurate data. Consistent logging. One trigger that ran reliably.

I’ve also built a meeting follow-up workflow where if I mention during a call that I’ll send something, the agent auto-drafts the email. Within one minute of hanging up, there’s a completed draft in my inbox. No to-do item added. No forgetting. Just a draft ready to review.

The follow-up workflow depends on the transcription. The transcription depends on the voice note habit. Simple foundation, then layers.

The start-small-iterate approach

There’s a principle I use in AI consulting called start-small-iterate. It sounds self-evident, but most people don’t apply it. They want to see the full architecture before committing to anything.

The problem is that the full architecture isn’t visible until you’ve started. You learn what the next step should be by running the first step and watching what happens.

One voice note workflow will tell you more about your actual workflow gaps than a week of planning sessions.

How to find your voice note equivalent

A voice note after meetings isn’t the only entry point. It’s just one example of the pattern.

The pattern is: find the manual, repetitive task that happens consistently but never gets done well. Something that should happen after every meeting, or every sales call, or every customer interaction — but doesn’t, because typing it out is too slow.

That’s your starting point.

Other examples that follow the same logic:

• After a sales call, the agent reads the transcript and populates the proposal template. Proposal goes out in minutes instead of sitting on a to-do list.
• After a client onboarding call, the agent triggers account setup, sends the welcome sequence, and logs the new client in the system.
• After any meeting where a commitment was made, the agent drafts the follow-up email so it’s ready to send immediately.

All of these start from the same place: one trigger, one action, run consistently.

A simple way to start this week

Pick the single most annoying manual thing that happens after meetings in your workflow right now. Not the most ambitious thing — the most annoying thing.

Build one agent around that. Let it run for two weeks.

Then ask yourself: what data does this workflow create, and what’s the natural next use of that data?

That’s your second workflow.

You don’t need to plan the whole system in advance. You just need to start somewhere specific and let the system design itself from there.

They run a supplement brand — creatine specifically — and they wanted to understand the market. Not in a vague, “what’s the industry doing” way. They wanted to know what consumers actually think about every major competitor. What they love. What frustrates them. Where there’s white space nobody has filled.

The thorough way to answer that question would be to read through thousands of Amazon reviews across a dozen products. Note the patterns. Cross-reference the complaints. Pull out the quotes that keep showing up.

Nobody does that. It takes weeks. You’d need a researcher or an analyst, and even then, you’re going to skim.

So I built an agent to do it instead.

What the agent did

The agent scraped all the Amazon reviews for every major competitor — roughly 10,000 reviews total across multiple SKUs.

It ran sentiment analysis on the reviews. Grouped them by theme. Flagged the recurring complaints. Identified the features consumers praised most. And surfaced the gaps — things multiple reviewers asked for that none of the products actually deliver.

Time to run: about 20 minutes.

Manual equivalent: somewhere between two weeks and a month, depending on how thorough you want to be. And even at the end of a month, a human analyst is going to miss things.

The output was a structured market report that told them exactly where the opportunities were.

The pattern keeps showing up

I saw the same dynamic when I was organizing an event and needed press coverage.

I needed to find journalists who cover Austin business and tech, understand what they write about, and reach out with pitches that actually fit their beat. Do it wrong and you’re just sending cold emails that get ignored. Do it right and you get coverage.

The old way to do this research: search each journalist by name, read their recent articles, check their LinkedIn, figure out the angle. For 30 journalists, that’s probably 15-20 hours of work — if you’re fast and organized.

I did it with AI deep research instead. Found the relevant journalists, pulled their recent work, identified the story angles that matched their coverage areas, and built the outreach list.

A few hours instead of 20+.

Austin Business Journal ended up covering the event.

Why research is the real AI opportunity

Most businesses are using AI to write things. Email drafts. Social captions. Blog posts. Marketing copy.

That’s fine. But writing is already fast. A decent writer can produce a solid draft in an hour. The improvement AI delivers there is real but incremental.

Research is different. Research is slow. Research is where you lose weeks.

And “slow” isn’t the only problem. The other problem is that expensive research often gets skipped or shortcuts get taken. You need to understand your market, so you read 50 reviews instead of 5,000. You need to know your competitors, so you look at their homepage instead of analyzing all their customer feedback. The quality of the research scales with how much time you put in — and nobody has that time.

AI changes the equation. You’re not just doing the same research faster. You’re doing research that wasn’t economically viable before.

10,000 reviews? No analyst is reading all of those without shortcuts. No team has the bandwidth. But an agent doesn’t get tired, doesn’t skim, doesn’t stop at page 3.

Research tasks AI can handle now

Here’s a short list of research categories that work well with AI agents today:

Competitor intelligence
Scrape reviews, collect feedback, identify patterns. Works across any consumer product category where public review data exists.

Customer sentiment analysis
Feed in support tickets, reviews, survey responses, or interview transcripts. AI finds the patterns faster than any analyst would.

Lead enrichment before outreach
Pull background on a company, its team, recent news, and funding history before a pitch. Works for sales calls, investor meetings, partnership conversations.

Journalist and investor research
Identify who covers your space, what they’ve written about recently, and what angles might land — before you reach out.

Market gap analysis
Compare what consumers ask for vs. what existing products deliver. Works anywhere public feedback data exists.

One thing to try this week

Pick one research task you’re doing manually right now — or skipping because it takes too long.

It doesn’t need to be a 10,000-review project. Start smaller. A competitor’s last 50 reviews. Background on five leads before calls this week. Recent coverage from three journalists in your industry.

Build the agent, run it, see what you get. You’ll know pretty quickly whether AI can absorb that research loop — and what that frees you up to do instead.

I showed up with a microphone and a notepad. Asked for permission to record. And then I spent the entire day watching.

Their weekly team meeting. Patient intake. How staff communicated with each other between appointments. The scheduling process. What happened when something went wrong.

Just observing. No solutions. No recommendations. Just watching how the business actually worked.

Why Most AI Consulting Starts in the Wrong Place

The typical consulting pattern goes something like this: you get on a call with a client, they explain their problems, you propose solutions, you build things.

The problem is that the problem description is almost always incomplete — and not because clients are withholding information. It’s because people describe their work the way they think it should work, not the way it actually does.

When you ask someone “what are your biggest time sinks?” they’ll tell you about the things they’ve consciously identified as problems. They won’t tell you about the 15-minute daily workaround they’ve been doing for three years without thinking about it. They won’t mention the verbal system their team uses to track tasks because the real system broke and nobody replaced it. They won’t describe the friction that’s become invisible because it’s been there so long.

Observation surfaces all of that. You see things the client can’t see precisely because they’re too close to their own work.

At the health clinic, one of the things I noticed early in the day was how their weekly team meeting ran. The doctor would delegate tasks to different staff members verbally. There was no central system for tracking those tasks. No automated follow-up. No accountability loop. Action items were assigned in the meeting and then tracked entirely in people’s memories.

This would never have come up in a discovery call. Nobody would have listed “our verbal task assignment system” as a problem. It just was how things worked.

The Microphone as Research Tool

The thing that makes the observe-first approach practical — and not just an expensive way to spend a day — is the recording device.

I use a small microphone I can wear or clip to a surface. I record everything throughout the day with the client’s permission.

At the end of the observation day, I don’t have notes. I have a full transcript of hours of actual work happening in real time: meetings, workflow discussions, problem-solving conversations, interruptions, questions from staff, explanations of processes.

Then I feed all of that through AI.

The AI processes the full transcript, identifies patterns, surfaces recurring themes, and helps me generate a structured implementation proposal: here are the workflows worth automating, ranked by frequency and time savings, with specific recommendations for each.

The clinic got a proposal the following week that was grounded in their actual workflows — not in how they thought their workflows worked, but in what I’d seen and recorded.

What the Data Shows That People Don’t

The key principle in agent building: automate the tasks that happen daily or weekly, not the rare impressive ones. High-frequency pain creates compounding return.

This is exactly why observation matters. The tasks people consciously identify as problems tend to be the big visible ones. But where time actually goes is in the small, daily, repeated frictions. The verbal task assignment with no tracking. The 15-minute email thread that happens every Monday. The three-step manual process everyone does every single day without realizing there’s a pattern.

You don’t find those by asking. You find them by watching.

How to Do This Yourself

If you’re consulting on AI for clients — or if you want to apply this to your own business — the observation framework is simple:

Before your first working session: Ask for permission to record. Explain that you’re there to understand how work actually happens, not to pitch solutions.

During the observation: Sit in on any meetings that happen that day. Ask people to walk you through their actual workflow in real time, not just describe it. Note every time you see something manual and repeated.

After the day: Transcribe your recordings. Feed the transcripts through AI with this prompt: “Identify the 5 most time-consuming repetitive tasks in this transcript, ranked by frequency.” Use that analysis as the foundation for your proposal.

The proposal you generate from actual observation data will be more specific, more useful, and more likely to get approved than any proposal generated from a 45-minute discovery call.

The observe-first methodology is one of several frameworks I cover in the 4-Day AI Sprint — a structured program for building practical AI workflows for your business.

Then she came to my AI workshop. Two days later, we got on a discovery call. First thing she said: “When I saw Lindy schedule a meeting for 6 cents… I told the VA I’m not gonna hire anybody right now.”

That sentence stuck with me. Because it wasn’t me trying to talk her out of hiring. She got there herself.

Her story

She runs a CPA and bookkeeping firm. Four employees. She’s at capacity — turning away new clients because there’s no room. She wakes up at 4:30am some mornings just to get through emails before the workday starts.

She fired her assistant in March. Not because of attitude. Because after a year and a half of training — every process documented, every preference explained, every workflow laid out — the assistant couldn’t adapt to the new tools the firm was moving to. Had to let her go.

And when she left, everything went with her.

That’s the part that doesn’t get talked about enough. When a human assistant leaves, the training leaves too. All that context about how you like things handled, what tone to use, which clients get priority — gone. You’re starting from scratch with the next hire.

So when the firm owner came to the workshop and saw an AI agent doing real scheduling work… she paid attention.

The 20-cent meeting

She hadn’t known our discovery call was scheduled by an AI agent until I mentioned it.

Lindy had handled the whole thing. Sent the initial email, replied within 30 seconds of every response, worked over the weekend when she followed up on a Saturday. When I told her it was AI, she laughed. She’d described the replies as “pretty humanistic.”

Total cost for scheduling the whole call: about 20 cents.

She’d been comparing $13/hour to that. And she’d done the math before I said a word.

The new question before every hire

A few years ago, the decision to hire an admin assistant was pretty simple: do you have enough work for them? If yes, hire. Figure out the rest later.

That math has changed.

Now there’s a step that comes first: what would this person actually spend their time on? Before you write the job description, before you post on Indeed, before you start interviewing — list the tasks.

Email management. Scheduling. Post-meeting follow-ups. Document routing. Calendar conflicts. Answering client questions that follow a predictable pattern.

Then ask: how much of that could AI handle?

She put it plainly on our call: “I’m not making any hiring decisions until I have a better understanding of what AI can do.”

That’s the new filter. And I think more business owners are landing there on their own.

What AI can’t replace (yet)

This isn’t an argument against hiring. It’s an argument for sequencing.

There are things an AI agent handles well right now: anything that’s high-volume, pattern-based, and doesn’t require the kind of relationship judgment a human brings. Scheduling. Routing emails. Drafting follow-ups. Creating meeting agendas from transcripts. Sending reminders. Answering FAQs.

There are things that still need a human: complex relationship management, calls that require real empathy, judgment calls in ambiguous situations, anything that benefits from genuine trust between people.

The practical question is: what percentage of the role is the first category?

For most admin and EA-type roles in small businesses right now, the answer is somewhere between 60% and 80%.

What I call the AI-first filter

Before the next hire, run through this:

1. List every task that role would own
2. For each task, ask: is this high-volume, pattern-based, and low-judgment?
3. For everything that lands in that category, ask: is there an AI tool that already does this?
4. What’s left over after the AI handles its share — is that enough for a full hire? Or a smaller one?

This isn’t about eliminating people. She has four employees she wants to keep. She wants to grow. But she’s being thoughtful about what the next dollar of headcount buys her.

That kind of thinking is what separates businesses that scale cleanly from ones that add people and complexity at the same rate.

One more thing about her former assistant

The story about the assistant who left stuck with me for another reason.

She’d spent a year and a half building that person up. All those training sessions. All those corrections. Every preference explained. And when the person left, the knowledge went with them — not just the skills, but the specific context about how the firm runs.

An AI agent doesn’t resign. You build the context once — the preferences, the tone, the routing rules — and it stays. It doesn’t get a better offer somewhere else. It doesn’t need to be retrained every 18 months because someone moved on.

For small business owners who’ve been through the hiring-and-losing cycle a few times, that permanence is worth something. Maybe more than the cost difference.

We ended up working together. She enrolled in a 30-day AI implementation. Every week, we deploy one new agent. By October 1st, her firm will have AI handling email management, scheduling, calendar coordination, and post-meeting workflows.

She’ll still have four employees. But none of them will be waking up at 4:30am to get through the inbox.

He’d been reading all the usual advice. Build an audience first. Run ads. Do content marketing. Reach out to strangers on LinkedIn.

I told him to set all of that aside for now and think of 20 people.

Not 200. Not a cold list. Not an email campaign targeting an industry. Twenty specific people he already knew who might plausibly benefit from what he was building.

Then I told him to text or email each of them personally. One by one.

That’s it. That’s the whole strategy for launching something new.

The Mindset Shift That Makes It Work

There’s a reason most people don’t naturally start here, and it comes down to a mindset problem.

When you’re trying to “generate leads” or “acquire clients,” it feels like you need to be convincing. You’re pitching. You’re selling. And the anxiety around that — of being rejected, of seeming desperate, of asking people you know for money — is enough to make most people avoid it and hide behind cold outreach instead.

But there’s a completely different way to think about it.

When I text someone about a new workshop or initiative, I’m not trying to sell them anything. I’m doing something I genuinely believe is valuable, and I want to make sure the people in my life who might benefit actually know about it.

The framing I keep coming back to: it’s an invitation, not a pitch. And if they can’t make it, or it’s not right for them — that’s fine. It was still worth letting them know. A good invitation is a gift even when the answer is no.

That one mental shift — from “I need to convince you” to “I wanted to make sure you knew” — changes the energy of the whole conversation.

How I Applied It With My First Workshop

When I ran my first AI workshop in Austin, I didn’t have a sales funnel. No ads. No landing page optimized for conversions. No cold outreach campaign.

I personally texted and emailed about 20 people I thought would genuinely get something out of it. People I knew, whose businesses I understood, who I had real reasons to think would benefit.

And I posted a simple Instagram story.

The workshop sold out in four days.

I thought it was a fluke. So I ran a second one the following month, same approach.

Sold out again. Five of the attendees from the first workshop came back — they wanted to stay current as AI kept moving.

The thing I didn’t fully appreciate until those first workshops is that the people in your existing network aren’t just potential clients. They’re also connectors. The message I send isn’t only “come to my thing” — it’s also “if this isn’t for you, do you know someone who would get a lot out of this?” One conversation turns into two clients. Two clients generate referrals. That’s how word-of-mouth actually works.

Why 20 Specifically

The number isn’t magical. But it’s useful.

Twenty forces you to be specific. You can’t get to 20 by thinking generically about “my target audience.” You have to actually think about real people — their names, their businesses, the specific reason this might be relevant to them right now.

That specificity is what makes personal outreach work better than any broadcast. When someone receives a message that clearly references their actual situation, they respond differently than they do to a generic announcement.

Twenty is also manageable. It’s not 500 cold emails scheduled through an automation tool. It’s a focused effort that you can do in an afternoon, and it’s sustainable even when you’re the only one on your team.

What to Say

The structure is simple:

1. Reference something specific about them or their business (shows this is personal, not blasted to everyone)
2. Describe what you’re doing in plain language (what it is, who it’s for)
3. Say why you thought of them (the reason should be real)
4. Include a low-pressure out (if it’s not for you, no worries — or, if you know someone who’d benefit, I’d love an intro)

You don’t need a fancy email. A text works. A voice note works. A quick DM works. The channel is less important than the fact that it’s direct and personal.

After the First 20

Personal outreach to your existing network is a launch strategy, not a long-term growth strategy. It works for the first version of something new, and it generates early momentum — which is the hardest thing to create from scratch.

Once you’ve run the thing, gotten feedback, and can point to real results, everything else gets easier. You have something to show. You have testimonials. You have stories.

And the next launch, even with the same 20 people, you’re no longer pitching something unproven. You’re inviting them to something that already worked for people they know.

Asian Efficiency runs workshops for founders, executives, and business owners who want to work more effectively with AI. If you want something more structured and self-paced, the 4-Day AI Sprint is the place to start.

When I sat down with them for a working session, they handed me a prospect spreadsheet. Hundreds of rows. Names, companies, properties, phone numbers.

The ask: can we personalize cold outreach for all of these?

The old way to do this is painful. You pick a name, look them up, find their LinkedIn, read about their company, figure out why your product or service is relevant to their specific situation, write a message that references something real, move to the next person, repeat for hours.

That’s why most cold outreach isn’t personalized. It’s not because people don’t care. It’s because real personalization, at scale, is just not humanly feasible.

We ran a Lindy agent instead.

What the Agent Did

The setup was straightforward. The agent connected to the spreadsheet, ran Perplexity research on each person and company, and generated a personalized LinkedIn or email message for each row. The whole batch took under 30 minutes.

But then Seth noticed something.

“How did it know this building was physician-owned?”

He was pointing at one of the messages. The draft referenced that the building appeared to be physician-owned and framed the outreach around a potential sale-leaseback opportunity.

Nobody had told the agent any of that. The spreadsheet didn’t specify ownership type. We hadn’t written any instructions about sale-leasebacks or medical office specifics. There was no if-then rule for “if physician-owned, then mention sale-leaseback.”

The AI had found it through research. It looked up the building, found information suggesting physician ownership, understood what that means in a commercial real estate context, and surfaced a relevant angle — without being asked.

Seth’s reaction was the one I watch for in every demo: “We didn’t even specify that the list had physician owners and real estate owners. We just gave you a list.”

The Difference Between Execution and Judgment

Most people think of AI in terms of execution. Give it a task, it does the task. Write this email. Fill in this template. Summarize this document. Faster, cheaper, same output.

That framing is accurate for a lot of use cases. And it’s genuinely valuable — automating execution can save enormous amounts of time.

But the physician-owned discovery isn’t execution. Nobody specified the task. The agent encountered information, interpreted what it meant in context, and decided to surface it as relevant.

That’s closer to what a good research analyst or an experienced junior colleague would do. You hand them a list, they come back not just with completed tasks but with observations: “By the way, I noticed this one seems to be a physician-owned building. You might want to mention sale-leaseback potential.”

This is what the “agent as teammate” idea actually looks like in practice — an agent that doesn’t just complete tasks but brings context and judgment to the work.

Why This Changes What’s Possible in Outreach

The conventional wisdom about personalized outreach is that it doesn’t scale. You can send a thousand generic emails, or you can send ten genuinely personal ones. That’s the trade-off.

AI breaks that trade-off.

I built a similar prototype for another client earlier in the year. The system took a prospect list, ran AI-powered research on each individual, and generated personalized LinkedIn messages — including specific details about their company, their recent work, and why the outreach might be relevant. What would have taken hours of manual research per contact ran in minutes for a full list.

The insight that sticks with me is this: the bottleneck in most sales isn’t the send. Everyone can send an email. The bottleneck is the research — figuring out enough about each person that the message actually feels personal and relevant.

When AI handles the research, the bottleneck disappears. And you get something else too: the AI sometimes surfaces angles you wouldn’t have thought to include.

What This Looks Like in Practice

For the real estate brokers, the workflow is now roughly:

1. Update the prospect spreadsheet with new contacts
2. Run the agent
3. Review the drafted messages and tweak any that need adjustment
4. Send

The research that used to be hours per person now runs in batch. The outreach is more personalized than they could have done manually. And occasionally, the agent notices something — like physician ownership and sale-leaseback potential — that adds a genuinely useful angle.

This isn’t magic. It’s an agent with access to a research tool, given instructions about what kind of insights are relevant for their business. But the output is qualitatively different from what the brokers were producing before.

Where to Start

If you do any kind of outreach — sales, partnerships, hiring, networking — the question is worth asking: what research is the bottleneck for you?

Is it understanding each company before a first call? Is it finding the right angle for a cold email? Is it knowing who to prioritize on a list?

Start there. Describe what a great junior researcher would do for you before each outreach. That description is your agent spec.

If you want to see how this kind of agent gets built from scratch, the 4-Day AI Sprint walks through prospect research, personalized outreach, and other practical workflows step by step.

“How did my email actually make it through your filter and flag you directly? I thought you had some kind of inbox agent.”

I do. But he assumed it worked like every other email automation setup he’d heard of. You know — subject contains “podcast” → archive. From this domain → move to folder. If/then. Conditions. Rules.

And that was my setup too, for years. It’s how most people approach inbox automation. It’s also why most people’s inbox automation eventually breaks down.

The Problem With Rules-Based Email Filtering

Email rules made a lot of sense before AI existed. You were essentially writing logic: if this keyword appears, do this thing. If the sender matches this pattern, do that.

The problem is that keywords don’t carry intent. The word “interested” in an email from someone asking about coaching is completely different from “interested” in a sales pitch or a newsletter. “Following up” in an email from a potential client you spoke to last week is completely different from a cold outreach sequence auto-following up for the fifth time.

Rules can’t tell the difference. They match patterns. They have no idea what the person actually wants.

So either your rules are too narrow (you miss things) or too broad (you get noise). Most people end up maintaining a complicated tangle of overlapping conditions that still doesn’t catch everything. Or they give up and go back to checking email manually.

What Changes When You Use Context Instead of Conditions

Here’s how I explained my setup to Patrick:

“I’ve programmed it now so that AI doesn’t work on if/then conditions. It can actually work on understanding an email in itself… interpreting context. If someone’s pitching to be a guest on my podcast, it automatically goes to archive. I just never see this. If it’s something like ‘I’m interested in coaching or interested in working one on one’ — that always goes direct to me. The agent will update me personally in Slack and say, hey, we have this email from so and so, is this something you want to reply to? And then it will actually help me draft the reply as well, but I always have to do a final sign-off on it.”

That’s the entire design. The AI reads each email. Understands what the person wants. Makes a routing decision based on intent, not keywords.

Podcast pitches get archived without me ever seeing them. They should go to a VA’s queue, not mine.

Coaching inquiries get flagged in Slack with full context. Who emailed. What they said. What they seem to want. Whether they’ve been in contact before.

Then the agent drafts a response. I read it, tweak if needed, and send.

Why Human Review Still Matters

This is the part people sometimes skip over when they set up inbox agents, and it’s actually the most important piece.

I do not let the agent send emails on my behalf. It drafts. I review. I send.

This is what I think of as “human in the loop” design. The AI handles the execution-heavy work — reading, routing, drafting — which is where most of the time goes. The human stays in control of anything that actually gets sent, especially anything that involves relationship-building or new contacts.

Editing a draft takes about 30 seconds. Writing a reply from scratch takes five to ten minutes. The leverage is real. But the quality control is what makes it sustainable long-term.

I’ve seen setups where people auto-send everything, and they almost always create problems. One client found his agent was sending oddly formal replies to close friends. Another had responses going out before he’d even looked at whether the opportunity was worth pursuing.

Drafting plus human sign-off is the right pattern for relationship-sensitive email. Let the AI do the thinking. Keep yourself in the loop for anything that matters.

Building It: Simpler Than You’d Expect

The way this works in practice isn’t complicated to set up.

The agent connects to your email inbox. It’s given a set of instructions written in plain English — not code, not formal logic — describing how to handle different types of emails. The instructions include examples where helpful.

For mine, it roughly says: if this is a request to be a guest on TPS, archive it. If this is someone expressing interest in my coaching or consulting services, message me in Slack with context and a draft reply. If this is something operational or from someone I’m actively working with, flag it separately.

The AI reads each email against those instructions, interprets what the email is actually about, and acts accordingly.

When I built a similar system for a CPA client who was drowning in email during tax season, the results were immediate. Within a few days she went from feeling overwhelmed by inbox volume to having a clean, filtered flow. The relief in her voice when she described it was clear — and she hadn’t changed anything about how she worked, just about how email reached her.

One Test Worth Running

If you’ve never tried AI-based email routing, the easiest way to start is to describe your inbox in plain language to an AI.

Write out: “I get these types of emails regularly: [list]. I want to ignore these: [list]. I need to see these immediately: [list]. I should respond to these within 24 hours: [list].”

Then ask the AI to draft a routing setup based on what you wrote. See what it comes up with. That exercise alone often surfaces patterns in your inbox that you hadn’t consciously noticed.

Once you can describe your routing logic in plain language, you can build an agent around it.

The 4-Day AI Sprint covers inbox management, email agents, and a dozen other workflows like this. If you want a structured way to apply this to your own work, that’s the place to start.

The audience was genuinely stunned. People who’d never heard of these tools. Couldn’t believe what they were seeing.

But here’s the thing. These same creators had published content about these tools. Videos. Short-form clips. Podcast episodes. The information existed. It was out there for anyone who wanted it.

The business owners in that room just hadn’t been watching.

The Online AI Content Problem

We’re in a saturated moment for AI content. Every creator, every influencer, every newsletter is publishing “this new AI thing will blow your mind.” The videos all start the same way. The thumbnails look the same. The hooks are identical.

Business owners aren’t dumb. They’ve noticed the pattern. At some point in the last year, a lot of them stopped clicking.

Not because they don’t care about AI. Because they’ve been burned too many times by hyperbolic content that either doesn’t apply to their business or requires a technical skill set they don’t have. The signal-to-noise ratio got bad enough that they just tuned out.

This creates a real problem for anyone trying to educate business owners about what AI can actually do. The content pipeline — videos, articles, courses, newsletters — isn’t reliably reaching the audience that most needs it.

But there’s a format that is still working: in-person.

What Changes in the Room

I started running in-person AI workshops in Austin earlier this year, partly because I noticed my online Asian Efficiency courses weren’t reaching local people. The content existed. Nobody local was signing up for it. So I brought it directly to them.

What I’ve observed in those workshops is that in-person delivery creates something video almost never does: the genuine “I didn’t know that was possible” moment.

I had a personal chef attend one of my early workshops. She had zero technical background. No coding experience. Had never built anything more complex than a spreadsheet. She showed up convinced that AI was probably “not for her.”

By the end of the day, she was building custom GPTs that automated parts of her client management workflow. Not because she’s exceptionally talented, and not because I’m an exceptional teacher. Because she saw it happen right in front of her — and couldn’t unsee it.

That’s the difference. When you watch a video explaining what’s possible, you receive information. When you see it demonstrated live, and then immediately try it yourself, something different happens in your brain. The barrier between “I know this exists” and “I can do this” collapses.

You Don’t Know What You Don’t Know

There’s a concept I come back to constantly when explaining why in-person workshops work: you don’t know what you don’t know.

When a business owner signs up for a workshop, they arrive with a list of problems they think AI could solve. Maybe three to five things. And those aren’t wrong — we cover them.

But once they see a live demo of AI doing a SWOT analysis on a business scenario, or drafting a proposal from a voice note, or running an inbox triage… they suddenly realize there are thirty more applications they hadn’t thought to ask about. Ideas come flooding in. Questions get specific and practical.

You can’t produce this effect with a video. They can watch a demo of the same thing and have a mild “huh, interesting” reaction. Seeing it in a room they’re in, with other business owners having the same revelation at the same moment, creates an entirely different level of engagement.

How I Fill Workshops With a Few Texts

The practical result of this is that my Austin AI workshops have been selling out. And not through elaborate marketing.

I send a few text messages to people in my network. I post an Instagram story. That’s it.

No paid ads. No sales funnel. No launch sequence. Just: here’s the date, here’s the venue, DM me if you want a spot.

Part of this is that I’ve been building a network in Austin since 2014, so I have relationships to draw on. But it’s also because in-person AI education is genuinely undersupplied right now. There’s enormous demand from business owners who want practical knowledge, who’ve given up on online content, and who respond immediately to the offer of a live, hands-on session.

The Broader Implication

A friend who has built a massive online audience was making the same observation from his side. He and his co-founder have spent years building that reach. They’ve published genuinely good content about AI tools. And yet when they get in front of business owners at conferences in person — people who should, in theory, be their audience — those people haven’t seen any of it.

“Business owners have tuned out to AI content because it’s so noisy,” he said. “But if you can get in front of them in person, it’s completely different.”

This is an opportunity for anyone doing AI consulting or education. The online market is oversaturated with content that mostly reaches people who are already plugged in. The offline market — local business owners, regional executives, professional communities — is largely untouched and highly responsive.

If you’ve been trying to teach AI through videos and courses and not getting the traction you expected, consider whether the audience you’re trying to reach has simply stopped watching. The in-person room might be where the unlocked demand is right now.

If you want to bring an AI workshop to your team or community, I run these in Austin regularly and have been expanding to other formats. Reach out directly to discuss — or check the 4-Day AI Sprint if you want the structured version you can go through at your own pace.

It works. But there’s a different play that I think is more interesting — and potentially a lot more valuable over time.

The idea: instead of charging for your time, take equity or a commission on results your automation actually generates. Build it once for one company. Then replicate it across every company in that industry with the same problem.

This is what I’ve been doing, quietly, for the past several months.

The Medical Building Deal

I’m working with a real estate company here in Austin that specializes in buying medical buildings. Their target sellers are doctors and dentists who own the building where they practice — dermatologists, primary care physicians, specialists — and who might want to sell.

Their lead generation process, when I first looked at it, was completely manual. A Philippines-based virtual assistant would go through public databases, identify building owners, paste names and contact information into a spreadsheet, and try to verify who actually owned what. Then a broker would work down the list, calling each one.

It worked. But it was slow, expensive to staff, and limited by how fast a human could click through databases.

I’m automating the entire pipeline with them. Automated research, owner identification, data enrichment, outreach sequencing — the kind of workflow that, once built, runs itself.

The deal structure in an arrangement like this: no project fee at all. In exchange for building and maintaining the system, you take a percentage of whatever commission the company earns on any deal that closes from leads the system generates.

The math on one transaction: a $10 million medical building, sold at a 2% broker commission, generates $200,000 in commission. A 20% share would be $40,000. From one closed deal.

Build it once. Every time a deal closes, the system generates a return.

Why Equity Beats Fees

The obvious attraction is upside. Project fees are capped — you scope the work, you get paid, you’re done. Equity positions grow with the business’s success.

But there’s something else. When you have equity in a company’s results, your incentives align with theirs in a way that straight consulting never does.

A friend who co-founded a large online-education business was telling me about a similar arrangement they have with a recruitment agency in South Africa they invested in. He said something that stuck with me: “When you’re just selling lead gen, there’s not really that much incentive to improve the conversion. But if you have equity, suddenly you care about all the other elements of the business. And there’s usually a lot of inefficiencies there too.”

When you have skin in the game, you notice things you’d otherwise let slide. You’re invested in the whole system working, not just the piece you built.

The Industry Replication Play

Here’s where it gets more interesting than a single deal.

Once you build a working automation for one company in an industry, you have something valuable: a proven template that solves a problem the entire industry has.

Medical office building brokers aren’t the only ones with manual lead gen pipelines. Every firm doing this kind of work — identifying building owners, sourcing off-market deals, doing outreach at scale — is running some version of the same process.

Once I can show that the automation works — real leads, real closed deals — I can take that template to every company in this vertical. Same equity structure. Same model.

Brad Jacobs wrote about this in How to Make a Few Billion Dollars. He’d identify industries stuck in outdated operational models — waste management, trucking, other fragmented sectors where nobody had bothered to modernize. He’d buy one company, build the technology, prove it worked, then acquire similar companies and roll out the same technology across all of them.

I’m not buying companies. But the underlying principle applies: go deep in one industry, build the automation once, then replicate it horizontally.

How to Think About Building This

Start with industries that are obviously manual. The best targets are businesses where a person is doing something repetitive that looks like data work — copying from databases to spreadsheets, verifying information by hand, sending similar messages to a list. If it involves judgment and relationship nuance, that’s harder. If it’s research and outreach at scale, that’s the sweet spot.

Look for high-stakes transactions. The equity model works best when individual deals are large. Real estate, healthcare contracts, professional services, commercial leasing — sectors where a single closed transaction generates enough commission that your 20% is meaningful.

The equity percentage should match your ongoing involvement. If you’re building and walking away, a smaller stake makes sense. If you’re maintaining and iterating over time, a larger ongoing percentage is appropriate.

Get the first one working before you think about scale. The replication play only works if the original automation actually delivers results.

Where This Fits

The AI consulting market right now has a lot of noise. Everyone is an automation expert. Project fees are getting competitive.

The equity model is different because it ties your compensation to outcomes, not deliverables. It also creates compounding value — a project fee is spent once and done. An equity stake in a working system keeps generating returns as long as the system runs.

Not every engagement should be equity-based. But for industries with high-value transactions, clear automation opportunities, and potential to replicate across multiple companies, this is the model I’m most excited about right now.

This approach is one of the core frameworks I cover in the 25X Productivity System — if you want to explore how to structure your AI work for maximum leverage, that’s a good place to start.

Near the end of our call, I told him something that surprised him.

“The email coordination we did to schedule this call — that wasn’t a person on my team. That was an AI agent.”

He paused. “I had no idea. I just figured it was someone you worked with.”

The agent had replied to him within 60 seconds. The total cost to coordinate the meeting: 11 cents.

This is the story I tell when someone says “but AI can’t handle the human-feeling stuff.”

The Setup

I’ve been running an AI coordinator called Linda for months. Linda is built on Lindy — a no-code AI tool I use for several automations. The instructions I gave Linda were written out once, in plain language: here’s how to check my availability, here’s how long meetings should be, here are the times I prefer, here’s what to do if someone wants to reschedule.

That’s it. From that point, Linda handles the back-and-forth.

When someone emails asking to set up a call, I CC Linda. She checks the calendar, replies with available options, confirms when they pick a time, sends a calendar invite, and handles any changes if something comes up. The whole thing runs without me.

What Used to Take 20-30 Minutes

Here’s what meeting coordination actually involves: reading the initial email, checking your calendar across days and time zones, writing a reply with options that work, waiting for their response, confirming, sending a calendar link, and sometimes going back-and-forth another two or three times.

For a typical meeting, that’s 20-30 minutes of email overhead, spread out over a day or two. Multiply that across a week of scheduling and it adds up fast.

Linda does all of that in under a minute, for 11 cents per meeting.

The number that gets people isn’t the 11 cents — it’s that the founder didn’t notice. He runs a company built on AI. He understands the technology. And the experience still read as human to him. Not because it was tricky, but because it was just… fine. Normal. Exactly what you’d expect from a competent assistant.

The Real Lesson

I used to think the benchmark for AI handling a task was “can it do this perfectly?” That’s the wrong benchmark.

The right benchmark is: does the person on the other end have a normal experience?

Linda makes minor errors sometimes. Occasionally the phrasing is slightly off, or she suggests a time I wouldn’t have picked. But in practice, none of that matters. People just treat it like communicating with a competent assistant. They reply, they confirm, they show up.

The bar for replacing calendar coordination isn’t perfect — it’s good enough that nobody notices. Linda cleared that bar a long time ago.

One More Detail

For a while, I had Linda reply as fast as possible. Then I noticed something: when she replied in under 60 seconds, some people got suspicious. The turnaround was too fast. Nobody expects a human assistant to respond in under a minute.

So I added a three-minute delay. It now waits a bit before sending, which makes the response feel more natural. That’s the whole fix.

This is a good example of how AI implementation usually works. The first version works technically. The second version works humanly. The adjustment is almost always small.

What This Means for Your Workflows

The question I get most often is some version of: “where should I start with AI automation?”

Calendar coordination is one of the highest ROI places to start, and here’s why:

It’s well-defined. The instructions are the same every time: check availability, send options, confirm. There’s no ambiguity about what success looks like.

It’s repetitive. Most people schedule dozens of meetings a week. Every one of those is 20-30 minutes of admin that can be automated.

It’s already good enough to pass the test. The founder didn’t notice. That’s the whole argument.

If you’re not using AI to handle scheduling yet, this is a reasonable place to start. Set up an agent with your availability preferences and scheduling rules. CC it on emails when someone asks to meet. See what happens.

The 4-Day AI Sprint walks through exactly this kind of setup — starting with high-ROI, low-risk workflows like this one and building from there.

Meeting after meeting. Deck sent. Follow-up call. Another week of silence. Follow-up again. Sometimes you close. More often, you’re just spinning.

I told him there’s a better format. He said it was the most useful thing he’d heard about fundraising in a while.

Here it is.

The Problem with Hand-to-Hand Combat

Most fundraising is what I’d call hand-to-hand combat. You identify a prospective investor, you schedule a meeting, you pitch them, you answer questions, you follow up, you wait. Then you do it again with the next one.

Each meeting starts from scratch. You have to rebuild context every time. And the investor pool — even if you have a good one — can only be accessed one person at a time.

For a $1M raise, this is manageable. For a $5M or $10M raise, you’re looking at dozens of these conversations stretched across months. By the time you’ve worked through the list, your early conversations are cold, your energy is depleted, and you’ve spent most of your time on process instead of on the deal itself.

There’s a leverage problem here. And the solution isn’t to get better at pitching. It’s to change the format.

The Fundraising Dinner

What I’ve been doing for several years now is hosting what I call a fundraising dinner.

The basic structure: invite around 30 investors to a buffet-style dinner. Send them the deck beforehand. They arrive knowing exactly what they’re walking into and why.

That last part matters more than it sounds. Investors who show up to the dinner have already self-selected. They read the deck, they decided it was worth their evening, they came. The people in the room are already warm before anyone opens their mouth.

Then the pitch happens — but it’s to a room, not a person.

What Group Format Does That One-on-One Can’t

Here’s where the dynamic gets interesting, and where most people underestimate the format.

In a one-on-one pitch, you’re the only energy in the room. If the investor is skeptical, you have to work against their skepticism alone. If they ask a tough question, there’s no one else to answer it from a different angle. The whole conversation depends on the chemistry between two people.

In a group setting, something else happens.

When someone asks a sharp question and gets a good answer, the room hears it. When someone follows up with their own version of the same concern, the room hears that too. And when someone — especially someone other investors know and respect — says they’re interested, everyone notices.

That’s FOMO. Real, organic, social proof playing out in real time.

You cannot manufacture this in a one-on-one meeting. It emerges naturally in a group. People watch each other. If a serious person is taking this seriously, it legitimizes the deal in a way no pitch deck can.

After the Dinner

One-on-ones still happen. But they’re a different kind of conversation.

By the time you’re meeting with an investor post-dinner, they already know the deal structure. They’ve met the founder or fund manager. They’ve seen other serious people engage with it in real time. The basics are already covered.

So instead of spending the first 30 minutes building context from scratch, you’re having the substantive conversation immediately. What’s the timeline? What does the cap table look like? Can you walk me through the downside scenario again?

These conversations close faster because the groundwork was already laid in the dinner setting.

I’ve used this format to close raises in a fraction of the usual time. The format does a meaningful portion of the work.

The Fee Model

For deals I’m interested in personally, I’ll set up an SPV and participate directly. The dinner format is still the mechanism, but I’m a co-investor alongside the people I bring in.

For deals where I’m facilitating but not investing, the fee is a flat project rate for the curated dinner — the investor curation, the deck review, the dinner itself, and the follow-up structure. I can’t guarantee that anyone writes a check, but I can guarantee that the right people are in the room and have seen the deal.

The guarantee is access and attention, not conversion. Though the conversion rate has been good.

Why This Works

The short version: it applies leverage to a process that’s historically unlevered.

Every optimization I’ve made in my professional life — in productivity, in my business, in my consulting work — has been about finding where you can use a structure or a system to multiply output per unit of effort. Fundraising one-on-one is one person selling to one person at a time. Fundraising in a dinner format is one founder selling to thirty people in one evening, with social dynamics doing much of the closing work.

The format respects everyone’s time. It creates better conditions for the deal to speak for itself. And it produces outcomes faster.

If you’re raising capital in the sub-$10M range — or if you know a founder or fund manager who is — feel free to reach out. I’m always open to taking a look.

If this kind of high-leverage approach to building your network and deal flow interests you, it’s part of a broader philosophy I teach in the 25X Productivity System — optimizing for output per unit of time, in every area of your professional life.

He expected something practical. “Data science” maybe. Or “computer science.” Something that sounded like a career.

I said: debate. Philosophy. Math. Physics.

He paused. “Those don’t sound like jobs.”

He’s right, they don’t. But they teach something that most jobs are going to need badly: the ability to think through a problem, evaluate information critically, and actually decide what’s true.

That’s the skill that matters now. And most people are still optimizing for the wrong one.

What Changed

For most of modern history, knowledge was genuinely valuable. People who knew a lot could command premium prices. Doctors, lawyers, accountants, consultants a significant part of what they sold was access to information that other people didn’t have.

If you knew how to read a balance sheet, that was worth something. If you understood the tax code, that was worth something. If you could diagnose a condition based on symptoms, that was worth something.

The scarcity of knowledge was the whole game.

AI changed the game.

Now you can ask an AI to explain the periodic table of elements, summarize recent case law, outline the symptoms of a rare condition, or walk through the logic of a financial model — and get a pretty good answer in seconds. For free.

The information isn’t scarce anymore. Which means the information isn’t the edge anymore.

What the Edge Actually Is Now

Here’s what AI still can’t do reliably: tell you whether the information it gave you is right.

It can produce a confident-sounding answer. It can cite sources. It can explain the logic. But it can’t evaluate that logic from the outside. It can’t catch its own mistakes the way a good critical thinker can. It doesn’t have a judgment layer that operates independently of its training.

That’s still a human thing. For now.

Judgment — knowing whether something is true, whether a source is reliable, whether a conclusion actually follows from the evidence — is the skill that doesn’t get commoditized when AI gets better. If anything, it becomes more valuable, because there’s more AI-generated content to wade through, and someone has to be able to tell what’s actually worth acting on.

I said this directly in the conversation with my friend: “Knowledge used to be very powerful. People who had a lot of knowledge could command premium prices. But nowadays knowledge is becoming more commoditized because of AI. You don’t have to memorize the periodic table anymore. You can just ask AI about it. But judgment and evaluation is something AI cannot do yet.”

That last part is doing a lot of work. Yet is a word worth paying attention to. But right now, today, the asymmetry is real.

Why This Is Hard to Act On

The tricky part is that most of our education system — and most career development — is still built around the old model.

School rewards memorization. Exams test recall. Credentials signal that you know a body of information. Even corporate training tends to be “here’s what you need to know about X.”

The few disciplines that genuinely teach you to think rather than just know are the ones that tend to get undervalued. Debate teaches you to construct and tear apart arguments. Philosophy teaches you to spot logical errors and hidden assumptions. Physics and math teach you to follow an argument rigorously to its conclusion and notice when something doesn’t add up.

None of those feel immediately practical. But the actual skill you’re getting from them is the ability to evaluate information rather than just retrieve it. And that transfers everywhere.

What This Means at Work

This isn’t just a college advice question. It applies to anyone in a professional context right now.

The people who are getting the most out of AI tools aren’t the ones who know the most about a subject. They’re the ones who can tell when the AI is wrong. Who can push back on a confident-sounding answer with a better question. Who can take what AI produces and actually decide what to do with it.

I see this constantly in the AI workshops and coaching work I do. Two people can use the exact same tools, with access to the exact same information. One of them makes dramatically better decisions with it. The difference is almost never knowledge. It’s judgment — the ability to evaluate what they’re getting and figure out what it means.

That skill is learnable. But it takes more than just knowing things.

The Practical Takeaway

If you want to be genuinely hard to replace over the next decade, the question isn’t “what should I learn?” It’s “how well can I think?”

A few places to start:

Practice being wrong deliberately. Take a position and argue for it, then argue against it. Notice where your logic breaks down. This is basically what debate trains you to do.

Get in the habit of sourcing. When AI gives you something confident, ask where it came from. Try to find it. If you can’t verify it, treat it as a hypothesis, not a fact.

Work on decisions under uncertainty. Most judgment isn’t about knowing the right answer — it’s about figuring out the best move when you don’t know. Practice making calls with incomplete information and reviewing how they turned out.

Read more philosophy and logic. I’m serious. One book on critical thinking or informal logic will do more for your judgment than most courses on “AI skills.”

The information age put a premium on knowing things. The AI age puts a premium on thinking about what you know.

If you want to build better judgment about AI — when to use it, how to evaluate it, where it breaks down — the 4-Day AI Sprint is designed around exactly that. Not just how to use the tools, but how to think about what they’re giving you.