I never published it.

I remembered writing it. I was a year and a half into home brewing at the time, sitting on my back porch with a glass of Poolside Shandy (my summer recipe, trademark pending, not really), and I thought I was writing about customer experience. Apparently, I didn’t think it was ready. I saved the draft and moved on.

I re-read it last week with fresh eyes.

I wasn’t writing about customer experience. I was writing about AI adoption. I just didn’t know it yet. And apparently, neither version of me thought it was worth publishing — until now.

Every lesson I pulled from five years of fermenting five-gallon batches in my garage maps almost perfectly to the conversations I’m having right now with colleagues, industry analysts, students, and organizations trying to figure out how to actually build AI capability. Not just buy it.

Let me show you what I mean. And let me add what seven years of hindsight gives me that 2019 Fred didn’t have.

Lesson 1: Results Are Only Good If You Can Repeat Them

What I wrote in 2019:

Sierra Nevada didn’t build their second brewery near their original one in California. They built it in Asheville, South Carolina, because the river water there has the same mineral composition as their source water out west. Same ingredients. Same process. Same result. Same customer expectation met, every single time.

Samuel Adams does the same thing. They engineer the water in their other locations to match the composition in Boston. They go to those lengths because their customers expect a consistent experience from every glass.

My point in 2019 was about brand consistency and customer experience. Meet the expectation every time, or you lose the relationship.

What it actually means in 2026:

This is the most underdiagnosed failure mode in AI adoption right now.

Companies celebrate the pilot. The proof of concept works beautifully. Leadership gets excited. The deck goes to the board or an executive team. Then they try to scale it, and nothing replicates. The model behaves differently in production than it did in the demo. The workflow that worked in one team breaks in another. Output quality degrades the moment another person runs the same process.

And everyone blames the technology.

The technology is not the problem. The problem is that nobody engineered for repeatability.

Sierra Nevada didn’t assume their second brewery would produce the same beer automatically. They studied the inputs, identified the variables, and intentionally controlled for them. Most organizations deploying AI skip this step entirely. They get a win, declare victory, and move on without ever documenting what produced the win in the first place.

Repeatable AI results require the same intentional engineering Sierra Nevada applied to water composition. Documented prompts. Standardized workflows. Defined quality benchmarks. A clear record of what input produced what output under what conditions.

Without that, every AI win is a one-time event. And one-time events don’t build organizational capability.

MIT’s NANDA initiative studied 300 public AI deployments and found that only 5% of AI pilots achieve meaningful business impact. S&P Global puts the abandonment rate even more bluntly: 42% of companies scrapped most of their AI initiatives in 2025, up from 17% the year before. Nearly half of all proof-of-concepts never make it to production.

That is not a technology problem. That is a repeatability problem.

Lesson 2: Experimentation Can Lead to Brilliance — or Disaster

What I wrote in 2019:

Home brewing taught me to document everything. Every change to a recipe, every adjustment to the process, every batch that came out wrong. Write it down, because you won’t remember six weeks from now when you crack the first bottle and try to figure out what happened.

ABT. Always Be Testing. I wrote that this was almost silly to call a lesson, because every marketer knows it. And yet even in the most mature marketing organizations, disciplined testing is not actually the norm. You have to document what works before you can experiment intelligently on top of it.

The frustrating thing about brewing, I noted, is that you wait weeks to find out if an experiment worked. Unlike digital marketing, where you can run an A/B test and have results by Thursday, brewing demands patience between hypothesis and data.

What it actually means in 2026:

AI collapsed that feedback loop to almost nothing.

You can test a prompt variation in thirty seconds. You can run a workflow experiment in an afternoon. You can iterate on an AI-assisted process faster than any A/B test I ever ran as a marketing leader.

That speed is the most powerful thing about working with AI tools right now. It is also, frankly, the most dangerous.

The faster you can experiment, the faster you can drift without ever building anything durable. I see this constantly. Teams are running AI experiments at a pace nobody could have imagined five years ago, and almost none of it is documented. What prompt produced that output? What model? What context was provided? What changed between the version that worked and the version that didn’t?

Nobody knows. Because nobody wrote it down.

The organizations building real AI capability treat every deployment like a brew log. What went in. What came out. What changed. What they’re trying next and why. Most AI experimentation inside companies today is completely undocumented, which means every win is accidental, every loss is unexplained, and institutional knowledge evaporates the moment the person who figured it out leaves the team.

Always Be Testing still applies. It just requires Always Be Documenting first.

Lesson 3: You Can’t Buy Your Way to Competency

What I wrote in 2019:

You can spend $7,000 on a home brewing system. Pumps, pots, oxygenation tools, a full professional-grade setup. I know people who have done it. And the equipment doesn’t make you a better brewer if you don’t know the fundamentals.

I started with a basic kit. A five-gallon pot, a spoon, some buckets. After a couple of batches and a few complaints from my family about the smell inside the house, I invested in some new gear to brew outside. The gear helped. But the gear was only useful because I’d already put in the time to understand the process.

My point in 2019 was about organizational investment. The best CX technology stack in the world doesn’t produce great customer experiences if you haven’t invested in the people running it. Who’s your rockstar developer? What are they doing to mentor the junior team members? Where’s the organizational knowledge being built, not just stored?

What it actually means in 2026:

This one landed harder than any other when I re-read it.

Organizations are spending real money on AI. Enterprise licenses. Platform fees. Implementation consultants. Customization projects. I’m not saying those investments are wrong. I’m saying they are the $7,000 brewing system, and most of the people who bought them have never fermented anything.

The tool is not the capability. The tool is the ceiling your capability can reach, if your team has built the foundation to get there.

AI literacy is the foundation. The ability to prompt effectively. To evaluate outputs critically. To understand where the technology is reliable and where it fails. To identify which workflows actually benefit from AI and which ones don’t. To know the difference between a good result and a confident-sounding wrong answer.

Without that foundation, you have expensive software and a team that doesn’t know how to use it. You have a $7,000 brewing system and no one who understands fermentation.

Pluralsight’s 2025 AI Skills Report found that 65% of organizations abandoned AI projects specifically because their people didn’t have the skills to execute them. Not because the technology failed. Because the humans weren’t equipped.

BCG found that only one in three employees say they’ve been properly trained on AI. McKinsey found that 80% of organizations know upskilling is the most effective lever for closing the skills gap — and only 28% are actually investing in it.

We know what works. We’re just not doing it. The investment that matters most right now is not the platform. It’s the people.

Lesson 4: Patience Is a Virtue, But Not at the Expense of…

The honest part:

Here’s where I have to come clean.

The original post ends here. Lesson four has a title and nothing else. The body is blank. I never finished writing it.

I have no idea what “not at the expense of” was supposed to say. My best guess is momentum. Or competitive position. Or the window of opportunity that closes while you’re waiting for certainty.

I’m going to finish it now. Seven years late.

What it actually means in 2026:

Patience in AI adoption is not a virtue. It is a liability dressed up as prudence.

I understand the instinct. AI is moving fast, the landscape is confusing, the ROI frameworks aren’t clean, and nobody wants to make a major organizational bet on something that might look completely different in eighteen months. I get it. I’ve had those conversations. I’ve felt that uncertainty myself.

But the organizations that waited for AI to “mature” before experimenting lost two years of institutional learning they cannot buy back. The learning curve is not theoretical. It’s real, and it takes time to climb. The teams that started building in 2022 and 2023, even imperfectly, even with tools that have since been superseded, developed judgment that the late movers are still trying to acquire.

The ones waiting now for a clear ROI framework before committing are making the same mistake with a shorter runway.

Only 6% of organizations currently qualify as AI high performers — meaning they’re seeing real, measurable impact on the bottom line. That means 94% of companies are still on the wrong side of the divide. The window is open. But it won’t stay that way indefinitely.

Patience is a virtue, but not at the expense of momentum.

The feedback loop in AI is faster than anything we’ve worked with before. The cost of a failed experiment is low. The cost of not experimenting, of waiting on the sidelines while your team watches from the edge of the pool, is measured in the capability gap between your organization and the ones that jumped in.

Jump in.

What This Is Actually About

I want to be clear about something: this isn’t a post about being prescient. I wasn’t. I wrote about brewing beer because I liked brewing beer, and I drew some marketing lessons because that’s what I did.

What I’m pointing at is something more useful than hindsight.

The fundamentals don’t change. Repeatability. Disciplined experimentation. Investing in people over tools. Not confusing patience with avoidance. These were the right principles for building great customer experiences in 2019. They are the right framework for building AI capability in 2026.

The technology changed. Human organizational behavior didn’t.

The companies struggling with AI adoption right now are not struggling because the technology is too hard. They’re struggling because they haven’t applied the same operational discipline to AI that any good brewer applies to a batch of beer. Document the inputs. Control the variables. Invest in the craft before you invest in the equipment. And don’t wait so long for perfect conditions that you miss the window entirely.

I’ve been thinking about this stuff for longer than I realized. Every week, I go down rabbit holes, experimenting with my own ideas, and the more I’m convinced that the gap isn’t technical.

It’s operational. It’s cultural. And it’s closeable.

Go back and read something you wrote five years ago. You might be surprised what you already knew.

The post I Wrote About Beer in 2019. It Turned Out to Be a Blueprint for AI Adoption. appeared first on According to Fred.

Let me say that again:

Voluntary. 300 teenagers. Five days a week. Four+ hours per practice.

All for a 15-minute show. Nine minutes of performance and six minutes of setup and teardown. And they crushed it.

If you’ve ever seen a high-level marching band performance, you know what I’m talking about. It’s not just music. It’s synchronized movement. It’s emotion. It’s storytelling. It’s precision on a scale most business teams could only dream of. And yet…they’re teenagers. No salary. No bonus. No KPI dashboard. Just accountability and shared purpose.

That got me thinking:

Why are high-performing teams so common in activities like this, but so rare in the business world?

Every Step, Every Note, Every Motion — Perfectly in Sync

To compete at this level, each band member has to hit every mark. No exceptions and no room for error. If you want to win at a high level, you must: 

• Play their music with exact pitch, tone, and timing
  
• March with precise foot placement, body angles, and visual expression
  
• Stay aligned not only musically, but physically, and emotionally, with 299 other people
  
• Execute feedback from peers and adults within minutes and hours, not weeks
  

In the world of marching band, perfection is the standard. Not because they’re forced to do it, but because they choose to. Every student knows their role matters. If one person misses a cue, it doesn’t just impact them. It shifts the entire formation. It affects everyone else’s timing. It throws off the story. It can mean the difference between making the top 15 schools in the finals or going home after prelims. 

There’s no room for ego. No one’s freelancing. They’re accountable to the whole.

It’s actually quite impressive to watch.  

In Business? We Drift

Now compare that to most corporate teams. I’ve worked at more than one company in my career, been on more than one team or project that just feels “off.”  Too many meetings with no agendas and no prescribed outcomes.  Lots of talking about the same issue with no resolution. That’s not how teams work.  Not how high-performing teams work.  Tell me you haven’t experienced at least one of these in your career.

• Feedback is often avoided because it feels “personal”
  
• Individuals optimize for their career, not the collective outcome
  
• Teams focus on visibility, not clarity
  
• We celebrate business, not precision

I tend to come back to this book every so often and re-listen to it (I’m big into audiobooks) by Patrick Lencioni.  In his book, The Five Dysfunctions of a Team, Lencioni writes one of the most common breakdowns in business is the avoidance of accountability. Teams are “so concerned about not jeopardizing relationships that they fail to hold one another accountable for behaviors and performance.” 

In a marching band, that mindset would get you benched — or worse, it would tank the performance for everyone else. In business, somehow, we let it slide. This is not only a team problem, but a leader problem.  Be it a team lead, a VP, or CEO. Accountability for your performance is not only a “you” problem, but a “leader” problem. 

The Competitive Edge: Peer-Driven Accountability

One of the most powerful things about watching my son’s band wasn’t just the talent; it was the trust. Trust from their peers and directors. Trust that they have a shared vision of success. 

When you think about your relationships, work or not, feedback is a polarizing experience. These students want feedback. They expect correction. Not to be judged, but to get better. They run the show hundreds of times because they know that mastery lives in repetition. They own their mistakes and fix them in real time. They cheer each other on. They give each other side-eye when someone’s off tempo. It’s not personal.  It’s the price of performing at a high level. 

And guess what? It works.

According to a New York Times Magazine article about Google’s quest to build the perfect team, teams with strong peer accountability outperform others significantly — not just in output, but in innovation and resilience. As the article notes, “teams that hold one another accountable shift from a culture of individualism to one of performance.”

In other words:

Accountability isn’t about control. It’s about commitment. And it is about growth. 

What Business Leaders Can Learn (and Do)

Here’s the kicker: nobody forced these 300 students to practice for 20+ hours a week, run drills in the rain, or take critiques from instructors, judges, and each other. They did it because they were part of something bigger than themselves.

If we want that kind of energy in the workplace, we have to create the environment for it:

• Set shared standards, not just individual metrics
  
• Normalize real feedback — not just during annual reviews
  
• Design for interdependence, not silos
  
• Reward collective wins, not just individual contributions
  
• Model it from the top — if leadership avoids accountability, so will everyone else
  

High-performing teams aren’t born. They’re built. They’re rehearsed. They’re led. And above all, they’re committed to the mission, not just the meeting.  And honestly, we need to train our managers, team leads, and executives to deliver feedback better.  To create a culture of positive outcomes from feedback, not fear of a bad performance review.  That’s not an easy task.  It takes leaders, of all kinds, to step up and earn the respect of their teams, so they know that when feedback is delivered, it is for a greater purpose, not criticism.  

From Field to Meeting to Zoom Call to the Boardroom

There’s a moment in every great marching band show where the music swells, the formation locks in, and the crowd goes silent — not because they’re bored, but because they’re in awe.

You don’t need a brass section or a color guard to create that kind of moment in your business. You just need alignment. Practice. Trust. Precision. And a shared desire to perform at a high level.

If 300 high school students can do it, what’s stopping your team?

The post What a Marching Band Can Teach Us About High-Performing Teams appeared first on According to Fred.

Sadly, it is everywhere. Generic blog posts. Unhelpful summaries. LinkedIn comments that read like ChatGPT tried to be inspirational but forgot the context. AI is writing more, and if we’re not careful, the result is that we’re going to be thinking less. 

This isn’t just a content problem. It’s a thinking problem.

The Rise of AI in Knowledge Work

Marketers, analysts, consultants, students — knowledge workers across the board are integrating AI into their workflows. I have, too.  I have several custom GPTs that help me do my work.  And that’s not necessarily a bad thing.  AI speeds up drafting, summarizes information, and automates tedious tasks. Nothing is perfect.  No matter how I use AI in my work, I’m still responsible for the output, and I always review the work before delivering it.  AI and the use of AI are not going away. Pandora’s box has been opened and is not closing. 

But there’s a difference between using AI to accelerate your thinking and letting it replace your thinking.  That’s where my growing concern lies. When outputs are taken at face value, or used without critical review, we end up with automation posing as insight. That’s AI slop in action.  And that’s content that looks credible but crumbles under scrutiny, ultimately risking your credibility and reputation. 

When Polished Meets Problematic: Deloitte’s Costly AI Misstep

Headlines were made recently when Deloitte admitted that it used AI, and inaccuracies were missed.  I think we’ve all thought the risks of AI slop were more theoretical. Now, we have evidence that it’s showing up in high-stakes environments and deliverables by one of the most recognized brands in consulting.

Deloitte Australia recently had to repay a portion of a government contract after submitting a report riddled with fabricated sources and bogus legal citations. The report, which analyzed IT systems for the country’s welfare compliance framework, included references that didn’t exist and court cases that were never filed.  Oops!  I’d hate to be the team that delivered that report.  

Deloitte later confirmed that generative AI was used during the drafting process, which isn’t necessarily bad. And while the tool produced well-formatted output, it also introduced “hallucinated” content that made it into the final report. Government officials flagged it. Media outlets piled on. And the credibility hit was real. 

“The AI-generated report was well-organized but full of inaccuracies. Ultimately, Deloitte had to refund part of its AU$440,000 contract.”

— Fortune

Where was the human in the loop? 

The Myth of the AI Shortcut

I use AI tools, custom GPTs, and 3rd third-party tools to help me draft content, strategies, etc, for work and for personal use.  There are times when I feel like the “AI shortcut” isn’t really there. I spend more time rethinking what I’m trying to say, refactoring prompts, and looking at the output. AI is not always a shortcut to getting what I want the final output to be at a quality level I desire.  That’s where I feel the critical thinking comes in. I want a better output.  And there are times I find it hard to articulate into a prompt or a custom instruction what is already in my head from years of experience.  So for me, AI is a tool, but not always a shortcut.  Others see AI as the “easy button” and thus a shortcut to a goal or deadline.  

The Deloitte situation should serve as a cautionary tale.  The companies that have the biggest names in the business, who have rolled out AI solutions first, who are considered seasoned professionals, can fall into the trap of taking the output at face value. When critical thinking, or just human oversight, is removed from the process, the results may look clean but lack depth, accuracy, or accountability.  That’s a dangerous place to be. 

I can guarantee that companies all over the world are seeing AI Slop show up in:

• Campaign briefs built entirely from AI-generated fluff
  
• Strategy decks that sound insightful but say nothing
  
• Research reports citing non-existent data points
  

And while these don’t always make headlines, they erode trust just the same.  Your clients will notice.  Your customers will see.  Years of building trust can be thrown away in a moment’s notice when your team is not using AI responsibly.  

Future Knowledge Workers: Gen Z Enters the Chat

OK, so knowledge workers, marketers, and others today are figuring out how to use AI in their work, and companies are still figuring out their AI policies and literacy programs. Here’s my larger concern. College students who are our next wave of knowledge workers are more comfortable using AI tools than most senior executives. But comfort isn’t the same as competence, and understanding the real-world impact of not critically thinking about how they use AI for work product.  We have a gap in our future knowledge workers not being prepared for using AI in the real world. 

“Critical thinking, problem solving, and adaptability are emerging as the most in-demand cognitive skills for Gen Z.”

McKinsey & Company

According to McKinsey, as AI tools become embedded in academic and professional life, students must learn how to question, refine, and contextualize what those tools produce — not just prompt and copy-paste.

Otherwise, we’re setting them up to become prompt jockeys, not strategic thinkers.

Therefore, I really hope universities and academic programs, even going down to high school or grade school, can find a way to adjust to helping students not just think about using AI responsibly, but also educate them on core critical thinking skills, finding ways to articulate what they are thinking into written form.  This will be a must-have skill to make better use of AI in the future.  It will also be a critical skill to separate signal from noise in the abundance of content that will continue to be created by AI.  

AI Isn’t the Problem. Thoughtless Use Is.

Generative AI gives us speed. It gives us structure. But it doesn’t give us intent, discernment, or values.

Gartner’s research backs this up: nearly 47% of digital workers struggle to find the information they need, even with AI-enhanced tools at their disposal.  I can personally speak to this in that I have connected tools like Microsoft Co-Pilot Studio (paid version) and ChatGPT to knowledge infrastructures like SharePoint and Google Drive and I still can’t find things I am looking for.  What is supposed to be a “simple task” ends up taking more time to get the correct information.   

It’s not just about access to tools — it’s about knowing what questions to ask, what answers to trust, and when something looks too good to be true.

Five Ways to Reinforce Critical Thinking in an AI World

Easy to say, not easy to do. No matter how much we want to trust our AI tools, we have to continue to bring critical thinking into the mix.  Here are five ways to reinforce our habits when working with AI tools. 

1. Challenge the output — If something seems perfect, ask why. Dig deeper.
   
2. Evaluate the source — Can the logic be traced? Are the citations real?
   
3. Push for originality — Reward thought leadership, not volume.
   
4. Create room for disagreement — If AI says X, what does your team think? Why?
   
5. Model smart AI use — Treat it like a junior analyst. Capable, but in need of supervision.

The biggest theme here is Human In The Loop. 

My Final Thoughts

AI isn’t replacing critical thinking. But it’s making it optional. And that’s the real risk.

In a world filled with AI slop, polished nonsense with no depth, the people who pause, ask better questions, and think a layer deeper will stand out. Can you stand up and justify your work product?  Can you use AI to help you craft a better output vs. just taking it at face value?  Can you shape our inputs to get better outputs? 

Those are the people companies will pay a premium for.

The post Thinking Critically in the Age of AI Slop appeared first on According to Fred.

The Importance of AI Literacy

I was recently on vacation down in the Turks and Caicos with my wife for some much-needed R&R. It was our 20th wedding anniversary trip, and the plan was to disconnect and just relax. During our time at our resort, we got into conversations with others who were there. One couple we met had kids roughly the same age as ours, and they lived in a city that I used to frequent with a past employer, so we had things to discuss. We ended up having lunch together one day, and the conversation led to AI and GenAI. My wife promptly rolled her eyes at me as our new acquaintances, who were in the banking industry, had some awareness of GenAI and ChatGPT, but had never really used it. After getting a look from my wife, I promised to show one thing and be done. So I whipped out my phone and opened ChatGPT to show him just what one of the sample prompts could do. It was a simple “Give me tips to overcome procrastination.” I clicked the prompt, the app started to write the response in real-time, and our new acquaintance had his mind blown.

This showed me, actually reminded me, that while I try hard to stay in tune with the latest and greatest in AI, not everyone is. AI literacy is a big deal and potentially a big problem if you do not take it into your own hands. Especially if workplaces and schools ban it.

Taking Control of Your AI Education

When something as big as Generative AI, and the advancement of AI in general, comes around, most people, companies, and those in positions of responsibility will take a “sit back and wait” approach. And for many valid reasons, it is a valid approach. It’s complicated. It has a lot of implications on how organizations and businesses work. For the education field, it has broader implications on how students learn, do homework, what is considered “cheating” and more.

But this isn’t the first time we’ve had a big technological change in our lives. Many of the same arguments being made today for the use of AI/GenAI were the same ones used when Google was introduced.

AI Will Become the New Normal

I wish I could tell you that all the good things that AI and Generative AI will provide us didn’t come with a dark side. The reality is that GenAI is introducing a whole slew of challenges from personal and corporate ethics to deep fakes and misinformation to hacking and cybersecurity to legal issues. While AI has been around for decades, that lonely day in September 2022 when Sam Altman, Greg Brockman and the OpenAI team launched ChatGPT for free and introduced a new way for the average human to interact with AI, Pandora’s box was opened and will never be shut again.

Since that day we now have a new way of working. A new way of learning. A new way of creating. A new way to engage with technology. For better or worse.

While GenAI will go through its own evolution from hype to questioning the value of it and the investments around continuing to develop GenAI it will become mainstream. In fact, Google, Apple and Meta are going to be the biggest players who will do it. And they are doing it now.

AI in Everyday Life – It is Here Thanks to Google, Apple and Meta

While I want you to better understand AI and GenAI specifically, companies like Google, Apple, and Meta are bringing it to you now. Today. To me, this is why you need to take your AI literacy seriously. You need to understand how each company is approaching GenAI and integrating it into your every day lives.

Apple is launching its new AI, called Apple Intelligence, with their next iPhone release in the fall of 2024. While Apple has been using AI in their phones for years, through features such as personalization, photo editing, video enhancements and more.

Google has been integrating AI into their platforms for years as well. Their latest foray is with an upgraded AI chatbot experience with the release of Gemini on Google phones. Gemini is also now featured in Google Docs, Sheets, and Slides if you pay for their upgraded plans.

Google is also bringing GenAI to search results. While there has been some gaffes already in the outputs created, this will only improve over time and will change the way we engage with search and search results.

Meta is adding their own AI engines into Facebook, Instagram, Threads and What’s App. You will start to see “Ask Meta AI or Search” appear in the search bars of these apps.

The point is, these technology companies are bringing AI to you, today, and doing it in very consumer-friendly ways.

Resources to Get Started on Your AI Literacy Journey

Getting started to learn more about AI and GenAI is not challenging. There are lots of articles published daily on the topic. Here are some of my favorite resources to help you get started.

• GenAI Tools – Here are some you can get started for free, or paid versions. Thre are many more than I list here, but these are good starting places.
  • ChatGPT – the one that started it all. I highly recommend trying the paid version for at least a month to see the difference. Can generate text, images, code and more.
  • Perplexity.ai – uses the Google API and pulls in more real-time content from the Internet in the response, with citations. With the paid version you can also access other models such as Claude.
  • Claude – a ChatGPT competitor, but more private in that it doesn’t have access to the Internet for anything after it has been trained. Claude maker, Cohere, prides itself on its take on security.
  • Adobe Firefly – generate images in a variety of formats and styles. Adobe claims it has trained Firefly on its own content, and thus, its images are commercially ready for use.
• Podcasts – a list of my favorite AI podcasts
• Free Education Resources – Check out some of these free resources from top companies and institutions
  • Google
  • IBM – Via Coursera
• People to Follow – if you use social media, here are a few people to follow and even turn notifications on
  • Yann LaCun – Professor at NYU. Chief AI Scientist at Meta. Researcher in AI, Machine Learning, Robotics, etc. ACM Turing Award Laureate.
  • Mustafa Sulyman – CEO, Microsoft AI | Author: The Coming Wave | Past: Co-founder, @InflectionAI & @GoogleDeepMind
  • Demis Hassabis – Co-founder & CEO @GoogleDeepMind – working on AGI. Trying to understand the fundamental nature of reality. Also revolutionising drug discovery @IsomorphicLabs
  • Andrew Ng – Co-Founder of Coursera; Stanford CS adjunct faculty. Former head of Baidu AI Group/Google Brain. #ai #machinelearning, #deeplearning #MOOCs
  • Paul Roetzer – Founder Marketing AI Institute (@Mktgai) and SmarterX (@SmarterXAI). Creator of Marketing AI Conference (MAICON). Co-host of The Artificial Intelligence Show.
  • Allie K. Miller – #1 Most Followed Voice in AI Business (1.5M followers). Nat’l AAAS Ambassador. Former Amazon, IBM. Fortune 500 and startup AI advisor, public speaker.

Every journey begins with a step. Just one step. Take yours today so you can be better informed about the changes that are coming your way with GenAI.

The post You’re Responsible for Your AI Literacy appeared first on According to Fred.

If you’re still on the fence about AI’s significance, buckle up. I believe with absolute conviction that within the next three years, every successful company will become an AI company in one capacity or another. Don’t believe me? Let’s explore the driving forces behind this unstoppable wave.

2023: The Year of Discovery

Last year was a watershed moment for generative AI. The public launch of game-changing tools like ChatGPT, DALL-E, Midjourney, Stable Diffusion, Claude, Llama, and Perplexity sparked a collective “a-ha moment” for businesses globally. It wasn’t the technology itself that was revolutionary – AI has been developing for decades – but the sheer accessibility and user-friendliness of these new tools.  

Suddenly, AI was not confined to the tech giants with teams of data scientists and PhDs. Its power became accessible through a simple text-chat window, prompting businesses to ponder its potential applications. This now raised the question: How can we put this tool to work for us?

So companies started to educate themselves and maybe put a small team together to define problem statements and define use cases.  The response varied: while some companies adopted a wait-and-see approach, others, including Coca-Cola, Walmart, EY, Accenture, McKinsey, dived in headfirst to gain a first-mover advantage.  

2024: Experimentation and Internal Revolution

2024 marks a year of bold exploration. Forward-thinking companies don’t just see AI as a shiny toy; they view it as a catalyst for reshaping their internal operations. This stage isn’t about launching AI-powered customer products just yet. Instead, businesses will focus on:

• Streamlining tasks: Departments across the board, from marketing to HR, will experiment with automating repetitive, time-consuming work. The goal? Free up employees to focus on strategy, creativity, and human connection. 
• Enhancing decision-making: AI’s capacity to analyze vast datasets will play a vital role in smarter decision-making. Expect real-time insights drawn from customer data, market research, and operational trends.
• Upscaling employees: A focus on upscaling will take center stage. Businesses understand that AI isn’t about replacing workers; it’s about equipping them with an incredibly powerful co-pilot.

Each of these comes with the understanding that there are teams internally who are becoming comfortable with AI and GenAI.  These organizations probably have established an AI Centers of Excellence or task forces focused on the liability of using these tools.  However, the risks of putting out public facing GenAI solutions at this point has proven to be risky.  Sorry Alaska Airlines. 

If we think about this year, 2024, early adopters will begin to really see the benefits and understanding how to deploy GenAI solutions at scale.  Other organizations will start to wake up to the fact that they need to start taking GenAI seriously.  If nothing else, the tools that companies use every day will start to publish GenAI features into their products that address these three areas, thus forcing companies to address GenAI in some way.  

2025: Realizing Value and the Competitor Gap Widens

By 2025, early adopters won’t just be fiddling with AI: they’ll be reaping the rewards. This is where the competitive playing field takes a drastic turn. The proof points pile up:

• Efficiency gains: Companies using AI to optimize workflows consistently outperform those that don’t. Reduced costs and faster turnarounds will be the hallmarks of success.
• Data-driven innovation: AI will help identify potential areas for new products and services, based on a deep understanding of markets and customer pain points.
• Competitive Pressure: Early AI adopters will set a new standard, forcing businesses in every sector to catch up or risk obsolescence.

New Large Language Models (LLMs) will have emerged, some being industry specific. Some companies may even build their own LLMs specifically to use with their own data. Additionally, every company will have tools in their technology stack that has AI features and functionality at the fingertips of their employees, and the value realization will start to become inevitable and evident.  Our risk tolerance will start to wane as AI literacy plateaus.  

2026: AI as the New Normal

In a stunningly short time, by 2026, we reach the tipping point where AI isn’t just an advantage—it’s table stakes:

• Software eats AI: Popular business software from CRMs to design tools will have generative AI features baked in. Using them without AI capabilities will feel archaic.
• The new AI workforce: AI-generated content, whether it’s draft emails, initial market reports, or social media copy, will be woven into workflows by a skilled human workforce trained to optimize the tool.
• Ethics and the Human Touch: Ethical concerns and AI regulation will come to the forefront as businesses grapple with the balance between efficiency and the irreplaceable value of human judgment and empathy.

The playing field flattens and AI and GenAI will be second nature.  We will have moved into a state of normalcy and everything from your smart home device to your enterprise applications will be using GenAI in ways we can’t live without. 

It’s Not Just Hype

These predictions aren’t mere speculation. Studies support the accelerated pace of AI adoption:

• A Gartner study found that by 2025, 75% of enterprises will deploy at least one AI solution within their businesses. (https://www.ciodive.com/news/gartner-AI-enterprise-projections-2024/580240/)
• McKinsey predicts that AI could deliver an additional global economic output of roughly $13 trillion by 2030, increasing global GDP by about 1.2% annually. (https://www.mckinsey.com/capabilities/quantumblack/our-insights/how-artificial-intelligence-can-deliver-real-value-to-companies)

Embracing the Inevitable

This AI-powered future can be intimidating. But for those willing to embrace change, it offers incredible opportunities. Companies that take AI seriously are the ones that will stay in the race. The next three years are going to be critical for every company as they address AI both internally for efficiencies, but also as they consider building products and services. Are you ready to make your company an AI company?

Disclaimer: Generative AI was used in the creation of this content.

The post The AI Revolution: Why Every Company Will Be an AI Company by 2026 appeared first on According to Fred.

The Marketing AI Show

Official Description

The Marketing AI Show makes artificial intelligence actionable and approachable for marketers. Brought to you by the creators of the Marketing AI Institute and the Marketing AI Conference (MAICON), join us for weekly conversations where we break down the top AI news stories and discuss what it means for marketers, leaders, and businesses so we can better use AI to transform businesses and careers. Enjoy The Marketing Artificial Intelligence Show for the latest in AI.

Spotify
Apple Podcasts
YouTube Channel

Why I like the Podcast

Paul Roetzer and his co-host Mike Kaput publish some of the most thoughtful podcast episodes on the topic of AI. Ex-agency founder and founder of the Marketing for AI Institute, Paul, made the shift to focus on AI after he sold his PR agency years ago. I think his past background helps him bring a journalistic POV to his podcast today. His focus on “following the founders” is what I think sets him apart from other podcasts. Paul has all the history on how the big LLM founders all came from the same labs, broke out for various reasons, and here we are with a network of AI founders that are driving our future of AI/GenAI. Paul’s level-headed analysis of the week in AI news is a masterclass on taking in fact, understanding what is speculation, and being clear on what he thinks is going to happen based on what information he has, which is usually just the same information we all have access to, news reports, articles, and public information. To me, this is a must listen every week they put out a show.

The AI Breakdown: Daily Artificial Intelligence News and Discussions

Official Description

A daily news analysis show on all things artificial intelligence. NLW looks at AI from multiple angles, from the explosion of creativity brought on by new tools like Midjourney, ChatGPT and AutoGPT to the potential disruptions to work and industries as we know them to the great philosophical, ethical and practical questions of advanced general intelligence, alignment and x-risk.

Spotify
Apple Podcasts
YouTube Channel

Why I like the Podcast

Nathaniel Whittemore is the author of The AI Breakdown, a daily podcast/video of the latest in AI news. Like Paul and Mike, Nathaniel breaks down the daily AI News. He does a bit of a quick round of the latest news in about five minutes, then takes a deep dive into one topic. The overall episodes are quick and can consumable and I like the singular focus of the quick topics of the day. AI has a lot to cover, so having a quick breakdown and then a deeper dive with his opinions sprinkled in is a nice set up. Topics are relevant. There is a lot to keep up with in AI news and The AI Breakdown covers the wide variety of topics nicely.

The Everyday AI Podcast

Office Description

The Everyday AI podcast is a daily livestream, podcast and free newsletter where we help everyday people grow their careers with AI. The Everyday AI podcast is hosted by Jordan Wilson, a former journalist who’s now the owner of a boutique digital strategy company with 20 years of martech experience.  Our main focus is to help you keep up with AI trends to make your job easier. Get your work done faster. Increase your output.

Spotify
Apple Podcasts
YouTube Channel

Why I like the podcast

We are all looking for practical ways to use AI in our daily lives. Jordan breaks down ways to apply the world of AI/GenAI to your work life. From understanding how to use ChatGPT better with prompting to understanding the other LLMs and tools out there, Jordan gives practical advice you can use tomorrow.

Gartner ThinkCast

Official Description

Gartner ThinkCast puts you at the intersection of business and technology with insights from the top experts on how to build a more successful organization, team and career in the Digital Era. Join us every other Tuesday to get your competitive advantage.

Spotify
Apple Podcasts
YouTube Channel

Why I like the podcast

While the Gartner ThinkCast isn’t only about AI/GenAI, it has been a topic over the last year. Gartner’s analysis and insights from its customers and analysts are well thought through and provide expert guidance as to the state of GenAI, how businesses can adapt to using it, and the risks/rewards.

While there are plenty of podcasts you could listen to, or even specific episodes, these are just a few of the ones I find valuable. Each provides a different level of insight, from practitioner-focused use cases to expert analysis. The world of GenAI is changing so fast. The insights from these shows give me a quick understanding of the week in AI, and I can listen to them as needed on the topics I find most interesting.

The post Top 4 AI and Marketing Podcasts to Follow in 2024: A Curated Guide for Professionals appeared first on According to Fred.

Sonita Reese is the Chief Encouragement Officer at First by Five, a consultancy helping entrepreneurs grow their business.  On this ‘Live’ episode we get into affects of COVID-19, work/life balance, and celebrating wins on teams to keep employee engagement up. 

Full transcript coming soon.

The post Growth & entrepreneurship, a conversation with Sonita Reese appeared first on According to Fred.

Digital policies, or policies in general, are often seen as a necessity to an organization. Yet, employees more often than not see them as an annoyance than what they are, a set of behaviors and rules that make organizations run. In my discussion with Kristina Podnar, consultant, podcaster, and author of Power of Digital Policy, we talk about many topics around digital policies and how organizations can effectively implement them so employees feel empowered by them, not burdened.

(Transcript coming soon)

The post Navigating Digital Policies, a Discussion with Kristina Podnar appeared first on According to Fred.

What I love about sports in the playoffs is that teams who are not supposed to be there, the underdogs, get scrappy. They get creative. They get gritty and have determination to prove that they belong in the fight. And with the right motivation from their coaches and each other they can do amazing things. They can pull off upsets. They can prove the naysayers wrong. They do belong. They do deserve to hoist the cup.

Everyone has Grit and Determination

Grit and determination is not just for athletes. For just as many cinderella sports teams that you read a headline about, I’ve seen way more stories on LinkedIn about individuals finding new jobs. They are sharing their metrics too. 150 days out of a job. 130 jobs applied for. 29 interviews. 3 ghosting by recruiters. 3 job offers. 1 new wonderful employer.

courage and resolve; strength of character.

definition of grit

I’ve personally never been in that position except maybe when I graduated college and was looking for my first job. I have certainly been fortunate. But these individuals are grinding it out. They are not only finding grit and determination to find a new job, in many cases they are finding better jobs. They are pivoting into something they really love or want to do.

firmness of purpose; resoluteness.

definition of determination

Everyone has or can have a situation that requires focus, embracing new behaviors, fixing bad habits, or changing your destiny. What I’ve determined is that it is a state of mind. To be courageous is a state of mind. To have resolve is a state of mind. And to get in that state of mind sometimes means you have to accept things you normally wouldn’t.

Embrace the Suck

I think one of the hardest parts of any situation that you come across that is unpleasant or unexpected is to take a minute to assess your situation, then just “embrace the suck.” What do I mean by that?

On one hand you can fight the situation ahead of you. You can pretend it isn’t as bad as it is. You can lie to yourself that it is “temporary” or “will just last <insert X period of time here>. Or you can grab the preverbal bull by the horns and embrace the fact that you are in a situation that you’d prefer not to be in.

That could be a project you’ve been given. It could be a job loss. It could be a personal situation. When you embrace the suck it is amazing how your attitude can change to focus on getting the task/project done and change your situation. To dig in and believe that you accomplish the goal or fix the problem. That takes grit. That takes determination. You embrace the suck and make plans and execute to make it better.

Back to my beloved Chicago Blackhawks. As of writing this we are down 0-2 in the first round of the playoffs with the Las Vegas Knights. We went to overtime in Game 2 and lost by one point. Every point is a battle. The players on both teams are grinding it out every line change (hockey term for a group of players who go on and off the ice together in rotations). It will take teamwork, grit and determination to advance to the next round. What’s the next round of your playoffs? Are you willing to go the extra effort to grind it out?

The post The Power of Grit and Determination appeared first on According to Fred.

I was excited to reconnect with an old colleague for this episode. Ryan Lunka is the COO at nChannel, a SaaS software that empowers retailers, wholesalers, and technologists with integration tools to sync data and automate processes among eCommerce, ERP, POS and 3PL systems.

In this episode Ryan and I get on the hot mic to talk about eCommerce, where his company plays a key role connecting systems together more seamlessly. We also talk about the latest rendition of the MarTech 5000 landscape infographic. It’s a crowded space. Will it look that way next year? Finally, we dive into the non-profits that he works with, Guitars not Guns and Can’t Stop Columbus.

Links to the sites discussed in the episode. 

nChannel

MarTech 5000 Landscape Infographic

Non-Profits Ryan is involved in: 

• Guitars Not Guns – Ohio Chapter
• Can’t Stop Columbus

Ryan’s Band – Local Tourists

• Listen to them on Spotify

Music provided by Epidemic Sound.

The post The MarTech 5000, eCommerce and Evolving Tech Landscape, a Discussion with Ryan Lunka appeared first on According to Fred.