Project Management That Works!

AI Driven PM - S2e13 - Season Finale - Gatekeeper - The Portfolio Agent

2026-08-13T22:16:50.901-05:00

The Biggest Problem in Project Management Isn't Execution

After 30 years and 150+ PMO implementations, I can tell you the dirty secret of portfolio management:

Most organizations don't have an execution problem. They have an intake problem.

They say yes to too many things. They commit to impossible dates. They overload their best people. And then six months later, they wonder why nothing finished on time and everyone's burned out.

Here's the thing: by the time a project is in trouble, the decision that caused the trouble was made months earlier. In a hallway. In a thirty-second conversation that nobody documented.

The Hallway Conversation That Kills Projects

You know how this goes.

New idea comes in. Someone asks how long it'll take. The real answer is six months. They want six weeks.

The PM has two choices: tell the truth and get labeled "not a team player," or agree to six weeks knowing full well it's impossible.

I had this exact conversation with a client. I told them August. They wanted April. I told them it would take fairy dust to make April work.

The guy looked at me and said: "So you're saying there's a chance."

We approved April. We put heavy assumptions on everything. Every single one of those assumptions failed. The project shouldn't have been approved in the first place.

That moment — right there — is where most project failures are born. Not in execution. Not in the retrospective.

At intake. In a conversation where someone was too uncomfortable to tell the truth and someone else was too optimistic to hear it.

Why Traditional Intake Processes Are Project Theater

Most organizations have intake forms. Governance committees. Stage gates. The whole apparatus.

My first question at any PMO assessment: "How many projects have you killed?"

If the answer is zero — and it usually is — the process is theater.

Here's what actually happens: Requests come in optimistic because requesters don't know what they don't know. Prioritization is political because the loudest voice wins. Capacity is ignored because nobody checks actual team workload. Dates are arbitrary because someone picked end of quarter, or before the conference, or because the CEO mentioned it in a town hall.

The result? You approve 15 projects when you have capacity for 8. You set your teams up to fail before they start. And then you hold them accountable for missing dates that were never achievable.

That's not portfolio management. That's organized chaos with better paperwork.

Introducing Gatekeeper

This is where Episode 13 lives — and where Season 2 comes full circle.

Gatekeeper is a portfolio intake intelligence agent. She sits at the front of the portfolio before a project gets approved, before a date gets committed, before a team gets assigned.

She asks the hard questions that humans avoid — because humans have politics, careers, and relationships to protect.

Gatekeeper doesn't.

She asks: Is the scope clear enough to estimate? What's a realistic effort range? Do we have capacity right now? What trade-offs are required if we approve this? What risks does this project carry? Is the requested date even achievable?

And she doesn't soften the answer.

Four Gates. No Exceptions.

Gate 1: Scope Clarity

Is the scope defined well enough to estimate? If someone walks in with "modernize the platform" or "improve customer experience," Gatekeeper asks clarifying questions. If the requester can't answer them, the project goes to "discovery required" status.

This gate alone would stop half the bad projects approved every year. You cannot estimate what you cannot define. Full stop.

Gate 2: Realistic Estimation

Gatekeeper pulls from Atlas practice libraries, applies PERT methodology, and produces a range — not a single number. If the requested date falls outside that range, she flags it immediately. Not after the kickoff. Not after the team is assembled. Right now, at intake.

Gate 3: Capacity Analysis

She looks at current portfolio load, team utilization, in-flight projects, and planned releases. She produces a capacity report that says something like: "Current portfolio is at 112% capacity. Adding this project increases that to 127%."

No more "we'll figure it out." The math is on the table before the decision is made.

Gate 4: Risk and ROI Alignment

ARIA-style risk assessment, compared against expected business value. High-effort, high-risk, low-value projects get flagged. Pet projects that would have snuck through on political capital get the same scrutiny as everything else.

The Portfolio Intake Report

After running a project through all four gates, Gatekeeper produces a report for the governance committee. Not a status update. A decision document.

Executive summary with a clear recommendation (approve, delay, or reject). Scope assessment with clarity score and key assumptions. Capacity impact showing exactly what gets displaced. Risk profile drawing from historical project data. ROI and strategic alignment scoring. And a recommendation narrative that's grounded in data — not someone's opinion.

Here's what that narrative looks like in practice:

"Defer to Q3. Project has clear scope and strategic alignment. However, current portfolio is at 112% capacity. Approving now would push three existing projects past their committed dates. Requested June 30 date is not achievable given a 5.5-month timeline and earliest available start of April 1. Recommend delay to July 1, revised end date December 15."

That's not a PM being difficult. That's the truth — documented and delivered before the damage is done.

Live Demo: The Phoenix Platform Modernization

We ran a real request through Gatekeeper: migrate an application from on-premises to AWS, refactor a monolith to microservices, upgrade the database from MySQL to PostgreSQL.

Gatekeeper's finding: this all-or-nothing bundle sat at the high end of risk benchmarks. Fifty percent chance of failure. Would cost 600K in opportunity cost displaced. Break-even required $5.55 million per year in savings.

Her recommendation: phased lift-and-shift to AWS instead.

That's the "so you're saying there's a chance" moment — except now the data is on the table before you make the bet.

Why > What: The Deep Diagnostic

We also walked Gatekeeper through a brand-new idea from scratch. She asked ten questions. Promised full Net Operating Value math at the end. A defensible go/no-go/defer/MVP recommendation.

At one point, I answered what we were building but not why we were building it.

Gatekeeper stopped me:

"You answered what. You didn't answer why."

That's the moment. The why is where the business case lives. The why is what justifies the investment. If you can't answer why, you don't have a project — you have an idea looking for a problem.

The timeline also failed the sniff test. Monolith to eight microservices, two-terabyte database, 150 tables — in three months. Industry pattern: nine to eighteen months.

Gatekeeper's read: "A three-month timeline tells me someone anchored on a date, not an estimate. Failure probability: fifty to seventy percent."

Her recommendation: a $150K single-service pilot. Extract one service. Pick the domain with the worst deployment friction and highest incident load. Touch only its tables — ten to fifteen of the 150. No full migration.

If we miss, we spend 1.25 million.

She produced a branded one-page decision record for executives: decision, NOV analysis, key findings, approved pilot scope, and open items before full program reconsideration.

That's what good governance looks like. Not a committee meeting where the loudest voice wins. A document. Data. A recommendation you can defend.

The Hard Truth About Discipline

Most organizations don't have a capacity problem.

They have a discipline problem.

They know they're overloaded. They know the dates are unrealistic. They approve them anyway. Because saying no is hard. Because disappointing a senior executive is uncomfortable. "We'll figure it out" feels easier than having the fight at intake.

Six months later, the delivery team takes the blame for missing a date that was never achievable in the first place.

Gatekeeper doesn't care about politics. She tells the CEO the same thing she tells a junior PM. Once the data is on the table, leadership can't pretend they didn't know. They can't blame the delivery team for missing a date they chose with full information in front of them.

That accountability shift — from delivery to decision — is the whole point.

The Full Circle: What We Built This Season

Let me take you back to where Season 2 started.

Episodes 1 through 9: Socratic prompting. AI as a thinking partner. How to ask better questions, coach more effectively, build influence, manage change, and measure what matters. How to use AI not as an answer machine, but as a mirror that helps you think more clearly.

Then we shifted.

Episode 10: ARIA — risk intelligence from organizational history. The lessons learned system I described in 2008, finally possible. She doesn't just store what went wrong. She tells you what to watch for before you repeat it.

Episode 11: Atlas — estimation with PERT and practice libraries. The estimation discipline I've been teaching for twenty years, now automated. She doesn't give you a number. She gives you a range, a confidence interval, and a set of assumptions you can defend.

Episode 12: PACE — sprint predictability and readiness debt. The execution accountability every agile team needs, now measurable. She doesn't just track velocity. She tells you whether your backlog is healthy enough to trust.

Episode 13: Gatekeeper — portfolio intake intelligence. The portfolio governance every executive claims to want, now enforceable. She doesn't just assess projects. She forces the conversation that everyone in the room has been avoiding.

Four agents. Four problems I've been fighting for three decades. One season.

Here's what I want you to understand: these agents amplify human judgment. They don't replace it.

ARIA doesn't decide which risks to mitigate — you do. Atlas doesn't approve the estimate — you do. PACE doesn't reduce sprint capacity — you do. Gatekeeper doesn't decide which projects to approve — leadership does.

She just forces the conversation.

The technology has finally caught up to the vision.

The Final Challenge

Thirty years. Seven books. 150+ PMO implementations. Thousands of project managers taught.

Here's what I've learned: tools don't matter. Methodology doesn't matter. Certifications don't matter — at least not the way we think they do.

What matters is this: Do you learn from your mistakes? Do you make decisions based on evidence? Do you protect your people from impossible commitments?

Because if you're still setting dates in hallway conversations, you're not managing a portfolio. You're managing chaos with a Gantt chart.

Stop playing games with impossible dates. Stop pretending you have capacity you don't have. Stop repeating the same mistakes because nobody captured the lessons.

Use AI to tell the truth. Use AI to force the hard conversations. Use AI to protect your teams.

Because we are not here to fill out forms and update trackers.

We are here to make dreams come true.

Thank you for Season 2. Now go build something great.

— Rick A. Morris, PMP Author | Consultant | Host, AI Driven PM

AI Driven PM: S2e12 - PACE - Predictability and Cadence Engine

2026-08-02T15:07:10.498-05:00

The Question Executives Are Actually Asking

Here's the question I get from every executive sponsor in every agile transformation I've ever run:

"Are we predictable?"

Not "are we busy?" Not "are we shipping features?" Not "is velocity trending up?"

Are the commitments we make to the business actually holding up?

That's the whole game. And yet — after 30 years and 150+ implementations — I can tell you that almost no team is measuring predictability directly. They're measuring velocity. They're tracking story points. They're running burn down charts.

And then they're wondering why leadership still doesn't trust the sprint.

This episode is about PACE — the Predictability and Capacity Engine. The third AI agent in this series. And it answers that question directly.

The Problem With Agile Metrics As We Know Them

Velocity is a lagging indicator. So is burn down. So are story points.

They tell you what happened. They don't tell you why stories slipped, whether you're getting better or worse over time, or what to do about it.

Here's the contrarian truth: velocity going up doesn't mean you're getting better. It might mean you're getting faster at moving unfinished work to the next sprint.

I've seen teams with 40% velocity growth and deteriorating stakeholder trust. Because the metric wasn't measuring what mattered.

The Distinction That Changes Everything

PACE makes one critical distinction that most agile tools completely ignore:

Is the problem capacity — or readiness?

Capacity is team size, sprint commitment, available hours. Readiness is whether stories enter the sprint with clear requirements, complete designs, resolved dependencies, and defined acceptance criteria.

You cannot fix a readiness problem by adding people. You cannot fix a capacity problem by tightening story quality. These are different problems with different solutions — and confusing them is expensive.

PACE separates them. Every sprint, every team, over time.

Two Metrics That Actually Matter

Slide Rate — the percentage of stories committed to a sprint that don't finish in that sprint.

Benchmark: under 15%, ideally under 10%. Consistently above 20%? You're either over-committing or accepting unready work. Probably both.

Readiness Debt — the percentage of stories that entered a sprint without meeting readiness criteria: requirements unclear, designs incomplete, dependencies unresolved, acceptance criteria undefined.

Here's why readiness debt is the more important metric: it's a leading indicator. It predicts slide rate before the sprint closes.

Forty percent readiness debt will cause slide. Not if. How much.

The Real Client Story

Large financial services company. Two years of agile. Forty-three sprint cycles. Nearly 50,000 user stories.

Surface metrics? Fine. Velocity trending up. Cost per story point trending down. Team getting more efficient by every standard measure.

But leadership had this persistent feeling that commitments weren't holding. They couldn't quantify it. They just knew something was off.

I pulled 48,621 Jira issues and ran them through PACE.

The finding wasn't what anyone expected. The team wasn't getting worse at execution. They were getting worse at readiness — and it was accelerating.

Slide rate had climbed from 11.8% to 18% for Team A, and the newer team was sitting at 28%.

The instinct in the room was to ask: "How do we increase velocity?" PACE revealed the real question: "Why are we accepting unready work into sprint?"

One story stayed with me. Marked complete. But couldn't be tested — undiscovered dependencies surfaced mid-sprint. The entire team spent that sprint discussing what to do with the ticket. Should have been resolved pre-sprint.

That's not an execution failure. That's a readiness failure. And you'd never see it in a velocity chart.

How PACE Works

Export your sprint history from Jira, Azure DevOps, Planner, or even a spreadsheet. PACE ingests it, normalizes the data, identifies committed, completed, and slipped stories, and calculates slide rate and readiness debt per sprint — then trends them over time.

Four deliverables come out the other side:

Sprint dashboard — color-coded by health. Green: slide rate under 10%, readiness debt under 15%. Yellow: 10–20% / 15–30%. Red: above that. You see the trend at a glance.

Best and worst sprint identification — best sprints are benchmark proof that the team can be predictable when conditions are right. Worst sprints are investigation targets. What happened? What entered unready?

Trend analysis narrative — "Slide rate increased from 10% to 25% over 12 months. The team isn't getting worse at execution. They're being asked to execute unready work." That distinction is critical — it tells leadership this is a process problem, not a people problem.

Action recommendations — targeted to what the data actually shows. Capacity recommendations for capacity problems. Readiness recommendations for readiness problems. Retrospective targets for the worst sprints.

Minutes to run. Months of insight.

The Cost Per Story Point Metric

This is my signature metric. Sprint by sprint, it shows team efficiency over time — not just "did velocity go up?" but "did we get more done per dollar?"

When you add three people, PACE shows whether you actually got more efficient, or whether you just added cost. When you split a team, PACE shows the productivity impact on both resulting teams — because splitting a high-performing team often tanks both for months before they stabilize.

The portfolio-scale version of this question: "If I add 40 people, how do I know we're more productive?"

PACE answers it. Trending cost per story point and velocity across the full backlog over time. Metrics that were previously impossible to calculate without a data science team and six months of work.

The Discipline Paradox

Here's the thing about discipline: it feels like friction. It feels like you're slowing down to do readiness checks, to enforce acceptance criteria, to push back on stories that aren't ready.

But discipline is what makes you fast.

When stories enter ready, they complete on time. When sprints finish clean, velocity stabilizes. When commitments hold up, stakeholders trust you. When stakeholders trust you, you get more autonomy, more resources, more runway.

PACE doesn't just measure output. It measures discipline.

And discipline compounds.

Portfolio Scale

Run PACE across 10, 20, 100 teams. Now you have a portfolio-level view: which teams are predictable, which are degrading, which have chronic readiness debt.

Compare Team A — 8% slide rate — to Team B — 30% slide rate — on similar projects. What's different? Spread what works. Identify where coaching is needed before the team hits a wall.

That's organizational learning at portfolio scale. Most organizations have the data for this already sitting in their sprint tools. They just don't have the engine to surface it.

PACE and ARIA: Better Together

ARIA — the risk intelligence agent from Episode 10 — operates at the project level. Health scores, risk indicators, executive dashboards.

PACE operates at the team execution level. Sprint discipline, readiness, predictability.

Together, they answer the two questions every delivery leader needs answered: Is this project healthy? (ARIA) and Is this team predictable? (PACE).

PACE's health check looks forward at the current sprint — how many stories are committed, how many have entered unready, what the projected slide rate is if current trends hold. Leadership doesn't wait until sprint close to know there's a problem.

Early warning. Portfolio-wide.

Next Up: Gatekeeper

Episode 13 is the finale — Gatekeeper, the portfolio intelligence agent that decides which projects should even start.

Most organizations say yes to too many things. When everything is a priority, nothing is. Gatekeeper reality-checks the portfolio, surfaces impossible dates, shows where capacity doesn't exist, and forces the hard conversations before commitment — not after.

That one's going to challenge some deeply held organizational habits.

For more resources: PMThatWorks.com

— Rick A. Morris

AI Driven PM: S2E11 - Atlas - The Estimation Engine

2026-07-17T10:09:16.030-05:00

The Estimate That Haunts You

Every PM knows the moment. Someone catches you in the hallway — or worse, in a meeting with the CFO — and asks, "How much is this going to cost?"

You don't have the requirements. You haven't talked to the team. You barely know what the project is. But you open your mouth anyway, because that's what PMs do. You give a number. And from that moment forward, that number is no longer an estimate.

It's a commitment.

I've spent 30 years watching good project managers get buried by bad estimates. Not because they were bad at math. Not because they were careless. Because they were asked to make a precision commitment at the exact moment they knew the least about the project. That's not a skill problem. That's a structural problem.

And it's one I built Atlas to solve.

The Real Problem with Estimation

Here's what makes estimation so maddening: most organizations already have the data they need to estimate well. They've delivered similar projects before. They know roughly how long certain types of work take. They have rates, roles, and historical actuals sitting somewhere.

The problem is that data isn't structured. It's not accessible. It lives in the heads of senior consultants, in old SOWs buried in SharePoint, in tribal knowledge that walks out the door when people leave.

So every estimate starts from scratch. Different PMs give different numbers for the same work. One person is optimistic. Another is conservative. Neither is grounded in a consistent baseline. The result is variance — and variance destroys credibility.

Most organizations think the answer is better estimators. What they actually need is a better system.

Meet Atlas

I named her Atlas after the Titan who carries the weight of the world. Because that's exactly what estimation feels like — and because Atlas the agent actually carries that weight so you don't have to.

At her core, Atlas does three things:

She maintains a practice library. Every service type your organization delivers — structured, versioned, and accessible. Service definitions. Phases. Modules and components. Roles and rates. Three-scenario effort estimates per module, per role. Assumptions and constraints baked in. When a project closes, actuals update the library. The system learns. It doesn't forget.

She guides PMs through a structured conversation. Module by module. She asks the right questions in the right order, validates your inputs, and builds the estimate as you go. You're not staring at a blank spreadsheet trying to remember everything. You're having a conversation with an agent who knows your practice library cold.

She produces three-scenario PERT estimates. Optimistic, most likely, pessimistic — with a fully formula-driven Excel workbook. Not a single number. A range. With the math visible and defensible.

Two Ways to Work

Atlas runs in two modes, depending on what you're starting with.

Standard workflow: You know the project shape. You know you're doing a CRM implementation with a data migration, a handful of integrations, and some custom reporting. You tell Atlas, she pulls from the practice library, walks you through component counts, validates assumptions, and builds the estimate. Fast, consistent, grounded in your organization's actual delivery history.

Discovery mode: You don't know the shape yet. Maybe you've got an RFP. Maybe you've got a requirements document that's 200 pages of "the system shall." You hand it to Atlas, she analyzes it, suggests component counts, and asks you to validate. "I'm seeing 4 data objects, 11 workflows, 20 custom scripts, 13 reports. Does that match your read?"

That second mode is where things get interesting. And honestly, it came from me hitting a wall.

The Insight That Changed Everything

When I first built Atlas, I designed her around the practice library. You know the service type, you pick the components, you build the estimate. Clean. Simple. Effective.

But then I started working on complex projects — ones where the requirements were dense and the component counts weren't obvious. I realized something uncomfortable: I had become constrained by my own thinking.

The practice library, which was supposed to help, was actually limiting me on complex engagements. I was anchoring to familiar component counts instead of letting the requirements drive the numbers.

The breakthrough was a two-phase approach. Use Atlas first to analyze the requirements and count components. Then feed those counts into the estimate. Let the requirements tell you what you're building before the practice library tells you how long it takes.

That realization unlocked discovery mode. And it led directly to the demo that still gets the best reaction when I show it.

500 Requirements, One Hour

Real project. Salesforce implementation. The client handed us a requirements document with 500 line items.

Old approach: senior architect spends two or three days doing line-by-line review, trying to group requirements into estimable components, hoping they don't miss anything. Best case, you get a first draft in a week. You've already introduced human variance and fatigue into the process.

New approach: I handed the document to Atlas.

She ran all 500 requirements through discovery mode. Came back with component counts: 4 data objects. 11 workflows. 20 custom scripts. 13 reports. 23 custom fields. And more. She also generated an assumptions document — a structured list of what she assumed to be true about each component — formatted for the architects to review and validate.

We went through two revision cycles based on architect feedback. Total time from document to validated estimate: about one hour.

The PERT output came back with a range of 5.7M, with an expected value of $1.8M.

Now, I know what you're thinking. That's a big range. 5.7M? How is that useful?

Here's the thing — that range is honest. And honesty is more valuable than false precision.

Why Three Scenarios Beat One Number

When you give a client a single number, you create false precision. You're telling them you know something you don't know. You're compressing all the uncertainty, all the risk, all the unknowns into one figure that will be wrong — and then you'll spend the rest of the project defending why it's wrong.

Three scenarios do something different. They communicate uncertainty explicitly. They show the client that the outcome depends on decisions that haven't been made yet — requirements that are still fuzzy, risks that haven't materialized, scope that could go either way.

The PERT expected value gives you a defensible anchor. The range gives you room to navigate. And the assumptions document gives you the conversation: "We estimated 11 workflows. If there are actually 17, here's what that means for the number."

That's not hedging. That's professional rigor.

And it sets up something even more powerful: scope control that's evidence-based, not subjective. When scope creep shows up — and it always shows up — you're not having a feelings conversation. You're having a math conversation. "We estimated 11 workflows, we're seeing 17. Here's the delta. How do you want to handle it?"

That's a completely different dynamic with a client.

The RFP That Needed an Answer by Friday

Let me give you one more scenario, because this one happens all the time.

Client comes back mid-project — or sometimes before the project even starts — and says they need componentized pricing. It's Wednesday. They need it by Friday. The requirements are still vague. And they have a very specific format they want the response in.

Old approach: PM and sales team spend two days frantically trying to reformat the existing estimate, guess at component breakdowns, and produce something that looks professional but was mostly assembled under pressure.

New approach: I gave Atlas the original RFP, the existing estimate, and the client's requested format. She produced a structured response with assumptions documented, PERT parameters shown, best/most likely/worst case added — in the client's exact format.

Wednesday to Friday. Done. With more rigor than the original estimate.

That's the shift. From scrambling to authoritative. From variance to consistency. From "I think it's around this" to "here's what it costs, here's why, here's the math."

The Loop That Makes Both Systems Smarter

I've talked about Atlas and ARIA in separate episodes, but I want to be clear about how they work together — because the loop is where the real organizational value lives.

Atlas builds the estimate at the start. ARIA protects it during execution. Atlas says "we estimated 11 workflows." ARIA watches for the 17th workflow and flags it as a risk.

But here's what happens at the end: when the project closes, actuals flow back. The practice library gets updated with real delivery data. ARIA's risk database gets updated with what actually happened versus what was estimated. Both systems get smarter.

Most organizations have lessons learned processes that nobody reads. What Atlas and ARIA do is encode organizational learning into systems that don't forget, don't retire, and don't walk out the door. Every project makes the next estimate better. Every execution feeds the next risk assessment.

That's not just efficiency. That's institutional memory that actually works.

What This Means for You

If you're a PM who's ever felt that knot in your stomach when someone asks for a number you don't have — this is what changes.

Estimation goes from four hours to ten minutes. From gut feel to structured conversation. From variance across your PM team to consistency grounded in your organization's actual delivery history.

And when you walk into that room with a client, you're not hedging. You're not apologizing. You're presenting a range with documented assumptions and visible math. You're saying: "Here's what it costs. Here's why. Here's what changes the number."

That's the difference between a PM who manages projects and a PM who commands the room.

Next Up: PACE

Episode 12 brings us to PACE — the Predictability and Capacity Engine. If you're running agile at portfolio scale and your sprint commitments are all over the place, PACE is what brings discipline back. We're talking predictability scoring, readiness debt, and what it actually takes to make portfolio-level agile work.

It's a good one. See you there.

— Rick A. Morris
PMP, PMI-ACP | R2 Consulting | Author | Host, Work-Life Balance with Rick A. Morris

EPISODE DESCRIPTION

Episode 11: Atlas — The Estimation Engine

You gave a number in a hallway once. Maybe a conference room. Maybe a Zoom call with the CFO. You didn't have the requirements. You didn't have the team's input. But you gave a number — and from that moment forward, it wasn't an estimate anymore. It was a commitment.

Bad estimates don't happen because PMs are bad at math. They happen because we're asked to make precision commitments at the exact moment we know the least. And then we spend the rest of the project defending a number we never should have been asked to give.

In Episode 11, Rick introduces Atlas — the AI estimation agent he built and deployed in production to solve the estimation problem at its root.

What Atlas does:

Maintains a versioned practice library — every service type your org delivers, with roles, rates, phases, modules, and three-scenario effort estimates per component
Guides PMs through a structured estimation conversation, module by module, grounded in your delivery history
Produces three-scenario PERT estimates (optimistic, most likely, pessimistic) with fully formula-driven Excel workbooks — not a single number, a defensible range

Two modes. One engine. In standard workflow mode, you know the project shape — Atlas builds from the practice library. In discovery mode, you hand Atlas an RFP or requirements document and she analyzes it, suggests component counts, and asks you to validate before a single hour is estimated.

The 500-requirements demo: Rick walks through a real Salesforce implementation where Atlas ran 500 requirements through discovery mode, returned component counts (4 data objects, 11 workflows, 20 scripts, 13 reports, 23 custom fields), generated a structured assumptions document for architect review, and produced a third-revision estimate — in about one hour total. PERT output: 5.7M range, $1.8M expected value. What used to take days of senior-architect line-by-line review.

Why three scenarios beat one number: A single estimate creates false precision. Three scenarios communicate uncertainty honestly, give clients room to understand what drives the number, and set up scope conversations that are evidence-based — not subjective. "We estimated 11 workflows, there are 17" is a defensible conversation.

The RFP scenario: Client needed componentized pricing in their specific format by Friday. Atlas took the RFP, the existing estimate, and the client's format — and produced a structured response with assumptions, PERT parameters, and best/most likely/worst case. Wednesday to Friday. Done.

The Atlas-ARIA closed loop: Atlas builds the estimate at the start. ARIA protects it during execution. When the project closes, actuals update the practice library AND feed ARIA's risk database. Both systems get smarter. Organizational learning encoded into systems that don't forget.

The transformation: Estimation from 4 hours to 10 minutes. From PM-to-PM variance to organizational consistency. From hedging to authority. From "I think it's around this" to "here's what it costs, here's why, here's the math."

Next episode: PACE — the Predictability and Capacity Engine. Portfolio-level agile predictability, sprint commitment discipline, and readiness debt. If your agile portfolio feels like organized chaos, this one's for you.

AI Driven PM - S2E10 - Introducing ARIA—The AI Agent That Turns Your Organizational Memory Into Defended Risk Intelligence

2026-07-02T21:21:30.372-05:00

For nine episodes, we've talked about how to use AI as a thinking partner.

How to prompt better. How to ask Socratic questions. How to stop commanding AI and start coaching it.

That's half the story.

The other half is showing you what happens when you actually build with it.

So for the next four episodes, we're changing gears. No more theory. We're showing you what I've built—real agents, production systems, tools running right now on real client projects.

And we're starting with the one that's most personal to me.

Her name is ARIA. The Advanced Risk Intelligence Agent.

The Vision I Wrote in 2008

Back in 2008 and 2009, I wrote two books where I described what I believed was the future of project management.

Not better Gantt charts. Not more sophisticated scheduling algorithms.

I described systems—actual systems—that could take everything we learned from completed projects and automatically apply them to new ones.

Systems that could look at a project before it started and say: "Based on everything we know, here's where you're going to get into trouble."

I laid out a vision of organizational memory. A database that captured not just what went wrong, but why it went wrong, and how often that same pattern showed up across different projects. A system that weighted lessons by severity and repeatability, so the most dangerous patterns surfaced first.

People loved it.

Here's what killed it: the manual effort.

You needed someone to interview every project team member after every project closed. Someone to categorize the lessons. Calculate the weights. Update the database. Run the queries. Build the reports.

And when that person left? The system died with them.

The theory was sound. The technology just wasn't there yet.

Fast forward to 2024. I'm watching large language models get better and better at structured reasoning. And I realized something:

Technology finally caught up to the idea.

What you're about to see is what happens when you take that 2008 vision and rebuild it with AI.

The Problem We've Been Ignoring for Decades

Let me describe how most organizations handle project risk.

New project starts. You open the kickoff meeting. Someone pulls up a risk register template—probably the same one that hasn't been updated in 20 years.

You ask: "What could go wrong?"

Budget overruns. Scope creep. Resource constraints. Vendor delays.

The usual suspects.

You assign probability scores. Low, medium, high. You assign impact scores. You multiply them. You get a number. You use that number to decide how much contingency to request.

And here's the question nobody ever asks: Where did those scores come from?

They came from someone's gut. Someone's intuition. Maybe someone who's been around long enough to remember a similar project. Maybe someone who's new and has no idea—they just pick medium because it feels safe.

And when the project closes—whether it succeeds or fails spectacularly—you do a lessons learned session. You write down what happened. You file it away.

And six months later, when the next project starts, nobody remembers any of it.

Right now, there are treasure troves of lessons learned sitting in Excel documents and SharePoint folders. Untouched. Unleveraged. Forgotten.

We've been doing this for decades. It's absolutely insane.

What We Should Be Doing Instead

Here's the vision. Here's what ARIA makes real.

We should be capturing every variance that matters—every schedule delay over a threshold, every cost overrun, every scope change that rippled through the project.

We should be structuring that data. What was the cause? What was the effect? What category does this belong to? How severe was it? Has this pattern happened before?

And then, when a new project starts, we should be able to say:

"This project shares characteristics with these five historical engagements. Those projects had these specific failure patterns. Based on that, here's your risk profile—not a guess, but a calculation."

That's exactly what ARIA does.

Why ARIA Is Different from Asking ChatGPT

You might be thinking: "Rick, I can already ask ChatGPT about project risks."

Sure. And it'll give you a perfectly reasonable list pulled from PMBOK and every PM article ever published on the internet.

But it won't know that your vendor management process breaks down consistently on projects over $2 million.

It won't know that your design phase always runs 30% longer than planned with remote teams.

It won't know that your change control process only works when a specific executive sponsors it.

ARIA knows—because ARIA is built on your lessons learned, your project history, your organization's DNA.

This isn't AI giving you generic advice. This is AI making your institutional memory operational.

And here's the part that changes everything for executive conversations:

Instead of walking into a budget meeting and saying, "We think we need a 15% contingency buffer because... it feels right"—

You walk in and say:

"This project shares characteristics with 12 historical engagements in our database. Those engagements slipped an average of 68 business days, driven primarily by requirements and scope failures. Based on that pattern, ARIA recommends a contingency of 129 business days and approximately $1.43 million. Here's why."

That's not a guess. That's a defended position.

The Math Behind ARIA: PERT

ARIA is built on the PERT methodology—Program Evaluation and Review Technique. This was developed by the US Navy in the 1950s during the Polaris missile program. They needed a way to estimate completion times when there was massive uncertainty.

Here's how it works:

For any risk exposure, you define three scenarios:

Optimistic (O): Best case. Risk doesn't materialize. Exposure = zero.
Most Likely (M): What you actually expect. ARIA uses a baseline of five days.
Pessimistic (P): Worst case. ARIA pulls this from your historical database—the actual delay when this risk materialized in past projects.

Then you calculate:

Expected Value = (O + 4M + P) ÷ 6

This gives you a statistically sound estimate that accounts for uncertainty. Not a wild guess. Not a simple average. A weighted probability.

And then ARIA takes it further. She calculates exposure—the combination of expected delay, probability you assign, impact you assign, category weight, and how much your organization historically suffers from this type of risk.

That exposure number, measured in both days and dollars, is what ARIA uses to rank your risks and calculate your contingency.

And here's the beautiful part: When a project closes and ARIA ingests the lessons learned, she updates the pessimistic scenario values. The database learns. The next estimate gets smarter.

ARIA's Six Modes

Mode 1: Lessons Learned Ingestion The fuel that powers everything else. Bring ARIA a completed project—closeout documents, status reports, post-mortems. She extracts variances over your threshold, structures them, asks you to confirm before writing to the database. No garbage in, no garbage out.

Mode 2: Risk Assessment The flagship. A new project starts, you give ARIA the basics, she runs your database against it using fuzzy matching, scores the top 20 most relevant historical risks, asks you two questions per risk (probability and impact), and generates a fully formatted Excel workbook with an executive summary, risk register, and PERT calculations. Presentation-ready in minutes.

Mode 3: Risk Coach Targeted guidance in the moment. You come to ARIA with a question—"We're about to start vendor negotiations. What should I watch for?"—or you feed her a status report that feels off. She analyzes it against the database and gives you a narrative memo: preventative questions, mitigation suggestions, early warning indicators, evidence signals to monitor.

Mode 4: Project Closeout When a project ends, ARIA walks you through a structured interview. What happened? Where did you deviate? What caused the variances? She captures it, flags anything volatile for the database, and ensures lessons actually get recorded for the next team. This feeds Mode 1. It's a closed loop.

Mode 5: Project Health Check For projects going sideways—or when leadership wants an honest read. ARIA analyzes your project documents, tracks sentiment over time (are updates getting more hedged? is tone shifting from confident to defensive?), and produces a color-coded assessment across eight dimensions:

Schedule trajectory. Budget integrity. Scope stability. Team dynamics. Stakeholder engagement. Risk posture. Delivery confidence. Process adherence.

With course correction recommendations prioritized by urgency.

Mode 6: Maintenance Behind-the-scenes database management. Category weight updates, metadata management, quality control. The housekeeping that keeps everything else trustworthy.

Watching It Work: A Live Risk Assessment

Let me show you what ARIA actually does.

The project: Dynamics 365 implementation for a manufacturing client. 2.7M—a 22% reduction). Nine months. Fixed price. Client wants a "like for like" replication of their current ERP. Thin requirements.

I gave ARIA those details and let her run.

Within two minutes, before she even asked me a question, ARIA flagged something:

"Two characteristics you've noted—a like-for-like replication with thin requirements, combined with a fixed-price contract that was cut 22%—align almost exactly with the single highest-severity record in the entire database."

She had seen this before.

Then she loaded the questionnaire—12 risks, in batches of five—each one with plain-language descriptions of what 0, 1, 2, and 3 mean, so you're scoring with context, not guessing.

The first question referenced a real historical engagement:

"An integrator never ran formal requirements gathering and configured the ERP to mimic legacy green screen behavior. UAT failed completely after two years. The systems integrator was removed. The project slipped 240 business days."

Then it asked: Is this likely to happen on this project? What's the impact if it does?

I scored it a 3 on both.

Four minutes later, ARIA generated the Excel workbook.

Executive Summary:

Total recommended contingency: 129 business days
Confidence band: 91 to 167 business days
Contingency dollar value: $1.43 million (approximately 68% of contract value)
Top 5 risks by exposure score
Executive narrative ready to present

PERT Calculations Tab: Full transparency. Every risk, every scenario, every multiplier, every formula. No black box.

Risk Register: Full scored list with probability, impact, exposure in days and dollars, source project references, and AI-generated mitigation strategies.

That workbook was ready in under 10 minutes.

Not a template. Not a guess. A mathematically grounded, historically evidenced risk assessment.

What This Really Means

For 30 years, I've been saying that project managers should be dream makers—not administrators. That our job is to clear the path so teams can do their best work.

ARIA takes one of the most time-consuming, highest-stakes activities we do—risk management—and makes it faster, smarter, and grounded in evidence.

That's time you get back.

Time to coach your team. Time to build stakeholder relationships. Time to think instead of calculate.

And that defended contingency number? That's your credibility.

Walking into an executive meeting and saying "here's what the data says we need, and here's why" is a completely different conversation from "I think we should add a buffer."

ARIA doesn't replace project managers. She amplifies us.

She gives us the organizational wisdom that used to walk out the door every time someone retired or changed jobs. She is your organization's memory—one that doesn't forget, doesn't get promoted, and gets smarter every time a project closes.

This is what I described in 2008.

This is what technology finally let us build.

Next time: Meet Atlas—the estimation engine. If ARIA protects you from risk on the back end, Atlas builds your estimates on the front end with the same rigor. Practice libraries, three-scenario PERT modeling, fully formatted Excel workbooks generated in minutes. Production ready. Running on real engagements right now.

— Rick A. Morris

AI Driven PM: S2E9 - Coach First, PM Second

2026-06-19T09:21:06.905-05:00

I almost skipped the coaching certification.

When I joined the John Maxwell team, the coaching program was part of the package. And honestly? I wasn't that interested. I was doing well in my career. I had built successful PMOs. I had a reputation for getting results.

What did I need a coaching certification for?

But I went. And a trainer named Christian Simpson changed everything.

He was talking about directive leadership—how most leaders just answer questions, tell people what to do, and keep everyone dependent on them for direction.

That was me. I was a directive leader. And I knew it, because the proof showed up every time I tried to take a vacation.

My phone would ring constantly. I'd come back to disaster areas. Everything waited for me because I had built a team that couldn't function without my answers.

I thought I was being helpful. I was actually being a bottleneck.

And then Christian said the thing that rewired how I lead:

"If you give somebody the answer, you rob them of a lifetime of learning."

That hit me.

If you give someone the answer, they'll ask the same question next time. Because they didn't learn—they were just told.

But if they find the answer? If they work through the problem, make the connections, arrive at the solution themselves?

They'll never forget it.

From Manager to Coach: The Shift That Changes Everything

There's a debate I've been in a hundred times about whether project management is "command and control."

When I went through my first Agile training, they described project management like Godzilla stomping through cities. Commanding. Controlling. Dictating.

And Agile? It was communal living, group hugs, servant leadership, butterflies everywhere.

I always pushed back on that.

I was never a command-and-control PM. Even before my personal development journey, I believed in enabling people over directing them. I just didn't have the language for it.

But here's the real distinction that matters:

Old PM role: Here's the plan. Here's your task. Go do it.

New PM role: What do you need to succeed? How can I remove the obstacles in your way?

One extracts work from people. The other builds capacity in people.

And in the AI era, that difference has never mattered more.

Here's why:

AI can automate task management. It can build schedules, generate reports, write requirements, summarize meetings. All the things that used to justify our existence as schedulers and documenters?

Gone. Or going.

But what AI cannot touch is the emotional side of project work.

Conflict between two senior engineers who both think they're right and neither is wrong.

A team member withdrawing quietly because scope keeps changing and nobody's noticing.

A junior PM who doesn't know how to push back on a sponsor without getting their head taken off.

That's where we live now. That's the new PM value zone.

And you can't navigate that by giving answers.

You navigate it by asking the right questions.

The Coaching Mindset vs. The Management Mindset

Manager	Coach
Provides the answer	Asks questions to surface the answer
Directs action	Creates space for discovery
Extracts work	Develops people
Tells people what's wrong	Helps people see what they haven't seen
Creates dependency	Builds capability

The shift isn't about being softer. It's about being smarter.

People own the solutions they create. When the answer comes from them—when they worked through it, wrestled with it, arrived at it—they execute it with conviction, not compliance.

And remember what we covered in Episode 8? Compliance leads to resistance. Resistance leads to revenge. Revenge leads to resentment.

Coaching is the antidote to all three R's.

The Core Coaching Skills Every PM Needs Right Now

Active Listening: Not just hearing words—hearing what's not being said. The fear underneath the frustration. The confusion beneath the compliance. The real blocker hiding behind "everything's fine."

Powerful Questions: Questions that help people think differently, not just answer you. "What options have you already considered?" beats "Have you tried X?" every time.

Holding Space: Creating safety for the hard conversations. Pausing. Sitting in the silence instead of rushing to fill it.

Capability Building: Developing people, not just extracting work from them. Asking "what would help you grow through this?" alongside "what do you need to ship this?"

Accountability with Empathy: High standards AND high support. Both. Not one or the other.

The Story That Made It Click

Let me tell you the best coaching moment I've ever been on the receiving end of.

I was building out the GrowthDay platform for Brendan Bouchard. And I was having a real problem with one of my team members. I was frustrated. Genuinely frustrated.

So I called Brendan.

And I went. I vented. I laid it all out—every frustration, every grievance, everything this person had done that was driving me crazy.

Brendan just listened. The whole time.

And when I was finally done, he asked me one question:

"Huh. Well, what did he say when you told him all this?"

Silence.

What did he say... when I told him.

I hadn't told him.

Not one word of what I'd just spent ten minutes telling Brendan had ever been said directly to the person I was frustrated with.

And in that moment I realized: I had been complaining to the boss about someone I hadn't even attempted to talk to myself.

Brendan didn't tell me that. He didn't say, "You need to go have that conversation." He didn't lecture me about going around someone.

He asked one question.

And I arrived at the answer myself.

That's a lifetime of learning in a single sentence.

I've never forgotten it. And I've never again brought a complaint to someone's supervisor without first having the conversation directly.

That's coaching.

Now Let's Use AI to Get Better at It

Here's the beautiful irony: The tool that people worry will replace human connection is actually one of the best tools I've ever found for preparing for human connection.

AI won't coach your team for you. But it will help you show up to coaching conversations more prepared, more thoughtful, and more focused on the right questions.

Let me show you three prompts I use.

Prompt 1: Coaching Conversation Planner (Your Non-Negotiable)

The situation I gave it:

Sarah—a senior back-end engineer on the Social Wishing project—has been withdrawing. She used to drive planning discussions and code reviews. Now she's quiet, says "whatever the team decides," works late but doesn't surface blockers, and gives short answers in one-on-ones.

Her velocity is still fine. Her engagement is not.

What ChatGPT gave me:

"Sarah still delivers. The risk sits in disengagement—senior engineers influence architecture, team energy, and decision quality. If she withdraws, the team loses signal."

Powerful questions by theme:

"What have the last few sprints felt like from your perspective?"
"When scope changes come up, what goes through your mind?"
"What are you carrying that the team doesn't see?"

Claude's opening:

"Sarah, I wanted to carve out some time that isn't about tickets or sprint status. How are you actually doing?"

And then: Full stop. Don't add qualifiers or softeners. Let her decide how much to give you.

If she says "fine" or "busy," Claude told me:

"Follow with: 'I believe that you're busy. What I'm asking is whether busy feels okay right now—or whether it's starting to wear on you.'"

That's not a script. That's a doorway.

Claude also gave me the question sequence:

Energy and load
Work experience
What she needs
What adjustment to watch for if she deflects

This took me two minutes to generate. The conversation itself might be the most important one I have this week.

Prompt 2: Powerful Coaching Questions Library

This one builds you a reusable toolkit organized by situation type.

Common PM situations:

Team member stuck and escalating
Two team members in conflict
Team member underperforming but unaware
Team member wants career growth
Team member resistant to change

My favorite questions from what the AI generated:

For a stuck team member:

"If you had to make this decision without me, what would you do?"
"What's the smallest step you could take to test a direction?"

For conflict:

"What outcome do you actually want from this situation?"
"What part of this disagreement is about facts versus preferences?"

For underperforming but unaware:

"What are you most proud of from the last month—and what would you do differently?"
"Is there anything getting in the way of your best work that I don't know about?"

The most important coaching principle the AI surfaced:

Don't lead the witness.

❌ "Don't you think we should handle the architecture this way?" ✅ "What architecture options do you see?"

❌ "Do you think it would help to talk to them directly?" ✅ "What options have you considered?"

Same destination. Completely different journey. And the journey is where the learning lives.

Prompt 3: Conflict Coaching Facilitator

The scenario: Two senior engineers at an impasse. Sarah wants microservices from day one (scalability). Tom wants monolith first (speed to MVP). Both are dug in. The debate is getting personal.

Claude's diagnosis:

"Architecture debates between senior engineers rarely stay technical this long unless something else is driving them. This conflict has three layers: 1. Risk tolerance and time horizon 2. Identity and credibility 3. Decision authority ambiguity

Until you address layer three, layers one and two will keep feeding each other."

ChatGPT's framing:

"Sarah sees Tom as cutting corners. Tom sees Sarah as slowing the team with unnecessary complexity. Both are trying to protect the project from risk—but they can't see that in each other.

The goal of your conversation is to reframe the debate from personal positions to shared project outcomes."

Questions for Tom:

"What risks do you see if we start with microservices?"
"What assumptions are you making about future scale?"

Questions for Sarah:

"What specifically makes you confident a monolith won't become a problem at scale?"
"What's your read on where the product is heading in 18 months?"

What the AI surfaces that's most valuable:

What's not being said.

Tom isn't saying, "I'm scared we're going to miss the launch date and I'll be blamed."

Sarah isn't saying, "I've seen monoliths become nightmare refactors and I can't watch it happen again."

Those are the real conversations. And you can't get there by asking about architecture.

Your Non-Negotiable Experiment This Week

Use the Coaching Conversation Planner to prepare for a real coaching conversation this week.

Then ask at least three coaching questions—and resist the urge to answer them yourself.

Here's what I want you to notice:

How does the person respond to questions versus directives?
Do they arrive at solutions you hadn't thought of?
Does coaching build capability and ownership in a way that managing doesn't?

Because here's the thing:

The tasks are getting automated. The human side of projects—the conflict, the fear, the ambiguity, the growth—that's becoming our entire job.

If you're still managing in a world that needs coaching, you're optimizing for a role that's disappearing.

But if you learn to ask the right questions?

You become the person no AI will ever replace.

Next time: Work-Life Balance 2.0—how AI changes the always-on PM trap. Excited to share some different ways of using AI to help us become not just better PMs, but better humans.

Want these prompts ready to copy/paste? Head to PMThatWorks.com for the full library.

Now go prepare for that coaching conversation. Somebody on your team is waiting for the right question.

— Rick A. Morris

The Prompts (Copy/Paste Ready)

Prompt 1 - Coaching Conversation Planner

You are an executive coach training project managers to coach their teams effectively.

First, ask me 4–6 questions about the team member, the situation they're facing, what I've observed, and what outcome I want from the conversation.

Then provide a coaching conversation plan answering:

What is the coaching goal for this conversation?
What powerful questions should I ask to help this person think through the situation themselves?
What am I listening for? (underlying concerns, assumptions, blind spots, emotions)
How do I balance support with accountability?
What's my opening? (How do I set the tone and create safety?)
What's my closing? (How do I ensure clarity and commitment to action?)

Team member situation: [Enter Context]

Prompt 2 - Powerful Coaching Questions Library

You are a professional coach creating a question library for PMs to use in team conversations.

Ask me 2–3 questions about common situations I face with my team (performance issues, conflict, low morale, ambiguity, or resistance).

Then provide a library of powerful coaching questions organized by situation type answering:

What are 5–7 coaching questions for each situation type?
What is each question designed to unlock? (self-awareness, ownership, options, or commitment)
When should I use open-ended questions vs. more directed questions?
How do I avoid leading the witness and let people arrive at their own insights?

Situations I commonly face: [Enter Situations]

Prompt 3 - Conflict Coaching Facilitator

You are a conflict resolution coach helping PMs facilitate team conflicts.

Ask me 4–5 questions about the conflict, the people involved, what each person wants, and what I've observed about the dynamic.

Then provide a facilitation strategy answering:

What is the underlying conflict? (goals, values, communication styles, resource scarcity, or misunderstanding)
How do I create a safe space for the conversation?
What coaching questions do I ask each person to help them articulate their perspective and needs?
How do I guide them toward mutual understanding—not just compromise?
What agreements or commitments do we need to leave the conversation with?
How do I follow up to ensure the conflict is resolved and not just paused?

Conflict situation: [Enter Context]

AI Driven PM: S2E8 - Resistance, Revenge, Resentment

2026-06-04T20:56:24.837-05:00

Stop Counting. Start Leading.

Let me tell you something I learned that changed the way I lead.

Around 2012, I got serious about personal development. I joined the John Maxwell team. I had phenomenal coaches and mentors around me. And one teaching from Paul Martinelli hit me in a way I wasn't expecting.

He was talking about relationships. And he said:

"The danger in relationships is counting."

Once you start counting—that's the third time they've done that, that's the fourth time that's happened—you've started keeping score.

And once you're keeping score, three things happen. He called them the Three R's.

Resistance. You start pulling back. Cutting the person off a little. Creating distance.

Revenge. You start doing the same thing back to them. They're going to do that to me, I'll do it to them.

Resentment. And this is the dangerous one. This is emotional withdrawal. Leadership doesn't care. Nobody's paying attention. I'm done.

Once resentment sets in, that relationship is over.

Here's what hit me: I'm data-driven. I count everything. And when I traced the roots of some of my most difficult client relationships, I could see exactly when I had started counting their failures instead of solving their problems.

I was tracking the wrong metrics. And it cost me relationships.

But that's not why I'm telling you this story.

I'm telling you this because those same Three R's? They're happening on your change initiative right now.

Your Tool Isn't Failing. Your Change Management Is.

Three months after rollout, 60% of people are still using spreadsheets.

Teams are calling the new PM tool "Rick's surveillance tool."

Two departments are openly hostile. Leadership announced the rollout and then disappeared. Training was a 90-minute session with no follow-up.

Sound familiar?

Here's the sequence:

Resistance: Passive non-compliance. "I'll just keep using spreadsheets until this blows over. Every few years they try something new. I'll wait them out."
Revenge: Active demonstration that it doesn't work. "You want me to use this? Fine. I'll use it. And I'll make sure everyone knows how painful it is."
Resentment: Emotional withdrawal. "Leadership doesn't actually care. Nobody reads these reports anyway. I stopped doing it six weeks ago and nobody noticed."

And here's the painful truth that Claude told me in our live demo and I couldn't have said it better:

"This is not a training problem or a tool problem. It is a trust problem with a workflow mismatch layered on top."

You didn't fail at selecting technology.

You failed at managing the human side of change.

People Don't Resist Change. They Resist Being Changed.

This is where DISC blew my mind.

I mentioned DISC in Episode 7. But here's the stat that changed the way I think about every rollout I've ever managed:

69% of the population is High S.

High S personalities love routine. They thrive on predictability. They come in, everything is in its place, and they do their best work.

They will change. But they don't like being changed.

And most organizations manage change initiatives like High D personalities designed them—fast, decisive, bullet point, done.

You announce it. You train them. You expect adoption.

And then you wonder why 60% of people are still using the old system three months later.

Here's the other piece nobody says out loud: People won't change unless they hurt enough that they have to, or care enough that they want to.

That's it. Those are the only two change levers.

Most change initiatives activate neither.

Why Change Initiatives Really Fail

It's almost never about the tool.

Here's what I've seen in 150+ implementations:

We focus on the WHAT. We ignore the WHO.

New tool. New process. New org structure. We spend 90% of our time configuring, integrating, testing, and training on the what—and about 10% on the people who have to actually change their behavior.

And then when adoption fails, we schedule more training.

More training on a tool people don't trust won't fix a trust problem.

What actually triggers the Three R's:

Change is done to people, not with them
No clarity on why the change matters
No voice or input in how the change happens
Broken trust from past change initiatives that went nowhere
Leaders who disappear after the kickoff

And I want to call out one thing that kills me in every large-scale implementation I've ever seen:

They cut training. They cut change management. They cut project management.

The ERP costs 2 million. The configuration runs over. And when they need to find money, they cut the things that ensure the whole thing actually works.

"We have people internally who can handle that."

No. You don't. Not because your people aren't capable—but because change management is a discipline, not a checkbox. And it requires time, intentionality, and someone who isn't also trying to do their day job.

AI Change Management Is Different

Here's what makes the current AI rollout wave particularly dangerous:

AI change is personal in a way that a new PM tool isn't.

When you roll out monday.com, people are annoyed. When you roll out AI tools, people are scared.

They're wondering: Is this the beginning of my replacement? Are they tracking my productivity to justify headcount reduction? Am I being automated out of relevance?

I've answered some version of "Will AI replace project managers?" dozens of times. The answer is the same every time: AI won't replace project managers, but you will be replaced by a project manager who knows how to leverage AI.

But when you're rolling out AI to a team that doesn't believe that? When leadership is absent, training was insufficient, and people don't see "what's in it for me"?

The Three R's hit faster and harder than with any previous technology adoption.

If you're not actively managing the people side of your AI rollout, you're watching the clock count down to resentment.

The Clarity Tool Lesson

Let me tell you one more story.

I used to do a lot of project rescue work. Organizations would call me when their implementation had gone wrong and they needed someone to come in and fix it.

For years, I supported CA Clarity—one of the most powerful PPM tools on the market. You could automate almost any process imaginable. Incredibly configurable.

But I'd walk into these organizations and feel the resentment the moment I walked in the door.

The tool hadn't been deployed well. The adoption had failed. People hated it. And the damage was done.

We could rebuild it. We could redesign it. We could deploy it properly this time, do real training, proper change management.

But the resentment gap was nearly impossible to overcome.

People had already decided. The tool was garbage. Leadership couldn't be trusted. Nothing would change.

So I had to transition my business from rescue work to implementation work.

Because trying to recover from resentment? That's a nearly impossible project.

This is why I say: Catch resistance early. Address it at resistance. Don't let it become revenge. And don't let revenge become resentment.

The Three Prompts That Help You Diagnose and Act

I ran three prompts live today using a scenario most of us recognize: A PM tool rollout three months in, with 60% still using spreadsheets and two departments calling it "Rick's surveillance tool."

Prompt 1: Change Readiness and Engagement Diagnostic (Your Non-Negotiable)

What it does: Assesses where your team sits on the change readiness spectrum, identifies what stage of the Three R's you're in, surfaces root causes, and gives you a 30-day action plan to shift sentiment and build ownership.

What the AI told me:

ChatGPT's change readiness diagnosis:

Most of the organization: between understanding and acceptance (they know the tool exists but don't believe in its value)
Hostile departments: at awareness only (they know it exists and actively reject it)

Three R's signals:

Resistance: Monday.com is being used as a reporting layer, not a system of work. Spreadsheets still run the actual business.
Resentment: Calling it "Rick's surveillance tool" signals distrust of leadership intent. They believe this serves leadership control, not team productivity.
Revenge: Double work creates visible friction. Teams signal frustration through inefficiency without open confrontation.

Claude's action plan:

Move 1: Co-creation with resistant departments.

"Do not push more training or communications into hostile departments. Go in and listen first. Facilitate working sessions where they redesign their monday.com boards to match how they actually work. Give them ownership of the configuration.

This is the only credible counter to the surveillance narrative. When they build it, it stops being yours."

That last sentence is everything.

Move 2: Activate the 40%.

"Identify two or three visible, respected people per department who are already using it effectively. Make them peer coaches—not ambassadors. Peer-to-peer adoption is faster and more credible than top-down instruction. Document one concrete time-saver or meeting eliminated per team as a proof point."

And the metric I loved most from Claude:

Number of times leadership references monday.com data in informal settings.

Because if leadership doesn't use it publicly, nobody believes it matters. That's not a usage metric. That's a trust metric.

Prompt 2: Resistance Root Cause Analyzer

The behaviors I gave it:

People say yes in meetings, then don't change behavior
Slack is full of "this tool is stupid" comments
One manager told their team: "Just keep using the spreadsheet. This will blow over."
Multiple IT tickets requesting to be removed from the system
Poor training participation (multitasking, minimal questions)
One senior engineer said publicly: "This is just another fad. Remember when we tried [previous tool]?"

What the AI diagnosed:

Type of resistance:

Passive: Yes in meetings, no behavior change
Active: Slack complaints and vocal opposition
Avoidance: Low training engagement
Sabotage: The manager telling their team to keep using spreadsheets

Underlying fears:

Fear of demonstrated incompetence (low training engagement = they don't want to look like they can't figure it out)
Fear that this change signals something about their future (Big Brother tracking)
Fear of workload increase without compensation ("You're increasing what I have to do without increasing what you pay me")

Unmet needs:

Need for input and co-authorship
Need for competence and psychological safety
Need for a manager who's actually aligned

The senior engineer's comment is the most important signal of all. When someone says, "Remember when we tried [previous tool]?"—they're not complaining about this tool. They're telling you: "I've been through this before. Nothing changed. Why would this be different?"

That's broken trust from past failures. And no amount of training fixes that.

Prompt 3: Co-Creation and Ownership Strategy

Here's the key reframe Claude gave me before building the strategy:

"The worst version of participatory change is pretending people have input they do not have. Name the constraint honestly before you invite collaboration."

What I could actually open up for input (even though the tool decision was final):

How boards are configured
What the required fields look like
Automation rules and permission structure
Update frequency and check-in norms
Who owns each board

That's not a small thing. When people design how their system works, it becomes their system. Not the tool leadership forced on them.

The pilot experiment approach: Run a 30-day experiment with one willing team, not to prove the tool works—to learn what actually makes it work for them. Then share those wins peer to peer.

The communication principle: Don't claim you're listening if you're not prepared to act on what you hear. Performative co-creation is worse than no co-creation. It validates the cynicism.

Your Non-Negotiable Experiment This Week

Run the Change Readiness and Engagement Diagnostic (Prompt 1) on a current or recent change initiative.

Then take one action: either invite co-creation or directly address a root cause of resistance.

Here's what I want you to notice:

Does giving people a voice shift sentiment—even when the change itself is non-negotiable?
Do you discover root causes you hadn't seen before?
Where on the Three R's spectrum is your team, really?

Because here's the truth:

You can have the best tool in the world. If you skip the people side, it will fail.

And once resentment sets in, you're not doing a rescue. You're doing a replacement.

Catch it at resistance. That's where change is recoverable.

Stop counting their failures. Start co-creating their success.

Next time: Coaching First, PM Second—why facilitation is one of your most valuable skills in the AI era, and how to lead without being the one with all the answers.

Check out the live episode on YouTube:

Now go find out where your change initiative really is.

— Rick A. Morris

The Prompts (Copy/Paste Ready)

Prompt 1 - Change Readiness and Engagement Diagnostic

You are an organizational change management consultant.

First, ask me 5–7 questions about the change initiative, the people affected, their current behavior and sentiment, past change history, and leadership engagement.

Then provide a diagnostic and strategy that answers:

Where is the team on the change readiness spectrum? (awareness, understanding, acceptance, adoption, or advocacy)
What signs of resistance, revenge, or resentment am I seeing?
What are the root causes of resistance? (lack of clarity, fear, broken trust, no input, poor execution)
What change management strategy should I use? (communication, co-creation, small wins, leadership modeling)
What specific actions should I take in the next 30 days to shift sentiment and build ownership?
How do I measure progress in change adoption beyond the number of people trained?

Change initiative context: [Enter Context]

Prompt 2 - Resistance Root Cause Analyzer

You are a behavioral psychologist specializing in organizational change.

Ask me 3–5 questions about specific resistance behaviors, what people are saying or not saying, and what happened leading up to the resistance.

Then analyze the root causes by answering:

What type of resistance am I seeing? (passive, active, avoidance, or sabotage)
What are the underlying fears or concerns driving the resistance? (fear of job loss, loss of control, incompetence, increased workload)
What unmet needs are people expressing through resistance? (clarity, input, support, or trust)
What past experiences are influencing current resistance? (previous failed changes, broken promises)
How do I address root causes rather than just symptoms?

Resistance behaviors observed: [Enter Context]

Prompt 3 - Co-Creation and Ownership Strategy

You are a change management facilitator specializing in participatory change.

Ask me 3–4 questions about the change initiative, who's affected, and where I could invite input or co-creation.

Then design a co-creation strategy answering:

Where in the change process can I invite people to shape HOW—not just accept WHAT?
What decisions can I delegate or open up for input?
How do I identify and empower change champions within resistant groups?
What pilot or experiment can I run WITH the team (not TO the team) to build ownership?
How do I communicate that input is genuinely valued and not just performative listening?

Change initiative and constraints: [Enter Context]

AI Driven PM: S2E7 - The Greatest Lie Ever Told in Project Management

2026-05-21T12:24:16.361-05:00

Why Influence Is the Only Skill That Actually Matters

Somebody told me early in my career that I owned my project.

Owned it. Like it was mine.

Like I could make decisions, adjust budgets on the fly, tell people what to do, and hold the team accountable because—hey—it's my project.

Let me tell you what I actually owned:

The blame. When things went wrong, I owned that.

Here's the reality of project management that nobody puts in the job description:

The people don't report to me
It's not my money
It wasn't my idea
I don't set the timeline
I don't control the strategy

And yet somehow, when the project goes sideways, everybody looks at the PM and says, "Why didn't you see this coming?"

That's the contradiction at the heart of our profession. And it's exactly why influence is the number one skill you need to develop.

Not scheduling. Not risk management. Not Agile certification.

Influence.

You Can't Make Anyone Do Anything

Let me be really clear about something:

As a project manager, you cannot make anyone do anything.

You can't make your team work faster. You can't make your sponsor change the date. You can't make your executive adjust the budget. You can't make your stakeholders show up prepared.

All you can do is influence the outcome.

Here's how I describe our actual job: We increase the percentage chance of hitting a date or budget by doing analysis, building plans, framing decisions, and then influencing whoever we're speaking with—team, sponsor, executive, customer—toward the outcome the project needs.

PMs operate in the influence space between silos.

We're the connective tissue. We're the translators. We're the ones standing between engineering's reality and the sponsor's dream, trying to close the gap without anyone getting hurt.

That's not authority. That's influence. And they require completely different skill sets.

The Three Currencies of Influence

If you haven't read Cialdini's Influence: The Science and Practice of Persuasion, go get it today. It feels like a sales and marketing book. But think about what we do every day:

Selling a position to move a date
Marketing the case for a budget adjustment
Persuading a skeptical stakeholder to trust the process

We are in sales. We just don't call it that.

From my experience, PMs work primarily in three influence currencies:

1. Credibility

People trust your judgment because you've earned it.

Not because of your title. Not because of your certification. Because of your track record, your preparation, your follow-through.

Credibility is the currency you build over time. And it's the one that gets spent fastest when you overpromise, underdeliver, or show up unprepared.

2. Reciprocity

You help others achieve their goals—and they help you achieve yours.

I think this is the most powerful influence lever PMs have access to. When you go to bat for someone, cover for them, solve a problem before they ask, create opportunities for them to shine—you build something that no org chart can give you.

Real influence is banked through real acts of service.

One important note though: Don't explicitly remind people of what you did for them and then ask for something back. I'll come back to this—Claude made this mistake in our live demo, and it's worth discussing.

3. Vision

You paint a picture of success so compelling that people want to contribute.

This is why we develop dream statements, mantras, and team motivation stories. We covered this in Episode 1. The reason I invest so heavily in clarifying the why behind every project is because people don't get inspired by Gantt charts.

They get inspired by a destination they believe in.

When they can see the win—when they can feel what success looks like—influence becomes almost effortless.

Three Common Influence Mistakes (That Kill Your Credibility)

1. Over-Relying on Logic

You build the perfect case. The math is airtight. The risk analysis is complete. You walk into the meeting absolutely certain that any reasonable person will see it your way.

And then the sponsor says, "We're making that date no matter what."

Logic without emotional intelligence isn't influence. It's a monologue.

2. Positional Pleading

"The CEO said so." "This is what the executive team wants." "I have authorization from leadership."

That's not influence. That's borrowed authority. And it erodes trust every time you use it, because people know you're not actually speaking for yourself.

3. Passive Aggressive Escalation

"I guess I'll just have to escalate this to the executive team." "I'll let them know we're going to miss the date."

This is an influence killer. Nobody rallies around a PM who threatens escalation. They shut down, get defensive, and you lose the relationship.

Real influence doesn't require threats. It creates alignment.

The DISC Secret Weapon

Here's something that transformed how I communicate with stakeholders:

One message. Four different ways to say it.

DISC is a behavioral assessment framework with four communication styles:

High D (Dominant): Bullet points, get to the point fast, no fluff
High I (Influential): People-focused, enthusiastic, what's exciting about this?
High S (Steady): Change-resistant, needs context, what's the impact on the team?
High C (Conscientious): Data-driven, detail-oriented, show me the facts

If you bring a five-paragraph explanation to a High D, they check out at sentence two.

If you lead with data and logic for a High S who's worried about their team, you miss the real concern.

Meeting people where they are isn't manipulation. It's respect.

And when you combine that with Socratic AI prompting—where you give the AI context about the stakeholder's style and ask it to help you craft the message—you unlock something powerful.

The Three Prompts (And What the AI Actually Gave Me)

I ran three influence prompts live using our Social Wishing app scenario: We need to cut three MVP features—AI recommendations, in-app messaging, and payment integration—to protect the launch date and save the team from burnout.

Prompt 1: Influence Network Mapper (Your Non-Negotiable)

What it does: Maps the key influencers and decision makers, identifies their positions and motivations, recommends influence tactics for each, and builds you a conversation sequencing strategy.

What I got:

ChatGPT opened with:

"You're not trying to win an argument. You're trying to align incentives around a shared risk. The hidden risk isn't feature loss. It's execution failure."

That reframe alone is worth the price of admission.

The sequencing strategy it gave me:

Engineering manager first — Meet privately, turn frustration into structured input. Ask them to quantify velocity trends, estimate burnout risk, and calculate realistic delivery probability. "I need your help protecting the team and protecting the date. I don't want this to become an engineering versus business fight."
Product owner second — Pre-align before the CEO meeting. Convert engineering evidence into risk framing.
Marketing director third — Build the narrative that gives the CEO something to say publicly.
CEO last — Walk in with the engineering risk doc, the product owner's alignment, and the marketing director's framing already in hand.

And then Claude gave me the closing guidance that absolutely nailed it:

"You're not building consensus. You're building a sequence of aligned conversations so that by the time you reach the CEO, the decision feels inevitable rather than contentious. Each conversation should leave the stakeholder feeling heard—not managed. The goal is that no one walks into the CEO meeting as a surprise voice of opposition."

That's the playbook. Pre-wire every conversation so the final meeting is a confirmation, not a confrontation.

Prompt 2: Stakeholder Persuasion Message Crafter

The specific situation: Convincing the executive sponsor—who loves AI and sees it as a market differentiator—to launch without AI recommendations.

What the AI gave me:

The core message:

"Launching AI recommendations before the user base exists to train them guarantees this feature underperforms at the exact moment it needs to impress the board."

The analogy:

"Netflix didn't launch with a recommendation engine. They launched with the catalog. The recommendation engine came after they had enough viewing data to make it work. An AI feature trained on zero user behavior doesn't differentiate you—it could embarrass you."

And then the strategic opportunity flip:

"A post-launch AI reveal gives you two press moments instead of one. We launched clean. We proved traction. Now we're releasing AI 1.1 with real usage data. That's a story—not a patch."

ChatGPT offered a different analogy:

"Launching without data-driven AI is like launching a Tesla without Autopilot enabled."

Both are strong. Both give the sponsor something to say in the boardroom. Leaders love a reference story that makes their decision sound precise, not reactive.

Prompt 3: Reciprocity & Coalition Builder

Here's where I want to be honest about the tool differences—because one of the most valuable things I can do in this series is show you where AI gets it right and where it gets it wrong.

Claude's suggestion for the engineering manager:

"I backed their QA resource request. Now I need to go to them first, privately, and name it directly: 'I went to bat for you on QA. I need you to go to bat for me—and for the team—now.'"

I pushed back on that.

Don't explicitly call out the favor. The moment you say "I did this for you, now you do this for me," you've turned reciprocity into a transaction. And the next time you do something generous for that person, they'll wonder what you're banking it for.

Real reciprocity doesn't need to be verbalized. It should be felt.

ChatGPT handled it better:

"You backed their QA request. That built trust. What they want now is team stability and credibility. Help them by making sustainable scope the official position—not a quiet concern. If you carry the political weight, they don't have to."

That's the difference. You're not reminding them of a debt. You're aligning your ask with their interests and removing a burden from their plate.

That's influence.

The Big Picture

Here's what I want you to take away from this episode:

You'll never have authority over the things that determine project success. Budget, people, strategy, timeline—none of it is yours.

But influence? That's entirely within your control.

Map your influence network. Understand what each person cares about. Sequence your conversations. Build reciprocity through genuine acts of service. Craft messages that meet people where they are.

And then watch what happens when a PM walks into a final decision meeting where the outcome is already inevitable.

That's not manipulation. That's masterful leadership.

Your Non-Negotiable Experiment This Week

Use the Influence Network Mapper (Prompt 1) on a real decision you need to influence this week.

Then have at least one conversation using the strategy or message AI helps you design.

Here's what I want you to notice:

Did mapping the influence network reveal leverage points you hadn't considered?
How did stakeholders respond when the message was framed around their priorities?
Did the sequencing strategy change how the final conversation landed?

Because here's the truth: The most effective PMs I've ever met don't win by being the loudest or the most technically correct. They win by knowing exactly who to talk to, in what order, with what message.

And now you have a thinking partner that helps you figure all of that out in minutes.

Next time: The Three R's—Resistance, Revenge, and Resentment. Why change initiatives fail, what happens when people get caught in the Three R's, and how to use AI to navigate your way out.

Want these prompts ready to copy/paste? Head to PMThatWorks.com for the full library.

Now go map your influence network. The decision is waiting.

— Rick A. Morris

The Prompts (Copy/Paste Ready)

Prompt 1 - Influence Network Mapper

You are an organizational influence strategist and stakeholder engagement expert.

First, ask me 4–5 questions about the decision I need to influence, who's involved, what their interests are, and what resistance I expect.

Then provide an influence strategy answering:

Who are the key influencers and decision makers in this situation?
For each person, what do they care about, what motivates them, and what concerns might they have?
What is each person's current position? (supporter, neutral, or blocker) and their level of influence?
What influence tactics should I use with each person? (credibility, reciprocity, vision, data, coalition building)
What is my sequencing strategy? Who do I approach first, and how does that set up the next conversation?
What message framing will resonate most with each stakeholder type?

Decision and context: [Enter Context]

Prompt 2 - Stakeholder Persuasion Message Crafter

You are a communication strategist specializing in persuasive stakeholder messaging.

Ask me 3–4 questions about the stakeholder, the message I need to deliver, their priorities, and what objections I expect.

Then craft a persuasive message by answering:

What is the core message in one sentence?
What framing will resonate most with this stakeholder's priorities? (data, vision, risk mitigation, opportunity)
What story or analogy makes the message memorable?
What objections will they raise, and how do I preempt them?
What ask or call to action do I make, and how do I make it easy for them to say yes?

Provide the message in an email or conversation script format.

Stakeholder and situation: [Enter Context]

Prompt 3 - Reciprocity & Coalition Builder

You are a relationship strategist helping PMs build reciprocity and coalition.

Ask me 3–4 questions about the people I need to influence, what they care about, and how I've helped or could help them.

Then provide a reciprocity and coalition strategy answering:

What have I done (or could I do) to help each stakeholder achieve their goals?
How do I frame my ask as aligned with their interests—not just my project needs?
Who are the natural allies I should recruit first to build a coalition?
How do I leverage early supporters to create momentum with skeptics?
What specific conversation should I have this week to build reciprocity and alignment?

Situation and key people: [Enter Context]

AI Driven PM: S2E6 - Net Operating Value

2026-05-07T10:14:00.000-05:00

Your Project Isn't Competing Against a Standard. It's Competing Against Everything Else.

Have you ever had a project going really well—hitting milestones, staying on budget, team is energized—and an executive pulls the plug?

You're furious. You're confused. You think, "How could they kill this? We were succeeding."

Here's the uncomfortable answer: Because they weren't evaluating your project. They were evaluating your portfolio.

And those are completely different conversations.

Most project managers optimize in isolation. We focus on our project. Our timeline. Our velocity. Our scope. We find our lane and we stay in it.

But executives? They're not optimizing your project.

They're optimizing across a dozen projects competing for the same people, the same budget, and the same strategic window.

And without a shared language for value—one that accounts for all the trade-offs—PMs and executives talk right past each other.

Your project looks green. Their portfolio looks broken.

That's the gap. And that's exactly what Net Operating Value is designed to close.

What Is Net Operating Value?

Net Operating Value (NOV) is a metric I helped develop to tell a more complete story about project value.

Here's the formula:

NOV = Expected Value − Effort Cost − Risk Cost − Opportunity Cost

Let's break that down:

Expected Value: Revenue, cost savings, strategic positioning, user impact. Everything you gain if the project succeeds.

Effort Cost: Budget, team capacity, person-months, timeline. Everything you spend to get there.

Risk Cost: Probability of failure or underperformance × the financial impact of that failure. What you might lose.

Opportunity Cost: What you are not building because you're building this.

That last one is the one most business cases never include. And it's the one that changes everything.

ROI asks, "Is this worth doing?"

NOV asks, "Is this the best thing we could be doing with these resources right now?"

That's a completely different question.

The AI Capacity Trap

Before we get into the prompts, I want to call out something I'm seeing happen in organizations right now.

They're seeing AI increase team velocity. Story points per sprint are going up. Delivery speed is improving.

And their first instinct is: Let's throw more projects at the team.

I've already had clients say, "If our team's capacity was 270 points before AI, shouldn't it be 390 now? Let's plan accordingly."

And look—maybe. You might see velocity increases. But here's the question you're not asking:

Can we build it? That's the wrong question. The right question is: Should we build it, given everything else we could build?

More capacity doesn't mean more projects. It means more opportunities to choose poorly.

The 2-1-0 philosophy exists precisely for this moment: You need to be two full quarters ahead in ideas competing for quarterly planning, and two full sprints ahead in fully defined, designed, and architected user stories.

Not so you can do more. So you have the tension to choose better.

Can we do this feature? Sure. Should we—given what else is in the queue? That's the real conversation.

Now Let's Do the Math

I ran three prompts live against our Social Wishing app—the bucket-list social platform we've been building throughout this season.

And the results were... illuminating.

Prompt 1: NOV Calculator (Your Non-Negotiable)

What it does: Calculates a full NOV assessment—expected value, effort cost, risk cost, and opportunity cost—and compares your project to a baseline threshold for approval.

I gave it this context:

10,000 users in 90 days
4.99/month premium, 10% conversion)
$300,000 budget, six months, eight people
Fully dependent on Facebook API
Opportunity cost: A flagship product feature we're not building that could drive $500,000 in upsell revenue with 80% confidence

ChatGPT asked me:

"What's your gross margin on subscription revenue?" (70%)
"What's your expected monthly churn rate?" (30%)
"Are you planning paid acquisition or assuming organic?" (Paid, $11 CAC)
"If Facebook API access is restricted, what percent of core functionality breaks?" (Total shutdown)

Then it ran the math.

Unit economics summary:

Monthly price × 70% margin ÷ 30% churn = $11 lifetime value per premium user
CAC = $11
Net unit contribution per premium user: 63 cents

ChatGPT's verdict:

"You're spending 50,000 in premium lifetime contribution. This is deeply negative unit economics."

Claude's NOV calculation: Negative $702,000.

"This project has a strongly negative NOV. Your alternative project produces positive expected contribution of $280,000 with far lower uncertainty. Build the flagship feature."

And just like that—in under four minutes—we had a business case analysis that would have taken a finance team days to produce.

That's the power of the NOV calculator.

Prompt 2: Value Assumption Stress Test

This is the one I love most.

It doesn't just tell you what your project is worth. It tells you which assumptions, if wrong, kill the entire value case—and what you can do about it.

I gave it the Social Wishing business case with these underlying assumptions:

10,000 users in 90 days via viral Facebook sharing
30% wish fulfillment rate
10% premium conversion
Users will trust the platform with personal bucket list information
Facebook maintains stable API access
Organic network effects keep CAC low

Then I answered honestly:

Never launched a consumer social product before
No evidence users will share wishes—just a hunch
No waitlist, no beta, no validated demand
Not solving an urgent problem—creating new behavior
No relationship with Facebook's API team

Claude's response:

"Good. Now we're thinking clearly. You just removed most of the illusion from the business case."

Then it walked through each assumption with best case, expected case, and worst case scenarios.

And then it said something I think every PM needs to hear:

"This is not a 25,000 behavior experiment."

It recommended a 45-day validation sprint instead:

Build a landing page, spend $3-5K in ads, measure cost per email signup
Start a private community of 100 people manually, see who posts wishes and who engages
If cost per signup exceeds $5-7 or organic growth assumptions weaken, you have your answer

Then it gave me the framing for the sponsor conversation:

"The question you should be bringing to your sponsor isn't 'Should we build this?' It's 'Can we spend 285,000?'"

That one reframe changes the entire conversation.

You're not saying no to the idea. You're saying yes to being smarter about how you validate it.

How many of us have had a CIO come back from a conference with a "cool thing" they saw? And six months and $300K later, we find out nobody actually wanted it?

This is how you avoid that.

Prompt 3: Portfolio Trade-Off Analyzer

Now let's zoom out. Three projects. One six-month window. Capacity for two.

The options:

Option A: Social Wishing (new product) — 300K cost, high risk
Option B: Flagship product feature (upsell to existing customers) — 200K cost, low risk
Option C: Infrastructure modernization (tech debt reduction) — 250K cost, medium risk

Which two do you choose?

ChatGPT and Claude gave me the same answer:

Option B + Option C. Kill Option A in its current form.

And here's the framing that I would use in the executive meeting:

"Option B is the cash generator. Option C is the capability builder. Together they fund growth AND protect our future velocity. That's a balanced portfolio."

That's not a financial argument. That's a story. Two sentences. Executives get it immediately.

For communicating the trade-off on Social Wishing, both tools gave me the same counsel:

"Don't frame this as killing creativity. Frame it as disciplined capital allocation."

Script:

"Our priority this year is predictable revenue growth and operational stability."
"For every dollar invested in the flagship feature, we get approximately $2 in risk-adjusted value. Social Wishing does not meet that threshold today."
"Social Wishing is interesting—but unvalidated. We will test demand with a capped experiment before committing full capital."

That's not a no. That's a responsible yes.

The Full Circle Moment

We spent all season building the Social Wishing dream. We clarified the vision. We wrote the team motivation story. We built the backlog. We ran the health diagnostic.

And now the NOV says: Don't build it. Not yet. Validate it first.

That's not failure. That's exactly how great project management works.

Dreams deserve data before dollars.

And the PMs who can have that conversation with their sponsors—who can say "here's the math, here's the risk, here's the smarter path forward"—those are the PMs executives trust with their most important projects.

Your Non-Negotiable Experiment This Week

Run the NOV Calculator (Prompt 1) on a current project.

Then identify: What is the one assumption that, if wrong, kills the entire value case?

Test it. Find the cheapest way to validate or invalidate it before committing more resources.

Here's what I want you to notice:

Does calculating NOV change how you talk about your project's value?
How do executives respond when your business case includes opportunity cost?
Did the stress test surface an assumption you've been avoiding?

Because here's the truth: Protecting your project isn't about defending it. It's about proving it deserves the resources over everything else competing for them.

That's thinking like a portfolio manager.

And that's how you earn a seat at the table.

Next time: Influence without authority—how to lead when you can't command. It wasn't your idea. They're not your people. It's not your budget. So how do you actually move a project forward?

Want these prompts ready to copy/paste? Head to PMThatWorks.com for the full library.

Now go run the math. Your dream deserves to know if it can stand up to the numbers.

— Rick A. Morris

The Prompts (Copy/Paste Ready)

Prompt 1 - NOV Calculator

You are a portfolio strategist and financial analyst for project investments.

First, ask me 5–7 clarifying questions about the project's expected benefits, costs, risks, and what else the organization could be doing with the same resources.

Then calculate a net operating value assessment by answering:

What is the expected value of this project? (revenue, cost savings, strategic value, user impact — quantify as much as possible)
What is the total effort cost? (budget, team capacity, person-months, timeline)
What is the risk cost? (probability of failure or underperformance × financial impact)
What is the opportunity cost? (what alternative projects or initiatives are we not doing because of this?)
What is the calculated NOV, and how does it compare to a baseline threshold for project approval?
What assumptions are most uncertain, and how would changing them affect the NOV?

Project context: [Enter Context]

Prompt 2 - Value Assumption Stress Test

You are a critical thinking coach and risk analyst.

Ask me 3–4 questions about the value assumptions underlying my project's business case.

Then stress test those assumptions by answering:

What are the 3–5 core assumptions that must be true for this project to deliver its expected value?
For each assumption, what is the best case, expected case, and worst case scenario?
How sensitive is the project's NOV to changes in each assumption?
What evidence or data exists to validate or challenge each assumption?
What experiments or MVPs could we run to de-risk the biggest assumptions before committing fully?

Project business case: [Enter Business Case and Assumptions]

Prompt 3 - Portfolio Trade-Off Analyzer

You are a portfolio management consultant helping executives make investment decisions.

Ask me 3–4 questions about the competing projects or initiatives in our portfolio and the organization's strategic priorities.

Then provide a trade-off analysis answering:

How do the competing projects compare on NOV?
What projects are must-dos (strategic imperatives) vs. nice-to-haves?
What is the optimal portfolio mix given current capacity and risk tolerance?
What projects should we greenlight, pause, or kill based on NOV?
How do I communicate trade-offs to stakeholders in a way that builds alignment rather than resentment?

Portfolio context: [Enter Competing Projects and Constraints]

AI Driven PM: S2E5 - Data Rules 2.0

2026-04-23T18:05:00.000-05:00

Let me tell you something I learned from Six Sigma that changed the way I run projects:

If you can't measure it, you can't improve it.

I came up through the DMAIC era—Define, Measure, Analyze, Improve, Control. I love data. I live for metrics. Give me a spreadsheet and a story to tell, and I'm happy.

But here's the uncomfortable truth I've discovered after 30+ years and 150+ implementations:

Most of the metrics we track don't tell the story we actually need to tell.

We obsess over story points. Velocity. Task completions. Hours logged. Burn rate.

And then an executive asks, "Are we going to hit the launch date?"

And we stare at our dashboard.

And it doesn't answer the question.

That's not a data problem. That's a framing problem.

The Activity Metrics Trap

Here's what most PMs measure:

Story points completed
Task closes
Hours logged
Budget spent to date
Number of commits to repo

You know what all of those have in common?

They measure busyness. Not achievement.

Activity doesn't equal value. And executives—even if they can't always articulate it—don't actually care about activity. They care about outcomes that drive business value.

I was working with a client recently who said their goal was a 2% net sales lift.

I asked, "If you land Walmart, do you win?"

They said, "No."

I said, "Okay—walk me through what this 2% net sales lift actually means."

And here's what was wild: They were using that metric to justify building a data warehouse, but they had no idea what data they were going to put into the warehouse to measure it.

The metric was real. The measurement plan was nonexistent.

That's the trap.

We pick metrics because they're easy to track, not because they answer the questions that actually matter.

We Are Lawyers. Our Sponsors Are Judges.

Here's a frame I use all the time that completely changes how PMs think about data:

Your job is to build a case. Your sponsor is the judge.

You gather evidence. You analyze the data. You present your recommendation. You let the judge decide.

And if you don't like the ruling? You don't argue in the courtroom.

You appeal.

You go back, review your data, figure out why it didn't tell the compelling story you needed, and you come back better prepared.

And here's the thing: If I have more data than you, I'm going to win the conversation.

Not because I'm louder or more senior or more confident.

Because data tells a story. And the PM who tells the better story wins.

The problem is we keep bringing the wrong data to court.

What You Should Actually Be Measuring

Here's what I suggest you measure instead of activity:

1. Value Delivered

Not "features shipped"—features in production being used.

I worked on the GrowthDay app build. We had all these features planned, but at the last minute, the founder said, "Wouldn't it be cool if we had a daily motivational segment—something that fires people up every morning?"

We almost cut it. Time pressure. Scope pressure.

We didn't cut it.

That tiny, last-minute feature became one of the stickiest in the whole app.

But here's the key: We only knew it because we measured how people were actually using the app. How many times. How long they stayed. Whether they came back.

If you're not measuring features being used, you don't know if you're delivering value or just shipping code.

2. Time to Impact

How fast do you go from idea to user value?

What's your average cycle time from "we need this" to "users are using this"?

That's a story executives actually care about.

3. Quality Signals

Defect rates. Technical debt. User satisfaction.

But here's the nuance: When you compress testing due to date pressure, defect growth becomes exponential—not linear.

I had this exact conversation in a live demo. The data showed:

Sprint velocity dropped
Defect rate rising
Testing coverage shrinking

I told the team: "If testing continues to erode, expect 15 to 18 defects per sprint within four to six weeks. The rework alone will cost more time than the testing would have."

That's the conversation a PM should be having with a sponsor. Not "we're at 80% of story points." But: "We're trading short-term velocity for long-term quality debt. Here's what that actually costs us."

4. Team Health

Velocity stability (not just velocity), morale, attrition risk, sentiment analysis.

A velocity drop after adding a team member? That's normal. Expected, even.

Knowledge transfer consumes senior capacity. Code review loads increase. A 20% velocity drop after onboarding one person is common in the first two to four sprints.

But if it hasn't recovered in three sprints? That's structural. That's something else.

Know the difference.

5. Stakeholder Confidence

Sponsor engagement. Clarity of vision. Meeting attendance.

When your sponsor starts missing meetings, that's a leading indicator—not a footnote.

The Metric I'm Most Proud Of: Scope Stability Index

Here's one I love that most teams don't track:

Scope Stability Index = New story points added ÷ Total committed story points

If that number exceeds 15% mid-sprint, execution predictability collapses.

Let me make that concrete. You committed to 30 story points for the sprint. During the sprint, 10 new points get added. That's 10 ÷ 30 = 33%.

Your sprint just broke.

Not because the team is failing—but because the input changed faster than the output could absorb it.

This is the conversation you bring to a sponsor: "Every time we add scope mid-sprint, we pay a compounding tax. Here's what that tax looks like in data."

That's a case. That's a lawyer walking into court prepared.

How AI Fits Into All of This

Here's the piece most people miss:

AI can automate all the activity tracking.

I have agents that pull data from JIRA, Microsoft Planner, ServiceNow, spreadsheets—normalize it, format it, and report it. Automatically.

That means I'm not spending 60% of my week staring backwards at what already happened.

I'm spending it on outcome measurement, impact analysis, and inventing new metrics that tell the parts of the story nobody else is telling.

AI can correlate your leading indicators (velocity, quality, team sentiment) with your lagging indicators (revenue, retention, delivery dates). It can isolate trends across data sources you'd never have time to manually connect.

But here's the catch, and I said this right at the top of the episode:

AI can't do for you what it can't do through you.

The metrics it surfaces are only as good as the questions you're asking. You have to know what story you're trying to tell before AI can help you tell it.

That's what these three prompts are designed to do.

Prompt 1: Metrics Dashboard Designer

Start here. This builds you a dashboard that actually answers the questions your executives are asking.

What it creates:

3-5 outcome metrics that prove value delivery
3-5 activity health metrics as leading indicators
Data sources and measurement approach for each
Green/yellow/red thresholds
How to present differently to executives vs. team vs. sponsor

What I got when I ran it for the Social Wishing app:

ChatGPT asked:

"What would cause leadership to declare this a failure at month six or nine?"
"How does this app make money in the first 12-18 months?"
"Do you have any analytics tooling selected?"

That first question? Bring it to your sponsor. Seriously. Ask them: "What would cause you to declare this a failure at month six?" You'll learn more in that 10-minute conversation than in three weeks of status reports.

The outcome metrics it generated:

New user signups per week
90-day active user rate
Wish progress rate
30-day retention

The leading indicators:

Visitor-to-signup conversion rate
Invite rate (% of users who invite at least one friend)
Time to first wish (median time from signup to first wish created)
Sprint predictability

The dashboard format for founders: Single slide, three rows.

Row 1: Growth (signups, conversion)
Row 2: Engagement (wish progress, invite rate)
Row 3: Retention (30-day retention, trend arrow)

Simple. Powerful. Tells the dream story.

Prompt 2: Predictive Risk Indicator Finder

This one is for when you feel something is wrong but can't quite articulate it yet.

What it does:

Identifies leading indicators that predict trouble
Correlates team health metrics with outcome metrics
Surfaces data you're NOT capturing (but should be)
Sets intervention thresholds
Coaches you on how to communicate risk without panicking the room

I gave it this project context:

Sprint velocity dropped from 35 to 28 points
Defects up from 7 to 12 per sprint
Code review cycle time averaging 2.3 days
Sponsor missed last 2-3 meetings
Team sentiment dropped from 8.0 to 6.5
8 new feature requests added this month, 3 original features cut

What AI told me:

"Velocity drop after adding a person is classic onboarding drag. A 20% drop is common in the first two to four sprints. If it doesn't recover within three sprints, the issue is structural—not onboarding."

"Your defect increase combined with shrinking testing coverage is the highest risk signal in your data. When testing coverage drops due to date pressure, defect growth becomes exponential, not linear. Expect 15 to 18 defects per sprint within four to six weeks if nothing changes."

And then—the one I loved most—it surfaced metrics I wasn't tracking:

Code review comment density per PR.

"High comment density means complexity or standards drift. Low comment density with long cycle times means avoidance. These require completely different interventions."

I would never have thought to track that. That's AI as a thinking partner.

Prompt 3: Vanity vs. Value Metrics Audit

This is the one that will save you from the most painful meeting of your career.

You know the meeting. Twenty executives in the room. You walk through your status report. They eat you alive.

Because your report answered "Are we busy?" instead of "Are we going to succeed?"

I've been in that meeting. I never want you to experience it.

I gave AI this list of my current metrics:

Tasks completed this week
Story points burned
Budget spent to date
Team utilization
Number of commits to repo
Lines of code written
Meetings held
Risks identified

And I told it: "Executives keep asking if we'll hit the launch date and user targets. My metrics don't answer that question."

Claude's response was brutal. And perfect:

"Why are you asking me questions, Rick? You've already diagnosed the problem yourself. Your metrics answer 'Are we busy?'—not 'Are we going to succeed?'"

"You have six vanity metrics out of eight. The executives are asking the right question. Your dashboard is giving them the wrong answer. It's not a data problem. It's a framing problem. You're reporting inputs when they're asking about outcomes."

Couldn't have said it better myself.

Then it gave me the swap:

Vanity Metric	Why It's Vanity	Replace With
Story points burned	Doesn't predict completion without trend	Forecasted completion date based on velocity trend
Team utilization	Measures busyness, not throughput	Scope stability index
Commits to repo	More commits can signal churn, not progress	Defect escape rate
Lines of code	More code often = more defects	Time to value / time to first wish

That table is a career-saver.

Your Non-Negotiable Experiment This Week

Two challenges:

1. Build your outcome metrics dashboard using Prompt 1. Take your current project and identify 3-5 metrics that actually answer your executive's burning questions.

2. Replace at least one vanity metric in your next status report with a value metric.

Just one swap.

Here's what I want you to notice:

How do stakeholders react when your report answers their actual questions?
Does better data help you spot risks earlier?
Do you feel more confident walking into that executive meeting?

Because here's the truth: The PM who tells the better story with better data wins.

Not because they're louder. Because they came prepared.

Next time: Net Operating Value—the metric I use for portfolio decisions. How to stack-rank your portfolio, make trade-off decisions, and help executives choose between good ideas using data that actually reflects business value.

Want these prompts ready to copy/paste? Head to PMThatWorks.com for the full library.

Now go build that dashboard.

— Rick A. Morris

The Prompts (Copy/Paste Ready)

Prompt 1 - Metrics Dashboard Designer

You are a data-driven PM coach and metric strategist.

First, ask me 4–5 questions about the project goals, stakeholders, team, and what success means in business terms.

Then help me design a metrics dashboard by answering:

What are the 3–5 outcome metrics that prove the project is delivering value?
What are 3–5 activity health metrics that are leading indicators of those outcomes?
For each metric, what is the data source and how do we measure it?
What thresholds or targets indicate green, yellow, red status for each metric?
How should I present these metrics to executives vs. the team vs. the sponsor?

Project context: [Enter Context]

Prompt 2 - Predictive Risk Indicator Finder

You are a predictive analytics expert for project management.

Ask me 3–4 questions about current project metrics, team dynamics, and any early warning signs I'm seeing.

Then analyze potential risk patterns by answering:

Based on the metrics I'm tracking, what are the 3–5 leading indicators that typically predict project trouble?
What correlation exists between team health metrics (velocity, morale) and outcome metrics (quality, delivery)?
What data am I not currently capturing that would give me an earlier warning sign of risk?
What specific metric threshold should trigger a project health intervention?
How do I communicate risk using data without sounding alarmist?

Current metrics: [Enter Metrics and Current Project Context]

Prompt 3 - Vanity vs. Value Metrics Audit

You are a metric strategist helping PMs distinguish signal from noise.

Ask me 2–3 questions about the metrics I'm currently reporting and what decisions those metrics inform.

Then provide an analysis answering:

Which of my current metrics are vanity metrics? (They look good but don't drive decisions.)
Which metrics are value metrics? (They directly inform action or prove impact.)
For each vanity metric, what is the underlying value metric I should track instead?
What questions should I ask myself to test if a metric is worth tracking?
How do I transition stakeholders away from vanity metrics they're used to seeing?

Current metrics: [List Your Current Metrics]

AI Driven PM: S2E4 - Do We Have to be the Domain Expert?

2026-04-09T11:24:00.000-05:00

I get this question all the time when I'm working with a new client or interviewing for a role.

"So Rick, are you an expert in the insurance industry?"

"No."

"Well... shouldn't you be? I mean, shouldn't a project manager understand the domain they're managing projects in?"

"Not really."

They look confused. So I follow up:

"How many people work here?"

"About 5,000."

"Great. So you've got 5,000 experts in insurance. What you need is someone like me who can get the best out of those 5,000 people."

That's the difference between domain expertise and project management expertise.

And most organizations don't understand it.

They prioritize hiring PMs who "speak the language of the business" over PMs who know how to facilitate, translate, and orchestrate expertise.

And that hiring bias? It backfires more often than you think.

The Conventional Wisdom (And Why It's Wrong)

Here's what most organizations believe:

You need a PM who understands healthcare, finance, manufacturing, [insert industry here]
They have to "speak the language" of the business
Domain expertise signals credibility and competence
Deep knowledge = better decisions

The reality? Domain expertise can be a hindrance just as often as it's a help.

Here's why.

When Domain Expertise Backfires

1. Domain Expert PMs Micromanage

When you know how to do the work, it's really hard not to tell people how to do the work.

A PM with deep domain expertise will hear an engineer say, "That'll take 10 hours," and think, "I could do that in two hours."

And then they start challenging estimates. Second-guessing approaches. Offering "suggestions" that aren't really suggestions.

That's not project management. That's micromanagement.

2. They Focus on WHAT, Not HOW

Domain expert PMs get obsessed with what's being built instead of how the team is building it.

They care more about the technical solution than the team dynamics, the stakeholder alignment, or the energy in the room.

And that leads to projects that might be technically perfect but operationally a disaster.

3. Personal Bias Takes Over

When you have deep domain experience, you carry bias—whether you're aware of it or not.

You think, "I've been there before. I've seen that approach. It doesn't work."

But here's the thing: Just because it didn't work when YOU tried it doesn't mean it won't work now.

Context matters. Teams matter. Timing matters.

And sometimes the team needs to try something, discover it doesn't work, and pivot. That's how ownership and learning happen.

When a domain expert PM shuts that down with "I already know that won't work," they kill ownership and innovation.

4. They Become Decision Bottlenecks

Domain expert PMs feel like they need to be involved in every decision because they "understand the implications."

So they become the bottleneck.

Every technical choice, every scope question, every trade-off discussion has to run through them.

That's not leadership. That's dependency.

What Great PMs Actually Do

Here's the truth most organizations miss:

A great PM knows how to ask the right questions—not provide the right answers.

Let me say that again for the people in the back:

You don't make decisions on scope, budget, timeline, or what's in or out. You make recommendations. You influence. But you don't own the decision.

Your job is to:

Translate between domains (tech to business, business to customer, customer to tech)
Facilitate expertise (create the conditions for experts to do their best work)
Ask the "dumb" question (the one everyone assumed was already answered)
Frame trade-offs (so the right people can make informed decisions)
Orchestrate, not dictate (you're the conductor, not the soloist)

And here's the magic: Facilitation expertise + deep learning desire > domain expertise.

Why?

Because you're unafraid to ask the next question. You're not stuck in "how it's always been done." You bring fresh eyes, challenge assumptions, and force clarity where experts have gotten comfortable with ambiguity.

When Domain Expertise DOES Matter

I'm not saying domain expertise is useless. There are contexts where it absolutely matters:

1. Highly Regulated Industries

Healthcare, finance, government—anywhere compliance is complex and non-negotiable.

In these environments, knowing which questions to ask requires baseline domain knowledge. You need to know what regulations exist so you know who to pull into the conversation.

But even then, you don't need to be the compliance expert. You just need to know when to engage one.

2. Deeply Technical Domains

If you're building your own AI/ML systems, embedded systems, or highly specialized technology, some technical fluency helps with translation.

But notice I said fluency, not mastery.

You don't need to code the solution. You need to understand enough to ask, "What are the trade-offs?" and "What happens if we're wrong?"

3. When You're the Only Person in the Room

If you're a solo PM in a startup with no dedicated domain experts, then yeah—you might need to wear both hats for a while.

But even then, your job is to build the team that replaces your domain gaps as fast as possible.

How AI Helps You Bridge Domain Gaps in Days, Not Months

This is where it gets fun.

One of the most powerful uses of AI for project managers isn't writing status reports or generating meeting notes.

It's becoming a domain learning accelerator.

You can use AI to:

Get up to speed on unfamiliar domains in days instead of months
Build stakeholder expertise maps so you know who to ask what
Generate facilitation scripts so you can lead technical debates without pretending to be the expert

Let me show you.

Prompt 1: Domain Knowledge Accelerator (Your Non-Negotiable)

This is your experiment for this week. Use AI to get up to speed on an unfamiliar domain—fast.

What it does:

Identifies 5-7 core concepts you need to understand
Explains each concept in plain language with analogies
Maps key stakeholder types and what they care about
Surfaces common PM pitfalls in that domain
Generates questions to ask experts

What I got when I ran it for the Social Wishing app:

ChatGPT gave me concepts like:

OAuth and API authorization flows
Graph API rate limits
Data privacy classification
Viral growth and infrastructure scaling

And then—here's what I loved—it gave me analogies.

For "viral growth and infrastructure scaling," it said:

Plain language: If growth spikes, your system must handle sudden load increases.

Analogy: It's like a small coffee shop that suddenly gets national press—but you only have one espresso machine. Service will collapse.

That's gold.

Now I can explain infrastructure risk to a business stakeholder without using the word "horizontal scaling."

I can say, "We just got national press, and we've got a line around the block—but we only have one espresso machine. We need to decide: Do we buy more machines now, or risk turning customers away?"

That's translation. That's facilitation. That's what great PMs do.

Prompt 2: Stakeholder Expertise Mapper

This one helps you figure out who knows what and who cares about what on your project.

What it creates:

8-12 key stakeholders by role (not name)
What domain expertise each brings
What each stakeholder's "win condition" is
Who to rely on for domain expertise vs. business context vs. technical decisions
Questions to ask each stakeholder type

What I got:

ChatGPT mapped out:

Executive sponsor (cares about market differentiation and user growth)
Product owner (cares about MVP clarity and scope control)
Back-end engineer (cares about API stability and Facebook integration)
Marketing director (cares about launch readiness and campaign metrics)
QA engineer (cares about testing strategy for third-party integrations)

Then it gave me questions tailored to each stakeholder.

For the marketing director: "What needs to be true in terms of experience or metrics for you to feel confident running a full launch campaign?"

For the QA engineer: "If you were going to design the testing strategy for the Facebook API integration from scratch, what would you prioritize?"

These aren't generic questions. They're role-specific, expertise-tapping questions that show you're learning—and give you credibility without pretending to know.

Prompt 3: Facilitation Over Expertise Script

This is the one I use when the team is stuck in a heated debate and I don't have the technical chops to declare a winner.

The scenario I gave it:

The Social Wishing engineering team is debating architecture.

Option A: Microservices from day one (more complex, scales better)
Option B: Monolith first, split later (faster to MVP, potential refactor pain)

I don't have strong back-end architecture expertise. Two senior engineers are dug in on opposite sides. The debate is getting heated, and we're burning time.

How do I lead this without pretending I know what's technically right?

What AI gave me:

Questions to draw out expertise:

"What's each of us assuming about how fast this app will scale—and are those assumptions written down anywhere?"
"What would have to be true about our growth trajectory for Option A to be clearly the right call? Or Option B?"
"Has anyone on the team built something similar before, and what happened?"
"Is there anything about our team's current skills or bandwidth that should factor into this choice that we haven't mentioned yet?"

Framework to organize the discussion:

Claude suggested:

Structured input from both sides (5 minutes each, no interruptions)
Engineering lead makes recommendation
I confirm alignment with business constraints
If no clear owner exists, escalate ownership before debating substance

That last one is killer: Find out who's going to make the call before you go into a full debate.

Authority without expertise:

ChatGPT gave me this framing:

"I'm not here to declare the technically pure answer. I'm here to ensure we understand the trade-offs and align the architecture to our business goals."

That's leadership.

You're not pretending to know the answer. You're facilitating the process that gets to the right answer.

Your Non-Negotiable Experiment This Week

Use the Domain Knowledge Accelerator (Prompt 1) on an unfamiliar area of your current project.

Then ask at least one question from the expert question list AI generates for you.

Here's what I want you to notice:

Did asking questions instead of pretending to know earn you more credibility?
(It almost always does.)
How much faster can you learn with AI as a tutor?
(Days instead of months.)
Did the "dumb" question you asked surface something nobody else was saying out loud?
(That's where breakthroughs happen.)

Because here's the truth: Asking questions doesn't make you look weak. It makes you look curious, coachable, and confident enough to admit what you don't know.

And that earns trust faster than pretending to be the expert ever will.

The Takeaway

Domain expertise is overrated for project managers.

Facilitation expertise is underrated.

Great PMs don't have all the answers. They ask the right questions and create the conditions for experts to thrive.

And with AI as your learning partner, you can bridge domain knowledge gaps in days—not months—so you can lead with confidence even when you're not the expert in the room.

So stop worrying about whether you "know the industry."

Start worrying about whether you know how to get the best out of the people who do.

Next time: Data-Driven Metrics 2.0—What metrics actually matter in the AI era, and how do we use AI to surface what's really going on in our projects?

If you would like to see the podcast live, check out this link: https://youtu.be/zqrspMN0gCM

Now go ask a "dumb" question. Your team is waiting.

— Rick A. Morris

The Prompts (Copy/Paste Ready)

Prompt 1 - Domain Knowledge Accelerator

You are a strategic learning coach helping a project manager quickly understand a new domain.

First, ask me 2–3 questions about the project, the domain, and what I specifically need to understand to lead effectively.

Then provide a learning plan answering:

What are the 5–7 core concepts or frameworks I must understand in this domain?
For each concept, explain it in plain language with an analogy to something more familiar.
What are the key stakeholder types in this domain and what does each care most about?
What are the 3–5 most common pitfalls or mistakes PMs make when they don't understand this domain?
What questions should I ask domain experts to demonstrate I'm learning and to uncover critical constraints?

Domain and project context: [Enter Context]

Prompt 2 - Stakeholder Expertise Mapper

You are a project stakeholder analyst.

Ask me 3–4 questions about the project, its goals, and who's involved or affected.

Then create a stakeholder expertise map answering:

Who are the 8–12 key stakeholders (by role, not name)?
For each stakeholder, what domain expertise or knowledge do they bring?
What does each stakeholder care most about (their "win condition")?
Which stakeholders should I rely on for domain expertise vs. business context vs. technical decisions?
What questions should I ask each stakeholder type to tap their expertise effectively?

Project context: [Add Context]

Prompt 3 - Facilitation over Expertise Script

You are a coaching expert helping PMs lead through facilitation rather than expertise.

Ask me 2–3 questions about a specific domain decision or technical choice the team is debating.

Then help me facilitate the decision by providing:

What open-ended questions should I ask to draw out the team's expertise?
What framework or structure can I offer to organize the discussion (without dictating the answer)?
How do I acknowledge my knowledge gaps while still leading with authority?
What decision-making process should I facilitate (consensus, consultative, executive call)?
How do I summarize and communicate the decision in a way that shows I understand the "why" even if I didn't provide the "what"?

Situation: [Enter Situation]

AI Driven PM: S2E3 - The Halo Effect

2026-03-26T14:46:00.000-05:00

Just Because You're Good at One Thing Doesn't Mean You're Good at Another

I do this exercise every time I speak at a PMI chapter or project management conference.

I get up on stage and say, "Everybody who's a practicing project manager right now, raise your hand."

Hands go up across the room. Hundreds of them.

"Okay, keep them up. Keep them up," I say. "Now—who came out of college wanting to be a project manager?"

And I watch every hand in the room drop.

Except for three or four people. Maybe.

That right there? That's the Halo Effect.

We were all good at something else first. I started as a technical engineer. You probably started as a developer, analyst, designer, accountant, or subject matter expert in your domain.

We didn't set out to be project managers. We fell into it because someone looked at us one day and said, "You're really good at [X]. You should lead the team."

And here's the problem: Being really good at [X] doesn't automatically make you good at leading people who do [X].

But organizations make this assumption all the time. It's called the Halo Effect—the cognitive bias that says if you're good at one thing, we assume you'll be good at something else.

And it's killing project success rates.

What Is the Halo Effect (And Why It Matters for PMs)

The Halo Effect is simple: Just because you're excellent at something doesn't mean you'll be excellent at something else.

But we promote like it does.

We take our best engineer and make them a project manager. We take our best project manager and make them a PMO leader. We take our top salesperson and make them a sales manager.

And then we wonder why they struggle.

Here's why: These are completely different skill sets.

Let me show you.

Great Engineer vs. Great PM: The Skill Mismatch

What makes a great engineer:

Deep focus and technical mastery
Love of solving hard, complex problems
Delivering working code or product
Individual contribution and craftsmanship

What makes a great PM:

Broad coordination across stakeholders
Fluency in navigating ambiguity
Managing energy, decisions, and people dynamics
Influence without authority

See the disconnect?

A great engineer wants to solve the problem themselves. A great PM needs to orchestrate others to solve the problem.

So what happens? The newly promoted PM tries to apply engineering rigor to people problems. They delegate, get frustrated when it's not done "right," and then just do it themselves.

They burn out trying to do everyone else's job.

Great PM vs. Great PMO Leader: Another Mismatch

Here's another one I see all the time: We take our best project manager and promote them to PMO manager.

And they crash.

Why? Because these are also totally different skill sets.

I wrote a blog post years ago called "The Kindergartner's Guide to PMO," and the key insight was this:

A project manager has been taught to color inside the lines.

A PMO manager has to establish the lines that everyone else colors in.

One role is about controlling a project within constraints. The other is about negotiating ambiguity and setting organizational standards.

Totally. Different. Skills.

And here's where I see it go wrong: The newly promoted PMO manager thinks, "I was successful doing it this way, so everyone should do it this way."

But that's not leadership. That's replication.

When I ran a PMO, I was very intentional about not forcing my way onto my team. I'd say, "Here's what we need to accomplish. You figure out how you want to do it."

You need meeting notes? Great. Do it in Word, dictate it, put it in bullet points in an email—I don't care. As long as we have clear, actionable notes, you do it your way.

That's how you build a high-performing team. You set the standard, not the process.

My Uncomfortable Truth About Being a PMO Leader

Let me tell you a story I don't share often.

When I ran a PMO, I found out I'm not a very good HR manager. At least not in the corporate sense.

Here's what happened:

We were a brand new PMO. Our goal was to hit 60% of our milestone and production dates. Industry standard at the time was around 30%.

We hit 99% in our first year.

Everybody crushed it. Not just met expectations—exceeded them. Our sponsors were thrilled. Our teams were energized. It was one of those rare moments where everything just clicked.

So when it came time for annual evaluations, I gave everyone fives.

HR came to me and said, "You can't do that."

"Why not?" I asked. "That's what happened."

"Someone has to fall below your expectations," they said. "Most people can meet expectations. Maybe one person can exceed. But you can't give everyone fives."

I thought that was the dumbest thing I'd ever heard.

I pushed back. And then they said the line that killed me:

"If you don't do this, then the problem is your expectations."

I looked at them and said, "Or it's yours."

They didn't like that.

I said, "You want me to forcibly tell someone they're not doing their job—or just barely meeting it—when what we're actually doing is exceeding every possible expectation and building leaders?"

"I'm not doing that."

So no—I'm not a good HR manager in the corporate sense. But I am a good people leader. And I had an incredible HR partner in Carrie Blaise (still one of my best friends) who taught me something invaluable:

Preparation is everything.

Carrie would come into our monthly one-on-ones with a bullet-point list of things he was concerned about, things he was proud of, observations he'd made. The level of detail he could hold onto and bring into coaching conversations was phenomenal.

And I learned: If I want to coach well, I need to prepare well.

That's where AI comes in.

AI as Your Coaching Preparation Partner

Here's what most people miss about AI: It's not just for typing emails or generating status reports.

AI is a thinking partner for coaching.

I use AI now to help me prepare for coaching conversations. I ask it to:

Assess skill gaps objectively
Build development plans for struggling PMs
Surface blind spots I might be missing
Frame hard conversations with empathy

I even use it for my own work. I've got a new methodology I'm developing, and I've had AI:

Do deep research on competing frameworks
Ask me 30-40 clarifying questions to refine my thinking
Look for holes in my logic
Make sure I'm not accidentally copying something I read years ago

AI runs research in the background while I do the high-value work.

And that's what today's episode is about: Using AI to help you become a better coach, a better leader, and a more objective evaluator of talent.

Three Prompts to Fight the Halo Effect

I'm going to walk you through three prompts I use to assess PM talent, build development plans, and create competency frameworks.

I ran all three live in ChatGPT and Claude. And interestingly, ChatGPT followed my Socratic prompting instructions better this time, while Claude kept wanting to skip ahead.

Just goes to show—you've got to experiment with multiple tools. What works best changes depending on the task.

Prompt 1: PM Skills Gap Analysis

This is your non-negotiable experiment for this episode. I want you to run this on yourself—or on a PM on your team.

Here's what it does:

Identifies the 8-10 core PM competencies needed for a role
Assesses which competencies the PM likely excels at
Surfaces growth areas and skill gaps
Provides a 90-day development plan

What happened when I ran it:

ChatGPT asked me great clarifying questions:

What authority do you formally have as a PM? Can you approve scope changes or push back on the sponsor?
How is success measured for you on this project?
What's the sponsor's level of PM maturity?

Then it gave me competencies like:

Scope and change management
Stakeholder management
Strategic communication
Risk anticipation
Team energy management

And a 90-day plan broken into:

Month 1: Shadow a senior PM on stakeholder conversations, practice framing scope changes
Month 2: Lead a retrospective, facilitate a decision architecture session
Month 3: Present a project health diagnostic to leadership

That's actionable coaching.

Claude, on the other hand, skipped my questions and went straight to the assessment. It said:

"Given your background and the context I already have on this project, I have enough to give you a substantive assessment without asking you to repeat yourself."

Not what I wanted. I wanted it to ask me questions because that's where the thinking happens.

But you know what? Claude's assessment was still good. Just a reminder—these tools have personalities. Experiment and find what works.

Prompt 2: Halo Effect Audit

This one's for diagnosing whether your organization is suffering from Halo Effect bias in PM hiring and promotion.

What it surfaces:

What skills you tend to prioritize (hint: probably technical ones)
What critical PM skills you're overlooking
Evidence that you're promoting the wrong people
How to change your selection criteria
Interview questions that reveal actual PM competencies

What ChatGPT told me:

"Your pattern is clear. You prioritize deep product knowledge, technical credibility, system architecture understanding, and individual performance reputation. The implicit belief appears to be: The best engineer will become the best PM."

Ouch. But true.

It then said:

"In a 200-person software company, PM success depends heavily on: stakeholder boundary-setting, scope governance, executive communication, strategic framing, conflict navigation, cross-functional influence without authority, and change containment."

None of which have anything to do with being a great engineer.

Then it gave me interview questions that would actually reveal PM competencies:

"You need to tell your executive sponsor the project will be three weeks late. Write the first three sentences of that email right now."
"How do you prevent scope creep when priorities change weekly?"
"Explain this technical roadmap to a non-technical executive in two minutes."

These questions don't test technical knowledge. They test influence, framing, and communication under pressure.

That's what separates good PMs from great ones.

Prompt 3: PM Competency Framework Builder

This one's for PMO leaders or anyone building career paths for PMs.

What it creates:

8-12 core PM competencies tailored to your org
Observable behaviors at "developing," "proficient," and "expert" levels
Which competencies matter most for junior vs. senior PMs
How to assess competencies in hiring/promotion
Development resources to support growth

What I loved from the output:

It gave me a competency matrix like this:

Strategic Alignment / Executive Communication

Developing: Shares updates focused on tasks, struggles to translate technical details
Proficient: Frames updates in terms of outcomes, timelines, and risks; pre-aligns executives before major decisions
Expert: Shapes priorities before execution begins; anticipates executive concerns and addresses them early; reduces rework caused by shifting direction

That's a promotion rubric right there.

If you're a new PMO manager and someone just asked you to define PM1, PM2, and PM3 levels—this prompt will give you 80% of the framework in 10 minutes.

Then you customize it for your org.

Your Non-Negotiable Experiment This Week

Run the PM Skills Gap Analysis (Prompt 1) on yourself or a PM on your team.

Be honest. Where do you think you're strong? Where do you think you're weak?

Let AI ask you clarifying questions. Let it surface blind spots.

Then identify the top two gaps and one concrete action to address each.

Here's what I want you to notice:

Were the gaps what you expected, or did AI surface something you weren't anticipating?
Did it reveal any blind spots you'd been avoiding?
How does having a structured 90-day development plan change the conversation?

Because here's the truth: Most PMs never get objective feedback. They get vague performance reviews and assumptions based on whether their last project shipped on time.

AI can give you the structured, evidence-based assessment most organizations never provide.

The Takeaway

The Halo Effect is real. And it's why so many great engineers fail as PMs, and why so many great PMs fail as PMO leaders.

Being good at one thing doesn't automatically make you good at another.

We need objective competency frameworks. We need to assess the actual skills that make PMs successful—not just assume technical mastery translates to people leadership.

And we need AI to help us do it objectively, without bias, without politics, and without the uncomfortable truth that maybe we've been promoting the wrong people for years.

So run that skills gap analysis. Be honest. Surface the blind spots.

Because the best leaders aren't the ones who were always great. They're the ones who were willing to see where they weren't—and did something about it.

Next time: People skills vs. domain expertise—which one actually matters for project managers? (Spoiler: It's not what you think.)

Want these prompts ready to copy/paste? See below!

If you would like to see the podcast live, check out this link: https://youtu.be/-9CvnXoPsFs

Now go find your blind spots. Your team is counting on you.

— Rick A. Morris

The Prompts (Copy/Paste Ready)

Prompt 1 - PM Skills Gap Analysis

You are a senior project management coach and assessor.

First, ask me 3–5 questions about the PM's background, current role, challenges they're facing, and what success looks like in their context.

Then provide an assessment answering:

What are the 8–10 core PM competencies needed for this role?
Based on what I've shared, which competencies does this PM likely excel at?
Which competencies are likely gaps or growth areas?
What specific behaviors or outcomes would indicate strength or weakness in each gap area?
What is a 90-day development plan to address the top 2–3 gaps?

PM background: [Add your background]

Prompt 2 - Halo Effect Audit

You are an organizational psychologist specializing in team composition and role fit.

Ask me 2–3 questions about how PMs are selected and promoted in my organization.

Then help me analyze our hiring/promotion patterns by answering:

What skills or background do we tend to prioritize when selecting PMs?
What skills critical to PM success might we be overlooking?
What evidence would indicate we're suffering from halo effect in PM selection?
How should we change our PM selection criteria to reduce halo effect bias?
What interview questions or assessments would reveal true PM competencies?

Organization context: [Add Organization Context]

Prompt 3 - PM Competency Framework

You are a PMO director building a competency framework.

First, ask me 3–4 questions about our project types, organizational culture, and what "great PM performance" looks like here.

Then create a PM competency framework answering:

What are the 8–12 core PM competencies for our context?
For each competency, what does "developing," "proficient," and "expert" look like in observable behaviors?
Which competencies are most critical for new PMs vs. senior PMs?
How should we assess these competencies in hiring and promotion decisions?
What development resources (training, coaching, mentoring) support growth in each area?

Organizational context: [Add Organizational Context]

AI Driven PM: S2E2 - What Do Project Managers Actually Do?

2026-03-12T12:53:00.000-05:00

So AI can write your status reports now.

It can take your meeting minutes. Update your project schedule. Do resource forecasting. Some tools even claim they can manage your risks and predict your delays.

Cool.

So what's left for us?

I'll tell you what's left: Everything that actually matters.

Here's the uncomfortable truth most PMs don't want to hear: Right now, you're probably spending about 60% of your time on coordination and busy work. That means only 40% of your job is focused on creating real value.

You can't avert a risk you didn't see coming if you're busy documenting what happened last week.

You can't clear roadblocks if you're stuck in your third status meeting of the day.

You can't anticipate needs or generate new ideas if you're staring at a screen updating who said what in yesterday's standup.

So if AI can handle status updates, meeting notes, risk logs, timeline adjustments, and all that other stuff—what's our actual job?

Let's talk about it.

We Make Dreams Come True (But What Does That Really Mean?)

I've said it before: Project managers are dream translators. But what does that actually look like in practice?

Here are the four things PMs do that AI will never replace:

1. Sense Making

We translate chaos into clarity for stakeholders.

Think about it: You've got twenty moving pieces, five competing priorities, three sponsors who all want different things, and a team trying to build something nobody's fully defined yet.

Your job is to be the centralized storyteller.

You take an abstract idea and turn it into concrete requirements. You hear a technical problem and frame it as a business value problem so a sponsor can actually make a decision.

AI can summarize. But it can't make sense of the why behind the chaos.

2. Decision Architecture

We design how decisions get made and by whom.

I don't know if anyone's ever said it that way before, but think about how often people try to force project managers to make decisions. That's not what we do.

Nowhere in the PMBOK does it say the project manager makes the decision.

What we do is:

Analyze options
Present trade-offs
Go for decision
Enforce the decision that was made

We don't own the decision. We own the process that gets to the right decision at the right time with the right people in the room.

AI can suggest options. But it can't read the room when a sponsor is about to overrule their entire leadership team because of a personal bias.

3. Energy Management

We keep teams motivated and aligned on what matters.

This isn't scope control. It's energy regulation.

Have you ever walked into a room where people are proactive, enjoying each other, having fun, being creative? You can feel that momentum. That's positive energy driving innovation.

I've also walked into rooms where you literally can't walk in because it's so tense. The air is frigid. People are angry. You can feel the toxicity.

We regulate that energy. We create the conditions where innovation can thrive.

AI can tell you morale is low. But it can't facilitate the conversation that gets the team back on track.

4. Conflict Resolution

We navigate competing priorities and personalities.

I once worked on a project where the CIO, CTO, CFO, and CEO all came to me separately and told me not to listen to the other three—just them.

You can't automate your way out of that.

AI can highlight the conflict. But it can't build the trust or influence the relationships that resolve it.

So here's the mantra for this episode:

AI Handles the Transactional. PMs Handle the Transformational.

AI does the reports, tracking, transcripts, and data analysis.

PMs do the vision, relationships, judgment calls, and influence.

That's the augmentation model. And if you get that balance right, you're not just surviving the AI era—you're thriving in it.

Today's Focus: Use AI to Surface What Actually Needs Your Attention

If AI is going to free up 60% of your time, you need to spend that time on high-value work.

So today, I'm giving you three prompts designed to help you think like a strategist, not a secretary:

Project Health Diagnostic (your non-negotiable experiment)
Decision Architecture Mapper
Energy Audit

I ran all three live in ChatGPT and Claude. And honestly? Claude surprised me with how good its coaching was in this round.

Let me show you.

Prompt 1: Project Health Diagnostic (The Non-Negotiable)

This is your weekly experiment. I want you to run this on a real project.

Here's the prompt:

You are an experienced PMO director reviewing project health.

First, ask me 3–5 clarifying questions about the project's current state, team dynamics, and stakeholder concerns.

Then, using my answers, provide a diagnostic report that answers:

What are the top 3 risks that need PM attention right now?
Which stakeholder relationships need strengthening and why?
What decision is being avoided or delayed that's creating drag?
Where is the team's energy or morale most vulnerable?
What one action would have the highest impact on project momentum this week?

Current project context: [Enter Context Here]

What I Tested It On

I used our fictional Social Wishing app project (the bucket-list social network from Episode 1).

Here's the context I gave it:

Month 2 of development
5 engineers, 2 designers, 1 QA
Sprint velocity dropped 20% in the last two sprints
Daily standups feel perfunctory
Sponsor keeps asking about launch date, but MVP scope isn't finalized
One senior engineer just gave notice
4 months and $200K left in budget

Both ChatGPT and Claude asked clarifying questions:

ChatGPT asked:

Who owns MVP scope decisions?
Are stories entering sprint fully defined or being clarified mid-sprint?
Has the sponsor agreed on success criteria for launch?

Claude asked:

Is the MVP not finalized because of genuine disagreement or because no one's forcing the decision?
Do you know why the senior engineer is leaving?
When stand-ups feel perfunctory, what's actually happening?

Here's Where Claude Crushed It

ChatGPT gave me a solid diagnostic. Top risks: uncontrolled MVP expansion, architectural fragility, team disengagement. Good stuff.

But Claude gave me coaching.

Check out this section on stakeholder relationships:

"The sponsor—urgently, but carefully.

Core problem here is a shared reality gap. They're operating in a world where their feature ideas are refinements. You're operating in a world where those are scope additions with real cost.

This isn't malicious. Sponsors often genuinely don't see the downstream impact of small additions.

What you need is a direct, structured conversation that makes trade-offs visible: Here's what's in MVP. Here's what each addition costs in time and money. Here's what we must cut to accommodate it.

The goal isn't to win an argument. It's to get them into the same decision-making frame as the rest of the team."

That's the kind of coaching I'd give a junior PM.

It's not just identifying the problem. It's framing how to talk about it in a way that doesn't create defensiveness.

And then it ended with this:

"The good news is none of this is fatal yet. Month 2, budget intact, and a team that's disengaged but not yet departed is recoverable. The window is short, but it's open."

That's empathy. That's perspective. That's what a senior PMO director would say.

Both tools recommended the same highest-impact action: Run a 90-minute MVP reset session with the sponsor and core team.

And both gave me an agenda for it.

Prompt 2: Decision Architecture Mapper

This one's for when decisions aren't getting made—or they're getting made by the wrong people.

Here's the prompt:

You are a project governance consultant.

First, ask me 2–3 questions about how decisions are currently being made on this project.

Then help me design better decision architecture by answering:

What categories of decisions exist on this project? (strategic, tactical, technical, resource, scope)
For each category, who should make the decision, who should be consulted, and who just needs to be informed?
What decision rights are currently unclear or creating bottlenecks?
What decision-making process should I establish for the most critical category?

Project context: [Enter Context Here]

What Happened

I told it:

Sponsor requests features mid-sprint and goes directly to developers
Senior engineer (who just left) had final say on architecture
Sponsor is also acting as product owner

ChatGPT told me I had a "governance vacuum."

Claude built me a full RACI chart and told me:

"The critical shift here is that scope moves to you. With a fixed budget and no senior engineer buffer, ungoverned scope is an existential threat."

Then it gave me the script for the hardest conversation:

"This isn't designing the architecture. It's the single conversation where you tell the sponsor that scope authority is shifting to you."

Again—coaching, not just output.

Prompt 3: Energy Audit

This one's for when something feels off with the team, but you can't quite articulate it.

Here's the prompt:

You are a team dynamics coach working with project managers.

Ask me 3–4 questions about team morale, motivation, and alignment with project goals.

Then provide an analysis answering:

Where is team energy highest right now? (What's motivating them?)
Where is energy lowest? (What's draining them?)
What misalignment exists between team priorities and leadership priorities?
What story or narrative could I reinforce to re-energize and re-align the team?
What one conversation should I have with the team this week?

Team context: [Enter Context Here]

What I Learned

Claude asked me:

"When the team talks about Social Wishing, what specifically lights them up?"
"How did the team react to the senior engineer's resignation?"
"Outside of stand-ups, where's the real conversation actually happening?"

That last question is phenomenal. Is it Slack? Hallway conversations? Lunch groups?

Because if the real conversation isn't happening in your ceremonies, you've got a trust problem.

Claude's narrative recommendation:

"We're two months in, and we just learned something expensive but valuable. The project needed better architecture before it could move. We're not behind. We're resetting with clear rules. People still here chose to stay. Now we're going to build the thing we actually believe in—with scope we can defend—and ship something we're proud of."

I would use that exact language in a team meeting.

Your Non-Negotiable Experiment This Week

Run the Project Health Diagnostic on one current project.

Use transcripts from your last few team meetings. Use emails. Use status reports. Give it real context.

Then act on at least one insight it surfaces.

Here's what I want you to notice:

Did AI surface something you were avoiding or hadn't articulated yet?
How much time did you save by having AI organize your thinking?
How much faster could you move if you had this clarity every week?

None of this is about status reports or meeting minutes.

This is about surfacing what actually needs your attention so you can spend your time on the transformational work that only you can do.

The Takeaway

Project managers aren't project secretaries.

We're sense makers. Decision architects. Energy managers. Conflict navigators.

AI should handle the busy work so we can focus on the stuff that actually moves the needle.

So stop spending 60% of your week looking backwards.

Start spending 80% of your week looking forwards.

Because that's where dreams come true.

If you would like to see the podcast live, check out this link: https://youtu.be/3sE6ynNyjIg

Next time: We're talking about the Halo Effect—why we keep promoting the wrong people into project management—and how AI can help you become the transformational leader your team actually needs.

Now go run that diagnostic. Your project is waiting.

AI Driven PM: S2E1 - Stop Asking AI to Write. Start Asking AI to Think.

2026-02-26T08:05:00.005-06:00

How to Turn Fuzzy Sponsor Dreams Into Clear Project Vision in Under 10 Minutes

Here's the thing nobody tells you at your PMP certification: You're not really a project manager.

You're a dream translator.

Every app you've ever loved, every bridge you've crossed, every streaming service you've binged—all of it started as somebody's dream. Your job? Make that dream come true without burning out your people or blowing up the budget.

But somewhere between the kickoff meeting and Sprint 37, we forget about the dream. We get obsessed with the date. The budget. The feature list. The Gantt chart that nobody reads.

And that's where projects go sideways.

The Elephant in the Room

Let's talk about the fear that's keeping half of you up at night: Will AI replace project managers?

Short answer? No.

Long answer? AI will never replace project management. But here's what I tell my consulting clients: You will absolutely be replaced by a project manager who knows how to leverage AI.

That's not a threat. That's reality.

I just read that IBM—the same IBM that said they'd stop recruiting for entry-level jobs because AI would handle it—just reversed course. They're doubling down on human hiring. Why? Because AI is effective, but human augmentation is where the real value lives.

AI can write your status report. It can't read the room when your sponsor's dream is dying and nobody wants to say it out loud.

The Value Paradox: Where Project Managers Actually Add Value

I ask my clients this all the time: What percentage of your week do you spend looking backwards versus looking forwards?

Looking backwards is updating documents, running status meetings, writing meeting notes, chasing down timesheets. There's some value there, but not much. You're reporting on things that already happened.

Looking forwards is anticipating needs, clearing blockers, mitigating risks, framing decisions, finding efficiencies. That's where all the value is.

So here's the real question: How is AI impacting project management?

It's shifting that percentage. It's letting you spend 80% of your time looking forward instead of 20%.

That's not automation. That's amplification.

The Problem With How You're Prompting AI Right Now

Most people treat AI like an intern.

"Rewrite this." "Summarize that email." "Make this sound more professional."

Commands. Tasks. Output.

And you know what? AI will do it. It'll give you exactly what you asked for—which is usually mediocre.

But here's what I've learned after 30+ years and 150+ PMO implementations: The best project managers don't command. They coach.

And if you want AI to actually help you think—not just type—you need to treat it like a thinking partner, not a typing service.

That's where Socratic prompting comes in.

What Is Socratic Prompting? (And Why It Changes Everything)

Instead of telling AI what to do, you ask AI to ask you questions first.

Here's the pattern:

Give AI a role (You're an expert project manager)

Ask AI to ask YOU clarifying questions (First, ask me 3 questions to understand the dream)

Tell AI to answer specific questions (Then answer: What's the vision? What are the outcomes? What are the risks?)

Ask for reasons, options, and trade-offs (Not just a single output)

This does two things:

You get better output because AI understands the context

You think better because the questions force you to clarify your own thinking

So instead of "rewrite this charter," you say:

"First ask me three questions. Then answer: What's the dream story? How does it motivate the team? What business outcome does this support? What mantra will keep us aligned?"

See the difference?

One gives you a document. The other gives you clarity.

Let Me Show You: From Fuzzy Idea to First Backlog in Three Prompts

I'm going to walk you through three prompts I use all the time. I ran these live in both ChatGPT and Claude (always use multiple tools—you'll get different ideas).

The example? An app idea I've had in the back of my head for years called Social Wishing—where people post bucket list items and their friends help make them happen.

Vague? Yes. That's the point.

Prompt 1: Vision Clarifier (Fuzzy Idea → Clear Vision)

Here's the prompt to copy:

You are an expert project manager and business analyst. I will share a vague idea from a sponsor.

First, ask me up to 3 clarifying questions to better understand the dream behind this idea.

Then, using my answers, help me think through the idea by answering these questions:

What is the clear project vision in 2–3 sentences?

What specific, measurable outcomes would show this dream came true?

Who are the key beneficiaries and how does this idea help them?

What are the top 5 risks or unknowns we should explore early?

Vague idea from the sponsor: [Paste Idea Here]

What happened when I ran it:

ChatGPT asked me:

When you picture this succeeding in 5 years, what's changed in people's lives?

Is this mission-first or venture-backed?

Who do you want to serve first?

Claude gave me a survey-style interface with options to select. Both made me think differently about the idea.

Then they gave me measurable outcomes:

"60% of posted wishes receive at least one concrete offer within 7 days"

"40% of wishes marked complete within 6 months"

I didn't come up with those. AI did. In about 90 seconds.

That's the power of asking AI to think with you, not just for you.

Prompt 2: Charter to Dream Story (Vision → Team Motivation + Mantra)

Once I had clarity, I needed to turn that into something my team could rally behind. Not just executives. The people actually building it.

Here's the prompt:

You are a senior project manager. I will share a project charter.

First, ask me up to 3 clarifying questions about the project and audience.

Then format the charter so that it explicitly answers these questions for executives and the team:

What is the dream story? (What are we ultimately trying to make true in the world?)

How does this dream motivate the team? (Why would they care about building it?)

What is the business outcome this supports? (How does it move a key metric or strategy?)

Is there a mantra or quick saying we can use to keep everyone aligned on the goal?

Present your output as:

1 short "dream story" paragraph

3–5 bullet points on team motivation

3–5 bullet points on business outcomes

3–5 candidate mantras/slogans

Project charter: [Paste Charter Here]

What I got back:

ChatGPT gave me this dream story: "Social Wishing exists to turn idle scrolling into human progress. We're building a platform where college students and retirees use their existing social networks to bring meaningful goals to life through shared skills, time, and encouragement."

Claude gave me: "For too long, social media has trained us to watch each other's lives instead of participate in them. Social Wishing flips that script."

I would use that line. I didn't write it. AI gave it to me because I asked the right question.

Mantras?

"Dreams deserve action"

"Scroll less, live more"

"Wishes are better out loud"

Again—10 minutes in, and I've got a vision, a team motivation story, and three mantras I can use in every standup to keep people anchored.

Prompt 3: Dream to First Backlog (Vision → Prioritized Features)

Now I need to get my product owner and team moving. I need an initial backlog that delivers early value and builds momentum.

Here's the prompt:

You are a product owner. I'll share a project vision.

Ask me 2–3 clarifying questions about scope, constraints, and what "first value" means to us.

Then help me think through the first backlog by answering these questions:

What are the 3–5 value themes that organize this work?

What 10–15 initial backlog items best deliver early value in those themes?

Why did you choose these as "first" items instead of others?

Vision statement: [Paste Vision Here]

What happened:

Both tools asked me:

What does "first value" mean? Someone posts a wish, or a wish gets fulfilled?

What's the absolute core if you had to cut everything else?

Then they gave me value themes:

Trust & Identity

Wish Clarity & Commitment

The Wish Loop

And 10-15 backlog items with rationale:

"First, completion requires clarity. Without structured wishes and a definition of 'done,' fulfillment rates will stall."

"Second, trust must precede action. If users don't trust identity controls, they'll only post safe or shallow wishes."

That's not a feature list. That's strategic prioritization.

And I didn't have to think it all up myself. I just had to ask the right questions.

Your Non-Negotiable Experiment This Week

Here's what I want you to do:

Use the Vision Clarifier (Prompt 1) on one real, fuzzy sponsor idea this week.

Not a fake example. A real one.

Notice:

How does the sponsor react when you ask clarifying questions?

How much faster do you get to the "why" instead of arguing about the "what"?

How much easier is it to explain the project once the dream is clarified?

Because here's the truth: When you talk to sponsors about their dream—not their project—the connection grows stronger, faster.

And that's where the real value of project management lives.

The Takeaway

AI won't replace you. But a project manager who knows how to think with AI instead of just commanding it? That person is going to run circles around you.

So stop asking AI to write.

Start asking AI to think.

And remember: You're not here to fill out forms. You're here to make dreams come true.

Want the full walkthrough with live demos? Check out Episode 1 of Season 2 of AI Driven PM on YouTube or wherever you get your podcasts. YouTube Link: https://youtu.be/_Sz-GR-t9qk

Now go make a dream come true.

— Rick A. Morris

The Quiet Failure Killing Your Transformation

2026-02-20T13:38:00.001-06:00

Your teams are not failing in the sprint. They're failing two sprints earlier.

Every time a sprint goes sideways, leadership asks: "What happened in execution?"

Wrong question.

Most delivery failures don't start in build. They start upstream, in the work you thought was ready but wasn't.

The Pattern Leaders Keep Missing

Here's what I've been seeing across every client engagement, every industry, every methodology: Organizations adopt fast. They execute slow. And the gap between the two is massive.

Look at these numbers:

Domain	Adoption Rate	Success/Scale Rate	The Gap
AI implementations (McKinsey)	88%	10%	78 points
AI production deployment (Stanford)	78% of businesses	Low deployment	~60-70 points
Automation initiatives (Stonebranch)	98%	Persistent challenges	50+ points
Agile transformations	80%+ adoption	10-30% effective	50-70 points

This isn't coincidence. This is readiness debt at scale.

What's Really Happening

The pattern looks like this across every organization I work with:

Adopt quickly because modernization pressure demands it
Skip readiness because upstream preparation takes time
Fail during execution as readiness debt compounds
Declare success anyway while quietly missing business outcomes

"We're doing Agile."
"We implemented AI."
"We completed the sprint."

But the business impact never materializes.

That's not execution failure. That's readiness failure.

Loud Failure vs. Quiet Failure

Most leaders can spot loud failure:

Project cancelled
Team disbanded
Initiative shut down

But quiet failure? That's everywhere, and it's invisible.

Quiet failure looks like this:

Initiative declared "successful" but delivers no measurable outcome
Framework adopted but discipline lacking
Teams busy but not effective
Technical success but business failure

The organization doesn't admit failure. It redefines success downward. Motion replaces progress. And the gap between adoption and results keeps widening.

Why This Keeps Happening

Multiple independent sources—across AI, automation, Agile, and ERP transformations—confirm the same root cause:

Implementation failures stem from inadequate upstream preparation, not technology limitations.

Forty-six percent of AI initiatives fail between proof-of-concept and production. That's not a technology issue. That's a readiness gap between "works in theory" and "scales in practice."

Automation studies highlight orchestration and governance gaps. That's upstream ownership failure.

Agile struggles rarely stem from stand-ups or retrospectives. They stem from unclear backlog ownership, unfinished decisions, and poor readiness discipline.

Different domains. Same pattern.

No one owns readiness before execution begins.

The Leadership Discipline That Closes the Gap

This is exactly why I created the 2-1-0 Execution Mantra. It's not just for Agile. It applies to any execution methodology.

2 means two units of work ready ahead. Product owns this.

1 means one unit fully designed and decision-complete. Architecture or enablement owns this.

0 means zero blockers when execution begins. Delivery owns this.

If you're not 2 ahead, 1 ahead, and 0 blocked, you're transferring risk into execution.

And execution is the most expensive place to discover risk.

The Real Executive Question

Stop asking:

"What's the velocity?"
"Why did we miss the sprint?"
"Can we commit to more?"

Start asking:

"Is the work truly ready before we commit?"

Because that 78-point adoption-execution gap isn't a methodology problem. It's a readiness discipline problem.

Until leadership owns readiness upstream, execution will continue to absorb avoidable risk.

The Bottom Line

Your organization doesn't struggle because it adopts too slowly. It struggles because it executes before it's ready.

Adoption is easy. Execution is hard. Discipline is what separates the 88% who adopt from the 10% who scale.

Before your next portfolio review, ask yourself one question:

Why are we allowing unready work into execution?

That's where predictability begins.

What's one thing your team committed to this sprint that wasn't truly ready? I'd love to hear your stories—hit me up in the comments or reach out directly.

AI Driven PM: AI Doesn’t Replace Project Managers—It Replaces Their Worst Tasks

2025-05-24T15:06:00.003-05:00

How I’m Using Prompt Engineering to Turn Reports Into Results

If you’ve ever lost half a day rewriting a status report for the fourth version of the same slide deck, you’re not alone. It’s one of the most frustrating parts of the job—and one of the least strategic.

That’s why I started using large language models (LLMs) like ChatGPT and Copilot. Not just for experimentation, but to systematically take work off my plate. But here’s the truth:

Generic AI gives you generic results.
Prompt engineering changes the game.

Why Prompt Engineering Matters (More Than You Think)

When most people talk about AI in project management, they focus on flashy dashboards or automated ticket creation. That’s fine—but it's not where the real leverage is.

The real impact happens when we combine clean project data with deliberate prompts—the kind that generate output we can actually use in executive reviews, steering meetings, and stakeholder updates.

“You can’t automate what you haven’t standardized.” — Rick Morris

Prompt engineering isn’t just writing a better question. It’s a system. And once you build it, the results speak for themselves:

Weekly status reports in minutes, not hours
Risk logs that auto-rate severity and recommend mitigations
Steering decks that don’t require a weekend to assemble

The Three Prompts That Changed My Workflow

Here’s what I actually use week-to-week—no gimmicks, no fluff:

1. Status Narratives That Make Sense to the C-Suite

I feed in structured data from Jira, Smartsheet, or even a quick bullet summary. My prompt is tuned to produce something short, plainspoken, and focused on action.

Key Tip: Ask the LLM to quantify variance and end with a one-sentence ask.

2. Dynamic Risk Logs

Instead of manually rating every risk, I now have AI assign RAG status based on impact × likelihood. It also flags missing mitigations or inconsistent timelines.

Bonus: Add a Markdown or JSON output format so you can plug it straight into SharePoint or your PMIS.

3. Steering Committee Slide Drafts

I use a slide-friendly prompt that generates headlines, bullet points, and speaker notes—based on live data from my working files. I still review and polish, but I’m no longer starting from scratch.

“If you’re doing the same thing every week, it should be automated.” — Rick Morris

It’s Not Just About Speed. It’s About Focus.

The first time I used prompt engineering to prep a risk log, I cut my turnaround time by 80%. But the real win? I used those hours to coach a product owner through a launch delay instead of formatting slides.

Here’s what’s changed in my actual metrics:

Status report time: Down from 6 hours/week to ~2
Risk updates: From 2 days to under 4 hours
Exec clarity: Survey scores on reporting jumped from 3.8 to 4.6 out of 5

“It’s not just about saving time. It’s about reallocating it to what matters.” — Rick Morris

What You Actually Need to Make This Work

You don’t need an AI team or a budget line. You just need a repeatable process:

Know where your data lives – PMIS, Confluence, Slack threads, spreadsheets
Standardize your outputs – Markdown for risks, JSON for status, PowerPoint XML for slides
Build and iterate your prompts – Make them tight, structured, and outcome-focused
Add guardrails – Validation scripts, human-in-the-loop reviews, compliance checks
Track the impact – Time saved, errors avoided, decisions accelerated

“A good process is one you don’t notice—because it’s working.” — Rick Morris

Final Thought: The Point Isn’t the AI—It’s the Outcome

LLMs didn’t make me a better project manager. What they did was give me time back—so I could show up where I was most needed: in risk meetings, in product launches, in conflict resolution.

Prompt engineering didn’t remove me from the loop. It just got rid of the noise.

“We don’t manage projects. We make dreams come true.” — Rick Morris

So if you’re spending more time narrating progress than driving it, prompt engineering might be your next best move. And if you want to see my actual prompts or workflows, just ask—I’m happy to share what’s worked and what didn’t.

Let’s build project management that works for the people doing the work.

AI Driven PM: The Future of AI and Project Management and How to Prepare

2025-04-05T11:23:00.005-05:00

What’s coming next—and are we ready for it?

AI is already changing how we manage projects. From automated reporting to risk forecasting, we’ve seen the early wins. But what’s around the corner is even more transformative. AI is evolving fast, and so is its role in how we lead teams, deliver outcomes, and shape strategy.

The next wave isn’t just about speeding up admin work—it’s about redefining project management itself. We’re talking about autonomous systems, intelligent collaboration, and real-time strategy support. The project manager of the future won’t just use AI—they’ll work with it.

Let’s look ahead at where AI is going in project management, and how we can prepare to lead with it, not just react to it.

1. Autonomous Project Management: From Assistant to Operator

Right now, most AI in project management acts like a very smart assistant—suggesting tasks, flagging risks, maybe generating a dashboard or two. But the next evolution is autonomy.

Autonomous project management systems will do more than assist; they’ll start to run day-to-day operations on their own.

🔍 What to expect:

AI dynamically assigning and reassigning tasks based on availability, capacity, and project changes.
Auto-generated status reports, meeting agendas, and risk updates—pushed in real-time to the right stakeholders.
AI making low-risk decisions without human sign-off, freeing up PMs to focus on higher-value work.

💡 Why it matters:
It’s not about replacing project managers—it’s about giving them space to lead. Admin-heavy PM roles will shift toward strategy, alignment, and vision, while AI takes care of the tactical grind.

📌 How to prepare:

Start identifying which parts of your current workflows are truly decision-based, and which are rule-based (i.e., can be automated).
Test AI scheduling or task assignment tools now, so your team builds trust and familiarity early.

2. Predictive Analytics: From Risk Alerts to Outcome Forecasting

We’re already seeing AI used for early warning systems—flagging when a project is slipping or when risks might emerge. The future is more proactive and far more sophisticated.

Advanced predictive analytics will allow us to model entire project lifecycles, anticipate performance bottlenecks, and forecast ROI across multiple scenarios.

🔍 What to expect:

AI generating “if-this-then-that” simulations based on real-time data.
Forecasts that take into account human behavior, historical performance, even market or economic trends.
Models that can recommend corrective actions, not just identify issues.

💡 Why it matters:
AI won’t just tell you there’s a problem—it’ll tell you what’s likely to happen next, and what to do about it. That’s a game-changer for both agility and strategic planning.

📌 How to prepare:

Start capturing project data with an eye toward trends, not just metrics.
Integrate predictive tools into retrospectives and planning conversations, even on a small scale.

3. Intelligent Collaboration: AI That Understands Teams

One of the most exciting (and underrated) trends is AI that understands how people work together.

The next generation of AI tools won’t just manage schedules and tasks—they’ll support team dynamics, flag potential miscommunications, and adapt collaboration processes to how teams actually work.

🔍 What to expect:

Sentiment analysis tools that detect signs of burnout, conflict, or disengagement in team communications.
AI-driven retrospectives that identify behavioral trends—not just task metrics.
Intelligent meeting assistants that track decisions, assign follow-ups, and summarize key takeaways.

💡 Why it matters:
Project success isn’t just about task completion—it’s about team cohesion, communication, and clarity. AI that understands people adds a new layer to how we lead.

📌 How to prepare:

Start tracking team engagement data—surveys, check-ins, communication patterns.
Test AI assistants that can join meetings or summarize Slack/email threads, and evaluate their fit for your team’s culture.

4. Strategic AI: From Tools to Thinking Partners

The most transformative trend ahead? AI that helps you think. Not just act faster or automate smarter, but actually elevate how you approach complexity.

These AI systems will support strategic planning, scenario modeling, and real-time portfolio optimization.

🔍 What to expect:

AI systems surfacing hidden patterns across multiple projects.
Decision-support dashboards that weigh trade-offs and make recommendations based on business goals.
Tools that link project outcomes to larger enterprise strategy—turning delivery metrics into strategic value.

💡 Why it matters:
Project managers are already being asked to act as strategic partners. AI will help close the gap between project execution and business value by translating delivery data into insight.

📌 How to prepare:

Map your projects to business outcomes, not just milestones.
Experiment with tools that connect delivery metrics to financial or operational KPIs.

What Does This Mean for Project Leaders?

We’re entering a new era where project managers must evolve from task managers to AI-augmented leaders. That shift isn’t just technical—it’s cultural, strategic, and human.

✅ The project manager of the future will:

Collaborate with autonomous systems, not just operate them.
Make decisions with predictive insight, not just historical data.
Focus on enabling people while AI handles the process.
Translate project data into strategy, not just execution.

This evolution won’t happen overnight, but it will happen. The teams that prepare now will lead the charge, not play catch-up.

Final Thoughts: AI Won’t Replace Project Managers—But It Will Redefine Them

AI is not just another software layer. It’s a catalyst for transformation, both in how we work and how we lead.

The future of project management isn’t just more efficient—it’s more intelligent, more adaptive, and more human-centered than ever before.

And the big takeaway? AI doesn’t remove the need for project managers—it reimagines the role entirely.

So, as we look ahead to autonomous systems, strategic decision support, and AI-driven collaboration—the question isn’t if you’ll use AI.

It’s how prepared you are to lead with it.

What AI trends are you watching—or already testing—in your projects? Drop your thoughts in the comments. Let’s look to the future, together. 🚀👇

AI Driven PM: The ROI Debate - Can We Really Measure AI’s Value in Project Management?

2025-03-01T18:54:00.001-06:00

AI in project management isn’t a futuristic concept anymore—it’s already here, promising efficiency, precision, and automation. But as organizations rush to integrate AI into their workflows, one question keeps coming up: What’s the real return on investment (ROI)?

Is AI truly delivering measurable value, or are we just dazzled by the potential? Project leaders are under pressure to prove AI’s impact with hard data, yet many struggle to quantify its benefits. If AI reduces administrative tasks or prevents risks, how do we measure that in dollars?

The debate isn’t just about whether AI works—it’s about whether we can track and communicate its success in a way that satisfies both leadership and the bottom line. Let’s break down what makes AI’s ROI so difficult to measure and how organizations can bridge the gap between AI hype and real business value.

The Promise of AI: Game-Changer or Overhyped Tech?

AI has already changed project management in tangible ways. Tools powered by AI are helping teams:

Automate repetitive tasks like scheduling, reporting, and resource allocation.
Predict project risks before they cause delays or budget overruns.
Optimize resource usage, ensuring the right people are assigned to the right tasks.

These are clear wins—but how do you put a dollar value on them? That’s where the ROI debate begins.

How to Capture AI’s ROI in Efficiency Gains

Measuring AI’s efficiency impact requires tracking:

✔ Time saved per task – Compare how long manual processes took before AI and after AI adoption.
✔ Reduction in project delays – If AI predicts risks earlier, does it decrease project completion time?
✔ Automation rate – Track the percentage of tasks that AI has taken over versus manual work.

✅ ROI Calculation Example:
If AI eliminates 10 hours of manual work per week and an employee’s hourly rate is $50, that’s $500 in weekly savings—or $26,000 per year for just one project manager.

Without these kinds of benchmarks, organizations risk adopting AI without ever proving its worth.

Where’s the Value? Tracking AI’s Business Impact

Most AI-driven project management tools don’t come with built-in ROI dashboards—which means organizations need to be proactive in setting up metrics that define success. The key is to focus on outcomes that directly impact the business.

1. Time-to-Completion Savings

AI-driven scheduling and predictive analytics can help shorten project timelines by streamlining workflows.

📊 How to Measure It:

Compare estimated project timelines before and after AI implementation.
Track the number of delays prevented through AI-powered risk detection.
Calculate the financial impact of faster completion—faster delivery often means earlier revenue recognition.

✅ ROI in Action:
If AI reduces a project’s timeline by 10% and that project generates $1M in revenue, AI has accelerated cash flow by $100K.

2. Risk Mitigation and Error Reduction

AI’s ability to predict risks and reduce errors can have a major financial impact, but it often goes unnoticed because companies don’t track the costs of project mistakes.

📊 How to Measure It:

Compare budget overruns before and after AI implementation.
Track how often AI flags potential risks and whether teams acted on them.
Measure the cost of rework before and after AI—are fewer mistakes happening?

✅ ROI in Action:
If AI prevents a $50,000 scope creep issue or reduces rework by 20%, those savings directly justify the AI investment.

3. Resource Optimization and Team Efficiency

One of AI’s biggest benefits is helping teams work smarter—not just harder. But without tracking how AI affects team utilization, organizations miss out on proving ROI.

📊 How to Measure It:

Compare team workloads before and after AI—are employees spending more time on strategic work?
Track AI’s effect on meeting times—has AI-generated reporting reduced unnecessary check-ins?
Measure employee satisfaction—are PMs spending less time on admin tasks and more on leadership?

✅ ROI in Action:
If AI reduces non-strategic work by 5 hours per week per team member, multiply that by their hourly rate and total team size—those numbers add up fast.

The Hidden Wins: The Value AI Brings Beyond the Balance Sheet

Not every AI benefit shows up in a spreadsheet—but that doesn’t mean it isn’t critical. AI is changing how teams work, communicate, and make decisions, leading to long-term advantages that are harder to quantify.

1. Better Decision-Making

AI gives project managers real-time data insights, reducing gut-feel decisions.
Predictive analytics help managers anticipate roadblocks before they escalate.

📊 How to Capture This Impact:

Conduct before-and-after decision audits—is decision-making faster and more data-driven?
Measure lead time on key project approvals—are managers reacting faster with AI insights?

2. Reduced Burnout & Improved Talent Retention

AI reduces mind-numbing admin work, giving employees more time for strategic work.
PMs who use AI spend less time in status meetings and more time on leadership tasks.

📊 How to Capture This Impact:

Track employee sentiment scores before and after AI implementation.
Measure PM turnover rates—if AI reduces burnout, retention rates should improve.

✅ ROI Takeaway:
Happy teams stay longer. Replacing an experienced PM can cost upwards of $50K in hiring and onboarding. AI’s ability to reduce burnout may save more money than it costs.

Making AI Pay Off: Strategies for Project Leaders

Project leaders who want to prove AI’s value need to be intentional about tracking its impact. Here’s how to make AI adoption more than just a tech experiment:

1. Start with a Pilot Program

Select a single project and measure AI’s impact against non-AI workflows.
Track KPIs like time savings, cost reduction, and error rates.

2. Benchmark Before and After

Use historical project data to compare pre-AI vs. post-AI performance.
Highlight areas where AI created measurable efficiency gains.

3. Demand Data Transparency

Choose AI tools that offer built-in analytics dashboards for tracking impact.
Work with vendors who can provide clear performance reports.

The Bottom Line: AI’s ROI is a Strategy, Not a Guess

The AI ROI debate isn’t going away, but that’s not a bad thing. It forces organizations to move past the hype and prove AI’s tangible value—whether through cost savings, efficiency gains, or strategic impact.

The key takeaway? AI isn’t just about automation—it’s about amplification. It’s making good project managers great by eliminating busywork, optimizing workflows, and providing real-time insights. But if you’re not measuring it, you’re not maximizing it.

So, how are you tracking AI’s worth in your projects? Let’s talk in the comments! 🚀👇

AI Driven PM: Transforming How We Lead Change

2025-02-15T15:13:00.003-06:00

Change is hard—there’s no way around it. Whether it’s implementing new processes, adopting new technology, or reshaping entire business models, change management can feel like navigating a minefield. Resistance from stakeholders, communication breakdowns, and uncertainty often stand in the way of success.

But what if AI could help? Not just by automating processes, but by actively supporting change management strategies—offering data-driven insights, improving communication planning, and even predicting stakeholder responses.

Welcome to the future of change management, where AI doesn’t just make life easier—it makes organizational change more predictable, strategic, and successful.

The Role of AI in Change Management

AI isn’t here to replace human leadership, especially in something as inherently human as managing change. Instead, it acts as a powerful tool that enhances decision-making, improves communication, and provides clarity in moments of uncertainty.

Here’s how AI can elevate your change management strategies:

1. Stakeholder Engagement: Knowing What Matters Most

One of the trickiest parts of managing change is understanding how it will impact different stakeholders—and how they’re likely to respond. AI tools can help by analyzing stakeholder sentiment and identifying key concerns before they become roadblocks.

🤖 How AI supports stakeholder engagement:

Sentiment analysis from emails, surveys, or social media platforms gives you real-time insights into stakeholder concerns.
Stakeholder mapping helps prioritize key players based on influence, engagement level, and likelihood to support (or resist) the change.
Predictive analytics can forecast how different groups might react, allowing you to proactively address concerns.

By using AI-driven insights, you can engage the right stakeholders at the right time with tailored messaging that resonates.

2. Communication Planning: Personalized and Data-Driven

Effective communication is the backbone of any successful change initiative. But generic, one-size-fits-all communication strategies often fall flat. AI can take your communication planning to the next level by helping you design personalized, data-driven messaging.

📢 How AI enhances communication:

AI can segment your audience and tailor communication based on stakeholder preferences, past behavior, and engagement patterns.
Natural language processing (NLP) tools can analyze the tone and clarity of your messaging, ensuring it’s clear, positive, and persuasive.
Chatbots and virtual assistants can provide instant responses to common stakeholder questions, keeping everyone informed and reducing confusion.

The result? More targeted, effective communication that builds trust and minimizes resistance.

3. Change Impact Analysis: Seeing the Bigger Picture

Understanding the full scope of change—who it affects, how it will disrupt current processes, and where risks lie—is essential for success. AI can help project managers visualize the ripple effects of change and make better decisions based on data.

🔍 Key AI tools for impact analysis:

Process-mining tools map out existing workflows, helping you identify which areas will experience the most disruption.
Predictive models simulate how different change scenarios will impact performance, helping you refine your strategy before implementation.
Risk assessment algorithms can flag high-risk areas, enabling you to mitigate issues early.

This kind of proactive approach ensures that you’re not just reacting to challenges—you’re anticipating and planning for them.

4. Monitoring and Continuous Improvement

The work doesn’t stop once the change is implemented. Monitoring progress and adapting your strategy in real time is crucial for long-term success. AI tools can offer continuous feedback and help you course-correct as needed.

📊 How AI helps with continuous improvement:

AI-driven dashboards provide real-time updates on key metrics, allowing you to monitor the impact of change initiatives.
Machine learning models can identify patterns and recommend adjustments based on what’s working (and what’s not).
Sentiment analysis can track how stakeholder attitudes evolve over time, helping you refine your engagement strategy.

Continuous improvement isn’t just a buzzword—it’s a data-driven reality with AI in your corner.

The Human Factor: Why AI Won’t Replace Empathy

AI is an incredible tool, but let’s be clear: It doesn’t replace human leadership. Change management is still about relationships, empathy, and understanding the human experience. AI provides data and insights, but it’s up to you to turn those insights into action.

The project leaders who will thrive in an AI-driven world are those who can balance data with intuition, using AI to enhance—not replace—their natural leadership skills.

How to Start Integrating AI into Your Change Management Strategy

Ready to make AI your change management co-pilot? Here’s how to get started:

🛠 Explore AI-Powered Stakeholder Tools – Platforms like Power BI, Tableau, and sentiment analysis tools can give you insights into stakeholder engagement.

🛠 Use NLP for Communication Audits – Test out NLP tools like Grammarly or Hemingway to improve the tone and clarity of your messaging.

🛠 Leverage Predictive Analytics – Tools like Alteryx or Microsoft’s Power Automate can help you simulate change scenarios and anticipate risks.

🛠 Embrace Continuous Improvement – Set up real-time dashboards to monitor and adjust your strategy on the fly.

Final Thoughts: Leading Change with AI by Your Side

AI-driven change management is more than a buzzword—it’s the future. By integrating AI into your change strategy, you’ll gain deeper insights, build stronger stakeholder relationships, and execute change initiatives with greater confidence.

But remember: AI is the tool. You are the leader. Use AI to amplify your strengths and lead change that sticks.

Are you already using AI in your change management efforts? Let’s swap strategies in the comments below! 👇

AI Driven PM: The Creative Side of AI - More Than Just Automation

2025-02-01T19:40:00.004-06:00

AI isn’t here to replace human creativity—it’s here to amplify it. Imagine having a brainstorming partner that never runs out of ideas, can instantly analyze trends, and gives you fresh perspectives based on vast datasets. That’s what AI brings to the table.

Here’s how AI is becoming a catalyst for creativity and innovation in project teams:

1. Idea Generation: Your AI-Powered Brainstorm Buddy

Stuck in a creative rut? AI can help. Tools like generative AI (think ChatGPT, Jasper, or DALL-E) are designed to spark new ideas, offering project teams a starting point for brainstorming sessions.

🎨 How it works:

AI can generate content ideas, design concepts, or even product features based on prompts you provide.
It offers suggestions you might not have considered, breaking the cycle of repetitive thinking.
It helps overcome creative blocks by offering unexpected combinations of ideas.

Think of AI as the teammate who always thinks outside the box—without any ego or resistance to feedback.

2. Identifying Market Trends: Staying Ahead of the Curve

Innovation doesn’t happen in a vacuum. To create something truly groundbreaking, project teams need to understand market demands, customer behaviors, and emerging trends. AI can process massive amounts of data in real-time, giving you insights that would take humans weeks to uncover.

📈 How AI supports trend analysis:

AI tools analyze social media, industry news, and consumer feedback to spot emerging trends.
Predictive analytics can forecast what customers will want before they even know it themselves.
AI can identify gaps in the market where innovative solutions can thrive.

With AI handling the heavy lifting of data analysis, project teams can focus on what to create, rather than spending time figuring out what’s relevant.

3. Enhancing Decision-Making: From Data to Innovation

Great ideas are only as good as the decisions that bring them to life. AI can support creative decision-making by providing data-driven insights that reduce uncertainty.

💡 How AI enhances decision-making:

AI models can simulate different scenarios to predict the outcomes of innovative ideas.
It helps teams prioritize ideas based on potential ROI, market demand, and feasibility.
AI tools can flag potential risks early, allowing teams to adapt and innovate before problems arise.

By blending AI’s analytical power with human intuition, project teams can make bold, creative moves with confidence.

The Human-AI Collaboration: The Perfect Innovation Duo

Let’s get one thing straight—AI isn’t replacing the creative spark that makes innovation possible. But it does offer the tools and insights to fuel that spark into a flame. The magic happens when project teams combine human imagination with AI’s processing power.

🤝 Here’s what that partnership looks like:

Humans bring empathy, intuition, and emotional intelligence.
AI brings speed, data-driven insights, and pattern recognition.
Together, they create solutions that are innovative, efficient, and relevant.

How to Integrate AI into Your Creative Process

Ready to make AI part of your innovation toolkit? Here’s how to get started:

🚀 Experiment with Generative AI Tools – Use platforms like ChatGPT, Midjourney, or Jasper to brainstorm ideas and explore creative concepts.

🚀 Leverage Predictive Analytics – Tap into AI-driven trend analysis tools to identify market opportunities and stay ahead of competitors.

🚀 Combine Data with Human Insight – Don’t just rely on AI outputs. Use them as a foundation for creative discussions and strategic decision-making.

Final Thoughts: AI Is the Ultimate Innovation Partner

AI isn’t just a back-office tool for automating mundane tasks—it’s a creative catalyst that can transform how project teams approach problem-solving and innovation. By integrating AI into your creative processes, you’ll uncover new ideas, stay ahead of market trends, and make smarter, more innovative decisions.

The future of project management isn’t just efficient—it’s imaginative. So, the real question is: Are you ready to innovate with AI by your side? 💡🚀

Got any cool stories about how you’ve used AI to spark creativity in your projects? Share them in the comments below! 👇

AI Driven PM: Training and Upskilling Project Managers for AI Integration

2025-01-18T19:12:00.001-06:00

The role of a project manager is evolving at breakneck speed, and AI is at the center of this transformation. While AI won't replace project managers, those who fail to adapt may find themselves left behind. The future belongs to those who can seamlessly integrate AI into their workflows, leveraging it to make smarter decisions, streamline processes, and drive efficiency.

So, how do we prepare project managers for this shift? It’s not just about learning how to use AI tools—it’s about developing a mindset and skill set that allows them to harness AI’s potential effectively.

The New Skill Set: What Project Managers Need to Thrive in an AI-Driven World

AI isn’t just another tool in the project manager’s arsenal; it’s a force multiplier. To work effectively alongside AI, project managers need to upskill in three critical areas:

1. Data Literacy: The Foundation of AI-Enabled Decision-Making

AI runs on data. If project managers can’t interpret and leverage that data, they risk making uninformed decisions. Data literacy isn’t about becoming a data scientist—it’s about knowing how to:
✅ Identify relevant data sources for project tracking and forecasting.
✅ Understand key metrics and how AI-driven analytics influence decision-making.
✅ Spot biases or inconsistencies in AI-generated insights.
✅ Communicate data-driven insights effectively to stakeholders.

Think of AI as your co-pilot. It can crunch numbers and generate reports, but if you don’t know how to interpret the data, it’s just noise.

2. Technological Proficiency: Learning the Tools of the Trade

AI tools are becoming increasingly sophisticated, from predictive analytics platforms to generative AI-powered project assistants. Project managers don’t need to become programmers, but they must understand:
🚀 How AI tools automate tasks like scheduling, risk assessment, and reporting.
🚀 Which AI-powered platforms best fit their industry and project needs.
🚀 How to integrate AI with existing project management software (e.g., Jira, Trello, Asana).
🚀 The ethical considerations of AI in decision-making.

A project manager who embraces AI isn’t just delegating tasks to a machine—they’re optimizing their team’s time and resources.

3. Critical Thinking & Strategic Adaptability: The Human Edge Over AI

AI is powerful, but it lacks one key element—human intuition. The best project managers will combine AI-driven insights with their own judgment to make better decisions.
💡 Challenge AI-driven recommendations when they don’t align with business goals.
💡 Think creatively about how to apply AI solutions beyond the obvious.
💡 Adapt quickly when AI insights reveal new risks or opportunities.

AI should enhance human decision-making, not replace it. Project managers who can think critically and pivot strategically will lead the AI revolution rather than be disrupted by it.

How to Get Started: A Practical Upskilling Plan

Not sure where to begin? Here’s a roadmap for developing AI-driven project management skills:

🛠 Take a Data Literacy Course – Platforms like Coursera, LinkedIn Learning, and edX offer courses tailored for non-technical professionals.

🛠 Experiment with AI Tools – Test out AI-driven PM platforms like ChatGPT, Microsoft’s Copilot, Notion AI, or any other AI tool to see how they can streamline your workflow.

🛠 Join AI & PM Communities – Engage in discussions on AI in project management through LinkedIn groups, Reddit forums, or industry events.

🛠 Stay Updated on AI Trends – Follow AI thought leaders and industry blogs to keep up with emerging trends and best practices.

🛠 Listen to AI Driven PM Podcasts – Go to aidrivenpm.com to see the latest!

Final Thoughts: AI Won’t Replace You—But a Project Manager Who Uses AI Might

AI isn’t a threat—it’s an opportunity. The most effective project managers won’t be the ones who resist AI but the ones who master it. By investing in data literacy, technological proficiency, and strategic adaptability, project managers can future-proof their careers and lead AI-driven projects with confidence.

The question isn’t if AI will change project management. The question is: Are you ready for it? 🚀

What’s your take? Have you started integrating AI into your project management workflow? Let’s discuss in the comments! 👇

AI Driven PM: AI Agents Are The Next Big Thing in Project, Program, and Portfolio Management

2024-11-18T16:00:00.005-06:00

"Organizations will not be able to compete globally without putting in place project management processes and continuing to develop their project managers to become leaders within the organization," I wrote in Stop Playing Games. That leadership role is evolving faster than ever with the upcoming release of OpenAI’s AI agents, expected in January. These tools are set to revolutionize how project managers approach their work, freeing us from the mundane and allowing us to focus on strategy, innovation, and team growth. Let us not spend our time looking backward, reporting on what has occurred. Instead, let us apply our skills in predictive analytics to elevate project delivery and drive transformative results.

What Are AI Agents?

AI agents are intelligent digital assistants designed to automate repetitive tasks, process large amounts of data, and provide real-time insights. They combine machine learning, natural language processing, and data analytics to support decision-making and free up time for more strategic and creative activities. These agents can integrate with tools like Jira, Google Sheets, and communication platforms, seamlessly fitting into existing workflows. By handling routine operations, AI agents allow project managers to focus on innovation and leadership, transforming the way we approach project management.

How AI Agents Will Transform Project Management

1. Simplifying Multi-Project Portfolio Management

Managing dozens of projects at once often feels overwhelming, with timelines and resources constantly competing for attention. AI agents will track project timelines across portfolios, flagging potential resource conflicts and suggesting real-time adjustments. By analyzing historical data, they can even predict delays before they occur, helping us optimize resource allocation and align all projects with broader organizational goals.

2. Strengthening Stakeholder Relationships

Stakeholder management is often the make-or-break factor in a project’s success. AI agents can analyze email threads, meeting notes, and communications for sentiment, identifying dissatisfaction or concerns early. They can propose tailored responses based on stakeholder preferences and even generate follow-up reminders, ensuring no relationship falls through the cracks. This proactive approach builds trust and strengthens collaboration.

3. Enhancing Change and Risk Management

In volatile project environments, change and risk are inevitable. AI agents will monitor metrics like sprint velocity, budget usage, and resource utilization in real time. They can identify risks such as potential delays or scope creep and suggest contingency plans. By simulating various outcomes, these agents provide managers with actionable options, enabling quicker and more informed decision-making.

4. Optimizing Team Productivity in Agile Projects

Balancing team workloads in Agile environments is no small task. AI agents will continuously monitor task distribution, identify under- or over-utilized resources, and recommend sprint adjustments. For instance, they can reassign tasks to prevent bottlenecks or suggest pacing changes to keep teams on track. This ensures teams remain productive without risking burnout.

5. Improving Customer Experience

Customer satisfaction is often the true measure of project success. AI agents will analyze feedback from surveys, customer service tickets, and user interactions, highlighting pain points and opportunities for improvement. They can even generate customer satisfaction scorecards to track progress over time, helping us exceed expectations and deliver consistent value.

6. Ensuring Compliance and Data Integrity

In industries like healthcare, finance, and technology, compliance is critical. AI agents will automatically audit project data against regulatory requirements, flag inconsistencies, and recommend corrective actions. They can simulate compliance scenarios, ensuring our projects stay audit-ready and meet industry standards without manual oversight.

7. Delivering Budget Insights and Control

Budget management is one of the most stressful aspects of any project. AI agents will track expenses, calculate metrics like Net Operating Value (NOV), and provide predictive insights into budget overruns. They can suggest adjustments to resource allocation or project timelines to keep costs in check while ensuring goals are met.

8. Empowering Team Development

Strong, engaged teams are the backbone of every successful project. AI agents will track performance metrics for individual team members, identify skill gaps, and recommend training opportunities. By offering personalized feedback, these agents help create a culture of continuous improvement, ensuring teams remain motivated and high-performing.

A New Frontier for Project Management

The introduction of OpenAI’s AI agents is more than just an upgrade—it’s a turning point for our profession. These tools will allow us to delegate routine tasks and focus our energy on strategy, leadership, and delivering meaningful results. They empower us to work smarter, not harder, by providing actionable insights, improving communication, and optimizing resources.

Leadership in project management has always been about more than completing tasks; it’s about driving change, inspiring teams, and creating measurable impact. With AI agents, we’ll have the tools to do this better than ever before. January marks the beginning of something extraordinary. Are you ready to embrace the future of project management? I know I am.

AI Driven PM: Leveraging AI to Transform Portfolio Management and Project Ranking

2024-10-30T08:51:00.000-05:00

In today’s competitive landscape, prioritizing the right projects is critical to success. With so many factors to consider—ROI, strategic alignment, risk, and resource availability—keeping a balanced and optimized portfolio can seem daunting. Enter AI, a powerful tool that can support project managers in making data-driven decisions, ranking projects based on potential impact, and continuously adapting as conditions change. Here’s a look at how AI can redefine portfolio management and project ranking, transforming the way we strategize and execute.

Define and Align: Setting the Foundation for AI in Project Ranking

The first step in bringing AI into portfolio management is defining the project scoring criteria. Without clear metrics, even the best AI algorithms won’t deliver meaningful insights. Start by identifying essential criteria that reflect your organization’s core values and goals:

Strategic Alignment: How well does the project align with core business objectives?
Financial Impact: What’s the projected ROI based on historical data and current trends?
Risk Level and Resource Needs: Is the project feasible within current resource constraints?
Customer Impact: Does this project address key customer needs or enhance satisfaction?
Innovation Value: Does this project push the boundaries of what your organization is known for?

With these metrics in place, AI algorithms can help predict project success by analyzing historical data, market trends, and customer insights, providing a preliminary score for each project. This clear, data-driven start enables project managers to make informed decisions that reflect both current needs and future goals.

Data is the Fuel: Integration and Quality Matter

A powerful AI model needs quality data to thrive. Collect data from various sources—historical project outcomes, resource availability, CRM systems, and external market data. This provides the AI with a well-rounded dataset that improves its predictions. Examples include:

Historical Performance Data: Collect success rates, budget adherence, and customer satisfaction scores from past projects.
Resource Data: Understand resource capacity and skills available across teams to ensure efficient project assignments.
Market Trends: Analyze market shifts and competitive dynamics, giving the AI insight into project feasibility and relevance.

Regular data updates ensure AI models stay relevant, and high-quality data integration paves the way for accurate AI-driven recommendations.

The Power of Prediction: Using AI to Score and Rank Projects

Once data is in place, AI can begin scoring and ranking projects. Machine learning models, such as regression analyses or decision trees, help predict the potential impact of each project. Techniques like clustering and natural language processing (NLP) can group similar projects and evaluate descriptions to assess strategic fit. Here’s how it works:

Predictive Models for ROI and Risk: Train models to predict each project’s ROI and potential risks based on past data.
Clustering for Similar Projects: Group similar projects to find high-priority candidates based on historical success.
NLP for Strategic Alignment: Analyze project descriptions to quantify alignment with core organizational priorities.

With weighted scores calculated for each project, AI provides a ranked list. Project managers can then balance this AI-based ranking with their own experience and insights.

Optimizing the Portfolio: Balancing Projects for Maximum Impact

Project ranking is only half the battle; managing the portfolio requires balancing resources, risks, and rewards. AI can simulate different portfolio compositions, using optimization algorithms to recommend the best mix based on current constraints. Some techniques include:

Optimization Algorithms: Use algorithms to balance resource allocation, budget, and deadlines.
Scenario Analysis: AI simulations offer insights into project combinations, showing likely outcomes based on variables like resource availability or changes in budget.
Dynamic Re-Ranking: Continuously monitor live project data, adjusting rankings as new information becomes available.

AI doesn’t just provide a static project ranking; it ensures that the portfolio remains balanced and adaptable, continuously optimizing for maximum impact.

Human Insight: Adding the Final Touch

No matter how powerful AI becomes, it can’t fully replace human insight. Regular portfolio reviews allow project managers to validate AI’s recommendations, taking into account strategic shifts, new information, and nuanced context. The human touch is essential for:

Validating AI Recommendations: Ensure AI’s rankings align with on-the-ground realities and strategic changes.
Managing Ambiguities: Address uncertainties that data alone may not capture.
Adjusting Criteria as Needed: Based on ongoing results, adjust criteria weights and scoring algorithms to refine AI’s approach.

By blending AI’s analytical power with human expertise, project managers can elevate their decision-making process, ensuring that each project delivers value aligned with broader goals.

Continuous Learning: The AI Feedback Loop

As projects are completed, updating the AI with actual outcomes creates a feedback loop, helping the AI refine future predictions. This cycle of continuous improvement enhances accuracy and keeps the AI model responsive to changing conditions. Steps include:

Post-Project Analysis: Feed final data back into the model to improve accuracy.
Trends and Recalibration: Identify shifts in priorities, such as increased focus on customer impact, and recalibrate AI scoring to reflect these.

With each completed project, AI learns and adapts, making future rankings and recommendations even stronger.

The Future of Project Portfolio Management

AI has the potential to revolutionize portfolio management by delivering data-driven insights, streamlining project ranking, and optimizing portfolio balance. By harnessing AI's predictive power and marrying it with human oversight, organizations can achieve unprecedented clarity and focus in project selection and execution. As Ralph Waldo Emerson wisely noted, “Do not go where the path may lead, go instead where there is no path and leave a trail.” Embracing AI in project portfolio management isn’t just about following trends; it’s about paving a new path to strategic success.

AI Driven PM: Uncovering Project Overruns with ChatGPT

2024-09-10T13:08:00.003-05:00

As project managers, we’ve all faced the challenge of figuring out what went wrong on a project after it's finished—why it ran over time, blew past the budget, or failed to meet expectations. Recently, we tackled this issue head-on by comparing two versions of a project plan—an initial one and a second from about six months later. Using ChatGPT, we dove into the details of the project to uncover the real sources of cost overruns and time delays, providing crucial lessons that any project manager can apply.

The analysis started with comparing baseline and actual data for each task. We fed both project plans into ChatGPT and guided it through specific prompts to pinpoint where the project diverged from the original plan. For example, tasks like “Requirements Definition” took longer than expected, and development costs were significantly higher than estimated. By leveraging ChatGPT’s ability to process large amounts of data quickly, we identified the exact points where things went off track. This kind of insight is only possible when you have baselined project schedules that are regularly updated, something every project manager should maintain.

One of the key findings came from identifying new tasks in the later project plan—ones that hadn’t been accounted for initially. This led us to uncover scope changes, such as additional development work or change requests, which drove up costs and extended timelines. Using ChatGPT, we could filter out irrelevant tasks and focus on the most impactful areas. The right prompts, like “What tasks are new in the updated plan?” or “Which tasks show the greatest cost overruns?” helped zero in on the problem areas, making the analysis both efficient and thorough.

In addition to identifying overruns, we used ChatGPT to formulate questions that project managers can ask before a project begins. Prompts like “Have task durations been validated by the team?” or “What’s your process for managing scope changes?” can help uncover potential risks before they escalate. ChatGPT can also be a great tool for facilitating lessons learned sessions, where you can use specific questions based on real data to guide meaningful discussions about what worked, what didn’t, and how to improve next time.

Key Steps to Analyze Your Project and Uncover Lessons Learned:

If you’re interested in using ChatGPT to analyze your project and discover lessons learned, here are some key steps you can follow:

Gather Your Project Documents:
- Start by compiling your project schedules, baseline plans, and any updates that show actual progress. Be sure to include key metrics such as task durations, start and finish dates, baseline costs, and actual costs.
Cleanse Your Data:
- Make sure your project files are free of unnecessary or incomplete data. Remove tasks that are irrelevant to the analysis (e.g., placeholders or completed without impact) and ensure that baseline and actual metrics are aligned. Ensure tasks are clearly labeled to make comparison easier.
Identify Key Areas for Analysis:
- Use ChatGPT to assist in comparing baseline versus actual data. Start with prompts such as:
  - “What are the differences in task durations between the two project plans?”
  - “Which tasks exceeded their baseline costs the most?”
  - “What tasks appear in the later version but not in the earlier one?” These questions can quickly highlight the tasks where things went wrong.
Run Comparative Analysis:
- Analyze specific metrics such as cost overrun, delays in task completion, and scope changes. Use detailed prompts like:
  - “Show me the tasks with the highest variance in planned and actual completion times.”
  - “Which tasks were added after the initial plan, and how did they impact costs?”
- This will allow you to isolate the tasks driving overruns.
Turn Findings into Actionable Lessons:
- Once the analysis is complete, use ChatGPT to help craft questions for future lessons learned sessions. For example:
  - “What would you change in task estimation to avoid overruns like those in Development?”
  - “How could earlier identification of resource bottlenecks prevent delays?”
  - “What processes need to be in place to control scope creep effectively?”
Document and Share Lessons Learned:
- Summarize the key findings from your analysis into a structured document that identifies specific overruns and their causes. Include clear lessons and actions that can be applied to future projects, ensuring that the knowledge is shared across teams.

Prompts to Try in Your Own Analysis:

Here are some additional prompts you can use when diving into your own project data with ChatGPT:

“Compare the baseline cost and actual cost for each task in my project plan.”
“List the tasks that caused the most time delays and explain how they impacted the overall timeline.”
“Identify the tasks where rework occurred, and what impact it had on project costs.”
“What scope changes were introduced, and how did they affect both time and budget?”
“How did resource allocation contribute to delays or overruns?”

By leveraging ChatGPT for these types of detailed project reviews, you can uncover insights that might otherwise be missed, turn project data into meaningful lessons learned, and prepare more effectively for your next project. Whether you’re identifying scope creep, resource bottlenecks, or task delays, this approach ensures a clearer understanding of where things went wrong—and how to avoid similar pitfalls in the future.

AI Driven PM: Claude Projects is a Game Changer!

2024-08-17T09:25:00.003-05:00

Let me share my experience with a tool that's quickly becoming a game-changer for my projects—Claude Projects. I've been using this tool extensively, and I have to say, it's been delivering incredible results. While its capabilities are particularly outstanding in the realm of software development, I'm convinced that its benefits would extend just as effectively to other types of projects.

Claude Projects is designed to streamline processes, enhance collaboration, and spark innovation in ways that I've found transformative. One of the features I really appreciate is its ability to upload and integrate various project documents into its knowledge store. I can upload everything from technical specifications and design documents to coding standards, architecture diagrams, and even historical project data. What this does is allow Claude to develop a deep understanding of the project's context, goals, and constraints—something that’s critical for any project, but especially in software development. For instance, when I upload our project's software architecture documentation, Claude provides suggestions and insights that align with the existing system design, helping to maintain consistency and reduce potential integration issues.

But that’s just scratching the surface. The custom instructions feature is another game-changer. It allows me to tailor Claude's behavior to meet the specific needs of my project. Whether it's preferred coding styles, naming conventions, documentation standards, or project-specific terminology, I can ensure that when Claude assists with code generation or review, it adheres to the practices we've already established. This feature also allows me to define the structure and format for development tickets or user stories, which has significantly reduced the time spent on ticket creation and refinement.

One of the most innovative features of Claude Projects is its ability to analyze front-end designs, such as those created in Figma. By uploading your Figma output, Claude can dissect the design and suggest a list of features based on the visual and functional elements of the UI. This integration is particularly valuable during the initial stages of development, where aligning the front-end design with backend functionality can make or break the project. Claude's analysis ensures that nothing is overlooked, and it often provides feature suggestions that enhance the user experience while maintaining design integrity.

What makes Claude Projects even more valuable is its role as a brainstorming partner. With its knowledge of our repositories and architecture, it helps generate lists of potential features based on our project goals and existing functionality. This has been particularly useful in our agile environment, where continuous improvement and feature ideation are essential. The tool can even break down complex features into smaller, manageable tasks, taking into account our microservices architecture or module dependencies.

I’ve been maximizing Claude Projects' impact by uploading a diverse set of documents—everything from technical documentation like API specs and database schemas to project management artifacts, business documents, and even historical data like postmortems from previous projects. This comprehensive input allows Claude to offer more nuanced and context-aware assistance, helping me make informed decisions, anticipate challenges, and identify opportunities for innovation.

While my primary use of Claude Projects has been in software development, I have no doubt that its powerful features would be just as beneficial in other types of projects. Whether you're managing construction, finance, or marketing initiatives, the ability to upload comprehensive project documentation and tailor AI-driven assistance to your specific needs is a significant advantage. Claude Projects is not just a tool; it's a catalyst for achieving excellence in project management across any industry. Give it a try!

AI Driven PM: Emotional Sprint Retrospectives

2024-08-03T14:00:00.001-05:00

In Agile methodologies, we often focus on processes and technical issues. However, integrating ChatGPT for "Emotional Sprint Retrospectives" can revolutionize team dynamics by addressing the emotional and psychological aspects of team performance. This concept, aligned with my principles of making emotional conversations unemotional, offers a novel approach to Agile retrospectives.

Emotional Sprint Retrospectives with ChatGPT

Concept: Utilize ChatGPT as a facilitator for emotional retrospectives, helping teams articulate their feelings, resolve conflicts, and foster a supportive environment. This goes beyond traditional retrospectives by integrating psychological safety and team bonding as core elements of Agile practices.

Implementation:

Anonymous Feedback Collection:
- How-To: Set up a session where team members can submit their feedback anonymously. Use a form or a survey tool integrated with ChatGPT like GPT Form Builder.
- Suggested Prompt: "Collect anonymous feedback from team members about their feelings, frustrations, and successes during the sprint."
Sentiment Analysis:
- How-To: Use ChatGPT's natural language processing (NLP) capabilities to analyze the collected feedback for prevalent emotions and underlying issues.
- Suggested Prompt: "Analyze the anonymous feedback for common emotions and highlight any significant trends or issues."
Facilitated Discussions:
- How-To: Organize virtual meetings where ChatGPT presents the aggregated feedback and suggests topics for discussion. Use ChatGPT to ask open-ended questions that encourage deeper conversations.
- Suggested Prompt: "Facilitate a discussion based on the feedback, focusing on team dynamics, workload stress, and interpersonal relationships. Ask questions like, 'What are some challenges we faced this sprint?' and 'How can we improve our collaboration?'"
Conflict Resolution:
- How-To: Leverage ChatGPT to provide conflict resolution strategies and mediate discussions. ChatGPT can suggest best practices for effective communication, empathy, and collaborative problem-solving.
- Suggested Prompt: "Provide strategies for resolving conflicts that have been identified in the feedback. Suggest communication techniques to improve team interactions."
Actionable Insights:
- How-To: Use the insights from feedback and discussions to develop actionable plans and psychological strategies to improve team emotional health. Implement stress-relief techniques, team-building exercises, or workload adjustments as needed.
- Suggested Prompt: "Based on the discussion, what are some actionable steps we can take to improve our team's emotional health? Suggest specific techniques or exercises."

Benefits:

Enhanced Psychological Safety: By addressing emotional well-being, teams feel safer to express their concerns and ideas, leading to a more innovative and productive environment.
Improved Team Cohesion: Understanding and addressing emotional dynamics can strengthen team bonds, leading to more effective collaboration and reduced conflict.
Higher Productivity: Teams that feel supported and understood are likely to be more motivated and engaged, resulting in higher productivity and better project outcomes.
Early Conflict Detection: Early identification of emotional distress and conflict can prevent escalation, ensuring issues are resolved before they impact project progress.

Making Emotional Conversations Unemotional

Drawing from my experiences, here are key strategies to blend these concepts:

Validate Emotions: Start by acknowledging the team’s emotions. Validating emotions doesn't mean agreeing with them, but rather recognizing their presence. For example, instead of dismissing a team member’s frustration about a deadline, acknowledge it and then move towards a solution.
Use Data to Drive Conversations: Shift the focus from feelings to facts. I emphasize the importance of data in making emotional conversations unemotional. For instance, if a team member feels overwhelmed, use workload data to discuss the issue objectively.
Positive Mindset: Approach each conversation with a positive mindset. Replace negative statements with constructive ones. Instead of saying, "We can't meet this deadline," say, "We can meet the deadline if we adjust these variables".
Structured Approach: Follow a structured approach to problem-solving. This involves presenting options and potential outcomes without becoming emotional. For example, outline what is needed to meet a project deadline and let the team or stakeholders make informed decisions based on the data provided.
Continuous Improvement: Integrate lessons learned into the process. By consistently applying these techniques and refining them, the team can continually improve their emotional intelligence and collaboration skills.

Conclusion:

Integrating ChatGPT into Agile methodologies for emotional sprint retrospectives introduces a powerful way to enhance team dynamics and project success. By blending the principles of making emotional conversations unemotional with innovative AI-driven strategies, we can create a more supportive and productive environment, ultimately leading to better project outcomes and higher team satisfaction.