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

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.

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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:

1. 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."
2. Product owner second — Pre-align before the CEO meeting. Convert engineering evidence into risk framing.
3. Marketing director third — Build the narrative that gives the CEO something to say publicly.
4. 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.

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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.

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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.

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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.

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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:

1. Did mapping the influence network reveal leverage points you hadn't considered?
2. How did stakeholders respond when the message was framed around their priorities?
3. 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.

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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

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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:

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

Decision and context: [Enter Context]

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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:

1. What is the core message in one sentence?
2. What framing will resonate most with this stakeholder's priorities? (data, vision, risk mitigation, opportunity)
3. What story or analogy makes the message memorable?
4. What objections will they raise, and how do I preempt them?
5. 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]

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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:

1. What have I done (or could I do) to help each stakeholder achieve their goals?
2. How do I frame my ask as aligned with their interests—not just my project needs?
3. Who are the natural allies I should recruit first to build a coalition?
4. How do I leverage early supporters to create momentum with skeptics?
5. 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

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.

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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.

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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.

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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.

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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.

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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:

1. Does calculating NOV change how you talk about your project's value?
2. How do executives respond when your business case includes opportunity cost?
3. 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.

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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

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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:

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

Project context: [Enter Context]

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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:

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

Project business case: [Enter Business Case and Assumptions]

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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:

1. How do the competing projects compare on NOV?
2. What projects are must-dos (strategic imperatives) vs. nice-to-haves?
3. What is the optimal portfolio mix given current capacity and risk tolerance?
4. What projects should we greenlight, pause, or kill based on NOV?
5. 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

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.

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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.

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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.

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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.

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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.

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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

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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:

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

Project context: [Enter Context]

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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:

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

Current metrics: [Enter Metrics and Current Project Context]

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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:

1. Which of my current metrics are vanity metrics? (They look good but don't drive decisions.)
2. Which metrics are value metrics? (They directly inform action or prove impact.)
3. For each vanity metric, what is the underlying value metric I should track instead?
4. What questions should I ask myself to test if a metric is worth tracking?
5. 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?

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.

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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.

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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.

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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:

1. Structured input from both sides (5 minutes each, no interruptions)
2. Engineering lead makes recommendation
3. I confirm alignment with business constraints
4. 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.

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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:

1. Did asking questions instead of pretending to know earn you more credibility?
   (It almost always does.)
2. How much faster can you learn with AI as a tutor?
   (Days instead of months.)
3. 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.

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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.

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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

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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:

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

Domain and project context: [Enter Context]

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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:

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

Project context: [Add Context]

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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:

1. What open-ended questions should I ask to draw out the team's expertise?
2. What framework or structure can I offer to organize the discussion (without dictating the answer)?
3. How do I acknowledge my knowledge gaps while still leading with authority?
4. What decision-making process should I facilitate (consensus, consultative, executive call)?
5. 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]