AI Driven PM: S2E3 - The Halo Effect

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.

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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.

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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:

1. Were the gaps what you expected, or did AI surface something you weren't anticipating?
2. Did it reveal any blind spots you'd been avoiding?
3. 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

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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:

1. What are the 8–10 core PM competencies needed for this role?
2. Based on what I've shared, which competencies does this PM likely excel at?
3. Which competencies are likely gaps or growth areas?
4. What specific behaviors or outcomes would indicate strength or weakness in each gap area?
5. What is a 90-day development plan to address the top 2–3 gaps?

PM background: [Add your background]

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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:

1. What skills or background do we tend to prioritize when selecting PMs?
2. What skills critical to PM success might we be overlooking?
3. What evidence would indicate we're suffering from halo effect in PM selection?
4. How should we change our PM selection criteria to reduce halo effect bias?
5. 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:

1. What are the 8–12 core PM competencies for our context?
2. For each competency, what does "developing," "proficient," and "expert" look like in observable behaviors?
3. Which competencies are most critical for new PMs vs. senior PMs?
4. How should we assess these competencies in hiring and promotion decisions?
5. 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?

 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:

1. Project Health Diagnostic (your non-negotiable experiment)
2. Decision Architecture Mapper
3. 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:

1. What are the top 3 risks that need PM attention right now?
2. Which stakeholder relationships need strengthening and why?
3. What decision is being avoided or delayed that's creating drag?
4. Where is the team's energy or morale most vulnerable?
5. 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:

1. What categories of decisions exist on this project? (strategic, tactical, technical, resource, scope)
2. For each category, who should make the decision, who should be consulted, and who just needs to be informed?
3. What decision rights are currently unclear or creating bottlenecks?
4. 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:

1. Where is team energy highest right now? (What's motivating them?)
2. Where is energy lowest? (What's draining them?)
3. What misalignment exists between team priorities and leadership priorities?
4. What story or narrative could I reinforce to re-energize and re-align the team?
5. 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:

1. Did AI surface something you were avoiding or hadn't articulated yet?
2. How much time did you save by having AI organize your thinking?
3. 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.

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

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:

1. Adopt quickly because modernization pressure demands it
2. Skip readiness because upstream preparation takes time
3. Fail during execution as readiness debt compounds
4. 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.

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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

 How I’m Using Prompt Engineering to Turn Reports Into Results

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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.

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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

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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

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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

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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:

1. Know where your data lives – PMIS, Confluence, Slack threads, spreadsheets
2. Standardize your outputs – Markdown for risks, JSON for status, PowerPoint XML for slides
3. Build and iterate your prompts – Make them tight, structured, and outcome-focused
4. Add guardrails – Validation scripts, human-in-the-loop reviews, compliance checks
5. Track the impact – Time saved, errors avoided, decisions accelerated

“A good process is one you don’t notice—because it’s working.” — Rick Morris

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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

 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.

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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.

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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.

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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.

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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.

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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.

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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. 🚀👇