What Project Server Taught Us About AI Adoption (That Nobody’s Using)
Why task-level AI adoption stalls — and what execution systems taught us 15 years ago
Three times this month, client leadership told me the same story:
“We bought Copilot.”
“People tried it.”
“Usage tapered off.”
“Leadership is asking what we actually got for the money.”
If you’ve led AI adoption in the last 18 months, this probably sounds familiar. The pattern is everywhere: initial excitement, scattered usage, gradual decline, awkward ROI conversations.
What’s interesting is that I’ve heard this exact story before. Just not about AI.
I heard it 15 years ago, during enterprise rollouts of Microsoft Project Server.
And that’s when it clicked for me:
AI adoption isn’t failing because the technology isn’t ready.
It’s stalling because we’re ignoring execution muscle memory.
The Problem We’re Not Talking About
Most AI initiatives today are focused at the task or activity level:
Write this email. Summarize that meeting. Generate a draft. Answer a question.
That’s necessary. I firmly believe AI belongs at the task level. It makes individual moments easier.
But here’s the uncomfortable truth organizations are running into:
Task-level AI alone is not investable.
Why? Because without an execution system behind it:
• There’s no baseline to compare against
• No variance to explain what changed
• No way to govern outcomes at scale
• No organizational confidence, just productivity moments
You get people saying “this is cool” in the moment. But executives don’t invest in cool. They invest in clarity, predictability, and reduced coordination cost.
This is the same mistake we made years ago when teams thought task lists could replace real schedules. Task lists told you what to do. Schedules told you when, by whom, at what cost, and what happens if something slips.
The difference was execution context. And we’re making that mistake again.
We Already Solved This Problem — We Just Stopped Talking About It
Before “AI adoption” was a phrase, organizations were wrestling with a different challenge:
• Project Managers chasing people for updates
• Weekly or bi-weekly status meetings per project
• Manual rollups and perpetually late data
• PMs spending more time preparing status than managing work
When organizations implemented Project Server with proper timesheet and progress-update flows, something important happened:
• Team members updated work once
• Updates pushed directly into the schedule
• Remaining work, estimates-to-complete, and resource demand updated automatically
• PMs stopped chasing people
• Status meetings were reduced by 75% or more in mature environments
• Leadership received earlier, more reliable signals
And here’s what made this interesting: this wasn’t Microsoft’s official playbook.
The documentation said timesheets were optional. Sales teams told customers “just use the schedule.” Product marketing focused on Gantt charts and resource leveling algorithms.
But a community of MVPs and implementation partners discovered the real adoption secret in the field. We learned—with real customers facing real adoption challenges—that the timesheet flow was the whole game. That’s what created the behavior loop. That’s what drove the 75% reduction in status meetings. That’s what made it stick.
The biggest win wasn’t the tool. It wasn’t even a feature. It was the behavior loop:
Update work → system reacts → forecasts adjust → fewer meetings → more trust
That loop became muscle memory. People didn’t think about “doing status” anymore—the execution system handled it.
The lesson wasn’t in the product documentation. It was learned by practitioners in the trenches.
And here’s the uncomfortable parallel: I’m seeing the same gap today with AI. The technology is impressive. The marketing is compelling. But the adoption playbook? It’s being written in the field again—by practitioners, not product teams.
This is exactly where AI adoption is breaking today.
Task AI vs Execution AI — This Distinction Matters
Here’s how I think about it now, especially for PMOs and portfolio leaders:
Task-level AI:
• Saves individual minutes
• Feels impressive in the moment
• Often optional
• Hard to govern at scale
Execution-level AI:
• Anchored to baselines and variance
• Explains impact (cost, dates, capacity)
• Produces defensible forecasts
• Becomes fundable and strategic
Task AI is about productivity. Execution AI is about organizational confidence.
That only happens when AI is anchored to execution truth—the same truth Project Server was designed to maintain: baselines, actuals, variance, forecasts, resource capacity, and portfolio-level tradeoffs.
Muscle Memory Beats Features — Every Time
Sticky notes survived for decades not because they were powerful, but because:
• The action was simple
• The feedback was immediate
• The habit was intuitive
Project Server succeeded in mature environments for the same reason:
• One update
• Everything else adjusted
• Less talking about work, more doing work
AI tools fail when they:
• Interrupt flow
• Feel risky or unpredictable
• Require people to think before they help
AI adoption succeeds when behavior becomes reflexive, not when features pile up.
This is the lesson Project Server taught us long before Copilot existed. And it’s the lesson we’re ignoring now.
Why PMOs Are the Natural Laboratory for Execution AI
PMOs sit at the intersection of:
• Messy, real-world data
• Political decisions and competing priorities
• Real delivery consequences
• Governance that actually matters
That’s not a weakness—it’s a strength.
If AI can work in a PMO environment—where schedules slip, resources get pulled, priorities shift, and every number is questioned—it can work anywhere.
Imagine Execution AI that could:
• Explain schedule variance in natural language: “Project Alpha is 3 weeks behind because the Design phase ran 40% over estimate, driven by scope expansion in Q2.”
• Surface emerging risks before they become fires: “Resource demand for Q4 exceeds capacity by 320 hours in the Engineering group—three projects are competing for the same skillset.”
• Recommend portfolio tradeoffs: “Delaying Project B by 6 weeks would free up the capacity needed to accelerate Project A by 4 weeks, improving overall portfolio value by 12%.”
• Generate truthful status narratives automatically: “Here’s your executive summary based on actual progress, baseline variance, and forward-looking risk.”
That’s Execution AI. And if it works in a PMO, it can scale to any execution environment.
This is why I believe PMOs—and the execution systems behind them—are not legacy concepts in the AI era.
They’re the foundation.
The Pieces Already Exist — They Just Need to Be Connected
Here’s what makes this moment interesting:
The technology components needed for Execution AI already exist in the ecosystem:
• Proven execution engines that understand baselines, variance, and forecasting
• Enterprise collaboration platforms where work actually happens
• Business intelligence tools that can visualize portfolio-level insights
• AI capabilities that can understand context and generate insights
What’s missing isn’t technology. It’s the execution layer—the connective tissue that turns task-level AI into organizational intelligence.
We don’t need to rebuild everything from scratch. We need to connect what already works with what’s newly possible.
Execution AI Is the Category We Need to Build Next
If AI is meant to:
• Reduce busywork
• Improve decisions
• Help organizations do more with less
Then maybe the path forward isn’t asking everyone to start from scratch with disconnected task tools.
Maybe it’s anchoring AI to execution systems and behaviors people already trust—and letting new muscle memory form naturally on top of that foundation.
Task AI will continue to evolve. That’s important. But without Execution AI, we’ll keep having the same conversation:
“We bought it. People tried it. Usage tapered off. What did we get?”
Execution AI changes that conversation to:
“Our forecasts are more reliable. Our teams spend less time on coordination. Leadership trusts our numbers. And we can prove the ROI.”
That’s the category we need to build next.
And I believe the organizations that figure this out first—the ones that anchor AI to execution truth instead of scattering it across tasks—will have an unfair advantage.
That’s the conversation I think our industry needs to be having next.
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What’s your experience with AI adoption in your organization? Are you seeing the same pattern? I’d love to hear what you’re observing—especially if you’re in a PMO or portfolio leadership role.
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