Is it unreasonable to want a clear path before committing to your next AI initiative?
You've seen pilots that work in demos but stall before production. You've watched promising initiatives quietly fade. The gap between what's possible and what's actually happening keeps widening.
Most organizations reach this point. Here's how the successful 12% moved forward.
No prep. No obligation. Just clarity.
The Reality of AI Initiatives
The numbers aren't the problem. It's what they reveal.
Never Reach Production
Show Zero ROI
CEOs at Career Risk
Feel They've Failed
"Feels like AI's everywhere except where it actually matters.
Everything works as a demo. Nothing works in production."
— Enterprise CTO, Nov 2025
Engaged and Consulted With Teams At
Google, Walmart, Nike, HotelPlanner, North American Bancard, Northwestern Mutual, HarvestRight, SRT Group, and others.
Seeing the Moments That Hint at the Real Problem
This isn't unusual.
Most organizations reach this point long before they realize it.
Finance asks for ROI and there's no clear answer.
Someone notes the pilot has been running for eight months with no path to production.
A senior engineer leaves and everyone knows the workload is about to shift again.
Your developers are doing everything they can — but AI application development is a new discipline.
Traditional software engineering doesn't map cleanly to LLM behavior, orchestration, safety boundaries, or production-grade data dependencies. Teams are learning while building, often without a clear definition of "production-ready."
The board review approaches.
The update deck looks the same as last quarter.
Priorities shift. Ownership is unclear.
Progress feels stuck even though everyone is working hard.
Everyone's working. But nothing's moving.
But picture the opposite for a moment:
Your team aligned, dependencies mapped, blockers visible, and every hour of work actually moving an initiative closer to production.
That's what becomes possible once the pattern is clear.
Work that demos well but can't run in production.
It's not a leadership failure. It's a structural pattern.
Not because the technology failed — but because the environment wasn't designed to support production.
The roadmap didn't account for the eight blockers.
Teams compensate instead of escalating.
Functions negotiate control instead of aligning.
Once you see which blocker you're facing — and what the 12% who reached production did differently — the next step becomes clear.
The Eight Blockers Behind 88% of Stalled Initiatives
The Eight Patterns That Quietly Define Your Trajectory
88% stall. 95% show zero ROI. This isn't random — it follows a pattern. Across 40+ AI program reviews, the same eight blockers repeat:
Architecture not built for production
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The hidden assumptions in prototype-grade architectures that silently break under real load.
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Why production failures often originate in components no one considered part of the "AI system."
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The point where scaling exposes gaps that demos never reveal.
Data dependencies that break downstream
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How a single undocumented dependency can cascade into system-wide fragility.
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Why AI workloads surface data quality issues traditional applications never encounter.
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The downstream breakpoints that only become visible once you map the pattern.
Unclear ownership or turf conflicts
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The early political signals that predict months of stalled progress.
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How ownership ambiguity delays production even when engineering is strong.
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Why "who is accountable" becomes more important than "is the model accurate."
Governance and safety constraints
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The moment responsible governance crosses into operational paralysis.
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How a single compliance interpretation can block production for an entire quarter.
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Why governance gaps surface late — usually right when leaders expect to scale.
Vendor-driven complexity
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The invisible complexity tax embedded in enterprise AI contracts.
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How vendor architectures unintentionally hard-wire fragility into your environment.
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The scaling cost that doubles once you move beyond pilot workloads.
Strategy–execution mismatch
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Why teams can hit every milestone and still fail to reach production.
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The common upstream decision that quietly determines the entire trajectory.
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How activity-based roadmaps mask structural gaps for months.
Capacity constraints
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How one overloaded senior engineer can cap your AI trajectory without ever signaling "no."
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Why AI amplifies staffing bottlenecks far faster than traditional engineering.
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The invisible queue that forms when specialized demand exceeds capacity.
Misaligned success metrics
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The KPI conflict that guarantees stalled progress long before anyone notices.
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How metrics designed to "prove progress" often prevent production entirely.
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Why alignment on success criteria is the earliest — and most overlooked — predictor of production.
Most companies don't realize which pattern they're in — or how early it sets their trajectory.
You don't need to solve all eight.
You only need to know which ones are blocking you right now.
That clarity is what the diagnostic gives you.
A Pattern I See Everywhere
A VP of Engineering told me his organization was running 37 AI pilots across six departments. Different vendors. Different objectives. Different metrics.
When I asked how they decide which ones will scale, he paused.
"We don't have a framework for that yet. We're just… keeping them all alive."
Three months later, the board asked why none were in production.
This isn't a leadership failure. It's a structural pattern.
You're Not Behind. The AI Environment Is Evolving Without Clear Signals.
88% of AI initiatives stall. 95% produce no measurable ROI.
Not because leaders are failing—but because the environment hides what matters until it's too late to course-correct.
The Gap Between What Many Say and What's True
What Many Say
"AI is our strategic priority"
"Promising early results"
"We're on track"
What's Actually True
The board is pressuring you
One demo worked
You're completely stuck
You feel that gap every day.
We make it visible and solvable.
25min
To Complete Clarity
100%
No Pressure
Free
Diagnostic
Why Everything Feels Impossible
The 4 Forces Creating AI Chaos
Above: Board Pressure
- • "Show ROI by Q3"
- • "Competitors are ahead"
- • "Why aren't we in production?"
Below: Team Chaos
- • 37 uncoordinated projects
- • No shared success criteria
- • Everyone building in silos
Inside: Political Turf Wars
- • CTO vs CDO ownership battles
- • Legal blocks everything
- • Security says "not yet"
Environment: External Chaos
- • Fragmented data across 12 systems
- • No observability or version control
- • Infrastructure can't support AI
No leader could derive clarity from inside this fog.
By the time the board asks why nothing's in production, you're already three quarters too late to fix it.
The diagnostic exists to prevent that moment from happening.
The Gap Between Industry Claims and Leader Reality
The narrative sounds smooth. The reality feels impossible. There's a reason.
The Industry Narrative
- → "AI transformation is accelerating."
- → "We're seeing promising pilots."
- → "Production is straightforward."
What Leaders Actually See
- ✓ Dozens of uncoordinated initiatives
- ✓ Unclear success metrics with board pressure
- ✓ No path from demos that work to systems that run
What the Research Shows
88% stall before production. 54% of executives feel they've "failed." 74% of CEOs are at career risk if they can't show measurable AI progress.
You're not failing. The system is designed to keep you confused.
Clarity doesn't come from working harder inside the noise.
It comes from seeing the structural forces causing the noise—and knowing exactly which one to address first.
The Same Patterns Keep Appearing... And It Is Costing Leaders Their Credibility
Leaders aren't lacking AI intelligence or effort. They're silently fighting structural forces that actively stall well intentioned AI initiatives.
I'm Alma Tuck.
For nearly three decades, I've worked inside complex technical environments where systems are messy, incentives conflict, and progress often looks nothing like the slide decks. Over time, I started getting called into a specific kind of situation — the moment when AI momentum quietly stalled, and no one could fully explain why.
I began hearing the same concerns from senior leaders:
- • AI initiatives that seemed promising suddenly plateau
- • Engineers burning out under unclear or shifting expectations
- • Stakeholders frustrated by slow or uneven progress
- • Politics and ownership disputes gridlocking decisions
- • Boards asking for answers nobody could confidently give
By the time I'm invited in, the pressure is usually high and the narrative unclear. Everyone is working hard, but the signals are noisy, and it's no longer obvious what's real or where the actual blocker is.
Across the last several years — in reviews with teams at Google, Walmart, Nike, HotelPlanner, North American Bancard, Northwestern Mutual, HarvestRight, SRT Group, and others — the same pattern kept emerging.
At one client, we traced eight months of stalled progress to a single API dependency no one had production ownership over. The architecture was flawless. The execution was blocked by politics. Nobody saw it — until we mapped the pattern.
The issue wasn't leadership capability.
It wasn't lack of talent, effort, or budget.
It was structural: a hidden production blocker shaping the entire trajectory long before anyone recognized it.
Once leaders could see that pattern clearly, their next move became obvious.
The AI Pilot-to-Production Diagnostic exists to make that moment of clarity happen earlier — before credibility erodes, before teams burn out, and before another quarter passes with no measurable progress.
40+
AI Programs
Reviewed
18
Months Advising
C-Suite Teams
Specializing in
AI Production Readiness
Engaged by teams at Google, Walmart, Nike, HotelPlanner, North American Bancard, Northwestern Mutual, HarvestRight, SRT Group, and others.
Sound Familiar?
What Executive Leaders Tell Me
We have 38 projects and no idea when we can ship.
— VP of Innovation, Manufacturing
The board keeps asking for progress I can't defend.
— CTO, Healthcare System
Every team has its own definition of AI.
— Chief Digital Officer, Retail
These aren't isolated problems. They're patterns — and they're fixable once you can see them clearly.
The AI Pilot-to-Production Diagnostic
25 Minutes to See What's Really Going On
A focused, structured conversation that reveals the core reason progress has stalled — and the one move that will actually change your trajectory.
We Start With Your Reality
A brief, candid conversation where I work to understand your environment as you experience it:
What's working
What's stalled
What's political
What the board keeps pressing on
What your instincts say is off
You don't need slides or preparation. My job is to extract the signal quickly and accurately.
I Map the Pattern
Across 40+ AI program reviews, the same eight production blockers appear.
In real time, we identify which category you're facing:
Architectural blocker
Data dependency blocker
Ownership/turf blocker
Governance/safety blocker
Vendor complexity blocker
Strategy/execution mismatch
Capacity constraints
+ a few others that show up repeatedly
You'll know which structural pattern is defining your reality.
You Get the Critical Insight
You'll know the structural reason progress has stalled, which blocker is defining your reality, and what the 12% who reach production do differently.
This isn't the full solution — it's the clarity that precedes the solution.
What You Leave With
The specific structural reason progress has stalled — explained in clear, non-technical language you can use immediately.
The exact blocker category shaping your trajectory, and why it matters more than surface symptoms.
What can safely stop or slow without political risk.
What requires escalation — and how to frame it so the board understands the underlying cause.
Where effort is being absorbed with no corresponding movement toward production.
A concise, defensible explanation you can share with executives or the board without overclaiming.
Some leaders stop here because this alone changes their approach.
If we see a path worth exploring together, I'll tell you. If not, you still get the insight.
Production Readiness Assessment (Optional)
Others move forward to the full assessment:
Map the 6-12 month path
Resolve ownership
Prioritize use cases
Build your board roadmap
You will know what's real, what's blocking you, and where to focus next.
The diagnostic isn't the solution. It's the moment you finally understand the problem.
Clarity Before Commitment
You shouldn't have to guess whether this will help. Know first, then decide.
The 25-minute AI Production Diagnostic is designed to give you complete visibility into what's blocking your initiatives—whether you work with me or not.
Clarity first. If working together makes sense after the diagnostic, we'll discuss what that looks like. If not, you still leave with insights you can use.
Your decision. Some leaders walk away with clarity and implement on their own. That's success.
No obligation. The risk is entirely on me. If the diagnostic doesn't reveal something you didn't already know, I haven't earned your time.
Your skepticism isn't cynicism. It's pattern recognition. Use it here.
Before You Book
This is a structured diagnostic designed to isolate which blocker is preventing your AI work from reaching production.
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No prep required — the signal comes from your environment, not documentation.
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No risk — if the diagnostic reveals nothing new, you've lost nothing.
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No exposure — the conversation is confidential and not recorded.
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No commitment — clarity comes first; decisions come after.
The first 20–25 minutes are focused entirely on clarity — mapping your environment to one of the eight structural patterns.
If the diagnostic reveals that I can materially help you, we'll spend the last 3–5 minutes discussing what working together could look like.
If not, the call ends there.
No pressure. No pitch. No follow-up sequence.
This conversation is fully confidential.
Nothing is shared. Nothing is recorded. You don't need to filter what you say — and that's exactly why it works.
You keep the clarity either way.
Apply for Your AI Pilot-to-Production Diagnostic
You don't need more input. You need to isolate the blocker. That's what this does.
Most leaders wait for clarity. The best extract it.
Apply for Your AI Pilot-to-Production DiagnosticA 25-minute structured diagnostic that isolates your blocker — and reveals whether working together makes sense. You keep the clarity either way.