If you scroll through LinkedIn, it looks like everyone has an “AI success story”:
- Press releases about “AI-powered transformation”
- Keynotes about “reimagining the business with AI”
- Internal emails about “successful pilots”
But when you pull back the curtains, a very different story shows up.
Capgemini’s The AI-Powered Enterprise study looked at nearly 1,000 large organizations implementing AI and found:
- Only 13% had successfully deployed AI use cases in production and were scaling them across multiple business teams
- 47% had launched pilots or proofs of concept that were not yet deployed in production
- The rest had a few use cases in production, but only on a limited scale
In a follow-up breakdown, Capgemini identified “AI-at-scale leaders” (13%) and “struggling organizations” (72%) the strugglers had started their pilots before 2019 and still hadn’t deployed even a single AI application into production.
That’s pilot purgatory.
So let’s talk about why so many AI projects live there, and die there.
1) The Numbers Behind Pilot Purgatory
From Capgemini’s global survey of organizations implementing AI:
- 13% – “AI-at-scale leaders”
- Multiple AI use cases
- Deployed in production
- Being scaled across several business teams
- 40% – “Limited scale”
- A few AI use cases in production
- But still isolated pockets, not enterprise-wide
- 47% – Pilots/PoCs only
- Have launched pilots
- Not yet deployed in production
- 72% – “Struggling organizations”
- Started AI pilots before 2019
- Still haven’t rolled out a single use case into production
So when someone says:
“We’re doing a lot with AI.”
A fair follow-up is:
“That’s great, but how much of it is out of pilot and actually running the business?”
For most large organizations, the answer is “not much.”
And the patterns that keep them stuck are surprisingly consistent.
2) Reason #1: Pilots Start Without a Path to Production
Most AI pilots start life like this:
- “We need to experiment with AI.”
- “Let’s find a safe use case and run a PoC.”
- “We’ll see what happens, then figure out next steps.”
What’s usually missing before the pilot starts:
- A clearly defined business owner
- A target KPI (revenue, cost, risk) and a baseline
- A set of GO/SCALE/STOP criteria
- A provisional plan for how this would roll out if successful
So the pilot becomes:
- A science experiment
- With no one on the hook to:
- Integrate it into core workflows
- Secure budget for rollout
- Push it through legal, risk, and operations
You end up with:
- A half case study deck
- Some positive anecdotes
- No production system
If a pilot doesn’t have an explicit path to production, it’s not a pilot. It’s a demo.
3) Reason #2: Ownership Is Fuzzy, So No One Can Say “Scale It”
The research shows that AI-at-scale leaders are far more likely to have joint ownership between business and IT, while strugglers tend to leave AI primarily in the hands of IT or central teams.
In organizations stuck in pilot purgatory, AI initiatives tend to:
- Live under:
- IT
- Data science
- A central “innovation” or “AI CoE”
- Report to leadership in terms of:
- “Pilot status”
- “Model accuracy”
- “User satisfaction”
What’s missing:
- A named business sponsor with P&L responsibility
- A clear statement of:
- “If this works, it changes this part of how we sell, serve, or operate.”
Without that ownership:
- IT can say:
- “We ran the pilot; the tech works.”
- Business can say:
- “We’re busy; we’ll get to that.”
Result:
- Nobody fights to get it through:
- Process redesign
- Change management
- Risk/legal reviews
- The pilot… stays a pilot.
4) Reason #3: Risk and Compliance Only See It at the End
Another common pattern:
- AI pilot is run in a sandbox
- The team gets excited:
- “Look at these results!”
- Then they take it to:
- Legal
- Compliance
- Security
- Risk
And that’s the first time those groups see it.
They understandably respond with:
- “What data is this touching?”
- “How do we log and audit?”
- “What happens when it fails?”
- “What are the harm scenarios?”
Often, the honest answers are:
- “We’re not sure yet.”
- “That wasn’t in scope for the pilot.”
So the initiative gets:
- Slowed to a crawl
- Confined to tiny, low-impact areas
- Or blocked entirely
The underlying issue:
- Risk/compliance weren’t treated as design partners
- They were treated as final gatekeepers
In AI-at-scale leaders, it is noted that they have stronger governance frameworks and clearer accountability. AI is tied into enterprise-wide objectives, risk frameworks, and oversight from the start, rather than being bolted on later. Stronger governance frameworks and clearer accountability are essential.
5) Reason #4: Processes and Data Aren’t Ready to Carry the Load
Even if the pilot model works, the rest of the system might not.
Typical blockers:
- Processes:
- No standard workflow to automate, just tribal knowledge
- Lots of exceptions and one-off rules
- Multiple teams doing the “same” thing differently
- Data:
- Spread across tools with no consistent IDs
- Quality issues no one wants to own
- Unclear lawful basis for some data uses
In pilot mode:
- You can sidestep this with:
- Manual “data patches”
- Heroic effort by a couple of people
- Limited scope
At production scale:
- Those shortcuts break
- You’d need:
- A data spine (clean-ish, connected data)
- Clear logging and monitoring
- Standardized interfaces
Organizations that never invest in that basic spine discover:
- Their pilot is a magic trick, not a repeatable system
- The cost and risk of “doing it properly” feels too high
- So they quietly de-prioritize the rollout
6) Reason #5: No One Wants to Pay the Change-Management Bill
Scaling a pilot means:
- Changing how people work
- Changing incentives and scorecards
- Training managers and teams
- Updating SOPs, documentation, and controls
Most organizations:
- Under-budget this
- Under-plan this
- Under-communicate this
So when the pilot team says:
- “We’re ready to scale this to 3 more regions/business units/teams.”
The response is:
- “We don’t have the capacity for that change right now.”
- “Let’s keep it as a pilot a bit longer.”
And “a bit longer” quietly becomes forever.
It is emphasized that the AI-at-scale leaders invest heavily in talent, training, and collaboration with partners, not just tech and they treat scaling as a first-class problem, not an afterthought.
7) Pilot Purgatory Symptoms: A Quick Self-Check
If you want to know whether you’re stuck in pilot purgatory, run through this checklist.
For your main AI initiative:
- How long has it been a “pilot”?
- < 6 months
- 6–12 months
- 12 months (major red flag)
- Do you have a signed-off owner who says, “If this works, it will change my KPI”?
- Yes, and they show up consistently
- Sort of, we have a sponsor in name only
- Not really, it’s “owned” by a project team
- Have risk/legal/compliance been part of design from the beginning?
- Yes, they helped shape scope/guardrails
- Only at the end, to “approve”
- Not yet
- Is there a documented plan for rollout if it works?
- With timelines, resources, and which teams are next
- “We’ll see what happens and decide later”
- No plan yet
- Have you put real budget into change management and training?
- Yes, it’s part of the business case
- Some, but not enough to change how people work
- Basically none
If most of your answers are in the second or third option, it’s not surprising if:
- You’re still in pilot
- You’re part of the 47% that launched pilots without deploying
- Or even the 72% who haven’t put a single AI use case into production despite years of “AI work”
8) What AI-at-Scale Leaders Do Differently
“AI-at-scale leaders” aren’t just lucky. They behave differently from day one.
Patterns you can borrow without a 100-page transformation plan:
- They start with business outcomes.
- Each major AI initiative has:
- A real KPI owner
- A target delta
- Clear financial logic (revenue, cost, risk)
- Each major AI initiative has:
- They design pilots as stepping stones, not science experiments.
- GO/SCALE/STOP criteria decided up front
- Risk, legal, and ops contributing early
- A draft rollout map exists before the first line of code
- They invest in the boring plumbing.
- Minimal viable data spine
- Logging, monitoring, and incident management
- Standard integration patterns and guardrails
- They budget for changing work, not just tools.
- Training
- SOP updates
- Manager enablement
- Communication and feedback loops
That’s why their pilots don’t sit in purgatory forever, they’re designed to grow up into production from the start.
Where The Scale Crew Fits In
If you’re reading this, seeing the numbers and thinking:
“We might be one of those organizations stuck in pilot land…”
You’re not alone, and you’re exactly who we help.
At The Scale Crew, we work with US startups, SMBs, and mid-market firms that:
- Are skeptical of AI theater
- Have already dipped a toe into AI (or are about to)
- Don’t want to become another “we’ve been piloting this since 2021” story
We don’t start by saying:
- “Let’s build a custom AI app.”
We start by helping you answer:
- Should you even be piloting this?
- Is AI the right lever vs. simpler automation or process fixes?
- If it does work, what’s the path out of pilot?
- Who owns it?
- What KPI must move?
- What does rollout actually require?
- Are your people, processes, and data ready to support a real deployment?
- Or will this pilot collapse the second it hits production reality?
That’s the core of our AI Readiness & Transformation Program:
- Stop you from running pilots that were never going to scale
- Help you decide where AI truly belongs (and where it doesn’t)
- Give your good pilots a real shot at becoming production systems, not just slides
If You Suspect You’re in Pilot Purgatory
We can help you:
- Whether you’re designing a real path to production, or just a permanent pilot
- Whether AI is actually the right tool for that job
- And what’s most likely to keep you stuck in purgatory before you sink more time and money into it.
