I spend a lot of time talking with teams that are trying to expand their AI efforts, and I’ve noticed a consistent pattern: AI pilots are multiplying across the board, but a majority of them fail to see the light of production.
Data tells a similar story: Only 26% of leaders report more than half of their pilots scaling to production. Meanwhile, 69% of practitioners (the front-line teams embedding AI into workflows) say most of their pilots are never scaled.
Chief People & AI Transformation Officer at Zapier.
That gap between ambition and execution is where momentum leaks out of AI programs. But when leaders spend time closing that gap, they can turn AI pilots into meaningful progress.
Where AI momentum starts to slip
As AI pilots move closer to production, the work changes shape.
Early progress often looks clean. Teams define a use case, test a model, and show early results. As pilots expand, they start touching real systems, shared data, security reviews, and downstream workflows. That’s where timelines stretch and attention fragments.
One of the most consistent signals in the data is where teams get stuck: leaders rank integration complexity and system sprawl as the biggest barriers to AI execution. Practitioners confirm the same reality, pointing to integration backlogs and policy delays as top blockers. What’s missing here isn’t effort or intelligence. It’s orchestration.
AI pilots are often designed as standalone wins. They prove that a model can work, but not that it can survive inside a web of existing tools, data sources, approvals, and workflows. When those connections aren’t planned early, teams end up rebuilding work that already “worked,” just not at enterprise scale.
This is where momentum quietly drains away. Each delay feels reasonable on its own. Taken together, they stretch timelines, consume trust, and make scaling feel heavier than starting.
Confidence fades when visibility lags
Another pattern shows up in how leaders and practitioners experience progress.
Eighty-one percent of leaders say they’re confident in their visibility into AI execution challenges. At the same time, 57% of practitioners believe leadership doesn’t fully see what’s happening day to day.
That disconnect matters because feedback usually arrives late. Leaders learn about failures after the fact, most often through escalations or informal conversations. By then, projects have already lost momentum or require rework.
What’s left is a cycle of fixing execution issues instead of moving work forward. That reactive rhythm makes AI feel unpredictable, even when the underlying goals are clear.
Leaders who close the gap don’t rely on confidence alone. They create shared visibility into execution, so friction shows up while there’s still time to address it.
What actually helps AI pilots turn into progress
You don’t have to rewrite strategy to close the gap between ambition and execution. Instead, spend more time where friction actually shows up. These focus areas consistently make a difference.
Start with clear ownership
AI pilots move faster when someone is accountable beyond the initial delivery. This is where internal AI champions come in. They’re the ones responsible for production outcomes and can be the clear decision-maker when tradeoffs arise.
That ownership helps teams resolve integration questions, prioritize follow-up work, and keep pilots from drifting into the ether of failed experiments.
Invest early in integration planning
Integration complexity is a common execution challenge across leaders and practitioners. When integration work is deferred until after a pilot proves technical value, teams often revisit assumptions under time pressure.
This is where it’s crucial to ask questions early and often about systems, data flows, and workflow dependencies to help teams design pilots with scale in mind. That early clarity reduces rework and shortens the path to production.
AI execution accelerates when teams share both a common foundation and a way to learn from each other. Standardized tools and approaches reduce the overhead of every new pilot by giving teams familiar patterns to build on.
Pairing that consistency with peer learning and internal upskilling helps knowledge travel across the organization. When teams share lessons from what worked and what didn’t, each pilot builds on the last. Over time, experimentation turns into repeatable capability instead of isolated wins.
Build governance into delivery
Governance pressure tends to increase as AI pilots move closer to production. When governance enters late, teams pause work while policies are interpreted, approvals are routed, and risks are reassessed.
Embedding governance directly into delivery workflows helps teams understand constraints earlier and move with confidence. Clear guardrails, applied consistently, reduce late-stage surprises and keep execution moving as pilots scale.
Create shared visibility into execution
Execution slows when leaders and practitioners see different versions of progress. Leaders often track milestones and timelines, while practitioners experience day-to-day friction through integration work, reviews, and rework.
Shared visibility bridges that gap. Live signals, clear escalation paths, and agreed-upon success criteria surface issues while they’re still manageable. That alignment reduces firefighting and helps teams stay focused as scope expands.
Budget for scale
Many AI pilots stall once early funding runs out. Integration, governance, and long-term delivery require sustained investment beyond the initial build.
Leaders who plan budgets with scale in mind give teams room to carry successful pilots forward. Dedicated funding signals that execution matters as much as experimentation, and it creates the conditions for pilots to mature into systems that last.
Turning ambition into results
The next phase of enterprise AI won’t be defined by who launches the most pilots. It’ll be defined by who learns how to see execution clearly enough to keep those pilots moving.
As AI becomes part of everyday operations, the advantage shifts toward leaders who stay close to the work as it scales, who notice friction early, and who treat execution signals as strategic input rather than noise. That kind of attention compounds. Teams spend less time recovering.
Decisions get made faster. Confidence grows where progress is visible.
Over time, AI stops feeling fragile. It becomes dependable. And when that happens, ambition no longer outruns impact, it sets the pace for it.
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