ADAPTOVATE, the global business agility and AI transformation consultancy, today released, The Execution Gap: Where Ambition Outpaces Action in the AI Era, a new research study conducted with Wakefield Research surveying 300 C-suite executives, spanning CEOs, CFOs, COOs, CMOs, CPOs, CHROs, CIOs, CTOs, and CISOs, at U.S. and Canadian companies with at least $10 million in annual revenue, on AI transformation readiness.
asserted
Ambition → release → readiness
The report’s central finding challenges how AI transformation stalls are usually explained: as a strategy or leadership buy-in problem.
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stalls → challenge → problem
78% of executives say they already know exactly what their organization needs to do to scale AI.
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organization → say → AI
And when Adaptovate asked the same executives an identical question with no mention of AI at all, 81% gave nearly the same answer: their organizations are better at identifying opportunities than acting on them.
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organizations → ask → them
That instinct-over-follow-through pattern may help explain a trend many industries lived through this year: organizations that acted on AI’s promise by cutting roles, only to find themselves rehiring once the operational reality caught up.
uncertain
reality → follow → roles
AI isn’t creating a new problem, it’s just the highest-stakes pressure test an old one has ever faced.
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one → create → problem
“After building Adaptovate across ten countries, I’ve now watched two transformations up close — Agile, and now AI — and this data confirms exactly what we see with clients every day: strategy isn’t the constraint anymore, it’s the ability to deliver it,” said Paul McNamara, CEO and Co-Founder of Adaptovate.
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McNamara → build → Adaptovate
“The firms that separate themselves over the next two years won’t be the ones with the best AI strategy.
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that → separate → strategy
They’ll be the ones who stop treating execution as a downstream detail and start engineering for it from day one.
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who → stop → day
83% say their own performance evaluation and job security already depend on their ability to scale AI — 22 points higher than the 61% who believe their company’s failure to embed AI within two years would be existential to the business.
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failure → say → business
The math is personal before it’s institutional: a company can draw on years of data, customer relationships, and IP built over time; an individual has only a resume and a job market crowded with peers all making the same case for why they’re still needed.
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they → ’ → case
Mid-size executives report the same level of personal job-security risk as leaders at large firms (81%), but are 30 points less likely to believe their organization’s risk is existential, landing almost exactly at a coin toss (51%).
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risk → report → toss
Individually, they feel their careers on the line.
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careers → feel → line
Collectively, they can’t agree the threat to the company is even real.
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threat → agree → company
Pilots Are Working.
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Pilots → work → ?
They Just Aren’t Spreading.
90% of executives are concerned that their most promising AI pilots aren’t scaling broadly across the organization, and 82% say internal gatekeepers — the people and processes that control how AI gets implemented, how models are overseen, and how workflows get redesigned — are actively slowing progress.
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workflows → spread → progress
But the friction may say less about gatekeepers holding pilots back, and more about how few AI pilots come in with the governance, clear metrics, and decision points needed to earn the right to scale.
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pilots → say → right
Only 19% of organizations have a company-wide plan to scale what works; 41% rely on a centralized transformation team, and 23% leave it to individual business lines with no throughline connecting them.
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% → have → them
55% of executives name a lack of skilled talent as their top barrier to AI progress, ahead of budget (47%), organizational resistance (44%), and unclear strategy (41%).
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% → name → budget
Notably, access to AI tools didn’t register as a barrier at all.
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access → register → barrier
But the same executives are chasing talent they expect to become obsolete: 88% believe AI agents will eventually eliminate many of the management roles they’re currently struggling to hire for.
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they → chase → roles
“The execution gap sits in the capability organizations deliberately build and set aside for change, and that’s a different thing entirely from the headcount running the business day to day,” said Ghaleb El Masri, Partner and Toronto Managing Director at Adaptovate.
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Masri → sit → Adaptovate
“Until that capability exists, the gap stays open regardless of how much talent or outside expertise an organization brings in.
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organization → exist → talent
Why Change Doesn’t Stick
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Change → stick → ?
While 96% of executives have worked with management consultants, only 20% say those strategies were effectively implemented, most often citing poor cultural fit (43%), missing technical expertise (40%), and misdiagnosed problems (35%).
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strategies → work → fit
Meanwhile, meaningful adoption inside a function typically happens because someone informally decided to own it, not because organizations built a deliberate mechanism for that ownership to hold.
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ownership → happen → mechanism
Executives are also split on where the friction really lives: 57% point to employees struggling with new workflows, 43% point to managers struggling to implement them.
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% → split → them
Most transformation plans only address one side.
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plans → address → side
The same capacity gap shows up again in how organizations plan for AI’s cost, not just its rollout.
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organizations → show → cost
94% of executives have factored AI token costs into investment decisions, but only 23% do so on a regular, systematic basis.
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% → factor → basis
Token cost visibility has outpaced token cost governance; the decisions of what AI capability to build and what it will cost to run are typically made by different people on different timelines, which is how organizations end up approving capabilities they can’t afford to scale.
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they → outpace → capabilities
Conclusion: Execution Is the Strategy
Across scaling, talent, change management, and now cost, the same pattern holds.
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pattern → hold → ?
Adaptovate’s conclusion: the organizations that close this execution gap won’t be the ones with the sharpest AI strategy.
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that → close → strategy
They’ll be the ones that treat execution capacity as the primary design problem, not an afterthought.
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that → treat → problem
The report includes a four-question framework executives can use to pressure-test their own organization’s readiness:
- Where will the business build its own AI capability, and where will it buy it?
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it → include → it
Category-by-category clarity on build-vs-buy prevents ad hoc, inconsistent decisions later.
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clarity → build → decisions
- Which parts of the business are expected to produce materially more output, rather than simply work with new tools?Distinguishes real productivity gains from surface-level adoption.
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parts → expect → adoption
- Which workflows specifically need to run leaner — not just which individuals need to be more productive, but which processes need fewer people or fewer steps?
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processes → need → people
- Who has the authority to track progress against these decisions and clear blockers when they appear?
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they → have → blockers
Without a named owner, even a good plan has no mechanism to move.
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plan → name → mechanism
…and 24 more, not listed.
The Aporia
