KPMG Global Tech Report 2026: What the AI Adoption Data Tells Business Leaders

KPMG surveyed 2,500 tech executives across 27 countries for their Global Tech Report 2026. The headline findings are optimistic. The detail underneath is considerably more interesting.

Only 11% of organisations are fully scaled on AI right now. Half believe they'll get there by December. That's either extraordinary ambition or a significant disconnect from reality, and the report doesn't really push on which one it is. What makes that figure harder to defend is that 62% of the same respondents expect to improve by just one maturity level in 2026. Those two numbers don't sit comfortably next to each other.

Two years of pilots and steering groups have produced a situation where most businesses are still experimenting but calling it transformation. Before setting targets for this year, it's worth being honest about whether your ambition is grounded in evidence or driven by not wanting to be the person in the room who sounds behind.

That said, the pace of model improvement is genuinely compressing timelines in ways that weren't predictable twelve months ago. The scepticism is warranted; however, the window is not closed.

The gap between average and excellent is not about budget

The survey puts average tech ROI at 2x. The top 5% of organisations are hitting 4.5x with lower relative investment. That difference doesn't come from better tools or bigger teams. It comes from tighter governance, actual decision-making, and execution that follows through rather than stalls at implementation.

Early adopters are also outperforming on returns, achieving 2.2x ROI against 1.4x for late movers. The gap between moving early and waiting for certainty isn't theoretical. It shows up directly in the numbers, and it's widening.

Smaller organisations are also outperforming their larger counterparts, averaging 3.6x ROI. Fewer silos and faster approvals account for most of it. The mistake many growing businesses make is importing enterprise-scale processes before they need them, which tends to eliminate the structural advantage they actually had.

55% of tech executives cannot explain what AI is delivering

That figure sits quietly in the report without getting the attention it deserves. These aren't organisations failing to adopt AI. Many are seeing real results. The problem is that half of them can't articulate those results in terms that mean anything to the people controlling the budget.

Standard ROI metrics were built for a different set of investments. They miss risk reduction, decision quality and cash flow improvements. 51% of tech executives in the survey acknowledge that legacy processes frequently contribute to poor ROI on tech investments, which means the measurement problem and the infrastructure problem are often the same problem wearing different clothes. If the KPIs haven't been updated, the business will keep underselling what's working, and that eventually shapes the wrong investment decisions.

Tech debt is a leadership decision, not a technology problem

69% of organisations made compromises on security, scalability and data standardisation to move fast. 63% say fixing those compromises is now slowing down the next phase of investment.

The high performers in this survey carry lower maintenance costs because they dealt with the underlying infrastructure before building on top of it. The organisations still working around unresolved debt aren't facing a technical challenge. They're facing a conversation that keeps getting deferred because it's uncomfortable. The data suggests that deferral has a compounding cost.

Agentic AI is not on the horizon. It's already being deployed.

88% of organisations are already investing in agentic AI. Within two years, digital workers are forecast to account for 36% of core tech team capacity, up from 28% today, while both permanent staff and contractor numbers are expected to decline. That shift is happening now in the organisations that are paying attention.

Zack Kass, previously at OpenAI, frames it well. Competitive advantage has moved away from access to models and towards the discipline to deploy them. Every significant workflow needs an owner, a default agent where appropriate, and a plan for when it goes wrong. Organisations without that conversation underway aren't being cautious. They're losing ground.

The workforce story is quieter than the headlines suggest

The report projects modest reductions in permanent roles, not the displacement scenario that fills column inches. High performers are actually retaining more permanent staff than average while moving faster on AI adoption than their peers.

The shift is in the shape of work rather than the volume of it. Managing agents rather than doing what agents now do. The capability that becomes critical is judgement, knowing when to trust an output and when to override it. That's harder to develop than most organisations currently plan for, and the gap tends to show up at the worst possible time.

Four things that really matter this year

Start with the data foundation, because everything else depends on it. AI is only as good as what sits underneath it, and modernising the stack while retiring systems that no longer earn their place is a prerequisite, not a phase you come back to later.

Governance needs to be built before scale, not after. Clear ownership, pre-deployment testing, and accountability at model level. The organisations that do this early spend less time managing avoidable failures and considerably less money explaining why they happened. If you're building this before you scale, 360 Strategy provides AI consulting in Scotland to help leadership teams get the governance framework right before the scale phase begins.

At some point the pilot phase has to end. Experimentation is cheap and necessary; however, staying in that mode indefinitely has real costs in competitive position and internal credibility. The report's data suggests the window for catching up is shortening, and it's shortening faster than most organisations currently assume.

Which leaves the communication piece, and it matters more than it gets credit for. If the board doesn't have a clear picture of what AI is actually delivering, that's not their gap to close.

Ready to build your AI governance framework before you scale? Book a 15-Minute Clarity Call.

Still queuing, or already moving?

The organisations doing this well aren't smarter or better resourced. They simply made earlier decisions, built better foundations and followed through when it got complicated. The gap between them and everyone else is mostly execution.

2026 is likely the year that gap becomes difficult to close. Most businesses are still queuing outside the Intelligence Age, convincing themselves that patience is strategy. The ones already inside aren't waiting to see how it plays out. The question worth sitting with is which side of that door you're actually on.

Mark Evans is the founder of 360 Strategy, a marketing strategy and AI consultancy based in Scotland.

Source: KPMG Global Tech Report 2026—Leading in the Intelligence Age.