KPMG report urges AI investment discipline as tech maturity lags

The AI maturity gap is now the real story

KPMG’s Global tech report 2026 lands on a simple but uncomfortable point for anyone trying to deliver transformation under real budget and deadline pressure: enthusiasm is not the same as execution. Half of tech executives expect to reach top tech maturity in 2026, but only 11 percent say they are there today, which means most firms are still chasing the finish line while already spending like they have arrived. For consultants, auditors, and advisers inside a Big 4 firm, that gap is not abstract. It shows up in project plans that slip, in teams that keep reworking the same business case, and in leaders who confuse activity with measurable value.

What the report actually measured

The report is based on a survey of 2,500 tech executives across 27 countries, with respondents spread across Europe, Middle East and Africa at 43 percent, Asia-Pacific at 29 percent, and the Americas at 28 percent. KPMG says the research was conducted in 2025 and then widened to look at predictions for 2026 and beyond, which gives the findings a useful split-screen view: current execution gaps on one side, and the next phase of AI and technology change on the other.

That structure matters because the report is not just about where technology is headed. It is about how quickly plans can become obsolete before implementation even finishes. In professional services, where teams are often selling change while living through it themselves, that is a familiar problem. By the time a transformation program clears governance, secures funding, and gets through early testing, the market may already have moved, and the client may be asking for the next version of the same solution.

Why scattered AI bets are not enough

KPMG says organizations are moving beyond scattered AI bets and embedding AI into workflows and offerings. That shift sounds positive, but the report’s numbers show how hard it is to make that transition real. Seventy-four percent of respondents say their AI use cases deliver business value, yet only 24 percent achieve ROI across multiple use cases. In other words, isolated wins are common; repeatable value is still much rarer.

The high-performer data makes that point even more sharply. Only 2 percent of high performers report several disconnected AI projects and teams, compared with 34 percent of others. That is the kind of statistic that should make any transformation leader pause before approving another standalone pilot. The lesson is not that more AI is bad. It is that fragmentation is expensive, because every separate workstream brings its own governance, integration issues, and operating overhead.

For KPMG people, this is where the internal accountability lens matters most. If a team says tech value comes from execution, the question becomes: who owns the workflow change, who owns the control environment, and who owns the adoption metrics once the pilot is done? Those questions are especially relevant when utilization targets are tight and client deadlines leave little room for experimentation that never scales.

What high performers are doing differently

Guy Holland, Global Leader, CIO Center of Excellence, KPMG International, says the report is meant to provide a synopsis of what high performers are doing better than most, and a checklist for tech leaders who want to improve performance, emulate those high performers, and deliver higher ROI. That framing is useful because it pushes the conversation away from hype and toward operating discipline.

High performers appear to be doing a few things differently:

• They reduce fragmentation instead of multiplying pilots.
• They connect AI work to workflows and offerings, not just to demos or proofs of concept.
• They measure value more carefully, especially when a single use case is not enough to justify the broader program.
• They build cultures that welcome change, rather than treating adoption as a one-time training exercise.

Those behaviors sound obvious, but they are often the difference between a transformation that survives the first quarter and one that becomes a cautionary tale by the second. In a consulting environment, that can mean whether a client program becomes a reference case or a write-off. In audit, it can determine whether new technology strengthens controls and evidence or simply adds complexity to an already crowded process.

The workforce impact is part of the story

The report also makes clear that people issues are still sitting underneath the technology story. KPMG says fears about job security persist even as firms push harder on adoption, which is a reminder that AI rollouts are not just about systems and data. They are also about how employees interpret change, whether they trust the strategy, and whether managers explain what is actually being automated, redesigned, or retained.

That matters inside KPMG because the pressure does not stop at strategy decks. Consultants are expected to translate technology into client value fast enough to protect margins and keep projects moving. Auditors have to understand how tech maturity affects controls, risk, and financial outcomes, while advisory teams are often asked to defend why one platform or model deserves funding over another. When people worry about job security, the quality of leadership communication becomes part of the operating model, not just an HR issue.

The report’s emphasis on building a culture that welcomes change is therefore practical, not decorative. If teams do not understand why a tool is being introduced, how success will be measured, or what happens when the pilot ends, adoption slows and value erodes. That can create a double hit: more spend on technology and more strain on the people expected to make it work.

What this means for delivery teams under pressure

The most useful reading of the report for KPMG staff is that the market is moving away from novelty and toward evidence. A good transformation story now has to answer three questions quickly: why this project, why now, and how will we know it worked? That is true whether the work sits in audit innovation, client-facing consulting, or internal modernization.

It also changes what strong leadership looks like. The leaders who will separate successful delivery from expensive overpromising are the ones who can keep investment disciplined, cut through duplicate workstreams, and insist on metrics that survive scrutiny. In a profession built on trust, that is a meaningful shift. The firms and teams that can prove value across multiple use cases, not just one flashy pilot, will be better positioned as AI moves deeper into everyday workflows and the pressure to show real returns keeps rising.

The bottom line

KPMG’s report reads less like a celebration of AI momentum and more like a warning about execution drift. The winners are not the teams buying the most tools or announcing the most pilots. They are the ones turning technology into coordinated operating discipline, with clear ownership, measurable outcomes, and fewer disconnected bets. In a business where time, headcount, and credibility are all finite, that discipline is becoming the real test of maturity.