The survey
TEKsystems polled 782 technology and business decision makers, ranging from C suite executives to directors and managers with direct authority or influence over digital transformation, across the US, Canada, China, India, Japan, Singapore, Belgium, France, Germany, Ireland, the Netherlands, and the UK. The result is the firm's State of Digital Transformation 2026 report, and its headline framing is stark: the gap between organizations that treat transformation as core strategy and those that treat it as a side project is widening, not narrowing, even as AI spending climbs across the board for both groups simultaneously.
One line from a financial services CIO quoted in the report captures the tone: when every workflow has an AI counterpart, transformation stops being a program and becomes the operating system. That is a meaningfully different claim than the usual digital transformation boilerplate, and the survey's own data backs it up with numbers that separate leaders from laggards on nearly every dimension measured, from investment confidence to workforce planning to the pace of enterprise wide rollout.
Leaders pull away on confidence, not just adoption
The most striking figure in the report is a confidence multiple rather than an adoption percentage: digital transformation leaders report being 2.5 times more confident that their 2026 investments will meet ROI expectations than laggards report for their own. That gap in confidence is arguably more consequential than any single adoption metric, because confidence drives the next round of budget approval, and budget approval drives the next round of capability, in a compounding cycle that laggards find themselves structurally locked out of year after year.
Seventy six percent of digital leaders expect increased 2026 spending compared with 55% of laggards, and that spending gap will only widen the capability gap further. Once a leader organization can point to measurable wins from last year's investment, the next budget conversation gets easier. Once a laggard organization cannot point to those wins, the next budget conversation gets harder, which is exactly the dynamic that turns a modest early lead into a durable structural advantage over a few budget cycles.
ROI timelines are stretching, not shrinking
Despite all the acceleration narrative around AI, the survey found ROI expectations are actually lengthening. Only 27% of respondents now expect to see ROI within six months, down sharply from 42% who expected the same in 2025. That thirty five percent relative decline in six month ROI confidence, occurring in the same year AI spending is climbing, suggests organizations are recalibrating toward more realistic payback timelines after a year of pilots that took longer to prove out than initial hype suggested.
We read this as a healthy correction rather than a warning sign, provided CIOs actually communicate the recalibration upward. The organizations at risk here are the ones whose leadership signed off on 2025 stye six month payback promises and have not yet reset that expectation with their own board or investors. A CIO walking into a Q1 review still promising six month payback on an initiative the data says now realistically takes longer is setting up a credibility problem that has nothing to do with whether the technology actually works.
What is actually blocking the laggards
Enterprise wide AI adoption doubled year over year in the survey, from 12% in 2025 to 24% in 2026, which sounds like broad progress until you look at what is holding the rest back. Environmental complexity, meaning fragmented systems, inconsistent data, and legacy architecture, ranks as the top blocker at 38%, followed by budget overruns at 30% and workforce upskilling gaps at 26%, and all three sit squarely in the category of problems that predate the current AI cycle entirely.
None of those three blockers are primarily technology problems, they are organizational and architectural debt accumulated over years, and that is precisely why simply buying more AI tooling does not close the gap for laggard organizations. A company with a genuinely fragmented data environment cannot shortcut its way to enterprise wide AI adoption no matter how much it increases its model spend, because the blocker sits underneath the AI layer entirely, in the same legacy ERP and integration debt this publication covers constantly, and that debt does not resolve itself just because a new model gets bolted on top of it.
The strategy gap is bigger than the technology gap
Perhaps the most useful number in the whole report is this one: 82% of digital leaders embed transformation as core business strategy, compared with just 34% of laggards. Digital leaders also plan workforce upskilling at more than double the rate of laggards, 76% versus 37%. Both of those are organizational commitments, not technology purchases, and they explain why laggard organizations with comparable technology budgets still fall behind year over year.
Our takeaway for CIOs building 2027 plans is to treat this report as validation that the structural and cultural work, embedding transformation into strategy, funding upskilling at scale, matters at least as much as which AI vendor gets selected. Organizations chasing the leader cohort by matching their tooling spend alone, without matching their organizational commitment, are very likely to end up funding the same environmental complexity and budget overrun problems that are already holding today's laggards back.
The technology adoption numbers worth tracking
Beneath the strategy level findings, the report includes a few technology specific figures worth pulling out on their own. Forty two percent of respondents report enterprise wide adoption of cloud native platforms and infrastructure as a service, while 37% are running generative AI at scale and another 17% remain in pilot phase, meaning more than half of surveyed organizations still have not moved generative AI past the pilot stage entering 2026. Fifty seven percent cite employee productivity improvement as their top AI use case, ahead of any customer facing application.
That productivity first pattern is consistent with what we hear from PE backed operators directly: the fastest, most defensible AI wins right now are internal, not customer facing, because internal workflows are easier to measure and carry less brand risk if something goes wrong during the rollout. CIOs deciding where to spend limited 2027 AI budget should weigh this data point seriously before chasing a flashier customer facing use case that the survey suggests most peer organizations have not yet gotten right themselves.
