The Confidence Gap

Here's a pattern worth sitting with. Eight in ten people say AI has made them individually more productive. Half say it's helping them make better decisions. By most measures, adoption has moved from experiment to habit. And yet, at the organisational level, the picture barely shifts.

The Finding

McKinsey's most recent Global Survey on AI found that only 37% of organisations report any positive EBIT contribution from AI use, essentially flat on last year, even as the share of companies scaling AI across the enterprise climbed to 44%. Individuals feel the benefit. The business case, for most, is still unproven. It's tempting to read that as a productivity story. It's really a trust story.

The Stanford AI Index's 2026 report on workplace adoption backs this up from a different angle. Fifty-eight percent of employees globally now use AI on a semiregular or regular basis at work. But only 53% say they trust it for work purposes, and in North America and Europe specifically, trust drops to somewhere between 40% and 48%, noticeably behind usage.

Who Feels It Most

The confidence gap isn't just uneven across business functions, it's uneven by seniority too. McKinsey's same survey found that midlevel managers and individual contributors report AI-related strain far more often than executives do, 47% versus 31%. The people closest to the day-to-day work of turning AI outputs into decisions are the ones feeling the friction most, while the people setting AI strategy are the least likely to notice it.

Why It Happens

People trust their own use of a tool more than they trust what the organisation does with its output. That gap between using AI and trusting it is the same gap McKinsey is seeing between individual productivity and enterprise financial impact. People are comfortable with AI doing the work. They're a lot less comfortable vouching for what comes out the other end, especially once it leaves their own screen and becomes someone else's report.

Governance lags behind strategy and training almost everywhere. The Stanford data adds one more layer worth noting: even where organisations say they support AI strategy and AI literacy, governance tends to lag behind both. Across almost every country surveyed, respondents rated their organisation's AI governance, the policies, monitoring, and accountability that make outputs trustworthy at scale, lower than its strategy or training. Strategy is easy to declare. Trustworthy data infrastructure is harder to build, and it shows.

The gap isn't evenly spread across a business, either. McKinsey's data shows strategy and corporate finance functions reporting some of the weakest revenue gains from AI of any business function tracked, just 26%, only IT scoring lower. That's a striking result given how much of the current AI investment conversation is happening precisely in finance and strategy teams. The functions doing the most talking about AI's potential are, so far, among the ones seeing the least of it show up in the numbers.

What the Research Shows

None of this is really about AI. It's about what happens when a lot of people start generating numbers quickly, without a shared, agreed answer to "which version is correct." McKinsey's high performers, the small group of organisations actually seeing meaningful EBIT impact from AI, aren't distinguished mainly by how much they spend. They're distinguished by discipline: they're roughly twice as likely to say senior leadership actively demonstrates ownership of AI initiatives, and to have defined processes for measuring impact in the first place. In other words, they've done the unglamorous work of agreeing what "good data" means before they scaled anything on top of it.

What Fixes It

Agree what good data means before scaling anything on top of it. That's a solvable problem. It's also, going by how many people hesitate when asked how confident they are in the numbers they present or receive, a more common one than most organisations would like to admit.

Close the gap deliberately, not by accident. If individual confidence in AI keeps outpacing organisational confidence in data, the gap doesn't close on its own. It tends to show up later, in decisions made on numbers nobody quite trusted enough to double-check.

Worth asking, honestly: if someone challenged the number in your next report, how sure would you be? If any of this sounds familiar, there's a five-minute assessment on our site that shows where an organisation actually sits on data maturity, a useful starting point before the next number gets challenged.

Sources & References

McKinsey & Company. Global Survey on the State of AI, May–June 2026. Stanford HAI. AI Index Report 2026, drawing on University of Melbourne / KPMG International workplace survey data.