Successful AI deployment
Deloitte’s Tech Trends 2026 report landed last month. They zero in on five forces reshaping the enterprise: AI going physical through robotics, the rise of agentic AI and a “silicon-based workforce,” the infrastructure reckoning as cloud-first strategies buckle under AI economics, the rebuilding of tech organisations around human-agent teams, and the cybersecurity paradox of AI as both threat and defence.
But one statistic jumped out: only 11% of organisations have successfully deployed AI agents in production.
Gartner predicts 40% of agentic AI projects will fail by 2027.
This is consistent with a pattern emerging across multiple other studies. McKinsey found 72% of organisations have deployed generative AI, but only 26% report measurable productivity gains and only 1% of executives describe their AI rollouts as “mature.” Google reports just 3% of organisations are “highly transformed.” Asana’s research shows 67% haven’t scaled AI beyond isolated experiments. MIT found that while 40% have piloted LLMs, only 5% have actually embedded them into workflows.
The usual suspects get blamed. Immature technology. Inadequate training. Lack of executive sponsorship. These factors matter.
But what if the real barriers are psychological?
What if we’re optimising for technical deployment while ignoring the behavioural substrate that determines whether anyone actually uses the thing?
As the UK Behavioural Insights Team puts it: “The promise of AI can only be fulfilled by understanding how and why people think and act the way they do.”
Deloitte’s report identifies the symptom of disappointing adoption, the gap between pilot and production. But it doesn’t provide the mechanism to diagnose why adoption stalls in a specific organisation. Is it motivation? People just don’t see genuine value. Is it capability? Confidence gaps prevent even the most willing adopters. Is it trust or identity threat? Professionals derive status from expertise. AI use can feel like an admission that twenty years of skill-building wasn’t quite enough.
The technology is here and organisations are rushing to deploy. What strike me as missing is the human side, and decades of behavioural scientific research point us to solutions. The organisations that close the pilot-to-production gap won’t be those with the best technology. They’ll be those that diagnose the human barriers first. Behavioral Science has a huge role to play here.
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Deloitte Tech Trends 2026
