A manager at the company started building with AI last year. He described what he wanted, iterated with the model, clicked through the result, and fed back the cases he had forgotten until the behaviour was right. An afternoon of that produced something that ran. He handed the branch to engineering and considered the work finished.
The branch sat for months, even though nobody was obstructing it. Review capacity hadn't changed, the architect's week hadn't changed, and the definition of "ready to publish" was the same as it had always been, with the same queue sitting in front of it.
An afternoon of work entered a pipeline, and the pipeline did what it was designed to do: absorb it at its own pace. The manager was measuring whether the thing worked. Engineering was measuring whether it could be operated, secured, tested and supported. Both were right. And the afternoon he saved bought the company nothing, because the step that got faster was never the step that set the delivery date.
That is the problem this article is about: AI accelerates the step in front of the person using it, and the steps behind that person stay exactly as slow as they were. The aggregate numbers say it is happening everywhere.
spend doubled while integrated workflows halved
Two numbers from the same survey. ServiceNow and ThoughtLab asked 4,500 executives across nineteen countries for their 2026 maturity index, and the third edition lets them compare against last year.
AI spend rose 110% in a single year. The share of organizations reporting streamlined, integrated workflows across business functions fell from 30% to 16%.
Spend doubled. Integration halved. The report's own explanation is fragmented platforms carrying a new wave of agent sprawl on top of them, which is a polite way of saying the tools multiplied faster than the plumbing.
It is the only integration figure in this year's research that has a previous year to compare against, and it runs backwards.
where the saved hours go
Follow it one step at a time, because each step is defensible on its own and the outcome is still near zero.
You buy licences and hand out tools. Reasonable enough; the per-seat cost is trivial against a senior salary. An individual who used to need three days to produce a piece of work now needs three hours. Also real; I have watched it happen at my own desk. But the workflow still allocates three days, and the ticket still waits for the weekly planning call. Two approvals sit in front of deployment, both sized for the latency of a human who had to read a document and think about it. So the individual, done by Tuesday, picks up the next ticket, and then the one after that. More work arrives at those two approvals every week than before. The approvals process it at the same speed they always did. Everything they cannot absorb sits and waits and becomes a backlog.
The company paid for speed and got a longer queue.
BCG's 2026 survey of 11,749 employees puts a number on the gap: 61% have limited or no guidance on what to do with the time AI saves them, and 45% are not reinvesting it into anything strategic. The hours are real. They just have nowhere to land.
In the same ServiceNow data, 59% are past piloting agentic AI and only 9% report meaningful progress on autonomous multistep workflows. Zero percent have built anything resembling a cross-functional, self-improving agentic operating layer. Only 16% have replaced fragmented legacy systems with an integrated platform, and 41% still name siloed data as a major obstacle. Cloudera's survey of 1,270 IT leaders (vendor-run, so weigh it accordingly) has 79% saying they cannot access all the data their initiatives need and only 30% with fully integrated sources. Adoption figures like these are worth reading carefully, because three studies measuring agentic adoption this year returned 17%, 23% and 59% by counting three different things.
Everything above comes from the people who bought the tools. Valtech asked the people who use them: a thousand professionals already working with AI daily. Asked where the most value would come from, 4.3% said more pilots. Integration of tools and data ranked first.
what 95% actually bought
Five percent of organizations in the ServiceNow data are redesigning work. The rest are pointing agents at workflows that already existed, in the shape they already had.
Deloitte's January 2026 survey of 3,235 leaders lands on the same spot from a different angle: 84% have not redesigned jobs or the nature of work around what AI can now do. My favourite pair in the whole report: 53% considered fewer management layers and smaller, more autonomous teams, and 16% actually moved to them. The idea got as far as a slide.
McKinsey have two surveys on this. Their State of Organizations report, with 10,018 executives: 88% deploying, 81% reporting no meaningful bottom-line gain. In their mid-2026 follow-up, which asks the same questions as last year, use rose to 89%. The share saying AI moved their profit at all stayed at 37%. The share getting significant profit from it stayed at 6%. What separates that 6% is not budget. Roughly three-quarters of them redesigned their workflows, against one-quarter of everyone else. McKinsey's own summary: conviction in AI is growing faster than the returns anyone can attribute to it.
All of that is self-report, and self-report on productivity is generous. Which is why the software engineering data matters more than it looks.
Faros AI, which sells engineering analytics and has a stake in this conclusion, instrumented 22,000 developers and compared each team's lowest-adoption quarters against its highest. Developers finished 34% more tasks. Each piece of work then waited 441% longer for someone to check it before it could go out. And the speed at which finished work actually reached users did not change.
That is the individual getting faster and the organization standing still, measured with telemetry rather than a questionnaire. Writing the code got cheap. Review, QA and deployment stayed exactly as expensive as they were, and everything that got written piled up in front of them. Faros is blunt that tightening review is the wrong response, because review was never the thing that changed.
DORA, a non-commercial research programme surveying close to five thousand developers, states the mechanism in one line: AI amplifies what is already there. Their 2024 edition put a number on the downside: for every 25-point increase in AI adoption, teams shipped 1.5% less and what they shipped broke 7.2% more often. More AI in an unchanged process did not mean more delivered. It meant slightly less, arriving slightly worse.
three moves that change the process
Decide where the saved hours go before you buy the tool. If an analyst saves six hours a week, somebody has to say what those six hours are now for. More time with clients. The project that has been waiting since March. One fewer hire next year. If nobody names it, the hours get absorbed back into the same job and nothing changes. "We got more productive" is not an answer to where the time went.
Count the steps, don't just speed them up. A three-hour task inside a five-approval chain is still a two-week task. The cheapest redesign move is deleting a handoff, and it is almost always resisted more than a six-figure platform purchase, because a handoff belongs to somebody.
Make each item cheaper to review. If the queue in front of review is the constraint, the fix is not more reviewers or faster ones. It is that each thing arriving needs less looking at. Put a human on the plan while the model is still writing it, so the implementation starts from something somebody already shaped. Have the repository check every incoming branch against its own architecture rules and refuse what does not pass. By the time a branch reaches a person, it has cleared both. Same reviewer, same week, far less to read per item. That is what got the manager's afternoon of work into production.
where the hours AI saved should land
The pattern in all of this is that AI speeds up the step a person does, and everything that step feeds into stays exactly as slow as it was. The spending is real, the individual gains are real, and the process is still shaped for the pace it had before.
So take one workflow you have already put AI into and answer two questions. Where are the saved hours supposed to go, and who decided that? If nobody can answer this, you bought speed for one step and a longer wait for everything after it.
That question is most of what an AI workflow audit does in its first week, and you can run it yourself on a whiteboard before anyone signs anything.
Original article: https://unlockedconsulting.ai/blog/hours-ai-saved-have-nowhere-to-go
