The Capability Gap
Vishal Sachar
Co-Founder & CEO of CLRT
Almost every organisation now uses AI somewhere. Almost none capture real profit from it. The distance between those two facts is the entire opportunity, and the entire risk, in this market.
The picture from McKinsey's State of AI is consistent and stark. By late 2025, 88 percent of organisations reported using AI in at least one business function, up from 78 percent a year earlier, and 62 percent were at least experimenting with agents. Yet only 39 percent reported any impact on enterprise EBIT, and most of those put it below 5 percent. Only about 6 percent cleared the bar of significant, measurable value. Adoption is nearly universal. Value is rare.
Set that against the local mandate and the exhibit writes itself. A government target sits above the market, pulling 295,000 companies toward agentic AI. The capability gap sits below, where adoption has happened but impact has not. The companies are caught in between, told to move, unsure how to move in a way that actually pays.
The reason for the gap is rarely the technology. Organisations buy tools and run pilots. What they skip is the harder work underneath: choosing the right workflow, redesigning it around the agent rather than bolting the agent onto the old shape, and verifying that the output can be trusted in production. A pilot that impresses in a demo and never reaches production is not adoption. It is theatre with a licence fee.
The firms that close the gap will not be the ones with the best model access. Everyone has that. They will be the ones who treat AI as an operating change to be designed, governed, and measured, not as a product to be purchased.
Adoption is the easy half. Impact is the half that pays, and it is still mostly unclaimed.
A deeper dive
McKinsey's own data points to where the gap closes, and it is not where most spend goes. Across the twenty-five attributes the survey tested against bottom-line impact, the single strongest correlate was the redesign of workflows. The high performers, that 6 percent, were more than three times as likely to use AI for transformative change rather than incremental efficiency, and most of them rebuilt their processes from scratch rather than layering AI onto the existing shape. The lagging majority did the opposite: they automated the current process and wondered why the profit line did not move. There is a mechanical reason. If you speed up a step that was never the constraint, you generate local efficiency that the bottleneck immediately absorbs, and enterprise EBIT never sees it. Value shows up only when the redesign removes the actual constraint, which is why workflow redesign, not model choice, is the variable that separates the 6 percent from everyone else.
Work with CLRT
Which side of the gap is your business on? A CLRT diagnostic places you precisely, and shows you the one workflow where a redesign would actually reach the bottom line.
Vishal Sachar
Vishal Sachar is the Co-Founder and CEO of CLRT, where he helps UAE businesses make sense of applied agentic AI and put it to work. He writes on agentic systems, AI governance, and the economics of automation. Reach him at vishal@clrtstudio.com or on LinkedIn.
