40% of companies ready to shut down AI-agent projects — three lessons from digital directors

Gartner forecasts that by 2027, 40% of corporate AI agent experiments will be shut down. Three digital transformation leaders who achieved real results explain why most fail — and how to avoid it.

Why Agents Don't Take Off

Hype around autonomous AI systems has led many companies to launch agents simply for the sake of implementation. Without a clear link to business metrics, such projects inevitably die at the first ROI check. A typical scenario: the pilot looks convincing in a demo, but in production the agent encounters dirty data, fragmented systems, and employees who don't understand why they need it. The result is a quiet project shutdown and a return to Excel or email chains. Gartner sees this scenario as standard for 40% of companies that launched agents in 2024–2025.

Three Lessons from Those Who Succeeded

Regardless of industry — finance, logistics, retail — successful implementations share one pattern: companies started not with technology, but with a problem.

• Specific business problem instead of "AI for everything." Agents were launched to address one pain point: automating invoice processing, monitoring warehouse inventory, initial processing of customer requests. Attempts to build a universal agent ended in chaos.
• Data in order before launch. No agent works more accurately than the data it receives. Successful teams first conducted an audit and cleanup of sources, only then deployed the agent. On average, this phase took 4 to 8 weeks — but it was precisely this that determined the difference between an agent that works and an agent that hallucinates.
• Employees engaged from day one. Teams that understood the agent removes routine and speeds up their work supported implementation. Where changes were handed down from above without explanation, employees found ways to bypass the agent or deliberately sent it non-standard requests.

"The most common mistake is to buy an agent and then figure out what to do with it.

The right order is reverse: find a problem that causes pain every day, and only then see if the agent solves it," says one of the interviewed directors.

Infrastructure Matters More Than Model

The third systemic reason for failures is technical. Agents deployed on top of poor data architecture begin to hallucinate, hang, or return incorrect results with non-standard input formats. One director describes a typical scenario: "We spent three months configuring the agent instead of three weeks because our internal APIs weren't documented. The agent didn't know what to request or in what format." Successful teams invested time in unifying the API layer and organizing data sources before launching the agent — this cost more upfront, but reduced the cost of production errors by several times.

What This Means

A wave of shut-down AI projects is inevitable — too many companies entered the technology without a clear plan and without infrastructure. But those who first determined the problem, got their data in order, and explained the meaning of changes to their team won't be affected by this wave. The gap between companies where agents work and those where they quietly died will only grow.