​Enterprise AI Needs A New Change Management Model

Kousik Rajendran is Co-Founder & CEO of Aivar Innovations.​

Enterprise AI is moving quickly, but getting an agent from prototype to production is still where many organizations are faltering.

In my view, there are three reasons: measurable ROI, trust and velocity. Deloitte found that while 74% of companies plan to deploy agentic AI within two years, only 21% report having a mature governance model for AI agents. That gap matters because agentic AI changes faster and behaves less predictably than traditional software, making conventional, linear change management insufficient. Organizations need a model that can continuously balance business outcomes, trust and speed.

In the traditional world, you develop code for a particular problem, deploy it and, because it is deterministic and relatively static, the way it solves the problem does not change until you release a new version. With agentic AI, the underlying models can change with very little notice. The API might remain the same while the behavior of the system changes underneath it.

Because of this, change management has to move dynamically with the technology and with the customer. It cannot be a linearly progressing, one-way process. It needs multiple loops structured around a path that is continually being revisited.

That is the thinking behind TRACE (Trust, Route, Adopt, Calibrate and Evolve), a change management approach for the Agentic era.

Trust Has To Work In Both Directions

The first challenge is that we generally see two types of users: people who over-trust AI and people who under-trust it.

Over-trust is already visible. People are sharing data they once might not even have sent through an API. Sensitive financial, HR or other classified information can get pushed into AI systems without enough thought about the implications.

Under-trust is the opposite problem. A leader or employee may simply decide, “The agent cannot work for this use case,” when it actually could.

The Trust stage begins with ownership.