A decade ago, "digital transformation" was the label attached to a wave of large enterprise initiatives that promised to modernize legacy processes with new technology. Many of those initiatives failed to deliver the promised results, and the reasons why are strikingly familiar to anyone watching how AI transformation projects are unfolding now.
The recurring failure modes
Digital transformation projects commonly failed when they were framed as technology initiatives rather than operational ones-when a new system was deployed on top of an unchanged process rather than the process itself being redesigned around what the new technology actually made possible. They failed when executive sponsorship was strong at launch but faded before the multi-year change management work was complete. And they failed when success was measured by technology deployed rather than outcomes actually changed.
How this is repeating with AI
The same patterns are visible in a meaningful share of current AI transformation initiatives: an AI tool layered onto an existing workflow without redesigning the workflow around it, executive attention that peaks at the announcement and fades during the harder implementation phase, and success metrics that track deployment-seats licensed, pilots launched-rather than measurable changes in cost, speed, or quality. The technology is different, but the organizational failure modes are largely the same ones that undermined the previous transformation wave.
What separates the successful cases
Organizations that have gotten real value from AI transformation tend to have redesigned the underlying process, not just added a tool to the existing one, sustained executive attention past the initial announcement into the harder work of change management, and measured success in outcomes-time saved, error rates reduced, cost avoided-rather than adoption metrics that can look good without producing real value.
The lesson worth carrying forward
None of this is a reason for skepticism about AI's transformative potential in specific well-chosen use cases. It's a reason for skepticism about transformation efforts that repeat the structural mistakes of the last technology cycle while assuming the new technology alone will produce a different outcome.