A steering committee approves the budget. A team of developers, data scientists and business representatives gets to work. Six months later, the model’s accuracy is impressive, but nobody uses the system.
This is not an isolated case. It is the most common pattern of failure in AI initiatives.
What is at stake
Anyone who approves an AI project is not investing in technology. They are investing in changes to workflows, decision-making processes and, sometimes, entire organisational structures. The algorithm is the means, not the goal.
When that relationship is reversed in day-to-day project work, the real risk begins. Not the risk of a poor F1 score, but the risk that the organisation will not adopt, understand or need the finished system.
The moment projects lose direction
In almost every larger AI initiative, there comes a point—usually between the first working version and the first real rollout—when the team stops talking about users and starts talking about features.
This is not a failure of the people involved, but a structural trap: the more technically complex an initiative becomes, the more the technology attracts attention. The business side loses touch. The steering committee receives progress reports about deployments rather than value realisation.
What it takes to stay on course
It takes someone who explicitly maintains the link between what is being built and why it is being built. Not as an adviser on the sidelines, but as part of project governance.
This is not a technical task. It is a leadership task—and it requires someone willing to ask uncomfortable questions while everyone else is refining the solution.