Successful AI implementation requires solving four issues in sequence: 1) Value Prioritization, 2) Decision Rights, 3) Data Governance, and 4) Incentive Architecture. Solving these out of order leads to wasted effort, such as redesigning incentives for tools that can't scale.
Vague answers like 'We're doing AI' are impossible for a board to govern because they can't be proven wrong. A genuine strategy presents a clear thesis with specific metrics, timelines, and financial projections, giving the board something concrete to hold leadership accountable to.
Asking business units for AI ideas results in them suggesting what they've seen from competitors or vendors. This approach imports another company's strategic context, which is likely a poor fit for your own, leading to mediocrity.
While every AI pilot may be individually justifiable, they often fail to be collectively coherent. A true strategy exists when initiatives build on one another, creating compounding value—like when clinical data feeds medical affairs AI. A collection of disconnected projects cannot be governed effectively.
Finance departments rightfully reject business cases that claim value from 'time saved' by AI. To be credible, the model must explicitly state how that saved time will be redeployed: to remove costs, increase throughput, or enable higher-value work. Without this, it's not a real financial model.
Employees don't adopt AI tools when the personal cost is immediate and visible, while the benefit is delayed, uncertain, and accrues to the organization, not their individual performance review. The solution is redesigning incentives, not more training.