When AI investments don't deliver, leaders often blame the technology, vendor, or data. The real issue is a "capability gap"—most users lack the specific knowledge to use the tools effectively. This misdiagnosis leads to costly, ineffective technology changes instead of addressing the core human skill problem.
Research shows cognitive, accuracy-based skills (like judging if an AI-generated draft is defensible) erode far more quickly than procedural skills (like running a workflow). This means teams lose their most critical risk-management capability—the ability to spot a plausible but incorrect AI output—first.
AI provides the most significant time savings on infrequent but critical tasks like annual payer dossiers or periodic safety updates. Because these workflows aren't performed daily, the team's ability to execute them decays rapidly from non-use. The most impactful applications are paradoxically the most likely to be forgotten.
Companies mistakenly treat AI training as a project with a completion date. In reality, AI capability is a depreciating asset with a measurable rate of decay. Budgets must shift from funding one-off "ignition" events to funding continuous maintenance to prevent inevitable skill loss and wasted investment.
While AI skills and knowledge decay over time, an employee's confidence often decays slowest of all. The real danger isn't an employee who knows they are unsure, but one who is certain about an AI process or rule that is now outdated. This "confident incompetence" creates significant compliance and safety exposure.
While well-intentioned, AI champion networks are often staffed by volunteers juggling these duties on top of their full-time roles. They are asked to outrun a relentless capability decay curve in their spare time, which is unsustainable. This model cannot systematically address the hardest, riskiest questions or keep an organization current.