This momentum is exciting, but also overwhelming. With limited capacity, technical debt, and governance still evolving, teams often face the same question: Where do we begin? According to MIT Sloan Management Review, legacy infrastructure and mounting tech debt remain core barriers to scaling AI efforts effectively.
Enthusiasm without focus leads to scattered pilots, shallow proof-of-concepts, and siloed tools that never scale. To move beyond experimentation, we need a smarter way to decide: which use cases should we prioritize — and why?
From possibilities to priorities
To make meaningful progress, organizations need a clear and shared lens for evaluating which AI use cases deserve attention now — and which can wait.
