Use Case Prioritisation & Portfolio Management

Most organisations can identify far more potential AI use cases than they can realistically pursue. The challenge is deciding where to focus. Effective prioritisation considers multiple factors: the potential business value of each use case, the feasibility given your current data and technical capabilities, the risk profile, the time to value, and the strategic alignment. A common framework plots use cases on two axes - business impact versus implementation difficulty - and targets the high-impact, lower-difficulty quadrant first. These early wins build organisational confidence and fund more ambitious projects. Portfolio management means thinking beyond individual use cases to the overall mix. You want a balanced portfolio: some quick wins that demonstrate value and build momentum, some medium-term projects that address significant operational needs, and a small number of ambitious bets that could be transformative if they succeed. Over-indexing on moonshot projects leads to disillusionment when none of them deliver quickly enough. Over-indexing on small wins means you never build the capabilities needed for more significant transformation. Regular review and reprioritisation are essential because both business conditions and AI capabilities change rapidly. The use case that was infeasible last year might be straightforward today, and the one that seemed urgent may have been overtaken by events.