Does the organization want to accelerate growth, differentiate the customer experience, improve decision quality, or create a new area of value? A clear direction matters when looking for AI use cases. Without it, the search can produce disconnected ideas for efficiency. Use case selection should be treated as the first step in organizational AI transformation.
Expressing strategic needs as measurable value hypotheses clarifies what is expected of each application. These needs can then be mapped to value streams, decision points, and the capabilities required.
In making these connections, I suggest looking at important moments in the experience of customers, employees, or managers. Where is information lost, a decision delayed, the range of options narrowed, or expertise prevented from reaching further? These questions help reveal what gap AI could fill beyond saving time.
Candidate use cases should be assessed for strategic fit, expected value, data readiness, risk, capability needs, and scalability together. An idea that looks valuable may not yet be ready for implementation if employees cannot define the problem or review the output. In that situation, investment in technology and capabilities needs to be planned together.
A use case portfolio can seek a balance between short-term gains and long-term transformation. Some applications improve existing work, while others rebuild workflows from the ground up. A small number of strategic applications create new products, services, or business models. How these three horizons fit into the organization's AI agenda should be a deliberate choice.
Ask yourself
- Which strategic outcome can we take to a different level with AI?
- What value can we create for customers or employees that is not possible today?
- Do we have the human capabilities this use case requires?
