Research · AI Strategy & Adoption

Use Case Prioritization

Every organization has more AI ideas than capacity to deliver them. Use case prioritization is the discipline of choosing the few that will produce measurable value, can be delivered safely and teach the organization something it can reuse.

Why it matters

Scattered pilots consume budget and attention without adding up to results. Many never reach production because they lacked a clear owner, reliable data or a problem worth solving. A deliberate portfolio makes it easier to fund the right work, stop the wrong work and show progress to leadership.

How to evaluate a use case

• Value: the size of the problem in revenue, cost, risk or customer terms, and how it will be measured.
• Feasibility: availability and quality of data, integration effort and technical maturity.
• Risk: impact on customers, employees and compliance if the AI is wrong.
• Readiness: a business owner who wants it and a process that can absorb the change.
• Reuse: whether the data, platform or pattern can serve other use cases.

Good first candidates

Strong early use cases usually have high volume, clear inputs, a measurable outcome and a person who can check the output. Examples include drafting responses for service agents, summarizing documents for review, classifying incoming requests and assisting developers. Fully automated decisions about customers or employees are usually poor first choices because the risk and regulatory exposure are higher.

Common pitfalls

• Choosing use cases because a vendor demo looked impressive.
• Scoring value without a baseline to measure against.
• Ignoring the data work needed to make a use case feasible.
• Keeping pilots alive after they miss their targets.

How to get started

• Collect candidate use cases from business units using a short, standard template.
• Score them on value, feasibility, risk and readiness.
• Fund a small portfolio with a clear success measure for each.
• Review the portfolio quarterly and stop what is not working.