How to Identify High-Value AI Use Cases

Start with business friction, not technology

The fastest way to create a weak AI backlog is to ask every department what it wants to do with AI. A better starting point is to identify where work is slow, repetitive, inconsistent, information-heavy or difficult to scale. The use case should be expressed as a workflow and outcome before it is expressed as a model or agent.

• Where do people re-enter or reconcile information?
• Where do queues, handoffs or review cycles create delay?
• Where do users repeatedly search for the same internal knowledge?
• Where does quality depend on reading, classification or summarization at scale?
• Where do teams spend time on routine follow-up or administration?

Define the workflow boundary

A useful candidate has a trigger, inputs, users, systems, decisions, exceptions and a completed outcome. If the workflow cannot be described from trigger to outcome, it is usually too early to estimate feasibility or value.

Separate the high-volume path from rare exceptions. AI may improve one part of the workflow without replacing the entire process.

Measure the current state

Prioritization improves when the current process has evidence. Useful baselines can include annual volume, cycle time, labor effort, rework, error rate, backlog, service level, conversion, customer effort or risk events. The goal is not to force every problem into a dollar value; it is to make improvement measurable.

Score feasibility with the same discipline as value

An attractive business case can still be a poor first AI project. Review whether authorized data exists, systems are accessible, representative evaluation cases can be created, users will participate, and failures can be contained.

• Data authority and quality
• Supported APIs or integration paths
• Representative test cases
• Human review and escalation
• Security, privacy and contractual constraints
• Latency and cost tolerance
• Named operating owner

Prioritize for learning as well as value

The best first use case is often not the largest theoretical opportunity. A bounded workflow with good evidence can teach the organization how to evaluate models, integrate systems, manage human authority and operate AI safely. Those capabilities can make later projects faster and more reliable.

Use a small proof when a key assumption is uncertain

If one or two technical assumptions dominate the decision, test those assumptions directly. A retrieval proof can test whether approved documents support grounded answers. An agent prototype can test whether a model can select tools reliably within a bounded task. A document proof can test extraction quality on representative samples.

The proof should end with a decision, not an indefinite pilot.

What should not be prioritized first

• Unbounded “AI assistant for everything” concepts
• High-risk decisions with unclear human authority
• Workflows with no baseline or acceptance method
• Projects that depend on inaccessible or unauthorized data
• Use cases with no operating owner
• Automation where deterministic rules already solve the problem more reliably

Move from use-case list to roadmap

A roadmap should sequence opportunities by evidence and dependency. Some initiatives require data cleanup, permissions, API work or governance before a proof is useful. Others can move directly into a bounded prototype. The roadmap should identify what must be true before investment increases.