A practical method for finding realistic AI opportunities in your business and ranking them by value, feasibility, data readiness and risk.
Start with problems, not technology
The most useful AI projects start from a specific, recurring problem: work that is slow, repetitive, error-prone or blocked by information that is hard to find. Starting from "where can we use AI?" tends to produce ideas that are impressive but not valuable.
Find candidate problems
Talk to the people doing the work. Look for tasks involving large volumes of text or documents, repeated decisions with clear rules and exceptions, searching across scattered information, or drafting similar content many times. Write each idea as a problem statement with the people affected and how often it occurs.
Score each idea
Rate every idea from 1 to 5 on four criteria. Business value: time saved, revenue, quality or risk reduction. Feasibility: whether current AI can do the task reliably. Data readiness: whether the needed data exists, is accessible and is of usable quality. Risk: the impact of a wrong output and any regulatory concerns.
Plot and shortlist
Place ideas on a grid of value against feasibility. High-value, high-feasibility ideas with good data and manageable risk form your shortlist. High-value but low-feasibility ideas may be worth revisiting as the technology or your data improves.
Check that AI is the right tool
Some problems are better solved with a process change, a simple rules engine or better search. If the task is fully predictable, conventional automation may be cheaper and more reliable.
Design a small pilot
Pick one shortlisted idea and define a pilot with a clear scope, a success measure, a baseline to compare against and a fixed timeline. Keep a person in the loop during the pilot and gather feedback from real users.
Decide what happens next
At the end of the pilot, compare results with the baseline. Scale it, adjust it or stop it. Stopping a pilot that did not meet its target is a useful outcome, as long as the lessons are recorded.
Key takeaways
- Start from specific, recurring business problems.
- Score ideas on value, feasibility, data readiness and risk.
- Confirm that AI is actually the right tool.
- Run a small, measured pilot before scaling.
If you want help running a use-case workshop or scoping a first pilot, our AI Consulting team works with teams at this stage.
AI Consulting Services- AI Risk Management FrameworkNIST · Frameworknist.gov
- People + AI GuidebookGoogle PAIR · Guidepair.withgoogle.com
- AI adoption (Cloud Adoption Framework)Microsoft Learn · Guidelearn.microsoft.com