To select and stop AI use cases well, write down before each pilot what success looks like and the condition under which you will stop. Then review every use case against those criteria at fixed decision points, and move budget from the ones that fall short to the ones that deliver.
Most organisations find it easier to start AI initiatives than to stop them. A pilot with no exit condition tends to keep running, using budget and team time without reaching production. The steps below show how to choose use cases with care and close the ones that no longer earn their place.
Why should you plan to stop AI use cases as well as select them?
An AI use case needs a clear way out because a pilot that runs without one rarely ends on its own. A use case is a specific situation where AI helps your business, such as forecasting demand or sorting incoming documents. Each one competes for the same data, people and budget. When weak use cases stay open, the strong ones get less attention. Stopping a use case is a normal portfolio decision, and it frees resources for the initiatives with a stronger case.
How do you select the right AI use cases to start with?
You select AI use cases by scoring each idea on business value and feasibility, then starting with those that rank high on both. Business value covers the objective the use case serves and the return it can bring. Feasibility covers the data available, the skills in your team and how well the use case fits existing processes. Plot the ideas on a value and feasibility matrix. A use case with high value but low feasibility can wait until its data or skills are in place.
What should you agree before an AI pilot starts?
Before an AI pilot starts, agree on four things: the baseline, the success measure, the stopping rule and the owner. The baseline is how the process performs today, without AI. The success measure is the change you expect, stated in business terms. A stopping rule is a written condition that ends the pilot when it is met, for example accuracy that stays below the level users need. The owner is the business sponsor who will run the result in production and take the decision.
When should you stop an AI use case?
You should stop an AI use case when it meets the stopping rule agreed at the start, or when its business reason no longer holds. Common signals include results that stay below the baseline, data that cannot be used for legal or contractual reasons, running costs that exceed the value created, and low adoption by the people meant to use it. A use case can also lose its purpose when the process changes or another solution solves the problem first. In each case, document what was learned and close it.
How do you build stop decisions into your AI roadmap?
You build stop decisions into your AI roadmap by adding Go/No-Go gates between phases and reviewing the whole portfolio at a regular rhythm. A Go/No-Go gate is a fixed decision point where a use case continues, changes scope or stops, based on the criteria set at the start. Keep the data work, findings and code from a stopped pilot, so the next initiative starts further ahead.
At Keyrus, we work closely with your team to select and manage AI use cases. Our approach takes each use case from exploration to operation, with Go/No-Go gates at every stage and full transparency on ROI. Talk to our experts to review the use cases in your portfolio today.
A portfolio of AI use cases stays healthy when stopping is planned from the start. Clear criteria make each decision faster, and the budget follows the initiatives that deliver.
Contact our experts to discuss your AI use cases.
A success measure states the result you want. A stopping rule states the result that ends the pilot. You need both, so the decision at the review does not depend on opinion.
No. It gives you evidence about what does not work in your context, and the data preparation done for it can often be reused.
Review each use case at the gates set in its plan, and the whole portfolio on a regular schedule, for example every quarter.
The business owner named at the start, with input from the data and technology teams who know whether the blockers can be fixed.