How to build an AI use-case backlog

Building an AI use-case backlog

The biggest trap in adopting AI is starting with the technology instead of the problem. Companies that ask "what can we do with AI?" end up with scattered, low-impact experiments. Those that ask "which of our problems does AI solve best?" build a prioritized use-case backlog that generates value systematically. This is the strategic asset that sustains a mature AI journey.

Where use cases come from

Good use cases come from real pain in the business. To find them, map tasks that are:

• Frequent: repeated many times by many people.
• Time-consuming: they consume significant time.
• Language or data based: reading, writing, analysis, classification.
• Error-tolerant with review: where the output is verified by humans.

Run workshops with each department to surface candidates. A one-to-two-hour session per area usually generates dozens of ideas.

Common use-case categories

Prioritizing with a value-effort matrix

Not every case deserves immediate attention. Assess each by business value and implementation effort, and classify:

1. Quick wins: high value, low effort. Start here.
2. Strategic bets: high value, high effort. Plan carefully.
3. Fill-ins: low value, low effort. Do when time allows.
4. Avoid: low value, high effort. Discard.

Also consider risk and data readiness as filters. A high-value case with disorganized data may need preparation before rising to the top.

Assessing each candidate

For each case, record:

• Problem and area: which pain and who suffers from it.
• Proposed solution: ready-made Copilot, agent or Azure OpenAI solution.
• Expected value: in time or cost ranges.
• Effort and dependencies: data, integrations, permissions.
• Risk: data sensitivity and impact of error.
• Owner: who sponsors and who executes.

From backlog to delivery

The backlog is not a dead list. It should be reviewed periodically by the AI committee, with cases entering, exiting and shifting priority based on results. Start with quick wins to build confidence and learning, and use that momentum to tackle strategic bets.

Choosing the right tool

• Copilot for Microsoft 365: broad productivity on everyday tasks.
• Copilot Studio: specialized agents over knowledge and actions.
• Azure OpenAI: custom solutions embedded in systems.
• Power Platform: process automation with or without AI.

AI backlog checklist

• Workshops with areas to surface candidates
• Cases assessed by value, effort and risk
• Prioritization matrix applied
• Right tool defined per case
• Quick wins selected to start
• Periodic review by the AI committee

How RHC helps

As a Microsoft Solutions Partner, RHC runs discovery workshops, helps build and prioritize the use-case backlog and recommends the right tool for each, from ready-made Copilot to Azure OpenAI. We turn scattered ideas into a clear roadmap, with quick wins up front and well-planned strategic bets.

Key takeaways

• Start with the problem, not the technology.
• Surface cases through workshops in the business areas.
• Prioritize by value, effort and risk, starting with quick wins.
• Keep the backlog alive, reviewed by the AI committee.