AI and Automation
Prioritizing AI Use Cases: Choose the First Process to Automate
A value, feasibility, and risk matrix for identifying high-volume, repetitive, costly, and sufficiently observable workflows.
Decision supported
Prioritise AI use cases with the same published criteria: business value, frequency, data readiness, verifiability, integration effort, reversibility, adoption, and consequence of error. Eliminate cases that fail a critical data or risk condition before comparing scores.
Executive summary
- Score problems, not executive enthusiasm.
- Use evidence and one baseline per candidate.
- Apply elimination gates before weighted ranking.
- Select a reversible pilot with rapid feedback.
Use one decision grid
Define each score, evidence source, weight, and owner. Publish the weights so the portfolio decision can be challenged.
Apply elimination gates
Reject or prepare first when authorised representative data is missing, outputs cannot be checked, error consequence is uncontrolled, or no workflow owner exists.
Validate top candidates on real samples
Test the leading cases against a simple baseline and difficult examples before funding a build. Score correction effort and integration, not model quality alone.
Choose a useful first portfolio
Balance one measurable quick proof with foundational learning; avoid ten pilots competing for the same data and specialists.
Decisions to make now
Recommended actions
- 01Measure ten candidate processes with one grid.
- 02Define data and risk elimination criteria.
- 03Validate the top three on real samples.
- 04Choose a reversible pilot.
- 05Re-score after the first pilot.
Watch points
- A sponsor choosing the solution before the problem.
- High scores based on estimates without baselines.
- A spectacular case with uncontrollable error.
Frequently asked questions
Should the highest-volume case always win?
No. Volume raises potential value and exposure; data, verifiability, integration, and risk matter equally.
How should weights be set?
Match strategy and risk tolerance, publish them, and test how sensitive the ranking is to each score.
Sources and verification
Last editorial verification: 14 August 2026. Links point to the source texts, authorities, and reference guides consulted.
- 01AI RMF Core: Govern, Map, Measure and Manage
NIST AI Resource Center. Accessed 14 August 2026.
- 02Architecting a successful generative AI proof of concept
AWS Prescriptive Guidance. Accessed 14 August 2026.
- 03The Adoption of Artificial Intelligence in Firms
OECD. Accessed 14 August 2026.
- 04Artificial Intelligence: An Accountability Framework
U.S. Government Accountability Office. Accessed 14 August 2026.
Related decisions
Continue with briefs that share the same operational, technical, or governance context.
AI and Automation
AI Automation in Algeria: Five Use Cases That Can Produce Measurable Value
Read the briefAI and Automation
AI ROI: Calculate Total Cost and the Value Actually Created
Read the briefDigital Transformation
Internal AI Adoption: Measure Useful Usage, Not Accounts Created
Read the briefNext step
Identify the first workflow to automate.
We start with the real flow, its exceptions, and one business metric to define a measurable pilot.