AI · Product Discovery · Governance
AI Evaluation Assistant
Shaping an enterprise AI-assisted workflow around users, decisions and human oversight.
What needs to be true before a person can trust and act on an AI-assisted recommendation?
Context
An enterprise assistant sits inside a workflow. Its usefulness depends on the user’s task, the context they are permitted to use and the decision they are trying to make.
Problem
A fluent recommendation can still be incomplete, unsupported or inappropriate for the user. Requirements need to address the whole decision process, including uncertainty and human review.
My Role
The BA focus is product discovery and requirements: understanding user groups, mapping the journey, defining roles and access, and making review and evaluation expectations explicit.
Stakeholders
Users, process owners, product and engineering teams, governance specialists and reviewers each help define what a useful and acceptable recommendation looks like.
What I Needed to Understand
Who uses the assistant, which decisions it supports, what information it can access, how a recommendation is reviewed and what happens when it cannot provide a reliable answer.
Approach
Start with a bounded workflow. Describe user scenarios and exception paths, then connect AI requirements to evaluation criteria and human decision points.
Key Requirements / Decisions
Access must follow the user’s permissions. Recommendations should support review, make uncertainty visible and allow correction or escalation. The record should distinguish AI suggestions from human decisions.
Process / Data Flow
A conceptual view of the flow, simplified for this public case study.
- 01User need + authorised context
- 02AI-assisted recommendation
- 03Human review
- 04Decision + audit trail
Challenges & Trade-offs
More automation can reduce effort while increasing the consequence of an unreviewed error. The appropriate balance depends on the decision’s impact, reversibility and available evidence.
Validation / Testing
Evaluate realistic tasks alongside incomplete inputs, unsupported requests and access restrictions. Review usefulness, evidence, appropriate abstention and the user’s ability to correct the result.
Outcome
This case study centres on workflow framing and evaluation requirements. It makes no claim about deployment scale, model accuracy or measured business gains.
What I Learned
AI is an enterprise workflow and decision-support problem, not only a model problem. Human oversight needs to be part of the experience from the beginning.
OPEN TO A THOUGHTFUL CONVERSATION
Complex problem?
Let’s make it clearer.
ideas → clarity → impact.