An Evaluation Framework for Enterprise AI
Evaluate potential AI initiatives through operating costs, workflow quality, and implementation tradeoffs, using documented assumptions rather than promised returns.
Vajriva · Educational analysis
2 min read (estimate)
AI initiatives need explicit evaluation criteria. A useful assessment considers operating costs, workflow quality, and the options a system could enable. This is an educational framework, not a report of client results.
An Evaluation Framework
Operating Costs Estimate potential value using documented assumptions, relevant costs, and an agreed comparison method. Include integration, review, inference, maintenance, and evaluation effort rather than considering model usage alone.
Workflow Quality Define what makes a response or action useful in its operating context. Evaluate errors, omissions, review effort, and exception handling against the existing workflow. Changes in speed should not be treated as improvements if quality or control deteriorates.
Implementation Tradeoffs Consider data access, integration dependencies, operating constraints, and the ability to maintain the system. A capability that is technically feasible may still be unsuitable for a particular workflow.
Agree on Evidence Before Implementation
Agree on evaluation criteria and a review point before implementation. Record the comparison method, relevant assumptions, and conditions under which a pilot should continue, change direction, or stop.
Questions to Resolve
- What business problem is being addressed?
- Who can authorize the workflow and review its outputs?
- Which data quality issues or access restrictions need attention?
- What actions should remain under human control?
The purpose of evaluation is to support a decision, not to guarantee a financial outcome.