Build an honest AI automation business case with measurable assumptions and a reversible pilot.
AI automation creates value only when a real outcome improves after implementation, exception handling, review, rework, adoption, vendor fees, monitoring, incidents, and ongoing change are counted. Start with a measured baseline and make uncertainty visible.
Measure the current workflow first
Record demand volume, arrival pattern, completion rate, handling time, wait time, repeat work, error and correction, escalation, staffing, systems, customer effort, and business impact. Separate work eliminated from work moved to customers, reviewers, engineers, or a new exception queue.
Include the total operating model
- Implementation, integration, migration, testing, training, and rollout effort
- Usage, model, platform, storage, support, and monitoring fees
- Human review, exceptions, quality sampling, and incident response
- Knowledge, prompt, model, provider, and policy maintenance
- Security, privacy, accessibility, legal, and vendor-management work
- Exit, portability, outage, and rollback costs
Compare outcomes, not activity
| Metric | Useful definition | Misleading substitute |
|---|---|---|
| Completion | Correct outcome without avoidable repeat | Conversations started |
| Efficiency | Total effort per accepted outcome | Model response time |
| Quality | Accuracy and control by issue class | Average satisfaction alone |
Build a decision-ready pilot
- Write baseline and cost assumptions.
- Choose a bounded cohort and task set.
- Define success, harm, and stop thresholds.
- Instrument automated and human work.
- Run long enough to observe exceptions.
- Compare scenarios and decide continue, change, or stop.
Challenge optimistic arithmetic
- Counting theoretical capacity as realized savings
- Ignoring adoption, rework, and displaced labor
- Applying an average to high-risk issue classes
- Assuming pilot pricing and performance remain constant
Use ranges and decision thresholds
Model conservative, working, and favorable cases for volume, automation rate, review burden, unit cost, defects, and adoption. Identify the variables that would reverse the decision. Update the model from observed pilot evidence rather than quietly replacing the original baseline.
Continue with the next decision
Sources and further reading
Primary and contextual sources used to verify definitions or give readers a relevant next resource.
- FTC advertising substantiation guidance Primary regulatory guidance that objective advertising claims require an adequate evidentiary basis before publication.