SMBs often ask: “Will AI replace this role?” That framing leads to bad ROI estimates and unrealistic expectations.
In practice, high-ROI AI projects typically do one of three things:
- Increase throughput (more work completed with the same team)
- Reduce errors/rework (quality improves, exceptions drop)
- Reduce cycle time (faster decisions, fewer handoffs)
A simple ROI model
Start with a baseline for the workflow you want to improve:
- Volume: items/week
- Time: minutes/item (or end-to-end cycle time)
- Cost: labor cost + tool cost + overhead
- Quality: error rate, rework rate, escalations
Then model the change from AI as a conservative range (best/expected/worst), not a single point estimate.
What teams forget to include
When ROI estimates miss, it’s usually because teams ignore the operational layer:
- Governance overhead: reviews, approvals, auditability
- Exception handling: what happens when the model is uncertain?
- Monitoring: drift, cost/burn, and changes in upstream systems
- Security constraints: access controls and data handling
None of these are optional in production. The good news: if you design for them early, you can still ship quickly.
A practical target for first pilots
For a first pilot, aim for outcomes you can verify within weeks:
- 20–40% reduction in handling time with human review
- Meaningful reduction in error rate or rework
- Shortened turnaround times on a high-volume queue
Once the workflow is stable and instrumented, expanding scope becomes much less risky.
Want a scoped ROI model for a specific workflow? Book a strategy call.