Project guide · AI and automation
Selecting one measurable artificial intelligence workflow trial
A low-risk trial from process baseline to continue, redesign or stop decision.
Starting point
What brings the project forward
A service business wants to use artificial intelligence across several teams but cannot identify a sensible first return. Staff spend time classifying inbound requests and drafting routine responses that already receive supervisor review.
Constraints
- Prove value with real operating cost
- Protect customer information
- Measure errors and review effort
- Retain a manual fallback
A workable delivery sequence
- 01
Process baseline
Measure volume, time, waiting, rework, error categories and the current approval process.
- 02
Risk and test design
Classify data and consequence, define excluded cases, build a test set from real work and agree how results will be scored.
- 03
Controlled pilot
Run with approved users and human approval, logging time, quality, corrections, exceptions and cost.
- 04
Decision
Compare full review effort and risk with baseline and continue, redesign or stop under pre-agreed criteria.
Destination
One clearly limited workflow has an owner, baseline, approved data boundary, test cases drawn from real work, a human decision point, failure monitoring and a measured scale-or-stop decision.
Business decisions
- Which cases are excluded
- Where the human decision occurs
- What quality and time thresholds justify scale
- What failure triggers rollback
Tests before go-live
- Baseline and pilot samples are comparable
- No prohibited data enters the tool
- Every output is reviewed during the pilot
- Exception handling and reviewer effort are measured
- Scale or stop decision uses documented measures
Handover material
- Process and data map
- Evaluation set and rubric
- Pilot log and metrics
- Scale, redesign or stop recommendation