Process measures
Compare cycle time, human effort, completeness, correction and escalation with the agreed baseline.
Results
Measure the work, not the demo.
Every launch compares operational performance, reasoning quality, authority compliance and known limitations against an agreed baseline.
The question
How will we know whether the AI worker improved the process?
01
Baseline documented
02
Success measures agreed
03
Real cases evaluated
04
Limits recorded
Evidence before claims
A model demonstration is not an operational result.
01The worker must be compared with the current process on representative cases, using measures that matter to the process owner and accountable teams.
02Until customer outcomes are independently supportable, Intelliger should publish the measurement method, clearly labelled examples and controlled-pilot results, not invented benchmarks.
Proof method
Connect every result to the underlying process evidence.
The method keeps illustrative ROI models separate from observed customer outcomes.
Observe
Measure the current process and its case mix.
Agree
Confirm measures and release criteria with the process owner.
Evaluate
Run historical or prepared cases through the configured worker.
Pilot
Use controlled real cases with the selected approval model.
Report
Compare results, limitations and next-scope options.
Operational proof
Measure routine throughput and the quality of difficult-case handling.
A useful worker must do more than automate the easy path. The evaluation should show how it behaves when evidence conflicts, a known exception appears or the case is genuinely unfamiliar.
Reasoning measures
Test evidence selection, hypothesis quality, uncertainty detection and compliance with authority limits.
Learning measures
Track whether reviewed novel cases improve later performance without regressing approved behaviour.
The outcome report separates demonstrated capability from open limitations, giving the process owner a defensible expand, revise or stop decision.
Honest reporting
Separate measured outcomes, demonstrations and illustrative models.
Every published result should state its source, case scope, baseline, deployment mode and measurement period.
Worker responsibility
Build a result the organisation can inspect.
The baseline and evaluation plan are agreed before the worker is deployed on production work.
Process baseline
Measure current effort, time, volume, quality and exception patterns.
Representative case set
Include common work, difficult exceptions and known failure modes.
Release criteria
Define acceptable completion, correction and escalation behaviour.
Outcome report
Document measured change, limitations and the decision on expansion.
Accountable role
Human judgement and approval
Decisions with material, regulated or professional consequences stay with the named accountable role.
No unsupported performance percentages
Illustrative models labelled clearly
Anonymised results identified as such
Baseline and case scope disclosed
Human controls documented
Limitations and exceptions included
Core measures
Use a small set of comparable operational indicators.
Select the measures relevant to the chosen process rather than presenting all of them as universal outcomes.
- 01Cycle time
- 02Human minutes
- 03Completeness
- 04Correction rate
- 05Throughput
- 06Cost per completed case
Build the evidence
Plan a launch with a baseline the process owner trusts.
Use real cases and agreed operational measures to decide whether and where to expand.