/ THE SHORT ANSWER
Choose one high-volume document step with a clear owner and reversible outcome, such as extracting fields, classifying a case, comparing evidence, or drafting a review note. Keep the system in assist mode while measuring accuracy by field or decision, missing evidence, exception rate, review time, and downstream rework. Do not automate final approval until the evidence shows where the system is dependable and the control design is adequate.
- 01Pilot one bounded step with a reversible output.
- 02Measure review effort and downstream rework, not only extraction accuracy.
- 03Use exceptions to improve scope, sources, controls, and ownership.
/ dotSuper point of view
The first document pilot should reduce one piece of cognitive or administrative load while making errors easier to see—not hide a decision inside an end-to-end automation.
What the evidence says
NIST’s AI RMF emphasises mapping the intended context, measuring performance and risk, and managing the system across its lifecycle.
The NIST Generative AI Profile calls attention to confabulation, information integrity, privacy, and human-AI configuration in generative systems.
A practical decision framework
The following framework is dotSuper’s operating synthesis of the cited guidance. It is designed to make the decision inspectable, not to imitate a platform ranking formula, certification checklist, or legal test.
- Select: stable document family, clear task, accountable owner, and baseline.
- Prepare: representative samples, labels, field rules, permissions, and exception classes.
- Assist: structured output, evidence reference, reviewer correction, and audit trail.
- Decide: compare time, quality, exceptions, rework, risk, and ownership against the baseline.
| Step | Decision to record |
|---|---|
| 01 | Select: stable document family, clear task, accountable owner, and baseline. |
| 02 | Prepare: representative samples, labels, field rules, permissions, and exception classes. |
| 03 | Assist: structured output, evidence reference, reviewer correction, and audit trail. |
| 04 | Decide: compare time, quality, exceptions, rework, risk, and ownership against the baseline. |
How to put it into practice
Sample across common, rare, low-quality, conflicting, missing, and adversarial documents. Split the set before tuning so final evidence does not reuse every development example.
Capture reviewer corrections as structured data with reason codes. Review whether errors cluster around specific vendors, formats, fields, languages, or process conditions.
- Name the accountable owner and the decision this work must enable.
- Record the current evidence, assumptions, exclusions, and next review trigger.
- Measure a useful outcome rather than treating publication or deployment as success.
What this page cannot conclude
- 01Performance on one document family may not transfer to another.
- 02High-consequence approvals may require domain-specific validation, segregation of duties, and regulatory controls.
- 03Publication, technical eligibility, or good practice cannot guarantee ranking, referral traffic, citation, adoption, or a business outcome.
Sources
Test the workflow before funding the solution.
The AI Readiness Sprint turns one operational constraint into a ranked decision, an accountable owner, and an implementation-ready first move.
Explore the readiness sprint