An AI-generated claim is a lead to verify, not evidence to award against.
A controlled sourcing workflow
Start with approved tools and approved data. Define the task and the output format before adding documents. Require source references and an explicit unknown state. A fluent summary can still omit a qualification or merge two suppliers’ positions.
Where assistance can be useful
| Task | Useful output | Required review |
|---|---|---|
| Market research | Candidate sources and questions to investigate. | Open primary sources, verify dates and confirm supplier existence and fit. |
| Requirements | Draft structure and ambiguity flags. | Business and technical owners validate the need and testability. |
| Proposal comparison | Extracted responses against a fixed schema. | Check each material entry against the original and preserve exceptions. |
| Contract preparation | Issue list and clause-location references. | Qualified counsel interprets legal effect; confirm document precedence. |
| Spend classification | Proposed category mappings and uncertain records. | Review high-value exceptions and reconcile totals. |
| Negotiation preparation | Questions, scenario outlines and issue summaries. | Validate facts, authority, alternatives and commercial judgment. |
Keep scoring, supplier selection, negotiation commitments and legal conclusions with authorized people. Do not allow an attractive generated rationale to substitute for the evaluation record.
Make the review observable
Maintain a simple log: task, tool or model, input scope, date, output, source references, reviewer, corrections and decision use. The detail should be proportionate to risk. Preserve the original documents and distinguish extracted facts from inferred recommendations.
Treat instructions embedded in supplier material as untrusted content. A proposal may include text that attempts to influence an automated reviewer. Use a controlled workflow that evaluates the supplier content as evidence and does not grant it permission to change the review rules or take external actions.
Measure whether the assistance improves the work
Test against a small set of manually reviewed examples. Track omissions, incorrect extraction, false citations and review time. A workflow that drafts quickly but creates extensive checking work may not improve the process. Revalidate after significant tool or model changes.
Sources & context
- NIST · AI Risk Management Framework ↗Voluntary risk-management foundation, including the Generative AI Profile. It does not certify a product or supplier.
- NIST AI 600-1 · Generative AI Profile ↗Primary reference for generative AI risk considerations.
The decision frameworks and illustrative examples are original editorial guidance. Sources support the stated context; they do not endorse this guide.