1. Automate the repetitive layer
Use AI for first-pass research, summarization, classification, draft generation, data cleanup and repetitive workflow steps. These tasks benefit from speed and consistency more than from final judgment.
The output should enter a review queue, not become the final answer automatically.
2. Keep evidence and source checks explicit
Any claim that affects pricing, compliance, supplier choice, legal exposure or customer communication should be traceable to an observable source.
A useful workflow stores the source, the extracted fact, the confidence level and the human who approved the decision.
3. Human judgment belongs at decision points
Negotiation strategy, supplier acceptance, market-entry commitments, pricing changes and sensitive customer decisions need accountable human ownership.
AI can prepare options, identify anomalies and surface trade-offs. The final decision should remain with the person responsible for the commercial outcome.
4. Measure the system, not the novelty
The useful metrics are cycle time, error rate, rework, conversion, response time and decision quality—not how many AI tools are connected.
If automation adds complexity without reducing work or improving outcomes, remove it.