How to Add AI to an Existing Business Workflow

Start with a workflow, not a company-wide AI plan

The useful question is not “How do we integrate AI into the business?” It is “Which recurring decision or document step creates enough delay or cost to justify an experiment?”

A practical process

1. Map the current workflow and exceptions.
2. Establish a baseline for time, quality, and cost.
3. Identify the step that requires language or pattern judgment.
4. Check whether ordinary rules, search, or software would be more reliable.
5. Build a limited test with representative data.
6. Define human review and escalation.
7. Run a controlled pilot.
8. Compare the pilot with the baseline.

Data and governance

Confirm who owns the data, whether it may be sent to a provider, how long it is retained, which users may access outputs, and how errors are corrected. In regulated or high-impact work, involve qualified legal, security, or compliance reviewers.

Avoid fake precision

There is no universal 90% cost reduction, 90% accuracy threshold, or fixed split between technology and change management. Use measured results from the organization's own workflow.

Scale only after evidence

A successful pilot should show repeatable quality, adoption, acceptable exception handling, and an operating owner. Expanding an unmeasured demo creates more risk, not more value.

Fact-check sources

• NIST AI Risk Management Framework
• NIST Secure Software Development Framework

Sources and product documentation can change. Recheck time-sensitive pages on the publication date.