How should a business start using AI?

Choose a workflow, not an AI slogan

Begin with work people already understand: preparing a recurring report, classifying requests, retrieving internal knowledge, drafting a first response, or identifying anomalies. Measure the current time, quality, delay, and error rate before changing it.

A suitable first use case is frequent enough to matter, bounded enough to test, and low enough in consequence that a supervised pilot is responsible.

Design the control points

• Define what data may enter the system and which data must remain restricted.
• Assign a person who owns the outcome, not just the tool configuration.
• Specify which outputs require human review and what reviewers should check.
• Create an escalation path for uncertain, sensitive, or high-consequence cases.
• Log failures and corrections so the workflow can improve rather than repeat hidden mistakes.

Measure usefulness before scale

Compare the pilot with the baseline. Time saved matters only if quality remains acceptable. Automation rate matters only if exceptions are handled safely. Adoption matters only if the workflow is actually more useful to the people responsible for it.

Once the result is repeatable, document the operating method and expand deliberately. The goal is not to insert AI everywhere; it is to improve specific work without weakening accountability.

Agree on the pilot's acceptance and stop conditions

NIST's voluntary AI Risk Management Framework and companion Playbook organize risk work around Govern, Map, Measure, and Manage. They provide useful context for identifying owners, understanding a use case, evaluating its risks, and deciding how to respond. A short pilot checklist does not establish compliance with a law, certification, or complete implementation of the framework.

Use this SavvyTank pilot decision record before enabling the workflow. Set thresholds appropriate to the consequences of an error; a low-risk draft and a consequential customer decision should not have the same acceptance rule.

• Baseline and scope: specify the task, current time and quality measures, allowed inputs, excluded cases, accountable owner, and who may use the output.
• Evaluation: assemble permitted examples of normal, difficult, and out-of-scope cases. Keep a separate set that was not used to adjust the workflow; record sample limitations instead of presenting the test as proof for every future case.
• Acceptance: define what reviewers will count as correct, incomplete, unsupported, or unsafe. Measure review and correction time as well as generation time, and record tool and operating costs before claiming savings.
• Stop and fallback: pause if restricted data is exposed, an unsafe output escapes the agreed review, or a pre-agreed error limit is exceeded. State who investigates and how people return to the previous process.
• Release and recheck: record the configuration, test date, reviewer, unresolved risks, and decision. Retest after material changes to the model, instructions, data, or workflow; an earlier pass is not a permanent guarantee.