AI operations and measurement

Choosing AI marketing use cases with measurable value

Choose an AI use case by the task it improves, the risks of an incorrect output and the evidence needed to judge it. Tool availability should not decide the business objective.

Define the current process

Describe the input, output, reviewer and decision the work supports. Record the time, error patterns and rework in the current workflow. Good candidates are bounded enough to compare, such as organizing approved interview notes or preparing a first draft from a verified brief. Avoid assuming faster generation means a better result.

Assess information and error risk

Identify sensitive material, permission constraints and the consequences of a misleading output. Decide what should not enter the system and which tasks require specialist oversight. Review the actual provider settings and terms before using business data; this guide does not establish a particular tool's protections.

Run a limited comparison

Use representative examples and explicit acceptance criteria. Measure reviewer effort as well as generation time, and record failures rather than only successful demonstrations. Assign ownership for monitoring and changes. A useful pilot ends with a decision to adopt, revise or stop, supported by the observed tradeoffs.

Put the decision into practice

  1. Define a bounded task.
  2. Record baseline quality and effort.
  3. Assess data and error risk.
  4. Include reviewer time in the comparison.
  5. Set adoption and stop criteria.

Bring the business question, the evidence already available and the constraints that matter. Discuss your next step with Nancy Chou.

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