Generative AI in marketing: choose useful applications

The key idea

Generative AI produces candidate outputs from instructions and context. Business value depends on the quality of the task, inputs, evaluation and integration into real work.

Our method

Compare applications across research, campaign development, adaptation and reporting. Choose one with reliable inputs and reviewable output. Define what the model may draft, what must be verified and who decides whether the result is usable.

How to assess progress

Track total process time and quality rather than raw generation speed. Keep rights, sensitive data and unsupported claims within the review process; do not assume one tool suits every task.

Decision

Decide which marketing bottleneck deserves a pilot using observed workload rather than enthusiasm for a format. List tasks your team repeats and distinguish producing options from verifying facts or making a commercial commitment. Choose an application where the output can be judged against a clear existing standard.

Exercise

During one working week, record five recurring tasks with preparation time, production time, review time and rework. Select two candidates and run each on an ordinary input and a difficult input. Ask the task owner to use the result in a realistic handoff, recording every additional explanation or correction required.

Deliverable

Produce an opportunity card for each candidate: task frequency, baseline effort, approved inputs, useful output, reviewer, quality requirement and integration dependency. Recommend one pilot and one deferred option, with explicit reasons. Include what evidence would make you stop or reconsider the pilot after several cycles.

Pitfall

Fast generation can move effort to another colleague. Include briefing, file preparation and downstream correction when evaluating recovered capacity. If the existing task is poorly defined, improve the task first; automating an unclear handoff usually produces more inconsistent work at greater speed.