Reviewed July 2026. This article was substantially updated to reflect current web standards and practices.
Generative AI is now part of many marketing and design workflows, but novelty is not a strategy. Its value comes from accelerating bounded tasks while people remain responsible for positioning, evidence, taste, rights, accessibility, and final decisions.
Use AI where verification is possible
Useful applications include summarizing approved research, creating controlled variations, organizing interviews, drafting metadata, expanding test cases, and adapting content into documented formats. Keep source material and acceptance criteria close to the task.
Do not outsource truth
Models can invent facts, citations, capabilities, and customer claims. Verify names, dates, prices, legal statements, technical details, quotes, and performance claims against authoritative sources before publication.
Protect data and rights
Define which customer, employee, analytics, and proprietary data may enter a model. Review provider terms, retention settings, copyright and licensing implications, and disclosure requirements for the organization.
Keep the brand recognizably human
Train the workflow on a real voice guide, original expertise, customer language, and specific proof. Human review should remove generic phrasing and ensure the result is useful, accessible, and consistent with the actual service.
Create a use-case register
List every AI-assisted workflow with its purpose, model or provider, data classification, owner, reviewer, output destination, known failure modes, and success metric. This prevents quiet experimentation from becoming an ungoverned production dependency.
A practical review standard
- Accuracy: verify factual, technical, legal, pricing, and attribution claims.
- Originality: add firsthand expertise, evidence, examples, and point of view.
- Brand: remove generic phrasing and preserve the approved voice.
- Rights: review source material, licensing, likeness, and disclosure obligations.
- Privacy: exclude unapproved personal, customer, employee, and confidential data.
- Accessibility: check structure, alternatives, contrast, motion, captions, and language.
- Performance: confirm the output helps the defined audience or task.
Designing AI-assisted experiences
Tell users when they are interacting with an automated system where that context matters. Set expectations about what it can do, provide sources or evidence when feasible, and offer a clear path to correction or a person. Do not let generated confidence hide uncertainty.
Evaluate the whole workflow, including the time spent prompting, reviewing, correcting, and managing risk. A slower conventional process may be better when errors are difficult to detect or costly to customers.