Content and content structure readiness checklist for generative AI
This checklist will help you assess your user research, content structure, content writing as well as ensure you have the proper content governance, genAI legal and regulatory compliance, and accessibility and inclusion standards.
Your organization must assess the risks of generative AI before starting projects or unleashing an AI-driven product into the world. You must ensure that your genAI product best serves your users and employees.
Issues include privacy, gender and racial bias, hallucinations (inaccurate, nonsensical, or factually incorrect information), security, and harm to people and the environment.
You can download this checklist as a document and take it with you wherever you go!
This checklist ensures you are ethically implementing genAI. It’s not a “shortcut” list to implementation.
User understanding & journey mapping
User personas and content journey maps exist and are updated regularly.
Content strategy is tailored to audience needs, intents, and pain points at each stage of the journey.
Content is available for the user tasks and questions.
Content structure & metadata
A shared taxonomy is used consistently across teams and systems.
Content assets are tagged with structured metadata (e.g., audience, format, lifecycle stage).
Metadata crosswalks exist to map terms between systems or business units.
Content authoring, modeling & modularity
Content is on-brand, targeted at specific audiences, up-to-date, and reviewed by subject matter experts.
Modular content types and components (e.g., CTAs, FAQs, feature blurbs) are defined and documented.
Templates or structured authoring tools ensure consistency and enforce logic across assets.
Governance & ownership
A formal content governance model outlines who creates, approves, and maintains content and content structure.
Approval workflows are in place for both human- and AI-generated content.
Review processes include checkpoints for regulatory compliance and brand alignment.
Systems & tools
The CMS/CCMS supports modular, structured content authoring and reuse.
Generative AI tools are integrated securely into the content lifecycle.
Analytics and feedback are used to continuously refine and improve content quality.
Accessibility, inclusivity & bias audits
Content is reviewed using accessibility standards (e.g., WCAG 2.1).
Inclusive language guidelines are documented and applied across formats.
Content is audited for representation and tone across different demographics, regions, and identities.
AI outputs are tested for biased phrasing, exclusionary logic, and cultural insensitivity.
Legal & ethical readiness
AI-generated outputs are reviewed for legal and regulatory compliance (e.g., disclosures, disclaimers).
There is a documented process for identifying hallucinations or unverifiable claims.
Contributors are trained to flag ethical risks in AI-generated messaging (e.g., health claims, financial advice).
Consent, data privacy, and usage rights are clearly addressed in content sourcing and reuse policies.
The organization communicates transparently when content is AI-assisted or AI-generated.
Innovation readiness
Pilot programs for AI-generated content are scoped, measured, and iterated responsibly.
The organization has a roadmap for scaling generative AI into high-value use cases.
Content operations are treated as a strategic, resourced function—not a cost center.
It’s a lot of work to ethically implement genAI! If you need any help, please reach out via our contact form.