A clear scope
What GOVERNANCE means here
Content governance makes automation accountable.
AI-Assisted Content Governance owns the specific workflow and deliverables described below; related pages supply complementary work and should not duplicate its scope.
The buyer problem
Where the work makes a difference
Quick drafts can introduce unsupported product claims, omit important qualifications or multiply low-value pages. Reviewers need a common standard and a way to stop incomplete information reaching publication.
Define the verified inputs, editorial standards, review roles and publication checks that every assisted draft must pass. Use AI to support research and production while protecting product accuracy, customer trust and the usefulness of each page.
The specific work
What we investigate and improve
- Define approved product data and prohibited unsupported claims.
- Create templates and prompts that preserve factual sources and uncertainty.
- Add expert review, sampling, versioning and rollback controls.
- Review current labeling, provenance and platform requirements for AI-generated assets.
BrownBag’s approach
From evidence to useful improvements
Define approved inputs and standards
Review the task, product sources and current drafts. Agree prohibited claims, evidence requirements, tool boundaries and the people who approve factual and editorial quality before publication.
Run a controlled content pilot
Prepare brief and review templates, then test representative and difficult products. Flag missing facts instead of inventing them, record corrections and check current requirements for generated content and assets.
Measure quality before expansion
Compare accepted output, total review time and defect rates with the existing process. Refine the workflow and hand over versioning, rollback and correction rules before expanding the publication scope.
Your handover
What you receive
- AI-assisted editorial policy and production workflow.
- Source-grounded templates and quality rubric.
- Approval and correction process.
- Pilot batch with documented error analysis before scaling.
Evidence, with context
How we measure progress
- Factual defect rate
- Review/rework time
- Useful content accepted per batch
- Duplicate or low-value page rate
- Correction turnaround
Scope your next step
Bring us the problem you want to solve.
These details help us agree the deliverables, responsibilities and review points:
- Approved product data and current drafting examples
- Editorial standards, tool-use boundaries and publication ownership
- A representative pilot and people who can verify the facts
Start with a focused scope. We confirm access, implementation ownership and any platform eligibility before the work begins.
Your questions, answered
Can AI write all our product descriptions?
It can assist, but factual inputs, quality checks and accountable review remain necessary. Start with a pilot rather than unreviewed mass publication.
Does Google ban AI-written content?
The concern is whether content is accurate, helpful and policy-compliant. Scaled low-value content can violate spam rules regardless of how it was produced.
Do AI-generated assets need labels?
Requirements differ by platform and asset. Check current merchant-feed and image-provenance rules before publication.