Quick answer: AI content governance gives content creators and reviewers a shared standard for how AI-generated content is created and judged in MLR review. Teams putting guardrails in writing are defining which uses of AI are permitted, restricted and prohibited, updating review and rejection criteria, and asking about AI use at intake so they can track where AI shows up and how it affects review.
AI is now part of the daily routine for most life science teams. In surveying done as part of Vodori's 2026 benchmarks report, 63% of respondents said they use AI daily or multiple times a day, but governance hasn't caught up. When asked about their organization's stance on using AI to assist with promotional content review, 24% of survey respondents indicated AI is approved for defined use cases with guidelines. Another 24% are still evaluating, 12% allow only limited use, and 15% don't permit it at all.
In The AI Efficiency Trap, Vodori's webinar with Compliance Forward, Annalise Ludtke of Vodori shared what customers are doing to close that gap. This post picks up where our overview of the session left off, with a closer look at guardrails: What to put in writing and how to tell whether it’s working. Watch the recording on demand.
Why do MLR reviewers need shared standards for AI-generated content?
Annalise shared an observation from Vodori's recent system owner panel. Customers noted that AI-generated content is often easy to spot, and when reviewers know a piece is AI-generated, they approach it through a different lens. For better or worse, it can be easy to reject it outright.
Without shared criteria, the same piece of AI-assisted content can get very different treatment depending on who reviews it. Vodori's 2026 State of Promotional Review Benchmarks Report describes the two failure modes: blanket rejection on one side, unchecked acceptance on the other. Written guardrails give content creators and reviewers a common understanding of what's acceptable and why content would be sent back.
That inconsistency matters because accuracy is what teams worry about most. Hallucinations topped the list of AI concerns in Vodori’s survey at 38%, with data privacy (25%) and compliance risk (22%) behind it.
Guardrails also start upstream. Jason Cober of Compliance Forward encouraged MLR teams to learn how AI-generated content enters the funnel in the first place. Much of today's variable content is built from patient and HCP personas. Knowing what those profiles contain and how the AI applies them helps reviewers spot where it's likely to fail. Sometimes errors can be fixed at the source by changing how the persona files are written.
What should an AI content policy include?
Vodori's 2026 benchmarks report recommends starting with a short document that defines when AI use is appropriate, when it isn't, and how reviewers should vote on AI-assisted content.
Some teams structure their AI content policy around three categories: which uses of AI are permitted, which are restricted, and which are prohibited. Jason's advice applies here: Do the groundwork of documenting guardrails and business rules before a full-scale rollout, not after.
How should promotional review and rejection criteria change?
The policy covers how content is created. Review criteria cover how it's judged. Annalise suggested putting rejection criteria in writing so reviewers can point to specific reasons for sending AI content back.
Updated review criteria can include:
- Add an "AI rework" vote reason. This gives reviewers an option between approving and rejecting for AI content that needs minor fixes.
- Name clear rejection triggers. Examples include unverified claims or content made with unapproved tools.
A dedicated vote reason also improves your data. Vodori already tracks why content gets sent back, and typos and errors led the list in 2026 at 23.9%. Without an AI-specific reason, AI-related rework gets folded into categories like that one, and you can’t see how much of it traces back to AI.
How can teams catch AI issues before formal review?
One option from Vodori's 2026 benchmarks report is a concept-review or pre-review step. It lets additional stakeholders flag AI and other content problems before formal submission, without blurring who has approval authority. The result is better content heading into MLR and less rework overall.
For modular content, Jason advised building guardrails before applying AI pre-checks. Useful checks include whether a module sits in the right place, whether it's paired with the right claim and whether required context and risk information come with it. Those checks take business rules and prompt work to get right.
How do you capture AI use at submission and track it?
You can't govern what you can't see. The simplest starting point is an intake question asking whether generative AI was used to create or materially change the content, and where.
Vodori's 2026 benchmarks report recommends going one step further: Disclose, then verify. Make disclosure a mandatory field. Then give reviewers a separate flag for AI-related concerns, even when the creator didn't disclose AI use. Tracking the two separately shows both how AI use is trending and where disclosure was off. The report treats a missed disclosure as a coaching moment, not grounds for automatic rejection.
Over time, that data shows whether your guardrails are working. Compare first pass approval, number of circulations and AI rework votes for AI-assisted content against everything else. Then revise the policy and criteria based on what you see.
Treat early guardrails as a starting point. Annalise noted that some customers are experimenting with guardrails while others have long-standing ones. Either way, writing them down creates shared knowledge across the team. She also recommended keeping rollout collaborative: Ask reviewers for feedback, and ask what's working and where they're stuck.
Frequently asked questions about AI content governance
What is AI content governance in MLR review?
AI content governance is the set of written rules for how AI can be used to create promotional content and how reviewers should evaluate it. It usually includes an AI content policy, updated review and rejection criteria, and a way to capture AI use at submission.
Should content creators disclose when they use AI?
Yes. A mandatory intake question about generative AI use helps teams track where AI shows up and how it affects review. Vodori's 2026 benchmarks report also recommends a separate reviewer flag for AI-related concerns, so teams can see when disclosure was missed.
Should reviewers reject AI-generated content?
Not by default. Vodori's 2026 report notes that documented guidance helps teams avoid both blanket rejection and unchecked acceptance. An "AI rework" or amend-and-resubmit option handles content that needs minor fixes. Clear rejection triggers, like unverified claims or unapproved tools, handle the rest.
What should an AI content policy include?
It should define when AI use is appropriate and when it isn't, and how reviewers should vote on AI-assisted content, such as reject versus amend-and-resubmit. Some teams organize these rules into permitted, restricted and prohibited uses.
Watch the full webinar
Guardrails were one part of a broader conversation about what AI is actually changing in MLR review. To hear Annalise Ludtke and Jason Cober cover measurement, AI pre-checks and audience questions on modular content,watch The AI Efficiency Trap on demand. For a recap of the key takeaways, read our overview of the session.
