Six months ago, your team published a detailed guide on data security best practices. Since then, your policies have changed. The article has not.
So when a customer asks your support chatbot a routine question and the bot confidently cites that guide as current policy, the advice is wrong. Your support team now has to explain why an official brand answer is outdated.
It’s a scenario that’s becoming more and more common as AI makes its way into customer service, e-commerce, and search. Since LLMs pull from published brand materials to answer user questions and shape buying decisions, outdated or incomplete content can carry severe consequences. According to The Conference Board’s October 2025 analysis, 72% of S&P 500 companies now identify AI as a material business risk, up from just 12% in 2023.
Content teams are feeling the pressure. Marketing collateral that used to be about engagement and reach now carries far more responsibility.
Why This Shift Is Happening Now
AI systems don’t distinguish between your latest product update and a blog post from 2019; they treat all indexed content as equally valid source material.
This creates a compounding problem. When ChatGPT, Perplexity, or Google’s AI Overviews pull from your content library, disclaimers disappear, dates vanish, and nuance evaporates.
This is what leads to scenarios like the one described at the top of this piece. Here are a few other examples of how content can go awry:
- A 2023 pricing page informs a sales conversation with a chatbot, and the customer pushes back when it becomes clear the quoted numbers no longer apply.
- A deprecated feature appears as a live offering in Google’s AI Mode, leading to confusion during customer onboarding.
- An old compliance explainer is surfaced on ChatGPT as guidance, even though the underlying regulation has changed. The company is forced into a reactive audit.
For regulated industries, the exposure carries profound risk: Financial services firms might face SEC scrutiny, and healthcare organizations that have to navigate HIPAA implications could find themselves correcting patient-facing guidance after the fact.
The New Risks Content Teams Are Absorbing
Content teams didn’t sign up to be compliance officers, but the risks have arrived anyway.
Consider what happened to Air Canada a couple of years ago: In a 2024 ruling, a British Columbia civil tribunal found the airline liable after its website chatbot cited incorrect information about bereavement fares, promising a discount that did not exist under current policy. When Air Canada refused to honor the discount, the customer pursued a claim and won. The tribunal ruled that the company was responsible for the chatbot’s statements, regardless of how or where the information was generated. What began as outdated guidance surfaced through AI ended as a legal and public accountability issue.
There are a few buckets that AI-related content risk tends to fall into. Here are some common failure modes to be wary of:
- Outdated information as “current” fact. AI systems resurface archived content without timestamps, so policies, pricing, or product details that no longer apply are delivered as if they were up to date.
- Inconsistent messaging across content types. Your blog says one thing, your help docs another, and your landing page a third. AI systems amalgamate those contradictions into confident answers that may be completely off base.
- Nuance and disclaimers stripped away. Legal caveats and contextual qualifiers rarely survive AI summarization. The careful language your legal team approved gets compressed into declarative statements.
McKinsey’s 2025 State of AI survey found that 51% of AI-using organizations have already experienced at least one negative consequence from AI deployment, with inaccuracy the most commonly cited issue. This represents structural exposure that content teams now own, whether they planned to or not.
Why Most Teams Aren’t Set Up for This Role
Content teams evolved to optimize for different metrics: speed, volume, engagement, traffic. But in many cases, the established workflows that serve those goals actively work against accuracy governance: Publishing calendars prioritize velocity, and editorial reviews tend to focus on voice and clarity. Legal approval processes that were designed for campaigns (discrete, time-bound assets) might not extend to evergreen content libraries that AI systems mine indefinitely.
And ownership gets murky fast. Who’s responsible for updating a three-year-old blog post when regulations change? Who audits help documentation when product features evolve? In most organizations, that accountability doesn’t exist.
Content teams sit at the center of this vacuum, creating the assets AI systems consume, without the mandate, tools, or headcount to manage the downstream risk.
How Teams Are Adapting Without Slowing Down
The organizations getting this right are building what we call the Content Risk Triage System — four interlocking practices that maintain velocity while managing exposure.
- Tiered review models. Not every piece of content carries equal risk. A best practice is to classify content by exposure: high-stakes claims (pricing, compliance, capabilities) route through legal review, standard editorial content moves faster with SME sign-off, and low-risk assets publish with editorial approval alone.
- Content risk scoring. Assign risk classifications at the brief stage. Content touching regulated topics, making quantifiable claims, or likely to be cited by AI systems should get flagged for additional verification before drafting begins.
- Clear ownership for content lifecycle. Designate owners not just for creation but for ongoing accuracy, e.g., one person who owns the quarterly audit of evergreen content and another team member who manages the sunset process for outdated assets.
- Treating content as living systems. Instead of “publish and forget,” treat your content libraries like software: versioned, maintained, and regularly patched. When policies change, content updates follow within defined SLAs.
What Content Leaders Should Do Next
Content leaders need practical systems that reduce risk without bringing publishing to a halt. These three steps are a reasonable jumping-off point:
- Start with an audit. Identify your highest-exposure content: pages making specific claims, documents AI systems frequently cite, assets in regulated topic areas. These are your first candidates for accuracy review.
- Set realistic standards. You can’t fact-check everything quarterly. But you can establish clear thresholds for what triggers review: regulatory changes, product updates, specified time intervals for high-risk content.
- Make risk management part of content strategy, not a bolt-on. Build verification into your editorial workflow. Include accuracy checkpoints in your content calendar. Staff appropriately for the governance work that now falls to content teams.
For organizations needing additional support, Contently’s Managing Editors can serve as an embedded layer of editorial governance, helping teams maintain accuracy standards without sacrificing publishing velocity.
The cost of fixing content after it spreads is far higher than the cost of managing it upfront. Don’t spend your next quarter doing damage control; put proactive systems in place today. It’s the resolution that will give back all year long.
For more on building content operations that scale responsibly, explore Contently’s enterprise content solutions.
Frequently Asked Questions (FAQs):
How do I know if my content library has risk exposure?
Start by auditing content that makes specific claims: pricing, capabilities, compliance statements, health or financial guidance, etc. Then identify assets that AI systems frequently cite by testing queries in ChatGPT, Perplexity, and Google AI Overviews. Content appearing in AI responses carries the highest exposure and should be prioritized for accuracy verification.
What do I need if I’m on a small content team with no dedicated compliance support?
At a minimum, assign clear ownership for content accuracy reviews on a quarterly cadence. Create a simple risk classification system that routes high-stakes content through additional review before publishing. Document your verification process so you can demonstrate due diligence if questions arise. These basics don’t require additional headcount, just intentional workflow design.
How do I get legal and compliance teams to participate without slowing everything down?
Build tiered review into your process from the start. Define what content types require legal sign-off versus what moves with editorial approval only. Create templates and pre-approved language for recurring claim types so legal reviews become faster over time. The goal is appropriate oversight, not universal bottlenecks.
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