By Paul Herbert, SVP, Digital Strategy, Inizio Evoke Comms
Talk of AI is still everywhere – you can’t scroll, search, or sit in a meeting without hearing about another way that AI has, or will, change the way we do things. AI is now part of the communications environment, whether teams plan for it or not. Among all the noise, here are 10 things pharma and biotech communicators need to be aware of (and do) now.
1. AI should be treated as a new audience, not a new channel
AI doesn’t distribute messages like media, social, or search. It interprets, reconciles, and synthesizes information on behalf of others, deciding what context matters, what’s credible, and what can be ignored. We now must design messages and content for machine interpretation, as well as human consumption.
Design communications for interpretation, not just distribution. Plan content around how AI will combine, prioritize, and explain your messages, not simply where they will appear.
2. AI can shape understanding before engagement occurs
For many audiences, AI-generated answers are no longer a follow-up to discovery, but the starting point. By the time someone reaches a website, article, or expert conversation, an initial frame of understanding may already be set.
Communications strategies must also now account for what happens before direct engagement occurs.
3. Visibility alone no longer suffices as a metric of success
Being mentioned in an AI-generated answer doesn’t guarantee the information is accurate, complete, or responsibly framed. In healthcare especially, presence without context can be misleading. The quality of the answer – not just inclusion – is becoming the more meaningful signal.
Use frameworks that evaluate answer quality — accuracy, completeness, and framing — and translate those findings into clear priorities for improvement.
4. Consistency across channels now outweighs perfection in any one place
AI systems learn from patterns over time. Disconnected or conflicting messaging across owned, earned, and third-party sources creates ambiguity that AI resolves by defaulting to generalized high-level answers. This might mean your treatment gets grouped with the rest, when you should really stand out as the right choice.
Strategic alignment across teams and channels is now a prerequisite for accurate interpretation.
5. Earned media becomes a key ‘authority signal’
Earned media continues to play a critical role, not just in shaping human perception (audiences still trust and engage with their preferred outlets), but in reinforcing credibility signals that AI systems rely on.
When trying to influence how AI responds, prioritize earned media that AI systems treat as authoritative and can access - make sure you or your agency maintain a list of media blocked by or to AI.
6. Paywalls mean earned media can’t rely on ‘Top Tier’ coverage alone
With more coverage moving behind paywalls, trusted national and health outlets still shape credibility but AI tools face access constraints and can’t always fully interpret the underlying content – especially nuance found beyond headlines and abstracts. Whether an outlet is paywalled needs to inform strategy.
Pair paywalled coverage with AI-readable explainers and structured owned content, ensuring key messages are echoed across accessible, trusted channels.
7. Expand the role of influencers and experts from amplifiers to contextual anchors
KOLs, advocates, and credible third-party influencers remain critical as trust and personal connection become more essential. People will still turn to trusted voices online even as AI-mediated answers grow, but teams must consider how influencer strategies can shape both the answers AI learns from and the context audiences apply to them.
Align with influencers for credibility and context, not just amplification.
8. Keep using paid — but factor in how and where it may shape AI answers
Paid campaigns still play a critical role in getting content in front of, and engaging, the right audiences in an AI-mediated landscape. While paid activity may not directly influence AI answers, it can help determine which content is seen, discussed, reused, and referenced – shaping the wider information environment AI systems eventually learn from. Used well, paid can increase signal exposure and velocity, even if it’s not the primary driver of AI outputs.
Prioritize paid amplification of authoritative, AI-readable assets and track where they travel, who engages, and which environments are visible to AI to shape ongoing strategy.
9. GEO is not a tactic – it should inform the whole communications plan
Generative Engine Optimization (GEO) is the strategic discipline of understanding and shaping how brands, data, and expertise appear in AI-generated answers. It’s now an important part of overall communications planning and isn’t something to tack on at the end of a campaign. GEO needs to shape how content, narratives, earned activity, and expert engagement are designed from the outset. Treated tactically, its impact will be limited.
Add GEO as a key part of your communications strategy at the outset.
10. You can’t influence AI understanding if you don’t know what it’s saying
Before optimization comes diagnosis. Organizations need visibility into how AI tools currently describe their category, their science, and their role. Without understanding the answers being generated today, efforts to influence future responses are guesswork.
Auditing how your story is currently showing up in AI is a critical first step in shaping communications programs.
AI-mediated search and answers aren’t replacing earned, owned, or paid channels – but they are changing how those channels get interpreted and combined. GEO is no longer a specialist add-on; it’s becoming table stakes for effective communications planning. Teams not adapting lose more than visibility: they hand narrative control of how their science, data, and reputation are understood and perceived to whatever AI scrapes next.
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