Pharma is shifting from traditional Share of Voice to Share of Answer (SOA), driven by mainstream HCP AI adoption and proliferating answer engines. This article establishes a framework for executing Answer Engine Optimization (AEO) across clinical and general LLM surfaces, deploying targeted paid AI media, and architecting cross-functional operating models to secure brand citations and anchor a successful pharma marketing strategy.
For decades, pharma relied on a ‘spray-and-pray’ model of marketing where Share of Voice (SOV) was achieved through volume alone. The tactic was simple – out-shout the competition. In recent years, that approach has begun to fail as digital platforms have fragmented and HCPs have increasingly demanded hyper-personalized content, delivered on their preferred platform at a time that is relevant to them.
Now, the rise of generative and agentic AI is transforming information-seeking behavior again. More than 8 in 10 US physicians now use AI tools professionally, a figure that has more than doubled since 2023. Even more striking, 65% of US physicians utilized a single engine, OpenEvidence, across nearly 27 million clinical encounters in April 2026 alone. This is no longer an early-adopter story – it is mainstream clinical behavior.
In a zero-click reality, the old tactics and metrics are obsolete. Brand visibility today depends on how well AI models understand, interpret and cite your content. As AI shifts how information is delivered and consumed, traditional SOV becomes irrelevant. You no longer need to just be at the top of a list of articles. Visibility is about being the definitive answer provided.
Changing Information-Seeking Behaviors: The Reality of HCP AI Adoption
The way we search for information has fundamentally shifted. There are now more than 230 million weekly health queries on ChatGPT alone. Search engines have also introduced AI Overviews (AIOs) which synthesize answers from a variety of sources.
From Search Clicks to AI Answer Engines
Users no longer dig through pages of search results. Instead, they ask specific questions and get answers without ever visiting an authoritative website. Users who encounter an AIO are only half as likely to click on links to other websites as those who do not encounter an AI summary.
1 in 2
HCPs use generative AI to access scientific information
94%
say AI makes it easier to find the information they need
7 in 10
say it helps them to make better treatment decisions
The Disconnect Between Field Force Realities and Digital Discovery
There is also a disconnect between how HCPs and industry perceive the importance of different information sources. HCPs already rate generative AI above sales force as a source of scientific information. In contrast, pharmaceutical executives vastly overestimate the importance of traditional engagements with sales reps and MSLs.
This gap means pharma companies are failing to prioritize what is now a critical information source for HCPs within general AI answer engines and AI-powered clinical decision support tools. If a brand is not in an AI-generated answer, it is like being on the third page of traditional search results – it may as well not exist.
The Limitations of Omnichannel Strategies
Omnichannel promised a seamless experience to drive impactful engagements with HCPs. But the reality is outdated campaigns, budgets being stretched too thinly and increased burden on field reps. Campaigns built on six-month-old data and infrastructure challenges mean messages are reaching HCPs outside of relevant decision-making windows.
Meanwhile, vendors continue to focus on vanity metrics like total impressions or SOV which fail to correlate with clinical intent or script lift.
This has had a serious impact on the effectiveness of omnichannel strategies. Strikingly, zero pharma leaders reported that omnichannel has been effective with no major limitations in our Q1 2026 survey with Biopharma Dive, and many have experienced significant challenges around implementation and enablement.
These results should raise serious questions – both about the value of messaging, which fails to reach HCPs at the right point in time, and how we measure marketing campaign success. Asking these questions is even more important as this shift in discovery dramatically alters brand visibility in new ways.
Share of Voice vs Share of Answer: The New Commercial Success Metric
When HCPs are turning to AI tools and agents for answers before brand or industry content, SOV is no longer the right metric to measure campaign success. Instead, the industry must focus on Share of Answer (SOA).
Defining Citation Share in a Zero-Click Environment
So, what is Share of Answer?
Unlike legacy metrics, SOA relies not on being the most frequent answer, but on a brand being the definitive answer, with its data used as the primary source synthesized by an AI engine.
It is about measuring a brand’s presence and positioning, or citation share, within AI-generated responses for the high-value clinical and category-specific questions being asked by HCPs today
Why Share of Answer is Binary, Not Cumulative
Content remains crucial to build trust and to earn citations in this new environment. AI engines scan the web and assess content for inclusion based on factors including structure, authority, expertise and trustworthiness. The content that engines deem to be clearest and most credible will be shown to users. In this environment, exposure is completely binary: your brand is either explicitly included in the synthesis, or it is skipped entirely.
While search engine optimization (SEO) still matters, brands must also look toward answer engine optimization (AEO) and generative engine optimization (GEO) to ensure their content structures are visible and citable for AI engines.
AEO is Two Games, Not One
To successfully capture SOA, brands must recognize that the AI engine landscape behaves like two entirely different channels. PFIQ’s analysis reveals a clear citation source inversion between general LLMs and endemic clinical engines:
- General LLMs (e.g., ChatGPT, Perplexity, Gemini): Early exploration surfaces are dominated by open community sources (38% of citations, with Reddit alone driving massive volume), and brand websites sit at just 9%.
- Endemic clinical engines (e.g., OpenEvidence, Glass Health, UpToDate): Point-of-care prescribing or other provider-focused clinical decision support tools where clinical authority sources jump to 78% of citations, while open communities plummet to 3%.
Optimizing for citation share in ChatGPT requires an open-community and third-party content playbook. Optimizing for OpenEvidence requires structured, peer-reviewed clinical evidence. The strategy doesn’t transfer, and in fact it inverts. It’s important to recognize that this isn’t a monolithic channel, and in fact you could break it down further into those engines that enable pharma ad placements and those that don’t (read on for more on media).
An Actionable Framework for AI Information Environments
Step 1: The AI Performance Audit
The first step for brands looking to explore AEO and shift to SOA as a meaningful success metric is to carry out a performance audit across question sets that mirror HCP and patient intent. For example: “What are the latest treatments for BRCA1-Associated Ovarian Carcinoma in 2026?”
This audit should consider:
- Whether brand content is cited when users ask queries about a specific indication.
- How often brand content is cited (citation share) and whether it includes a link.
- How this compares to competitor citation rates and links.
Step 2: The Technical Audit and Optimization Roadmap
Once the performance audit is complete, a technical audit should be carried out to develop a clear roadmap for optimization.
This is not a one-off project. AI visibility is not guaranteed and models frequently retrain. Instead, the roadmap should be continuously assessed and improved to account for drift as content landscapes and tools continue to evolve.
Step 3: Navigating the High-Cost Frontier of Paid Media in AI Answer Engines
The third step is to be strategic about the emerging commercial frontier of paid AI media. We can’t just start buying media on every answer engine available. Endemic clinical engines that take ads, like OpenEvidence and Epocrates, are commanding unprecedented CPMs in the $70-$1,000+ range (compared to $30-$80 for traditional medical display).
Buying media just because the inventory exists is a fast track to wasted spend. Brands must avoid treating the AI media landscape as a single category. Instead, smart spending requires matching the surface to where your specific HCPs query, targeting the high-intent clinical query itself rather than a broad impression, and layering HCP NPI affinity data over the placement.
The precise, data-driven approach offered by optichannel is still vital to ensure brands are investing in the right places to engage HCPs at the exact right moment when content will have the most impact. The industry must ensure it does not just extend broad-reach approaches into new AI channels if it wants to meaningfully engage with HCPs.
Moving Beyond Digital Marketing: The Cross-Functional AI Operating Model
Because AI-generated answers directly influence point-of-care prescribing decisions, they carry the exact same regulatory weight as a sales aid or website. Therefore, AEO can no longer sit isolated within digital marketing.
To win Share of Answer, pharma must break down traditional silos and stand up a singular, cross-functional operating model consisting of four core pillars:
- Brand: To own audience definition and message positioning.
- Medical Affairs: To lead publication strategy and ensure clinical accuracy.
- Regulatory: To manage MLR compliance and label-tethering.
- AI Governance: An emerging, vital function to maintain auditability, model accountability, and a defensible documentation chain.
The Next Frontier: Injecting Answer Engine Context
Structuring your data for AI engines is only the baseline.
The next frontier of winning Share of Answer isn't just about what the AI reads. It will become about who it is answering, and AI models are only as good as the context they are fed.
Visibility in a zero-click reality also requires a dynamic context layer—powered by a headless and industry-specific Model Context Protocol (MCP)—capable of passing live HCP preference and behavioral signals directly into LLM assistants, agents, and chatbots agnostically. By injecting this deep behavioral context into the AI ecosystem, brands can ensure that the answers synthesized by AI are automatically personalized to the exact moment of clinical intent.
Time to Finally Break through the Noise
Clinical decisions are increasingly being made before customers ever land on a brand’s site. But the speed of the change in information-seeking behaviors has left many teams struggling to keep up. With the industry and HCPs out of alignment on where value lies, urgent action is needed to ensure brands can still meaningfully engage and ensure their treatments reach patients.
Success will belong to those brands willing to stop relying on broad, ineffective campaigns and shift their focus to the precision of optichannel. By unifying optichannel behavioral insights with AEO content structuring, brands can ensure that when HCPs ask AI assistants for the best treatment, their therapy is the answer given. Those unable to adapt their content and their foundational AI data infrastructure will find themselves more lost in the noise than ever.