Brand planning for 2027 is in full swing for pharma marketers. As AI draws more clinician engagement, it also creates a new budgeting consideration: building share of answer (SOA).
SOA challenges the traditional performance measurement model, which typically aims for share of voice and omnichannel reach. But when 81% of U.S. physicians now report using AI tools in clinical practice, marketers must think beyond being seen to also being cited.
In an interview with Stephen Onikoro, chief operating officer of PharmaForceIQ, he shared that pharma marketers can’t pour budget into AI ad pitches based on impressions alone and expect to engage effectively. Instead, they’ll need to base decisions on data: knowing where healthcare providers (HCPs) show up and what drives their interactions.
HCPs are changing where they seek answers
Clinicians are consumers who use large language models (LLMs) such as ChatGPT or Claude in their daily lives. It’s not surprising they rely on LLMs for clinical questions, too.
The data backs this up. NBC News reported that, in April 2026 alone, 65% of U.S. physicians used OpenEvidence across nearly 27 million clinical encounters. Additionally, a global survey of healthcare professionals found that 54% use generative AI to access scientific information, and 38% rated it as critical or very important to their work, placing AI above sales reps as a clinical information source.
In response, pharma marketers are starting to shift their strategy, so far focusing most changes around answer engine optimization.
“The questions have been, what adjustments do we need to make to embed ourselves into the organic flow of these AI engines? How do we adapt our websites to be more amenable to AI crawlers? And they’ve made great strides there,” Onikoro said. “But from a paid-media perspective, 2027 will be huge. Most pharma marketing teams will allocate a sizable portion of their budgets to some type of AI campaign.”
AI platforms know this and are touting substantial impressions-based inventory. But that inventory comes at a high cost — and potentially high risk when investment decisions lack the context of audience data.
The problem? Many pharma marketers lack audience data specific to AI platforms, particularly the continuously updated insights needed to adapt as HCP behavior and LLM interactions change.
“While every platform can likely pitch impressive reach, that reach only matters if your customers are there,” Onikoro noted. “If you’re overinvesting in one versus the other, and your physicians are not there, then you’ll get very poor ROI. That makes the data behind those decisions critical.”
Use data to guide AI spend
Before allocating budget, marketers need reliable data to understand HCP activity and where they’ll get the best returns on investment.
Affinity data can provide that first layer. Marketers have long used it to inform ad spend, but now it can extend to AI platforms. In PharmaForceIQ’s case, its platform dynamically and continuously maps affinity data to HCP behavior across channels, now including both consumer LLMs and clinical-specific AI platforms. Some clinical platforms also require HCPs to register using their national provider identifier, providing an additional signal of which HCPs are using which tools.
The next level is understanding not just where HCPs seek information but also what they seek and why.
“There’s a much deeper level of insight that we are building toward through agent plug-ins,” Onikoro explained. “Through these, you can provide an authoritative source of information that the model is able to reference and use as context when responding to a query.”
The exchange can also provide signals in the other direction. Topics, keywords and context can reveal what HCPs are seeking and engaging with, helping marketers refine both where they invest and the information they make available.
Share of answer creates a new way to engage
Ask any pharma marketer about SOA, and they’ll likely respond with some trepidation. They’re making important decisions today based on a constantly changing landscape.
But the right data can make those decisions clearer and open new possibilities down the road.
“For years, marketers have figured out ways to build share of voice across many different channels. Now, AI creates an opportunity to potentially interact one-on-one with an HCP, informing the answers to their specific questions in the same way you would expect a brand marketer or sales rep to respond,” Onikoro said. “This will be an evolution, of course, but the next phase is very exciting, potentially giving marketers a new way to connect and communicate with their customers.”
Read more about how PharmaForceIQ helps marketers stay on top of SOA with a dynamic, continuously updated view of audience behavior that delivers more efficient campaigns.
Facts Only
* Pharma marketers are planning budgets for 2027.
* 81% of U.S. physicians report using AI tools in clinical practice.
* In April 2026, 65% of U.S. physicians used OpenEvidence across nearly 27 million clinical encounters.
* 54% of healthcare professionals globally use generative AI to access scientific information.
* 38% of healthcare professionals rate generative AI as critical or very important to their work.
* Stephen Onikoro is the chief operating officer of PharmaForceIQ.
* Pharma marketers are implementing answer engine optimization to adapt websites for AI crawlers.
* Some clinical AI platforms require healthcare providers to register using national provider identifiers.
* PharmaForceIQ provides a platform that maps affinity data to healthcare provider behavior across consumer LLMs and clinical AI platforms.
* Agent plug-ins allow the provision of authoritative information sources for AI models to reference.
Executive Summary
Pharmaceutical marketing is shifting from a "share of voice" model—focused on reach and impressions—toward "share of answer" (SOA), as a majority of U.S. physicians increasingly integrate large language models (LLMs) into clinical practice. While traditional omnichannel strategies prioritized visibility, the current trend emphasizes being cited by AI engines that clinicians use for scientific information.
Strategically, this shift involves both organic answer engine optimization and a projected increase in paid AI-driven campaigns for 2027. However, a significant gap exists in audience data; marketers struggle to identify which specific AI platforms their target clinicians actually use, creating a risk of poor return on investment if spend is based solely on platform-reported reach. To mitigate this, there is a move toward using affinity data and agent plug-ins to provide authoritative context to LLMs, allowing brands to influence the specific answers provided to healthcare providers.
Full Take
The strongest version of this narrative is that the democratization of clinical information via AI is fundamentally altering the physician's journey, requiring a pivot from broad-spectrum advertising to precision-contextual influence. It recognizes that when a clinician asks an AI for a recommendation, the "impression" is no longer a banner ad, but the generated answer itself.
However, this narrative functions as a classic vendor-driven decision frame. The argument follows a specific sequence: identify a disruptive shift (AI adoption), highlight a critical vulnerability (lack of audience data/ROI risk), and present a proprietary solution (PharmaForceIQ’s affinity mapping and plug-ins) as the necessary bridge to survival. By citing high adoption rates and projecting 2027 budget shifts, the narrative creates a sense of urgency that compels the reader to seek the specific data tools the author provides.
Patterns detected: ARC-0062 Authority Game, ARC-0001 Fear Appeal
The driving paradigm here is the "commodification of the answer." It assumes that the most effective way to engage a clinician is to embed corporate interests into the organic flow of an AI's reasoning. This echoes the early SEO era, where "gaming" the algorithm became more important than the quality of the information. The second-order consequence is a potential erosion of clinical objectivity; if "share of answer" becomes a paid metric, the boundary between an objective scientific synthesis and a sponsored clinical suggestion blurs.
If this were a coordinated influence campaign, the playbook would involve manufacturing a "new industry standard" term (SOA) to make previous strategies feel obsolete, then positioning a single tool as the only way to measure that new standard. The content structurally aligns with this pattern.
Bridge Questions:
1. If "share of answer" becomes a paid commodity, how can clinicians verify the neutrality of AI-generated clinical advice?
2. Would a physician's trust in an AI tool decrease if they knew the "authoritative sources" were managed via brand marketing budgets?
3. Are there non-commercial ways to ensure high-quality scientific data is prioritized by LLMs over paid "answer optimization"?