Pharma Marketing Hits Its AI Inflection Point in 2026

Recently, OpenAI launched ChatGPT Health. Anthropic followed days later with Claude for Healthcare. Google quietly pulled AI-generated health summaries after an investigation found they were serving misleading information. The message is clear: AI is now the front door for health information—and the race to own that door is accelerating.

Six months ago, a physician searching for dosing guidance on a GLP-1 would have clicked through to the prescribing information. Today, they’re asking ChatGPT—and the answer may or may not mention your brand.

That’s the shift. Not theoretical, not coming soon—already here.

To understand what this means for pharma marketing, I reached out to ten industry leaders across agencies, brands, and technology companies. I asked them: What should the industry be excited about? Where’s the biggest gap between hype and reality? What aren’t we talking about enough?

Their answers don’t form a tidy consensus. Some see breakthrough potential; others urge caution. Some are building the infrastructure; others are questioning whether we’re building on solid ground. What emerges is an industry at an inflection point—moving fast, but not always in the same direction.

Here’s what they told me.

Your Brand Now Lives in AI Answers

Consumer behavior has already shifted—and many brands haven’t caught up. When patients and physicians search for treatment information, they’re increasingly turning to AI tools that synthesize answers rather than serve links. If your brand isn’t surfacing in those responses, you’re losing ground you didn’t know existed.

Eric Solomon, Practice Lead of Paid Media at Real Chemistry, has watched this shift accelerate. His team developed Health GEO—what he describes as “the LLM version of SEO for healthcare brands”—a tool that tracks how often and how accurately brands appear in large language model responses. Dozens of clients quickly leveraged the offering in 2H 2025, and the list keeps growing.

But measurement is only half the battle. Real Chemistry also built Compliance Compass, a tool that scrapes FDA warning letters and cross-references them against client content—flagging potential issues before they become problems. “That was a quick solution for a real healthcare client problem,” Solomon notes.

His broader point cuts deeper: “The consumer behavior shift is more important than the AI technology itself. It’s about rising expectations around information access and intent.” Brands that don’t track their presence in AI answers are operating blind.

From SEO to AISO

If Eric Solomon is measuring the problem, Harlan Schwarz is prescribing the solution. The EVP of Media at Inizio Evoke has been tracking how search behavior is fragmenting—and the numbers demand attention.

“Today, 60% of searches end without a click as users increasingly rely on AI-generated summaries,” Schwarz observes. “54% of queries are personalized through AI-driven behavioral analysis, and 45% are handled by chatbots or virtual assistants. Trust in AI is growing—68% of generative AI users believe the information they receive—yet many don’t realize they’re interacting with AI.”

The strategic response requires rethinking how pharma brands create and distribute content. “Brands should publish through third-party medical publishers, peer-reviewed platforms, and expert-authored pieces,” Schwarz advises. “Collaborate with advocacy organizations, influencers, and journals—not just branded sites. Fund content placements that act as signal-boosters for AI: earned and paid placements in trusted environments like PubMed and LinkedIn.”

He calls this the shift from SEO to AISO—AI Search Optimization. “Tracking the AI footprint—not just clicks—will define competitive advantage in this new era.”

The Next Audience Currency

While others focus on where AI is today, Lauren Jacobson is watching where it’s headed. The Head of Performance Planning for Pharma at Initiative sees a shift coming in how audiences are built and targeted—one most marketers aren’t yet preparing for.

“AI search platforms require logins and personalization,” Jacobson observes. “That makes enhanced AI search intent one of the most powerful future audience signals.”

She maps out the trajectory: users will opt in or out of search-based targeting, AI platforms will package anonymized intent audiences, and advertisers will target based on what people are actively asking—not just passively browsing.

“For pharma, this is enormous,” she says. “Early-stage symptom exploration, category entry moments, pre-diagnosis education opportunities. Someone asking an AI about fatigue and joint pain is signaling intent in a way that browsing WebMD never could.”

Her prediction is blunt: “AI search intent is the next great audience currency.” The brands that figure out how to reach patients at the moment of inquiry—compliantly and at scale—will have an advantage that’s difficult to replicate.

Dashboards Are Dead, Agents Are Here

For Manik Khanna, CEO and Co-founder of SemantIQ Health, the excitement isn’t about better analytics—it’s about a fundamental shift in what AI systems can actually do.

“For the last twenty years, we’ve built dashboards and point analyses,” Khanna explains. “AI is finally becoming operational. We’re moving from static analytics to agentic systems that can run end-to-end workflows: audience design, measurement, planning, and optimization on top of governed healthcare data.”

But Khanna is blunt about where the hype collapses. “The biggest myth is that you can just ‘add AI’ and suddenly data becomes usable,” he says. “Without a strong data foundation—a semantic layer and transparent logic—all you get is more confusion.”

His most quotable line cuts to the heart of the matter: “You cannot prompt your way into domain expertise.”

For pharma, the stakes are higher. “In regulated industries, an answer is only valuable if you can explain where it came from, reproduce it, and defend it,” Khanna argues. “Every metric needs lineage, logic, and governance built in—not bolted on later.”

His view is clear: “The future isn’t AI that answers questions. It’s AI that explains how it gets to an answer.”

The Plumbing Problem

Matt Ryklin, VP Data Ecosystem and AI Partnerships at PurpleLab, is excited about what’s finally possible—and clear-eyed about what’s holding the industry back.

“The industry should be excited about clean rooms finally letting pharma marketers target and measure dynamically and efficiently,” he says. “For years, healthcare data couldn’t plug into the programmatic ecosystem the way retail or CPG data could. That’s changing fast.”

But there’s a catch—and it’s not where most people are looking.

“Sometimes we forget the foundation,” Ryklin observes. “We’re layering AI on top of fragmented data and expecting magic. The models are great—but the problem is underneath. How does data flow between pharma companies, agencies, DSPs, and measurement partners? That pipeline has long been held together with manual processes and good intentions.”

His focus is what he calls the connectivity layer. “AI creates the opportunity for truly dynamic measurement—ask any question, get a custom answer. But that only works if data can move between partners securely and in near real time.”

The unsexy truth: “Clean rooms and privacy-safe identity are the infrastructure that makes AI actually useful.” Without the plumbing, the AI is just a smarter tool sitting on the same broken pipes.

From Transactional Targets to Outcome-Driven Audiences

Brad Fox, SVP of Health Media at dentsu, sees AI reshaping something fundamental: how pharma audiences are defined in the first place.

“Historically, audience building has been a transactional exercise,” Fox explains. “A brand or agency asks a provider to build an audience off a definition of ‘patients diagnosed with X’ or ‘patients diagnosed with X and prescribed Y.’ Anyone who has navigated a personal health journey knows it’s rarely that simple.”

The reality is messier—and more meaningful. “There are pre-diagnosis signals, multiple provider interactions, treatment trial-and-error, and ongoing care decisions that unfold over time,” he observes. “The traditional approach compresses this complexity into a single endpoint, omitting the nuance, and the journey, that can meaningfully improve relevance and reach.”

AI changes the equation. “Predictive AI and neural networks trained on anonymized health outcomes let us model patterns across the full continuum of patient journeys—capturing pre-diagnosis indicators, diagnosis trajectories, treatment initiation, adherence, and more,” Fox says. “Instead of static labels, our approach & partnerships enable us to design outcome-driven audiences that reflect how real people move through the healthcare system.”

Creative Testing Without the Wait

While much of the AI conversation centers on data and targeting, Daniel Lopez sees breakthrough potential in creative development. The Director Digital Investment at Havas Media Network has been deploying AI tools that are fundamentally changing how pharma creative gets tested.

“We’ve shifted into a world where we no longer have to wait weeks for creative testing with individuals who may not even show up,” Lopez explains. The alternative: AI-powered attention mapping that shows exactly where viewers focus—and whether that attention lands on elements that matter.

“We use heat mapping to see where users are naturally drawing their attention,” he says. “Are they looking at the hero image? The CTA? Or—critically for pharma—are they actually seeing the ISI?” In an industry where Important Safety Information isn’t optional, knowing whether patients register it changes the creative calculus.

The other breakthrough: synthetic patient personas. “Feeding AI what we know about our target audiences, we can create digital personas to test creative before a human ever does,” Lopez explains. “We can test emotional response across personas like a multiple myeloma patient who’s been through three treatment rounds.”

The traditional focus group isn’t dead, but it’s no longer the only option—or even the fastest one.

Competitive Intelligence Gets a Compliance Lens

Eric Steigelfest, CEO & Co-Founder of PurePlay AI, sees an opportunity hiding in plain sight: competitor creative as a window into regulatory strategy.

“Pharma has always done competitive creative intelligence, but most of it is surface area,” Steigelfest observes. “We count formats, track claims, screenshot the hero frame. That’s useful, but it misses the strategic core. In a regulated category, the competitive advantage is rarely the idea itself—it’s the interpretation of constraints.”

Two brands can read the same FDA guidance and arrive at very different creative executions because they made different decisions about what risk to foreground, how to sequence fair balance, and what tradeoffs to accept. Those decisions are the real competitive blueprint.

“CTV is not a banner. It’s audiovisual persuasion under regulatory pressure,” Steigelfest explains. “Every competitor spot is a compressed record of how a brand navigated MLR, medical, and legal to land on an executable ‘yes.’ When you treat competitor creative as data, not inspiration, you can extract a compliance strategy.”

This requires multimodal AI—models that interpret what is said, what is shown, and when. “Where does the ISI begin relative to the main claim? How long does it remain on screen? Is it visually dominant or minimized?” Those aren’t just creative choices. Those are regulatory bets.

His bottom line: “The brands that win in 2026 won’t just have better creative. They’ll have better regulatory interpretation—operationalized.”

AI Can’t Model Irrational Humans

Seema Keswani offers a necessary counterpoint to the AI optimism. As SVP of Strategy at VCCP Health—the Challenger Agency for Challenger Brands—she brings a philosophy built on asking questions others don’t. “The best insights come from challenging what is seemingly right in front of us,” she explains. Put into action, the VCCP approach has illuminated a fundamental limitation too few marketers are discussing.

“There’s an emerging trend: AI-powered personas for market research,” she observes. “Train an AI on customer data and ask these ‘digital twins’ for feedback. It sounds like a breakthrough. Importantly, it sounds efficient.”

The problem? “These technologies risk making one critically incorrect assumption: that human health decisions are fundamentally logical.”

She quotes Rory Sutherland: “Not everything that makes sense works, and not everything that works makes sense. Because the human mind does not run on logic.”

The examples hit close to home. “Ask people why they brush their teeth and they’ll say ‘for healthier teeth.’ Yet behavioral observation reveals something different: people brush before social events. The real motivation isn’t health—it’s confidence.”

In healthcare, the stakes compound. “Patients stop taking life-changing medications because they ‘don’t feel sick.’ They avoid proven therapies because of social stigma. These irrational human truths may never surface in AI learning data. But these truths unlock brands. They propel brands from Challenger to Leader.”

‘AI-Powered’ Doesn’t Mean Pharma-Ready

Leah Venturina, Senior Director of Programmatic Media at Klick Health, brings battle-tested pragmatism to the AI conversation. Through evaluating many AI tools, she and her team have seen too many failing to live up to their hype, falling short when they hit the realities of pharma marketing.

“Many AI tools look impressive in isolated pilots, but programmatic success in pharma requires repeatability at scale across diverse disease states and audiences,” she explains. “When AI is designed with pharma constraints in mind, it can genuinely reduce friction and raise the floor on execution—but only a small subset of tools are actually there today.”

Her diagnosis extends to the vendor landscape. “Right now, everyone is launching an ‘AI-powered’ solution and positioning it as industry agnostic, but very few are built for pharma’s strict regulatory environment. This flood of new vendors often creates more work for marketers, as the lines between true viability and mere novelty become blurred.”

She also points to another challenge few acknowledge: “AI adoption in our industry accelerated rapidly, often bypassing foundational training necessary for responsible use. Without baseline knowledge, teams risk over-trusting outputs in a high-stakes category where errors have significant consequences.”

Venturina’s bottom line: “The industry is moving past the novelty of ‘AI-powered’ to focus on whether these tools can run compliantly, repeatedly, and transparently in real-world activation.” The vendors who can prove it will win. The rest will fade into the pile of pilots that never scaled.

* * *

Ten voices, ten vantage points—but the signal cuts through the noise.

The behavior shift isn’t coming; it’s here. Patients and physicians are already asking AI for answers, and brands that don’t show up in those responses are invisible in moments that matter. The infrastructure underneath—clean rooms, data connectivity, governance frameworks—will separate companies that can deploy AI from those stuck in perpetual pilot mode. And the human element remains irreducible: in a category defined by irrational decisions, fear, hope, and stigma, the marketers who remember the limits of logic will outperform those who forget.

The contributors to this piece don’t agree on everything. But they share one conviction: the window for waiting and watching has closed. The dynamics shaping pharma marketing in 2026 are already in motion.

The question isn’t whether AI will transform this industry. It’s whether you’ll be ready when the transformation is complete.

This piece was written by Jason Lotkowictz SVP, Life Sciences at PurePlay.