At 2026 Veeva Commercial Summit, I sat down with brand leaders from Gilead Sciences, Boehringer Ingelheim, Pacira Biosciences, and more to discuss patient and HCP engagement.
This is a topic where everyone agrees on the aspiration. We want to get the right information to the right person at the right moment. We want patients and HCPs to feel supported. We want omnichannel to be more than a slide in a brand plan.
And yet, much of the actual brand experience still looks like this: a patient or physician arrives on a website, scans a navigation bar, clicks through several pages, and either finds it or gives up. The site may contain the information. The problem is that the experience assumes the user has the time, patience, and vocabulary to retrieve it.
That is the gap Veeva Ostro’s conversational AI is designed to close.
Here are the main takeaways from our discussion.
Most brand websites are not suffering from a lack of content. In fact, the opposite is often true. There is a large amount of high-quality, MLR-approved material already available.
The problem is that the content is trapped inside a static architecture.
A doctor with a specific question does not want to browse a website as though they are reading a brochure. A patient looking for information does not want to guess which tab contains the answer. Both groups want the same thing everyone now expects from digital experiences: ask a question, get a relevant answer, and move on.
A brand leader at a top 20 biopharma, described the shift clearly. Brand teams need to move beyond one-way, static touchpoints and create more of a two-way dialogue. An HCP should be able to come to a brand site, ask a specific question, and receive an immediate, compliant response.
Geetha Parachuru, director of commercial digital products at Gilead Sciences, made a related point. Physicians are busy treating patients. They should not have to search across a site to find specific, time-sensitive information. The answer should be available at the moment they need it.
This sounds obvious, but it represents a meaningful change in how brand teams think about engagement. The goal is not just to increase clicks, views, or time on site. The goal is to answer the questions patients and HCPs actually have.
Every industry is talking about AI. Biopharma has a more specific question: can this system be trusted not to hallucinate, improvise, or wander into off-label territory?
That question is not a bureaucratic obstacle. It is the right question. Legal, regulatory, and compliance teams are responsible for making sure brand communications are accurate, approved, and appropriate. If an AI system cannot operate inside those boundaries, it does not matter how impressive the demo is.
A leader explained why Veeva Ostro was different for their team. Ostro only pulls from 100% MLR-approved content. It does not invent new claims. It does not generate unsupported answers. Its precision AI interprets the question and retrieves answers exclusively from approved material.
That distinction helped them make the case internally. The value proposition was not that the AI is smart. It was that the system is constrained to approved content by design.
Still, internal buy-in does not happen automatically. Teams need to bring legal, regulatory, medical, commercial, and digital stakeholders into the process early. They need to answer practical questions. They need to show how the system works, where the content comes from, and what guardrails are in place.
Kip Finch, senior associate director on the mature assets team at Boehringer Ingelheim, emphasized the importance of an internal champion: someone who can follow up with stakeholders, keep momentum, and move decisions forward.
When that foundation is in place, implementation can happen quickly. One panelist shared that their team went live with Veeva Ostro in just four weeks.
The slow part is usually not the technology. It is the organizational trust-building required to use the technology responsibly.
For years, omnichannel has had a simple slogan: deliver the right content, in the right channel, to the right person, at the right time.
This is a good slogan because it is correct. It is also a frustrating slogan because it is much easier to say than to do.
Stacy Stone, executive director of omnichannel marketing at Pacira Biosciences, described how AI is changing that. Whether a patient or HCP enters through an email, a banner ad, or a brand site, the experience can now become more relevant immediately. Instead of sending everyone to the same static destination, the brand can respond to what the person is actually trying to understand.
That is the real promise of AI for brand engagement. Not a novelty. Not a chatbot for its own sake. A scalable way to deliver useful, personalized, approved information.
It also creates a new kind of insight for brand teams.
A traditional website can tell you what people clicked. It can tell you where they dropped off. It can tell you which page had more traffic. These are useful signals, but they are still indirect. They do not always tell you what people are trying to learn.
A conversational experience can.
One panelist shared that their team discovered physicians were asking about an important aspect of their drug’s administration. As she put it: “Veeva Ostro delivers the Commercial Evidence needed to uncover hidden friction points. For us, it revealed that HCPs required better access to surgical wristbands used to ensure safer administration of our drug, an unmet need a traditional brand website would have missed.”
That is a good example of the difference between analytics and evidence. A clickstream might show that a page is underperforming. A conversation can reveal the hidden operational problem underneath the engagement data.
Patients and HCPs are not just generating traffic. They are telling brand teams, in their own words, what they care about, what they cannot find, and where the current experience is falling short.
Nearly every biopharma company now has some version of an AI mandate. That is understandable. The technology is important, and no one wants to be late.
But “do something with AI” is not a strategy. It is an ambient pressure.
The stronger approach is to begin with a real business or customer problem. Where are patients getting stuck? Where are HCPs failing to find information? Where are brand teams flying blind? Where is there a meaningful gap between the experience the brand wants to deliver and the experience users actually receive?
Geetha’s advice was to think strategically and act tactically. Understand the current state. Identify the pain point. Then determine where AI can help.
Another leader added that the content foundation matters. Modular content, claims, and approved source material are not side issues. They are what make compliant AI possible. If the foundation is weak, the AI experience will be weak too.
This is the less glamorous but more useful version of the AI story. The best use cases often do not start as abstract innovation projects. They start with familiar problems that brand teams have been trying to solve for years.
Patients cannot find answers. HCPs do not have time to dig. Brand teams do not know which questions are going unanswered. Static websites are not built for dialogue.
AI becomes valuable when it solves those problems.
For too long, brand sites have placed the burden on patients and HCPs. The information may be there, but the user has to do the work of finding it.
That model made sense when websites were the best digital interface available. It makes less sense now.
With Veeva Ostro, leading biopharma teams are replacing static brand experiences with compliant, conversational engagement. Patients and HCPs get faster access to approved answers. Brand teams get a clearer understanding of intent, friction, and unmet needs. Legal, regulatory, and medical teams get the confidence that the system is grounded in approved content.
The result is not just a better website. It is a better way for brands to listen, respond, and learn.
To learn more about how leading brands are replacing static sites with conversational AI, read this white paper.
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