Why Human Judgment Still Matters in AI-Powered Biopharma Marketing

1. Can you tell us about Underscore and the challenges biopharma marketers turn to your team to solve? How does your focus on rare disease and oncology shape your approach?

Underscore is a specialist media agency built for complex biopharma, particularly rare disease and oncology. Clients bring us the problems that are hardest to solve: finding small patient populations, reaching the right healthcare professionals, connecting fragmented data and preparing specialty brands to launch without wasting time or investment.
Those challenges shape how we approach the work. In rare disease, the patient population may be very small and diagnosis can take years. In oncology, treatment decisions can involve multiple stakeholders. We have to understand those realities before deciding how to reach people, what the data is telling us and where media investment can make a difference.
Our team has worked across more than 50 specialty launches. That experience helps us recognize patterns and ask better questions early, especially when a brand has very little room for wasted spend. We bring that understanding into the decisions we make with clients, so the media plan reflects the business and the people it needs to reach.

2. As AI makes sophisticated marketing tools more widely available, where does deep category expertise still change the quality of a decision in biopharma?

Technology can help teams analyze information and move faster and more precisely, but the value of AI depends on how that information is interpreted and applied. Biopharma recommendations must account for small patient populations, long paths to diagnosis and treatment decisions that may involve multiple stakeholders. Those conditions change how you plan media and evaluate performance.
Deep category experience changes the quality and speed of the decision: it helps a team recognize when a signal matters, when it is misleading and when the data simply does not know enough. AI can accelerate the work, but it cannot manufacture judgment or perspective that isn’t there. I believe that experts + carefully curated AI drive the best results. Being expert enables you to spot when AI is connecting signals that don’t have realistic potential or are rooted in unstable proof points.

3. In rare disease and oncology, patient populations can be small and the path to treatment complex. How should those realities shape the way marketers interpret performance signals and decide when to act?

Start by defining what the data can actually tell you and where its limitations are. A signal may look clear in a report but become much less useful once you consider the patient journey, the concentration of relevant healthcare professionals, access, or the commercial constraints facing the brand. For example, driving traffic to” brand.com” or “diseaseEd.com” may aid awareness if the people landing there are the right people, but without qualifying the visitors, you may be wasting resources without realizing it. I think it’s important to have everyone aligned on what success looks like and moving in unison with the same understanding of how the key metrics and KPIs drive action from the beginning.
This is also where category experience matters. Teams need to ask whether the data is thin, biased, delayed or easy to misread. They also need to understand what would change the recommendation. More data does not remove the need for those questions.
In a specialty launch, there isn’t room for wasted spend. The team interpreting performance needs to understand the business well enough to challenge an apparent success or recognize an early warning. That judgment should influence how the campaign is optimized and where the next dollar goes.

4. Agencies often describe AI in terms of efficiency. What should clients look for to understand whether those efficiencies are translating into better strategic thinking and more valuable work?

Clients should expect to see the value AI creates. It may show up as faster analysis, more frequent optimization or greater output. But the more important test is whether experienced people are spending less time producing the work and more time improving the decisions behind it.
I do not think lower fees are the automatic answer. The more useful question is whether AI is making the agency's contribution more valuable. Is the team getting to a useful answer faster? Does it have more time to pressure-test a recommendation? Can the client point to a change in how the work gets done?

An agency saying it uses AI is not enough. If the client cannot identify anything that is faster or more useful, it should ask harder questions about what the technology is delivering.

5. Much of an agency planner’s judgment has traditionally developed through hands-on research and analysis. As AI takes on more of that work, how can agencies build the experience their next generation of leaders will need?

We have to redesign apprenticeship intentionally. The risk is that agencies automate junior work, celebrate the efficiency and then discover a few years later that they have stopped developing senior thinkers.
I do not believe we should preserve manual work for its own sake. If a task can be responsibly automated and does not require human judgment, we should automate it. But junior people still need to learn how to frame a problem, evaluate evidence and understand why a recommendation was made. 
That means bringing them closer to the decisions sooner. What question are we really answering? What does the data not know? Where is the commercial risk? In healthcare, AI can synthesize information quickly, but teams still need to understand the reality of a patient journey and the constraints on a healthcare professional's attention. AI should help junior people become better thinkers sooner. We can accelerate how their focus on diving into more specificity rather than sorting and sifting more widely.

6. Underscore’s focus has narrowed over time toward complex biopharma. What did that evolution teach you about the difference between capabilities that travel across industries and expertise that becomes more valuable through specialization?

Our path was not perfectly linear. We started more broadly and had capabilities that translated across verticals. Diversification created growth opportunities, but it also helped us understand where our experience made the greatest difference to the quality of the work.
That became clearest in complex biopharma, including rare disease, oncology and specialty launches where the margin for wasted learning is small. We could ask better questions, see risk earlier and connect media decisions to commercial outcomes with more precision. Narrowing our focus was a decision to concentrate on the work where we had earned the right to lead.
There is also a personal dimension for me. Rare disease has affected my own family, so I understand why smaller patient populations deserve the same depth of attention, expertise and investment as much larger ones.

7. Category credentials and promises of senior involvement are common in agency pitches. How can a CMO determine whether that expertise will meaningfully influence the decisions made after the business is won?

A credentials deck full of category logos proves exposure. To understand the depth behind it, a CMO needs to ask how much of the agency works in the category, how long the people leading the account have been doing this work and what they have learned from situations like the client's.
The expertise should be evident in the conversation. Can the team explain where conventional wisdom breaks down? Does it know which data limitations matter? Can it describe what it would do differently because it has seen a similar pattern before?

Clients should also test the promise of senior involvement. Ask who will be working with you every week and what decisions senior leaders will personally help you make after the first 90 days. Then speak with existing clients about who shows up when the business gets difficult. That will tell you more than a staffing slide.

8. When a CMO reviews an agency roster, how can they look beyond satisfaction with today’s work to determine whether their partners are equipped for the commercial challenges ahead?

 I would start by asking where each partner is being asked to solve problems it is not built to solve. A roster review does not have to mean replacing everyone. It should clarify where each agency creates value and where the business has outgrown the original arrangement.

Look at the hardest commercial problems coming over the next two or three years and work backward. Those might include launch complexity, patient finding, payer dynamics or data limitations. Does the roster reflect where the business is going, or where it was when those agencies were hired?

There are also situations where a holding company is the better choice. A global CMO who needs creative, media, commerce, production and technology under one operating model may reasonably prioritize geographic scale and integration. A specialist has to earn its place by helping the client make better decisions with measurable impact. The right choice depends on the problem the client needs to solve.