How Good AI Fails Federal Market Access
Bottom Line |
AI cannot interpret how the clinical mission actually shapes procurement across VHA, DHA, and IHS. Without on-the-ground account intelligence, pharmaceutical and device manufacturers increase execution risk. |
Market access teams routinely run regulations, policies, and strategic questions through large language models (e.g., Claude, ChatGPT, etc.) for the Veterans Health Administration (VHA), the Defense Health Agency (DHA), and the Indian Health Service (IHS), which is a shift that brings efficiency to these teams. It also introduces a less visible risk: in federal markets, the distance between a correct general response and the best next action is unusually wide.
The efficiency case for AI assumes that the user can distinguish a generic response from a good answer. When market access teams lean on generalized models, they often fail to consider how the AI model is drawing largely on publicly available information and condensing it into a digestible response influenced by how the prompt was written. This broad response is genuinely useful as foundational knowledge and for question forming. It does not, though, account for the critical need to overlay specialized market knowledge. So, for the AI information to truly be useful, the user must be able to both successfully contextualize the response and understand which parts of the response to pressure-test either within the model itself or through independent research outside the model.
Both highly capable AI users and experienced market access professionals face the same limit: a model can only be steered as precisely as the user's deep knowledge base of the specific market or sub-market. Within federal market access, specifically, an AI user must know which follow-up question to ask, if there are assumptions that should be challenged, and if proposed recommendations do not reflect how this market operates. Without a current federal-specific context, even an adept user with a strong team can inadvertently accept a well-constructed response because nothing looks wrong with it on the surface since the AI draws on enough logic to make sense. As such, the risk is not the tool itself or the team using it. Instead, the risk is the assumptions and inputs in the prompt itself and the analysis applied as the user reviews the output.
The cost of not having a complete perspective rarely shows up at the outset. Plans that are built on generalized information do not typically fail until early milestones are missed or downstream execution stalls – often unnoticed unless an external trigger forces the issue. At that point, work that could have been sequenced deliberately is compressed or an operational bump that could have been smoothed turns into an unnecessary emergency. To the detriment of the product and the process, expectations have been set with leadership by the time these gaps surface, and, typically, recalibrating them becomes difficult. Layering an informed perspective from a specialist actively calling on the health system onto any AI outputs helps ensures both momentum for the product and the establishment of long-term collaborative trust with it.
None of this argues against generative AI. It is the most effective tool for an initial information layer (e.g., market orientation, definitions, organizing internal knowledge, etc.). Speed has distinct value. However, true partnership with federal health systems requires an understanding of how the clinical mission informs decision making. Industry leaders who set a clear division of value between AI and federal market specialists capture the swiftness AI offers without absorbing its execution risk.
