Authors:
By Kirk Shepard, Co-Founder of MAPS, and Girish Pashilkar, CEO of BP Logix
Drawing on the 2026 AI in Medical Affairs Industry Benchmark Survey and a senior leadership roundtable convened at MAPS Americas 2026
The Mission-Critical Role of Literature Monitoring
Medical Affairs professionals believe in AI. The data is clear on that. In the 2026 AI in Medical Affairs Industry Benchmark Survey, 86% report being confident or very confident that AI will improve their function’s efficiency over the next two to three years. That belief cuts across role, organization size, and seniority.
What’s happening with that confidence is harder to read.
Only 20% of respondents describe their organizations as leading edge on AI adoption. The largest group, 41%, identifies as cautious. Another 33% call themselves fast followers, and 6% remain in wait-and-see mode. When senior Medical Affairs leaders gathered for a roundtable at MAPS Americas 2026 in March and were asked what they were actually doing with AI, the picture that emerged was modest. The work clustered at the edges of the function: writing assistance, document summaries, meeting prep.
That work has value. It is also a long way from transformation.
What “using AI” actually looks like right now
The survey shows where AI has gained a foothold in Medical Affairs. Plain language summaries and literature monitoring tie for the most-deployed use cases, with 44% of respondents reporting each in production. First draft generation follows at 39%, document intake and data extraction at 34%, and medical information response drafting at 33%. These are not pilots anymore.
The roundtable conversation at MAPS Americas 2026 surfaced something the numbers cannot fully capture: how teams are using AI day to day. Microsoft Copilot was the most widely cited tool in the room. At smaller organizations, ChatGPT. These are general-purpose tools designed for individual productivity, opened on demand for whatever task is in front of someone. They’re capable, and they’re what most teams have access to. They are also different in kind from AI embedded inside the workflow itself. One participant described using AI to prepare briefs before HCP visits. Another talked about quick abstract wordsmithing. A third described using it for brainstorming and document summaries.
The pattern across the room: AI is something people open when they feel like it, not something that runs as part of how work gets done. As Richa Garg, Principal Product Manager at BP Logix, observed of the discussion, “The room is using AI for menial tasks. Core Medical Affairs workflows are still untouched.” Clinical study reports, health authority engagement, and other higher-stakes regulatory work remain off-limits even at organizations with AI deployed elsewhere.
That distinction matters. It explains why the same teams that report multiple AI use cases also report that AI has not fundamentally changed how they operate. Faster individual tasks. Same underlying process.
Keyword Strategy Remains a Persistent Challenge
When the survey asked respondents to rank what is holding their organizations back, validation and accuracy came out on top at 47%, ahead of compliance and regulatory concerns at 43%. That ordering is worth pausing on.
Compliance is real. Medical Affairs operates inside one of the most heavily regulated industries. The data points to a different leading concern: whether AI output is good enough to trust. Teams do not yet have the validation frameworks, internal governance, or confidence in output quality to push AI closer to high-stakes work.
The validation paradox shows up in how teams handle AI output today. 49% of respondents use full manual SME review for every piece of AI-generated content. Only 15% have moved to a hybrid approach combining automated and human review. AI is being used in production. It is not yet trusted enough to streamline the review work it was meant to accelerate.
The roundtable conversation reinforced this picture. AI-generated medical summaries came up several times as a use case people had tried and walked away from. “It was so high-level it was useless,” one participant said. Publications surfaced as a particular friction point. “I struggled in publications because there are a lot of external regulations involved, like GPP,” another participant noted. The absence of clear FDA guidance came up repeatedly as a significant inhibitor. Without defined guardrails, organizations cannot confidently move into higher-stakes territory.
Validation and accuracy were cited as the top barrier to AI adoption, closely followed by compliance and regulatory concerns.
What teams actually want next
When asked where they want to go next, 38% of respondents named literature monitoring and synthesis as a top future investment area. That is notable because literature monitoring is already the most-deployed AI use case at 44%. Teams want to push further where they have already started. First draft generation follows at 29%, then medical information response drafting at 25%, document intake at 22%, and internal training at 16%.
The MAPS Americas roundtable temperature check confirmed the pattern. Three topics tied for the most attention when participants placed their priority dots on the use cases board: literature monitoring, first draft generation, and internal training. The tie itself tells a story. The field wants to push AI further into production workflows. It also knows its teams are not yet equipped to get there.
Shepard was struck by the range of people gathered: “Different parts of Medical Affairs, different company sizes, all grappling with the same questions. The quality of that discussion reflected where the field actually is: thoughtful, cautious, and ready to move when the conditions are right.”
Literature monitoring & synthesis led future interest among survey respondents, followed by first draft generation and med info response drafting.
The workflow integration gap
There’s a difference between AI as something people invoke and AI as something that runs. Tools that live in a chat window speed up the tasks people put in front of them. AI that runs as a defined step inside a process, with structured inputs and outputs, validation checkpoints, and accountability built in, changes how the work itself gets done.
The two capabilities Garg walked through during the roundtable fit a different model than the Copilot prompts and ChatGPT queries that surfaced earlier in the discussion. The energy in the room shifted noticeably during the demos. Participants leaned in. Questions changed shape, from general curiosity about whether AI was ready for Medical Affairs to specific, practical questions about whether the system could handle this kind of intake or that kind of routing.
What became clear in the room was that for many participants, the demos introduced a category of AI use they hadn’t been imagining. The mental model going in was AI as something you open: a chat window, a prompt, an answer. The demos of Approvia showed that AI could run as a step inside a workflow your team has already designed.
Reflecting on the discussion afterward, Shepard observed: “Watching the questions change showed me something specific about where the field is. Most teams haven’t seen what AI looks like running inside a workflow. Until they see it, the idea stays abstract. The demos made it concrete in real time.”
Literature monitoring & synthesis led future interest among survey respondents, followed by first draft generation and med info response drafting.
INTERESTED IN HOSTING A ROUNDTABLE?
This article summarizes a Roundtable hosted by a MAPS Partner Circle member that brought together leading experts from across the industry. If you are a solution provider interested in hosting your own Roundtable – in-person or virtually – please check out our Media Planner or contact Luke with MAPS: [email protected].
