Can AI help biopharma sales reps become more effective?
By Jamil Siddiqui, managing director, and Pablo Suero, manager, Deloitte Consulting LLP
The biopharmaceutical industry is undergoing a fundamental transformation of its commercial value chain, shifting from siloed functions to an integrated ecosystem where data and insights connect marketing, medical affairs, and field engagement (see Navigating the future of commercial in biopharma). Considerations around this shift are likely to be important for field force teams (sales representatives, medical science liaisons (MSLs), and key account managers) that often need to navigate a complex and fragmented information landscape.
According to the results of a survey conducted by the Deloitte Center for Health Solutions, biopharma sales reps spend roughly two-thirds of their workday researching prospective clients, reviewing products, evaluating prescribing trends, scheduling meetings, and completing various administrative tasks. Less than one-third of their time is spent interacting with customers. Even more telling, nearly 70% of respondents said administrative work, such as completing post-meeting reports, adds little to no value to either their company or to their relationship with clients.
In response, some biopharma companies are beginning to use generative and agentic artificial intelligence (AI) to reimagine how their sales reps might interact with health care professionals (HCPs include physicians, nurses, front-desk staff, and office administrators). The technology is also being used to measure the effectiveness of interactions between field teams and HCP clients.i
Having access to accurate and relevant data can be key to success in biopharma sales.ii But the information sales reps often rely on can be fragmented and outdated by the time it reaches them. Biopharma companies typically integrate external third-party data into a broader data ecosystem. For reps who operate in this often complex and dynamic environment, data that is weeks or months old can be irrelevant and ineffective.
This is where AI could make a meaningful difference. Rather than just directing data streams to flow into one place, AI can turn fragmented signals into timely and actionable guidance. For field force teams, the benefit can extend beyond efficiency. Actionable guidance can give sales reps the ability to walk into meetings better prepared to move the relationship forward.
A day in the life of a biopharma sales rep today
Consider this: Kevin is a fictional sales rep for a biopharma company. Yesterday, he met with several HCPs. Before each meeting, he accessed multiple portals, apps, documents, and dashboards to piece together a view of the customer (patient mix, prescribing patterns, prior interactions with colleagues, and local market dynamics). The information was incomplete and some of it was outdated. For one physician, Kevin found three different records. He also had to make sure he had the latest updates across his company’s product portfolio. The bulk of this research took place in the parking lot just before walking into the meeting. After the meeting, Kevin filled out forms, scheduled a follow-up appointment, and decided which details from the interaction were important enough to log. Then he drove to the next appointment.
Kevin’s experience is not unique.iii While he had access to a considerable amount of data, it wasn’t particularly usable. When information is scattered, outdated, or inconsistent, reps might spend too much time interpreting signals and not enough time engaging customers. However, field force teams can be skeptical about AI and its value. Some may worry that another layer of technology will mean more alerts and new dashboards competing for their attention. If an AI tool makes recommendations without context, or if it offers suggestions that feel generic, mistimed, or hard to trust, general adoption might not be achieved. The technology should do more than aggregate data or automate tasks. It should help prepare reps for meetings, prioritize what matters to their clients, and help them understand why a recommendation is relevant. The real opportunity is not to make Kevin faster, it is to make him more effective before, during, and after each HCP interaction.
A day in the life of a biopharma sales rep tomorrow
Consider how Kevin’s day might look using AI that is built to support him. On the way to his first appointment, Kevin receives a short audio briefing that provides a few insights that could be relevant to the upcoming meeting. This might include changes in the physician’s prescribing patterns, conferences the physician has recently attended, prior interactions the physician has had with Kevin’s colleagues (such as an MSL) and recommended next steps. That kind of support could make a meaningful difference. Rather than overwhelming reps with data and alerts, AI can help prioritize the signals that are most actionable. When recommendations include the reasoning behind them, they can be easier for reps to trust and incorporate into the workflow.
After the meeting, Kevin records a brief verbal recap while driving to the next appointment. The system automatically logs the interaction and sends tasks to different teams (e.g., MSLs or field reimbursement managers) to answer questions that came up during the meeting. It also drafts a follow-up email to the physician and his office manager with recommendations on the next best action based on the physician’s information and level of interest. Kevin only needs to review, edit, and approve the messages rather than writing something from scratch. Routine tasks such as scheduling, documentation, and follow-up coordination happen in the background, freeing Kevin to focus on the part of the job that matters most to him: having more insightful conversations that help move client relationships forward.
At scale, that impact could be significant. A Deloitte analysis estimates that—over five years—generative and agentic AI could deliver up to $7 billion in value for a large biopharma company, with productivity gains compounding over time and cost savings reaching 20% to 30% by the third year (see Realizing the value of artificial intelligence in life sciences).
Conclusion
Over the past two decades, the commercial model in biopharma has become more constrained. Field force teams are often smaller than in the pastiv, physician access has grown tighter, and hybrid engagement has become part of the landscapev. In that environment, the next competitive advantage may come from turning data into meaningful insight. For biopharma companies that embrace it, AI can serve as a practical copilot, surfacing relevant insights, guiding next best actions, and helping reps navigate complex HCP interactions with greater confidence and precision.
AI can also create an opportunity to redefine how success is measured. Rather than focusing primarily on activity metrics such as call volume or meeting counts, leaders can assess interaction quality. This can include information such as follow-ups, the timeliness and relevance of the content provided, and whether the rep connected the HCP to an MSL or other colleague when needed. That kind of measurement tends to incentivize smarter work that can align the field force with the omnichannel reality of today’s customer.
The biopharma sales rep of tomorrow is likely to win based on judgment, trust, and relationships. AI can help reduce the friction around those interactions, freeing reps to spend less time managing systems and more time creating value for HCPs, patients, and the business.
Acknowledgment: Nitin Solanki, senior manager, Deloitte Consulting LLP
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Endnotes:
iAgentic AI and the future of pharma sales, Hexaware Technologies, March 13, 2026
iiThe crucial role of real-time data use in pharma marketing, IQVIA, November 7, 2024
iiiBad data is expensive: Harnessing high-quality data for biopharma innovation, pharmaphorum, April 24, 2025
ivInfographic: Pharma sales force by the numbers, BiopharmaDive, September 18, 2017
vPharma’s salesforce is suffering — what does version 2.0 look like?, PharmaVoice, March 28, 2023
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