Where will AI take the pharma field sales force? Two scenarios

Where will AI take the pharma field sales force? Two scenarios

By Rohit Gupta and David Laros, Beghou Consulting

Will AI replace pharma sales reps?

It’s a question commercial leaders are undoubtedly starting to ponder as the technology advances and its applications in the industry come into clearer view. Pressured to deliver results under compressed launch timelines and, in some cases, fewer resources, commercial leaders are looking for ways to operate in a leaner, yet still highly effective, manner. So, there are headwinds facing the traditional pharma sales model. Given the great potential for AI to enhance operational efficiency and improve insight-gathering and -dissemination, it’s likely that the advancement of AI tools will only strengthen those headwinds and expedite the pace of change.

The commercial leaders who drive their organizations toward AI maturity will put themselves in a position to harness the power of this technology for the good of their organization and their patients. But what will this mean for the pharma field sales force? Will AI’s long-term impact on pharma sales efforts be significant or muted? Let’s consider the following two scenarios.

1. AI takes over the field sales effort: In this scenario, AI advances to the point where it can transform pharma sales efforts and effectively replace a significant part of a sales rep’s job. In the near-term, AI could boost efficiency and prompt field action by providing better insights and action recommendations (e.g., next-best action guidance, high-quality summaries and reports, etc.). Longer term, it’s not far-fetched to envision AI leading the field sales effort and the rep playing a high-value, precision role in engaging with HCPs. To boil it down, whereas today AI is well-suited to be the rep’s “copilot,” in the future, the rep could potentially be AI’s “copilot.”
2. AI remains a supporting player: The other scenario to consider is that AI never overcomes the hurdles it currently faces related to data, and the technology fails to come close to being a replacement for the dynamism and ingenuity of the human mind. AI tools may support human actors by feeding them insights and streamlining information gathering. However, in this scenario, they remain boxed into that support role. Another reason AI may remain a supporting player is the fact that it’s unlikely every customer will embrace AI-delivered promotion from pharma. A mix of promotional tactics delivered via a coordinated omnichannel effort will remain the best way to engage customers. AI will be a key part of companies’ promotional efforts, but not the centerpiece.

To be clear, we are not making predictions or taking sides. For example, while we think the first scenario is a legitimate possibility, we are skeptical that it will ever become a reality given the headwinds we will discuss. But given the fast pace of technological change today, it is important to consider and plan for all possible eventualities. Let’s explore both scenarios in detail.

Scenario 1: AI revolution

The pharma sales rep has impact at three levels:

• Customer experience: Reps are on the front lines of the customer relationship. When they do their job well, they enhance a customer’s experience with the brand and thereby strengthen the company’s relationship with the customer.
• Revenue: The rep’s core purpose and most significant impact is driving sales and increased revenue for the company.
• Return on investment: Sales reps are a pharma company’s most impactful promotional channel.

Initially, AI will impact the middle of the pyramid. AI tools can help reps increase revenue for their organizations by guiding them on next-best actions and helping them usher customers through the sales funnel. In this world, the rep is still driving sales, and AI is in a supporting role. However, AI is adding value by helping the rep more efficiently process information and make good decisions.

As AI advances, it could penetrate the next two layers of the pyramid (ROI and CX). Consider a few examples:

• Sophisticated asynchronous communication: Website chatbots today are clunky and often unhelpful. But imagine a highly trained and sophisticated “AI rep” that coordinates seamlessly with an “AI MSL” to deliver the information an HCP seeks in a compliant way. Today, if an HCP asks a sales rep for information that only an MSL can deliver, it halts the conversation and creates a suboptimal customer experience (these situations will only increase in a world dominated by complex rare disease therapies). However, if this process was AI-enabled, the AI rep and the AI MSL could coordinate in real-time to deliver the information the HCP seeks. And it could deliver this information asynchronously, when the HCP needs it, not just when the rep or MSL is in her office. In this way, companies can improve the effectiveness of their interactions with increasingly digitally savvy HCPs, eliminate delays in information delivery, and ensure HCPs have all the information they need to make informed medical decisions.
• Information management: A significant piece of a sales rep’s job remains largely administrative. However, this work is still valuable. Take updating the CRM as an example. While data entry is rote work, it is crucial that a company has accurate information about its target HCPs. A key part of a rep’s job is parsing through all the information a company has about an HCP and uncovering the current reality (e.g., what sub-specialty are they focused on? Which of the five addresses in the CRM is accurate?). AI can do some of this work today and soon may be able to do all of it, eliminating the need for the rep to update information in a CRM. For example, in addition to handing the data-entry portions of the job to AI, companies could deploy AI tools to scrape information on the web and integrate it with the company’s data to create a richer and more holistic view of an HCP, thereby facilitating more precise targeting.
• Insight dissemination and internal coordination: Internal coordination and strategy discussions are crucial to ensuring the sales team is aware of key insights and pursues the company’s goals in a coordinated fashion. However, AI could eventually largely manage this effort. It could pull insights from a company’s interactions with HCPs and surface those insights for the broader sales team, cutting down significantly this internal coordination and meeting time.
• Data synthesis and decision-making: As the amount of data in the industry expands, companies can increasingly leverage AI for data synthesis and, eventually, automated decision making. It’s an evolution that will be driven by AI tool sophistication and data availability. But even using public data sources, companies can leverage AI to efficiently synthesize and draw insights from data. A potential future state is fully automated AI-driven decision-making, fueled by unencumbered access to public data and large numbers of private data sources.

In this “AI revolution” scenario, rep workloads will decrease, and, as a result, the number of reps a company needs would go down. Though embedding AI tools in a pharma sales organization requires upfront effort and costs, these tools would generate ongoing return on investment and be more cost effective than hiring an army of human sales reps. Companies could cut costs while retaining the current value they generate from sales reps. Are we at the point where AI can be deployed across the “rep impact” pyramid today? No. But it’s possible in the not-so-distant future. And once we get to the point where AI has penetrated all three levels of the pyramid, it is essentially serving as the mirror image of the rep. In that world, the human rep could easily shift to a supporting role for the AI rep.

The bottom line is that if AI unfurls as it looks like it will, there will be use cases where it can replace the sales rep.

Scenario 2: AI limitations

The counterpoint to the “AI revolution” scenario rests on the continued primacy of human ingenuity as well as some significant headwinds that could hold back AI technology advances. Here are some of the challenges:

• Data fragmentation and accessibility: While one side of the argument is that the continued explosion of data in the pharma industry will fuel unchecked advancements in AI capabilities, the counterpoint is that with this explosion comes increased fragmentation of data. As more data emerges, more data suppliers will emerge, as well. Pharma companies therefore will never realistically be able to access all the data they need, which will in turn hinder the power of AI tools.
• Increased focus on data privacy: A related challenge is the increased focus on data privacy. While the trajectory appears to be increasing access to healthcare data, it’s not hard to envision a world where more and more data is off limits to pharma companies due to increasingly stringent data privacy regulations.
• Abstraction and nuance: Today, AI can find relationships between things that already exist, but it can’t abstract and argue, forging new ground beyond what already exists. Similarly, AI in its current form cannot replace a sales rep’s understanding of the nuances of human relationships. An HCP’s glance, tone of voice, or subtle behaviors can all be important inputs that the rep uses to determine her next best action in the relationship and optimize sequencing of messaging in the room. AI can suggest actions today based on data it has access to, but it can’t come close to replicating the observational power and intuition a smart human brings to her interactions.
• Customer appetite: Do pharma companies’ customers want to engage with AI all the time, some of the time, or never? While even under the most skeptical analysis AI will infiltrate more and more of the pharma commercial effort, it’s likely that some high-value customers will continue to prefer more traditional modes of promotion from pharma companies. Therefore, coordinated omnichannel marketing that includes a mix of channels and messages, tailored to the individual customer based on rigorous data analytics, will remain the optimal approach for most pharma companies, even as AI advances.

In this “AI limitations” scenario, AI technology will undoubtedly improve and facilitate more sophisticated data querying, insight sharing, and streamlining of processes. However, it will remain hemmed in by some significant limitations. If this scenario plays out, AI will be a valuable contributor to the field sales effort but will never come close to playing the leading role.

The way forward

While it is fascinating to consider the possibilities, pharma commercial leaders need a way forward. In our view, commercial leaders must seek AI maturity. Of course, maturity will look different for every organization, but, at a high level, leaders should find ways to leverage AI safely and compliantly to:

• Increase operational efficiency.
• Improve insight gathering.
• Optimize decision making.

In the short term, AI will be a key supplement to commercial teams, helping them operate faster and more effectively – and ideally make better, more informed decisions. At the same time, commercial leaders must consider potential future states and position their organizations to capitalize on the coming advancements in AI technology. What that means for the sales force is still to be seen. But the most competitive commercial organizations in the coming years will position themselves to agilely capitalize on advancements in technology to better engage their customers.

Rohit Gupta is VP of analytics strategy and transformation and David Laros is VP of digital strategy, analytics, and insights at Beghou Consulting.