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From Skepticism to Success: Navigating AI Adoption in Life Sciences Sales

June 25, 2024 – 10 min read

A few months ago, I wrote an article (Artificial Intelligence, ChatGPT, and the Future of Life Sciences Sales) about the disparity between the amount of discussion about AI in Life Sciences sales (a lot!) versus the actual examples of AI in use (a small number of examples, many leaning towards the Marketing side of the commercial equation).

Part of the challenge is thinking about AI as a technology. While AI certainly is a technology, it is also an entirely new way of thinking about selling. In the schema of “Mindset, Skillset, Toolset,” many think of AI as part of a toolset. However, to truly leverage AI and use AI to drive field excellence and performance requires a mindset shift. Sales teams need to actually think differently about selling with AI, not just act differently. And if you’ve ever tried to get somebody to think differently, you know that’s asking a lot.

In the schema of “Mindset, Skillset, Toolset,” many think of AI as part of a toolset.  However, to truly leverage AI and use AI to drive field excellence and performance requires a mindset shift.

What AI is Good At

At first glance, AI seems a perfect fit for Life Sciences Sales. One of the biggest challenges we hear from Life Sciences Sales teams is the sheer volume of data and information they are expected to learn, understand, and leverage in their work. And AI is great at doing just that– taking large data sets, analyzing them, and generating a plan of action.

AI is not magical, although sometimes people talk about it like it is. A few of the features that set AI apart include:

  • Analyzing massive data sets. This is an AI superpower—the ability to access, digest, and analyze incredible amounts of information and establish connections and correlations between data points. Arguably, AI knows more than any human does—which can be both a good and a bad thing.
  • Identifying trends, commonalities, and preferences. The version of AI we tend to encounter most is Preference Analysis, typically through search engines and online shopping. Ask Google, “What kind of dog is good with children?” and suddenly, all of your sites are flooded with ads for dog food and pet care. Search for “gas grills” on Amazon, and suddenly, Amazon assumes your world revolves around grilling. This tactic can be annoying, but it can also be useful, as AI brings the things you care most about to the top of your search list.

    This feature should be very useful for life sciences sales; most reps have hundreds of practices they call on, with all of the doctors, nurses, PAs, and coordinators that go with that. You would think that having technology that can analyze their wants and needs would be very useful to a sales team. And sometimes it is— but as we’ll discuss later, sometimes it most definitely is not perceived that way.
  • Learning and Changing Perspective. Perhaps the most meaningful feature of AI is its ability to shift its perspective based on what it knows and how it’s been trained. This is what separates AI from a database; both are able to store and interpret large amounts of data, but AI has the ability to “think” differently based on what it learns. And, in some ways, that also makes AI scary—because the answer from AI depends a lot on who (or what) is training it.

Why Everybody Loves AI—and Why Sales Doesn’t

One of the things I noted in my previous article was that it’s difficult to find really good examples of AI in Life sciences sales. There are a lot of Life Sciences examples, but they tend to be on the Medical or Marketing sides of the business. Probably the best sales example is Next Best Action, and we’ll get to that particular love/hate relationship in a moment. So why aren’t sales going all-in on AI? A lot of it has to do with a salesperson’s own core beliefs about themselves:

  • Individualized Accomplishment & Recognition: Life sciences sales are full of individual recognition—just ask any President’s Club member about that while they’re passing you a pina colada during that reward trip to Hawaii. Organizations have built incentives and rewards around individual accomplishments and are then surprised when those same individuals balk at having an AI mentor hanging over their shoulders.
  • Technology Saturation: Some life sciences sales teams are virtually drowning in technology, from CRM to strategic territory planners to insights engines and on and on. Under the circumstances, it’s not too surprising that yet another new technology platform isn’t necessarily welcomed with open arms. For some, it may feel like just one more box to check—when what they really want to be doing is talking to customers.
  • Relationships and the Salesperson’s Instinct. While many selling skills can be taught, salespeople pride themselves on building relationships, understanding their customers, and anticipating and responding to their needs. These instincts are based on extensive observation, conversations, and trust-building. It’s not surprising that some salespeople resist “letting the machine do my thinking for me.”

Why “Next Best Action Works” – and Why It Doesn’t

One of the most common applications of AI in Life Sciences selling is Next Best Action (NBA), usually part of an overall omnichannel strategy. NBA is a great example of why selling teams do not immediately embrace AI.

Omnichannel strategies make use of what AI does best—gathering and analyzing large amounts of data from multiple sources and recommending and identifying the Next Best Actions a rep can execute to address the needs of a customer. This is a wise application of AI, leveraging the power of the AI engine to do something that would otherwise be prohibitively time and resource-intensive.

However, the impact of NBA has been mixed. At its core, omnichannel/NBA is really a marketing strategy rather than a selling strategy, so for salespeople, it may already feel like something that’s being done “to them” rather than “for them.” For many reps (and their leaders), the NBA collides with some essential selling mindsets. How can a computer do the kind of critical thinking that I have been honing through years of practice? How can insights from technology be effective in this most human of endeavors? Who is this computer to tell me what to do? And perhaps most common: this is just the “flavor of the month,” and “if I just keep my head down, this will all blow over soon.”

How is it possible for sales leaders to shift these entrenched mindsets?

The Essentials of AI Change Management

Researcher Chris Argyris noted that our actions are derived from our “mental models”—our mindsets. From these mindsets, we derive strategies, and from those strategies, we create tactics. When we fail to get the outcomes we want, we tend first to examine our tactics and then our strategies. The hard part is when we must examine our mindsets— the core beliefs that drove our actions in the first place. Shifting strategies is difficult, but shifting mindsets—that’s some heavy lifting.

How do you shift mindsets? There are four key drivers:

  • Beliefs: If our core beliefs drive our mindset, then shifting mindsets means having some malleability in our core beliefs. Some core beliefs are set in stone and nearly impossible to move. However, the core beliefs that drive adoption of AI likely have some flexibility to them. For leaders, this usually means looking at the reasons why their teams believe what they do.
  • Consistent Reinforcement: We’ve all experienced “flavor of the month” initiatives, where something that was critical one day is all but forgotten the next. True change requires consistent and persistent reinforcement. Not only do people require ongoing reminding of the importance of the change, but they also need to believe the change is really something the organization is committed to.
  • Accountability. With many types of change, there is an almost irresistible urge to put things back the way they used to be. How many times have we heard, “We tried that, and it didn’t work”? The tendency to backslide, to go back to the way it used to be, is natural; consistency is nature’s comfort food. This is the time for leaders to send and reinforce the critical message of change: the only way is the way forward.
  • Scaffolding/Support. At the same time, as we reinforce and hold accountable, we have to acknowledge that changing mindsets is challenging and emotionally difficult. Even when a team member is willing to shift, they often need help and support. They should have places to turn for help, be it a leader, a colleague, or a resource center.

Thinking Differently Takes Time

The hardest part of shifting mindsets is having patience. We spend most of our lives developing our core beliefs, our mindsets. Most people are not going to change in a blinding flash; rather, they are going to change over time as practice and application make new behaviors more familiar and more proof points make the new mindset feel viable. That’s why change initiatives need multiple touchpoints over time. Progress also comes with doubt and backsliding; that’s normal. Give people a chance to test, apply, practice, and see value before expecting wholesale change.

notes on pad with pen next to coffee and biscuit

Most people are not going to change in a blinding flash; rather, they are going to change over time as practice and application make new behaviors more familiar and more proof points make the new mindset feel viable.

AI in Sales Readiness

So, where is AI being readily adopted in sales? There are some solid examples of AI adaptive technology being used in driving sales-readiness. Reps are expected to learn and retain vast amounts of information, including disease state, capabilities of their portfolio and their competitors’ portfolio, clinical studies, approved messaging, formulary status, and payer landscape—not to mention the preferences of every single HCP they call on. It’s a lot, and it’s the primary reason that some reps give speeches instead of engaging in conversations—because they just can’t retain everything.

AI Adaptive systems like Axonify and Allego can help drive retention and rep confidence through an automated and systemic reinforcement strategy, providing just enough information in the flow of work to keep a rep’s knowledge fresh. The AI can analyze each rep’s knowledge and performance and feed them just the information they need—so every rep has a unique experience. Studies show that reps who use these systems regularly can increase their knowledge base by 25% or more, leading to higher HCP engagement and market share growth.

In Summary

AI appears destined to grow as a presence in our professional lives. There are many ways that AI can make our lives easier, enhancing and simplifying everyday tasks. But for many people, AI comes with a healthy dose of cynicism. Can AI do everything it claims? And even if it can, does that make our world better or worse? The use of AI can challenge our mindsets and even the very core beliefs that our mindsets are based on. In order for AI to truly take hold in the world of life sciences sales, the rules of change management apply: don’t just show me how to use this new technology; show me why.

PDG extends its sincere thanks to Rich Mesch for authoring this insightful article.

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