AI and machine learning are transforming both pharmaceutical drug discovery and commercialization. By compiling vast streams of data on the habits, practices, and choices of healthcare providers (HCPs), and then applying AI and machine techniques, companies have the opportunity to adopt a highly personalized customer engagement strategy. In other words, sales and marketing teams can focus on what is most meaningful and actionable for both the HCP and the pharmaceutical company.
Previously, only large companies had access to such capabilities. Now, AI is accessible to emerging biotechs and mid-sized companies that may lack a large internal IT department or budget, with options including modular software and open, cloud-based platforms. Today, AI is for all.
But the pharmaceutical industry is resistant to change. And though many are happy to commit themselves to small pilot AI programs, there can be a reluctance to make larger, more significant changes. In my view, the once-upon-a-time hurdles for pharma organizations – costs, tech maturity, and culture – are no longer barriers preventing commercial success, but outdated misperceptions persist.
Investors have poured billions of dollars into AI and machine learning in recent years. The result? Modern solutions built using scalable technology models that bring the cost down. Early commercial AI offerings tended to be highly customized tools with very specific goals, today’s best solutions are platforms that tend to be easier to implement and more flexible, with modules that can be added incrementally as needed. A project can start small, and then grow across the breadth of a brand – or an entire enterprise. For instance, an emerging digital therapeutic company can invest in a modern intelligence platform and focus first on field-force enablement (which allows companies to make decisions based on real-time evidence) but then unlock other modules or capabilities, such as marketing automation, content selection, and channel orchestration, over time to optimize the customer journey.
Modular solutions also allow companies to move at their own pace – progress quickly or take a more measured approach. For example, a company that wants to optimize content use can start by training the AI solution to follow basic rules. However, as the company learns more about its customers and content usage, that data can inform the AI platform to continuously optimize the type of content and the delivery of that content to meet the HCPs needs. Technology has matured to allow for a continuous cycle of “more data in, better insights out.”
Ultimately, adopting AI is a culture shift. In today’s market, where the total cost of ownership is plummeting and technical maturity is skyrocketing, the only thing holding all sized companies back from adoption is user acceptance. I firmly believe that, as trust between humans and machines continues to build, users will recognize that AI is critical to deepening HCP engagement in today’s digitally-driven commercial model.
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