After navigating unprecedented disruption and adapting go-to-market actions, expectations for customer interactions have fundamentally changed. Companies must be more intelligent and strategic than ever in customer engagement. How are leaders gaining an edge? Machine learning and artificial intelligence are becoming linchpin programs in commercial operations.
As AI continues to mature, the utilization of machine learning applications in omnichannel orchestration for life sciences businesses is growing exponentially. Improvements in AI are driving deeper, stronger connections between HCP and life sciences organizations. For those lagging behind in adopting intelligent, omnichannel platforms, the gap between them and industry leaders is already large and widening.
There are a few commonalities that we’ve identified among leading organizations. Here are 4 trends in AI that all life sciences companies need to recognize and adopt within their own organization to improve customer interactions and facilitate Next Best Action this year and beyond.
More Finely-Tuned Adoption of AI Technology
Over the last few years, the utilization of machine learning and AI within the life sciences industry has gone from mysterious and emergent to a powerful technology revolutionizing customer engagement. As the category has matured and buyers have become more aware of the possibilities of AI, it is being adopted more broadly and with more precision across biopharmas and medical device companies.
Internal Champions of AI
AI technology is being propelled forward thanks to a number of factors including the increased hiring of Chief Digital Officers from outside of traditional health and life sciences industries, expanded focus on data science and analytics within businesses, and improved proficiency by machine learning companies in accelerating education and adoption among new users. Buyers of machine learning applications within life science are more sophisticated and knowledgeable than ever, and vendors are rising to the occasion to drive adoption across new markets.
Internal teams are also demonstrating a greater capability to drive the adoption of AI technologies by informing strategy and building out models that fit their business. Knowledgeable customers expect vendors to provide technical expertise connecting their systems and making AI actionable. In turn, this interaction is creating broadly effective AI engagement.
How Aktana worked with Genentech’s internal data science team to build an AI deployment roadmap.
Expansion into Emerging Markets
As companies have become more well-versed in deploying AI solutions and customizing the models to be effective, they are becoming more emboldened to test the tech in new markets. Companies are more confident than ever in attempting to roll out AI market strategies to new lines of business, brands, and geographies. AI is expanding the resource base for life sciences organizations to begin engaging emerging markets effectively.
Evolution (and Emulation) of the Medical Science Liaison
Medical science liaisons (MSL) have played an integral role in supporting research and establishing positive relationships with health care providers (HCPs) and key opinion leaders. They also serve as internal SMEs for sales reps and have been key training leaders.
As sales leaders in life sciences organizations place greater emphasis on providing data and in-depth experiences, MSLs are becoming increasingly critical for fostering HCP relationships and keeping HCPs informed along the customer journey —even though their role remains distinctly separate from the sales function.
MSL Insight Improves AI Systems
Leading life sciences businesses are now recognizing that the pure, two-way sharing of information between MSLs and HCPs should serve as a model for all sales interactions. AI technology can assist in these interactions as well. The AI systems used by sales for customer engagement can be better informed by the data and insights gained from an organization’s MSLs. In doing so, they can improve the sales interaction without any direct involvement in the exchange.
AI has also unearthed new ways for MSLs to benefit their organizations and improve engagement with HCPs. MSLs are now proactively working with marketing and sales to develop knowledge around therapeutics and decision-making factors. AI then can better predict when these conversations with MSLs need to take place as well as the content of these conversations.
Need for Greater Transparency in AI Technology
We’ve stated previously that AI is more mature and better understood across life sciences organizations; however, in many cases, AI and machine learning technology are still viewed as black-box solutions. Life science organizations are increasingly interested in understanding and adopting AI, so it is incumbent on providers to be clear and transparent about how AI arrives at its predictions and recommendations.
AI is an assisting technology, not a replacing technology. Simply put, machine learning makes dynamic decisions based on trends in provided data. AI is a suggesting tool, but it still requires a human to make a choice. Customers need to be informed in order to have confidence in the recommendations produced by AI technology. Clear communication is crucial for all vendors in AI to begin introducing trust and transparency into AI discussions.
Understanding the Context Behind AI Recommendations
Transparency goes far beyond merely defining AI for customers, however. Many organizations’ understanding of AI technology is sophisticated to the point that they effectively vet an AI solution vendor and empower their teams to use them. Vendors will need to partner in this effort in order to facilitate self-service solutions. To accomplish this, transparency means demonstrating the data and reasoning behind each recommendation made by a machine learning engine. This degree of transparency is necessary in order to equip customer analytics teams with the knowledge they need to bolster their own feedback loops and the confidence to improve their own AI systems.
Prioritizing Management of Data and Content
Machine learning applications are only as effective as the data on which they operate. While not a new problem, the prioritization of data hygiene and content management practices in order to enable rapid adoption of AI technology is an emergent focus for life sciences businesses.
Entity Resolution for Data Management
Data and content present fairly similar challenges in preparing for AI utilization. For data, the most significant barrier is presented by duplicated entries across siloed data systems. In order to address this overarching problem, life sciences businesses are now prioritizing entity resolution as a critical step in data management. Entity resolution is the process of determining whether multiple records (across multiple data streams) reference the same entity (eg. a record for an HCP is listed under different unique identifiers in different systems). Organizations looking to maximize their return on AI technology are recognizing the importance of consistent data and taking action to clean up their data in the immediate term.
Developing a Master Source for Content
Content management is also in need of attention when implementing machine learning GTM strategies. Without a master source for all marketing content, preparing and delivering the most relevant content as identified by the model is not possible. AI models can also determine the relevancy of a piece of content and inform potential gaps in content requiring attention. With more organizations placing importance on adopting AI technology, efforts to ensure all content is housed in a manageable location are becoming paramount. These content management steps are being prioritized by leading businesses to create intelligent engagement and move marketing motions from next best action to next best experience.
Adopting AI to Drive Intelligent Customer Engagement
Each year a new set of challenges arises for connecting with HCPs. Making the most of their limited time and presenting the most useful information available is especially necessary today. After years of advancement, AI is no longer a mysterious technology, and a growing number of premier life sciences organizations are employing AI solutions to drive intelligent customer engagement. Adopting an intelligent, AI-led customer engagement solution requires significant effort and education, but if employed correctly, ensures long-lasting success.