Democratizing AI: Bridging the Intelligence Divide in Commercial Life Sciences


Artificial intelligence (AI) and big data/analytics have been identified as top technologies that are transforming pharmaceutical drug discovery and commercialization.1 While already making headlines for their potential in drug discovery, these technologies will also play a major role in optimizing marketing and sales. In fact, AI is expected to have a bigger uptake in commercial in the next few years, up four points to 22%%.

Alan Kalton

Alan Kalton

Many companies are leaning into this trend. A 2021 analysis showed an unprecedented 45% increase in AI-driven omnichannel actions, directly impacting the engagement experience of more than 700,000 healthcare professionals (HCPs) in the US.2 What’s even more telling, however, is that leading life sciences companies are now scaling AI capabilities simultaneously across multiple geographies, divisions, and therapeutic areas, rather than taking a more conservative, brand-by-brand or geography approach. In just one example, a top-20 biotech expanded the use of AI across more than 40 countries.

That’s great news for multinational, enterprise life sciences companies. However, emerging biotechs and mid-sized companies have been historically reserved about AI, hesitant to adopt intelligence solutions enterprise-wide, despite proven benefits. If they make the investment at all, smaller companies tend to carefully slow-roll AI deployment—at first, in production mode—which can be self-defeating as AI performance improves in real life when fuelled by more and more data inputs. The persistent belief that implementing AI is too heavy a lift or expensive is a misperception today. AI is no longer a luxury—a Rolls-Royce technology only attainable by organizations with significant resources. Modern technology means AI is accessible to all companies. A 2022 survey of senior technology executives reveals that 26% of companies (across all industries) now have AI systems in widespread deployment—more than double the 12% in last year’s survey.3 Modular software and open, cloud-based platforms that seamlessly integrate with internal and external data sources combine to bridge the intelligence divide in the commercial life sciences industry.

And not a moment too soon. Large or small, pharmaceutical, medical device, and animal health companies can no longer delay adopting AI to guide customer interactions. Doing so risks losing HCPs’ attention, or worse, their trust. There are too many channels for commercial teams to adeptly coordinate, and HCPs are increasingly feeling overwhelmed by impersonal content pushed out to them from all directions. Only AI can efficiently process millions of terabytes of data and provide accurate, data-driven recommendations on the next best actions to take with customers.

The AI haves and have-nots: 3 barriers busted

Companies in the healthcare industry are tapping into AI’s limitless potential to take sales and marketing performance to the next level. One of the most notable ways AI is making an impact is by enhancing decision-making with actionable, data-driven insights.

AI can process millions of data points in seconds to enable a deeper understanding of customer profiles, treatment patterns, attitudes, and behaviors. Such in-depth knowledge of healthcare professional (HCP) personas considerably improves segmentation and targeting, enabling companies to develop highly customized engagement strategies faster and easier than before. Marketing departments can quickly engage specific physicians based on patient types, geography, and prescribing behavior without the need for focus groups, surveys, and other expensive and time-consuming market research methodologies. Content, too, can be accurately personalized to meet the needs of HCPs.

The companies using AI—the “haves”—are well-positioned for commercial success. This is magnified 10x for organizations working to optimize an omnichannel engagement strategy.

Almirall, a rapidly growing pharmaceutical company, has set its sights on omnichannel engagement in its drive to become the leader in medical dermatology in the US and recently incorporated AI into commercial operations nationwide on the way to that goal. “To become a leader, you have to act like a leader,” says Vincent Cerio, head of commercial operations at Almirall. “Part of that effort means replacing clunky, outdated processes with agile processes using modern technologies like best-in-class AI systems that enable dynamic targeting, strategic segmentation, and personalized HCP engagement.”

Cerio continues, “We are resistant to change in the pharmaceutical industry, and for good reason. However, there is a new generation rising through the ranks that are more familiar with advanced data analytics technologies like AI and see it as a crucial part of the tech mix. It’s a breakout moment.”

Still, some companies remain hesitant, committing only to piloting small AI programs, if at all. They still believe the three most common misperceptions that keep AI out of reach. But the once-upon-a-time hurdles for small to mid-size organizations—costs, tech maturity, and culture—are no longer barriers preventing commercial success.

1. Initial investments extend further

Investors have poured billions of dollars into AI and machine learning in recent years, hoping that the technologies will become the next frontier of drug innovation. In fact, the money funneled into AI-focused pharma companies increased 30% in 2020 compared to 2019, according to biotech analysis company Deep Pharma Intelligence.4

The result has been significant investments made in developing modern solutions built using scalable technology models that bring the cost of those solutions down. As a result, many more companies—not just deep-pocketed Big Pharma—have followed other consumer industries by wading deeper into AI to boost their brands, as the technology matures and gets cheaper.

Aktana, for instance, has invested heavily to enable scale by productizing its best-in-class technology assets developed over the last decade to create an off-the-shelf intelligence platform. This contrasts with the highly custom solutions from five or 10 years ago that came with expensive services costs and ongoing maintenance. A modern platform that employs a “build/buy once and deploy multiple times” approach reflects an economy of scale and lowers the total cost of ownership by at least 60% (depending on the size of the deployment) while empowering companies to cost-efficiently deploy AI and scale across regions, countries, therapeutic areas, and brands.5

“It’s an ecosystem of capability rather than a series of costly point solutions, each requiring expensive services. The total costs go down and the power of AI goes up with each new team using the system,” explains Cerio.

2. Technology maturity grows for greater flexibility

Early commercial AI offerings were hampered by the singularity of their approach. Most solutions fell neatly into one of two buckets: machine learning or expert systems (an if-then, rules-based approach). Neither technology alone does a great job replicating the way humans really think. Without necessary context, machine learning technologies generate conclusions that aren’t always practical in the real world. At the same time, it would be impossible to catalog every possible scenario a user might encounter with rules alone.

If AI 1.0 was defined by technologies working in isolation, the next generation is all about synergy. AI 2.0 blends various analytics technologies and human insights, to provide reliable, human-enhancing levels of intelligence for better decision-making. Simply put, AI solutions have matured, been proven, and are no longer a risky investment for small to mid-size life sciences companies.

In addition, today’s AI solutions are uniquely designed to allow companies to incrementally add capabilities as they mature, and users become more comfortable with the technology. A successful AI program will havea scalable platform at its foundation, with modules that can be added for new and expanding use cases. For instance, an emerging digital therapeutic company can invest in a modern intelligence platform and focus first on field-force enablement, but then unlock other modules or capabilities such as marketing automation, content selection, and channel orchestration over time to optimize the customer journey.

“The integration of AI is a natural part of our growth strategy so we looked for a flexible, modular solution that can easily evolve in lock-step with our corporate growth rather than having to reverse-engineer our AI system to catch up to our organization years to come,” adds Cerio. “The process to get us operational was seamless, and now we are looking for ways to deploy the solution in additional ways or to other teams such as our medical science liaisons.”

Further, a modular solution allows 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 loop of more data in/better insights out.

3. New ways to resetting corporate culture

Driverless cars and doctorless diagnosticians must work flawlessly every time. Likewise, their users must be confident that such AI-driven innovations will work as expected. Adoption of these technologies—and, hence, their tremendous benefits—hinges on trust. It is a catch-22—and can be a cause for resistance at any company, but especially growing organizations.

As AI evolves into the more advanced, contextual version that is emerging today, its predictions are becoming increasingly pivotal to treatment decisions. Customer-facing teams are better equipped than ever to deliver the right information to HCPs at the right time—for instance, highlighting a new therapy with fewer side effects to a recently diagnosed patient. The challenge is most users have little visibility into how AI generates conclusions, and this lack of transparency inevitably casts doubt. Many of the algorithms used for machine learning—particularly popular deep learning approaches—do not transparently convey the “how” or “why” behind the recommendation.

The lack of rationale hampers our ability to let go of misgivings and accept the accuracy of machines whose mechanics we cannot easily see or comprehend. As such, human beings want computer systems to produce transparent explanations for their decisions—a form of science known as explainable AI (xAI). Fortunately, the most advanced AI solutions in commercial life sciences now incorporate xAI with a blend of other analytics technologies and human insight to improve decision-making by building real-life context and plain-language explanations into recommendations.

Almirall took change management very seriously before deploying AI nationwide. “We hired a consultant to develop a thorough change management plan, and the results have been tremendous with user adoption far quicker than expected,” says Cerio. “Specifically, we identified our company’s personality and then worked to bridge the gap between what people think and how they need to think about the change. It’s a thoughtful documented process that revolves around concisely communicating the ‘why.’ Peer influence has also been key to success as the first adopters were asked to honestly provide feedback to their peers—the good and the not-so-good. And it has gone off as planned.”

Ultimately, this is a culture shift. The democratization of AI, therefore, is not all about the financial discrepancy between the “haves” or “have nots.” Rather, in today’s AI market where the total cost of ownership is plummeting and the technical maturity is skyrocketing, the only thing holding companies back from adoption is user acceptance. Explainable AI is the key.

As trust between humans and machines continues to build, AI 2.0 will become foundational to deepening HCP engagement in today’s digitally driven commercial model. And, as nearly half of all global healthcare companies plan to implement AI by 2025 saying it is crucial for business operations,6 the barriers to adoption are crumbling. The biotech, pharmaceutical, medical device, digital therapeutics, and animal health companies that recognize this and embrace behavioral change will see they have nothing holding them back.

Alan Kalton is Senior Vice President of sales for Aktana and GM of Aktana’s European division. 


  1. GlobalData, Pharma Intelligence Center, “AI and Big Data Will Continue to Disrupt the Pharmaceutical Sector,” (July 15, 2021). See full resource here.
  2. PharmaVOICE, “How AI is Helping Pharma Solve Some of its Covid Problems” by Meagan Parrish (January 25, 2022). See full resource here.
  3. MIT Sloan Management Review, “Companies are Making Serious Money with AI,” by Thomas H. Davenport and Randy Bean (February 17, 2022). See full resource here.
  4. Deep Pharma Intelligence Report, as seen in Financial Times (February 2022). See full resource here.
  5. Aktana data—based on average operating value between 2018 and 2021.
  6. PharmaNewsIntelligence, “AI in the Pharma Industry: Current Uses, Best Cases, Digital Future,” by Samantha McGrail (April 30, 2021). See full resource here.
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