Staying Ahead of the AI Curve in Commercial Pharma

Contributed by:

Matthew Van Wingerden, VP, Analytical Services , Aktana

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The commercial model within life sciences is ripe for change. In a landscape with harder to reach healthcare providers (HCPs) who are faced with more marketing noise than ever, the need for improved engagement is significant. According to DRG Digital, only 12% of HCPs said they had emailed with their rep in the past six months. More than a third said they wanted to. This data indicates our industry isn’t personalizing for HCPs as best as we can. This is one of the most significant challenges artificial intelligence (AI) looks to solve in commercial pharma.

By using AI, we can turn mass marketing into one-to-one personalization. This enables marketers and sales reps to offer each HCP the information they require, how and when they need it.

Although AI is a powerful tool that can make strategies more agile and improve HCP relationships, there are a few things to understand upfront. Here are three ways to ensure long-term success incorporating AI, even as the industry continues to shift.

Identify Specific Areas for Impact

With the pressure to implement intelligent technologies, many companies are ambitious about expected results, without having a clear understanding of what’s possible. Prior to implementation, we must first understand what AI can achieve to set realistic expectations for teams, stakeholders, and leadership.

Depending on your organization, you will have varying levels of readiness for adoption of AI technology. It’s essential to begin with a specific goal in mind to ensure intelligent technology is bringing value based on what’s important to your business. For example, one top global pharma company wanted to better support their reps juggling multiple products so they knew what to prioritize, when and why. Nine months after launch, reps who used Aktana’s suggestions had achieved 10% higher goal attainment than those who did not.

Harmonize with Human Intelligence

There is a common misconception that implementing AI into your workflows and strategies eliminates the need for human intelligence. However, projects that see the most success incorporate a blend of both automatic and manual approaches. In this AI-assisted approach, AI models are integrated directly into marketing tools, but at the same time, the marketer has the ability to not only visualize what is predicted to happen, but can control that output by applying constraints or new logic on it. In this way, the best of both worlds is utilized.

In the field, users are empowered to accept, dismiss, and give their feedback, which makes them feel more trusting of AI technology as it learns from their behavior. With AI, commercial teams can monitor what’s working and what isn’t to adapt to customer preferences over time. This allows teams to make continuous improvements to the customer journey by indicating preferred channels or methods of communication to encourage better engagement from HCPs.

Pave the Way for Explainable AI (xAI)

Currently, artificial intelligence can detect nuanced patterns in data that aren’t easily noticeable to humans, but it can require training with annotated data sets. As time progresses, AI workflows will get better at explaining to users why systems are making interpretations and subsequent decisions.

To begin paving the way for explainable AI today, it’s important to educate your leadership and commercial teams about AI technology, how it works, and, when possible, provide reasoning behind suggested next best actions.

This development toward more explainable AI, rather than deep learning technology, where the machine understands more than users, is significant as the commercial model evolves. Explainable AI will allow users to understand, trust, and break down organizational challenges that exist. Once it becomes more commonly used, AI technology will become intuitive even for people who have minimal experience with it.

For now, organizations that approach AI technology with a specific goal in mind and an appreciation for human interaction with AI will lead the revolution in life sciences. In commercial pharma, using AI to drive a new model for HCP engagement translates to better decision-making by physicians, supporting better patient outcomes across the board.(PV)

Aktana is a pioneer in AI-enabled decision support for the global life-sciences industry. Its proprietary platform harnesses machine learning algorithms to enable commercial teams to seamlessly coordinate and optimize multichannel engagement with healthcare providers.
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