Beyond the Early Adopters: Mainstream Adoption of AI in Life Sciences Training

Along with our partners from LTEN (Life Sciences Trainers and Educators Network), Quantified recently hosted a webinar titled “How AI is Transforming Learning in Life Sciences: Expert Practitioners Speak.” Industry leaders spoke up and shared their perspective on adoption of generative AI by life sciences training teams. We heard from:

• Kevin Kutler, the current Head of Sales Training at Regeneron and former head of US Training at Novartis. Kevin brought over a decade of experience in sales, marketing, and operations to the discussion.
• Rashonda Burkett, Director of Medical Capabilities at Novartis and former medical liaison with UCB and Sanofi. She shared her perspective as an industry veteran with a background as a Primary Care Clinical Pharmacist.
• Andreas Mueller, former CIO of Novartis and an Advisory Board Member with expertise in digital transformation at major firms like Deloitte and ZS Associates. Andreas highlighted the strategic importance of digital technologies in global training initiatives.

These experts underscored the rapid adoption of AI technologies and their impact on training teams in life sciences. If you’d like to understand how leading experts see the market, watch the webinar.

The Geoffrey Moore Model of Technology Adoption: A Quick Review

Even with as much insight and expertise as the panel brought to bear, one of the bigger insights came from polling the audience of over 140 industry professionals. You may be familiar with the Geoffrey Moore model of technology adoption, first proposed in his book “Crossing the Chasm.”

Although the book was first published in 1991, it’s been frequently revised and continues to inform commercialization of technology. Leaders, buyers, and analysts routinely cite it as an authority.

At the crux of the book is a psychographic model of technology adoption:

• Innovators are willing to take substantial risks in adopting new technologies and are typically 2.5% of the market.
• Early Adopters are enthusiastic about technology but are more risk-averse. They need to see the Innovators have success first. Early Adopters may let innovators take the initial risk, but they move quickly in order to realize first-mover advantage over the competition.  Early Adopters are 13.5% of any market segment.
• Early Majority users are pragmatists. They see the market moving, as started by the Early Adopters. They realize that adopting the new technology helps them maintain an edge in the marketplace. Early Majority represents 34% of the market.
• Late Majority users represent the next 34% of the market, but they adopt new technology as a defensive move in order to catch up and avoid being left behind. If you’re continually playing catch-up, you know the feeling of being in the Late Majority.
• Laggards are 16% of the market. We all know a few laggards. They tend to resist change, even when there are compelling reasons to adopt. They grumble about having to learn new technologies and processes, but are eventually swept along by the wave of change.

Generative AI Has Crossed the Chasm for Life Sciences Educators

Here’s how it breaks down. Innovators and Early Adopters represent the “pre-chasm” segments, which total 47%. The remaining segments – Early Majority, Late Majority, and Laggards – total 53%.

With fully 35% of respondents believing that we are in the Early Majority stage of adoption, it’s clear that that many teams – such as Sanofi’s North American Learning Experience Team – are treating this as a business imperative to remain competitive.

In short, with over half of respondents in Early Majority or later stages, AI for training in life sciences has crossed the chasm.

In that environment, training leaders who think of adoption of generative AI as experiment or a pilot may find themselves left behind in a rapidly evolving market.

Case Study: How Sanofi Used AI to Certify 100% of the Team on Product Launch

The Evolution of AI Role Play in Training

AI-driven role play is one of the most promising, and high-ROI, areas for AI adoption in life sciences training. Through simulated interactions and scenario-based learning, AI role play provides a risk-free environment for learners to practice and improve their performance.

For leaders and managers, AI role play is a scalable and objective way to administer certification programs and get teams trained and into the field and selling more quickly. These realistic simulations also boost confidence in handling real-world situations, from patient interactions to regulatory compliance.

Related: 6 Mistakes You Can Avoid with AI Pharma Sales Training Simulations

The Risks of Lagging in AI Adoption

In an industry as competitive as pharmaceuticals, lagging behind in technology adoption can mean missing opportunities for effective training. The risk of being outpaced by competitors who deploy AI for training is now a real risk, not a hypothetical scenario. Experimenting with and adopting AI is no longer about keeping up with trends. Instead, use of AI is rapidly becoming a must-have in order to stay competitive in training capabilities.

For sales and training leaders in pharmaceuticals and medical devices, the message is clear: the time to act on AI for training teams is now. Whether it’s through enhancing existing training programs with AI role-plays or adopting AI-driven analytics to personalize learning and assess effectiveness, the opportunities are vast.

Enterprise-Ready AI is Ready for Prime Time

AI for training teams is rapidly becoming an expectation. Adopting AI fully means more than simply using ChatGPT and other easily available tools in order to generate content. Enterprise-ready AI, such as Quantified’s Simulator, is already delivering returns to teams in the early and late majority phases of adoption.

By embracing AI, training leaders can not only improve the ROI and speed-to-market of their programs, but also ensure that their teams are equipped to meet the challenges of today and tomorrow.