When it comes to AI in L&D, we’ve all heard the hype – how it will revolutionize the training landscape, improve efficiency and effectiveness, and bring a new level of personalization to the industry.
This is all true, but what kind of impact can it have on training in a niche like pharma? As L&D professionals in pharma know all too well, the pharmaceutical industry is full of unique characteristics that present particular challenges when compared to rolling out L&D in any other industry.
We realised the best way to understand how AI is impacting pharma L&D is to ask the people who are living and breathing it every day. After speaking to four experienced L&D professionals across the pharma and healthcare space, we’ve compiled their views in one place.
Here’s what they had to say:
Is AI Ready to Make an Impact in Complex Fields Like Pharma and Healthcare?
In highly regulated industries like pharma and healthcare, there can often be a natural hesitation to dive head-first into new technologies.
But has AI reached a stage where it’s ready to be integrated even into these complex fields? We asked the question, and the consensus seems to be yes among our panel of experts.
Laxmi Russo, Senior Director, HRBP and Talent Development & Learning told us that AI was “definitely” ready, with Dr. Ritesh Verma, Head L&D at Alembic Pharmaceuticals likewise in total agreement.
Emma Foxon, Senior Learning Strategist in the Healthcare and Pharmaceutical Industry, believes AI “already is [ready] and it’ll be here to stay” before wisely adding that we “have to now assess new ways of introducing Human-in-the-loop (HITL) practices to ensure we’re using AI ethically and practically, not just using AI for AI’s sake.”
“There’s a fine line between the buzzwords and enhancement of human & AI practices”, Emma added.
Andrew Olliver-Jones, Senior Change Management and L&D Consultant, agrees that AI is ready to make an impact – with some caveats. He stated that “the technology itself is certainly ready, as generative AI and adaptive learning tools can process colossal volumes of complex data — from clinical trial results to shifting regulatory guidelines — and convert them into accessible, bitesize learning far quicker than traditional methods”.
However, Andrew added that “tech readiness is only half the battle. In a highly regulated, high-stakes sector like pharma, precision isn’t optional. AI models have a habit of delivering absolute nonsense with unshakeable confidence. That authoritative hallucination triggers a real cognitive risk where people stop questioning the output because it sounds so polished.
In Andrew’s view, “while the tech is ready, L&D’s immediate priority isn’t just rolling out shiny new tools; it’s building robust governance, ensuring human oversight, and fostering a culture of healthy scepticism” so that L&D teams know how to interrogate AI rather than trust it blindly.
AI is being widely touted as a transformative technology, and it’s clear that it’s ready to make an impact even in these most high-pressure industries.
What are the Most Effective or Interesting Use Cases for AI in Pharma Right Now?
With AI evidently primed and ready to leverage in pharma and healthcare L&D situations, what are the most impactful use cases that are building excitement among our panel?
AI-Assisted Metrics & Measurement
Emma highlighted that in her view, right now “the most effective modality I have seen is gathering user metrics and assessment criteria, over a period of time from pre launch to post launch. The findings of how L&D trainings were engaged with and what impact they had in the commercial field setting was astounding. The case was compared to a similar “non AI” launch strategy – the findings were more in depth, more insightful and I believe better decisions were made due to AI modalities being used.”
“However there were some gaps, mainly assessment of true behavior change in the field on an individual level – we still need the human touch”, Emma was quick to note!
Leadership Development & Coaching
Laxmi told us that her favorite use case for AI in pharma L&D right now is “AI-supported leadership development and coaching. For example, leaders can use AI to practise difficult conversations, explore different scenarios and approaches, or prepare for a challenging leadership situation and receive immediate feedback. That complements, rather than replaces, the human coaching relationship.”
AI-Powered Roleplay Conversations
Andrew told us that his favourite application at the moment is “AI-driven roleplay for scientific conversations, because Medical Science Liaisons (MSLs) and commercial reps spend a lot of time having complex, nuanced conversations with doctors (e.g. explaining how a drug works or answering tricky clinical questions) and making a mistake in the real world carries real regulatory and reputational risk.
Andrew added that “while companies like Roche get a lot of attention for using AI in early drug discovery, this kind of simulation is where AI is making a massive, practical difference to people on the ground. Using voice or avatar platforms to simulate a busy, demanding doctors gives teams a completely safe space to practice and build confidence before they ever step into a room with a clinician.”
Dr. Ritesh concurred, noting that he was interested in the “use of Generative AI platforms as practice coaches, to help commercial teams with sales pitches” in particular.
We’ve seen this in action already, as Bayer leveraged VirtualSpeech for a variety of use cases, including to prepare field reps and commercial teams for high-stakes negotiations with AI-driven avatar roleplays. This gave teams a risk-free space to work on handling tough objections and to receive instant feedback, boosting employee confidence and consistency.
Increased Relevance & Personalization
Laxmi also added that AI can be leveraged to “personalize learning and make it much more relevant to the individual and the business need. In a biotech environment, capability needs can be very different across Clinical Development, Research, Regulatory, Medical and other functions. AI can help curate learning based on an individual’s role, experience and development needs, rather than relying on a standard learning programme for everyone.”
Andrew seconded this view, stating that “personalized micro-learning is a great example. Instead of dragging busy scientific staff through hours of generic, annual compliance e-learning, AI can quickly figure out what someone already knows and only test them on the bits they actually need to refresh. It saves huge amounts of time while still keeping standards high”.
With exciting AI use cases already being put to the test by L&D teams in the pharma and healthcare space, we’re eager to see how this trend continues to develop.
What’s Next for AI in Pharma L&D?
With AI established as an already-powerful tool in pharma L&D teams’ arsenal, we asked our panel of experts what they expected to come next, and what their overall thoughts on AI in this complex space.
Emma’s biggest takeaway is that “we’re in the “boom” phase of AI, we will get to some normalcy on when and where AI should/could and will be used as standard in pharma, we are just finding our path”
Emma also added that “it’ll be interesting over the next 2 or 3 years to see what happens with the integration of AI into normal workflows. The assessment of data is fractional in my opinion, compared to what it will be. So I think this will be our biggest challenge – understanding what the data is telling us, being aware of any trends or bias and ensuring when we look at AI infrastructure we look at how humans (HITL) will be required to ensure we are assessing it ethically.”
Emma concluded by saying that “the industry is looking for a “solve all” solution, which there needs to be some hesitancy around, proper business use cases need to be identified looking at the “what are we “solving for” in order for AI integration to have success factors.”
This need for hesitancy was also expressed by both Dr. Ritesh, who advocated for “limited usage as the pharma industry is highly regulated” before recommending “judicious use” of AI, and Laxmi, who stated that she “would be very conscious of responsible AI. In a scientific environment, accuracy, confidentiality, intellectual property, data privacy and human oversight are critical”.
Laxmi stated that she sees “AI as a powerful tool to augment our experts and make learning more personalised and accessible, but not as a replacement for scientific expertise, human judgement or the relationship between a leader and their coach”.
Laxmi also believes that “the real opportunity is to move beyond using AI simply to create learning content. In pharma and biotech, L&D can become much more closely connected to workforce and talent strategy. For example, as AI and digital technologies increasingly influence drug discovery, clinical development and other parts of the business, L&D can help identify what capabilities the organisation will need in the future, understand where the gaps are, and determine whether we should build, buy or borrow those capabilities”.
Andrew’s view is that “as AI takes on the heavy lifting of content generation and data retrieval, L&D can shift focus back to what AI can’t replicate: critical thinking, ethical decision-making, patient-centric empathy, and authentic leadership. Technology gives us speed, but human expertise provides the direction”.
The key theme among this panel of L&D experts? AI usage in such a cutting-edge space like pharma requires responsibility and human intervention to safely make the most of the technology’s potential.
Wrapping Up
If you’re ready to bring safe, scalable, AI-powered conversation and soft skills training to your L&D strategy, Book a Demo with VirtualSpeech to see how we help organizations like Bayer transform field readiness.