Why AI’s real value in life sciences training is human, not artificial
3 mins
AI is not reshaping global learning in the dramatic, job replacing way some had predicted. Instead, AI is expanding what learning teams are capable of. When used responsibly, it strengthens creativity, supports personalization and brings new insight to learning programs.
For Learning and Development professionals in Life Sciences, this shift is especially meaningful. Training must be scientifically accurate, globally consistent and ready for compliance review at any moment. As AI becomes more embedded in learning ecosystems, Life Sciences teams are taking on evolving responsibilities around data quality, content governance and learner experience design.
Two recent webinars hosted by RWS, in collaboration with Training Industry and ATD, brought together experienced learning leaders to discuss how AI is influencing these realities. While the conversations spanned multiple industries, the ideas and lessons shared are highly applicable to Life Sciences training teams today.
Moderated by Michael Coates, Business Development Director at RWS, the first session titled eLearning’s AI Tightrope: Real Stories From Those Who Walked It (and Stuck the Landing) featured Ryan Austin, CEO of Cognota, Damon Patterson Sr., Director of Learning Operations at Align Technology, and Dominique Biliato, Global Learning and Development Lead at SAI360.
The second webinar, Hearts, Minds, and Algorithms: How L&D Can Master Human Connection in AI Enabled eLearning, welcomed Jim Guilkey, President of S4 NetQuest, Rebecca Krauland, Vice President of HR Solutions at Lockton Dunning Benefits, and Carolina Denkler, Senior Account Manager at Third Term Learning.
Together, these experts explored how AI and human creativity intersect and what it takes to innovate responsibly. Their insights offer valuable direction for Life Sciences organizations balancing scientific accuracy with global scalability.
Turning AI’s potential into real impact
Across both discussions, a clear point emerged. AI’s role in learning is no longer theoretical. It is already helping organizations improve speed, scalability and quality. For Life Sciences teams, which must deliver training that is compliant, multilingual and scientifically accurate, these improvements are particularly powerful.
AI is increasingly supporting learning teams by simplifying content workflows, accelerating global localization and revealing performance insights that were previously difficult to capture. Examples include adaptive learning paths that personalize scientific training and coaching tools that guide teams between sessions.
Behind the scenes, AI is also giving teams better visibility into return on investment. Learning leaders can now track engagement, measure time and cost savings and make more confident decisions based on data rather than assumptions. For Life Sciences organizations where auditability and accountability matter, this shift is significant.
Why empathy still matters most
Although AI can accelerate content creation and personalize learning, the panelists agreed that empathy remains essential. This is especially true in Life Sciences, where training often includes complex or sensitive content, cross cultural nuance and topics that directly affect patient safety.
Carolina Denkler cautioned that over automation can lead to content that feels generic or emotionally flat. “AI generated content just comes out generic and cookie cutter most of the time,” she said. “The information’s there, but it’s just not engaging.”
Rebecca Krauland emphasized that empathy is the foundation of engagement, describing it as the “cost of engagement.” AI can identify learner patterns, but it takes human creativity to interpret them in a way that is relevant, inclusive and credible.
The panelists also noted that while AI can assist with basic coaching, deeper skill development, especially in clinical or scientific environments, still depends on human judgment, trust and connection.
Responsible AI begins with strong foundations
Both webinars highlighted the same caution. AI only creates value when built on solid foundations. Data integrity, governance and transparency are essential for any successful implementation, especially in regulated industries.
Rebecca Krauland emphasized the importance of preparation. “You have to have solid data and governance in place before you even consider those types of tools.” Without structured data, AI cannot perform reliably.
Damon Patterson described AI as a force multiplier that can amplify strengths or weaknesses. Governance determines the outcome. Transparency also matters, particularly in Life Sciences, where learners want to understand how AI influences their training.
Jim Guilkey reinforced that readiness matters. The most effective organizations align systems, clarify workflows and ensure teams understand both the capabilities and limitations of AI before scaling.
Design thinking will shape AI’s future in learning
Looking ahead, the panelists agreed that AI will not replace instructional design but instead make it even more important. Effective learning still begins with clear objectives, thoughtful design and a deep understanding of the audience.
Jim Guilkey emphasized this point. “Don’t let the technology drive how you’re doing your training. Let the design drive that.”
Carolina Denkler noted that AI’s greatest strength may be its ability to extend human creativity. When guided by strategy and empathy, AI can help global learning professionals design training experiences that are efficient, engaging and emotionally intelligent.
For Life Sciences, this combination of design thinking and responsible AI offers a path to learning that is both scientifically rigorous and deeply human.
A balanced path forward for life sciences learning
Across Life Sciences organizations, L&D leaders are recognizing that AI’s success depends on balance. It requires the right mix of innovation and responsibility, automation and empathy, speed and scientific integrity.
The organizations advancing most effectively are strengthening governance, improving workflows, preparing teams and designing learning with intention. Most importantly, they are redefining the role of human expertise in an AI supported environment.
As both RWS webinars demonstrated, AI does not diminish the role of learning professionals. It elevates it. When used responsibly, AI becomes a bridge that helps make learning more adaptive, more globally consistent and more meaningfully human.
Watch the webinars
To explore the full discussions and hear directly from the experts, watch the recordings of eLearning’s AI Tightrope: Real Stories From Those Who Walked It (and Stuck the Landing) and Hearts, Minds, and Algorithms: Mastering Human Connection in AI Enabled eLearning.
Watch the webinars on demand and discover how Human plus AI can help Life Sciences learning teams build smarter, more compliant and more human centered training experiences.