Artificial Intelligence is transforming corporate learning, but not every organization is doing it in ways that employees actually trust. In this episode of Workplace Stories, we talk with Peter Manniche Riber, Digital Learning & AI Leader, about how his team built AI-powered learning tools that employees truly believe in.
From creating the “Dilemma Coach” and “IDP Coach” to redefining personalization and data privacy, Peter demonstrates what happens when innovation is combined with practicality, and why sometimes the smartest move is to build, rather than buy.
You will want to hear this episode if you are interested in...- [00:00] Why “believability” is the key to AI adoption.
- [04:50] How Novo Nordisk’s “Dilemma Coach” and “IDP Coach” came to life.
- [09:00] Why less data, and the right data, creates better personalization.
- [17:00] Balancing privacy, ethics, and personalization in AI learning.
- [25:30] Working with works councils and data regulators early.
- [33:00] Scaling learning equity and access across global teams.
- [39:40] What AI means for strategic workforce planning.
- [41:30] Peter’s advice for L&D leaders ready to experiment with AI.
The Power of “Believability” in AI LearningAt Novo Nordisk, Peter’s team coined a simple but powerful concept, believability. It means people will only engage with AI tools if they recognize themselves and their context in the experience. Through hundreds of user tests, they found that when an AI response feels personal and relevant, adoption skyrockets.Rather than hoarding corporate data, they ask employees directly about their goals, challenges, and career aspirations. This approach not only keeps data secure but also ensures every interaction feels real, human, and trustworthy.
Why Novo Nordisk Built Its Own AI ToolsWhen it came to designing learning applications, Peter’s team decided to build rather than buy. The reason? Control, context, and compliance. Off-the-shelf tools couldn’t meet Novo Nordisk’s strict privacy standards or reflect its unique leadership culture. By developing internally, the team could train AI on company-specific frameworks, design intuitive UX guardrails, and maintain full ownership of their data, while spending less than a handful of traditional e-learning modules would cost.
Redefining Data and TrustInstead of scraping internal systems, Peter’s philosophy is simple: ask people. Employees willingly provide fresh, accurate context when they understand how it’s used. Transparency and consent are baked into the process, with large-font screens explaining how data is handled and why it matters.The result? Nearly 90% of employees feel completely safe using these tools, a remarkable trust level for AI-driven systems inside a regulated, global company.
The Future of L&D and AI ExperimentationPeter’s message to learning leaders: stop waiting for perfection and start experimenting. You don’t need a massive budget or a team of data scientists to create meaningful AI applications. What you need is curiosity, clear hypotheses, and the courage to learn by doing.AI won’t replace thoughtful design or human judgment, but it can unlock a new era of personalized, scalable, and believable learning.
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