From overcoming initial anxieties through hackathons and playful experiments, to setting an ambitious organizational roadmap for AI, Dessalen Wood shares how Syntax is embedding artificial intelligence across departments, focusing on pragmatic progress rather than hype.

You’ll hear stories about driving excitement, learning by doing, and the all-important challenge of measuring real impact. More than just technology, this episode dives into the culture shifts, collaboration with IT, and leadership mindsets that are pushing companies out of their comfort zones and into the future, while keeping authenticity and humanity front and center.

You will want to hear this episode if you are interested in...

• 00:00 Overcoming AI fear through collaboration
• 03:30 Defining AI readiness today
• 09:55 AI's role in business transformation
• 15:46 AI anxiety in the workplace
• 22:05 Making AI adoption fun
• 28:11 AI expertise requires human touch
• 36:42 AI strategy: Three layers explained
• 41:31 True transformation vs. improvement
• 53:21 Rethinking work, technology, and AI

Overcoming AI Anxiety

Early stages of AI adoption in organizations are often marked by fear. Employees worry about being displaced, making mistakes, or failing to keep up. At Syntax, Dessalen Wood and her fellow leaders tackled these concerns by creating safe, engaging, and transparent opportunities to experiment.

One of the most effective strategies was an organization-wide AI hackathon. Everyone, regardless of their role, was invited to submit ideas for automation and improvement—ideas that the tech team then built. Not only did this demystify AI, but it also provided a healthy dose of competition and excitement. Dessalen describes that, “Instead of people fearing automation, it became a competition... People were saying, please, automate my tasks!” This shift from apprehension to enthusiasm helped break through adoption barriers and foster a culture of creative problem-solving.

Structuring Success: A Multi-Layered AI Roadmap

Syntax’s approach moves AI from a buzzword to a set of actionable strategies. The leadership distinguished between three core areas:

1. Department Initiatives: Leveraging AI for productivity and process improvement within teams
2. Customer Value: Enhancing solutions and services delivered to external clients
3. Business Transformation: Reimagining core business models and operations for strategic advantage

Many organizations mistakenly assume one AI initiative will magically improve all three—but real impact comes from tailored strategies for each. In practice, this means differentiating between continuous improvement (making existing tasks more efficient) and true reinvention (fundamentally transforming how and why work gets done).

The creation of AI champions, employees trained as internal advocates and solution designers, helped ensure that innovative ideas didn’t just sit in a backlog. Instead, those not ready for large-scale investment could be adapted, piloted, and iterated by these champions, keeping the spirit of experimentation alive while prioritizing resources for the highest-value initiatives.

The Human Element: Authenticity, Experimentation, and Measurement

As AI tools become more prevalent, a new challenge emerges: maintaining authenticity in communication, development, and leadership. The team discussed the “hollowed-out leader” phenomenon—where over-reliance on AI could dilute critical thinking and personal investment. Dessalen explains why expertise, context, and human customization are more important than ever: If it doesn’t demonstrate expertise and isn’t highly curated, it just turns people off.

Measurement is also evolving. Early wins in AI productivity are being tracked, not just in terms of completion rates or tool adoption, but in demonstrable business outcomes and stretch goals. Syntax uses tools that help employees articulate their productivity gains and set new impact targets, ensuring that activity translates into organizational value.

Resources & People Mentioned

• Experience Qualtrics Management Resources 

Connect with Dessalen Wood

• Dessalen Wood on LinkedIn 

Connect With Red Thread Research

• Website: Red Thread Research
• On LinkedIn
• On Facebook
• On Twitter

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Manuel Smukalla, Global Talent Impact, Skills Intelligence, and Systems Lead at Bayer, joins Workplace Stories to unpack one of the most ambitious organizational transformations underway today. As Bayer confronts significant market, legal, and profitability pressures, the company has taken a radically different approach to how work, leadership, and talent are structured, rethinking everything from management layers to career progression.

In this episode, Manuel walks through Bayer’s shift to Dynamic Shared Ownership (DSO), a decentralized operating model built around networks of teams, 90-day work cycles, and leaders who coach rather than control. He explains why skills visibility became a foundational requirement for this model to work and how Bayer is using skills data to democratize opportunities, improve talent flow, and fundamentally rethink careers inside a global enterprise.

You’ll hear how Bayer reduced management layers by more than half, redesigned leadership expectations through its VAC (Visionary, Architect, Catalyst, Coach) model, and moved toward a culture where employees are empowered, and expected, to own their work, development, and impact.

You will want to hear this episode if you are interested in...

• [01:01] Why Bayer embarked on a radical organizational transformation.
• [04:30] What Dynamic Shared Ownership really means in practice.
• [06:55] Moving from hierarchical structures to networks of teams.
• [10:40] Why skills visibility became a critical business problem.
• [14:05] How 90-day work cycles change accountability and outcomes.
• [18:10] Building organizations around customer problems, not functions.
• [21:15] Launching skills profiles as a starting point, not an endpoint.
• [23:00] How Bayer’s talent marketplace democratizes opportunity at scale.
• [27:00] The three pillars of a skills-based organization.
• [33:00] Rethinking careers, performance management, and feedback.
• [43:10] The VAC leadership model explained.
• [52:30] Measuring success in a decentralized organization.
• [53:45] Advice for organizations considering similar transformations.

Dynamic Shared Ownership: Redesigning How Work Gets Done
At the core of Bayer’s transformation is Dynamic Shared Ownership, an operating model that replaces traditional hierarchies with flexible networks of teams. Manuel explains how Bayer reduced its management layers from thirteen to six and reorganized work into 90-day cycles focused on clear outcomes. After each cycle, teams reflect on what worked, what didn’t, and whether the work should continue at all.

This approach decentralizes decision-making and forces a shift away from command-and-control leadership. Leaders are no longer expected to direct every task; instead, they create the conditions for teams to succeed, setting direction while trusting teams to determine how outcomes are achieved.

Skills as the Engine of Talent Flow
For Dynamic Shared Ownership to function, Bayer needed a new way to understand and deploy talent. Manuel shares a pivotal realization: managers were turning to LinkedIn to understand employee skills because the organization lacked internal visibility. That insight sparked Bayer’s skills journey.

Rather than starting with complex taxonomies, Bayer focused first on skill visibility. Employees created and maintained skills profiles, supported by workshops on how to describe capabilities effectively. Over time, this evolved into a talent marketplace that matches people to work based on skills, not job titles, career level, or location, helping democratize access to opportunities across the enterprise.

Moving Talent to Work, Not Work to Talent
Manuel outlines three defining pillars of a skills-based organization. First, talent must move to work rather than work being constrained by static roles. Second, organizations must commit to permanent upskilling, recognizing that development is continuous, not episodic. Third, opportunities must be democratized at scale, reducing reliance on manager sponsorship or informal networks.

Bayer’s marketplace supports fixed roles, flex roles, and fully agile project-based work, encouraging employees to actively shape their careers while remaining accountable for outcomes. This model challenges long-held assumptions about promotions, ladders, and linear advancement.

Leadership and Performance in a Decentralized World
Leadership at Bayer has been redefined through the VAC model: Visionary, Architect, Catalyst, and Coach. Leaders set direction, help teams design how value is created, remove barriers, and support rapid cycles of learning. This requires significant unlearning for leaders shaped by traditional hierarchies.

Performance management has also shifted. Goals are set in 90-day cycles at the team level, with feedback coming from peers and work leads rather than solely from a direct manager. Over time, this creates richer data on contribution and impact, but also demands a cultural shift toward transparency, shared accountability, and continuous feedback.

Connect with Manuel Smukalla

• Manuel Smukalla on LinkedIn

Connect With Red Thread Research

• Website: Red Thread Research
• On LinkedIn
• On Facebook
• On Twitter

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Christine Crouch, Senior Director of Learning at General Mills, joins Workplace Stories to discuss a massive shift in how one of the world's legacy food companies approaches talent development. General Mills has recently transitioned to a centralized and integrated learning model.

In this episode, Christine lays out one of the clearest cases for centralization we have heard. While efficiency is a benefit, she argues that the true drivers are decision-making power and better data. By unifying the function, General Mills gains a stronger view of learning activity and business needs, creating the strategic infrastructure necessary for the future of work.

You’ll hear how Christine’s team manages to be centralized without losing the "local feel" through a robust Learning Business Partner model. She also details how centralization unlocks the ability to correlate learning metrics with talent outcomes like retention and performance. Finally, Christine shares her philosophy on AI, not as a replacement for human connection, but as a tool to elevate the human side of learning.

You will want to hear this episode if you are interested in...

• [06:07] Background on General Mills and its culture.
• [07:00] The shift from decentralized to centralized L&D.
• [11:11] How to make centralization feel local to business stakeholders.
• [18:30] The Learning Business Partner model explained.
• [21:07] Correlating learning metrics with talent outcomes.
• [27:58] Managing "rogue purchases" in a centralized model.
• [34:20] Why AI will elevate, not replace, the human side of learning.
• [47:35] Piloting AI coaching tools like "Nadia".

The Strategic Case for Centralization
For many organizations, the move to centralize L&D is purely a cost-cutting exercise. However, Christine frames the shift at General Mills as a play for better data and strategic decision-making. A centralized function provides a unified view of the organization's needs, allowing L&D to prioritize investments that drive enterprise-wide capabilities rather than just solving isolated functional problems. As AI accelerates, this strong data infrastructure is what will allow the organization to distinguish between what people actually need to know versus what can be offloaded to technology.

The Learning Business Partner Model
Centralization often brings the fear of losing touch with the business. General Mills solves this through the "Learning Business Partner" role, individuals who sit on the leadership teams of specific functions or segments but report back to the central L&D organization. These partners act as a bridge; they understand the HR strategy and business plans of their specific function while ensuring continuity with the broader enterprise goals. They are expected to be performance consultants first, identifying the root problems to solve rather than just taking orders for training.

AI: Elevating the Human Element
Christine’s approach to AI is grounded in optimism and human-centricity. She believes AI will not replace the human side of learning but elevate it. General Mills is actively piloting AI for tasks like personalization, automation, and coaching via a tool called "Nadia," which acts as an "always-on" coach. However, Christine emphasizes that deep skill building, like change leadership, still requires human connection, peer discussion, and the ability to "read the room," skills that AI cannot fully replicate. 

Connect with Christine Crouch

• Christine Crouch on LinkedIn 

Connect With Red Thread Research

• Website: Red Thread Research
• On LinkedIn
• On Facebook
• On Twitter

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On the latest episode of Workplace Stories, we sit down with Koreen Pagano, author of "Building a Skills-Based Organization," to talk about one of the hottest and most complex topics in the world of work: how organizations can become truly skills-based, and what that really means in today’s rapidly changing, AI-driven landscape. The conversation was loaded with practical insights, candid stories, and wisdom from the front lines of workforce transformation.

Koreen shares her journey from ed-tech and product leadership to guiding hundreds of organizations through the maze of skills transformation. We discuss the crucial front-of-house and back-of-house elements, from clear communication and partnership models to building the right data and technology infrastructure. You’ll hear fresh perspectives on using skills data as an early signal for retention, the shifting role of tasks versus skills, and what it means to future-proof your workforce for ongoing change. 

You will want to hear this episode if you are interested in...

• [05:17] Skills vs job architecture approaches.
• [10:04] Navigating skills-based organizations.
• [14:33] Workforce data challenges with AI.
• [23:04] Skills over jobs for strategy.
• [27:04] Building resilient data systems.
• [34:33] Building trust in skill data.
• [39:32] Predicting employee retention through data.
• [45:59] Helping organizations align AI transformation with business goals.

Why Skills Still Matter in a “Task-Talk” World
There’s a persistent misconception that the age of “skills” has passed and that “tasks” offer a more practical lens, especially with AI in play. Koreen shares how, at a recent industry event, she heard professionals say, “We don’t need to worry about skills, we have to focus on tasks.” But she thinks that it’s misguided to abandon skills just when organizations are barely starting to understand and leverage them.

While tasks describe the work to be done, skills reflect the underlying human (and sometimes machine) capabilities that make that work possible. Both are crucial, but without a foundational understanding of your organization's skills, mapping tasks is like building on sand.

Front of House, Back of House, and Getting Skills Right
We need to balance “front of house” and “back of house” considerations when building a skills-based organization. Organizations often focus either on external communications, partnerships, and culture (front of house), or purely on technology, data, and infrastructure (back of house), but rarely both. Koreen is unique in straddling the two, and it’s this holistic approach, blending people and process with tech and data, that sets successful organizations apart.

The Evolution of Data and the Rise of Skills Verification
Organizations are beginning to realize that their skills data isn’t just about upskilling or reskilling; it’s tightly connected to business-critical outcomes like retention, performance, and the ability to adapt to market shifts. Koreen shares compelling examples of using skills data to provide early warning on issues like employee retention, demonstrating data-driven HR in action.

She also shared her pragmatic “3Vs” model for validating skills data: Validity (how well the data measures what it claims to), Variety (different types of data from varied sources), and Volume (quantity and frequency of data collected). You can make solid business decisions with basic self-reported skills data, but for higher-stakes calls, like hiring, you need much more rigorous, validated information.

Jobs, Skills, and the Trap of Static Structures
Often, organizations anchor their skills strategy to their job architecture. Consultants and technology vendors frequently push companies to start by mapping skills to static jobs. We discuss why this is a dangerous place to “end”, because jobs, roles, and the tasks that define them are changing faster than ever, especially with AI in the mix. Koreen advocates for designing skills data that is flexible, lives independently, and can be mapped to jobs and tasks as they evolve, never becoming held hostage by legacy structures.


Goals Over Tasks
Perhaps the most powerful call to action was the need to focus less on micromanaging the “how” (a long list of tasks) and more on the “what and why”, the goals, outcomes, and genuine business objectives. In a future where work is constantly shifting, organizations that empower people around purpose, supported by dynamic skills data, will outperform those stuck mapping today’s tasks to yesterday’s job charts.

Building a skills-based organization isn't a project with a tidy endpoint, it’s a transformation. As Koreen reminds us, it’s hard, messy, and as much about culture as it is about data. But for the organizations (and the people) willing to experiment, adapt, and keep skills at the center of strategy, the payoff is a workforce that’s ready for whatever comes next. 

Resources & People Mentioned

• Building the Skills-Based Organization: A Blueprint for Transformation by Koreen Pagano 

Connect with Koreen Pagano

• Koreen Pagano on LinkedIn 

Connect With Red Thread Research

• Website: Red Thread Research
• On LinkedIn
• On Facebook
• On Twitter

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In this episode, Stacia and Dani sit down once again with Cole Napper, author of “People Analytics: Using Data-Driven HR and Gen AI as a Business Asset.” A year after his first appearance, Cole returns with bold insights about the seismic changes facing HR and people analytics, and why now is the time to rethink how we define value in the workplace.

Cole argues that the future of HR depends on shedding its transactional skin and embracing a new, data-driven paradigm. He discusses why traditional models like Dave Ulrich’s COE framework won’t survive the decade, how organizations can “discorrelate” from market forces by proving business value, and why fear, not technology, is the biggest obstacle to transformation. 

With sharp humor and evidence from his own research, Cole makes the case for a redefined HR: one that blends human strategy with AI-powered intelligence to drive growth, not just efficiency.

You will want to hear this episode if you are interested in...
[00:00] Building a new HR paradigm in the Gen AI era.
[06:00] Why people analytics hit its “identity crisis” after 2022.
[12:00] How to prove HR’s business value beyond metrics.
[19:00] The decline of the Ulrich HR model and what replaces it.
[24:00] The future of AI-driven workforce transformation.
[33:00] The tension between the HR and finance worldviews.
[46:00] Why data infrastructure is suddenly “sexy” again.
[52:00] Three possible futures for HR in the next decade.

Building a New Paradigm for People Analytics
Cole’s new book calls for a reset in how organizations use data, not as an isolated reporting function but as a business accelerator. He reveals how people analytics can move from being “scorekeepers” to strategic partners by tackling the questions behind the questions: Why is it happening? What should we do about it? His message is clear, analytics must tie directly to revenue, cost, or risk reduction, or it’s just a hobby.

The End of HR as We Know It
Cole predicts that the Ulrich model, the long-standing HR framework of COEs, service centers, and HRBPs, won’t survive the coming decade. As generative AI automates much of HR’s transactional work, only the strategic and human elements will remain. He and the hosts debate what should stay human and what can be delegated to machines, exploring the fine line between technological efficiency and organizational soul.

AI, Accountability, and the Future of Work
Cole cautions that while AI’s potential is vast, it cannot replace human accountability. Drawing a parallel with the evolution of chess, he argues that AI will transform HR’s “game,” not erase it. The goal isn’t to align around AI as a tool, but to use it to unlock entirely new possibilities in how we work, learn, and grow.

Infrastructure, Not Illusion
For all the hype, Cole reminds leaders that the foundation of AI success lies in data infrastructure, “the least sexy but most essential lever.” Without it, organizations risk failure in the next wave of transformation. Investing in data quality, architecture, and scalability today determines who thrives, or disappears, tomorrow.

Resources & People Mentioned

• People Analytics: Using Data-Driven HR and Gen AI as a Business Asset by Cole Napper

Connect with Cole Napper

• Cole on LinkedIn

Connect With Red Thread Research

• Website: Red Thread Research
• On LinkedIn
• On Facebook
• On Twitter

In this episode of Workplace Stories, we sit down with Lori Niles-Hoffman, global learning strategist, EdTech advisor, and author of The Eight Levers of EdTech Transformation. With over 25 years of experience implementing large-scale learning systems, Lori brings a no-nonsense, deeply human perspective to how organizations can thrive at the intersection of technology, data, and talent.

Lori reveals why EdTech success isn’t about shiny tools, it’s about mastering eight foundational levers that determine whether your learning strategy creates value or chaos. From ecosystem thinking to stakeholder management, she explains how leaders can future-proof learning strategies through data, design, and disciplined experimentation.

You’ll hear candid insights on how AI is reshaping L&D, not by changing the rules, but by exposing where we’ve been weak all along. Lori also shares why the “backend just got sexy,” and how the next competitive edge won’t come from beautiful interfaces, but from the quality of data and insights driving them.

You will want to hear this episode if you are interested in...
[00:00] The eight levers shaping EdTech transformation.
[06:00] Lessons from 25 years in enterprise learning systems.
[09:00] Why most L&D tech investments fail before they start.
[14:00] The rise of data literacy and “sexy backends” in learning design.
[17:00] Why clean data matters more than new tool.
[24:00] A breakdown of the eight levers and how they work together.
[29:00] Stakeholder management and ecosystem thinking in practice.
[35:00] The new role of AI in exposing weak learning strategies.
[39:00] Why skills, not titles, will define the future of learning.
[41:00] The human side of transformation: keeping people at the center.

The Future of Learning Isn’t About Tech, It’s About Leverage
Lori’s framework flips the script on how organizations approach learning transformation. Rather than starting with technology, she urges leaders to first clarify their target operating model, data readiness, and stakeholder relationships. The result? Smarter decisions, stronger credibility, and sustainable change.

Her book’s eight levers, ranging from content strategy to ecosystem alignment, help leaders navigate the “medium term” (through 2028) of rapid evolution in learning technology. As Lori puts it, the goal isn’t to adopt AI or automation for their own sake, it’s to make learning adaptive, outcomes-focused, and undeniably relevant.

Data, Ecosystems, and the “Sexy Backend”
Forget fancy dashboards, Lori believes the true revolution is happening behind the scenes. As user interfaces disappear and voice or text prompts replace them, differentiation will come from data governance, interoperability, and predictive insights. The backend, she says, is now where strategy lives.

She emphasizes that AI doesn’t change the levers, it exposes their weaknesses. The organizations winning in this new era will be those that invest in clean data, aligned systems, and smart stakeholder engagement.Skills as the Spine of the Future WorkforceAmong the eight levers, Lori highlights skills as the “spine” connecting every other element of learning strategy.

She challenges L&D professionals to stop chasing shiny taxonomies and instead treat skills like a supply chain, measured, managed, and constantly replenished. The goal isn’t just mobility or efficiency; it’s resilience.

Resources & People Mentioned

• L&D Tech Ecosystem 2020
• Skills Oddyssey
• Learning Strategy paper
• Lori's book

Connect with Lori Niles-Hoffman

• Lori on LinkedIn

Connect With Red Thread Research

• Website: Red Thread Research
• On LinkedIn
• On Facebook
• On Twitter

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Just two years ago, AI was a shiny new object in L&D, with most professionals dabbling in small pilots and content creation experiments. The latest findings reveal an inflection point: the majority of L&D teams are now actively using AI, not merely testing it.

This week, on the podcast are Donald H. Taylor and Eglė Vinauskaitė, the minds behind a groundbreaking new report, "AI & Learning 2025: Race for Impact." We’re exploring the rapid changes AI is bringing to Learning and Development (L&D), from early experimentation to widespread implementation, and what it means for the future of work.

In this conversation, you’ll hear about the three distinct futures for L&D departments, how AI is moving beyond simple content creation into qualitative analytics and adaptive learning, and why team culture and leadership are crucial for success. We also dig into some big philosophical questions: How do we keep humans at the center of tech-driven workplaces? And how will AI reshape the very definition of value in L&D?

This episode is packed with insights, data, and stories from organizations at the forefront of change. So, get ready to rethink how learning happens and how impactful workplace transformation can be.

You will want to hear this episode if you are interested in...

• [00:00] How AI is transforming Learning and Development.
• [05:40] Transition from experimentation to mainstream implementation of AI in L&D.
• [13:31] Debunking the maturity model.
• [16:03] AI integration culture in organizations.
• [25:07] AI's impact on L&D values.
• [38:54] Necessity for L&D to demonstrate clear impact and unique value beyond content.
• [47:36] Leadership Beyond the L&D silo.
• [52:25] Introduction of the “transformation triangle”: three potential strategic futures.

The Rapid Evolution of AI in L&D
AI usage still predominantly supports content creation and design, but there’s an intriguing rise in more sophisticated applications, especially data analysis, dynamic feedback, and even AI-driven coaching. For L&D leaders, the big question is no longer “should we use AI?” but “how can we use it to unlock deeper value for our organizations?”

What Sets Successful L&D Teams Apart?
A critical insight from the report is the role of mindset and organizational culture in successful AI adoption. Teams thriving with AI aren’t necessarily bigger or better-resourced; they are “open” teams, led by individuals who embrace risk, imperfect information, and proactive change. These leaders are comfortable experimenting without knowing all the answers, an essential trait for the current landscape.

True transformation requires more than tech skills; it demands business acumen, a robust understanding of performance, and an ability to integrate learning with business strategy. L&D teams must move from being passive order-takers to strategic partners, actively shaping how people develop and perform.

AI: Threat or Opportunity for Traditional L&D Roles?
For some, the rise of AI in learning is unnerving. Tasks once considered core, like instructional design or content creation, can increasingly be automated, often cheaper and faster than before. Taylor cautions that unless L&D professionals shift their value proposition from content production to driving true impact, their roles risk being diminished or redefined.

But there is an opportunity for L&D to expand its influence. Rather than being relegated to the background, teams can now focus on performance support, skills stewardship, and facilitating human growth, areas where strategic thinking and deep expertise are critical and cannot be automated away.

Three Futures for L&D: Skills Authority, Enablement Partner, Adaptation Engine
Perhaps the most provocative segment of the episode introduced three possible “futures” for L&D roles in the AI era:

Skills Authority: L&D becomes the custodian of skills, owning skill taxonomies, plumbing, and strategic workforce development. This future demands advanced expertise in identifying, building, and tracking capabilities crucial to business success.

Enablement Partner: Here, L&D empowers employees across the organization to create their own learning solutions. The team shifts from direct content delivery to building infrastructure, processes, and trust, letting expertise flourish where it’s needed most.

Adaptation Engine: The most radical scenario, where L&D is absorbed into cross-functional teams focused on rapid business adaptation. Learning professionals blend with design, tech, and operations to solve holistic problems, making learning indistinguishable from broader performance improvement.

While AI will eventually become as invisible as electricity, the human element in learning, facilitation, creativity, and stewardship remains paramount. The priority for leaders now is to harness AI thoughtfully, ensuring it serves genuine learning and performance goals rather than just delivering faster horses. 

Resources & People Mentioned

• AI in L&D: The Race For Impact
• AI in L&D (4 Reports) 

Connect with Donald H. Taylor and Eglė Vinauskaitė

• Egle Vinauskaite on LinkedIn 
• Donald H Taylor on LinkedIn 

Connect With Red Thread Research

• Website: Red Thread Research
• On LinkedIn
• On Facebook
• On Twitter

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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 Learning
At 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 Tools
When 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 Trust
Instead 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 Experimentation
Peter’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.

Resources & People Mentioned

• Novo Nordisk

Connect with Peter Manniche Riber

• LinkedIn: Peter Manniche Riber

Connect With Red Thread Research

• Website: Red Thread Research
• On LinkedIn
• On Facebook
• On Twitter

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On the show this week, Nadia Uberoi, Head of People at Garner Health, joins us to explore how HR can move beyond compliance to become a solution-centric and outcome-driven function, aligning closely with the business’s mission and deliverables.

Nadia shares practical details on how Garner Health has developed agile people systems and cultivated a high-candor culture rooted in real-time feedback and organizational transparency. We discuss how conscious alignment between HR and business strategy depends on both robust systems and hiring people with the right agile mindset.

You’ll hear more about Nadia’s unique approach to performance management, Garner’s decision to publicly share 360 feedback, and how a culture of candor empowers everyone, not just HR, to take ownership of change. Nadia also offers an inside look at the mechanics of building and scaling an intentional organizational culture, and the lessons she’s learned from fast-growing companies.

You will want to hear this episode if you are interested in...

• [05:16] Aligning HR with business strategy and high candor cultures.
• [07:28] Seeking CEO buy-in for strategic alignment.
• [10:37] Balance short-term solutions and long-term infrastructure planning.
• [13:48] Focus on continuous improvement and clarity of responsibilities.
• [21:23] Quarterly planning with smaller, manageable initiatives enables better adaptability and faster impact.
• [29:04] Real-time feedback improved HR-business alignment by enabling quick adjustments.
• [33:31] Real-time feedback and collaboration enable immediate improvements.
• [48:22] Intentionally build and systematize culture for business impact.

Resources & People Mentioned

• Principles by Ray Dalio
• Lattice
• Garner Health   

Connect with Nadia Uberoi 

• Nadia Uberoi on LinkedIn 

Connect With Red Thread Research

• Website: Red Thread Research
• On LinkedIn
• On Facebook
• On Twitter

Subscribe to WORKPLACE STORIES