The AI Learning Revolution Is Already Here. Most Companies Aren’t Ready.
The AI-Powered Revolution in Learning + Talent Development: 5 Key Webinar Takeaways
The talk about AI isn’t just hype. The learning and talent landscape is undergoing its most significant transformation in decades, and the shift is happening faster than anyone expected. That urgency set the tone for The AI-Powered Revolution in Learning and Talent Development, the first webinar in Degreed’s three-part series exploring how AI is reshaping the way organizations build skills, empower their people, and plan for the future.
The conversation brought together two of the most influential voices in the industry: Josh Bersin, global analyst and CEO of The Josh Bersin Company, and Heather Stefanski, Chief Learning and Development Officer at McKinsey. Nikki Helmer, Chief Product Officer at Degreed, led the conversation, which unpacked what AI is enabling today, how learning leaders are responding, and what it will take to build a resilient, future-ready workforce in 2026.
The brightest minds in learning and talent aren’t just talking about adding a chatbot to your LMS. They are sounding the alarm for how radically the ground is shifting under HR, L&D, and every company trying to build a future-proof workforce.
Here’s the punchline: AI isn’t enhancing learning. It’s rewriting the rules of how organizations build capability. If businesses don’t get ahead of it now, 2026 is going to hit hard.
Below are the 5 biggest takeaways from the conversation, and what they mean for your 2026 planning cycles.
Takeaway #1: Learning is shifting from a publishing model to a dynamic content system.
For years, learning strategies focused on what Josh Bersin calls the publishing model. Learning teams operated like content publishers, producing, tagging, and distributing courses. Success was measured by participation: attendance, completions, and clicks.
But AI is forcing a new paradigm.
“I’m talking about a new revolutionary approach where you use a dynamic content system as your core learning experience,” said Bersin.
AI allows organizations to go far beyond simply creating more courses. Instead, it enables learning teams to map, measure, and accelerate skill development adaptively, based on real workforce and organizational needs. In that system, success is no longer measured by how much employees learn, but by whether they can actually perform new tasks and acquire new capabilities faster.
Implications for 2026 planning
- Learning organizations must prioritize capability building over course catalog expansion.
- AI-powered tools will increasingly automate low-value tasks (tagging, search, basic content creation), freeing L&D teams to focus on strategic design.
- Skill data becomes the foundation for workforce strategy.
Takeaway #2: AI is creating a new operating system for talent.
The webinar discussion underscored an essential truth: Organizations are only beginning to grasp the scale of change AI brings.
Bersin pointed out that AI is collapsing traditional L&D and HR workflows—content creation, skill identification, job architecture, and even coaching—into fluid, adaptive systems. AI is redefining how companies understand talent. Once-linear processes are fundamentally changing, including job descriptions, roles, career ladders, and competencies. This demands a major shift in how we think about “learning” in the context of work, and how that function integrates with talent mobility, performance, and business strategy.
AI is making everything dynamic: work, skills, teams, and learning itself.
Bersin put it plainly: “AI alters how we define work, build teams, and plan talent pipelines.”
This isn’t simply tool adoption. It’s a restructuring of talent systems.
Implications for 2026 planning
- Talent and learning strategies must break down siloes and work as a unit.
- Organizations need robust governance around AI adoption, especially ensuring accuracy, fairness, and transparency.
- Expect AI to become a standard layer embedded throughout or foundational to learning platforms, much like mobile and cloud did in prior transformations.
Takeaway #3. L&D needs to move out of learning and into designing ways of working.
Stefanski delivered what might be the most transformative challenge to learning leaders: L&D must stop defining itself by the content it produces and instead reposition itself as a strategic architect of development and career acceleration.
Stefanski argued that the traditional identity of learning teams is holding companies back. Instead of building courses, L&D teams should be designing ways of working, shaping talent experiences, and influencing the workflows employees use every day.
According to Stefanski, one of the biggest shifts required is for learning teams to get out of learning, strictly speaking. “If we actually think about the work we need to do, we need to be a part of designing the work, and designing the technology,” she said.
This is not semantics. It is a structural redefinition of purpose. McKinsey now refers to L&D as a “development organization,” a team whose mandate is to accelerate careers, increase performance, and shape how work gets done.
Implications for 2026 planning
- L&D teams must redefine their identity around performance, career mobility, and ways of working.
- Content creation should no longer be the primary output of the function.
- The role of L&D becomes about orchestrating development experiences, not owning all learning assets.
- The question for leaders shifts from “What training do we build?” to “How do we redesign work so people learn as they do it?”
Takeaway #4. Use AI-embedded tools to make learning integral to working.
One of Stefanski’s most provocative claims was that L&D should be spending 70% of its time inside the workflow, where real performance happens, not inside course development cycles. This reframes what “learning technology” actually means. It’s no longer about building modules or even recommending content. It’s about embedding tools that enable skill-building inside the flow of everyday tasks, where employees feel the impact instantly.
To illustrate, she shared McKinsey’s “Lilly” initiative, an AI-powered storytelling coach embedded directly into PowerPoint. As consultants create client slides, Lilly guides them through narrative structure, clarity, and persuasion—in real time, inside the actual tools they already use.
The result of this integration of learning into work will be the ability to accelerate the time to proficiency of strategic skills, providing tremendous value for the organization. Stefanski argues for identifying use cases and establishing metrics that prove faster time to proficiency, which she sees as the ultimate metric for L&D and one that proves real business value.
Implications for 2026 planning
- Prioritize AI-driven agents and workflow-integrated tools for performance acceleration.
- Partner closely with product, engineering, and operations teams to design learning into work.
- Adopt time-to-proficiency as the ultimate metric, instead of completions or satisfaction scores.
Takeaway #5. The future of learning isn’t AI-enabled. It’s an AI-native development ecosystem.
Throughout the webinar, one theme crystallized: AI isn’t just transforming learning technology; it is also transforming what we expect learning to be. The organizations that thrive in the next era will be those that embrace a fundamentally different vision of L&D’s purpose.
In this new era, learning ecosystems will no longer act as course libraries supported by AI. Instead, they will become AI-native development ecosystems, built around skills, workflows, and human connection.
In an AI-native ecosystem:
- Skill-building happens in the tools employees use every day, through AI agents that coach, prompt, and accelerate proficiency exactly when needed.
- Learning is delivered through intelligent, embedded performance support, wherein AI surfaces guidance, feedback, and practice exactly at the moment of need.
- Digital pathways become fully personalized by AI, rather than manually curated content sequences.
This is not a subtle shift. It requires rethinking what learning is, where it lives, and who drives it.
And it demands that organizations be bold enough to let go of the old model, centered on courses, content ownership, and learning “events” that take place independent from daily work. It is imperative that they move toward a system where development is woven into the fabric of work itself.
Implications for 2026 planning
- Focus on how to reshape workflows, roles, and development with AI, and not how to retrofit legacy systems and processes.
- Redirect spend from content creation tools to technologies like AI agents, workflow coaches, and embedded performance tools that bring learning into the flow of work.
- Elevate L&D’s role in enterprise transformation to become a strategic driver of capability and performance.
- Plan for the cultural impact of AI, not just its technical implementation. Leaders will need new coaching skills, teams will need support navigating change, and employees will need clarity about how AI supports (not replaces) their growth.
Looking ahead: What does this mean for 2026 and beyond?
The insights shared by Helmer, Bersin, and Stefanski suggest that 2026 won’t be defined by incremental updates or new learning technologies. It will be defined by organizations’ willingness to redesign learning around development, work, and culture, rather than content.
If you’re planning your 2026 learning and talent strategy, here’s what you need to put at the top of your list:
1. Redefine L&D as a “Development Organization.”
Shift the mandate from informing employees to accelerating their careers. Replace “What learning should we build?” with: “How do we design work, tools, and experiences that make people better faster?”
2. Invest in workflow learning, instead of more courses.
Prioritize AI tools that embed coaching, feedback, and guidance into the workflows where performance happens. This is where 70% of future learning impact will be created.
3. Build adaptable workforce and talent models.
Static roles and lengthy career pathways are giving way to skills-based mobility and fluid team structures.
4. Let AI personalize and automate digital learning.
Reserve manual course creation for true regulatory or compliance needs. Let AI generate personalized learning paths and adaptive practice, freeing L&D teams to focus on the strategic work only humans can do.
5. Adopt proficiency as the north-star metric.
Move beyond completions, attendance, and satisfaction. The key question now is: “How fast can our people become great at the work that matters?” This shift aligns learning impact directly with business performance, and it’s where there’s the greatest opportunity to utilize AI-embedded tools.
Learn more about the AI Revolution in Learning
2026 will be the year organizations stop designing learning systems and start designing development ecosystems, embedding learning into work, balancing AI-driven personalization with high-impact human experiences, and redefining L&D as a strategic partner.
Learning technology will no longer be something people go to, but something they experience every day. The conversation with Nikki Helmer, Josh Bersin, and Heather Stefanski is part of a much larger, ongoing dialogue about how AI will shape the future of work.
If your organization is preparing its 2026 strategy, now is the moment to reimagine what learning can be, and how AI can bring that vision to life. Watch The AI-Powered Revolution in Learning + Talent Development and the other two webinars in the series on demand to learn how.
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