There are so many factors that can affect workforce development and how well it works (or doesn’t) inside your organization. But at a moment when one of the biggest business challenges is helping workforces upskill in AI, the importance of consistent and effective learning can’t be overstated. 

If employees can learn quickly and effectively, your business can adapt faster. And there’s a science behind making that happen.

Understanding how the science of learning works in theory, in practice, and in business will empower you to use it to your advantage, creating a high-powered engine for workforce transformation.

In Theory: What is Learning Science?

Learning science is the study of how people acquire and apply knowledge. It combines findings from behavioral science, cognitive neuroscience, sociology, and more.

As with any science, there are subsets and offshoots, but let’s focus on what’s most applicable to the workforce. Adult Learning Theory, also called andragogy, highlights six key components:

1. Relevancy: This is a common topic among learning and development (L&D) professionals. People have to know why they are learning something. They have to be able to see the connecting thread between what they are learning and their work or challenges.
2. Practicality: In addition to wanting to understand why and how it applies, adults prefer practical, concrete examples and hands-on application of what they are learning. When they see how to execute on learning topics in a real-world situation, they are able to practice accordingly and apply more effectively.
3. Intrinsic motivation and independence: Often, mandatory trainings can make people less motivated. To learn best, adults need the freedom and independence to guide their own development in ways that matter to them. 
4. Extrinsic goal setting: Despite being highly internally motivated and independent, adults also need to see progress as a result of their investment in learning. Establishing clear goals, milestones, and achievements along the way demonstrates progress and  success, and will also help boost motivation.
5. Referenceable and shareable experiences: Unlike children, adults have a lot of pre-existing knowledge and experiences. That previous knowledge needs to be respected and accounted for, but it also means that adults can learn a lot from one another. Collaboration can be a very effective tool among adult learners. 
6. Ownership and respect: Because adults have their own sets of experiences and knowledge to work off of, they need to feel as if their expertise is both heard and accounted for. It’s important to treat adults as equals to any instructors. Don’t talk down to them, even when they’re in the early stages of learning a new skill.

But understanding theories and putting those theories into practice are very different (we’ll talk more about experiential learning later, too). Knowing how to practically apply these theories in a modern and innovative work environment is what will make the difference between light learning at work and transforming an entire workforce to effectively acquire new skills to take on the future.

In Practice: Apply the Science of Learning to Workforce Transformation

There are many ways to apply learning science principles to workforce development programs, and as technology evolves, more opportunities will arise. Many of the practical applications we’re going to focus on here are innovative approaches that will help employees grow capabilities better than traditional methods.

Personalize learning with AI and contextual skill data.

What we define as “personalization” has changed dramatically in the last few years. Since the internet kickstarted a content and e-learning explosion years ago, people have been able to find information on almost anything they want to learn. Early on, that felt like a type of personalization. It probably even satisfied the relevancy principle of adult learning, to an extent, because it was the best way to date to access tailored content.

Now, so much more is possible, and just being able to find content on a specific topic isn’t enough. When every other channel from social media to video streaming is automatically curated to people’s preferences and past content consumption, employees know they are wasting time sorting through heaps of information just to find one article or podcast that’s applicable to their desired skill and proficiency need. 

AI has enabled a new level of access to content that’s tailored based on skill level, role, business goals, and personal focus areas. This ties back to the principle of referenceable experiences; in this scenario, the learner’s prior knowledge and experience is being directly taken into consideration when content is curated and provided to them. 

It takes more than a generic LLM to offer this level of learning personalization. The AI that is enabling tailored learning support has to have the right context. It needs accurate skill data, learning content, and learning science principles built in. Otherwise, it’s a “generic-in, generic-out” scenario, which is what many content recommendations already are. For example, content could be recommended based on a specified skill focus, but could be too novice or too advanced for the learner, resulting in an ongoing search for the right content. 

This level of tailored learning, combined with other AI capabilities, can also offer unique, on-demand development experiences by making content interactive—think conversations with AI coaches, automatically generated quizzes, and multimedia learning pathways curated in a matter of minutes. This gives employees an engaging and easy way to own their learning journey. They have experiences, not just static content, available at their fingertips.

These innovations double down on adult learning science principles by making relevant content instantly available to employees, then allowing them to easily lead their own development journey with AI-native experiences, all while giving them content that takes their previous knowledge and experience into consideration.

Applied adult learning science principles: Relevancy, Ownership and Respect, Referenceable Experiences

Build upskilling momentum with goals, milestones, and recognition.

Certificates and badges are a straightforward way to acknowledge accomplishment when someone develops a new skill. But progress is a pre-requisite for completion, and you can create opportunities to reward progress as well. Learning science tells us that intrinsic and extrinsic motivation are important. If every goal achieved provides positive reinforcement that boosts learning productivity and engagement, then it makes sense to include more milestones along the way.

Managers are in a unique position to celebrate these small wins. Recognition in meetings or public channels can be as meaningful as any concrete award for adult learners, who prioritize respect but also appreciate acknowledgement. In an episode of Degreed’s Learning Algorithm podcast, How Managers Can Use AI to Develop Their Teams, Casey Adams, Vice President of Solutions Consulting and Enablement at Degreed, recommends spotlighting and celebrating in-progress learning successes to his broader team as a form of recognition. 

Adams says it can be as simple as saying to an employee, “Hey, you learned this new thing, show the team what you’re doing,” and giving them the platform to share their new skills. That acknowledgement recognizes modest achievements, creates an opportunity for collaboration, and builds a stronger team learning culture.

Applied adult learning science principles: Extrinsic Goal Setting, Intrinsic motivation and independence

Map skills to roles to guide mobility and transformation.

Empowering your employees to develop their skills means giving them a clear map from Point A to Point B. Whether upskilling is intended to support a large-scale workforce transformation or an individual career move, it helps if your employees know what skills they need at what skill level to reach their goals.

Start with what’s current. Map which skills and corresponding proficiencies are needed for each role within the organization. This sets clear expectations for employees, and allows leaders to easily suss out skill gaps.

From there, you can set clearer goals for future skill development and identify which skills are needed at which level of proficiency. For example, to move from a contributor to a manager level, an employee might need to move from a level 3 in leadership to a level 5. Alternatively, all employees may need to go up one skill proficiency level in AI by the end of the year to keep pace with change and organizational transformation objectives. Putting concrete numbers to these goals help bring the relevancy and practicality principles to life for your employees. 

Though this concept isn’t new, it is suddenly readily achievable and scalable. New AI technology ensures this is not a manual mapping and linking process. It’s automatic.

Once this information is outlined and available to leaders, managers, and individual employees, it becomes easier for leaders to make data-driven talent decisions and for employees to meet expectations and even take on stretch assignments and responsibilities. Suddenly, employees are empowered to own their own journey, again because they actually have the map to get where they want to go, which links to the intrinsic motivation and independence principle of learning science.

Applied adult learning science principles: Relevance, Practicality, Intrinsic Motivation and Independence

Use AI and experiential learning to build real capability.

It’s time to move beyond passively consuming learning content. Can you learn from articles, videos, and podcasts? Of course. But, to use an analogy from Degreed Founder and CEO, David Blake, reading about running doesn’t make you a skilled marathon runner. 

As people, we need practice to learn. We need real experience. We need trial and error. The more we do something, the more confident we become. Our How the Workforce Learns Gen AI report found that the most confident Gen AI users were:

• Nearly twice as likely to use Gen AI daily
• 4x more likely to apply it to real problems
• 32% more likely to learn on the job
• 77x more likely to engage with and become proficient using Gen AI

Essentially, the most confident users were the ones actually using it, not just passively consuming learning content on what Gen AI is and how to use it. This is an example of experiential learning in action. Those who are taking an active step in the process of developing a skill are becoming more competent at applying it in their day-to-day lives. This is the transformative difference between understanding AI and using AI. Knowing this, L&D teams can spend time weaving more of those experiences into workplace upskilling.

Traditionally, experiential learning had to happen outside of the e-learning workflow. Often, it required time from a manager or L&D employee to create, administer, supervise, and offer feedback. If it took the form of a quiz, someone had to spend time creating that quiz. If it was a practice pitch or conversation, someone else had to take time to play along, respond, and react, then provide feedback. 

Instead, employees can now use an AI agent to:

• Create one-on-one coaching moments
• Practice for presentations
• Role play important conversations
• Assess learner knowledge

This agent would be as effective or more than a human coach because it would have access to that person’s skill level, their background, their goals, and foundational organizational knowledge. Suddenly, e-learning can go from passive content consumption to an interactive learning experience.

Experiential learning delivered through AI gives employees the chance to apply learning in a low-risk environment, prepare for the high-impact moments, reflect, and get instant feedback on their work and progress. This directly leverages the learning science principles of practicality and intrinsic motivation. Teams can see the clear applicability of this skill in their daily work, leading to a higher level of internal drive to improve, while the on-demand aspect gives them desired independence in the learning process.

This strategy has an added business productivity bonus: If employees are allowed to practice and apply learnings in real-world scenarios, they might also be completing real work while they do it. Combining learning with work output is a win-win. 

Applied adult learning science principles: Practicality, Intrinsic Motivation and Independence

In Business: Use Learning Science to Create a Competitive Advantage.

The half-life of workforce skills, or the time it takes for a learned skill to become outdated, is about four years, whereas it used to be 10, according to Forbes. AI skills have an even shorter shelf-life, at about two years. 

Your business runs on those skills. Learning helps employees keep them up to date, and using learning science to optimize learning ensures they can do that as quickly and effectively as possible. 

In other words, learning and development is critical because it’s the backbone of any forward-looking business’ goal of adaptability and long-term success. If your employees can acquire essential, emerging skills and capabilities as they emerge, they can lead in those areas. And if they can’t (or don’t) learn the new skills shaping the market, your business could rapidly fall behind. 

It’s not just a question of being prepared for AI. AI happens to be the most pressing area for upskilling currently, but if you embed learning into the way teams work in your organization, it will help them be ready for and adapt to whatever comes next. Employees today need to be lifelong learners to keep up with continual change, and learning science can empower them with the right tools, habits, and opportunities for growth and innovation.

Learning science applied at scale helps make development more effective, impactful, and long-lasting. As a result, businesses can:

• Accelerate upskilling
• Make data-driven decisions about talent and development
• Lead in moments of key opportunity (like AI transformation)
• Build a resilient and adaptable workforce

Using the Science of Learning and Cognitive Neuroscience to Your Advantage.

Ultimately, if your workforce can learn and develop faster, your business will be set up to thrive, no matter what the next big change or challenge is. An essential ingredient for success is understanding the principles of learning science and how they can accelerate upskilling. If your people are enabled to adapt and motivated to do so, they’ll be ready for anything.

The post Learning Science in Theory, in Practice, and in Business appeared first on Degreed.

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. 

The post The AI Learning Revolution Is Already Here. Most Companies Aren’t Ready. appeared first on Degreed.

Throughout 2025, in conversations with executive leaders in human resources, IT, and learning and development, as well as industry analysts and my team of product engineers, one theme kept resurfacing:  

In 2026, your ability to build new capabilities will matter more than any single technology choice.

Here are the seven learning and development trends shaping that future:

1. Skill Frameworks Get Sharper and More Popular.

Skill taxonomies were created as a way to organize and catalog workforce skills across an organization, but they’re confusing and overly complicated to apply. People are turning to skill frameworks instead. 

A skill framework creates a clear link between roles and skills so each employee knows exactly what skills and proficiency levels are needed for their job and any position they hope to move into. They know exactly where they stand and there’s always a clear growth path, as learning becomes far more targeted and aligned to work.

2. Content Libraries Become Ingredients, Not Destinations.

Enterprise companies are paying millions for content libraries that don’t get used enough to justify the expense. With AI dynamically curating content, these libraries will no longer be the go-to place for employee learning. Instead, libraries will become more backend “ingredients” that form the basis of the AI-curated pathways and experiences. 

In 2026, libraries will shift from “places employees go” to raw materials that AI assembles into contextual, personalized pathways. Relevance becomes the differentiator, rather than catalog size, which means that many organizations will be able to reduce their investments. The future belongs to content that AI can remix for specific skills, levels, and moments of need.

3. Leadership Programs Blend In-Person Experiences with AI Support.

Companies will start to reinvest in immersive, in-person leadership experiences, using AI to provide continuous reflection, coaching, and conversation practice before, between, and after important interactions. Leadership development is critical for all businesses, so that the most influential people on your team have the skills to help, promote, and participate in workforce change management. 

4. Transformation Requires Human-Enablement, Not Just Technology.

Businesses right now are stacked with AI tools and capabilities, and investing more all the time. Yet, the ROI is lacking. Nearly 95% of businesses have seen zero return on in-house AI investments and only 15% of generative AI (Gen AI) users report their organizations see significant ROI from it. 

Why? Because capability, not just access, determines whether technology has an impact.

In 2026, learning teams will take on a central role in change enablement, building confidence, mindsets, and behaviors so people can work differently, not just use new tools. The role of learning will be to help employees make sense of change and transform at the pace of technology. 

5. Capability Dashboards Become the New Measure of Transformation.

Leaders are shifting away from tracking content consumption (like completion metrics). In addition to time savings and efficiency metrics, they are looking for proof that workforce transformation is actually occurring. 

They want visibility into: 

• Skill readiness 
• Proficiency growth
• Behavioral adoption
• Team-level capability gaps

Capability dashboards will become a central mechanism for tracking progress and proving that transformation is actually happening.

6. Learning Teams Evolve Into Cross-Functional Agents.

Learning can no longer operate in isolation. They have to be reimagined to support business goals through the wide-ranging expertise needed across the entire organization. 

The new model will include performance consultants, AI orchestrators, and data partners. Leaner teams will function strategically, embedded across business initiatives to accelerate execution and reduce time-to-impact for business-critical initiatives. 

7. Reflection Becomes a Regular Development Ritual.

Reflection has always been a key part of learning frameworks. Studies show that reflection can help knowledge retention and outcomes. Yet, it’s often difficult to operationalize. 

All of that changes with AI, which has enabled regular, productive, and conversational check-ins that can help summarize and cement new growth or information. Individuals can summarize what they’ve learned, practice scenarios, or prepare for upcoming conversations. Teams can debrief together more regularly.

The result: more readiness, more clarity, and more confidence.

Learning Is Changing, But It Remains the Key to Success.

AI is speeding everything up—but capability will determine who keeps up.

Organizations that thrive in 2026 won’t be the ones with the most tools. They’ll be the ones who build a workforce that can adapt, grow, and perform in an environment defined by constant change. Part of that means keeping up with the learning and development trends that are most proven to accelerate workforce transformation.

That’s because learning is no longer just an L&D or HR function. It’s your operating system for transformation.

The post Top 7 Learning and Development Trends for 2026 appeared first on Degreed.

What Is Adaptive Learning?

Adaptive learning adjusts automatically to the needs of the individual based on their skills, role, goals, and proficiency level. It’s highly personalized, responsive, and interactive. It’s contextual, it’s dynamic, and most importantly, it cuts down on time wasted; no more content scavenging, no more time spent on content that’s irrelevant to experience level, and no more waiting for feedback. That way, every moment of development is useful. 

The personalization is fed and enforced by the rich data and analytics that arises from the learning process. Adaptive learning provides more than just completion data: there’s real measurement of knowledge gain and skill growth. 

What Does Adaptive Learning Look Like in Practice?

Adaptive learning answers a longtime need in the learning industry: The ability to learn in the flow of work. For example, AI capabilities make it possible to produce topic-rich, accurate quizzes at scale to easily test knowledge retention. Conversations with AI can adapt in real time, allowing employees to practice challenging soft skills or presentations on complex topics. Adaptive learning makes it possible to provide customized, role-specific paths and instant feedback, so that it’s easier to benchmark performance and iterate. 

This level of flexibility and personalization means learning and work can entirely coexist and boost each others’ effectiveness Here are some example scenarios:

Example: Your team needs to quickly master complex new market regulations. Rather than having them complete a single, static training, AI-generated quizzes allow you to assess understanding.

Simply checking a “completed” box, doesn’t mean your team is actually prepared to apply their knowledge in the field. Any skill or knowledge gap can directly impact business outcomes and performance, so it’s essential to find out what your people actually know. Once you see where the gaps are, you can curate the right content to fill the gaps for each individual, rather than providing another blanket, one-size-fits-all training session for everyone that misses the mark after the first one.

Example: Your company is launching an important new product and your sales team needs to deliver the new pitch. You can provide them with an AI-powered coach that’s always available and gives real-time feedback.

This allows them to practice their pitch risk-free. They can iterate, apply feedback, and improve their approach before stepping in front of your potential customers. As they learn and practice, they are engaging in practical skill-building with real business impact.

Example: The launch of a new AI tool has direct application for your product team, and they need to build capabilities in an emerging industry skill. You are able to get them up to speed more quickly with AI-curated and expert-checked learning pathways.

As the skills needed constantly evolve, content pathways can now be generated at the same pace, which means your people can absorb relevant content faster. Degreed Open Library, for example, provides pathways on the most in-demand emerging skills in the market, and every pathway is automatically updated biannually to ensure content is fresh and accurate.

Use cases for adaptive learning are growing, as day-to-day work requires more interactive, dynamic, and diverse learning modalities to keep pace with the capabilities employees need.

How Do You Enable Adaptive Learning?

Context is the key. AI has opened the door for adaptive learning experiences, but to be successful, AI first needs the right context. Otherwise, the information it provides is no more tailored than a general LLM or AI assistant. 

To ensure AI is purpose-built for learning and upskilling your team, it needs a context in:

• Learning science
• Verifiable skill data
• Integration into systems
• Organizational context aligned to strategic goals

With that as the foundation, the AI is then set up to successfully adapt to the needs of the individual. 

What’s the Future of Adaptive Learning?

Tech capabilities are growing every day. We do not know what will be possible two years from now, but I assure you that learning at work will become a lot less like a static training session and a lot more like one-on-one coaching with a trusted expert. Learners will be laser focused on content that is actively growing their knowledge and skill set, and they will be putting their new knowledge and skills into practice in low-risk scenarios.

L&D is in the process of evolving from providing content that supports business objectives to delivering AI-native learning experiences that proactively progress business objectives. 

The post Adaptive Learning: What It Is and Why It Matters appeared first on Degreed.

That is how Degreed Founder and CEO David Blake set the scene for Vision 2025, our annual, product-focused event. And it’s true. This rapid tech evolution doesn’t just change the way we work and live, it changes the way we learn now and in the future. 

To adjust to the scope of the AI transformation at hand, learning needs to become as adaptive as your people and your technology. It needs to be personalized and relevant to maximize skill-building. It has to be responsive to the needs of your people and your business at scale. That learning and adaptive resilience is going to be critical for the AI era. 

“As we navigate the shift, there are going to be consequences—winners and losers—and we want to make sure that we all end up on the winning side,” Blake said.

What is Adaptive Learning?

Adaptive learning is highly personalized and responsive. It’s customized with learner-specific paths and real-time feedback, making it highly interactive and tailored to individual work.

“[Adaptive learning] is learning that adjusts automatically to the needs of the individual based on their skills, their role, their level of proficiency, and their goals,” says Nicole Helmer, Chief Product Officer at Degreed. “It’s contextual, it’s dynamic, and most importantly, it eliminates waste. So every moment of development is useful.”

Here’s what we’re doing inside Degreed to make that happen:

Automatic Quiz Generation

Skill-building in the age of AI can’t only be about efficiency, it also has to be about effectiveness. It’s no longer just about content completion, it’s about content comprehension.

Are your people really learning and able to apply new skills? Where are the gaps? Now, Degreed Maestro will be able to automatically generate quizzes so learners can test their knowledge. 

With quiz results, admins and leaders can also see summaries of the results to pinpoint, then target, critical skill gaps.

Skill Proficiency Tagging and Role-to-Skill Mapping 

For personalized learning to be effective, it needs to consider skill proficiency, not just what skills are on an employee’s profile. 

That’s why we’ve enabled bulk skill proficiency tagging, which lets users automatically tag a large volume of content with specific skills and proficiency levels. AI will analyze content titles, descriptions, and metadata, then combine it with your company’s skill taxonomy to ensure tagging accuracy.

From there, the new role-to-skill workflow will come into play. It offers a simple, scalable way to define role expectations and suss out skill gaps. It provides a structured, easy way to map skills and target proficiency levels for every role, and then it uses that data to guide employees toward targeted learning.

Model Context Protocol (MCP) 

AI that’s operating without context is about as useful as your favorite maps app without the GPS. In a learning system, it’s the context (e.g., skill data, organizational goals, role details) that will take AI from offering generic information retrieval to providing personalized learning experiences.

Model Context Protocol (MCP) gives AI a consistent, governed way to tap into the right context from Degreed and connected systems wherever those AI models live, whatever platform they’re built on (yes, including other MCP-enabled tools like Gemini and Copilot). Through MCP, AI has access to the skill data, roles, learning history, and guardrails that matter. That way, it can better personalize development and guide your people to what’s next.

Experimental Innovation

In the face of AI, learning will continue to evolve rapidly and the Degreed AI Experiments Lab is already prototyping the development of the future. 

Among these experiments are several multi-step, AI-native learning experiences, including mini coaching moments, AI scoring on learner practice projects, and even smart questions and feedback loops that are woven into learning and aggregate response data.

On top of that, we continue to refine Maestro for real, in-the-flow-of-work learning experiences. We’re also growing Degreed Open Library, our repository of learning pathways on the most in-demand skills in the market. These pathways come at no extra cost to Degreed Learning clients. 

All of this is only the beginning of an era of dynamic and responsive learning that’s personalized like never before. You can lean into these adaptive learning experiences to prepare your workforce for the AI transformation and beyond. 

The post The First Steps to Dynamic, Adaptive Learning at Work appeared first on Degreed.

The first was the printing press, which codified and spread knowledge at a scale the world had never seen.

The second was the industrial school system, which was built to skill up entire generations for factory and office work.

The third was the internet, which unlocked access to knowledge for billions. It took learning beyond traditional classrooms—into the workforce, into your homes, across your lifetime.

Now, we’re entering the fourth moment: The rise of AI.

Efficiency Isn’t Everything

AI is already embedded in our meetings, documents, and systems. But just because AI is infused into the work, it doesn’t mean people are learning. If anything, so far, AI has made people more efficient, but not more capable. Yet.

That’s a problem. Because most organizations are using AI for one thing: efficiency. That’s great, but speed does not equal skills.

Infrastructure Still Matters

We’ve seen this pattern before. When YouTube arrived, it revolutionized content distribution. It made countless learning resources available everywhere.

But it didn’t solve organizational learning. It didn’t create more capability. It also didn’t solve organizations’ needs for managing employee learning.

Why? Because infrastructure still matters. We need systems, context, feedback, and outcomes. 

That applies to AI too. A chatbot on your company portal is not a learning strategy. A CoPilot that summarizes meetings and HR policy documents will not build your bench strength in those topics. Answers don’t build capabilities.

What separates serious, successful AI learning systems isn’t going to be the model, it’s going to be the infrastructure behind it. It’s going to be the foundation that the AI is based on, including:

• Learning science
• Verifiable skill data
• Integration into systems
• Organizational context aligned to strategic goals

If your AI tools don’t have that, they will not be optimized for learning. That structure is what makes the difference. That’s what will determine the learning impact of this moment.

The Challenge to Upskill Better and Faster at Work

Let me be direct: According to WEF and Accenture, 60% of the world’s workforce need to upskill in the next five years. That’s an increase of 10 percentage points from 2020. And only about 40% of C-suite leaders say they are prepared, which is down 10 percentage points from 2020. 

We knew this skill gap was coming five years ago, and leaders are even less prepared for it now. This means that more people need learning and more skills, quicker, now than ever before in history. 

The concept of “just-in-time learning” was built in the third wave, the internet wave, and it was all about connecting people to content when they need it. But now, work itself is changing. Tasks are being automated. Roles are more fluid. Knowledge has become cheap, yet judgment, adaptability, and creativity are not.

We need a new model for learning. One that matches the pace of change and the reality of today’s AI world. That future looks something like this:

• Adaptive learning skips what people already know and targets the exact skills they’re missing.
• Real-time skills intelligence lets you close gaps before they slow you down. 
• AI helps people get smarter and better at their work, not just faster.

And that future? It’s here.

The post Why AI Infrastructure is a Learning Differentiator appeared first on Degreed.

When I first stepped into management as a millennial leader, I didn’t have decades of leadership experience to lean on. What I did have was curiosity and a willingness to try new tools. I experimented with platforms that helped me onboard faster, understand my team’s strengths, and keep projects moving. That openness to technology wasn’t just convenience. It became a way to fill in gaps and lead with confidence.

It got me thinking, is the flexibility of the millennial generation the key to bridging the gap between traditional ways of working and the new tech-first tactics? We’re digital natives who’ve grown up making technology second nature. We introduced our workplaces to Slack and Zoom. And yes, we showed them all–probably more than once–how to export that doc as a PDF. Now, we’re ready to champion AI-powered learning. 

This is my call: L&D leaders, use us to ease the AI transformation. When L&D leaders make us partners, adoption won’t trickle in through slow rollouts. It will spread like a wildfire. We’ve done it before and we can do it again. Here’s how to activate your millennial managers so you can personalize learning better than ever before:

Lesson 1: Adoption Starts With People, Not Platforms.

Corporate learning has always chased personalization. For years, it meant nothing more than a recommended course list based on your department. Today, AI has changed the game. Platforms can identify current skills, map them to career goals, and adjust learning pathways as people grow.

But here’s what I’ve learned as a manager: technology doesn’t drive change by itself. People do. My team was never excited about “a new system” just because it came from HR. They got on board when they saw me using it, sharing results, and showing them how it made their work easier.

Millennial managers are uniquely positioned to spark that kind of adoption. Why? We:

• Make up the largest share of the workforce, many now in leadership roles
• Are more comfortable experimenting with AI tools than other generations
• Value growth, work/life balance, and want to create career opportunities for our teams

When managers use AI for learning, they normalize it for their employees.

Lesson 2: AI Removes Barriers and Elevates Coaching for Personalized Training

The first time I piloted an AI learning tool, I noticed something right away. My team didn’t spend hours searching for resources anymore. The platform pushed exactly what they needed at the moment they needed it.

AI in learning looks like this:

• Onboarding that clicks: New hires get pathways built for their role from day one.
• Skill gaps closed in real time: When regulations change or a new system goes live, people can upskill immediately.
• Personalized growth: Employees see learning tied to their individual career paths, not just generic compliance courses.
• Retention through relevance: People stick around when they see their manager investing in their future.

For me as a manager, the biggest shift was that Degreed Maestro, our AI purpose build for learning, helped me coach better. Instead of guessing what my team needed, I had insights into their skills and progress. That made my 1:1s more meaningful and our work more effective.

Lesson 3: Managers Multiply the Impact of AI

Here’s the difference I’ve seen first-hand:

• Without managers leading, AI feels like another HR initiative. Adoption is slow, and employees are skeptical.
• With managers leading, AI feels like a team advantage. Employees see real benefits in their day-to-day work.

When I shared how AI helped me conduct competitive intelligence research in half the usual time, the rest of the team leaned in. It wasn’t about me “selling” them on technology. It was about showing them what was possible.

This is why millennial managers are multipliers. Our willingness to test and share gives AI in learning credibility across every generation we lead.

Lesson 4: L&D Leaders Can Activate Millennial Managers Now

So how can you activate millennial managers to accelerate AI learning adoption?

1. Start small: Invite a group of millennial managers to pilot AI-curated pathways in areas like leadership or digital skills. [Degreed Open Library]
2. Give us a platform: Encourage us to share wins and stories with peers. A quick case study or team success story builds trust. 
3. Equip us with context: Don’t hand us scripts. Instead, show us how AI connects to skill growth, retention, or productivity. We’ll translate that for our teams.
4. Recognize us: Highlight millennial managers who lead adoption in company communications. Visibility motivates us and validates the effort.

Quick Assignment: Identify five managers in high-change roles. Give them access to an AI-powered learning pilot. Ask them to present outcomes like faster onboarding or higher team engagement at the next leadership meeting.

AI has made personalized learning possible at scale. But adoption depends on people, not platforms. As a millennial manager, I’ve seen how quickly teams respond when they see technology making their work easier and their future brighter.

That’s why companies can’t overlook this generation. We’re not just comfortable with AI, we’re confident with it. And when organizations empower millennial managers to lead the way, AI in learning won’t just be implemented. It will be embraced.

When millennials are empowered and set the tone, organizations don’t just keep pace with change. They set the pace of change.

The post Personalized Training 201: Millennial Managers can Unlock AI Adoption appeared first on Degreed.

This course isn’t Econ 101 or the History of Western Civ, but it is learning. We’re digging into how corporate learning has changed more in the last decade than in the previous few combined and why the syllabus looks nothing like it used to.

Corporate learning has changed more in this decade than it has in the previous few decades combined. And the pace of change accelerates daily. Even just a few months ago, “personalization” in training meant surface-level recommendations from an algorithm that suggests courses based on your department or job title. Helpful, but often generic.

With AI, personalization has finally caught up to its promise. Modern platforms can understand an employee’s current skills, career goals, and even how they like to learn, then adapt in real time as those needs evolve. It’s not just matching people to content anymore. It’s building truly individualized and adaptive learning journeys that grow with them. 

Class is officially in session; let’s dive into the first lesson.

Lesson 1: AI-Powered personalization is more efficient.

The shift isn’t just theoretical, it’s already transforming businesses. Companies using AI-powered learning platforms report a 57% jump in training efficiency, with employees completing courses faster, retaining more knowledge, and applying it more effectively on the job.

Personalized training speeds up AI transformation and compliance requirements, fosters behavior change, and ramps up leaders in record time. For leaders, this is a game-changer. Learning is no longer a “nice to have.” It’s a direct lever for performance, agility, and retention in the face of AI. 

Lesson 2: The new way to personalize is nothing like the old way.

Here’s how AI redefines personalized learning compared to traditional approaches:

AI is now woven into nearly every business function–78% of companies report using AI somewhere in their operations, according to McKinsey. Training and development shouldn’t be the exception.

Lesson 3: There are a lot of places you can weave AI into learning strategy.

For this lesson, we’ve brought in our learning strategy expert, Stephen Elrod, SVP of Global Delivery. According to Stephen, the question is no longer if you should adopt AI for learning, but how. Here are small, tangible steps you can start with today:

1. Audit your learning stack: Invest in platforms that go beyond content delivery. Look for systems that adapt to individual needs and build capabilities, not just information sharing.
2. Pilot AI-driven pathways: Start with one skill area (e.g., compliance, leadership, or digital skills) and test with a small group.
3. Integrate learning into workflows: Add micro-learning resources where people already work.
4. Raise AI literacy: Run a short workshop to help employees understand AI in learning.
5. Protect trust: Establish a clear data and ethics policy so employees know how their information is being used.
6. Secure executive sponsorship: Share one business case (like improved retention or reduced training costs) with leaders to get buy-in. 

Lesson 4: AI can be the new 24/7 workforce coach.

AI isn’t replacing human learning, it’s enhancing it. It’s giving every employee the equivalent of a personal coach, surfacing opportunities that are timely, relevant, and actionable. And it’s helping organizations scale that level of personalization across the entire workforce.

The takeaway: AI has turned personalized learning from an aspiration into a reality. Companies that embrace it won’t just keep up with change—they’ll lead it.

Homework Assignment for L&D Leaders

What’s a good lesson without a little reflection?

1. Choose one high-priority skill area in your organization (e.g., leadership, compliance, or digital skills).
2. Map how employees are currently trained in this area eg.what tools, content, and methods are used.
3. Identify at least two gaps where personalization could improve outcomes (for example, tailoring by role, adding real-time feedback, or integrating into workflows).
4. Draft a one-page plan for how AI could close those gaps.

Bring this back to your next team meeting and share one idea you could pilot in the next 90 days.

The post Personalized Training 101: Your Guide to Using AI for Learning appeared first on Degreed.

AI has arrived—not as a back-office tool, but as a front-line collaborator reshaping how we work, think, and create. Business cycles are accelerating. Knowledge work is evolving. The biggest question for today’s leaders: Is your workforce ready?

The old playbook—static training, one-size-fits-all programs, disconnected learning—can’t keep pace. To stay ahead, organizations must move beyond content delivery to strategic skill development at scale.

As Siya Raj Purohit, Education Go-To-Market at OpenAI, said at LENS 2025, “When people could start talking to AI, when the interface became language, something fundamental shifted.” Communicating with AI in everyday language makes it accessible to your entire workforce.

This shift has cracked open an entirely new way of learning where people and AI co-create value in real time. The challenge now isn’t just technological, it’s talent-based.

The Shift Has Already Begun

We’re not approaching an AI moment—we’re in it. As language becomes the interface, AI becomes more than a tool. It becomes a partner that enhances human thinking, creativity, and decision-making. “AI isn’t just a tool,” Purohit said. “It’s a thinking partner that can help employees at all levels.”

And it’s already having real-world impact. According to a report by the International Monetary Fund (IMF), 60% of jobs will be reshaped by AI. And by 2030, 50-67% of workers will need reskilling, depending on their location.

And yet, there’s a gap between expectation and readiness. Our 2025 How the Workforce Learns GenAI report shows that 48% of professionals expect their responsibilities to change due to Gen AI, but only 22% feel very confident using it.

The confident few aren’t waiting. They’re automating tasks, generating insights, and making better decisions today.

So, while AI may be disruptive, the real challenge—and opportunity—is human. This is a talent problem, not a technology problem.

From Disruption to Opportunity

Despite the magnitude of AI-driven change, forward-looking organizations aren’t bracing for impact, they’re moving from reaction to transformation.

Purohit outlines four stages of AI transformation:

1. AI Literacy. Build foundational understanding for individual employees by encouraging everyday use. You should see boosts in efficiency, work quality, creativity, and innovation.
2. Custom Workflows. Equip teams with AI that fits their roles and supports team-level problems. Focus on automation, extensibility, and collaboration.
3. AI-Enhanced Operations. Create repeatable processes across the organization to drive productivity. Gain operational efficiency and cost savings.
4. AI-Infused Offerings. Innovate with AI at the core of your business to deliver AI-powered products and services. You’ll enhance your product value and customer engagement.

“Each of these steps builds momentum and maintenance impact,” Purohit said. “And as learning leaders, I think you all are in the best position to lead this transformation.”

The New Learning Playbook: From Literacy to Innovation

“What changed wasn’t just how advanced these models became, but how they could be used… to explore ideas, build skills, unlock creativity,” said Purohit. But to build an AI-ready workforce, you need more than AI tools. You need an AI-powered learning partner. Degreed Maestro helps you move from AI ambition into real outcomes, like everyday AI mastery, personalized learning at scale, learning aligned to business goals, and a workforce built for innovation.

1. AI Literacy from Daily AI-Powered Learning

When your workforce understands how to use AI, it can help them do their jobs better. Building AI fluency starts by embedding AI into daily development. Degreed Maestro makes this easy with:

• Personalized, curated learning pathways adapted to roles and goals.
• AI-powered coaching in the flow of work.

2. Custom AI Workflows Create Adaptive Workforce Learning Experiences at Scale

Once individual employees are using AI every day, Degreed Maestro can equip entire teams with AI tailored to their function and skill needs. That includes:

• Role-plays help sales teams and other functions practice relevant conversations.
• Coaching at scale for all leaders, not just senior leaders.

3. AI-Enhanced Operations Drive Smarter Skill Management

To scale transformation across your entire workforce, you need smarter processes, not just more content. Degreed Maestro turns L&D into a strategic engine for performance, agility, and growth by supporting:

• Skill reviews that replace time-consuming manager evaluations with AI-guided assessments.
• Data-driven insights that connect learning to skill gains and performance outcomes.

4. AI-Infused Offerings Come from Innovating With a Smarter Workforce

AI can support your organization in a multitude of ways, but to fully leverage its benefits, bake it into your products and services. Degreed Maestro can prepare your entire workforce to lead the change by:

• Aligning learning with your business and talent strategies.
• Accelerating readiness for new roles and setups through adaptive learning journeys.

Now’s the Time to Lead

“It’s on us as learning leaders to make sure our workplaces are ready,” Purohit said. “This is not about tools. It’s about building environments where people can grow, experiment, and hopefully do their life’s best work.”

The age of AI demands more than digital transformation. It calls for a workforce learning transformation that puts people at the center and empowers them to adapt, grow, and lead.

Reimagine learning not as a support function, but as a strategic enabler of workforce resilience and innovation. Learn more about how AI is changing learning in How the Workforce Learns Gen AI.

The post AI Makes Workforce Learning Unstoppable appeared first on Degreed.

In Degreed’s AI Experiments Lab, we are using AI as an opportunity to reimagine learning content. With it, we can invest in adaptive, efficient, and effective approaches that deliver better outcomes for both employees and organizations.

The Old Way to Tackle Learning Content

Traditionally, L&D, HR, and talent professionals  created, curated, or purchased content (often in the form of courses) and tried to make it available where and when people needed it.

That content strategy was linear, resource-intensive, and often missed the mark on personalization and timeliness.

The New Way to Enhance Learning Strategy

Today, AI-enabled tools and chatbots are redefining how we access and consume content. Here’s how to break down three components that will help you build an AI-powered content and learning strategy: Knowledge Sources, Transformed Content Objects, and Dynamic Learning Experiences.

1. Knowledge Sources

Knowledge sources are the foundational layer for all modern AI-powered learning strategies. They are the source of truth that everything is built from. Knowledge sources can include:

• Dynamic data from web sources: Real-time updates pulled from the internet or APIs. Useful for recency, but requires filtering and context.
• Model training data: Broad, pre-learned information from public sources. Useful for general knowledge, but may be outdated or lack specificity.
• Licensed content libraries and expert resources: Curated, high-quality materials from trusted vendors or specialists. Offers credibility and narrative depth.
• Internal documentation and proprietary content: Organization-specific materials like manuals, process guides, and internal wikis. Highly relevant, but often fragmented or out of date.

Together, these sources form the raw material that fuels both transformed content and dynamic experiences.

2. Transformed Content Objects

Transformed content objects are structured learning assets created by reformatting knowledge sources into polished, reusable formats. These are especially effective when you want to take dense or complex source material and deliver it in more accessible, engaging forms that can be reused and shared across teams.

Examples include:

• Slide decks
• Explainer videos
• Infographics
• One-pagers and job aids
• Audio summaries or podcasts

Best suited for:

• Topics requiring human oversight or approval
• Scenarios where consistent messaging and branding matter
• Creating multimedia formats that require more time, specialized skills, or budget to produce

Most importantly, transformed content objects allow for a layer of editorial control and customization that ensures accuracy and tone. This is ideal when quality, branding, and repeatability matter, making it a core strategy for learning teams seeking scale and consistency.

These assets are pre-built and reviewed, offering clarity, engagement, and control when precision and polish are critical.

3. Dynamic Learning Experiences

Dynamic learning experiences use AI to generate personalized content and interactions in real time, based on the same knowledge sources. Unlike transformed content, these experiences are not reviewed or packaged in advance—they’re created on the fly, adapting to user needs, contexts, and questions.

Examples include:

• Chatbots for Q&A or task support
• Voice assistants for walkthroughs
• Adaptive quizzes and exercises
• Live translation tools
• Scenario-based simulations

Best suited for:

• Situations requiring just-in-time information
• Personalized or context-specific learning
• Flexible delivery without prebuilt content

By adapting in real time to a learner’s role, prior knowledge, behavior, or skills, dynamic experiences can offer uniquely relevant guidance that evolves with the individual. This makes it possible to deliver more meaningful and contextualized support without waiting for traditional course development cycles.

However, it comes with trade-offs: the lack of pre-review means you need strong source content and governance to ensure accuracy, and analytics are often less consistent or structured. Still, when used well, dynamic learning can provide high-impact support that feels more like a conversation than a course.

How Degreed Helps

We’re reshaping what it means to be a Learning Experience Platform (LXP) today by enabling organizations to operationalize each layer of the new content strategy—starting from the source.

1. Activating Knowledge Sources
Degreed integrates with and brings together more learning-oriented knowledge sources than any other system by connecting internal content, licensed libraries, and external data in a unified platform. 

Our platform helps ensure your knowledge base is current, credible, and discoverable, so that it serves as a strong foundation for AI-driven experiences. To strengthen the trust and transparency, we’re also building advanced content evaluation and auditing tools to help you assess and validate your knowledge sources. 

Prioritizing high-quality inputs improves consistency, accuracy, and the impact of everything that follows.

2. Transforming Content with Control
Degreed supports the creation of polished learning assets through workflows that reformat and enrich your content—turning documents into videos, podcasts, summaries, and more. 

In addition, through Degreed Open Library, we provide access to expertly transformed, multi-modal content on the most in-demand topics at no additional cost to our Degreed Learning clients. 

These tools empower admins with faster, more flexible creation of engaging learning materials. Whether you’re publishing for onboarding, compliance, or upskilling, you get consistency, oversight, and brand alignment.

3. Delivering Dynamic Learning Experiences
The Degreed AI engine, Maestro Studio, powers real-time, personalized interactions like chatbots, search assistants, adaptive learning exercises, which are all grounded in your company’s internal knowledge. These experiences:

• Meet learners in the moment
• Offer as-needed support and contextual learning
• Enable dynamic personalization based on a learner’s role, behavior, or context
• Do not require traditional course development cycles

From Content to Experiences

As L&D shifts from delivering static content to designing adaptive learning experiences, the path forward isn’t about producing more content; it’s about orchestrating experiences that resonate, evolve, and drive measurable outcomes for individual learners and enterprise businesses.

The post Add AI into your Content and Learning Strategy appeared first on Degreed.