That’s the learning efficiency paradox. AI is meant to create greater efficiency and productivity, but employees have to be able to use it well. If they can’t, they may not make any gains and AI may even become a blocker. 

Businesses are not yet seeing the ROI on their AI investments, and this is why. Speed isn’t everything. AI works fast, but unless we reimagine work and learning, that speed comes at the cost of people, processes, and real innovation. Without guidance and intentionality along with it, people can become overwhelmed, processes can descend into chaos, and “innovation” is limited to whatever commoditized knowledge and ideas AI has to offer.

The tension between scale and substance, speed and depth, activity and impact, is reaching a fever pitch. It’s not enough to create and complete training quickly; people need to absorb and apply knowledge effectively.

It’s time to redefine efficiency in a way that’s sustainable for long-term business success. In most organizations, AI transformation initiatives are targeting speed and productivity above all else. But that can’t last because people won’t be able to keep up. 

Efficiency is not just about moving faster, it’s about doing things better. And it starts with people, not technology. 

The Efficiency Paradox and the Human Side of AI Transformation

Getting AI transformation right may seem like a technology problem, but it’s also a people problem. AI was supposed to increase efficiency, but many businesses aren’t seeing the results yet. In fact, nearly 95% of businesses saw zero return on in-house AI investments and only 15% of Gen AI users report their organizations see significant ROI from it.

Perhaps understandably, the tendency is to view the lack of progress as a technological issue, so companies are continually funneling money into AI initiatives. After 85% of leaders increased AI investments over the last year, nine out ten enterprise leaders still expect to spend more on Gen AI next year, according to Knowledge at Wharton (88%) and Deloitte (91%).

Yet, despite the sharp uptick in AI spending, The Federal Reserve Bank of St. Louis reported that in the last year, AI use at work has only risen from 33% (2024) to 37.5% (2025). Similarly, a global PwC report found that 14% of respondents are using Gen AI daily, compared to only 12% in 2024. The increases in use are not yet aligning with the massive spending on these initiatives.

The bottom line? To recognize the deep value AI promises, employees will need to upskill both faster and more effectively, as the number of skills they need and the speed at which they need them continue to grow. To do that, businesses need to invest in the human side of AI transformation. In the end of the day, AI initiatives need to be effective for the people who will use them to innovate, to find new ways of working, and to ultimately drive business growth.

AI’s Role In the Learning Efficiency Paradox

While AI is certainly driving the urgent change that businesses are grappling with, it’s also contributing to the solution. That’s the other piece of the paradox.

How? 

AI’s capabilities, when used correctly, open the door to untapped potential in enabling the fast and thorough skill development people need to keep pace with AI. You need learning efficiency. Static learning content libraries and self-service development won’t do the job any more. AI unlocks opportunities for personalization, interactivity, and innovation. 

Personalization

When your employees have access to the most relevant content possible, it means they don’t have to waste precious learning time looking for the right content. AI can use skill data and the foundations of learning science to ensure content is always relevant for the learner’s role and skill level. 

Interactivity

AI allows for more responsive content than has ever been possible before. This goes beyond personalization: You can practice real conversational scenarios, practice for key interactions, and get immediate feedback. This is an unprecedented way of learning that helps cement capability. 

Innovation

Innovation comes in many forms, and AI has a lot of potential to drive creative improvements to existing processes. According to McKinsey, half of AI high performers expect to use AI to transform their businesses, mostly through redesigning workflows. Reimagining traditional processes and procedures can be the key to greater efficiency, with human and AI capabilities operating in tandem. 

The learning efficiency paradox is both an opportunity and a new challenge. Take the opportunity to join in-depth, expert-led discussions on solving this efficiency paradox at Degreed LENS 2026 in Orlando, Florida. From an agenda filled with workshops, roundtables, and sessions rich with insights, you’ll learn from and network with the best in the business.

The post What Is the Learning Efficiency Paradox? 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.

1. AI-generated content
2. Customized feedback and coaching moments
3. Surveys, data, and debrief conversations

1. AI-Generated Content

Let’s start with multi-modal learning content generation. We’re exploring ways that you can use AI to help generate content or use your existing documents or Sharable Content Object Reference Model (SCORM) files as the starting point. From there, it can be quickly transformed into learning resources of any length or format. You can edit the content produced, with multimedia options for text, images, graphics, and videos—or even slides.

This is an easy way to keep fit-for-purpose learning content engaging, diverse, and always relevant. And it’s one we plan to launch in early 2026.

2. Customized Feedback and Coaching Moments

Degreed Maestro is about more than conversations with AI. It’s about creating high-impact, comprehensive learning experiences. To do that, we’re exploring multi-step AI experiences that combine multiple formats to provide the learner with opportunities for improvement, such as customized feedback or mini coaching moments.

For example, after practicing a sales call with Maestro, it would provide scores and feedback based on my performance, showing me what I did well and what I need to improve. It would also provide mini coaching moments or a chance to replay and practice the specific things I need to work on. 

3. Surveys, Data, and Debrief Conversations

We’re also excited about a new way to use Maestro through natural, AI-powered debrief conversations. These encounters can drive learning and reflection while surfacing valuable insights along the way. 

Instead of formal surveys that produce fatigue and rushed, incomplete answers, Maestro can weave smart questions into everyday conversations or draw insights from existing ones with no extra effort required. In these settings, people tend to share more openly and in greater depth than they would in a traditional survey, especially when they know their responses can remain confidential. 

In one example, we asked employees how they’re using AI in their roles via a quick conversation with Maestro. Maestro gathered the responses and created a live dashboard to aggregate the results. From there, we could even chat with it about the data to explore further trends. 

This approach makes it straightforward to establish a baseline understanding of an individual employee’s skills, needs, and experiences, to then tailor learning to individual needs. The measurement of impact available afterward uncovers a depth and richness of insight that’s simply out of reach with traditional methods. It’s real-time understanding that was previously invisible. 

Stay Updated

Imagine what you could achieve with that level of clarity about employees, their needs, and the impact of your learning programs. We’d love your feedback as we keep exploring, so follow me on LinkedIn or sign up for our AI Experiments Lab newsletter to stay updated on our latest tests.

The post AI-Generated Content, Coaching, and Interactive Data appeared first on Degreed.

• Healthcare
• Finance
• Manufacturing
• Technology
• Professional Services

Each pathway blends AI precision with expert validation, helping organizations speed up workforce time-to-readiness in their industry’s most sought-after skills, all while saving costs and time on manual curation.

How Is a Learning Pathway Created?

Every Open Library pathway starts with guided AI that is trained to surface credible, skill-aligned resources, rather than random web content. Human experts then refine tone, relevance, and design for clarity and deeper cognitive learning, following proven learning frameworks like Bloom’s Taxonomy and Coherence and Signaling principles.

Taking a collaborative approach, with human polish and oversight applied to AI output, maintains learning science at the core, driving higher learner completion rates and building skills that transfer more effectively on the job.

The Industry Edge: Pathways Built for Workforce Readiness

Our latest industry bundles are designed to solve specific challenges in each sector:

• Healthcare: Building empowerment, communication, and decision-making confidence in clinical and non-clinical teams.
  
• Financial Services: Enhancing trust, leadership, and compliance-readiness.
  
• Manufacturing & Advanced Tech: Upgrading workforce adaptability through analytics, safety, and innovation pathways.
  
• Professional Services: Strengthening client management, consulting, and data-driven problem-solving.
  
• Retail & Consumer: Empowering managers and associates with digital, interpersonal, and operational skills.

Open Library’s research-based structure and AI-powered scalability allows organizations to quickly roll out relevant learning across roles and geographies as needs evolve, without starting from scratch each time.

What Are the Results? 

Put simply, the outcome is learning that’s simple to implement, scalable, and cost-effective. As a result, Degreed clients are now turning to Open Library not just to complement content vendors, but to replace them.

• A telecom company is moving away from LinkedIn Learning in favor of Open Library and the added relevance and quality it provides.
  
• A global mining company was able to eliminate hours of manual quality assurance (QA) and reduced content vendor spend by leveraging Open Library’s auto-refreshed, high-quality pathways.
  
• A non-profit healthcare organization saved time and budget while boosting learner confidence and peer-to-peer learning.

In total, Open Library drives measurable value:

• 60% of Degreed clients now use Open Library, with adoption up 13% month over month.
  
• Content completions increased 13% between August and September, which shows rising engagement among global users.

The Future of Open Library

Open Library is evolving beyond a curated catalog of content. Specifically, it’s becoming a multi-format learning ecosystem by combining AI-powered summaries, Maestro coaching, interactive simulations, and future premium marketplace options.

By 2026, Open Library will feature:

• 500+ pathways and 7,000+ content items
  
• 16+ ready-to-launch bundles
  
• Integrated AI coaching and leadership learning journeys
  

Basically, it’s learning that scales and evolves with your business, and without added cost.

Explore the new industry pathway bundles and see how Open Library helps your teams build the right skills—more quickly, and with lower spend and less effort.

The post 150 Learning Pathways for Industry-Specific Workforce Readiness appeared first on Degreed.

Completion rates prove it got done, and compliments prove employee satisfaction, but they don’t prove learning effectiveness. Execs want tangible evidence that the investment paid off. You need to show that people retained the information and are ready to use it on the job. And if it didn’t work, you need to identify where more learning is needed. 

So, what’s the answer? Test it.

Quizzes, a new experience within Degreed Maestro, enable you to quickly create and deploy AI-generated quizzes to ensure knowledge retention and measure learning effectiveness. So, next time your execs ask if it worked, you have relevant data to support your answer.

The Old Way: Manual Quiz Creation

Seeing the value of quizzes? Easy. Executing them manually, accurately, and at scale? Difficult.

Creating and deploying quizzes at scale is traditionally manual and incredibly time-consuming. It often requires extensive input from busy subject matter experts (SMEs) to draft questions, which is an inefficient use of their high-value expertise and often delays deployment. 

Reliance on manual creation and SME availability makes it difficult to scale and maintain up-to-date quizzes. This is more true now than ever, given that the skills and knowledge employees need are constantly changing. 

The New Way: AI-Generated Quizzes

With Maestro quizzes, admins can create quizzes in minutes, drastically reducing the time and effort spent. Through a text-based conversation with Maestro, you can guide Maestro to create a custom quiz on any topic and adjust parameters, like the number of questions and difficulty level. It can also create quiz questions based on existing documentation. This is a simpler way to harness the knowledge of SMEs without having to involve them in manual question generation. 

After employees finish the quiz, in-app reports enable admins to gauge their level of knowledge retention. These reports help identify consistently incorrect answers and, therefore, knowledge gaps where more learning is needed.

The quizzes are directly connected to the Degreed platform, allowing you to manage workflows like assigning quizzes or reporting on results. That makes it easy to identify where more learning is required so you can create more effective experiences that close knowledge gaps, learning more across the business.

Create Business Value

Real-world Applications for Quizzes

Example: Preparing for a Product Launch

During a new product launch, your revenue teams are inundated with information: value props, product functionality, go-to-market strategies. It’s a lot to take in, and it’s difficult to gauge whether your people are ready. 

With Maestro quizzes, you can evaluate whether your people have a grasp on key components of your new product. You can even create different quizzes for different audiences, since the information your sales team needs to remember is different from what your implementation team needs to know. By using existing documentation, you can generate quizzes focused on these different audiences within minutes. 

Example: Checking Long-Term knowledge Retention for AI Transformation 

Many organizations have ambitious targets for adopting AI in the business. This knowledge likely doesn’t exist at scale within the business, so organizations need extensive training and upskilling. By embedding a quiz at the beginning and at the end of a pathway, you can measure how much the workforce learned about implementing AI, so you can tie learning back to strategic business initiatives.  

Example: New Customer Onboarding Process

Quizzes can reinforce process and procedure updates. If you’ve launched a new customer onboarding procedure, employees need to remember the correct process, what to do, and what not to do. You’ll have visibility into their readiness and procedural sticking points that may require extra training.

Quizzes As Part of a Larger Learning Journey

Quizzes are powerful by themselves, but are enhanced when combined with other learning initiatives, such as pathways or academies. By including quizzes at the end of these experiences, you can evaluate the effectiveness of those programs and identify where they may need new or different content. 

Embedding quizzes inside Degreed also improves the learner experience, providing a UI that is consistent across experiences. And it helps you consolidate tech, reducing cost and administrative load with fewer platforms to maintain.

The post Create AI-Generated Quizzes to Measure Learning Effectiveness appeared first on Degreed.

Instead, that skill-building depends on context. AI needs information like:

• Who’s the learner and what is their skill level?
• What does “ready” look like for their role?
• What are the foundations of learning science and how are they applied in this situation?

MCP (Model Context Protocol) brings that context and relevancy.

Think of it like this: You could use an old school map without a GPS. But it’s more difficult and less personalized to where you are. 

Think of MCP as the GPS for AI learning. 

Instead of adding “AI features” into a portal, MCP simplifies integrations, giving AI access to information in other tools and databases and providing 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. In the GPS analogy, this allows for real time rerouting to avoid traffic, recommendations of nearby attractions, and overall route intelligence. 

Through the MCP interface, AI has access  to broader contextual information to provide more targeted, personalized learning in real time.

With MCP, AI can move from generating content to providing learning context that helps drive capability, so that employees and businesses can develop and apply new skills more quickly.

What Is MCP? What Role Does It Play in Learning?

MCP, is the connective layer inside Degreed, built to make AI learning assistants truly useful at building capabilities. It gives any approved AI agent—like ChatGPT, Claude, Copilot, or an internal assistant—a governed, real-time snapshot of what matters for learning and performance, including:

• The learner’s skills, goals, and role
• Historical learning and skill data from Degreed 

Think of Degreed MCP as a “context envelope.” With that envelope in place, AI becomes a much more accurate support in the learning process. It can analyze all data related to the employee to provide the right learning at the right time.

How MCP Works in the Flow of Learning

On Tuesday morning, a sales manager opens Copilot and types: “Help my team get ready for Friday’s product pitch.”

Without MCP…

Copilot can find sales decks, playbooks, and pitch documentation, but it can’t tell who’s presenting, what skills they are missing, or how to help them improve.

With MCP…

Degreed and Maestro to bring missing context into view: who’s on the team, what each person already knows, and where they need coaching to tune up their skills.

Together…

Copilot surfaces the right materials, messaging, product overviews, and client data, while Maestro adds AI-native coaching conversations that guide each rep through practice modules and feedback loops that strengthen their delivery.

The manager then assigns everything directly in chat, and MCP writes the updates back to Degreed—so every skill, coaching activity, and readiness metric stays governed, current, and measurable.

There’s no extra portal needed or re-prompting required to remind the AI who’s who. The context and personalization from this exercise follow the learner from tool to tool, so the assistant stays helpful across technologies and the data remains secure.

What MCP Does for L&D, HR, and IT

Faster time to readiness: Onboarding ramp plans automatically adapt to each role, person, and deadline.
Higher adoption: Learning appears inside the tools people already use and is tailored to their needs.
Auditability: Every learning and skill action is governed and explainable.
Data you can trust: Degreed remains the single source of truth; MCP simply surfaces that data in real time.

Built to Fit your Tech Stack, Not Replace It

MCP works across your tech ecosystem, connecting signals from platforms like Workday, Salesforce, and any LMS, without duplication. It’s vendor-neutral and least-privileged by default, which means that MCP only accesses the minimum data and permissions necessary to perform its function—nothing more. Use your AI of choice, and your governance and security rules will still apply.

As Nikki Helmer, Chief Product Officer at Degreed, shared during Vision, “MCP doesn’t just make AI sound smarter. It helps it make smarter decisions—ones that align learning to business goals, reduce risk, and build real readiness.”

The post Announcing Degreed MCP: The Moment AI Learning Gets Its GPS appeared first on Degreed.

Continuous rollouts of new tools without sufficient time to adapt is leading to change fatigue, fragmented adoption, and disengagement. When employees are overwhelmed or feel unsupported, productivity drops and performance stalls.

Yet, your business still needs the adoption to stay relevant, see the ROI, and grow your business.

As a leader, you can make it easier.

In moments like these, you need a united front and a confident workforce more than ever. Organizations where CHROs and CIOs align on AI upskilling, cross-functional collaboration, and ethical governance, companies are three times more likely to develop a Gen AI-ready workforce. 

To get there, key members of the C-suite have to band together and give employees the leadership and guidance they need to grow. 

Why CHROs, CLOs, and CIOs for AI Adoption?

As you start to see this large-scale workforce transformation for what it is—people who need to learn a new technology—it’s obvious why the key stakeholders here are Chief Human Resource Officers (CHROs), Chief Learning Officers (CLOs), Chief Talent Officers (CTOs), and Chief Information Officers (CIOs). 

Framing it this way also showcases why these teams have to come together to be effective at readying their workforce for AI.

The Art of AI Alignment

Employees aren’t resisting AI, they’re resisting the confusion that comes with it. 

They’re tired of unclear expectations, shifting tools, and too few answers. To drive meaningful adoption, align HR, L&D, and IT around a common goal: delivering clarity and direction that ties directly to business outcomes. When people understand the “why” and the “how,” adoption becomes progress instead of pressure.

Once you’ve got buy-in from all the leaders, here’s what you have to work together to do:

1. Establish a framework with clear AI guidance.

Above all else, people need to know what they can do with AI and what they can’t. No employee wants to be the one putting the company at risk, but without a clear strategy and framework, they’re left to guess. Whether it’s what platforms they use, how they use them, or what they can use them for, people need the guardrails. 

Here are some questions you can consider when creating AI guidance:

• What is safe AI use? 
• What does it mean to use AI responsibly at your company? 
• Are there any AI regulations your company is subject to? (e.g., the EU AI Act)
• Which platforms can they use? Which can’t they? Why?
• What work can be done by AI and what can’t? (This one may require a little experimentation.)
• What are the expectations for employees?
  

2. Establish a plan of action.

As the leaders in the midst of a full-scale workforce transformation, you need clear delineation for which departments will handle which aspects of adoption. For example, there may be some portions of the transformation that are best-served by certain teams and other components that could be owned by any function. Clarity is key.

Here are some questions to consider as you make your plan:

• Who will be responsible for AI tool governance?
• Who will communicate AI guidance, news, and information with the workforce?
• Who will create the learning and upskilling opportunities in AI?
• How will your employees learn to use AI appropriately?
• How will you all work together on a daily, weekly, or monthly cadence to stay in sync?

Boost AI Adoption with a United Front

Why are organizations with aligned leaders so much more likely to have a team that’s ready for AI? Because that alignment gives employees two key components of successful Gen AI learning: Support and infrastructure.

Once aligned, you can empower your employees to develop the confidence needed to easily adopt AI through learning experiences—both hands-on AI practice and self-guided learning resources.

As part of that learning, they can also experiment with AI within the new guardrails. Experiential learning is one of the best ways to develop skills and in the process of trial and error, your employees will also be able to suss out the value of different AI tools for different use cases across your organization.

That confidence they’ll develop is the key to beating AI fatigue. Compared to others, Very Confident Gen AI users are:

• 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
• 38% more likely to get support from peers and mentors
• 77x more likely to engage with and become proficient using Gen AI

With confidence, your employees are no longer wasting brain power trying to figure it all out. Instead, they have the resources, the limitations, and the expectations. They approach AI refreshed. They can experiment and grow with renewed energy.

The post Align Your CHROs, CLOs, and CIOs to Grow AI Adoption 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.

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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.