How is it possible to deliver more results with shrinking resources?

What’s blocking transformation initiatives? 

Change is no longer a phase. It’s the environment we operate in. Employees are experiencing approximately ten “planned change programs” per year, according to McKinsey, which is five times more than a decade ago. Business priorities evolve before initiatives are complete, and the workforce is under pressure to adjust as change occurs. It is falling on HR, learning, and IT leaders to create a system, rather than another one-time rollout plan, to address this by consistently building new capabilities and confidence to meet the moment. 

But most traditional development processes still treat change like it’s episodic, whereas what is really needed are systems built for continuous adaptation. It’s about building adaptability. Fast.

To support a growing need for continual development alongside change management, we are developing a number of new functions within Degreed Maestro, our AI that’s purpose-built for learning. At LENS we announced these latest innovations** which will be launching soon. They make up a suite of AI-powered experiences designed to turn change management into a repeatable, scalable process.

Driving Skill Proficiency, Alignment, Performance, and Efficiency

To drive effective workforce transformation, L&D and business leaders must find a way to move  from the status quo to a system for continual development that supports the organization’s strategic goals. This will take more than innovative thinking. It will take proficiency, alignment, performance, and efficiency.

Here are four ways to address this challenge, and how new Maestro features** which will be available soon can help. 

1. Proficiency: Identifying the Status Quo

Before you can move toward a goal, it’s critical to understand your starting point. In the case of large-scale transformation initiatives, you must understand the skill profile of your workforce. Traditionally, capturing high-fidelity skill data wasn’t scalable, often taking 10 minutes to assess just one skill.

Maestro Skill Check-Ins allow you to assess 15 skills in 10 minutes, all calibrated to your organization’s unique definitions of proficiency. This allows you to build an accurate picture of workforce skills, moving more quickly from guessing to knowing.

2. Alignment: Synchronizing Strategy and Sentiment 

Transformation often fails due to “friction” or a lack of buy-in that usage data alone can’t explain. Low usage tells you there is a problem, but knowing why a program isn’t working starts you on the path to a solution. Are people confused? Do they feel like the change isn’t relevant to them? Are they running into specific challenges?

With Guided Conversations, Maestro conducts nuanced interviews to uncover the “why” behind the data, asking employees what’s blocking them or where they are hesitant. It then turns those conversations into structured, actionable insights so you can more easily identify and address barriers to transformation.

3. Performance: Closing the “Know-Do” Gap 

Knowledge is only valuable if it is applied. The “Know-Do” gap is where training often fails. To bridge this, employees need to move beyond passive content consumption into interactive practice.

Maestro Roleplays now include custom rubrics. This allows you to define the exact criteria for success and gives employees immediate, structured feedback and coaching. It ensures they don’t just “know” the material, but can execute on it under pressure.

4. Efficiency: Doing More with Less 

The administrative burden of managing a transformation, especially with shrinking resources, can be overwhelming. You need ways to do more with less, and do it faster.

Ask Maestro is a conversational interface for eliminating manual hurdles and navigating Degreed. Whether it’s instantly transforming a PDF into a rich, interactive quiz, finding content in seconds, or building a complex pathway in minutes rather than hours, Ask Maestro automates the L&D “busy work” so your team can focus on high-value strategy.

Maestro in Action: Navigating a High-Stakes Sales Pivot

Let’s consider two common business challenges:

1. Your sales team needs to roll out a new methodology to boost revenue. This is a business-critical change because last year, 70% of B2B reps fell short of their quotas as market complexity outpaced traditional training.

2. You need to develop strong, new leaders with the skills for today’s challenges in a world where leadership has never been more important. But research shows that 60% of new managers fail within their first 24 months.

Here’s how Maestro works in action for these real transformation moments:

Driving Transformation with AI Innovation

As we closed out LENS, one theme was clear: Driving transformation is more important than ever. And transformation fails in the moments when people hesitate or where skills don’t translate into action. 

“Although we don’t know what the future of an AI world might look like, there isn’t a positive outcome that doesn’t involve human development,” Zoe Botterill, Head of Learning and Development at Pearson, told LENS attendees.

To meet this challenge, organizations can’t rely on static programs or one-time initiatives. They need a systematic way to:

• Understand workforce readiness in real time
• Identify and remove friction before it compounds
• Build capability through practice, not just exposure
• Keep people aligned as priorities evolve

Degreed Maestro is designed to support that shift by connecting skill data, real-time insight, and guided development. Instead of wondering if a new methodology is sticking or if your new leaders are prepared for their first challenging conversation, you have the data to prove it. The shift is moving from managing the library to managing the movement, ensuring that every employee is equipped to pivot as fast as the business does.

Because the goal isn’t to get ready. It’s to stay ready.

Join our Degreed in Action webinar series for a deep dive into these announcements, or reach out to your account team for a personalized walkthrough of the new Maestro features.

**The functionality described in this blog is coming soon.

The post Driving Measurable Transformation with Degreed Maestro appeared first on Degreed.

This is a big missed opportunity. 

Sales teams drive revenue. The faster new sellers ramp and start generating pipeline, the faster the business sees results. Coaching is one way to get that boost. In a Korn Ferry study, 94% of top sales organizations said coaching improved seller performance.

Normally, in order for sales members to practice pitching conversations and improve performance, they need time from leaders who can role play and provide them with feedback. But those leaders are busy and often manage large teams, which makes time-to-readiness longer for new sellers.

How TEKsystems Used AI Sales Coaching and Role Play to Grow Readiness

TEKsystems, a global technology and business services company that operates as part of Allegis Group, found a clear opportunity to improve sales readiness and performance through Dynamic AI learning experiences with Degreed Maestro.

Chris Harry, Chief Talent and Learning Officer at TEKsystems, explained that the first three phases of their sales funnel (prospecting, generating interest, and qualifying) were a huge indicator of holistic deal success.

“What we know as a company is, if we can move that business deal velocity at a particular rate and get to qualifying, we are 60% more likely to win the business and fill the business as required,” Harry said.

The TEKsystems team understood that improving performance in these areas of the funnel had the potential for massive return on investment (ROI). They began by onboarding a small cohort of new sellers with Maestro AI role playing experiences in an effort to improve sales learning and time to ramp.

To accomplish this, they created Maestro experiences focused on four key skill areas:

• Elevator pitch: Introducing the company and its services
• Handling objections: Responding to common pushback
• Discovery questions: Asking the right questions to understand customer needs
• Full prospect call: Practicing the full conversation with all the necessary pieces

Adding AI Coaching to Human Mentorship

These experiences were never meant to replace human coaching. When new sellers could practice and learn from an expert, prior practice with Maestro complemented and made the time spent with sales leaders more effective. 

It’s the balance of more immediate, feedback-provided role plays and the human-to-human mentorship that will go on to make role-based learning faster and more effective overall.

Each AI learning experience built on the previous one. On average, learners spent about 15 minutes each on Maestro experience, which cumulatively accounted for about an hour of training per learner across the four focus areas. And even that short time investment produced measurable improvements in confidence and readiness.

Seeing the Why and ROI of AI Learning Experiences

The early pilot revealed something important: Small, targeted practice opportunities can have a measurable impact on sales readiness.

Within their pilot group, 25% of the cohort landed a services meeting after being at TEKsystems for less than six months. Previously, developing a services deal took new sellers 36 to 40 months. In other words, one quarter of new sellers achieved a milestone that previously took years. According to Harry, that’s a strong, data-backed signal that the group’s extra coaching preparation accelerated the path to revenue.

“Our ROI on just this group could be a force factor within 12 months from launching this,” Harry said.

Why did dynamic AI learning make such a big difference?

A big benefit of the AI approach for the learners was psychological safety. Many new sellers hesitate to practice repeatedly in front of their managers or peers. AI learning created a safe space where they could experiment and refine their approach without judgment, before they were ready for a more critical audience. 

“Overall, we found astonishing feedback,” Stefanie Kuehn, Senior Program Manager, Organizational Development at TEKsystems, said. “Their confidence grew. They were able to role play in a safe environment, and that meant not having a leader or a mentor over your shoulder listening in. But they were able to do the Maestro experiences multiple times over until they felt that they were good enough to then go ahead and approach that call or test out their ability.”

Expanding AI Role Playing Across the Business

As an industry, we’re seeing a shift. Learning and development teams are becoming more embedded and better integrated across functions, rather than operating as a separate team. TEKsystems’ plan to expand Maestro AI role playing across more use cases in the organization is a case in point of this new cross-functional approach to L&D.

“Maestro really speaks for itself,” Kuehn said. “It’s an excellent tool to use. It really has a lot of great potential to get integrated into more than just where our sales development reps are.”

Kuehn specified that they are now looking to expand Maestro use into the talent delivery side of the business. The team plans to hire 500 new recruiters, embed Maestro into their onboarding, and build their confidence before they start hands-on coaching experiences with their leaders.

From there, the team plans to expand Maestro learning experiences beyond onboarding into day-to-day, continuous learning, or “hyperskilling,” as the company calls it, meaning accelerated skill building at every level. 

After all, all employees could improve performance with more opportunities for practice. AI-powered learning experiences make that practice scalable. For organizations like TEKsystems, the result has been faster time to readiness and stronger performance for sellers. But that’s only the beginning.

The post TEKsystems Improves Sales Readiness with Dynamic AI Learning appeared first on Degreed.

AI is also changing how people learn at work. 

This moment represents both urgency and opportunity for human development. For learning programs, traditional courses and static content libraries aren’t cutting it anymore. They can’t keep up with the speed at which needed skills evolve and they aren’t personalized enough to support the kind of continual development that employees need to remain relevant and agile in their roles.

But AI can help make upskilling more dynamic, interactive, and therefore more effective. For example, this can look like: getting instant feedback, talking through high-stakes conversations, or practicing a presentation. 

Let’s take a deeper dive into how human-AI interactions are changing learning, and how to make the most of these opportunities—for both the organization and individual employees.

AI is Expanding Into What Previously Were Human-Only Interactions

One of the most significant developmental shifts is the move from human-to-human interactions to a mix of human-AI collaborations. Activities that once required peer or mentor relationships—like coaching, role plays, and simulations—are now being augmented by AI capabilities.

Getting enough regular feedback interactions, coaching sessions, and role plays was less feasible when it was just humans to humans. There are limited hours in the day, scheduling conflicts, and competing responsibilities at play. Managers and colleagues don’t always have time to support a team member who wants to practice that important presentation for the third time, even if they need the extra practice. 

In the words of Nicole Helmer, Degreed’s Chief Product Officer, during a recent webinar: “These were human-only interactions. They’re now human and AI interactions, and there’s tremendous value to be unlocked by thinking about the scalability and the personalization that comes from those sorts of AI experiences.”

These newer human-AI interactions are not replacing interpersonal connection, they are expanding opportunities to learn more effectively, develop skills faster, and as a result, they grow human potential better. Now, managers don’t have to do all of the one-on-one coaching themselves, and can instead leverage AI to help them upskill their teams. 

There will always be a need for those one-on-one human interactions, they can become more purposeful and intentional, since AI tools can now provide some ongoing interactive coaching and feedback.

Human-AI Learning that Stays With You

One of the most exciting developments in the intersection of AI, learning, and human intelligence is how technology is making knowledge more accessible within daily workflows. Degreed is formalizing Model Context Protocol (MCP) capabilities that allow learning content and recommendations to become available through enterprise-level AI agents, ensuring that knowledge finds the learner through different platforms and tools, when and where they need it.

Much like how Google Maps can provide relevant directions to a new place because it knows your current location, MCP-enabled systems understand your work and knowledge context to deliver appropriate learning recommendations. This creates a more seamless integration between work and learning, breaking down the barriers between the two.

“If that’s now where people are experiencing their working life, we want to be able to offer individuals the right nudges, the right discovery, the right learning initiation in that flow of work,” Helmer notes.

Double Down on Human Capabilities

It’s all too easy to assume that your workforce can rely on AI, whether it’s for information, strategy, or advice. Leaders have to be cognizant to avoid this tendency, and to guide employees to avoid it as well. 

The value of human capability doesn’t decrease in an AI-enabled workplace. It increases. But only if there’s a deliberate effort to go beyond reliance on AI for fast and easy access to information. As Helmer said: “We have got to be training people to think, not just to access answers.”

The real goal of AI transformation is far more complex: to complement and empower humans to grow their capabilities and increase what’s possible. 

Judgment. Adaptability. Critical thinking. The ability to navigate ambiguity. These capabilities are not generated by AI tools, and they can’t be replaced by them either. These human skills are built through deliberate practice and development over time. Employees know it, too. According to Degreed data, seven of the 10 most in-demand skills for 2026 are human skills like leadership, communication, and problem solving. It’s clear that employees and leaders alike are investing in developing their human skills alongside their AI skills, recognizing the essential importance of both. 

Make Human Development More Dynamic and Effective

Static courses and episodic development programs are no longer enough to meet the needs of today’s employees, as they seek to keep pace with the rapid change caused by evolving technology. Human-AI learning collaboration offers a new model:

• Humans provide context, judgment, and creativity.
• AI offers scale, personalization, and continuous reinforcement.
• Together, they build capability that evolves as fast as work does.

Organizations that thrive in this environment won’t be the ones with the most AI tools. They’ll be the ones who use AI to help people develop more effectively, and as quickly as the skills they need change.

To learn more about this human-AI learning collaboration, watch the full Degreed in Action webinar series. 

The post From Courses to Conversations: Collaborative Human-AI Learning appeared first on Degreed.

Most platforms were built for stable roles, predictable skills, and linear change. Today’s reality includes mid-year strategy shifts and monthly AI tool evolutions. Expectations are changing before teams have fully come up to speed on previous standards.

In this environment, it’s no longer enough for learning to simply deliver information. Learning has to help people adjust quickly, repeatedly, and in-flight. 

This era of constant change has been amplified into a pressure point by the growing accessibility and innovation of AI tools, which are evolving how we work. AI isn’t the main character of this story, though. Change is. Constant change is what’s pushing learning technology to move beyond content toward something more practical, more experiential, and more aligned to how work actually happens.

In a Degreed webinar on The AI-Powered Future of Learning Technology, leaders from Boehringer Ingelheim and Fosway Group shared what’s actually changing (and what’s working) as AI moves from experimentation to execution.

1. AI can finally bridge the gap between business strategy and L&D.

Business leaders know where they want to go. What’s often missing is a clear, shared view of which workforce capabilities need to change to get there.

Martin Hess, Chief Learning Officer at Boehringer Ingelheim, described a simple but powerful approach: Use AI to analyze business strategy documents and produce a first pass at the skills required to execute them. 

Suddenly, instead of having abstract conversations about transformation, learning leaders can walk into the room with something tangible that builds the initiative’s momentum: “Based on your strategy, here are the ten skills that look critical. Let’s pressure-test this together.”

The result is faster alignment, stronger engagement from leaders, and adaptive learning that’s anchored to real business priorities, instead of generic capability frameworks. AI doesn’t replace human judgment here. It removes friction and gets everyone on the same page faster.

2. Virtual Reality (VR) and AI tools are making realistic practice possible and scalable.

For decades, L&D has talked about the importance of practice to cement capabilities, especially for those “soft” or intangible skills that come into play during sales conversations, leadership moments, and difficult feedback interactions.  

Most employees rarely get enough safe, repeatable opportunities to try a skill before it matters. Personal coaching is effective, but expensive. Role-play works, but only for small groups. Most employees never get enough practice to build real confidence.

AI and VR tools are opening up practice opportunities in ways we’ve never seen before. AI-powered simulations and VR environments now allow people to rehearse real-world scenarios like sales pitches, high-stakes negotiations, and performance conversations. Not by watching a video or reading an article, but by actively participating, responding, adjusting, and trying again. This leads to faster time-to-readiness. 

“No company on this planet, I believe, can afford to provide a personal coach to everyone,” Hess said. “Now this becomes suddenly affordable and feasible, and we can provide a very hyper-personalized experience to pretty much everybody at scale, which is revolutionary.”

This sets work up for two major changes:

1. Practice is finally scalable. What used to require expensive one-to-one coaching or small-group role plays can now reach more people without sacrificing relevance.
2. Psychological safety improves. Employees can experiment, make mistakes, and build more confidence when they can practice without fear of being evaluated by another human. The stakes are lower. 

In essence, all employees can now practice repeatedly for business-critical moments, in context, and without fear of judgment.

Practice stops being a privilege for a few and becomes part of how capability is built across the organization. These kinds of tools give more people a safe space to build confidence before it matters, so real conversations go better.

3. The future of L&D relies on teams upskilling themselves in this evolving learning technology.

Instead of depending on legacy and manual processes, organizations are redeploying talent and relying more heavily on AI-enabled workflows. This is as true for learning and development as it is for every other function in the organization. The implication is unavoidable: Learning teams need to be as fluent in AI as the workforce they support.

At Boehringer Ingelheim, the response was decisive. The learning function went “all in” early by upskilling not just L&D, but senior HR leaders and executives, as well. Leaders weren’t expected to become technologists, but they were expected to understand what AI can do, how it works, and where it creates risk.

In L&D departments, this is a moment of choice: The acceptance and application of AI can either erode L&D influence or expand it, depending on whether teams step into the opportunity that this new operating model offers.

Leteny advised learning pros to pick up the pace in their AI upskilling so that they can help lead the movement and dictate how it shapes the tech stack: “You need to run quick now if you’ve not already gotten there, because the business is doing it already and it might be imposed on you from the business perspective as to what tools you can use.”

This is more than a moment of deciding which tools will be used. It’s a moment for transforming how L&D performs its role through the use of AI. And a lot of professionals are looking at content creation. 

4. Using AI to support content creation is a growing goal for L&D teams.

Content creation is easily one of the most common AI use cases for learning technology solutions. According to research Leteny cited from the Fosway Group, 71% of respondents say they are going to use AI to support content creation.

This process looks different depending on the need. For example, new AI capabilities can support content by:

• Converting dense or static materials into more engaging, multi-modal formats
• Refreshing outdated content quickly as policies, tools, or priorities change
• Simplifying complex information into clearer, more digestible learning moments

However, it’s important to note that this is supported. Humans will still need to be in the loop to ensure quality, expert-driven content. It means that some learning professionals will be able to focus on more strategic, expertise-driven tasks instead of tedious, manual work.

5. Proactive governance and intentional compliance will streamline learning technology.

AI-powered technologies have opened the door to the new security practices and standards that have to be addressed as part of any tech implementation. More guidelines are coming out around AI each year. The European Union was the first to introduce a comprehensive AI law, and in the U.S. alone, hundreds of AI-related regulations emerged in 2025. 

Due to constant AI evolution, legal teams are under immense pressure. They are encountering many AI tools and learning technologies for the first time, and yet, need to keep the company safe and compliant. But things are improving. The longer legal teams operate in this AI-driven environment and the more familiar these tools become, the faster these governance conversations happen.

In this new era of learning tech, it’s essential to treat legal and governance partners as collaborators, instead of blockers. Involve them early. Give them space to learn alongside L&D, provide input on security and guardrails, and help minimize the hurdles that would otherwise come later. 

Hess put it best: “Bring the legal people along. If you have work councils, bring them along. Take them on the journey because they are also at the beginning. Appreciate that, and have mercy with those people. That’s very, very important.”

A more grounded future for learning technology

There is no definitive end state for learning technology. It must continue to evolve alongside organizational needs and workforce capability. But one thing is for sure: Learning teams and the technology they employ must help organizations continually translate shifting priorities into skills. It’s essential that they create space for applied practice, scale capability without scaling teams, and evolve responsibly alongside governance, regulation, and trust.

That kind of change doesn’t happen in isolation. It’s leaders, vendors, and employees in learning, HR, and IT who are building this new reality. As Leteney put it, “It’s conversations between the vendors and the corporates that are the most powerful here, so that you can together figure out what the future does.”

For organizations and L&D teams that are willing to learn fast, embrace AI, and bring people along, that future of learning technology is already taking shape.

→ Watch the entire AI-Powered Future of Learning Technology webinar and two other sessions in our AI-Powered Revolution series.

The post How AI Is Shaping the Future of Learning Technology appeared first on Degreed.

AI alone is not an advantage; it is already democratized and as readily accessible to your competitors as it is to you. So, the differentiator becomes your people. This year more than ever, we need to be investing in human development as much or more than we invest in technology. 

As leaders, we need to not only make each human more productive, but also ensure each one is smarter and better skilled than yesterday. That is what’s going to be the prerequisite for organizational health and growth in the AI era.

The Learning Landscape in 2026

So, it all starts and ends with people. That was true last year, and the year prior. What has changed are the tools we have at our disposal to enable our people. Let’s take a look at how that is evolving:

1. The importance of skills is growing. The rate of innovation means skills are changing faster than ever. The half-life of skills is shrinking dramatically, especially for AI skills. We’re starting to see organizations thrive as they enable and lean into skill-based adaptability. Having a clearer picture of your workforce skills illuminates where employees can upskill, adjust, and grow quickly and in all the right ways for the business.
2. AI can power personalized learning experiences that were never scalable in the past. Employees can now practice and apply new learnings on demand in a low-risk environment. AI coaching empowers and enables every employee to learn at the time that is right for them and receive personalized, timely feedback to help them cement knowledge and upskill more effectively. Plus, AI is continually improving its ability to incorporate and retain individual learning context, which means the feedback it provides is also getting better and better.
3. AI just opened a huge frontier of skill, learning, and content data. AI provides the ability to evaluate a large body of learning experiences. You can now look at all learning content, articles, courses, podcasts, classes, and degrees collectively, and collect the data to see which content and pathways are actually creating the skill-based outcomes your organization is looking for. This is an entirely new set of data than what has been available in the past.  It makes it easier to deploy impactful skill development at scale.

AI raises expectations. It doesn’t remove work.

AI won’t replace workers. 

Here’s why. We can either take the efficiency AI offers and enable each individual to do more work with the same hours in the work week, or we can use it to do the exact same amount of work in fewer hours. The smart decision is to take the efficiency gains. Grow output with the same number of people instead of slowing output and reducing the number of people.

There will always be a competitive advantage to the human working the additional hour.

Even as AI gets more sophisticated, it’s not eliminating work at scale. According to Forbes, leaders use AI more than managers, and managers use AI more than individual contributors. Think about that: AI use is being modeled at the top levels and progressively on down. Yet, you likely aren’t seeing leaders suddenly inundated with free time because they are using AI most. AI isn’t replacing their work. It’s raising the bar for efficiency, effectiveness, and output. 

We won’t suddenly work fewer hours because AI is smarter. Employers will expect more impact from the same time investment. That’s good news for the employment market. 

According to the McKinsey State of AI report from 2025, 80% of respondents said their company made efficiency an AI objective, “but the companies seeing the most value from AI often set growth or innovation as additional objectives.” These factors are where the real value of an AI-powered future lies. We don’t just do more of what we do now faster. We find new ways of working, of developing, of innovating to grow the business.

And AI can’t do this by itself. Future-ready workforce solutions need human growth and creativity in order for AI transformation to work successfully at scale. Full stop.

The future belongs to better learners

In an AI-powered world, the most important skill isn’t knowing everything. It’s knowing how to learn what’s next. The future of work won’t be defined by how intelligent our machines become. It will be defined by how intentionally we invest in human capability.

The companies that win in the next era of work won’t be the ones with the most advanced AI. They’ll be the ones that have cracked the code of personalized learning at scale. The ones that build the most agile, capable, and continuously learning workforce. 

So, I ask again: With AI getting smarter every day… are your employees getting smarter too?

Because they should be.

The post AI Is Getting Smarter Every Day. Is Your Workforce? appeared first on Degreed.

In a discussion about “The AI-Powered HR Team of the Future,” Ali Bebo, Chief HR Officer at Pearson, and Susie Lee, Chief Learning Officer in Residence at Degreed, addressed what that shift really means, and what HR leaders should do next. Here are the top takeaways:

1. Career ladders are breaking. Career systems are replacing them.

For decades, most organizations relied on a simple idea of linear career progression: climb the corporate ladder. Junior to senior. Individual contributor to manager. Up and to the right. 

That model no longer works for today’s non-linear career paths. 

Roles are changing too quickly. Skills expire faster. And pushing everyone into management roles just so they can “advance” creates disengagement on both sides. 

Today’s workplace is getting messier for employees and managers alike, especially with the outdated “career ladder” model still considered the norm in many employees’ minds. Bebo mentioned that as career progression gets more complicated, more and more employees are getting trapped in transition points, unsure where to go or how to improve. Meanwhile, HR is trying to answer questions like: “How do we successfully bring people in from school into work? How do we successfully move people up through different career paths that are in engineering or in operations or manufacturing?”

This will continue to be a growing challenge for HR. That’s why businesses—and HR teams in particular—need to reimagine this career progression structure and take ownership of this shift. The alternative, as Bebo said, is more of a flowing, integrated system: “It’s much more about creating career systems so people can have a bit more of a GPS system inside of career architecture or career framework to really figure out what it would take to move from one role to the next to be able to be prepared to go from one role to the next.”

But even within a system, there are still transition points where people need to move from one phase to another. For this, Bebo’s answer is hyper-personalization. It’s a dynamic talent system that responds to skills, background, and goals of the employee. 

What’s the difference between a system and a ladder? This kind of adaptable career system:

• Breaks roles into tasks, skills, and workflows, not static job descriptions
• Enables lateral, diagonal, and project-based movement, not just promotions
• Supports dual career paths, so employees can grow as experts or leaders
• Acts like a GPS for talent, helping people navigate what’s next, not just what’s up

In cooperative, tailored systems like this, employees don’t get stuck at transition points, unable to change or advance in their role. And organizations gain more flexibility to redeploy talent more fluidly as priorities change.

2. HR needs to shift to a product mindset to lead AI transformation.

HR management can no longer operate as a service function. It has to think and act like a product organization. For years, HR and L&D teams have been structured around programs, policies, and processes. Work was delivered in cycles: design, roll out, measure, then start again with something new. That model assumes relative stability in roles, skills, and business needs over a certain period of time.

The rate of change in the AI era breaks that assumption. 

By adopting a product mindset, HR professionals can reframe and reimagine how they deliver learning and talent development. In a product-oriented HR model:

• Employees are treated as users, not recipients, of programs.
• Careers, learning, and talent experiences are designed as living systems, not one-time interventions.
• Success is measured by adoption, outcomes, and continuous improvement.
• HR works in tight partnership with technology, data, and business teams, iterating based on real usage and feedback. (Bebo even playfully described the HR and IT organizational partnership as “the new power couple.”)

When AI is constantly reshaping workflows and bringing new skills to the forefront, HR can’t afford long planning cycles or overindexing on content. The half-life of skills is shrinking dramatically, especially for AI skills. HR teams need to ship, test, learn, and adapt, just like product teams do.

“We almost need to shorten our development timeframe to the agile way of working, so that it’s quarterly releases that are just continuing to add more value to what we offer,” Bebo suggested.

Because of the speed needed, this mindset is especially critical as organizations move toward dynamic career systems and AI-enabled development. HR solutions and systems have to evolve continuously based on skill demand, employee behavior, and business priorities.

Without a product mindset, HR risks spending too much time creating the “perfect” development opportunities, skill strategies, and architectures, only for them to be outdated upon release or shortly thereafter. A product release framework creates a more agile operating system, making talent and skill development programs available faster and ensuring constant iteration to improve as information changes.

3. Learning is now the most valuable skill.

With an estimated 65% of job skills expected to change by 2030, no organization can pre-train its way out of disruption. The only sustainable advantage in the coming years will be learning agility. That’s why “learning to learn,” as Pearson puts it, has emerged as a business-critical capability. 

Learning has become the power skill that stands the test of time. If your employees can learn quickly, they can keep pace with transformation. And if they can transform at pace, your business is adaptable and set up to meet whatever comes next.

AI accelerates this lean into learning by providing hyper-personalized content, and even guidance, in the flow of work. But the human capabilities of  adaptability, reflection, and application remain essential. It’s people who have to learn.

HR and L&D can make learning more effective by helping people focus on the skills that ladder up to broad, essential capabilities for organizational innovation and growth. For example, at Pearson, one area of interest is innovation, and Bebo points out that there are many “subskills” that feed into that—skills like creativity, client expertise, problem diagnostics, and problem solving. 

Once specific skills are homed in on, leveraging key learning science findings can help make upskilling even more effective at an individual level. 

The organizations that win won’t be the ones with the most courses or the best AI tools. They’ll be the ones that help people build capability at speed in key areas. They’ll be the ones with employees who learn most effectively and have the clearest guidance about what it’s important to learn next.

What this means for leaders and the future of HR

Career ladders are giving way to career systems. Jobs are being unbundled into skills and tasks. AI is becoming a daily collaborator. And learning agility is the currency of the future. These changes are practical and urgent. 

To be proactive, HR leaders should be asking:

1. Where are our career structures too rigid for today’s work?
2. Which tasks (not roles) are most ready for AI augmentation?
3. Do our learning strategies build adaptability, or just credentials?
4. How are we measuring success: efficiency alone or workforce readiness?

The human transformation that’s essential for success starts with Human Resources. Empower your HR team to take that lead. Watch the full “The AI-Powered HR Team of the Future” webinar to learn more about what comes next.

The post What to Know About the Future of HR and AI in 2026 appeared first on Degreed.

Yet, the learning and development metrics that most businesses track have long focused on exposure, consumption, and completion: Attend the class, finish the program, check the box. 

Real development doesn’t happen as a result of skimming a slide deck or attending a one-time workshop. It happens through application, practice, feedback, and iteration. It happens when people try, reflect, adapt and try again.

And that’s where managers come in.

Managers are uniquely positioned to turn learning into development by creating the space to apply and pressure test new skills. They unlock moments to practice and experiment, and they can lead by example. 

That’s always been true, but what has changed is how quickly and effectively managers can make skill development happen with AI.

But first, let’s take a step back. Why does experiential learning and practice matter so much?

Experiential Learning for Team Development

Early in my career, I led the retail training team at Cabela’s, an outdoor retailer. There, learning was practical and experiential by necessity. Employees weren’t just expected to know about the outdoor products they sold, they had to demonstrate how to use them. Fly fishing demos. Dutch oven cook-offs. Tent speed setups. We had to have active experience using the products.

Customers expected expertise in the products we carried, and the business’ success depended on it. If you were selling it, you were expected to know how it worked because you’d actually done it. These events and demos helped us get real experience with the products we were selling. We practiced our skills in real time. Learning was hands-on, social, and… fun. And it worked.

That same principle applies in today’s workplace, even when work is largely virtual. People learn best when they practice in conditions that resemble reality.

But as effective as “real-world” practice is, it’s still practice. At Cabela’s, we were learning and attempting to use these products, but in a gamified way where we were surrounded by peers. We weren’t learning at the moment that a key sale was at risk. 

Practice in safe, low-risk environments builds confidence. Repetition builds muscle memory. Feedback accelerates improvement. And it’s the manager’s job to coach their team, helping them upskill and be well-rehearsed for the moments that matter. 

How Managers Create the Right Learning Opportunities

Organizations can provide employees with the content, the tools, and even the key focus areas for upskilling, but it is managers who are best positioned to build a culture of development and create opportunities for real-world practice. Managers can say, “I know how hard you’ve been practicing, why don’t you try that next presentation?” or “You’ve been learning how to do that with AI, why don’t you try it for this project?”

Managers have four key responsibilities to help boost development:

1. Define the boundaries. Teams need clarity on which skills matter now and which ones will matter next. That starts with clear goals and a shared understanding of what “good” looks like now, and how the standard is changing.
   
2. Encourage—and model—experimentation. Telling people to use AI is easy. Learning out loud is a lot harder. When managers experiment themselves, they can share what works (and what doesn’t), and learning accelerates. It sets the tone and standard for the whole team. 
   I once had to build basic business intelligence dashboards without any prior experience. My first version wasn’t great. I shared it anyway. Someone on the team said, “Hey boss, nice idea, but this isn’t very good,” and they took it further and did it ten times better. That person became the expert, the team got better data, and learning was celebrated as valuable for everyone on the team. But they were unleashed to try and fail because I had done so first.
3. Build the learning culture. Culture shows up in small behaviors and habits. How often people are encouraged to practice. How often they get feedback. How safe it feels to try and fail. When learning is integrated into the job, not an extra chore, development follows.
   
4. Highlight learning wins. Organizations celebrate deals closed, products shipped, and targets hit. Learning deserves the same spotlight. When managers showcase newly developed skills, recognize progress, and reward experimentation, they reinforce behaviors that drive long-term growth.

But even the best leaders face real constraints. Time is the biggest one. Calendars fill up quickly, coaching becomes reactive, rather than proactive and development slips.

This is where AI-powered learning experiences fundamentally change the game.

Using AI to Amplify and Maximize Personalized Learning

No one wants their first attempt at a new skill to happen in front of a customer or critical stakeholder. A missed deal. A broken relationship. Lost credibility. 

In high-stakes moments, “learning on the job” is a luxury most teams don’t have.

That’s why practice matters. And it’s why managers can’t leave development to chance. Traditional learning makes this harder than it needs to be. It introduces friction at every step—searching for the right content, waiting for feedback, or trying to find a safe moment to apply a new skill. All of that eats into already limited time. And when time runs out, practice is the first thing to go.

AI changes that. 

AI allows managers to shift learning before the moment of risk. It compresses feedback loops. It personalizes practice. It lets people rehearse in realistic, low-risk environments on their own time and in their own context, so they’re ready when it counts.

Here’s how managers can enable AI in practice:

Streamline processes and standards

If you want your team to practice using AI, make it easy. Create clear standards and repeatable environments, like a custom AI agent, an AI workflow, or even a shared prompt. Then, encourage the team to share early and often the successes they have with everyone. This enables them to work faster and, ideally, to continue iterating on the process. 

Empower the team with AI learning tools and rewarding development

Not all AI is created equal. The right AI matters here. The most effective tools combine skill data, internal company knowledge, adaptability, and contextual learning science principles built in. Teams should be able to use AI tools to practice new skills immediately, in their own context, at the correct level of expertise, and in their own language. That speed and right-sizing will translate directly into confidence, readiness, and performance. 

Lead by example to build the desired culture

Managers still set the tone. Use AI yourself and find out what works (or what doesn’t). Invite your team to improve on it and reward discovery of new and better ideas. Uncover the possibilities of AI together. When experimentation becomes normal, learning accelerates. And when priorities inevitably shift, teams that know how to learn can shift with them.  

I’m fortunate to have tools like Degreed Maestro readily available to create realistic simulations like sales conversations, objection handling, leadership dialogues, and consulting scenarios that my team can use to practice repeatedly. The biggest benefit is they can get feedback on how they are doing, without needing me to provide it in every session. My biggest constraint as a leader is my full calendar, and now it doesn’t have to impact my team’s development. I can turn feedback cycles from weeks and months to days and hours. 

Manager Development with AI

As teams level up, managers have to level up too. AI can help managers rehearse high stakes conversations, refine communication, and deliver more targeted coaching through data-driven insights.

With the right data and AI-enabled guidance, managers can focus on what matters most: guiding people from where they are today to where the organization needs them to be next. This is also one of the fastest ways to accelerate individual career growth.  

AI, Personalized Learning, and Leadership

AI doesn’t replace learning or leadership. It amplifies both.

When managers use AI to scale practice, personalize feedback, and celebrate learning in action, development becomes part of everyday work. Teams move faster. Confidence grows. Learning stops being something people consume and becomes something they do.

That level of transformation starts with managers. And it is carried forward by their teams.

The post How Managers Can Use AI for Team Development 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.

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.