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.

That’s the question that is top of mind for HR, learning, and IT leaders alike. Technology is shifting faster than teams can operationalize it. The result is that company goals and workforce strategies often are no longer fixed destinations. In fact, employees are experiencing approximately ten “planned change programs” per year, according to McKinsey, which is five times more than a decade ago. 

Traditional development methods can’t keep up with that. It’s time for a perspective shift.

How to Strengthen Adaptive Change Management and Employee Development

We should be asking, how can we keep pace with a moving target?

Before AI started driving technology innovation at a furious pace, it made sense to determine a goal to support your strategy, adjust work to meet that goal, and continue on that path until it had been achieved. That’s not what work looks like anymore; the world is evolving too quickly to see the process through from beginning to end. 

Employees are noticing the lack of effectiveness in how these shifts are handled. In 2025, only one fourth of employees surveyed felt their organizations effectively managed change rollouts across the business, and nearly half said it increased their workload.

At that rate, change isn’t sustainable. In a modern workplace, the only way to manage the current pace of change is to enable your workforce to continually adapt. Then, instead of missing targets, you’ll be looking for the next ones and feeling confident you can keep up.

That’s the shift. But knowing you need adaptability isn’t the same as operationalizing it.

To make change stick, you need a way to move individuals through it. It isn’t enough just to launch initiatives and hope adoption follows.

Apply the ADKAR Model to Drive Individual Change

Adaptive change fails because people aren’t ready to act on it.

That’s where the ADKAR model comes in. Built on the idea that organizational change only happens when individuals change, ADKAR gives leaders a simple way to identify where progress is breaking down, and what to do about it. It focuses on five outcomes every individual needs to move through:

• Awareness: Do people understand why this change matters right now?
• Desire: Do they actually want to engage with it?
• Knowledge: Do they know what to do differently?
• Ability: Can they apply those changes in real work?
• Reinforcement: Are those behaviors being sustained over time?

Most organizations stall somewhere in the middle. They communicate the change (awareness), maybe even provide training (knowledge), but never confirm whether employees can apply it under real work conditions. Or they skip reinforcement entirely, assuming once a behavior is introduced, it will stick.

In a world where priorities constantly shift, those gaps show up faster at scale.

The ADKAR model works because it gives you a diagnostic lens. Instead of asking, “Why isn’t this working?” you can pinpoint the exact barrier. Is it a lack of clarity? Resistance? Missing skills? Or simply no reinforcement?

Once you know where individuals are getting stuck, you can respond with precision, whether that includes targeted communication, hands-on practice, or ongoing feedback loops.

This approach helps turn change from a one-time rollout into a repeatable, scalable system. It’s not enough to introduce new ways of working. You have to ensure people can adopt, apply, and sustain them as conditions evolve.

How to Address Change Business-Wide

Understanding how individuals move through change is only part of the equation. The next challenge is scaling that across the business, so strategy translates into consistent behavior. Your workforce doesn’t need better planning to make this happen. Not only do they need the organization and leadership to help provide stronger change management. They also need more in-the-flow opportunities to help them develop the required emerging skills.

To keep pace, you need a system that connects what people know, how they feel, and what they actually have the capability to do right now. That means moving beyond static training programs. It means building a repeatable way to activate and accelerate always-on readiness.

Here are the steps to addressing big workplace shifts:

1. Set the baseline. Don’t make assumptions about skill proficiencies. Knowing what employees know (and don’t know) is an essential part of effectively enabling them throughout a transition. Without this knowledge, you’re operating blind.
2. Find out what’s blocking adoption. When adoption of a new method, process, or technology stalls, you can’t fix the problem until you know what’s causing it. Too often, organizations rely on surface-level learning data like completions or logins. But that doesn’t tell you whether employees feel confident, understand the shifts they need to make, or believe the  changes apply to their role. Once you have a better grasp of these factors, you can help employees develop in the right areas.
3. Provide experiential learning opportunities. According to McKinsey, nearly 90% of leaders are seeking a significant change in how to develop employees. And this is now possible, because AI capabilities have illuminated an entirely new learning landscape. Employees can access one-on-one coaching and participate in skill rating sessions in real-time to meet personalized development needs as they arise.
4. Pick up the speed of transformation. The longer it takes to build, update, and deploy learning experiences, the more likely your approach will fall out of sync with current reality. To keep up, organizations need to reduce the operational drag around development by automating content creation, simplifying pathway design, and delivering the right learning to the right people without manual effort.

Successfully managing these four steps is what most organizations are aiming for in this AI-driven era. 

Prepare for Constant Workforce Transformation with AI-Powered Learning

Even the best laid business plans and strategies fail if the workforce doesn’t have the capabilities needed to execute them. And in a world where priorities shift constantly, stalled progress is a huge risk. The organizations that keep up are the ones that can continuously:

• Understand workforce readiness in real time
• Identify and remove friction early
• Build capability through practice, not just exposure
• Adapt faster than the pace of change

That’s the shift. Change isn’t something you manage once. It’s something you execute continuously.

Degreed Maestro, our AI tool that’s purpose-built for human learning, is designed to help you do exactly that.

The post Driving Change Management When Change Never Stops appeared first on Degreed.

This isn’t a surprise to most HR professionals experiencing the transformation in real time. 89% of surveyed HR senior leaders told CNBC they believed AI would reshape HR job roles in 2026.

At Degreed LENS 2026, Claudio Muruzabal, Board Member, Global Business Transformation Advisor and Former Chief Business Officer, and Erik Lossbroek, CRO at Degreed, discussed “The End of HR (As We Know It).” While the session made it clear that HR continues to be essential and is not going anywhere, it also underlined the fact that the HR function is undergoing a deep overhaul as a result of emerging technology and larger-scale organizational shifts.

Automation is Redefining HR’s Role

More and more HR tasks are being automated or turned over to AI. In 2025, 66% of HR teams used generative AI and approximately 77% of HR organizations had a technology initiative in place to improve efficiency, according to The Hackett Group.

But this effort isn’t new.

“HR has been ahead of the curve in terms of bringing AI to the business,” Muruzabal said. He went on to explain that HR has often been one of the first business functions using AI—for hiring, recruitment, assessment, and more. 

Now, AI tools are getting sophisticated enough to truly automate some of the repetitive tasks that bog down HR teams. Muruzabal said that this kind of automation is making many HR functions much more self-service for employees, while reducing the load for HR from that type of work. 

But when that happens, the work HR teams do isn’t eliminated; it’s extended into new areas. And that work is increasing. That same study from The Hackett Group found that HR workloads were actually set to rise 10% in 2025. As more menial tasks disappear, HR professionals can focus on more strategic, large-scale business initiatives, like capability building.

“If we can envision that model, in which we use automation even more in all of the traditional functions, and we focus our resources on the future and development, we’re talking about a function that is very different from the function we’re all used to,” Muruzabal said. “And it’s up to us—the ones who we believe that we need to focus in the learning space to make that happen—to create the capabilities and the affordability to invest more in this continuum.”

That focus on learning and workforce development is what will make organizations successful long-term, because that is what will allow businesses to better adapt to change.

Moving From Development Programs to Capability-Building Systems

The new mandate to adapt is falling to HR, talent, and learning teams. To meet these growing development needs, it’s not enough to curate and launch programs semi-annually. 

According to Financial Times, traditional learning programs can take three to six months to roll out. AI capabilities and tools change significantly in the time it takes to research, build, and launch initiatives like those.

“What you need is to be focused on developing talent every day,” Muruzabal advised.

In another LENS 2026 event session, Zoe Botterill, Head of Learning and Development at Pearson, shared the need for learning and development teams to move to more of a product mindset. Through that lens, the team would develop programs in shorter sprints, continually iterating and building on learning experiences while they are operating in the organization.

But this is a huge shift away from traditional training models. Creating a learning solution that’s iterative and responsive means moving from program-based talent development to a comprehensive system that continuously adapts to develop workforce capability. Such a system will connect skills to business priorities in real time and evolve as roles change. It will grow with the business.

This is where HR can step in and activate their experience in setting up org-wide, people-centric systems to support strategic initiatives for the business. But transformations of this scale don’t come without barriers or cross-functional impact.

AI Raises the Stakes for HR

The rise of AI tools, trends, and capabilities will inevitably create winners and losers—companies that will thrive in the new world and those that won’t be able to keep up. Regardless, the work of HR teams is not going away. If anything, work is more intense and demanding than ever, though it will affect business functions differently.

“There will be some segments and markets that will be more impacted than others, but in the longer run, there is a much bigger opportunity to really create a bigger pie for everybody,” Muruzabal said. “I’m not afraid of AI.”

As part of this transition, there has to be a growing focus on technological organization, access, and structure, which will bring in IT, legal, and compliance into the mix. 

“Governance is as important as technology itself,” Muruzabal said. “Making the right governance decisions in terms of technology will become even more important in the future.”

The weight of governance will likely also become an increasingly important element of HR responsibilities. This is especially true as talent development technology becomes more integrated and personalized, and the sensitive human data those systems contain is shared across your ecosystem to leverage emerging AI capabilities like Model Context Protocol (MCP).

According to a 2026 survey of CHROs, the top cited barriers to AI adoption are organizational concerns, such as employees’ fear of job loss, budgetary pressures, and security and compliance needs. Not the technology itself. 

HR Transformation In 2026

HR is feeling the pressure to ensure the workforce keeps up with an ever-evolving workplace while also adapting and evolving their own HR models and systems with new technology.

 Through this transition, we’re seeing:

• A shift in the focus of HR from small-scale, day-to-day tasks to more strategic, business-critical initiatives, thanks to automation and AI.
• A move from one-time training programs to more holistic, responsive systems for always-on capability building.
• A rise in the importance of human work and tech governance, even as more tasks get outsourced to AI.

This unique convergence with technology will be HR’s new operating model for 2026. 
For more great insights, sign up for our six-part Degreed In Action webinar series.

The post HR + AI: A New Operating Model for 2026 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.

The rise of AI tools has led us to demand more from the same people. But they’re struggling to keep up. All the focus has been on implementing the tools, with much less emphasis on enabling the humans.

It’s a paradox and a reality for HR, learning, and tech leaders right now. The company needs to show ROI on their AI investment, but the missing ROI isn’t a technology problem, it’s a human readiness gap.

Last week, Degreed CEO David Blake took the stage at LENS 2026 to put a name to this new era of highly demanding work: The Age of Intensification.

In this new age, work, learning, and talent development has to change. At LENS, industry experts and Degreed clients and leaders touched on these changes and how to adapt. Here were some of the highlights:

Humans can still win in the Age of Intensification

The world is compressing and the rate of change is accelerating. Yet, AI hasn’t reduced human workload. It’s expanded what’s possible and raised the base level (and the bar) for performance. 

Now, the ask is for people to do more in the same amount of time. Humans have to learn to use AI, and use it well. All without an over-reliance on the technology. It’s demanding. Intense.

Yet, having access to AI tools doesn’t inherently make them useful. It’s only when those tools are elevated by people and embedded into processes that the real ROI will emerge. And after the massive investment businesses made on AI, leadership teams expect that measurable return. 

“Your CEOs need to see a return on it, and that pressure and that mandate is coming to you,” Blake said.

Transformation isn’t optional. The only question is whether it’s intentional and effective. And technology alone can’t drive that kind of transformation. We know because the technology is here, but the return isn’t. Only 25% of AI initiatives deliver expected ROI, according to IBM.

In this Age of Intensification, there’s also a persistent fear that AI will displace human work. But the message from LENS was more nuanced. Not only can AI not replace human work, but it was never designed to. According to the world’s first Chief AI Officer, Sol Rashidi, it was meant “to facilitate, to accelerate, to amplify the magic that we bring to the workforce.”

Although it’s easy to fawn over the capabilities AI has to offer, it’s missing critical logic, novelty, leadership, and judgment. Anyone can access AI tools, but your company is the only one with your unique workforce characteristics and skill sets. That’s what defines your business.

Or, as Rashidi reminded LENS attendees: “Technology is amazing. We’re more amazing. Don’t lose that.”

Learning must become as dynamic as change

When change is continuous, development can’t be episodic. Static learning content isn’t going to cut it when the entire world is becoming responsive and personalized.

During his session on Degreed AI Labs, Taylor Blake described what an AI-native development model could look like: “I’m going to propose that the AI Native Model is going to look something like this. Learning experiences are going to be more specialized to the task. They’re going to be more personalized to the individual. They will be more situational to the moment.”

This principle also applies more directly to the process of creating and distributing learning content. As pointed out in Financial Times, traditional learning programs can take three to six months to roll out, and AI can change entirely in that time. That’s why, although learning teams are masters at traditional content authoring and curation, this new age is calling for something more dynamic: a product mindset that plans for a fast launch and regular adjustments. 

Learning can no longer be a one-time program rolled out annually. 

“We’re going to iterate and learn as we go,” Zoe Botterill, Head of Learning and Development at Pearson, said.

Efficiency alone isn’t enough

When pressure increases, most organizations add more—more programs, more tools, more initiatives. In fact, McKinsey has found that employees now experience 5x more change programs than they did a decade ago. But the reality is that 59% of change initiative value is lost between the initial idea and the execution of these initiatives. This explains in part why 89% of leaders are looking for a drastic change in how their organizations develop employees. They understand that this is where the value will be created or lost when it comes to AI investments.

“When pressure goes up, most organizations respond by adding something. My challenge to you is, as a high-performing team: Pause. Think through how you can simplify. Focus on the capabilities, the skills, and the workflows,” Jennifer Sutherland, Global Head of Learning Enablement at ZS, said.

More isn’t always better. In fact, sometimes, it’s detrimental. Rashidi told LENS attendees that the endgame isn’t to be more productive, it’s to be more effective: “We’ve got to help our organizations to stop being overly obsessed with productivity and efficiency, because this just measures ‘more.’ But what if we are doing ‘more’ of the wrong things?”

Efficiency for the sake of efficiency isn’t returning the dividends many leaders think. In fact, in some areas, it’s weakening your overall strategy.

“Efficiency doesn’t create strength in your workforce,” Blake said. “Think about it. If you can automate away a job, you’ve probably already done so.”

Safe experimentation and practice are key

Another critical piece of agility is having the curiosity and courage to experiment is becoming more and more important, especially as technology continues to evolve at high speed.

“You have to be able to fail because it’s in the failure that you find the gold, the treasure, and the lessons,” Antonia Jackson, Learning and Technology Partner at HubSpot, said.

And part of that experimentation means pressure-testing your systems and tools. If you’re trying a new technology, Carlo José, Global Head of Learning and Development from GSK, said pilots are exactly the right time to challenge the system.

“Break it now while we’re in testing stages so we can figure out how to evolve,” José said.

Another way this principle comes into play is in learning itself. New AI tools, like Degreed Maestro, give users the ability to practice, receive feedback, and even fail at new skills like presentations, sales pitches, or business-critical conversations. And with AI, they can do so without judgment.

TEKSystems, for example, used Maestro to deliver personalized practice opportunities to their sales teams at speed and scale.. And here is some of the feedback they received: 

“Their confidence grew,” Stefanie Kuehn, Senior Program Manager, Organizational Development at TEKSytems said. “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 until they felt that they were good enough to then go ahead and approach that call or test out their ability.”

Human Readiness Drives the Future

The biggest takeaway we kept hearing over and over again? Technology alone isn’t enough for AI transformation. You can have all the AI tools in the world, but if your workforce is unprepared, you won’t see that ROI.

“It’s about people and how people use technology for the business,” Ingrid Urman, Global L&D Director at Tenaris, said.

Businesses aren’t seeing the ROI from AI because even though they got the tools, they didn’t focus on upskilling the people. If we expect people to do more work in the same amount of time during this Age of Intensification, then we have to empower them to be proficient and effective using these tools. 

This is the real work of transformation: building human capability to match technological ambition.

The insights from the LENS stage are an indicator of what’s coming, but amidst all of that, it’s crucial to remember our role as humans in shaping that future. 

“Find your edge,” Rashidi said. “Continue to be creative. Connect the dots where machines cannot. Be accountable for your organization. You have a moral responsibility.” 

Technology will keep accelerating. Expectations will keep rising. Human transformation is what determines whether businesses keep up or fall behind.

For more information on the Degreed product announcements and updates made at LENS, please register for our six-part Degreed In Action webinar seriesor read our product announcement press release.

The post Workforce Transformation in the Age of Intensification 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.

Nearly every organization has broad access to AI tools, but your workforce is unique to your company. People are the thing that sets your organization apart. Ultimately, that’s going to be your competitive differentiator. And your people are (smartly) doubling down on human skills.

New insights from Degreed show the top 10 skills professionals are building for 2026. And here’s the thing: Seven of the top 10 are human or business-centric skills, like leadership and communication.

Top 10 Skills Professionals Are Building for 2026

Based on learning pathways created in Degreed in 2025, the top 10 skills professionals want to develop in 2026 are*:

1. Leadership
2. Communication
3. Project Management
4. Problem Solving
5. Customer Service
6. Microsoft Excel
7. Data Analytics
8. Python
9. Adaptability
10. Stakeholder Management

The list makes it clear that the development of technical skills remains essential. Data analytics and Python continue to grow in importance. 

Yet, the majority of this list reflects something more foundational. As automation scales, the value of judgment, coordination, and influence increases. Those are the skills that competitors don’t automatically have access to just because they have ChatGPT.

There’s another important takeaway here: Not all tasks can be done by AI. In fact, according to the World Economic Forum, “tasks tied to empathy, creativity, leadership, and curiosity” only have a 13% potential for AI transformation. They require too much human judgement and experience to be automated.

As technology advances, complexity increases and change accelerates. For employees, complexity requires human or soft skills and capabilities to amplify the value of technology while remaining adaptive and agile through the change technology is creating. These skills are what help AI investments translate into sustained performance. In other words, imagine how chaotic and stressful it would be to guide your company through AI transformation without good leadership, communication, problem solving, and stakeholder management.

AI Literacy Is a Growing Need for Workforce Readiness

While human skills are essential for sustained growth and resilience, organizations are not slowing down on AI implementation. AI transformation is no longer optional, but where it often breaks down for organizations is on the human layer. Employees need to be able to use AI tools successfully and productively, otherwise the transformation stalls.

It’s all about workforce readiness, which is why hiring managers are already looking at AI literacy as a must-have. In fact, more than half of hiring managers say they would not hire someone without AI literacy skills. The expectation is clear: Employees need to understand how AI works, where it adds value, and how to use it responsibly in conjunction with their human expertise. Even when new employees bring the fresh AI skills, hiring for those skills only addresses the problem in the short-term. Focusing on upskilling your existing workforce is a safer long-term strategy for keeping pace with any unforeseen changes that may occur.

AI fluency has become foundational capability, and it’s one that must be constantly maintained. AI and other digital technology skills have about a 2-year shelf life, which means they expire almost twice as fast as traditional skills.

For businesses, the advantage of AI literacy comes from combining that fluency with skills like leadership, communication, and problem-solving to realize greater sustained value of transformation initiatives. That’s why forward-thinking professionals are leaning into both areas.

Popular Skills Across Industries

The top ten skills listed above are aggregated from generalized data across businesses, but every industry has its own set of essential emerging skills. There are varying human and technical skills that stand out as up-and-coming in each industry. 

According to our data, here are some of the skill trends across key industries:

• Financial services professionals are strengthening leadership and stakeholder management alongside analytics capabilities.
  
• Healthcare teams are focusing on inclusivity, collaboration, resilience, and data-informed decision-making.
  
• Manufacturing and energy organizations are prioritizing leadership, project execution, and change management, as automation reshapes operations.
  
• Professional services and IT show a blended profile, combining demand for programming and analytics with strong interest in project management, communication, and problem-solving.

Across the board, industries demonstrate the need for a combination of human skills and AI or tech skills. It’s the combined strength of both that sets businesses up for success.

Human Skills + AI Literacy Is a Business Power Move

In any industry, technical capability can enable efficiency, but human capability will determine impact. AI systems can generate recommendations, but great leaders guide teams to interpret, innovate, and execute on them. AI can process data at scale, but teams must decide what to do with the results. 

Leading organizations strengthen both dimensions. Adopting AI tools faster isn’t enough. Your organization needs experienced, knowledgeable employees who can guide AI transformation from the start and sustain ongoing transformation.

*Top skills are based on the number of learning pathways created in the Degreed platform specific to those skills in 2025.

The post 70% of the Top Skills for 2026 Are Human Skills 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.