For years, compliance ruled as a top corporate training priority.

Not anymore.

For the first time, upskilling and reskilling are ascendant, according to Fosway Group.

But even with that momentum, there remains a lot of head scratching happening among learning and business leaders over how to make skills work.

What does it mean for organizations trying to build agile, skills-first learning strategies?

“That’s still an ongoing conversation,” says Fiona Leteney, Senior Analyst at Fosway Group.

In the final episode of our three-part podcast series, Leteney and Degreed Co-CEO Max Wessel unpack the shift, explore the 2025 Fosway 9-Grid for Learning Systems research, and discuss how buyers can thoughtfully work with learning tech vendors.

Where Skills Strategy Breaks Down

While enthusiasm for skills is growing, execution remains complex. Organizations are grappling with granularity—”Are we managing 50 skills or 50,000?”—along with data exchange challenges and systems that don’t talk to each other.

Too often, talent and learning data are still siloed—leaving teams without a clear view of employee capabilities or opportunities to reskill for open roles. Forward-thinking business leaders understand that systems need to integrate and scale with minimal friction and quickly deliver measurable outcomes.

Leteney frames it like this: “Interoperability and the integration of systems within an ecosystem is one of the most important things. ”

Interoperability vs. Consolidation

In some cases, employees have to log into three separate platforms just to access one course. That’s inefficient and unsustainable.

The pressure to consolidate tech stacks and cut costs is real. But when decisions are made without input from learning teams, critical capabilities can get lost.

Advice for Learning Leaders

When asked how organizations can rise to the skills challenge, Leteney is clear: “Partner with your vendors… We saw in all the research, often it’s the partnerships where the value is, so that you can be part of the conversation as a buyer, you can influence the roadmap and you can influence the direction of travel—and also give the reality checks to the vendors.

“The devil’s always in the details, and every organization’s different, and it’s finding those vendors that you can actually have that conversation with.”

That kind of collaboration is exactly what Degreed was designed to support.

We power connected ecosystems that make skill development work—from granular data insights to automated and AI-enhanced experiences that adapt in real time.

Whether you’re launching a new strategy or rethinking your entire learning tech stack, Degreed can help you make it seamless—and scalable.

Let’s talk about how Degreed can help you future-proof your learning ecosystem.

The post New Podcast: Is Your Learning Tech Ready for the Skills Era? appeared first on Degreed.

As the gap between business needs and workforce capabilities persists, people need more control over how they develop. The knowledge they pursue needs to be hyper relevant.

Moving your people from Point A to Point Z in today’s world of work is more than a project. It’s bigger than a pilot. And that’s why workforce transformation is more than a buzzword.

We think about workforce transformation as changing your company’s structure and skills—to align your people with business strategy. Done well, it can have a profound effect on their ability to perform at their best.

The Importance of Shared Leadership

Like many other worthy business endeavors, workforce transformation requires collaboration among HR, Learning, and IT leadership.

Some organizations have created dedicated roles to lead the charge: Organization & Transformation Officer, Development & Change Management Lead, and Vice President of Workforce Planning, among other titles.

Innovative leaders know that transformation isn’t about repackaging L&D. It’s not about more training. It’s not even just about reskilling. 

Structuring for Change

To keep pace with shifting demands, successful organizations are rethinking how they build, organize, and support talent at scale. 

These organizations are:

• Establishing a language for skills that’s consistent and organization-wide.
• Identifying skill gaps through accurate, connected data.
• Helping employees adapt to new ways of working.
• Delivering personalized, business-aligned development at scale.
• Measuring whether skills are actually being built—and where.

These actions represent strategic changes that affect workforce planning, promote internal mobility, advance recruiting, and grow careers. They require clear ownership and coordinated execution.

The Critical Role of the C-Suite Stakeholder

At many companies, CHROs are defining workforce strategy. CLOs are making learning more relevant and aligned. CIOs are responsible for delivering infrastructure that supports flexibility, security, and interoperability.

None of this comes together in isolation. That’s why leading organizations are aligning their people systems and strategy around a shared foundation: skills.

Creating a Common Skills Language Across the Business

One of the biggest blockers to transformation is inconsistent skill data. Different systems define and track skills in different ways—or not at all. The result is duplicate effort and unclear decision-making.

Clarity is key. That’s why Degreed Skills+ eliminates roadblocks by integrating skill data from across your HRIS, LXP, LMS, and talent marketplace—and then cleaning, normalizing, and organizing it using AI. It  suggests and resolves synonyms, defines proficiency levels, and enables customization based on your unique organizational language.

With a shared skills framework, your cross-functional teams can finally work from the same data and unify how skills are defined and tracked—so you can identify gaps, measure progress, and plan with confidence.

Delivering Personalized, Scalable Growth

Once skill gaps are identified, the next big step is delivering meaningful development.

What makes learning meaningful? Relevance. With Degreed Learning, content, mentors, and pathways are tailored to each employee’s role, goals, and skill profile. Instead of navigating overwhelming content libraries, learners are guided toward what they need when they need it. Further personalizing the experience, Degreed Automations handles assignments, updates, and nudges automatically—triggered by employee actions or milestones.

And to make employee development even more adaptive, Degreed Maestro, our AI purpose-built for learning, uses role, context, and historical data to recommend content, coach employees, and assess progress. It reduces wasted time, boosts platform engagement, and improves learning retention.

Supporting Change as Your Strategy Unfolds

Transformation doesn’t end at go-live. As priorities evolve, systems need to support continuous adaptation.

As your organization adopts new technologies, embraces internal mobility, or rethinks how teams are structured, employees need to understand what’s changing—and how to keep up.

Degreed supports change readiness with features like skill-level descriptions, coaching, and role-specific Learning Plans and Pathways. Employees can see where they stand, what to focus on next, and how to grow into evolving roles.

What Leading Teams Are Doing Differently

What sets successful teams apart is not only vision, but also execution.

Forward-thinking organizations are: 

• Standardizing skill definitions across teams and systems.
• Prioritizing development aligned with business needs.
• Reducing friction in how people access and apply learning.
• Measuring progress in terms of actual skill growth.
• Using real-time data to make decisions about talent development and deployment.

A system like Degreed connects the dots across these workflows, reducing manual effort and creating visibility across functions—without adding complexity.

What comes next depends on the steps you take now.

Preparing for AI. Improving retention. Boosting employee performance. Whatever the goal, the pressure to adapt is no longer theoretical—it’s operational.

With tools like Degreed Skills+, Degreed Learning, and Degreed Maestro, you can define the skills that matter, develop them with purpose, and track progress with clarity. That’s how transformation becomes operational—and sustainable.

Learn more.

Get a clear picture of what’s coming from Degreed. Check out our seven-part Degreed in Action webinar series and choose your sessions to find out more about our innovations in AI, skills reporting, automations, Degreed Professional Services, Degreed Academies, and more.

The post What Workforce Transformation Actually Requires appeared first on Degreed.

But just 20% of leaders at enterprise companies say they have the talent they need to leverage generative AI.

Furthermore, AI threatens to displace droves of employees. Globally, 300 million full-time jobs could be exposed to automation, especially those in finance and banking, legal services, media and marketing, and IT.

If you want to fully leverage this developing technology—while reducing AI disruption across your organization—skill building will be critical. Savvy learning and business leaders are planning to not only upskill workers to use AI in their current roles—but also to reskill AI-displaced workers into new roles.

Reskilling for New Roles

AI is already displacing workers and will continue to do so. Tech giant Cisco laid off 5,500 employees in 2024 so it could invest more in AI. Meanwhile, IBM was expected to lay off or slow hiring for a whopping 26,000 roles—8,000 of them in the next five years—due to AI disruption.

But displacement doesn’t have to mean layoffs. Four in 10 surveyed leaders expect no change in headcount due to AI. They’ll need to reskill their employees to achieve that. By retaining most of a workforce, they’ll hold onto all the expertise in their organizations—like years on the job, company-specific knowledge, and rapport among workers.

Keeping Up with AI

You can reskill displaced workers into other roles, but you’ll need to start now to minimize layoffs. AI is advancing so quickly that workers need new skills now just to keep up. The good news is that skill building doesn’t have to take a lot of time. Just ask Verizon.

During the COVID-19 pandemic, Verizon closed 70% of its physical stores—while simultaneously needing far more telesales and remote customer service reps. Verizon retrained nearly 20,000 displaced workers—some of them completing just eight additional hours of training before starting their new roles. How was it possible? In-store workers already had plenty of customer service skills, brand knowledge, and product experience. 

If you see the threat of displacement as you forge into AI territory, look for adjacent roles for at-risk employees.

For example, if a new AI chatbot displaces some of your customer service staff, you could quickly reskill people into sales positions, because they already have high emotional intelligence, product expertise, and communication skills.

Staying Curious

Another key to the Verizon success was foresight. While leaders couldn’t have predicted the pandemic, they had already been experimenting with remote work. The company had piloted remote customer service and telesales programs for some employees. It had invested in the right technology and knew how to train people into those roles.

So when disruption came, it was just a matter of scaling those efforts.

While you can’t predict exactly how AI will disrupt your business, you can start building some tactical advantages now. Make small investments to explore AI uses so you’re ready to lean into them if it makes sense to later. Stay curious about AI even in its early stages, even if it’s not as capable as you’d like yet.

An impressive Ericsson success metric reveals the power of this path: Five years ago, the telecom giant began upskilling a relatively small group of 300 scientists to use artificial intelligence. Ericsson used Degreed to support its focus on 5G and AI technologies, emphasizing agility and ensuring employees had the necessary skills to meet evolving industry demands. Now, more than 30,000 of the company’s 100,000-plus employees are considered very proficient in AI.

Check out Degreed Experiments for some L&D-specific examples of ways you can start planning for—and start using—AI today.

Reskilling for AI Roles

While some workers will be displaced because of AI, others will need to leverage it to keep their companies competitive. Upskilling workers in their current roles is critical. You’ll also need to be ready to move staff into new AI-support roles. For example, you might need someone to track the value creation of your AI initiatives, validate AI-created content, or conduct internal audits to ensure regulatory compliance.

Here, too, there’s no time to waste. AI is developing so quickly that failing to keep up with the necessary skills could sandbag your investments.

Using AI to Teach AI

Luckily, AI is proving to be just as useful in L&D as it is elsewhere. Consider making your first AI investments in learning, so you lay the groundwork for developing an entire workforce of AI-savvy smarties. 

• Use generative AI tech to quickly create and market learning content throughout your company.
• Use automations like those offered by Degreed to quickly deliver the right content to the right people—and keep them engaged in learning.
• Embrace AI coaches like Degreed Maestro to pinpoint individual needs and personalize learning at scale.

Teach employees how to learn, not just what to learn, so they become skilled at reskilling themselves. They’ll become better at keeping up with the ways AI impacts their work than you are.

Planning for AI Disruption

Because AI promises to drastically change the way people work, it’ll also change the way people organize work. How do you structure new roles around the future of this technology? Right now, with AI disruption still so fresh, it’s nearly impossible. But you can find ways to build adaptability right into your talent.

Consider changes made and shared with us by the technology transformation experts at CI&T. Leaders there have democratized AI skills so workers find ways to use it in their roles. All employees can train to use its AI development tool (FLOW) to benefit from AI in their roles. And it’s working. Since its launch, 38% of employees use FLOW, 12% have been certified, and CI&T has successfully adopted it for 9 of 10 clients in 6 months.

Another way to get ready? Build in the ultimate adaptability by becoming a skills-first org (SFO). 

A skills-first organization prioritizes skills as the organizing unit for its workforce. It leverages skills data from platforms like Degreed to inform talent and management practices. These insights help leaders understand workforce capabilities, development needs, and deployment options.

Focusing on skills (rather than job titles or formal educational requirements) gives you a structure for homing in on the exact skills workers need, speeds them toward upskilling and readiness, and helps you flexibly organize workers to deploy projects as needs arise.

Making the shift to a skills-first strategy is at least as big of a change as adopting AI. That’s why we created The Ultimate Guide to a Skills-First Future. Download it today to start building the kind of adaptability you’ll need to help your workforce keep up with AI disruption.

Find out more.

Explore The Ultimate Guide to a Skills-First Future—to find out why skills-based strategies matter and how you can get started.

The post Catch Up and Keep Up: Skill Building Amid AI Disruption appeared first on Degreed.

With 98% of business executives planning to incorporate more skills-based approaches, aggregating, analyzing, and acting on essential skills and skill data is no longer an option for L&D, HR, Talent, and Change Management professionals.

Indeed, savvy organizations are already incorporating skills-based strategies, and they’re seeing impressive results. They’re 98% more likely to retain high performers, 57% more likely to be agile, and 107% more likely to place talent efficiently.

It’s time to put skills and skill data to work. 

Let’s face it: harnessing skills is complicated when you’re getting started. 

Luckily, Degreed has been working on this for years. Recognizing essential skills, making skills actionable, and driving impactful learning has underpinned the Degreed mission from Day One. For years, we’ve helped organizations simplify the process of identifying changes in their skill supplies. We’ve helped them scale learning through personalized development. And we’ve helped them measure change to show impact.

Degreed can help you too, by putting essential skills and skill data to work across your organization, so you can develop your workforce to be ready for anything.

Using Skills to… Pinpoint Needs

To get started, you need skill data. But obtaining data on your workforce is easier said than done. That’s why Degreed tackles this challenge in three ways.

1. Inferring Skills

Skills inference extracts information about your employees’ skills from existing sources like resumes and job descriptions, so you can quickly and easily gather data on the skills of your workforce. We offer skills inference in multiple aspects of our solution.

For example, when learners onboard with the Degreed LXP, we help populate their skill profiles by suggesting skills based on their job roles and resumes. And by using AI to automatically map skills to roles, we make it easy to get started identifying what skills you need for the roles at your organization. We’re even exploring new AI skills inference capabilities to discover hidden talent at your company.

2. Assessing Skills

It’s important to understand not only what skills your workforce has, but at what levels of expertise. That’s why we offer multiple methods to indicate proficiency levels, including Self Ratings, Manager Ratings, and Peer Ratings to provide a high fidelity view of your people’s skills. Adaptive questionnaires provide another method for validating your people’s skills and skill levels.

3. Managing Skill Data

To manage skill data, you need tools to analyze and understand your skill supply, fill data gaps, manage your taxonomy, and normalize skill data and labels from across your ecosystem. Degreed Skills does exactly that.

Degreed enables skill scale normalization. A skill scale measures skill proficiencies, typically using a numeric range. With Degreed you can normalize inconsistent scales from across your learning and HR tech stack—so you can measure all skills against the same rubric: yours. 

Let’s say your organization wants to measure skill proficiency using a 4-point scale, but your HCM uses a 5-point scale and your LXP an 8-point scale. Degreed normalizes those differences from across your tech stack, so you can see all your skill data in your preferred scale.

Similarly, the tools across your tech stack likely use different skill labels. For example, your HCM may use the word “Innovate” as a skill label, but your LXP may use “Innovative” as the label for the same skill. With Degreed, you can normalize skill labels so they’re consistent. In addition, Degreed removes duplicates, misspellings, and synonyms and accounts for multiple languages. 

And while AI makes recommendations for normalization, with Degreed you’re ultimately in the driver’s seat. You can override AI recommendations, meaning you have more control over your data.

In addition, our taxonomy management features empower you to create and leverage a bespoke taxonomy that meets your organization’s needs.

Using Skills to… Personalize Development

Once you have skill data, it’s time to put that data into action to drive better outcomes. But how do you use skill data to better personalize learning?

Degreed helps you. . . 

Orchestrate learning using skill data. 

To orchestrate learning, you need to deliver the right learning to the right people at the right time. Degreed empowers this in many ways.

With Degreed, you can create segments of learners based on many factors—including their skills and skill levels—and then automatically assign learning or send messages and nudges to those segments. 

For example, you can create a segment of everyone at your organization with a skill level 1 in Artificial Intelligence and then assign everyone in that segment foundational learning that is essential to leveraging AI at your business. You can even use Automations to send nudges that remind learners to complete that content.

Degreed also helps you. . . 

Connect learners based on based skills.

Research shows that 75% of people prefer learning with others over learning by themselves. Harnessing skill data is a perfect way for you to facilitate that colleague-to-colleague learning collaboration.

In Degreed, manager dashboards show people leaders data about what skills their team members have, enabling those managers to optimize team strengths and coach individuals on where to focus for maximum growth. With Degreed, you can also use skill data to automatically recommend mentors to individual learners based on the skills learners want to grow. 

And Degreed helps you. . . 

Curate experiences for skill growth.

High-quality curation incorporates skill data, to ensure people receive the right learning and experiences they need to grow critical skills at speed and scale. 

In Degreed, you can curate Plans and Pathways to target the growth of skills important to your organization’s success. In addition, you can create Academies that provide your people with cohort-based learning opportunities for deep skill building. 

In addition, Degreed helps your people to. . .  

Discover resources that are personalized according to an individual’s skill profile.

Learning is impactful when it’s relevant to the learner, which is why it’s so important to help your employees discover the right content for their needs and goals.

When learners search for learning resources inside the Degreed LXP, our AI surfaces personalized, relevant content and experience recommendations based on the individual’s skills, goals, and experiences. 

Using Skills to… Measure Change

Once you’ve accomplished skill growth, it’s important to show off your results. With Degreed, you can measure skill growth to demonstrate the impact of learning across your business. 

Degreed skill analytics provide powerful visualizations that simplify complex skill data—so you can uncover the impact of learning on skill growth, identify supply and demand of skills across your organization, and make smarter investments in your people. 

All employees using Degreed have a skill profile where they list their current skills, they select focus skills, they obtain skill ratings, and they track experiences and content tied to their skill development. Not only do these profiles help drive a personalized learning experience, they also allow individuals to track their development and growth. 

But measuring change shouldn’t happen in a silo. Combining data from across your Learning and HR ecosystem provides a more holistic view of change and helps you capitalize on it, for example by identifying talent for projects and roles. That’s why interoperability achieved through integrations like our bi-directional skills sync with Workday is so important to Degreed. It allows distinct systems to easily share information and work together, and it reduces the manual work of combining data from multiple tools.

What This All Means for Your Business

Pinpointing needs, personalizing development, and measuring change are great, but why do they matter?

These actions matter because they unlock your ability to develop a workforce that’s ready for anything. From market shifts and competitive threats to economic shocks, there’s no telling what the future holds. That’s why it’s so important to develop an agile workforce and put mechanisms (like efficient, personalized learning) into place to facilitate agility and allow your people and your business to respond effectively to changes and challenges.  

This is how you transform the people you have into the employees your business needs. 

Find out more.

Start using skills to transform your workforce.

Leading organizations around the globe use Degreed to prepare for the future, pinpoint their needs, personalize development, and measure change.

Ready to learn more about how Degreed can help you know, learn, and grow the skills your business needs? Get a Degreed demo today.

The post Knowing and Growing the Essential Skills Your Business Needs appeared first on Degreed.

Well, learning a skill so deeply that it never leaves you is a complex process—and finding ways to teach skills deeply at scale is even harder. But that’s exactly what L&D professionals have been trying to do for decades.

It’s what Charles Jennings wanted to accomplish in the 1990s when he created the 70-20-10 learning model based on how he’d observed people learned: with a ratio of around 70% experiential learning, 20% social learning, and 10% formal training. Since then, countless organizations have divvied up their L&D resources according to this ratio.

More recently, industry analyst Josh Bersin, founder and CEO of the Josh Bersin Company, has advocated for an academy-based approach. He’s called academies the future of learning at work, and we agree with him. But that doesn’t mean that the 70-20-10 model (or a host of other approaches, for that matter) have to be left in the dust. In fact, you can build learning academies that align with 70-20-10 — and include other tactics that enhance learning programs even further like contextual learning, cognitive wobble, interleaving and spacing, and capstone projects.

What are learning academies?

Bersin describes the modern learning academy as a “place to learn,” where numerous learning resources and modalities come together. A learning academy is a skill-based experience that hones in on relevant, focused, and personalized learning at scale. An academy focuses on driving mastery and confidence—two things that helped me learn to ride a bike as a kid.

If that sounds open-ended, you’re right. Academies are flexible and can align with your business goals, whether that’s boosting sales, keeping up with rapidly evolving technologies, or developing skilled leaders.

70-20-10: Thinking Outside the Box

Unlike many learning opportunities, academies don’t have to fit into one of Jennings’ three 70-20-10 pillars. Instead, think of the academy as a box—a container large enough to hold all three pillars.

A holistic 70-20-10-based academy could include:

• Formal training: Regular lessons that form the knowledge base of the learning experience.
• Social learning: Cohorts that group employees of similar skill levels so they can support each other, share ideas, and give feedback.
• Experiential learning: Hands-on projects and assignments that translate knowledge into practical job skills.

Beyond 70-20-10: Content vs. Context

Imagine you’re a cycling coach and you want to teach cycling at scale in an academy setting. You might start by showing learners the basics of how the bike works, where the brakes are, and how to balance on two wheels. You might include some YouTube videos, a labeled diagram of a bicycle and its parts, and a before-you-ride checklist. This is content, and it’s critical for imparting knowledge.

But students can’t learn to ride a bike from YouTube. They need context to apply skills on the open road. They have to feel how to balance. They have to pull back on the brakes to get a sense of how the bike stops. And they have to topple over a few times to know how to take a turn.  Context comes from applying knowledge. Doing, rather than knowing.

Incorporating a range of modalities, an academy can deliver context from multiple sources, including:

• Peers: Discussion prompts inspire employees to share their experiences.
• Instructors: Live events create real-time interaction among workers.
• Work: Projects require practicing skills in hypothetical situations.
• Industry expertise: External certifications or courses make real-world applications clear.

As you can see, context can also align with the two larger pillars of the 70-20-10 framework — experiential and social learning — and tip the balance in their direction.

Cognitive Wobble: Right-sizing Challenges

Cycling primarily builds quad, glute, and calf muscles, while academies typically build mental muscles. Strengthening physical and mental muscles requires struggle. Right-sizing that struggle in ways that don’t overwhelm learners is critical to their growth.

James Nottingham, best-selling author of Challenging Learning, describes his approach to teaching people: “Instead of giving them clarity, [my purpose is] creating confusion, or cognitive wobble. Like when you are learning to ride a bike and it wobbles—I am trying to create that mental wobble so they have to think about it more.”

Although Nottingham applies his strategies to child education, cognitive wobble can maximize adult learning too. Nursing students who participated in productive failure—or trying to solve a problem before being taught how—reported that the struggle helped them learn.

The key is finding the sweet spot between challenging and overwhelming. You wouldn’t send a novice cyclist down a steep mountain trail, nor would you give a world champion training wheels. Nottingham identifies three mental states shared by learners. People learning are: relatively comfortable, relatively uncomfortable, or panicked. Academies can help you make learners relatively uncomfortable. How? You can admit them at any level and personalize their learning journeys.

Applying the 70-20-10 Model:
Activities that initially encourage failure bring in experiential and social learning before formal training occurs — giving you more opportunities to bolster learning that's hands-on and collaborative.

Interleaving, Spacing, and Retrieval 

Whatever level your cycling students start at, you wouldn’t teach just one concept at a time, nor would you only train on Saturdays. You’d have them train daily, and you’d alternate between activities. Leg days might be Mondays and Thursdays. Core days might be Tuesdays and Fridays. On Wednesdays, you might switch it up completely and focus on bicycle mechanics.

Academies allow you to do the same for employees. They interleave multiple topics so people learn several topics at once. Academies also space out training instead of cramming it all into a weekend seminar. Interleaving and spacing make learners switch mental gears between lessons — which over time builds better recall.

Applying the 70-20-10 Model:
Learning programs that pigeon-hole modalities into formal, social, or experiential learning have limited ability to interleave and space out learning. This is because learners tend to tick off one modality at a time, which limits how much they switch between topics or allow their minds to consolidate between learning sessions.

Capstone Projects: Putting it All Together

A capstone project is like a bicycle race. Riders enter the event and finally get a chance to fully showcase their skills. And while those participants might not be ready for the Tour de France, a local race might teach them to pace themselves, share the road, and safely push themselves to compete. In this sense, a capstone project can help to provide hands-on experience and round out an academy pathway.

Capstone projects check mastery while pushing learners to achieve something. For the cyclist, it might be finishing a race. For an employee, it could be creating a deliverable.

The best capstone projects:

• Align projects with business goals
• Incorporate hands-on (experiential) learning
• Involve a team to complete the objective (social learning)
• Include a presentation and feedback

Capstone projects can vary widely depending on the goals of your academy. Examples include:

• Research reports: Learners pose a business question that requires crunching data or interviewing customers. They design a database query or study to generate insights and then write up the results.
• Project proposals: Employees think through a project from a fresh perspective, then they pitch it to leadership as an innovative idea.
• Internships and apprenticeships: Employees produce deliverables while learning from teammates from another department. Deploying an opportunity marketplace allows companies to offer this capstone experience at scale.
• Group projects: Employees identify a business problem, plan and execute a solution, then present results.

Are learning academies worth it?

If your company follows the 70-20-10 framework, you can build compatible learning academies that teach employees deep, unforgettable skills. This kind of learning can reskill employees worried about losing their jobs to technology. It can train talent that in turn fuels sprawling business expansions. It can help leaders promote from within and avoid hiring externally.

Academies have long been an ideal way to train employees at scale, but building them used to be too expensive for most organizations. The good news is digital products like Degreed Academies are designed to help you plan and deliver the targeted skill development programs your organization needs at a lower cost than building them on your own. They also help you measure outcomes that align with key business objectives, so your leadership can see the value learning programs provide to your organization. 

Ready to find out more?

Get a demo of Degreed Academies today.

The post Like Riding a Bike: Going Beyond the 70-20-10 Learning Model appeared first on Degreed.

Just like Mike, everyday workers need to build skills with practice. Skills are needed to perform well and can help lay out a path forward to the next role. Skills empower workers to achieve results and build experience. 

Skills are today’s currency of work. Your people and their skills are your organization’s biggest assets. Assessing, growing, measuring and cultivating skills enables your talent and your company to succeed.

A skills taxonomy can help you make sense of what your people can offer as you work toward achieving business goals. 

A skills taxonomy is:

A hierarchical system of classification that can categorize and organize skills in groups or “skill clusters.” A skill taxonomy is very structured and will usually include the skills that are most important to business goals, sometimes with the skills’ definitions as well.

A skills taxonomy gives much needed structure to a company’s abilities to assess, grow, measure and cultivate key skills that translate to business results.

Skills vs. Competencies 

When you’re creating a skills taxonomy, it’s important to distinguish between skills and competencies.

A competency is “knowledge, behaviors, attitudes and even skills that lead to the ability to do something successfully or efficiently.”

A skill, on the other hand, is “learned and applied abilities that use one’s knowledge effectively in execution or performance.”

Skills are the components that go into building a competency. For example, the ability to communicate effectively is a competency. Skills that help you communicate effectively include writing concisely, speaking confidently and crafting informative, easy-to-read documents.

Going back to our basketball analogy, a player needs to know how to make three-point shots, layups, free throws, jumpers, fades and step backs. Each type of shot requires different skills. These skill sets are developed through practice, and the team must know where each player’s strengths are in order to make plays and score. One team member might have a knack for setting up a pick and roll, another might be especially good at getting fouls called to help rack up points. Knowing these skills and competencies helps develop crucial team strategies and helps players and coaches make decisions. A team’s ability to insert shooting competencies into the right structure and format contributes to scoring enough points to win.

Skill Taxonomies vs. Competency Models

Skill gaps are widening across industries, and traditional talent models like competency models, aren’t sufficient to help plug those gaps.

Those competency models are too complex and static, and they’re never used the way L&D leaders hoped, because they become quickly outdated. Contrast that with skill taxonomies that focus more on what people can actually do for a particular job. They’re dynamic and constantly updated as new skills emerge and others fade.

When your company builds and uses a skills taxonomy that reverberates with workforce strengths, it can upskill or reskill talent in key areas that need improvement. Aim. Shoot. Score!

Supporting Your People

A taxonomy-based skills strategy can help you differentiate your company when employees are evaluating the next steps of their careers. In today’s competitive job market, retaining talent can cut hiring costs and build a culture where employees see how they can grow and develop internally. A skills taxonomy — in a cohesive way — shows employees how they can demonstrate skills and competencies.

If employees have a clear direction, they can build skills and competencies to perform better in their current roles and obtain future roles. While the line from skill building to promotion isn’t always crystal clear, people certainly benefit from having a rubric for what to work on.

Having a good understanding of skills taxonomies can equip you to help develop upskilling and reskilling programs for your employees. 

Advancing L&D

By partnering with business units to evaluate key skills needed in each function, L&D can play a pivotal role in targeting relevant learning opportunities that help employees build skills and competencies. 

Depending on the companies’ tech stack, some HRIS (Human Resources information systems) feature skills taxonomies and can track and measure skills across the organization. 

Having a system and the data to map out where skills gaps occur, developing programs to close them, and building on current skills and competencies is a competitive advantage. 

A skills-based approach can future-proof your company, even if every one of your employees isn’t an A-Team all star. A skills taxonomy can provide your employees with the structure, clarity and paths needed for them to grow at your organization.

The post What Is a Skills Taxonomy? And Why Is Your Competency Model Obsolete? appeared first on Degreed.

The most innovative L&D teams use skill data to create personalized learning experiences, because understanding what skills people have and want makes for better, more relevant content recommendations, mentorships and on-the-job experiential learning. At a more macro level, learning teams use skill data to understand the supply and demand for skills across their organizations.

The more skill data your open ecosystem generates, the more opportunity there is to make smart, informed talent decisions.

And this is why we’re so excited about our new partnership with Workday. Degreed is now a Select Partner in the Workday Software Alliance Program — enabling people to have more relevant learning opportunities and deeper skill profiles and learning teams to understand workforce skill supply and demand better.

Via a bi-directional information exchange, skill data from Workday HCM, Workday Learning and Workday Skills Cloud will be pulled into the Degreed platform (and vice versa), complementing the Degreed skills data and taxonomy. More specifically, the two-way data exchange will influence Degreed user profiles, recommendations, opportunity matching, Skill Coach, reporting and every other Degreed function that uses skills natively. Profiles will be the most noticeably impacted area, with user skills being added and updated.

The ultimate goal is to make the lives of learning and people leaders easier.

“The Workday and Degreed partnership will bring richer skills insights to the market with customers getting the best of Workday data plus Degreed,” said Tiffany LaBare, Director of Strategic Partnerships at Degreed. “Given that over 7 million individuals rely on the Degreed learning platform to continuously improve, there is a constant stream of skills data being generated that complements the data collected by Workday. This ensures that learning and people leaders can capture all forms of skill data and make it actionable in areas like learning, productivity, performance management, and talent planning.”

Meeting and Exceeding Business Goals

Senior executives and CEOs want their teams to lead with advanced analytics, according to Deloitte. “The amount of data available to organizations every day continues to proliferate at a staggering volume… But not every organization is optimizing the opportunities available.”

In that same study, organizations that reported having the strongest cultural orientation to data-driven insights and decision-making were twice as likely to have reported exceeding business goals in the prior 12 months. Findings from the Degreed report State of Skills 2021 show advanced data analytics rank among the top ten in-demand skills among HR professionals.

More Really Is Better

This means more data points, and it also means a more diverse set of data points. Diverse data sets can ensure you’re getting the most accurate and comprehensive view of the skills your organization has and those it needs.

Companies that take a wide-angle approach to data collection — looking at inputs from a diverse range of sources and systems — cultivate what’s called a “signal advantage.” The result is a more expansive view of patterns, risks and opportunities to act on. A signal advantage allows you to build alerts around big shifts in your skill data to keep you ahead of change.

Skill Data Doesn’t Have to be Overwhelming

If you want your organization to create real business value by connecting workforce learning to more holistic — and more helpful — information, it’s time to embrace an advanced skill data strategy. Whether you take it fast or slow, understanding what skill data is, where it comes from, and how to get it in abundance can open up a world of new information to your organization. Our new partnership with Workday is designed to help open up that world too. 

To further understand skill data, download The Ultimate Skill Data Handbook from Degreed. Or contact Degreed to discuss skill data with us in more detail. We’d love to answer your questions.

With skill data, you can make informed decisions and support dynamic talent initiatives that personalize learning, engage your people, provide new career opportunities and help meet your most important business goals.

The post New Degreed & Workday Partnership: More Skill Data for Smarter L&D appeared first on Degreed.

The benefits of upskilling are huge. PwC found that successful skill-building programs lead to stronger culture, higher productivity, and accelerated innovation. Effective upskilling also makes it easier to attract and retain talent.

The perils of ignoring a skill strategy are notable, too. CEOs struggling with the skills gap are worried about missed market opportunities, rising talent costs, stunted growth, and a lack of innovation. They also expect quality standards and customer experience to suffer.

Mastercard has proven how effective upskilling pays off. Facing fintech startups in the wake of the recession, CEO Ajay Banga insisted that this payments company would become a technology company. Mastercard’s L&D team responded by revolutionizing its digital offerings, especially for the crucial Operations & Technology division. From 2016 to 2019, employee adoption of digital learning climbed steadily to 96% and the average number of learning platform logins per employee tripled.

In that same timespan, Mastercard’s stock price tripled too, as the company broke through in key areas like mobile payments and cybersecurity. Was the stock surge just a coincidence? The learning leadership doesn’t think so. “That represents Mastercard having the right skills and people to explore new payment technologies,” declared Steve Boucher, the VP of Global Talent Development.

Time to Shift the Paradigm

If the benefits of upskilling are so obvious, why aren’t most companies making progress? The problem is the paradigm. For too long, upskilling has focused on the company, not the individual.

The old models of work typically centered on the company. Leaders relied on a “command and control” management style, forcing employees into “one size fits all” training programs. Companies wanted to dictate when, how, and what people learned and tried to tie a predictable return on investment (ROI) calculation based primarily on employee participation. Decades of research have shown how ineffective this approach has been.

These days, leading analysts like Deloitte’s Center for the Edge are calling for a new model of work that puts individuals at the center. As artificial intelligence and machine learning make uniquely human capabilities more valuable, companies can no longer treat people like interchangeable parts. Workers thrive with variety, fluidity, and autonomy.

Focusing on individuals will transform upskilling efforts. Employees can direct their own development, recognizing and addressing personal skill gaps. Employers can get real-time data and insights about people’s skills and can make it easy for people to find projects and positions that match emerging skills, rewarding the workers who learn and grow within the company. In this paradigm, people can easily see the link between their learning and the skills they are building for their careers creating a more engaged, purposeful, and impactful workforce.

Align Individual Experience with Company Value

When organizations are effective with upskilling, the approach aligns the individual experience with value for the company.

The individual upskilling experience starts with each employee’s aspirations. Once it’s clear what people want, you can start conversations about the skills they’ll need to get there. Personalized learning platforms like Degreed make it easy to deliver content to close their skill gaps by leveraging data generated by its users. Then, individuals can put new skills into practice with stretch assignments, become more well-rounded as workers, and even take on entirely new roles within the company.

Focusing on individuals also creates value for companies. Affirming employees’ career goals helps establish trust so you can have an open conversation about strengths and weaknesses.

As workers learn on digital platforms like Degreed and strategic learning programs from your company, leaders can track progress via data dashboards and use the insights to inform supportive check-ins. The end result is a win-win: internal mobility rewards loyal workers and is often far more efficient than hiring outside talent.

Five Changes to Make in Your Organization

Alright, let’s get into the details. What specific shifts will support an effective upskilling strategy focused on individuals? To get started, try these five adjustments in the following key areas:

1. Recruiting: from external talent to internal talent.

External hires are costly and risky. But internal mobility can improve retention and accelerate the learning process. Deloitte found that 76% of top talent acquisition teams look to hire internally, compared to just 17% of low-performing teams. 

2. Capacities: from static competencies to dynamic skills.

Many people don’t know the difference between competencies and skills. Competencies usually include attitudes and behaviors. Skills, on the other hand, reflect transferrable expertise. Competency models were designed to keep people in one role, but shifting to a skills strategy can enable much greater flexibility for individuals and organizations.

3. Performance management: from top-down evaluation to employee experience feedback.

Managers are key to helping their employees build skills and progress in their careers. This all starts with having a career conversation. In addition, when employees know what’s expected of them, when they can use their strengths every day at work, and when they are recognized and rewarded for the great work they do, it’s an all-around win. Finally, managers should help employees find projects and stretch assignments so people can actually apply the skills they’ve learned. This may even lead to a completely new role inside the company. The role of managers in effectively upskilling the workforce is to focus on being “career coaches” and helping people grow and thrive in the company. Yet, many managers are not tuned in to their team’s development. To compensate, I recommend asking for feedback on employees’ experiences. Do they have clear expectations? Are they using their strengths and earning recognition? Can they find opportunities to grow?

4. L&D: from one size fits all training to a continuous skill strategy.

For generations, companies trained large groups on discrete objectives. Teams gathered to learn a management technique or the latest software program. But this approach could never keep pace with digital disruption. Now top firms like Unilever are using digital tools to create a lifelong learning ecosystem. Workers are always exploring and building the emerging skills that they’ll need for their unique journeys.

5. Career development: from high-potential programs to building skills for everyone.

In the days of pricey in-person training, many employers treated career development as a perk for “high-potential” employees. This exclusionary approach had predictable downsides, making participants more entitled and alienating those who didn’t get invited. As online platforms drive down the cost of upskilling at scale, it’s time to get more inclusive. Looking for a model? One major bank pioneered a genius “funnel” approach that lets people prove their own potential.

Time to Take the Next Steps

If you’re ready to get started, check out 7 Steps for Upskilling Your Workforce for a clear framework on how you can build an effective upskilling strategy.

Skills are the key to the future. The only question is which organizations will be effective with upskilling. Now is the time for a strategy that elevates the individuals who make your organization great.

The post Now It’s Personal: Effective Upskilling Addresses the Individual appeared first on Degreed.

Earlier this year, we started our inquiry into a fundamental question:  

What skills contribute to DEIB (diversity, equity, inclusion, and belonging), specifically fostering diversity, creating equity, enabling people to feel included, and building a culture of belonging in the workplace? 

We’re now about 70+ articles, 20+ interviews, and 2 roundtables with ~50 people each into this project, so we thought it worthwhile to pull up and summarize what we’ve learned so far. Here are four insights we’ve identified to date: 

• The roles of senior leaders, managers, and employees differ 
• Lots of skills appear to be important 
• Skills increase by level 
• Same skills, different context 

The roles of senior leaders, managers, & employees differ  

We asked a wide range of folks about the specific roles and responsibilities that different employees have in fostering a culture of DEIB—focusing specifically on how those roles and responsibilities vary by level.  

Senior Leaders 

So far, we’ve consistently heard that the role of senior leaders is to set the tone and reinforce appropriate skills and behaviors. Some of the specific responsibilities include the following:  

• Champion, vocally support, endorse, and promote DEIB efforts 
• Drive the agenda for culture change, set goals, and create accountability 
• Develop policies, procedures, and practices by seeking input from a diverse group of employees to build structures for DEIB culture 
• Model the behaviors of the DEIB culture and foster an environment in which people feel safe 
• Challenge organizational / systemic / policy disparities 
• Evaluate DEIB initiatives and change programs periodically to assess their effectiveness 

Managers 

Managers, by contrast, are responsible for creating the conditions that allow a culture of DEIB to thrive. Some of the specific responsibilities in doing that include:  

• Create psychological safety within their teams that’s required for DEIB to be a reality 
• Set clear expectations for employees and hold them accountable 
• Model appropriate behaviors for employees 
• Foster an inclusive workplace by raising awareness for the needs of team members, ensuring equitable practices and development of their teams 
• Proactively seek out different perspectives, understand people’s challenges, and find solutions with their interests in mind 

Employees 

As you might expect, employees are generally expected to focus on activities within their control, such as improving themselves and engaging in appropriate behaviors. But, interestingly, even though we’ve heard about the power of grassroots efforts with DEIB, starting or engaging in those efforts isn’t an explicit expectation we’ve heard anyone mention.  

Here are some of the specific responsibilities we’ve heard: 

• Identify opportunities to learn about DEIB and improve their level of understanding 
• Engage and participate in DEIB initiatives at the workplace 
• Provide honest and useful feedback about DEIB initiatives 
• Proactively take initiative to advance DEIB (e.g., improving DEIB communication, avoiding microaggressions, and showing empathy) 
• Feel safe in exhibiting vulnerability in how they show up in the workplace 

Lots of skills appear to be important 

After establishing the DEIB roles/responsibilities of employees at different levels, we then asked folks to identify the skills that these different groups need to fulfill those responsibilities. As you might expect, this exercise generated a LONG list of skills—at one point, we had more than 75 discrete skills identified as critical to creating a culture of DEIB!  

Which skills have been mentioned most frequently? They include: 

• Communication skills (including listening, storytelling, nonverbal communication, etc.) 
• Empathy 
• Giving feedback 
• Flexibility 
• Self-awareness 

But there are a lot more than that. We will not share the comprehensive list because we will be testing that list in our upcoming survey. And we don’t want to bias you too much before you take our survey on this topic, which you can take RIGHT HERE. (Sneaky how I did that, wasn’t it?!) 

This exercise, though, has generated 2 primary insights:  

1. The issue of whether something is a skill or competency seems to really trip people up. Based on our previous research, Skills vs. Competencies, we know a lot of folks struggle to articulate the difference between a skill, a competency, a behavior, and a trait. So we addressed this issue in that report, saying it doesn’t really matter as long as everyone in your org knows what you’re talking about.  

However, for this study, we’re finding that people haven’t thought about the basic building blocks for creating a culture of DEIB. Instead, they’ve focused on more abstract competencies (e.g., inclusive leadership) or outcomes (e.g., everyone feels included). Therefore, when we ask them to identify the skills to create that culture of DEIB, they struggle to answer it succinctly.  

2. There’s no real clarity on which skills are most critical. While this is a core reason we started this research, the breadth of perspectives on critical skills for DEIB is remarkable. This could be due to: 

• Unique org-specific factors that influence DEIB skills (e.g., culture, leader type, individuals’ perceptions) 
• A lack of deep thought about the skills that drive DEIB 
• A challenge in separating DEIB skills and knowledge 
• Or some other factor 

We’re continuing to explore this subject through our survey.  

Skills increase by level 

When we began this research, we saw several skills frameworks implying that the DEIB-related skill sets of employees, managers, and senior leaders may somewhat overlap, but are largely discrete, such as shown below: 

However, this was not reaffirmed by our interviews. Instead, we consistently heard from folks that DEIB skills build by level—and rarely are any skills subtracted. In other words, the skills sets are additive, whereby managers need more skills than employees, and senior leaders more skills than managers. We’ve illustrated this concept in this graphic below: 

The idea of additive skills is incredibly helpful, because it can influence how we construct expectations of employees by level and how we teach these skills.  

Same skills, different contexts 

We’ve also consistently heard that DEIB skills shouldn’t be taught separately from other leadership skills—but that’s exactly how they’ve been taught for decades in many orgs. Some of the reasons we heard for this contradiction include: 

• All DEIB-related training was done by groups outside the learning or leadership function (i.e., provided by a centralized D&I team or employee resource groups) 
• The learning or leadership development team’s lack of knowledge about relevant DEIB-specific contexts to build into existing leadership training 
• The lack of a mandate for learning or leadership development teams to integrate DEIB-specific content into existing leadership training 

We heard loud and clear that this approach needs to stop—as it makes DEIB “another” thing that people must do. Instead, leaders should be integrating DEIB contexts into existing leadership skills trainings, which will then normalize the everyday use of skills that help create a culture of DEIB.  

What happens next?  

Well, there you have it: 4 initial findings. As mentioned above, this blog is a progress update—not a final report—on what we’ve seen to date, so these are not our final findings. However, we like to “think out loud” with our research process and share where we are at the moment.  

The next step in our process is to get quantitative data to understand this topic at a larger scale. We have conducted a survey, with anyone employed at an org with more than 100 people eligible. Now that the survey is closed, we’ll analyze the data, conduct some additional final interviews, and publish our final report in September.  

We’re looking forward to unveiling our conclusive research this fall!

The post DEIB & Skills: What We’ve Learned So Far appeared first on Degreed.

This blog will expand beyond the basics into the different methods of structuring your organization’s skill data. There are many overlapping (or even contradictory) ideas in the market about what it means to have a skill strategy and how that relates to a skill taxonomy or ontology. 

These ideas aren’t inherently simple, but they don’t need to be overly complicated. That’s why we’re breaking them all down for you below.

Organizing Skill Data

Skill Strategy

Definition: A strategy for talent development that prioritizes skills as a way to measure the ability of your people. This measurement is then aligned with the work that your organization needs to get done and the career opportunities that exist internally. Skill strategies can vary greatly between companies and can use any combination of upskilling technology, skill taxonomies, skill ontologies, skill clouds, or none of those.

Why it matters: Using a skill strategy as opposed to a competency model (or in tandem with one) can help make your workforce more agile and enable opportunities for internal mobility and career growth.

Skill Taxonomy

Definition: A hierarchical system of classification that can categorize and organize skills in groups or “skill clusters.” A skill taxonomy is very structured and will usually include the skills that are most important to business goals, sometimes with the skills’ definitions as well.

Why it matters: This can help workers understand which skills they have from the taxonomy, how they relate to organizational needs, and what they should learn next. It can also help show what skills are included in various categories of skills. The purpose of the framework is not to capture every skill, but rather to capture information about the most essential skills that are relevant to your business strategy.

Skill Ontology

Definition: A set of skills and their relationships between one another.

Why it matters: A skill ontology allows organizations to define and measure relationships between skills (and even jobs and people). It helps create a common language and understanding of skills across a variety of different dimensions or platforms. For example, across individuals, teams, and companies, the definition and terminology used to describe a UX designer will vary. A skill ontology is able to aggregate all of that data and recognize that different systems are talking about the same entities and build relationships between them. Another way of looking at an ontology is that it is a “smart system” that helps maintain, aggregate, and simplify the skill data within a taxonomy.

Skill Graph

Definition: A skill graph shows the relationships between other skills and determines how skills map to roles, content, and other skill-related features. It’s often simply a visual representation of a skills ontology. 

Why it matters: Understanding how different skills are related to one another (and how closely related) can inform how artificial intelligence and models offer upskilling and mobility opportunities. For more on skill models, check out Part 1 of our dictionary.

Skill Cloud (Also called a Skill Inventory or Skill Registry)

Definition: An inventory of skills across organizations that includes all known skill terms. It is the data set that is used to evaluate skills to include in organizational skill lists, ontologies or taxonomies. It is basically a single source of truth for any skill, but it does not order or categorize skills like a taxonomy does.

Why it matters: A skill cloud helps organize and standardize skills across an organization, but a skill cloud alone does not make these skills actionable. They simply sit in the cloud.

Skills I/O

Definition: Degreed’s Skills I/O (which stands for input/output) is able to manage skills, skill data, and the structures mentioned above. You can use the Skills I/O to build taxonomies, manage multiple skill sources, integrate different taxonomies, and edit the skills in your organization. 

Why it matters: Whereas taxonomies, ontologies, and graphs help us understand skills in relation to our business objectives, but our Skills I/O is able to put those concepts into practice together.      

For more skill data definitions, keep an eye out for our final installment of the Skill Data Dictionary and download our Ultimate Skill Data Handbook today!

The post Skill Data Dictionary Part 2: Organization appeared first on Degreed.