In partnership with

⬇️ Inside this issue:

• How higher education is helping schools and employers speak the same skills language

• Bruce asks: Are durable skills really new, or just a new name for something familiar?

• Why Wall Street is putting a price tag on AI skills

INTERESTING READS

🎯 States are embracing skills based hiring, but how do we know if it’s actually working?

🎓 College transcripts may be due for an upgrade, and LERs could be the answer.

🤖 From CHRO to Chief Work Officer, AI is changing what it means to lead HR.

📊 11 million U.S. workers may need to switch careers over the next decade.

EDUCATION

What are skills ontologies and why should we care?

There's a word popping up more frequently in conversations about digital credentials, skills-based hiring, and the future of education: ontologies.

Sounds complicated, right? But behind the fancy terminology is a surprisingly simple idea: What if schools, employers, and technology platforms could all understand skills in the same way?

A college might teach "professional communication," while an employer is looking for "stakeholder engagement." A training program might call something similar "business communication." These are different descriptions of potentially overlapping abilities, but how does an employer know they're related? And how can someone prove they have the right skills when everyone uses different language?

That's where skills ontologies come in, and higher education is helping show us why they matter.

So, what exactly is an ontology?

According to the World Wide Web Consortium (W3C), an ontology helps define concepts and the relationships between them so computers can understand and use that information. In the skills world, think of it like a family tree connecting abilities such as project management, budgeting, communication, and problem-solving. By showing how these skills relate, ontologies can help schools and employers recognize someone's capabilities, even when they use different words to describe them.

Higher education is helping connect the dots

Colleges and universities are in an interesting position because they're responsible for teaching skills, assessing them, and helping students demonstrate what they've learned. But traditional transcripts don't always tell the whole story.

Imagine a student who organizes a university fundraiser. Along the way, they manage a budget, coordinate volunteers, communicate with sponsors, and solve problems. Those are valuable workplace skills, but they aren't necessarily reflected in a list of courses and grades.

That's one reason institutions are exploring Learning and Employment Records (LERs), digital credentials, and better ways of describing what students actually know and can do.

One tool supporting this work is Credential Engine's Credential Transparency Description Language (CTDL). Yes, another acronym. Bear with us.

CTDL helps organizations describe credentials, programs, and skills consistently so different systems can understand the information. While it isn't exactly the same thing as a skills ontology, it supports a similar goal: making information easier to connect, compare, and use.

What Tennessee is doing differently

The Tennessee Board of Regents offers a real-world example of how higher education is putting these ideas into practice.

Through its TBR CRED initiative, Tennessee is working to make student achievements more visible across 37 public colleges and universities. Rather than relying exclusively on traditional transcripts, the initiative uses Comprehensive Learner Records (CLRs) to capture a fuller picture of students' learning, including achievements that don't always fit neatly into traditional academic records.

But what's particularly interesting is how Tennessee is using CTDL to describe program-level skills and competencies consistently.

Think of it as creating a common language that other schools, employers, and technology platforms can potentially understand. When institutions describe learning in compatible ways, it becomes easier to connect that information across different systems.

The goal isn't simply to hand students more digital credentials. It's to make those credentials more useful when they leave campus.

What can employers learn from this?

Employers face a similar challenge. One company might call a skill "team leadership," while another calls it "people management." Someone could spend years developing those abilities without ever earning a certificate or holding a management title.

Skills ontologies can help organizations recognize connections between abilities, even when they're described differently. This becomes especially relevant as employers increasingly use AI to screen resumes and match candidates with jobs.

AI can process thousands of applications, but that doesn't automatically mean it accurately understands someone's capabilities. If the information going into a hiring system is inconsistent or incomplete, the results can be too.

Higher education's approach offers three practical lessons:

• Agree on what skills mean. Before investing in technology, establish clear definitions of the abilities you're trying to recognize.

• Show the evidence. Skills become more meaningful when organizations can explain how someone developed or demonstrated them.

• Make skills understandable beyond your organization. A credential shouldn't only make sense to the institution or company that created it.

These principles might sound straightforward, but they're important foundations for making skills-based hiring work.

Making skills mean something

The promise of LERs has always been bigger than giving people digital versions of diplomas and certificates. It's about helping people communicate what they know and can do, regardless of where they learned it.

Higher education is helping move that vision forward by exploring ways to describe skills more clearly and connect learning to employment opportunities.

And while the technology behind it might sound complicated, the bigger idea isn't.

If we're going to build a world where skills matter more than job titles or degrees, we need a shared understanding of what those skills actually mean.

That's what makes ontologies worth understanding. Not because everyone needs to become a technology expert, but because everyone deserves a better way to show what they can do.

Speak naturally. Send without fixing.

Wispr Flow turns your voice into clean, professional text you can send the moment you stop talking. No rough transcription you have to clean up. Actual polished text, ready for email, Slack, or any app.

Speak the way you think. Go on tangents. Change your mind mid-sentence. Flow strips the filler, fixes the grammar, and gives you text that reads like you spent five minutes writing it.

Start flowing free

SKILLS

What the heck are durable skills?

My oldest daughter graduated from high school with a transcript spread across three different PDFs and… almost as many time zones.

Our family had taken a sabbatical in San Diego, where she attended High Tech High and by college application time, I had no idea how an admissions officer could look at those transcripts and figure out what she could actually do.

So what did I do?

For every STEM-related course, I created a PDF describing what she had done and the skills she had developed. Then I convinced her college and career advisor to attach it to her applications.

Surprise: my daughter, the solid “B” student, was accepted into all three universities she applied to.

But I was left wondering: What happens to students who don't have a parent who knows how, or has the time, to make their learning visible?

A decade later, we have a name for many of the skills I was trying to surface: durable skills.

So, what are durable skills?

The simplest explanation I've found is that durable skills transfer and last.

Think communication, collaboration, critical thinking, creativity, adaptability, and decision-making. They aren't tied to one particular job. Communication matters whether you're a nurse, teacher, electrician, software developer, or CEO. Their value doesn't disappear every time technology changes, either. Durable skills can deepen with experience and look different in different contexts, but their underlying value remains.

Organizations like America Succeeds have developed frameworks to help educators and employers define and recognize these skills. But the more I read about durable skills, the more I had a nagging thought: Haven't we been talking about these skills forever?

We called them soft skills, then employability skills. The 21st-century skills movement gave us the 4 Cs: critical thinking, communication, collaboration, and creativity. More recently came transferable skills and human skills. Now we have durable skills.

The skills are supposed to be durable, even if their names, apparently, are not.

Maybe… the renaming is the point

My first reaction was skepticism. Do we really need another term?

Soft skills reminded us that technical expertise wasn't enough. Transferable skills emphasized what people could carry between jobs, while human skills gained relevance as technology became capable of doing more of the work humans once did.

Now, amid rapid advances in AI, we're asking another question: What lasts?

In that environment, durable starts to sound less like marketing and more like a useful distinction.

But, are they really durable?

Calling a skill durable makes a pretty big promise, especially when we start thinking about how those skills transfer between different environments.

A Getting Smart conversation with Michael Crawford and Chris Unger, hosted by Mason Pashia, pushed my thinking about how difficult these skills can be to assess and reliably signal to others.

Someone might collaborate brilliantly on a high school robotics team, but does that mean they'll collaborate equally well in a hospital? Maybe, but context matters. Critical thinking presents the same challenge. Thinking critically about a medical diagnosis requires different knowledge than thinking critically about a financial statement.

This raises an important question for educators, employers, and credential designers: If we're going to recognize these skills and ask others to trust them, how do we make them visible and credible?

I don't think the answer is simply creating another list of skills. We need to give people opportunities to demonstrate them and capture meaningful evidence of what they can actually do.

Take a student working on a robotics project, for example. Instead of simply claiming she's a good collaborator, she could share the project, explain her individual contribution, and include feedback from teammates and her teacher. That gives someone else evidence to examine rather than just a claim to accept. Seeing her demonstrate collaboration in another setting would make that evidence even stronger.

This emphasis on observable behaviors is also reflected in ETS's research on assessing collaboration, while research on employers' perspectives on micro-credentials reinforces the importance of demonstrating skills in realistic settings.

For educators and credential designers, a practical starting point is to choose one durable skill, identify what demonstrating it actually looks like, and consider how that evidence could be captured and shared. Rather than simply stating that someone has strong critical thinking or collaboration skills, we should be able to show what they've done that supports that claim.

Looking back, that's exactly what I was trying to do for my daughter. At the time, I thought I was solving a transcript problem, but I was really solving a visibility problem.

So yes, I think the new name is worth it if it gets us having more meaningful conversations about which skills we value, who has them, and how people can develop and demonstrate them.

Durable skills aren't new. Maybe we just need to get better at showing our work.

BY THE NUMBERS

Some Wall Street firms are tying pay to AI skills

25% of financial firms now link raises, bonuses, or promotions to employees' AI proficiency, according to a survey of 500 finance executives. Another 42% have formal criteria for evaluating AI skills. AI literacy is quickly becoming more than a workplace advantage. It could soon influence how employees are rewarded and promoted.

Source: Reuters, October 14, 2026

KNOWLEDGE

What to read, watch, and listen to this weekend

📚 Read: Forget the Career Ladder — Modern Work Is a Weave by Lynda Gratton for The Times

The traditional career ladder is starting to look a little outdated. Professor Lynda Gratton explores why career growth may have more to do with weaving together different experiences, skills, and opportunities than climbing toward one job title. A thought-provoking read for anyone rethinking what career progression should look like.

📺 Watch: The Thinking Game by Google DeepMind

Ever wonder what actually goes on behind the scenes of some of AI's biggest breakthroughs? This documentary follows the scientists at Google DeepMind as they tackle complex challenges, make discoveries, and push the boundaries of what technology can do. A fascinating look at the people, persistence, and creativity behind the AI revolution.

🎧 Listen: How AI Is Changing Talent, Not Just Tasks from Harvard Business Review

As AI takes on more workplace responsibilities, what happens to the opportunities people traditionally relied on to learn and grow? Salesforce's Paula Goldman explores how AI is reshaping entry-level roles, talent development, and the value of human judgment. An interesting listen for leaders thinking about how tomorrow's workforce will actually build its skills.

FOR FUN’SIES

💌 Have more feedback? Reply to this email and tell us. We read each one.

🗣️ Want to be featured as a contributor? We’re looking for industry people to write editorials. 

✊🏼 Want to sponsor or partner? Connect with us about advertising opportunities.