Change is now a relentless daily operating rhythm.
Most employers (63%) are citing skill gaps as the biggest barrier of workforce transformation, according to the World Economic Forum.
Every learning and talent leader is watching this happen in real time. Workforces are unable to keep pace and progress on the AI transformation executives expect cannot be demonstrated through completion metrics alone. The question business leaders are unfortunately asking in response is: Why invest more in learning when the function has historically struggled to demonstrate measurable business results? The short answer is because that truth is quickly becoming outdated.
AI will only create value if people know how to apply it to their work. Learning can now build capability and, with AI, can measure it through performance and skill application. This tracking can happen within something Lori Niles-Hofmann has coined as “closed loop reporting.” Once skill level can be proved through performance, it can also be linked directly to business results.
Now, learning, talent, and HR leaders can take this skepticism with fear or with the ambition to outlearn the change.
Two Paths: Leaning on Old Volume or Tracking New Value
There are two ways this can go for learning and talent development. One path leads to smaller teams, smaller budgets, and a slow fade into irrelevance. It’s already happening in companies where learning was built around delivering programs and driving consistent performance, but never adapted to meet current business needs.
A learning leader at one of Degreed’s global manufacturing clients put it bluntly: “AI will disrupt everything. If we don’t position ourselves in a new way, we will vanish. In three years, we will not be needed anymore.”
That’s someone inside a learning function, looking at his own team and doing the math. He chose the other path: on that one, the function forward fast, and it’s required to reimagine learning as a value-driver for new and emerging business strategies, like AI adoption and transformation. And for that new strategy, he said, “If I had to boil it down to one sentence, it’s from volume to value.”
Making Business Value the Learning Metric
Volume metrics and value metrics require different definitions of success.
Volume measures whether learning happened: courses completed, hours logged, seats filled. Value measures whether the business is any more capable than it was before and whether that capability produces results the business needs: revenue growth, productivity, efficiency, reduced risk, or cost reduction.
Transitioning from volume to value changes what gets scored and how. A chief technology officer at a global learning company summed up the new starting point this way: “When we think about work, we think of the outcome, and think backwards through what is that combination of automation and human together that will best achieve that outcome.”
Start from the outcome the business needs. Work backward to determine how the work must change, what people need to do, and where AI can help. Then establish a baseline with the business owner so you can measure whether those capabilities and results improved.
Beginning with a priority outcome and mapping outward can look something like this:
| Employment stage | Employee needs | Business needs | Business metric |
| Onboard | “Get me ready fast” | Minimize ramp time & cost | Time to Readiness |
| Perform | “Equip me to master my work” | Grow capacity without more headcount | Output per Person |
| Develop | “Get me ready for what’s next” | Fill roles from within | Plan Fulfillment |
| Adapt | “Keep me employable” | Redeploy instead of replace | Retention Savings |
Moving from volume to value also requires specificity about whose performance needs to change and how. Someone six weeks into a new job needs something different from someone who’s been in the same role for six years, and a learning strategy built for “everyone” ends up serving no one.
What Learning Value Actually Looks Like
TEKsystems shows what this model looks like in practice. It started with a specific group, a specific performance need, and a business outcome: help newly hired sellers become effective in deal-solutioning conversations faster.
Historically, new sales-development hires needed 36 to 40 months before they could hold their own in a deal-solutioning conversation. That’s years of ramp time before someone in that role was producing measurable business value.
To shorten the ramp, TEKsystems built AI-powered roleplay into onboarding using Degreed Maestro, giving new hires a space to practice those conversations before a real deal was ever on the line.
“We found that 25% of that original cohort… were able to land a meeting with that buyer within six months of tenure,” says Stefanie Kuehn, Senior Program Manager at TEKsystems. “And we are more likely to win business once we get that meeting with that buyer—about 60% of the time.”
The learning metric shifted from something like the number of roleplays completed to whether or not new sellers could reach the right buyers sooner. That’s a business outcome, counted in closed deals and win rates, and it’s the difference between measuring learning activity and measuring the value learning creates.
Learning Builds the Needed Capability to Adapt
Somewhere in your organization, someone is deciding whether learning is still earning its seat at the table. Budgets are getting reviewed. Headcount is getting questioned. AI is creating one of the clearest opportunities for learning to prove its business value.
That potential alone will not protect the function. Learning has to prove it.
The organizations making the strongest case for L&D are tying every dollar spent to something that already matters to the business: how fast new employees become productive, how many roles are filled internally, or even how successful AI transformation is.
When it comes to AI transformation, businesses that drive adoption of new AI tools and workflows are far more likely to see their investment pay off: 86% report meeting or exceeding ROI expectations, compared to fewer than half of the organizations where adoption lags, according to Deloitte. Learning is one of the biggest levers a company has for closing that adoption gap.
That’s why learning matters now.
Companies will not get value from AI simply by buying the technology. They will get it when their people can use that technology to work differently and produce better results. Learning proves its value when it can show that connection. Moving from volume to value is how.
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