Remember when corporate learning and development (L&D) meant mapping out rigid, multiyear competency programs? In the AI era, that approach is a relic that threatens competitive readiness.
The World Economic Forum projects that 39% of workers' core skills will be outdated by 2030. That means standard L&D can no longer keep pace with technology's shelf life. Yet enterprises haven't caught up to this reality. A Gartner survey of CxOs and business leaders found that “just 20% believe their workforce is truly AI-ready.”
The answer isn't more training. Forward-thinking enterprises are abandoning check-the-box learning exercises. They’re driving a fundamental shift away from skills training toward talent development and assessment.
“We’re not focusing on skills for skills sake,” says Josh Newman, vice president of workforce skills and talent readiness at ServiceNow. “We’re building skills in the service of talent readiness, with a dynamic skills taxonomy that’s a living, breathing understanding of the capabilities we already have and the capabilities we need.”
What’s the difference? Skills development is about learning to do a specific thing tied to a specific job function. Talent development is about building the potential and readiness of a workforce.
As AI constantly shifts the goalposts for what a specific job function requires, rigid skill building becomes obsolete, forcing L&D leaders to pivot toward talent development and dynamic learning models as a more effective way to ensure organizational readiness.
Conventional training methods—workshops, self-directed courses, and annual compliance cycles—were built for a world where skills had long shelf lives.
Today, the lifespan of AI skills is so short that tools often evolve faster than a company can develop and deliver a course about them. Indeed, in fast-growing fields such as AI, “by 2030, the half-life of technical skills will drop from eight years to as little as two,” according to Gartner.
Beyond the shelf life of the content, traditional learning formats are inconvenient, usually requiring employees to stop what they’re doing, log in to some kind of learning platform, and take a course. Most people don’t have time for that during the workday, and if training is pushed to after hours, employees run the risk of burnout.
In addition, isolated training sessions create a disconnect where knowledge evaporates before it can be used.
The deepest flaw, however, isn't the lifespan of the content. It's what we've been measuring all along.
“Legacy L&D would try to measure what you know, when all that really matters is what you can actually do. Those are two different things,” says Pasquale Fontanetta, group vice president of global learning solutions at ServiceNow.
“Just because you pass a test doesn't mean that you can do the work,” he continues. “We’re shifting from summative to formative assessments, which is a coupling of badging, credentialing, certification, and on-the-job training where you can, with high confidence, say that a person is qualified.”
Certification doesn't prove readiness. And if you don't know who's ready for the work ahead, you can't move fast when the work requirements change.
The real competitive risk lives in the distance between knowledge and capability. To succeed, organizations must eliminate the divide between learning and doing, which means learning must happen in the regular flow of work.
ServiceNow has developed an assessment called a talent signature that’s built into the ServiceNow University learning platform. Fontanetta calls it “the DNA of each learner.” It recognizes who someone is, their role, their credentials, and their collaboration patterns. It then synthesizes those signals continuously to surface strengths and readiness gaps in real time.
This is an infrastructure approach that connects three things that are rarely managed together:
- What people can do
- What work needs doing
- What outcomes that work produces
That data has almost always lived in separate systems. Learning platforms historically didn't talk to performance systems, workforce management, or operational data. When work moves faster than your systems can see, and when AI agents join teams and take on new roles before job descriptions can be written, that separation can become a liability.
Consider a support team deploying an AI agent. Suddenly, humans are doing quality assurance work that wasn't in their original job description. A talent signature can read that emerging capability demand and surface learning paths before a productivity bottleneck occurs—all within the platform where the work is already happening.
Leaders can see whether people are ready for new work, where learning should land, and how roles should evolve. This shift from an annual snapshot to a real-time signal lets L&D leaders identify readiness gaps, route learning to the appropriate person at the correct moment, and flag emerging role requirements before business units may even realize they need them.
Most L&D programs involve curating existing content such as knowledge base articles, videos, and documents and organizing it into a collection of learning modules. That still has its place, but there’s a better way.
“We’re at this mind-blowing moment where we’re moving from modularization to atomization,” Fontanetta says.
Whereas a modular approach might carve a big learning course into sequential chunks a learner completes in a specific order, atomization removes the enrollment model entirely. There’s no specific course curriculum or predefined learning sequence.
Instead, you have a small “atom” of learning dropped right into your workflow when you need it, delivered by an AI agent with contextual understanding of what you’re working on.
Consider the contrast:
- Modular: Your company launches an Excel course for data analysis. It might include several breakout courses for which you enroll, block time, and complete in order. That’s standard training.
- Atomized: You're building a report in Excel and type a date as 1/5/25 in one field and Jan. 5, 2025, in another. Both are right but inconsistent, and Excel starts acting strangely. An agentic prompt surfaces immediately—not in a separate app or after the fact—with a contextual explanation and a quick fix.
“It's an agentic window that has contextual understanding of what's on your screen,” Fontanetta explains. “And then based on where you're struggling, it helps you right there in the work.”
Talent readiness used to be an HR metric. Now it’s a strategic dependency akin to infrastructure uptime. It goes mostly unnoticed when it's working but can be catastrophic when it fails.
For decades, workforce readiness lived in the background, a line item in an annual review, a box checked by compliance training, a sleepy problem for L&D to solve quietly and mostly out of sight.
The organizations that treat talent readiness as infrastructure, not overhead, will see the shift coming before it arrives. They'll know which teams are already absorbing new AI-driven work, which roles are evolving before anyone's documented the new requirements, and where the next bottleneck will form.
The organizations that don't will find out through attrition and missed opportunities. They’ll realize that the workforce they built for last year's business isn't the workforce this year's business needs.
Find out how ServiceNow can help you make the shift to talent development.
