In a world driven by artificial intelligence tools, what’s next for learning and development?
The top factors driving a learning “reset” include demand for rapid upskilling and reskilling; a greater need for AI literacy across the organization and within the L&D function; and cost or efficiency pressures, per Gartner research.
While the brisk pace of technological development makes the need for change clear for all to see, the demand for more L&D is “increasingly coming from the top,” Rebecca Burton, senior principal analyst in Gartner’s HR practice, said during an Oct. 26 session at the Gartner HR Symposium/Xpo.
When Gartner polled board directors on the top tasks for CHROs to get ahead of AI, L&D-related aspects — such as more employee training programs, especially around AI-related skills, and worker redeployment — took up three of the top five spots.
“Pretty lofty expectations from both our CHROs and our boards,” Burton noted — and that can drive heavy anxiety for L&D leaders. “We have worked really hard to get here, but the path ahead is not going to be easy.”
Burton outlined four trends L&D leaders should keep in mind heading into 2027, especially amid change that isn’t slowing down.
1. L&D needs a scalable reskilling process amid new “chaos”
Burton acknowledged that the very idea of workforce reinvention can be overwhelming and easy to misunderstand and outlined ways employers are looking at this issue in the field.
For example, “skills chaos” has skyrocketed, defined as not only the increasing number of skills needed for a job but the sped-up turnover and expiration of skills. “The number of skills required for a single job is continuing to climb annually by about 5%,” she said, while a third of skills required in 2025 may no longer be required by 2030.
“This kind of skills chaos means our jobs are changing rapidly, too,” Burton explained — meaning “job chaos” is also here to stay. Per Gartner research, 32 million jobs may need redesigned annually through 2031.
Gartner defines workforce reinvention as the need for jobs, skills, mindsets and ways of working to shift quickly to achieve business results. “So large segments of our workforce are going to have to pivot as AI takes on work and as decision-making changes,” Burton said. “This is not a static event. This is an ongoing opportunity for us. And a big part of our job is to be able to set up systems to help manage this change.”
L&D generally has to stop thinking of itself as simply a learning provider for the organization, she continued. The department has expertise in skills and behavior change that can help employees adapt to ongoing transformation — a key issue, as continued disruption can easily shatter workplace culture.
Reskilling remaining employees will be central to L&D’s role moving forward. “We need a repeatable process for that reskilling, not a series of disconnected programs,” Burton said. Make job changes more visible to workers, since employees “cannot prepare for change that they do not see coming,” she noted, and help employees self-assess their skills so they can be ready to adjust for new job realities.
2. Prepare for the growing risk of critical knowledge loss
The knowledge and expertise present in the workforce “is something that organizations often overlook when they’re thinking about workplace transformation and change,” Burton said.
For example, organizations are dogged by brain drain due to more worker retirements and layoffs triggered by automated tool adoption. “Together these forces are creating a growing risk,” Burton said. “Organizations are losing critical knowledge faster than they are transferring it.”
But employers need to do more than simply document that knowledge. Instead, organizations need to instill what Burton called collective intelligence, or the ability to systematically connect and act upon the collective know-how of people and tools.
Knowledge transfer is thus a key duty of L&D, and the structure of such can vary widely from informal meetings and file sharing to targeted mentoring programs, job rotations and broad knowledge management platforms.
Burton encouraged L&D leaders to consider what exists in their organizations right now. “What can you leverage to be more intentional with knowledge transfer? Because for most organizations I work with, it is very ad hoc … and that is creating a risk for that enterprise.”
In other words, L&D needs to be a steward of organizational knowledge, especially in the age of AI, and ensure it can move at scale. Some companies have implemented knowledge manager roles specifically to ensure this takes place, Burton said.
3. Implement AI coaching for managers — carefully
While human-to-human coaching remains highly favored, it can be difficult to scale, especially for roles that are already busy, like managers. Enter AI-powered tools, which are “becoming a really exciting way to scale development for our employees,” Burton said.
Close to 8 in 10 CHROs surveyed by Gartner said their managers were overwhelmed by the scope of their responsibilities, a sensation AI is only worsening. But AI could also ease some of the administrative burdens common to the role and improve decision-making, Burton said.
Gartner predicts that by 2028, 40% of employees will be first trained and coached by AI when entering a new role — up from less than 5% currently. And 38% of HR leaders said they are piloting or using AI coaches for leadership development specifically.
Burton advised some caution in deploying these programs, however. “If AI starts to replace some of those meaningful interactions we have human-to-human and manager-to-employee, that can be risky,” she said. “Frankly, some organizations that have deployed this had their employees feeling ‘the ick.’ They don’t want to engage with this. They want that human connection.”
Related, these tools can potentially erode trust if an employer doesn’t do their due diligence in assessing data risks and AI bias with vendors, Burton said.
But ironically, poor implementation could lead to manager skills, including coaching, actually atrophying. “What happens when you’re outsourcing those capabilities? We need that critical thinking, we need those coaching capabilities for our managing populations,” she noted.
In response, many organizations are leaning into a blended approach, where humans play a key support role for AI solutions that can be deployed at high-stakes moments in the employee life cycle. It’s all about helping managers show up better prepared for the challenges in their day to day, Burton said.
4. Training needs to combat “cognitive surrender”
As employees shift into heavier AI use, such as delegating work to AI agents, learning support has had to evolve alongside — partly to combat “cognitive surrender,” Burton said.
Gartner defines cognitive surrender as the tendency to stop thinking critically or independently and instead rely on external sources, like AI, to make decisions and judgments. “We actively should be fighting against this,” she said.
The risks of this behavior are high. Consider a nurse who accepts an AI risk assessment without realizing patient data is missing, or an attorney who submits an AI-generated brief without verifying its sources, Burton said. To fight this impulse, employers can institute training exercises that make learners investigate an AI-generated response, such as by reviewing its claims and verifying its information through primary sources that are not AI.
Burton called this exercise a “hallucination hunt.” Common flaws that AI may introduce to work include fabricated or misattributed citations, missing caveats or dependencies, or even an entirely false premise.
“We have to adopt an untrusted until validated mindset when working with our AI outputs,” she said.