The Future of AI in L&D – What Might Happen Next

By Tristia Hennessey

AI capability is compounding, not incrementing. Improvements are stacking on top of each other. What felt experimental twelve months ago is operational today. What feels advanced today - will be baseline tomorrow.

Employees are three times more likely to be using gen AI than their leaders expect, according to McKinsey's June 2025 research – and that is already old news in terms of the pace of progress. The workforce isn't waiting for your strategy - they're already building their own habits.

For L&D leaders navigating the next two to ten years, the real question is whether you'll shape how AI is used in your organization or simply react to what's already happening.

Near Term (2-5 Years): Learning Moves Into the Workflow

The next few years will accelerate a shift already underway: learning embedded in the flow of work rather than stacked on top of it. McKinsey's October 2025 article on the future of the CLO describes AI-powered coaching tools guiding call center agents in real time (I am working on a training project with these AI support tools right now in fact), evaluation systems reducing manager bias, and project management platforms surfacing stretch assignments automatically.

The near-term challenge for leaders: building internal capability to optimally utilize these tools. At top industry conferences in 2025 (like ATD, TechLearn and DevLearn) sessions and conversations on cross-platform integration, AI readiness, and vibe coding drew significant interest. The people getting real value from AI understand problem framing, tool selection, process evaluation, and iteration loops - not just simple prompting.

Budget pressure reinforces this. Organizations are doing more with less, and AI offers efficiency gains when paired with human judgment and clear quality standards. But without those things, they are recipe for disaster.

Longer Term (5-10 Years): New Interfaces, New Roles

The technology stack for corporate learning will look different in five years. Wearables are one reason why. As AR glasses, haptic devices, and biometric sensors mature alongside AI wearables, the delivery mechanisms for learning will shift. Imagine field technicians receiving real-time guidance through smart glasses, or leadership development programs that adapt based on biometric stress indicators.

These technologies remain early-stage, but L&D leaders planning five to ten years out should track their convergence with AI-driven personalization.

Succession planning and knowledge transfer from retiring workforces will intensify pressure on these systems. ILT and in-person learning events are making a comeback in some organizations precisely because human connection and mentorship can't be fully replicated or replaced by AI.

Face-to-face learning will likely become a premium product in an age where trust in digital media erodes due to the widespread use of AI tools. The challenge L&D leaders will face is integrating high-touch, human-centric approaches with AI-enabled scale and efficiencies.

What This Means for Strategy

Three practical implications for leaders planning now:

First, treat AI upskilling as change management. McKinsey's December 2025 research found that seven in ten employees largely ignored formal AI onboarding videos, preferring trial-and-error and peer learning (in short, your uncanny valley AI talking heads are not effective). Consider providing more structured learning opportunities with practice activities, scenario-based simulations, and live (human) facilitators to improve confidence and adoption of AI tools.

Second, establish policy guardrails before scaling. Acceptable use policies, data governance frameworks, and clear boundaries on where AI can and cannot be applied give employees confidence to experiment without fear of crossing lines they can't see.

Third, invest in skills intelligence. The organizations gaining ground are mapping current capabilities, predicting future needs, and personalizing development at scale. This goes beyond traditional competency frameworks - it requires integrated systems that track skill gaps in real time, surface learning opportunities based on actual work patterns, and connect workforce capabilities to business strategy. With experienced workers retiring and institutional knowledge at risk, this kind of visibility becomes essential for succession planning.

The gap between where your workforce is and where your strategy is will only widen if you wait. The organizations that move now will shape how AI gets used. The ones that don't, will inherit whatever habits their employees build on their own - and worse, will lose ground to the organizations who planned ahead and proactively integrated efficiencies.