It’s difficult to think of a technology that has grown as quickly or transformed as many industries as AI, and in the past year or so it has become part of the everyday toolkit for workplace learning. It began as an experiment in a handful of proactive teams but has now spread across the industry as AI capabilities have become more accessible and expectations for speed and scale have risen. Learning and Development (L&D) teams are under pressure to build skills faster, support hybrid workforces, and respond to shifting business needs without adding headcount. And AI offers a way to keep pace – automating routine work, generating content on demand, and giving teams the breathing room to focus on strategy and business goals. In many organisations, this first wave of adoption has helped L&D keep up with rising demand and shrinking timelines.
This is backed up by Rise Up’s 2025 State of Learning Report, which reveals only a third of organisations have yet to implement AI as part of their L&D strategy, down from more than half the year before. But while adoption is rising quickly, the depth of that adoption is uneven. Most teams are using AI to make existing processes more efficient, not to redesign how learning works or accelerate how skills are built. Our report suggests that L&D is entering a pivotal moment, where the basic foundations of AI are in place, but its most meaningful applications, such as personalisation and targeted capability development, are still emerging. The next stage will determine whether AI becomes a true strategic asset for learning or remains little more than a time-saving tool.
Efficiency at scale – How L&T teams are using AI today
The first wave of AI adoption in L&D cantered on speed and efficiency. More than half of organisations are now using AI to generate learning content, with 53% relying on it to draft materials, build exercises, or streamline course creation. Another 33% use AI to automate routine processes – everything from enrolment workflows to administrative tasks that once consumed hours of manual effort. These applications have given L&D teams a practical entry point into AI, because they reduce bottlenecks, lighten operational load, and help teams deliver training faster without sacrificing quality. For leaders, this has made AI and easier ‘sell’ because it’s no longer an abstract idea.
But this efficiency focus also highlights the limits of where many organisations currently are. Automation helps L&D do more with less, but it doesn’t fundamentally change how people learn or how skills develop inside the business. In most cases, AI is being used to accelerate existing processes rather than unlock new capabilities. It’s a valuable step, but an early one – useful for keeping pace with rising demand, but not enough to meaningfully influence business outcomes or reshape learning strategy.
The personalisation gap
Despite the rapid rise in AI adoption, there’s still a heap of untapped potential in personalisation. Only 21% of organisations are currently using AI to personalise learning pathways, even though 56% agree that personalisation improves engagement and accelerates time-to-skill. That disconnect is interesting because it highlights a missed opportunity. Personalisation is where AI can have its most transformative impact, like adjusting content difficulty, format, and pacing in real time. It can surface the right learning at the right moment or help employees progress based on what they know rather than what a generic curriculum assumes they know. When done well, it shortens skill development cycles and keeps learners motivated, reducing one of the biggest friction points in traditional training.
The challenge is that personalisation isn’t something that can be ‘switched on’ – even with all the right AI tools in play. It requires a shift in culture and mindset too. Many L&D teams are still rooted in linear program design, where learners move through the same sequence regardless of their starting point. AI-enabled personalisation breaks that model, but it also demands clarity around what organisations are trying to achieve. That’s where time-to-skill really starts to matter. Instead of attempting to calculate traditional ROI, which often fails to capture learning’s true value, time-to-skill offers a direct measure of how quickly employees gain the capabilities the business needs.
What’s holding teams back?
Even as AI adoption accelerates, a significant number of L&D teams say they lack the confidence or capability to use it in more advanced ways. According to our report, 62% cite a lack of AI knowledge as their biggest barrier, and 55% say they don’t have the skilled personnel needed to implement AI-driven learning effectively. Nearly half point to budget or resource constraints, and others mention integration challenges that make it difficult to connect AI tools with existing learning systems. These obstacles create a natural ceiling, where teams understand the potential of AI, but the practical steps required to unlock that potential feel out of reach. As a result, most organisations remain focused on narrow, efficiency-based use cases rather than building toward deeper strategic impact.
The good news is that addressing these barriers doesn’t mean reinventing the wheel. But instead of trying to quantify traditional ROI, which often fails to capture the complexity of learning outcomes, L&D teams can anchor their AI strategies around time-to-skill, capability progression, and clear indicators of learner performance. These metrics offer a more direct view of how quickly employees are developing the skills the business needs, and they align far more naturally with what AI is designed to improve. When teams track time-to-skill consistently, it becomes easier to identify where personalisation or adaptive pathways can make a difference, build targeted AI use cases, and justify further investment. In that sense, overcoming AI barriers is less about technology adoption and more about establishing the right measureme
