AI in L&D is transforming how organisations train, upskill, and future-proof their workforce. But here’s the catch: while some L&D teams are using AI to scale content creation effortlessly, others are missing out on its game-changing potential in areas like adaptive assessments, personalised learning paths, and predictive L&D planning.

Don’t mistake AI to be just about automation, it’s about building smarter, data-driven learning ecosystems that drive real business impact. The question is, are you using AI in learning and development to its full potential or just scratching the surface?

To uncover the reality of AI adoption in workplace learning, we turned to the Tenneo Industry Report. With extensive industry research and insights from top L&D leaders, this report reveals not just where AI is making an impact, but also the critical gaps holding organisations back from achieving true learning success.

Are organisations embracing AI strategically, or are they missing out on its most powerful capabilities? The findings from the Tenneo Industry Report may surprise you.

The Current AI Adoption Landscape: What the Data Reveals

AI in L&D is accelerating, but its implementation remains uneven across different functions. While some use cases, like training content creation, have seen significant traction, other areas such as assessment design and personalised learning paths are still playing catch up.

This discrepancy highlights a gap between the potential of AI and how it is currently being utilised across different use cases in workplace learning. Here are some numbers to back this claim: 

• 55% of L&D teams use AI-driven tools for content creation, enabling scalable, personalised, and multilingual learning materials.
• Only 4% of organisations leverage AI for assessment creation and learning path design, missing out on opportunities for adaptive learning.
• 13% of L&D teams use AI for translation tools, highlighting a gap in providing accessible, localised content.
• 21% of organisations integrate AI into operational tasks like scheduling and feedback collection, enhancing efficiency in L&D.

This fragmented adoption of AI use cases suggests that while organisations recognise its transformative potential, many are still unsure how to maximise its capabilities beyond content generation.

AI’s Expanding Role in L&D: Key Use Cases & Future Trends

AI in the field of L&D is evolving from basic automation to intelligent, strategic applications that drive personalisation, efficiency, and measurable impact. The afore mentioned statistics are a clear indication that while some organisations have embraced AI for content creation, others are still exploring its potential in assessments, learning paths, and predictive workforce planning.

Let’s look at the key use cases where AI in learning and development is making a difference, and where it still has room to grow. These use cases will provide valuable insights into how the future of AI in L&D is shaping.

1. AI in Content Creation – Faster, Smarter, Scalable Learning Content

Keeping up with constant learning demands is one of the biggest challenges for L&D teams. AI-powered content creation eliminates bottlenecks, allowing L&D teams to generate high-quality, personalised learning materials at scale. It’s no surprise that 55% of L&D teams already leverage AI for automated course content creation.

For L&D leaders, this means faster content production without compromising quality. AI helps create learning materials tailored to individual skill gaps, ensuring every employee gets the right training at the right time.

As AI continues to evolve, it will move beyond automation, enabling interactive, scenario-based learning that enhances engagement and knowledge retention.

2. AI in Assessment Creation – Measuring Real-World Impact, Not Just Knowledge You can't improve what you don't measure. Assessing employee learning has traditionally been static and one-dimensional. Many L&D teams rely on basic quizzes and completion rates that do little to measure real competency growth.

Yet, only 4% of organisations currently use AI for assessment creation, leaving a huge gap in understanding training effectiveness.

AI can change the game by creating adaptive, real-time assessments that evolve based on a learner’s responses. Instead of a one-size-fits-all test, AI can personalise assessments to measure actual skill application in real-world scenarios. For L&D teams, this means greater accuracy in identifying skill gaps and ensuring that training delivers measurable business impact.

3. AI in Learning Path Design – Personalised Growth Journeys, Not Generic Training

Every employee learns differently, yet most training programs still follow rigid, one-size-fits-all structures. The lack of personalised learning paths means employees often receive irrelevant or redundant training, leading to lower engagement and wasted resources. Despite this, only 4% of organisations currently use AI for learning path design.

AI can build dynamic, individualised learning paths that adapt based on job roles, skill levels, learning preferences, and career goals. Employees receive tailored recommendations rather than generic training, ensuring that learning is meaningful, engaging, and directly applicable to their work.

For L&D teams, AI-powered learning paths mean higher engagement, better retention, and more efficient skill-building.

4. AI in Operational Work – Freeing Up Time for High-Impact Learning

L&D leaders often struggle with time-consuming administrative work, from managing training schedules to tracking completion rates. AI is already streamlining these tasks, with 21% of organisations using AI for scheduling, feedback collection, and program coordination.

By automating repetitive L&D operations, AI allows teams to focus on strategy, not spreadsheets. Instead of spending hours tracking attendance or chasing employees for feedback, L&D professionals can use AI to generate instant reports, analyse training engagement, and proactively identify learning gaps.

5. AI in Data Analysis – Turning Insights into Strategic Action

Many L&D teams collect training data but struggle to translate it into meaningful insights. Only 11% of organisations currently use AI for data analysis, despite its ability to measure learning effectiveness, predict future skill needs, and track training ROI.

AI-powered analytics enables L&D leaders to go beyond completion rates and understand how training impacts performance and business goals. With AI, organisations can identify trends, optimise learning strategies, and make data-driven decisions that ensure training investments deliver tangible results and improve training impact.

6. AI in Skill Mapping – Aligning Workforce Development with Business Goals

Most organisations understand the importance of tracking workforce skills, but traditional skill mapping methods are often manual, time-consuming, and outdated by the time they’re completed. AI can automate this process, providing real-time insights into workforce capabilities, skill gaps, and emerging learning needs.

While only 9% of L&D leaders are actively using AI for skill mapping, this use case promises to grow rapidly. For L&D leaders, AI-powered skill mapping means greater alignment between employee development and business strategy.

Instead of guessing which skills will be needed in the future, organisations can proactively upskill employees based on AI-driven insights, ensuring the workforce remains agile and future-ready.

7. AI in Strategic Planning – Anticipating Tomorrow’s Learning Needs

Most L&D strategies are built reactively, based on past performance rather than future needs. However, AI is now enabling predictive learning strategies, helping organisations forecast skill shortages, industry trends, and evolving learning demands.

Despite this potential, AI adoption in L&D strategy remains low with only 17% L&D leaders using it. This indicates that many businesses are yet to leverage AI’s full potential in shaping their workforce development.

With AI-driven workforce learning strategies, L&D teams can anticipate the skills employees will need months or years in advance, ensuring training programs are aligned with both immediate and long-term business priorities.

The ability to plan ahead rather than react will be the defining factor for organisations looking to stay competitive in a rapidly changing landscape.

Conclusion: AI in L&D – The Future is Here, Are You Ready?

The future of workplace learning is no longer about just keeping up, it’s about staying ahead. As AI continues to evolve, organisations that embrace its advanced use cases and applications will see higher learning impact, stronger workforce agility, and measurable business outcomes.

If you’re looking to build a future-ready AI-driven learning strategy, the Tenneo Industry Report is the roadmap you need! This in-depth research report not only uncovers how AI is shaping workplace learning but also provides practical solutions and strategic insights to help L&D leaders seamlessly integrate AI into their learning ecosystems.

Whether you're exploring AI in L&D for the first time or refining your learning strategy, this report acts as a powerful guide to drive real transformation.

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