If 2026 has proven anything to Learning and Development (L&D) leaders, it’s that producing more training content doesn’t mean employees are learning more.
Over the course of 2026, AI has enabled L&D teams to create courses and quizzes in minutes, but has it actually helped develop staff or allowed them to find useful answers to their problems? Many would argue no. So, how can L&D teams make better use of AI in 2027?
Fred Thompson, CEO of Thirst, explores five ways successful AI implementation will shape workplace learning over the coming year, and why L&D teams will need to look beyond content creation to make a meaningful difference to employee performance.
Why AI needs to move beyond content creation
The first wave of AI in L&D was all about production – something that has been widely accepted by L&D professionals across the board. For under-resourced L&D teams, it has been fantastic at cutting the production time of courses from weeks to hours. But there is a risk of doing this.
When content becomes nearly free to make, as AI has enabled it to become, it’s understandable that many businesses have picked this up and run with it. For the majority of these businesses, their training libraries have grown exponentially, and staff have been assigned more modules than ever. But this has become a poisoned chalice that businesses need to avoid. AI has enabled businesses that have gone it alone to create a culture of learning for learning’s sake, with no real understanding of what makes a course interesting. So, while there is more training material available, if it’s not actually benefitting anyone, what’s the point?
Another issue has been ensuring learners have access to the information they need from the correct person in their team. Sure, it’s great having a formulaic course. But what if someone has a question; how do they reach out to the employees with the knowledge? This is another problem that needs to be addressed. In essence, AI solved the content problem for businesses, but risks creating new ones instead. Addressing this will be the difference between those that succeed in 2027 and those that won’t.
Five ways successful AI implementation will change workplace learning in 2027
Learning will become part of everyday work
For too long, if an employee has encountered a problem, they are directed to a forty-minute module or asked to wait for a training on the issue (which can sometimes take place weeks later); it’s fair to say that they won’t be able to solve the challenge in front of them quickly.
In 2027, smart AI implementation will be able to address this by allowing employees to ask questions as they work and receive answers drawn from their organisation’s own processes and guidance in real time. This could mean finding the right procedure or understanding how to complete an unfamiliar task without having to search through an entire training library.
This doesn’t mean structured courses no longer have a place. But for everyday questions, the focus will shift towards giving employees relevant information at the point they need it.
Personalisation will move beyond course recommendations
Traditionally, personalised learning has meant choosing between different versions of the same course. This isn’t helpful. Employees may work in similar roles, but they can have very different levels of experience or learning requirements. Assigning everyone the same material risks leaving some employees behind while asking others to revisit information they already know.
Over the coming year, AI has the capability to increasingly tailor learning to an individual’s role, existing knowledge and areas of difficulty. For example, a new starter may need step-by-step guidance. Yet an experienced employee may only need an update on a process that has changed. This means that in 2027 L&D teams will have the opportunity to make learning more relevant and reduce the time employees spend working through material that offers them little value.
Skills gaps will shape learning priorities
As we have already established, having more courses doesn’t mean employees are taking more information on board. As roles and working practices change, L&D teams will need a clearer understanding of where expertise already exists and where additional support is required.
AI can support this by mapping learning content to specific skills and helping teams identify gaps before they become operational problems. This will also help businesses recognise where important skills are missing in a team. Learning can then be directed towards building that capability across the team, giving training a clearer purpose.
Internal knowledge will become easier to capture and find
Some of the most useful knowledge in a business tends to sit with the colleague who knows why a process works a certain way, or the manager who repeatedly answers the same questions. The problem with this is that getting that information on paper from these key individuals is often a problem, as they tend to be time poor.
AI will make this process easier. A key individual can simply record themselves explaining something, and AI will transform this into working documents and explanations that will be easy to access for all employees. This means practical knowledge can become readily available without always having to track down the person who has the answer. Be mindful, though, that capturing that knowledge will only be part of the job. Businesses will still need managers or trusted employees in the loop to check that it is accurate and keep it up to date.
Measurement will move closer to business outcomes
Completing a course tells an L&D team that someone has worked through the material. It doesn’t necessarily show whether they can apply it. As AI helps connect learning data with business performance, greater attention can be paid to how quickly new starters get up to speed and put their new skills into practice. For L&D leaders, this will allow them to focus on helping employees do their jobs and identifying where further support is still needed.
To conclude…
It’s clear that if employees aren’t given time to learn or encouraged to ask questions, introducing AI won’t remove those barriers. Managers will still need to make development part of everyday work. The quality of the information will matter too. An answer drawn from an outdated policy can still be wrong, however quickly it arrives. Human oversight will therefore be essential when learning material is created and when existing knowledge is reused. Experience and judgement will be equally important. AI may help someone find an answer, but understanding when that answer doesn’t fit a particular situation will still require support from knowledgeable colleagues.
Success in 2027 now depends on how well businesses bring these elements together. The opportunity is to help employees find reliable answers, develop relevant skills and share what they know. L&D teams that keep those needs at the centre of their approach will have a clearer purpose for AI than simply filling a larger training library.
