Learning Technologies 2026 – how much does this matter?
This is our report from the 2026 Learning Technologies conference.
Author: Martin Couzins
Having talked the audience through the latest trends in learning technology, Dani Johnson, Co-founder and Principal Analyst at RedThread Research, asked the question: “How much does this actually matter?”
It led into a panel debate about how learning leaders view learning tech developments and how they are using tech in their organisations.
That question – how much does this actually matter? – was driven by the idea that better learning platforms will only ever deliver marginal gains compared to embedding learning in work systems.
AI provides organisations with the opportunity to do exactly that, at speed and scale. And to some degree employees are already doing that. They are using AI to adapt their workflows and processes whether organisations are aware of that/sanction it or not.
Beyond the AI marketing hype
Embedding learning in work systems would be a real challenge for the learning technology market. One way in which vendors are aiming to remain relevant is to focus on the functionalities that business cares about the most, such as assessment, performance tracking, enablement, analytics and measurement and skills.
As an aside, the nuance in these areas is worth exploring further if you are a buyer. For example, AI and more immersive experiences are shifting assessment from measuring what you know to what you can do. Johnson made the point that buyers need to go beyond the marketing hype to really understand what vendors now mean by assessment.
That said, the question about how much this really matters is not going to go away. As Johnson said, what if the platforms and tools L&D practitioners have been obsessed with for the past decade aren’t where the real impact lies? Maybe L&D teams should focus less on standalone learning technology and more on embedding learning in workflows – working with colleagues around the organisation to figure out how the tools they already use every day can be turned into engines for development.
Reducing cognitive load
Enablement, one of those business functionalities that vendors are focusing on, seems to be an area of L&D that more accurately reflects how adults learn at work. In a session exploring frontline worker enablement, the panel discussed why traditional L&D that focuses on inputs such as courses and content doesn’t cut it for workers for whom work is highly structured and task-focused, heavily managed and constrained, physically and emotionally taxing, mobile etc. Enablement focuses on the system in which the work is done – the processes, policies, systems, tools and communication. Much of this system is designed to reduce cognitive load to make it easier to do the job.
In the frontline environment, working for the manager and the team can be more important than working towards corporate goals. That means managers are critically important to how the team works, even though they will be under pressure to hit targets. Enablement is at the heart of sales training too – is it time to scale this approach to all workers?
The panel shared some tips for L&D professionals:
Design for the system, not just for learning – design the system to remove cognitive load so that workers do not have to remember lots of information to do their jobs.
Provide work stability – many frontline workers are on unpredictable shift patterns and short-term or zero-hours contracts. Start by making shifts more predictable and ensure pay is on time. Then focus on more on the ongoing learning.
Managers are a priority – support them so that they can make quicker and better decisions and so that they can support their team effectively.
Co-create tech solutions – focus on the biggest work challenges and build for those.
AI was ubiquitous across the conference and exhibition. My AI takeaway was to explore further what Giovanni Giamminola, author of The Augmented Manager, calls cognitive governance, the way in which we think with AI – what we retain for ourselves, what we delegate and how we think with AI.
Giamminola shared what he calls the four “postures” of thinking with AI, the stages to go through rather than jumping straight into asking it to do the thinking for you.
- Exploratory posture – clarify what problem really needs solving and use AI to help find first principles, not just variations of known solutions.
- Generic posture – once you have framed the problem, ask AI to generate ideas and options.
- Critical posture – ask AI to critique your own idea or decision – look at what would make your idea fail, for example.
- Operating posture – use AI to format the content.
Why is cognitive governance important? So that you can answer the question: Who is actually doing the thinking?, says Giamminola.
That’s two big questions to take away from the event:
- How much does this actually matter?
- Who is actually doing the thinking?
