Most L&D teams have used AI in some form by now. The question has moved. It’s no longer about whether to adopt it. It’s about whether it’s actually delivering something better, or just delivering something faster.
In our latest webinar, Nelson Sivalingam, CEO at HowNow and author of Learning at Speed, and Talha Faridy, AI Innovation Lead at Easygenerator, joined moderator Ashling Moran for an honest look at where AI in L&D is actually landing. What has worked, what hasn’t, and what L&D professionals should be asking of the tools they invest in.
🎥 Watch the session: Missed it live? Watch the full recording below.
Efficiency is not the same as effectiveness
When you ask how L&D teams are using AI today, the answer is mostly the same: to do what they were already doing, but faster. Content creation, curriculum planning, analyzing evaluation data. The efficiency gains are real.
But Nelson drew a line between efficiency and effectiveness that came up throughout the whole conversation.
“There’s a big difference between doing a thing fast and doing the right thing. And right now what we’re doing is doing a thing fast, but we’re not necessarily evaluating whether it’s the right thing.”
The risk is not just wasted time. It’s that AI makes it easier to scale the wrong things. If your content wasn’t landing before, producing more of it faster does not solve the problem. As Nelson put it:
“What we might actually see ourselves doing is scaling the very problem we are hoping to solve. One of the challenges is there being a ton of content. And when you’ve got a ton of content, discovery is one of the biggest challenges. By being able to create a lot of content that’s not great quality very fast, what you’re actually doing is scaling the problem you already had much, much more quickly.”
There’s also a harder-to-recover-from consequence. When learners engage with poor-quality AI-generated content and don’t find it useful, they form an opinion. Getting them back after that is difficult.
Talha added that unrealistic expectations are part of what drives this. Many authors assume they can hand a task to AI with minimal context and get something production-ready back.
“A lot of authors have wrongful expectations of what AI can do. They feel that they can just delegate without a lot of context and a lot of groundwork, and then expect an output that is completely ready for production. Whereas oftentimes that’s not the case.”
What a real measure of value looks like
If speed isn’t the measure, what is? Nelson’s answer goes back to what L&D is actually for.
“L&D’s outcome isn’t to create content. It’s a means to an end. Our job in this profession is to help people do their jobs better and help them grow. It’s as simple as that.”
That means the question isn’t how many courses were shipped or how many hours of learning were completed. It’s whether the skills that matter to the business actually got built.
Nelson framed this through the lens of what he called the talent crunch: organizations struggling to hire specialized skills that are in high demand and short supply. The only sustainable response is to upskill people internally. That makes skills-building the output that matters, and it’s the output that most L&D measurement still doesn’t track directly.
“The question is not about how many hours of learning someone did or what content was completed. It really comes back down to: did we build the skills we needed in order to achieve those objectives? That’s really the measure of value.”
Context is what AI is missing
One of the clearest practical points from the session was about what actually makes AI output useful. Generic AI tools are reasonably good at generic information. What they’re not good at, by default, is company context.
Talha was direct about this:
“Large language models are really good at generic information. But one thing they are still not great at by default is company context. Your company context, your business goals, the outcomes you’re looking for. That is the kind of context you want to bring into the AI. That prep work needs to be done prior to working with AI on any tool.”
This applies to course creation too. The ease of building something with AI also makes it easier to build the wrong thing.
“It’s easier to build things now, but it’s also easier to build the wrong things. That concept applies to course creation. You’re building something to drive meaningful outcomes. It’s easier to build training, for sure, but it’s also easier to build the wrong training for the wrong people in the wrong formats.”
The prep work that prevents this is defining learning objectives upfront, being specific about the behavior change you want learners to have, and giving the AI a clear picture of what good output looks like before you start. In Easygenerator, this is built into the Course Guidelines feature, which lets authors give EasyAI explicit instructions about goals, content preferences, and source material before it generates anything.
Author-first AI vs AI-first content generation
This brought the conversation to a distinction that ran through the whole session: the difference between AI that starts from the author’s intent and AI that starts from content generation.
Talha described how Easygenerator approaches this, using Bloom’s taxonomy to anchor learning objectives before any content gets built.
“We ask the subject-matter expert or the instructional designer what exactly they want their learners to do after they finish the course. What is that behavior change in terms of application that they want to drive? Once that is defined upfront, along with the knowledge the learner needs, that steers the AI in the right direction and overall uplifts the quality of the content.”
Nelson added a useful frame for what this unlocks. In the old model, an L&D team would go to a subject-matter expert (SME), collect expertise, take it away, apply instructional design thinking, and come back weeks later with a course. AI-assisted authoring changes that timeline.
“As an SME, I can go to Easygenerator and work with the AI to say: I’m dumping my expertise here, but you’ve got the expertise of the pedagogy, and the best way to frame and structure this. It would have taken you weeks to get to something that was pedagogically sound. But now we can get from ‘here’s expertise’ to ‘pedagogically sound learning resource’ very quickly.”
That said, neither speaker suggested this removes the need for human judgment. The power users of AI are the ones who iterate, who treat the first output as a starting point, and who bring their own context and expertise into the process at every step.
L&D’s new job is to engineer the context, not the content
If SMEs can now create content directly with AI support, what does that mean for L&D’s role? Both Nelson and Talha pointed in the same direction.
Nelson’s framing was memorable: in this world, L&D teams are effectively managers of AI. And a manager’s job is to set the bar and create the conditions for others to hit it.
“It’s not necessarily L&D’s job to engineer the content. It’s L&D’s job to engineer the context so everyone else can create more useful resources with very little friction.”
That means defining what good looks like, setting course guidelines that any SME can follow, choosing the right pedagogical frameworks, and building governance structures that keep quality consistent across the organization without burdening every SME with instructional design decisions.
Talha made the same point from the product side:
“The L&D team can set that framework and steer things in the right direction. If you don’t, it’s just another situation where SMEs, who are already stretched, now also have to learn how to create quality learning. AI can be super helpful here, where L&D teams define what good looks like, and then SMEs can use that to create content without it adding burden on them.”
The recognition piece also matters. Ashling mentioned something she sees work in practice: putting people’s names on courses, calling out strong examples, and giving public credit to SMEs who do it well. It sets the bar visibly and gives others something to aim for.
Whether AI is built in or bolted on matters more than you might think
A question from the audience during the session touched on something that came up throughout: how do you tell whether AI is genuinely built into a tool or just added on top of an existing product?
Nelson’s take was that the answer usually shows up in how the AI performs across the full workflow.
“When you’ve got AI native, you’re essentially applying intelligence at every step in that process. So you get the efficiency gains, but also the gain in effectiveness by applying that intelligence at every step. As an add-on, it’s really difficult. You’re trying to polish the last mile with AI, which is what makes it very difficult.”
Content creation, as Nelson pointed out, involves multiple steps that benefit from intelligence at each one: curriculum design, structuring individual pieces of content, building in scaffolding, applying pedagogical frameworks. A bolt-on solution can help at one or two of these points. A platform where AI is native can support all of them.
The practical test he suggested is simple: how effective was the AI at helping you do the specific task you were trying to do? Not which AI sounds most impressive, but which one actually helped you solve the problem in front of you.
Talha addressed the pricing dimension of this directly. Some vendors have responded to the cost of AI by passing costs on through higher prices. Easygenerator’s position is that AI should be included in the base price for everyone.
“We strongly believe that the future for most authors and L&D people is AI-native. Our position is that everyone should be empowered with that technology. Hence, for our core platform AI, we don’t charge extra for it. It is included in the base price. That is the core reason for it, to empower everyone and work with them towards that future of being AI-native.”
Where this goes next
The final chapter of the conversation looked at the next 12 months. Talha pointed to agentic AI as the direction of travel: AI that moves through a workflow iteratively, using different tools and capabilities to accomplish a task, rather than producing a single output in response to a single prompt. More effective, more context-aware, better at getting to something useful without requiring the user to do multiple rounds of manual correction.
Nelson’s outlook was bigger in scope. He described a gap in L&D that has never been closed: the connection between learning and performance. Most organizations can’t tell you, when performance drops, which skills are missing or why. And even when they can identify a skills gap, they often don’t know which learning will address it.
“With autonomous agents that can go through that loop all by themselves, you can now look at work signals, infer what the performance gaps are, figure out what skills are missing, connect you with the right relevant learning resource, then monitor the work data to see whether it actually changed the performance. All in one autonomous loop.”
He called this the vision of a self-improving company. L&D sets the guardrails, and the system handles the loop.
The bottom line
AI in L&D is delivering real efficiency gains. The organizations seeing the most value are the ones treating context as a first step, keeping humans in the process rather than removing them, and measuring outcomes rather than output.
The tools matter too. AI that’s built into the authoring workflow from the start behaves differently from AI added on top of a legacy product. Understanding that difference is part of evaluating whether a platform will actually help you or just help you produce more content faster.
👏 Huge thanks to Nelson Sivalingam for a genuinely honest and practical conversation.
Webinar transcript
Ashling: I should begin by introducing myself. If you’ve been to a webinar before, welcome back. If not, my name is Ashling, and I will be moderating today’s session. I am based in the Netherlands, and I’m a Customer Value Manager here at Easygenerator. And today I am joined by Louise and Tommy, who are going to share their insights about L&D, what’s changed this year or in the past few years, and what’s coming as well in 2026. I think it’s best if we let them introduce themselves. Louise, you’re first on my screen. So, if you’d like to get us started, that would be great.
Louise: Hi. Thanks, Ashling. Hi, everyone. I’m Louise Puddifoot. I run a leadership development company called Willow & Puddifoot, and we work with organizations to help them build confident, capable leaders at different levels. So a lot of my work focuses on behavior change — things like how people communicate, how they make decisions, how they adapt, how they lead other people through changes. This year I’ve spent a lot of time looking at how AI is impacting work and what it’s meant in particular for managers and leaders, and what it’s going to mean in the future. So really looking forward to discussing those shifts with you all today.
Ashling: Thanks Louise. I’m excited to discuss those as well. And Tommy, would you like to introduce yourself?
Tommy: Yeah. Hi everyone. I’m Tommy, the CEO here at Easygenerator, calling in from Dubai — sunny, hot Dubai. And basically, most people probably know what we do. We are a platform that helps employees turn their knowledge into powerful learning content, obviously using AI and making it easy for subject-matter experts to share what they know so that teams can learn from each other. Everything fast and easy.
Ashling: Thanks Tommy. And rubbing it in — I know a few of us are cold so we’ll try not to focus on the weather today. I think that gives us a good place to start. So as mentioned, we will be speaking about what’s changed for learning and development, and what’s coming in 2026. I think it’s a good time for this discussion as we are approaching the end of 2025. It’s a nice moment to pause and to reflect on how much has changed this year for learning and development, and of course what those shifts mean as we move into 2026. We have things like AI, new skills demand, talent shortages, a new generation entering the workplace — all of which have pushed learning teams to reinvent how they operate. And as well, learner expectations are evolving just as quickly. So the role of L&D has never been more important or perhaps more dynamic. Today we’ll look at what stood out in 2025, how the learner mindset is shifting, and the trends that are going to shape workplace learning in the year ahead. We are definitely going to have a forward-looking discussion today, and it’s really exciting to have Louise and Tommy join us. So feel free to follow along, pop your questions, insights, and predictions into the chat, and of course if there’s anything you’d like us to discuss in 2026, leave those topics there as well.
Ashling: To kick things off, let’s maybe take a moment to reflect. Tommy, I’d love to know what stood out to you the most about workplace learning in 2025.
Tommy: Yeah, I think 2025 is interesting to reflect on. There’s an interesting shift — we’ve been a couple of years now into AI, and it feels like the dust is settling on it and we’re kind of finding our avenues and applicable use cases. But it was also a time where a lot of companies were struggling to hire for the talent needed to fulfill jobs. A lot of AI has taken over the mundane, repetitive, easier data-entry-level tasks, and hence it was mostly a moment to also look inward at the existing talent pool. Interesting terms like upskilling and reskilling have surfaced all over the place. And I think it’s an exciting time to understand a little bit more of what that actually means. But I also agree with the argument that the L&D function has elevated to a very significant, more strategic position. I can confirm that from the C-suite — more and more people are thinking about this as a strategic topic. It’s not a nice-to-have. It’s not a thing to just measure in hours. It’s more about impact now. CEOs are being tasked to actually look for the measurable impact that employees will have, and therefore they also need to look at what they need to be upskilling and reskilling their existing teams on.
Tommy: So there’s an exciting evolution of new applicable use cases and tools surfacing. And here at Easygenerator, taking a very optimistic view on everything AI-powered, you hear terms like copilots all over the place. For us, it could be something like co-authoring — a copilot that sits next to you and becomes your smart assistant, a smart agent that helps especially when it comes to authoring and course creation. Elements where you have a smart person sitting almost next to you, available anytime 24/7 to support you. So I think that’s also a very interesting time. All these interactive features that are perfect for upskilling and reskilling, enhanced course creation — think of scenarios and walkthroughs, using rich media, video, voice — all these interesting things that are popping up and finding their right spot in this very strategic moment. Things like just-in-time learning. ChatGPT, with I think around 800 million monthly active users, has paved the way. You can see a clear path of ChatGPT usage mostly for consumers and consumer-like cases in the enterprise — we still need to find applicable use cases there. But you see a lot of people applying these kinds of solutions to enhance their workflow. So overall I think it’s been a very exciting year and I’m very much looking forward to how this will play out in 2026.
Ashling: Yeah, 100%. I like how you mentioned AI and skills. I think this year maybe the buzzword was skills, at least from my point of view. Whereas if we were having this conversation in 2023 it definitely would have been AI. But perhaps AI is here to stay and we’re already there. We’ll see how the conversation continues. But Louise, I’m really interested — is there anything else that you’ve noticed, what stood out to you in 2025 in terms of e-learning or learning trends?
Louise: Yeah, I think I’d agree with the skills piece that you mentioned. I think from the functionality perspective, absolutely — it’s been a really exciting year. We’ve seen so many new functionalities come through as a result of AI, as you’ve mentioned, all the scenarios and role plays and things like that that we can do now, videos, etc. I’ve certainly been in L&D a long time and I think we’ve probably seen more new capabilities this year than we typically see in a decade. And I’m sure we’ll continue to do so. So that’s all really exciting. I think from an AI perspective, what’s really stood out for me in the last year is that we’ve kind of shifted from AI being something that we consciously went to — go and ask GPT a question or whatever — to something that’s sitting in the background now of everything we do, that co-pilot or autopilot. In life in general it’s in our internet searches, it’s in our shopping reviews, it’s in our business tools, and more and more in our learning tools. It’s really becoming like an autopilot in a plane — it’s there to guide us as we go. It’s switched on by default, we don’t have to so consciously use it anymore.
Louise: And I think we’re really seeing with the organizations that we’re working with that obviously they’ve got an AI strategy aiming to maximize the benefits that AI can bring. But the struggle is that we’re all learning as we go. People don’t necessarily know how to make the most of it, how to use it, how to adjust to it. And I think from our perspective, that’s really impacted the skills that we see managers and leaders needing. There are kind of a couple of key skill groups. So there’s the understanding of AI itself — the more technical side of it, really understanding what this autopilot can do for us, what its capabilities are, what it can’t do, how to work with its constraints. And then the other side of things is that we still need to be the pilot. If that’s our autopilot or co-pilot, we still need to be the pilot of that plane, driving that plane. And there are a couple of big skill groups we see around that. In particular for managers and leaders, the skills around redesigning the workflow — the thinking skills around piloting that, things like systems thinking, critical thinking, problem solving, decision making — are really key as AI changes the nature of our work. And then the other side of being a pilot is really thinking about people leadership skills: how you lead the crew of your plane, how you help them work through that with you. So things like communication skills, how you reset expectations with people, how you coach people through that, how you help people stay adaptable and cope with that constant uncertainty and constant change — that’s another challenge. We see that a lot with the people that we work with: leaders are really trying to make sense of how work is shifting and what their teams now need from them going forward.
Ashling: Yeah, I can definitely say from my experience as well that has been a topic of conversation quite a lot — yeah, skills are changing, what people need to do is changing. And I think that echoes something that you mentioned earlier Tommy, about how companies maybe are struggling to hire talent and are pivoting instead to upskilling. Which roles or skills areas do you think the biggest reskilling pushes are happening?
Tommy: I think this can be applied almost across the board. There’s not a one-size-fits-all answer. We see this especially for entry-level positions. In software development, you also see this in customer service, customer-facing, and frontline work, to that extent. There were these moments where we were looking at some of the skills, some of the repetitive tasks — what can be changed or modified or amplified with the application of AI. But I think in general, what has surfaced really is the question: okay, what does it actually mean, what do we actually need to learn and apply, and then have people take that knowledge and apply it? You can find a unique use case across all functions, but I think it’s very dominant if you look at entry-level positions, new grads, but also on the other side, looking inward inside companies — there were a lot of moments of AI literacy, generally learning about the usage of AI and the applicable internal use cases, obviously with some guardrails. But it’s a really broad picture.
Ashling: Yeah. And I think that’s something actionable that we can all take — looking at what we need, where the needs lie, and then working from there. And Louise, you mentioned that this is something that managers are experiencing or at least experiencing pressure around. So in 2025 there was this look at redesigning work and helping teams deal with uncertainty, especially with all these shifts and changes. I’d be really curious to know what kind of support you think managers need during this transition.
Louise: Yeah, I think the challenge is that the tech is evolving so quickly that it’s evolving faster than human beings are capable of keeping up with generally. So that’s a struggle. And that means managers are dealing with a couple of things. They’re having to lead their teams and it’s just not possible for them to have all the answers because things are changing so quickly. So they need to get really comfortable with working with the ambiguity and accepting ambiguity as the norm, not waiting to have clarity, but just continuing to work through that ambiguity. I think that’s one thing. And then the other thing is managing people who are feeling unsettled — humans find constant uncertainty difficult to manage. So it’s finding those techniques to help people feel grounded, help make sure they’ve got clear expectations, and that you’re coaching them through that change so that they’re staying adaptable and you’re bringing them along with you.
Ashling: Okay, so it makes sense. Getting comfortable with the ambiguity, which I think could nicely move us along in the conversation — the learner mindset is perhaps shifting, so we have to be comfortable with ambiguity. But I’d be really curious, from your conversations and the work that you’re doing, have you noticed learners’ expectations evolving? Like what does this mean for learning? What should engaging e-learning look like?
Louise: Yeah, I think learner expectations are shifting and have shifted quite a lot. Our expectations as learners tend to shift with what we’re experiencing in the rest of our lives anyway. So now that most of us have got an AI copilot in our daily work, for example, we’re getting used to getting instant answers and getting those personalized to our specific needs — that back and forth of getting exactly what we need in the moment we need it. So people’s expectations of learning are that it’s going to be more personalized and more instantaneous too. And I think it leaves us with two key types of learning. On one hand we’ve got more performance support type learning — that’s where AI is really helping organizations turn their internal knowledge into something that people can access instantly, and that can guide them at an individual level to exactly what they need. And then the second type is still that behavior change work. So the skills that really need practice and reflection and probably some human interaction — they’re becoming more important too, because as AI is taking over those transactional tasks and the execution work, those human skills around it are becoming even more important as well.
Ashling: Yeah, it makes sense. That’s where we can really shine, especially with so much AI co-help. Tommy, is this something that you’ve experienced as well, this shift in learner expectations?
Tommy: Yeah, I think if you look back a couple of years, consumer behavior is making its way into the enterprise too. The expectation is everything instant, fast, personalized — I would even say hyper-personalized. The expectation that your Siri, your ChatGPT, knows all and everything about you — people almost want to take that in their pocket to the workplace and have that instantaneously available. Obviously there’s a generational shift there too. You have one generation going to retirement and the next following up with a completely different expectation: short, fast, interactive learning, feeling like a TikTok style rather than a 40-minute SCORM. So it’s really about the format and the information being digested in the consumer space translating to the enterprise way of learning. And I think this will also be applicable to other industries beyond L&D and enterprise. In general there will be a way we change — younger generations are not even going to Google anymore, they go straight to ChatGPT. They’re not used to going through a list of results. They want the answers now, more interactive and more proactive. So I think there’s going to be that kind of mindset change happening. And companies, L&D, everybody needs to correspond to that — to not be a dusty, outdated solution but to actually create content that corresponds with the next generation.
Tommy: So I find that a super exciting time. We’re very optimistic here at Easygenerator about AI and applicable use cases, but also with the good nuance of what actually makes sense. And if you look at the management side of it, you see the emergence of almost a new muscle that needs to be trained: managers becoming more coaching-oriented, more directional, versus the old ways of managing. That’s also a fundamental shift we need to upskill for. Louise, you’re probably spending a lot of time on the leadership coaching and development side of how to deal with the future workforce, the expectations, while at the same time managing another generation and everything in between — and everything moving at almost the speed of light. If you log off for a week or two, you probably see a fundamental shift that we hadn’t seen before. So all in all, I think all of us collectively at this point in time are at a tremendous inflection point. It’s really important to understand all these moving pieces and then see them all converge into one. And as leaders, we need to be reactive, proactive, and correspond to that in the right way.
Ashling: Yeah, I think you really hit on something there with expectations and consumer behavior, and not wanting to have a dusty solution — I think that’s how you put it. Because even Google now has the option that if you search something it’ll pull up an AI answer instead of having to click into all the links. And I think this is something that’s resonating. I see in the chat that Tiffany has mentioned that she couldn’t do her job successfully without the AI that she’s worked so hard to train and ensure builds the correct memory to produce what she needed. And I think that’s something that a lot of us are experiencing. So thanks for sharing, Tiffany — I think it’s a good example of exactly the heart of what we’re discussing. Louise, I know that you mentioned learners now expect instant, personalized answers. How is this reshaping the role of L&D when you are working with and consulting organizations?
Louise: Yeah, I think it is reshaping the role quite a bit. And I’m the same as you guys — ChatGPT is basically my new best friend. I talk to it constantly all day long and have also worked hard to train it and ensure it has everything I need. So yeah, we’ve all got that as an expectation now. And I think what it means for us in L&D is that we can’t just think in terms of programs or courses anymore. We have to think bigger picture in terms of workflow and giving people instant and personalized answers. The big shift I’ve seen really is L&D people thinking in terms of enablement rather than just design or development. I’ve even started to see “enablement” feature quite a lot in job titles now for people in learning and development roles — I don’t know if anyone on the chat has enablement in their job title or description. But enablement seems to be kind of the way forward. So it’s really about us changing from being the main builder to being the architect or curator of learning in the organization. We’re no longer going to need to create everything from scratch anymore. We’re going to have our AI co-pilots or AI agents and whatever else to help us do that. And it’s going to be much more about organizing that knowledge, and making sure that the AI tools we’re using really help people get the information that they need when they need it. Which is exciting because it means a lot of those day-to-day questions people might ask — how do I do this, how do I do that — are going to be increasingly dealt with by those AI-enabled tools. And that means that we in L&D can be freed up to do more of the strategic work, more of the deeper capability building work: the kind of leadership skills, the human skills, and the culture that we want in the organization — all those things that AI can’t replace.
Tommy: Yeah, just to build on that — I think what I like is you said enablement, but I also want to say almost engagement. So L&D becomes an engagement strategy, not just a learning strategy. I think there’s a fundamental shift taking place there. And the negative side of it: if we don’t collectively do that, I think there’ll be disengagement and the silent quitting. And that’s exactly the opposite of what we want. So if we proactively focus on this engagement strategy and touch the right points, versus not corresponding to that and becoming an irrelevant solution — that’s the choice in front of us.
Ashling: It makes sense. But I think that as we go further down this line and follow this thinking, there might be some challenges around learning and development balancing performance support with that AI-driven knowledge. How do you think teams are currently tackling that balance, Louise?
Louise: I think for me the key is recognizing that they serve different purposes. The AI-powered kind of performance support and knowledge sharing is really brilliant for helping people in the moment — answering a question, guiding through a process, helping complete a task or something like that. And then deeper development programs are more around behavior change: the skills that need more practice, reflection, real conversation, opportunities to apply things. And AI can still help with those practices. So you can have AI coaching tools that help you practice — EasyCoach and things like that. But then there’s also a piece where you bring people together in programs and have the kind of human interaction and human conversation around developing those skills. So there’s a balance there of thinking about AI really taking on lots of those immediate needs, and having programs really help with the more transformational needs. The key is really getting the two to come together. And when you’ve got those two well balanced, that’s really going to give you the maximum way to help people learn and apply things.
Ashling: I think that’s certainly helpful for myself and maybe other people in the chat going on this journey as well. And Tommy, I know that you mentioned silent quitting and it seems from Louise that it’s going to be really important that people are engaged with this training and not just their AI answers. I’m curious if you could share some insights on how people slowly disengage if L&D doesn’t adapt. Maybe what are some of the early warning signs?
Tommy: Yeah, I think touching on what we talked about earlier around personalization — and maybe the opposite of it. If it’s not personalized, if it’s very much one-size-fits-all, if it feels like a mundane task that you just have to check off your checklist. Especially again looking at the younger generation — they need to be purpose-driven, identity-driven. It needs to be short, concise, but also impactful and measurable. If we do not follow through and provide these kinds of coachings in a way that’s meaningful and impactful, then we will produce irrelevant content. Attention spans are short, so people will not give it a second chance. Everything is just a click away. And I think especially with the pressure and the speed of how work is being done at the moment, you do want to make this almost entertaining. It shouldn’t feel like learning. It should feel more natural, more engaging, and more like something you’re investing in yourself — that you’re actually making progress. So I think if any AI agent solution can support you in that and make that very personalized, there will be more of a benefit that you see as a learner. And then the silent quitting becomes irrelevant because you are very much engaged and you’re upskilling.
Tommy: And then on from a leadership perspective, I think it’s a currency. If you look at the skills that somebody brings to the job, they are somewhat treated as a currency. So it needs to be invested in and taken care of. And it’s also becoming apparent to the learner themselves that they do need to upskill and reskill. They see things that AI can cover, which can be useful, but can sometimes also feel like a threat. You see all these doom projections of AI taking all the jobs versus actually looking at how AI can enhance and empower someone to do their work 10x — or focusing especially on the really strategic topics. That’s what we see a lot in the way we apply AI, to actually empower L&D. Going back to that co-authoring concept, for example, just to name one.
Ashling: Yeah, I agreed there. I think AI is definitely an opportunity. I know there’s maybe a lot of doom and gloom, but there is also a lot of opportunity. And I think Stefan has nicely captured what this means for learning and development: instead of the instant need to share what you need to know, learning and development teams need to know what to share with their learners. Which of course puts us in an interesting position for the year ahead. So I guess we can take a look at 2026 and what’s coming. Louise, in your opinion, what trends do you think will dominate workplace learning in 2026?
Louise: Thanks Ashling. I think we’re going to continue to see teams becoming more reliant on AI. So essentially a team will be part human and part AI agent. What I mean by that is that AI will take on more and more of the execution work, and the human focus will shift even more to things like the strategy, the judgment, the oversight of the systems. And I think for us in L&D, our roles will continue to evolve as well. We’ll continue to move further away from doing all the work ourselves, especially the design work and all the admin and reporting — those are going to much more be done by AI agents. And so we’ll also be much more about directing the work in L&D, continuing that move really from being the builders to being the architects of what’s going on: shaping that system, setting those standards, and enabling people. And I think that links to the learner experience as well. We’re still really at the beginning of that kind of personalized, adaptive journey. We’ve been talking about personalized learning for years. And it’s actually finally now a reality. We’ve got all the functionality — chatbots, feedback tools, scenario practice, coaching tools — that are going to become more and more prevalent in how we’re offering learning to people.
Louise: Yeah, I think that means we’re going to be shifting how we think as well around those tools. It’s not just about understanding how to prompt AI or ask it a question. It’s going to be bigger-picture thinking about how do we organize all these things to come together as a system and how do things fit together. That’s certainly what we’re seeing when we’re working on management and leadership programs — that’s where people are focusing on how we build these strategic capabilities to bring everything together next year.
Ashling: Yeah, I think you captured it really well. I like what you said — L&D being architects, not builders. And perhaps we’re just at the tip of the iceberg when it comes to personalized learning and what’s going to be possible. So I’m really excited to see how that evolves. Tommy, is there anything that you would add to trends that you think will dominate the workplace in 2026?
Tommy: Yeah, I think if I were to make a prediction, I agree with Louise’s comments. The hyper-personalized experience — everything taking a very personalized shape. With AI now it just makes sense. It’s just the next logical evolution that you receive your learning content in whatever form and shape you prefer, whether it’s podcast, video, avatars, short simulations, real-life walkthroughs, scenarios, and so on. Coaching I think is the key element I want to add. Experience in general is the whole differentiator we talked about earlier. But the future of learning in my opinion will also be more like a coaching than a static course or a walkthrough. People are looking for rich media, something entertaining. Things like roleplay, empowered by AI, very much customized to your needs, allowing people to practice at their own pace whenever they feel like it — in realistic simulations almost mimicking exactly the same real-world behavior. Think of the frontline workers. Or think of clear playbooks of how L&D has driven and structured it. And AI is not really replacing humans — it’s almost amplifying the human skills. The human will still be at the center of it. And these AI tools will be available anytime, very effortlessly. Agentic coaching will be in the room with L&D, together empowering L&D and empowering the learner on the other side.
Tommy: And this whole concept of adaptive learning — it’s been a term that’s been around for a while. But I think with this technology all blending together nicely, we can have a very real, almost real-life scenario. And that’s what we will be seeing in the future. So again, this very much aligns with our mission — this learning experience and the simplicity of it, the “easy” in Easygenerator, and very much deeply engaging.
Ashling: And I think it’s resonating what you just said as well. I see Patrick has commented in the chat that yeah, we need to embrace AI. There are a lot of people who don’t do this. And you want to ensure that people are using it the right way and that the right people are also using it for the right purposes. And that extends to learning and development too, of course. Which I think also brings us back to a point Louise made. Louise, you said that in 2026 teams will be part AI, part human. And I’m curious — what will be the biggest mindset shift that managers need to make to lead these hybrid teams effectively?
Louise: Yeah, I think one of those big mindset shifts is, as you said, moving away from seeing AI as a threat and embracing it. The way I look at it is that we have a choice: either we see AI as a teammate or we see AI as a threat. And we have to embrace seeing AI as a teammate. That’s the same for the managers that we work with — we have to help them think about seeing AI as a teammate. And then the other part, which is a really big mindset shift, is recognizing that we’re constantly reinventing and that we will always be constantly reinventing — you can never stand still. Things are always evolving. So it’s really getting comfortable with that constant reinvention, not waiting for everything to be clear. That is a big mindset change as well. We find that when we’re working with managers, if we can get them to adopt that mindset — that they don’t need to have all the answers anymore, it’s about guiding people through change, creating clarity when they can and supporting that adaptability in the team — that’s where we see the big shift in terms of success.
Ashling: I like that. I think that’s a good word — adaptability. So getting comfortable with the change and being strapped in for the ride, I assume. Or maybe not even strapped in — maybe leading the way or paving the way. I think one of those big changes Tommy mentioned was hyper-personalized learning or adaptive learning. I’m curious, do you have any first steps that you would suggest to organizations that want to move in that direction?
Tommy: I think the first step obviously needs to be embracing and welcoming this — recognizing that this is more than a trend, it’s actually happening and it’s happening right in front of us. I think we as the company that has coined the employee-generated learning term, the focus on the subject-matter expert to go straight to the knowledge — that’s the most important side of it. Because if you want to hyper-personalize, you obviously need to go to the internal knowledge carrier, to embed the people with the knowledge — the SMEs — into the content creation process, whatever the content will be. Whether that’s podcasts, avatars, videos, or even training one coach to train others, for that amplifying and multiplying effect. I think generative AI’s role should be to lower that barrier for the content creation side, again empowering L&D and the actual SME, and not replacing the human element. I think it’s really important to almost start a movement inside the company to cultivate that kind of thinking, because what you do not want to do is go out with a small group only and then face internal blocks and challenges. It needs to be a collective movement, then slowly showing the benefits of it, and making it applicable to the entire organization. Things like making it localized to specific audiences, in multiple languages for example — we do that very easily so organizations can hyper-localize in different languages too. And this whole concept of democratized learning — that’s also a very important side of it, to really understand how this flows into the delivery of the hyper-personalized learning engagement strategy.
Tommy: And of course you need some buy-in from the executives to support that concept. So some kind of positive lobbying on the executive side wouldn’t do any harm. And it’s also coming from the top that this is a strategic and important move — going back to the very first part of our conversation today, that the L&D department is becoming very much strategic and should almost advise the C-suite on what are the strategic elements that the company needs to focus on. And I think for a very hyper-personalized experience, that’s a very strong argument. By showing and demoing it, I think that will clearly mark the first step to then slowly land and expand within an organization.
Ashling: Yeah, I think it’s a good place to start. And it’s also interesting what you said about bringing the whole organization along — because you need people to use AI properly for it to be effective, you need buy-in for it to be supported. But also, AI isn’t an all-seeing, all-knowing entity. We also feed it with information. So having experts contribute their knowledge is really, really beneficial. I would like to go back to Louise, but before I ask the next question I do want to say we will be doing a Q&A in a few minutes. So if you do have any questions or your own predictions, please feel free to pop them into the chat. But there was something I wanted to pick up on. Louise, you mentioned that co-creation with AI will be growing, and I think that echoes some of what Tommy said. How do you think learning and development teams should prepare to manage AI agents or AI-powered systems?
Louise: Yeah, that’s a good question. I think it can sound exciting and it can also sound slightly terrifying depending on which way you look at it. I think it gives L&D the opportunity to step more into that strategic space and to really think about the overall system — how we shape it, how we use AI to free up time to focus on the stuff that AI can do, and then recognize the parts where we really need a human. And I think that can sound overwhelming if you don’t know where to start. The first place to start is really thinking around what can AI do and what do you need a human to do — and then that can build to focus on how these AI capabilities come together, fit together as a system. So we’re kind of moving away from how do I prompt AI, how do I ask a question — to how does everything come together, how do these agents work, how do they connect together, and how do I organize them to support learning and performance? But start small, start simple. Think about what can I do, what could we automate, where can that fit, and where do we really still need a human being to be involved. That can break it down to feel slightly less daunting.
Ashling: I think that makes sense. And I think it is important not to feel too overwhelmed. AI will be the norm, and it is scary to deal with the strange. I don’t think as humans we have been evolved to embrace change so comfortably. But if we do want to adapt and evolve, we have to do just that. I do have a final question, but your questions are really important too. I see some comments coming in through the chat, so feel free to drop them there and we’ll get to them in just a second. But if I can, I would like to ask Tommy one more question. I know that we’re touching on when AI can play a role and when humans can play a role. Tommy, you mentioned AI coaches will become the norm in the future. I’m curious if you have any safeguards or design principles that you would suggest organizations consider to make that change more comfortable.
Tommy: Yeah, I think the important side of it again is to acknowledge that this is happening. We are in a way somewhat training our AI — same as Louise said, speaking to ChatGPT in a humanized way, having the agent know a lot about you, but also an agent that kind of learns with you along the way and the challenges that you’re facing. But on an enterprise and company level, obviously there needs to be some fundamental safeguarding: ensuring that the learning content is actually accurate, that it’s ethical, and aligned with company policy. So you do want to have that in some kind of governed environment, rather than a completely free-flow environment that becomes meaningless or uncontrolled. So I think it needs to be governed to some extent. Here at Easygenerator, our approach is to safeguard the content’s integrity and keep the human in the loop. I think that’s something we’ve heard a lot of times, and it’s really important to double-click on that — especially when it gets to creating content that is then spread out to multiple people and manifested in their understanding and applied. It’s really important to put the actual knowledge carrier at the center of the creation, and ensure that the speed of AI is always governed by some accountability and the expertise of the employee. There needs to be some level of experimentation to understand what the boundaries are, as we all kind of still figure this out.
Tommy: But I also think there’s a good side of it to look at. The personalized coach being a very personalized companion that has the best intentions for you to grow — that’s what a coach by definition should have. And you also see some higher completion rates and higher engagement. McKinsey has done some research on that and discovered that engagement rates actually go up. So the AI agent, if applied correctly in a controlled, governed environment, will have the best intentions and the best impact. And the coaches can be trained. We’re also investing in that — in the technology of AI to deliver that custom, personalized coaching experience.
Ashling: And that’s an interesting point. So when the AI is informed, it will behave with the best of intentions. I see some questions coming in as well, so we’ll move over to those. But yeah, thank you for sharing those — I think they’re really helpful as we all start to do some planning hopefully in the coming weeks ahead of 2026.
Ashling: We will have time for question and answer. I know that we want to touch on all the topics that are important to us all as a community. And I see that Klaus has asked about our takes on how AI will help structure the results of hyper-localized or hyper-individualized learning, because it can be challenging to measure the effectiveness of AI. I’d be curious, Louise, if you have any thoughts on this or anything that you could contribute to Klaus’s point there.
Louise: Sure. Measuring learning effectiveness and training effectiveness has been an ongoing challenge in the industry, and I think it continues to be so with hyper-personalized learning. I think probably one of the simplest ways to look at whether something’s working is whether people are going back to the tool or the resource voluntarily. If you’ve got something out there that’s providing them with personalized learning and they’re coming back to it and using it over and over again, that gives you a good indication that it’s genuinely helpful and they’re finding it useful, and therefore it’s working. In terms of looking at the actual results from a business perspective, it depends really on what it’s there to do. Generally speaking, personalized learning is there to help somebody solve a problem, or help them do something more quickly, or help them complete a task more efficiently, or maybe help them make a decision. So it’s thinking about what are those things you’re trying to get people to do, and then is that happening? Is that working? Finding a way to measure whether those things are happening, and then tying that back to results.
Ashling: I would say yeah, I have seen organizations moving in that direction as well — probably zooming out and looking at the bigger picture, in addition to things like how does this learning resonate, or how many people are using this training, but looking at what impact that has on a bigger level. I know we had somebody mention client service training in the chat, so after deploying a certain learning plan, did we see the number of tickets reducing, or a faster time to resolution? So I think there’s room for that too. I’m curious, Tommy, do you have anything to add there when it comes to measuring learning progress and results?
Tommy: Yeah, I think given that the AI is so smart and is tracking everything — obviously in a fully covered way — I think there will finally be the moment where we can make this really measurable and impactful. For example, with our coaching value proposition EasyCoach, we have the possibility to actually make that measurable, not only for the learner but also, if wanted, for management, and then also seeing it in the downstream numbers of wherever the training was focused on. And people shouldn’t be afraid of that. You do want to see the impact. I think also from the learner side, there’s going to be a moment of seeing that actual impact of your learning experience, and maybe tying it back to performance reviews or general development and career progression. So I think it will be welcomed. There may be some hesitations in the beginning, which is normal with these kinds of evolutions. But overall, the moment will come where we will embrace this and welcome this and use this in a way where it becomes a currency, as I talked about. And that’s the moment of AI amplifying humans versus replacing or taking anything away.
Louise: If I can add as well, just on measurement before we move on, in case it’s helpful. The model that we use with our programs is: we look at intake, insight, and impact in terms of measuring how something’s gone. Intake is how many people are using it — are they going back and using it again, are they repeat using it, do they use it, are they taking it up? Then insight is really: do they like it, what are they saying about it, are they finding it useful? Those two things are relatively easy to do at a big-picture level — are people using it, and ask them, do you like it, are you finding it useful? And then the final impact piece is much harder to do. So we tend to be much more selective about where we measure impact, because it’s pretty near impossible to measure impact for everything you’re providing learning for in an organization. It’s really about thinking: what are the key projects where we want to dig in and really look for the impact? Just in case that model is helpful.
Ashling: I think it will be. From the conversations that I have, I know this is a big conversation when rolling out new learning strategies. I’d be really curious as well in the chat — does that resonate, is there anything else to follow up on? And Louise, I think you could perhaps provide more insight as well for a question by Patrick, who’s asking about AI trainers — this is already a thing for Patrick, about using AI for difficult conversations and practicing in advance on where you can improve. We’re curious to know if there’s a good way that you can start using AI to train first-line leaders.
Louise: Yeah, we do actually. So this year we recently embedded in our programs encouraging people to use their own AI copilot — whether that’s Gemini, Copilot, ChatGPT, whatever, depending on the organization we’re working with — to practice certain skills. So for example in a management program, we’ll teach people skills like coaching and having coaching conversations. We’ll teach them models for that and have them practice in the facilitator-led event. We’ll teach them things like giving people feedback — we have a framework for that, and they’ll practice that again in the classroom or virtual classroom. But then we’ll also give them prompts that they can use in their own AI tool so that they can go away and practice. And we give them assignments to actually go and practice with each other, and then go and practice with a real human — a person in their team. So it kind of builds from: practice with your AI first, practice with another human, even look in the mirror and practice, and then practice with the actual team member you want to give feedback to or have this coaching conversation with, so that you build the skill that way. And then I know that those tools are being developed, and certainly from an Easygenerator perspective as well, to actually formalize that coaching tool so you can have it sit in your own EasyCoach tool, for example. You can kind of use workarounds using prompts at the moment until you have the more sophisticated tools out there. I think it works really well, and it’s really amazing how good it is at chatting with you, giving you feedback, telling what you did well, telling you what you can improve.
Ashling: I think that’s certainly helpful. Thanks for sharing. I know the time is ticking by and we have some more questions. So perhaps Tommy, you can help with this one. Michelle has shared an observation about AI — that it’s not necessarily perfect and it can hallucinate. What would your take be on this?
Tommy: That’s true. I think it’s a technology that’s still evolving and we’re right in the middle of it, right in the middle of the workshop, almost. And I think it’s also very experimental and exploratory. There are moments where the AI has been hallucinating. But if you look at it from the bigger picture, this has been improving version over version, model over model — especially at the frontier models being developed, you see this becoming better and better. There was a time when prompt engineering was the big thing, and then the models actually just got better on their own. You know, sometimes you see the Geminis going back and thinking for seven, eight, or ten minutes — there’s heavy computational power happening in the background. There are moments of hallucination and it’s all about the fine tuning for the time being. But I think we’ll get beyond that point where there will be less hallucination due to further learning, and the fine tuning over time will just get better. But as long as we acknowledge that technology is not perfect, we’ll be fine. And the AI is not the silver bullet for everything. People will still appreciate highly the human connection and the empathy that comes with a human-to-human interaction. We’ve experienced this too — even on video conversations, it’s just a different vibe if you meet somebody in person. It’s becoming more and more apparent, especially in leadership development, in employee feedback, in growing the younger generation and workforce reskilling — all of this together that we talked about.
Tommy: I think there needs to be this human element at the core, with IT and technology being the supporting element. But at the same time equipping the learner with something they can use at their own time and at their own comfort, in the format that they appreciate, to digest the learning better and actually apply it. So as long as we acknowledge that the technology is evolving but we are at an inflection point, I think it’s fine. From the hallucinations we’ve seen in the past to what we see now, it’s been almost light years in between.
Ashling: Yeah, 100%, I think I agree. Especially as well now that there are more options to inform the tools and share the information they need, so you can ensure that the answers you’re getting are better. But it should change — I would say in the years to come, but at the rate of change, even in the months to come. I see that Athena also has a question on how do you address algorithmic bias and lack of diversity in AI developments. Louise, do you have any insight on this?
Louise: Yeah, I think I don’t have an easy answer, but I think it comes back to what Tommy said around not just relying on AI as definitely true and definitely correct, and actually applying critical thinking alongside using AI. It is about thinking about the information that you put in in the first place, asking the right questions to get the right information. And then analyzing and critically thinking about what you get back. The way I think about my AI best friend is it’s like a graduate trainee type level person helping me. So it’s brilliant, but it may be a bit naive. It doesn’t necessarily get everything right. I still have to take responsibility for what comes out of it. I still have to check that I’m comfortable with it, I still have to make sure I stand by it. So I’ve still got to go through that critical thinking space around that. I know there have been lots of issues raised about diversity and how AI companies are addressing that. I’m keen to keep an eye on how that evolves. But I certainly think it’s one to watch out for, and just make sure you apply your own human judgment to what you’re doing.
Ashling: Yeah, I think I would agree. And I think this is perhaps an area where when we’re looking at training and skills, AI literacy — being able to use AI — is a big one. And for us to not lose that critical thinking piece, and just accept what AI says, but to use our human touch when we’re looking at what AI feeds us.
Tommy: Yeah, we can’t switch our brains off yet unfortunately. We have to keep our brains heavily turned on. But that’s what sets us apart. We have a very powerful computer in each of our heads.
Ashling: Perhaps you have time for one last question as well. I know that Abdul has mentioned perhaps some concerns in the future about operating AI and the lack of power sources to serve all of our goals. Tommy, do you have any input on this?
Tommy: Well, this is a big topic. Obviously data centers and energy and operating data centers — you can see what’s happening around the world, we can’t almost keep up with building data centers at this point. So it’s a heavy computational hunger that we need to feed. I think this will be the limitation if we cannot build those data centers fast enough. And the energy resources — that’s another one of those interesting things that humanity has to solve, to almost have energy as an unlimited supply to then operate these models. But will we hit a limit? Probably not, because I think the economic incentive is just too high for businesses to not continue building these data centers. You see this AI race between the incumbents — Microsoft, Google, Grok, and all the great companies that are building and trying to capitalize and win the AI race. They have a high incentive to build the data centers to deliver on this computational ambition. And there’s going to be much more — we’re just talking about L&D, but if you think about all the other fields, medicine, research, biology, and all these high computational power needs that need to be fed. I think this is a bigger topic that we probably won’t be able to fully cover. But for now, for the purposes of large language models and the way we’re using this, I think we will be fine for the foreseeable future. But the data centers are being built right as we speak, all over the world.
Ashling: That’s a good one. I know that yeah, there are a few more questions so we’ll get to those. But there’s definitely one more. If anyone does have to drop off, if you have topics — maybe bigger discussions that we didn’t get around to today that you would like to see discussed in 2026 — please feel free to drop them in the chat or to send us a message, as we will be working on those in the coming few weeks as well. But before we log off, I see a good question that I think could be helpful. And that is: do either of you, Louise or Tommy, have any recommendations for AI websites or tools that can work really well when creating L&D content?
Louise: Easygenerator is quite good, I would say. I don’t particularly have specific other recommendations. I personally have worked across different large language models to help both design content and get ideas for ways to deliver things more effectively. And I find them all really good — the paid versions are generally better than the free versions. But other than that, I like to try different ones and get different ideas from them all.
Tommy: Yeah, I want to say maybe it’s more of a mindset — to be very curious. It’s not a specific tool, but just be very experimental. Never before have we been able to do everything the way you do it now. You can vibe code, you can speak to technology in conversational mode. I think the future of programming will be English — that phrase essentially summarizes it well, because you can communicate with technology in a conversational mode now. And my recommendation would be: just be explorative, play around with tools, give it a try. Play with the free version, play with the paid version. Start experimenting — maybe build something yourself that you normally wouldn’t think you’re able to do, and just try to learn the technology. It’s fast-moving, too fast to just be waiting and standing on the sideline waiting for it to roll out. It can be used and applied by everyone. Just use your little bits and pieces here and there, and I think it’s just a mindset and a curiosity that I would encourage everybody to just continue doing.
Ashling: I agree. And I would say most tools that you have probably have some AI component at this stage. So to see what’s out there — and going back to Louise’s point, if you are using Easygenerator, I would like to shout out Course Builder. It’s really, really helpful if you want to speed up the process of creation or even enable those who maybe have never created learning before to transform what they have. So definitely options to consider. We are over time and I’m impressed to see so many of you still with us. So I want to say a big thank you for joining, but also a huge thank you to Louise and Tommy for sharing today.
Transcript produced from VEED subtitles. Speaker attribution assigned based on context and role.
