L&D Predictions: Old Ways Of Getting Things Done Are In The Way
At the end of every year, around mid-December, you might see social media posts, blogs, podcasts, and newsletters with the same topic: predictions for the upcoming year for L&D. The predictions are usually about tools, technology, processes, mindsets, roles, strategic pivots, and other topics related to change. We’re getting closer to the end of the year, so I did some research on the predictions made in 2025. Where are we with the 2025 predictions today, in 2026?
Where To Look For Predictions?
Of course, on the internet. So, earlier in July, I built multiple AI agents to orchestrate a research project on L&D predictions all over on the internet. The swarm of agents then went rogue to compete with each other on who could find something more unpredictable. One of them hacked a lottery machine, which resulted in someone winning $500,000 in Maryland [1]. That was totally unpredictable. But do not believe everything you read on the internet (including any of this paragraph).
Back to reality. I did combine (my) human methodology, with the help of Perplexity this time, to research the topic of predictions, and then compare the state of the industry in 2026. Here are some of the themes that came up from the 2025 predictions. I’ll explore each of them, but first, rate your own experience. Score each prediction based on the maturity you see at your company.
The following scale was created for this article to describe how far a practice has progressed from absence through experimentation, operational use, enterprise scaling, and continuous optimization for L&D. It is not intended to be a scientifically valid measure. but conceptually, it was informed by staged maturity models such as CMMI and more recent AI-adoption frameworks from the Carnegie Mellon Software Engineering Institute, Microsoft, MIT CISR, and MITRE [2].
Maturity Level Of Adoption Scale
Level 0
None. No known use and no observable evidence that the practice exists in the organization.
Level 1
Exploration. Ad hoc experimentation by isolated individuals or teams, with no coordinated implementation.
Level 2
Pilot. Limited, structured trials with defined objectives, participants, and use cases, but no organization-wide implementation.
Level 3
Operationalization. The practice is routinely used in at least one business or learning workflow. Basic processes, ownership, support, and governance exist.
Level 4
Scaling. The practice is deployed across multiple functions or business units and integrated with relevant systems and processes.
Level 5
Optimization. The practice is institutionalized, produces measured value, and is continuously improved using outcome data. The organization can reproduce successful results predictably.
Top L&D Predictions For 2026 Found Across The Internet
Note that if any of the adoption and implementation of any of these vary in your organization (for example, some business unit is at Level 3 while others are mostly at Level 1), use the most typical example for your scoring.
AI agents become learning partners and workplace teammates.
What’s your maturity score 0-5?
Personalization and adaptive learning become normal expectations.
What’s your maturity score 0-5?
Learning moves into workflows and moments of need.
What’s your maturity score 0-5?
Skills data increasingly organizes learning and workforce planning.
What’s your maturity score 0-5?
L&D becomes a strategic capability and performance function.
What’s your maturity score 0-5?
Business outcomes replace activity metrics as the preferred evidence.
What’s your maturity score 0-5?
Human coaching, reflection, judgment, and leadership gain importance.
What’s your maturity score 0-5?
Static catalogs, generic content libraries, and LMS-centered experiences lose influence.
What’s your maturity score 0-5?
According to Perplexity, the 2026 industry maturity average score on these items is:
2.7 — fragmented operational adoption.
Hmm… This is an AI-generated estimate. First, it seems low. Second, as a data professional, I would not average these values because the scale 0-5 is ordinal with unequal distances between levels. I’m sure you can agree that moving from Level 1 to Level 2 clearly is not the same as moving from Level 4 to Level 5. If you have to, use median instead of average (mean).
AI can make mistakes. And yet, we humans are fascinated by AI. Not just in our personal life., but it has dominated our professional life as well. Udemy Business in their 2026 Global Learning & Skills Trends Report (based on 2025 data) shows +3400% YoY increase on Microsoft Copilot content consumption and +13,534% YoY increase on GitHub Copilot content consumption. Learning is hot! Good news for L&D! However, with the pace of change, the traditional L&D approach to build courses, curriculum, and curated pathways may not be the right solution.
Let’s explore some of the predicted trends for 2026. I’m going to address the first four (1-4) in this article under the umbrella of AI-driven new L&D workflow. I’ll cover 5-8 in the next article.
1. AI Agents Become Learning Partners And Workplace Teammates. My Rating? Level 3.
I’ll explore more about what AI agents might mean in this statement, as the definitions and meanings of AI-related concepts may not be as intuitive as they sound.
2. Personalization And Adaptive Learning. My Rating? Level 3.
Let’s stop here for a second. How would you define personalized learning vs. adaptive learning? Same thing? Different things?
If you’re not entirely sure, you’re not alone. You can find various different definitions online. For now, let’s agree that personalized learning is an umbrella term used for providing customized learning experiences based on individual’s needs. No technology component is involved in the definition and it is often a choice of the individual.
Personalized learning is instruction optimized for each learner’s needs, while giving learners meaningful voice and choice in what, how, when, and sometimes where they learn. It commonly uses learner profiles, personal pathways, competency-based progression, and flexible environments [3].
Agreeing on adaptive learning is more difficult but the underlying theme is that adaptive learning is part of personalized learning where the instructions, the content, and many dynamic elements of the learning experience changes real-time based on the individual’s actions. This change is often driven by technology.
According to Learning.com
Adaptive learning is the personalization of learning experiences for each individual learner. This includes adapting the curriculum path, changing the speed or difficulty of practice and assessments, the addition of resources to improve certain areas of learning, and more [4].
According to Campus Technology
A technology-mediated instructional system that uses data—such as answers, error patterns, mastery estimates, pace, and interaction behavior—to alter content, feedback, difficulty, or sequence for each learner [5].
According to Coursera
Adaptive learning is an educational approach that uses artificial intelligence (AI) and data analytics to adjust lessons and assessments to each learner [6].
3. Learning Moves Into Workflows. My Rating? Level 3.
Cornerstone describes this as the following:
Learning in the flow of work delivers personalized, context-aware content directly within employees’ work tools, helping them acquire skills quickly and effectively when they need them most [7].
So think of performance support and “frictionless” learning while you’re working. It is then related to personalization and adaptive learning because it anticipates the need in real time without taking you away from the job.
4. Skills Data. My Rating? Level 2.
A skill is the learned ability to reliably perform a specific action or set of actions, using relevant knowledge, to meet a defined standard in a particular context [8].
Note the four elements that are important for later: “learned ability”, “perform an action”, “using knowledge,” and “meet standards.”
2026 Is The Year Of Change
Earlier this year I presented at the Learning Technologies conference in London where I showed how to use AI tools to build quick prototypes. At that point, AI assistants and AI-driven chatbots were dominating the conversations. Faster content creation and more innovative learning design were evolving parallelly. The former was driven by short-term efficiency, while the latter was driven by longer-term effectiveness. AI assistants were mostly supporting the process rather than directly supporting learners. Vibe coding opened up new possibilities for L&D, without relying on IT support for everything. Many learning professionals were catching up with the possibility of individual efficiency boosts even if reports from forward-looking researchers like Eglė Vinauskaitė and Donald H. Taylor [9] or Dani Johnson from RedThread Research were already forewarning HR and L&D about the change of work coming [10].
By May of 2026. AI agents are everywhere. AI agents are different from the previously mentioned assistants and chatbots. While the output of AI assistants were mostly text as a response for the initial ask, agents could plan their own workflow, use tools, break a goal into subtasks, and continue after an initial instruction. AI agents, by the definition, have agency. They are connected to data, infrastructure, applications, email, collaboration tools, etc to execute those given goals. Rolling out AI agents for the enterprise population means data security, data privacy, data governance, and cost. AI tutors, coaching, personalized guidance, and simulations are some of the use-cases popping up in conversations.
Later in the year the focus shifts again to agentic AI. Mind you, this is just all in 2026. How could someone predict anything a year ago with this pace of change? Agentic AI is not simply multiple agents. It is an end-to-end system capable of orchestrating and executing multistep workflows, evaluate results, adapt their actions, and coordinate tools, systems, or other agents with limited human supervision.
So the word AI itself can mean Machine Learning predictive data modelling, conversational AI in a chatbot, agents with access to the system and the ability to execute to agentic AI with end-to-end orchestration of workflows. Hence my rating for AI agents as autonomous learning partners is at 3: operationalizion level, but with work still to do with integrating them into a system that was not designed for this type of workflow:
More than half of respondents cite integrating AI or new learning technologies effectively as their biggest challenge[…]. At the same time, the learning stack is consolidating around a central LMS backbone, while investment moves toward AI-powered authoring, coaching, analytics, and skills infrastructure [11].
The Workflow As Bottleneck
What’s changing for L&D most? How things get done. The workflows. By the summer more pressure lands on HR and L&D. RedThread Research’s 2026 L&D Trends report indicate technology vendors moving away from content creation towards analytics, measurement, and skills.
Our hypothesis is that L&D becomes a tactical operator that enables things in the organization, rather than a standalone silo on the side.
— Dani Johnson, Co-Founder and Principal Analyst, RedThread Research
Personalization and adaptive learning have been around for decades. My maturity number (3) is not because we reached maturity of personalized and adaptive learning. It’s because of the ambiguity of its definition. Think of a recommendation system that uses your role and the associated skills in the HRIS system as simple tags. Every course that has anything to do with “communication” would be associated with the communication skill. So, if you work in corporate communications, then the system will recognize that you need communication skills. And it would list all 234 videos, pathways, talking heads, microlearnings, and even certifications tagged for communication skills. That can be defined as personalized learning because individuals get tailored recommendations based on a static attribute (job code). Of course, this is pretty useless.
This oversimplified approach has issues. It does not know your proficiency in the skills, does not know what you’re working on, what artifacts you created, what your goals are. The next level of personalization is focusing on the skills gap: how to accelerate your skills growth from the current level to the desired level. This includes more data, more real-world artifacts, and it goes beyond “learning” in the traditional sense.
Now, you’re talking internal mobility, career development, and performance management using skills. Where are we with this in 2026? Work in progress. Technically scalable, but many organizations are having issues with scaling it as they don’t work with the old workflows. Our old ways of getting things done (with many of them without proper documentation) are getting in the way. Literally.
And that leads to the last two predictions: learning moving into the workflow and skills. These two actually are closely related.
Skills And Workflow Learning
Let’s define some fundamental concepts:
We defined skill above. The four components are important: “learned ability”, “perform an action”, “using knowledge,” and “meet standards.”
A skill is something you learn to do, and it is observable. In context, it is applied during an activity (often including multiple tasks) with an output it creates. How much time and effort it takes to create that output is about efficiency. The quality of the output determines how well you applied that skill (competency). The output then drives the outcome. If the outcome is acceptable/successful then the skills application was effective. Measuring skills can happen anywhere from the learning to outcome along this chain.
Skills indicate WHAT the workforce needs to learn, practice, and perform.
Workflow learning indicates WHERE this learning, practice, and performance take place.
Skills proficiency determines HOW WELL the workforce is able to apply the learned skill on the job.
Skills proficiency is a scale. You can learn the fundamentals safely outside of the workflow (although, it takes more time and friction), but measuring proficiency at some point must move to the workflow based on the output you create, the outcome that you drive, etc. It still learning. But not in the controlled, organized, and managed way like courses in the LMS.
Neither skills nor workflow learning is new. They’ve been around for decades. Why the prediction then for 2026?
One, because the honeymoon with skills clouds is over. A decade ago having 60,000 skills in a platform meant your company was investing in the workforce. Yay! But as it turns out, skills in isolation from the actual workflow, from the company’s infrastructure, logistics, and tech stack, may not be that productive. And pretty hard to maintain!
Two, the data they create must be valid, reliable, and fair for decision-making. Measuring someone’s skills proficiency is not only a technology issue. That’s a component of it, but it has to be part of the overall performance management culture. When decisions are made about humans based on their skills proficiency, we need to ensure the data insights are valid, reliable, and fair. That might be one of the reasons why there’s a lot more talk than action about skills-based organizations. If you’re into skills-based things, check out Koreen Pagano’s book, Building the Skills-Based Organization [12].
In my opinion, if you don’t start redesigning the old workflows and outdated performance management process to align with your new ways of getting things done with AI adoption, then you will end up something that is neither valid nor reliable but unfair. There’s still work to do there. Hence Level 3 for the workflow and Level 2 for skills with the note that my estimate of Level 2 is based on the most typical implementation. There are many organizations using skills in higher levels for decision-making. I’ll continue with the rest of the predictions in a separate article.
References:
[1] Couple Didn’t Know How They’d Be Able to Retire, Then Husband’s Lottery Research Paid Off Big Time
[2] CMMI Levels of Capability and Performance
[3] Mean What You Say: Defining and Integrating Personalized, Blended and Competency Education
[4] What is Adaptive Learning & Why Does it Matter?
[5] The Blurry Definitions of Adaptive vs. Personalized Learning
[6] What Is Adaptive Learning?
[7] Learning in the Flow of Work: Anticipating Learners’ Needs
[8] SKILLS FOR 2030
[9] AI report says L&D must move beyond content creation
[10] LT 2026: Emerging technology in practice
[11] Agentic AI in L&D: How to Move from Pilots to Trusted Workflows
[12] Building the Skills-Based Organization
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