We’re barely into 2026 and it already feels like the ground is shifting under organizations and employees. When everything is moving fast, skills become the stabilizer for companies trying to stay resilient and for employees figuring out new technologies, new expectations and, frankly, new identities at work. Chief learning officers (CLOs) sit right in the middle of that swirl, charged with helping people perform today and stay ready for whatever’s coming next.

As we look ahead to the rest of the year, the CLOs who will really stand out are the ones who can pair experimentation with the fundamentals of how people learn. Having the latest artificial intelligence (AI) tool isn’t enough anymore; the game is being able to plug it into real business needs and teach people to use it in ways that actually stick. My hope is that this piece gives you a clearer sense of how to do that.

AI Is Defining 2026 — and Accelerating Everything

The tone for the year was set early: NVIDIA rolled out new chips that will power the next wave of AI supercomputing, Docebo bought 365Talents to deepen its skills intelligence stack, and CES was basically a showroom of innovative robotics and smart devices. Predictions are already suggesting that AI agents will be embedded in 40% of Global 2,000 roles. That’s a significant shift in day-to-day workflows.

CLOs Will Feel the Pressure to Modernize

Everyone is waiting for the “next ChatGPT moment” that instantly resets expectations for what talent needs to know. That puts real pressure on learning teams to build agility into their systems and strategy: moving people where their skills are needed most, building new capabilities quickly and proving out those skills with real evidence, not just seat time.

Anchor Your Strategy in What Actually Works

Amid all the hype, some learning fundamentals still matter. Merrill’s Instructional Design Principles, for example, continue to be one of the most practical guides for teaching new technologies. Demonstrate → apply → activate → integrate → engage. That arc is tailor-made for AI features:

• Show the tool in action.
• Let people apply it to their own tasks.
• Help them layer it into existing workflows.
• Support integration into daily habits.
• Then give them room to explore and experiment.

It’s active, it’s hands on and it mirrors how real proficiency develops. For example, a learning experience might begin by demonstrating an AI agent feature in action.

The next step would involve a learner applying their knowledge to their role, solving a problem or coming to a suitable output using the AI agent. Then they must activate their knowledge, by building on their existing knowledge base and workflows now that they understand what the AI tool does and how it could apply to their tasks. The fourth step, integration, comes once the tool becomes part of the learner’s daily work. Finally, “engage” is fulfilled when the learner can confidently use the AI tool in different ways and feel able to explore its uses in other scenarios.

What Skills Should We Be Developing Now?

Bloom’s Taxonomy offers a helpful lens here. AI is rapidly taking on lower-order tasks such as classify, summarize, recall and sort. Human tasks and skills are, therefore, shifting upward to analyze, evaluate and create. This ability to work alongside AI is something we’re calling “evolved skills,” or understanding how to use an AI tool to get to an outcome through iteration and failure. That shift should influence everything from your learning design to your assessments.

Scenario-based learning, problem-solving labs and iterative challenges are becoming the new standard for training and applying higher-order skills. You’re not teaching learners about AI, instead, you’re teaching people how to work with AI, fail with it, recover and try again.

Tie Every Learning Investment to the Business

CLOs know this already, but 2026 will make the link even tighter. Sales pipeline, onboarding time, customer satisfaction… those aren’t downstream measures anymore; they’re inputs into the design of your learning strategy.

To hit them, you’ll need tight alignment with functional leaders such as the chief information officer (CIO), chief financial officer (CFO), chief operating officer (COO) and the business unit heads who feel the impact of AI changes most acutely. AI rollouts shift skills fast, sometimes overnight. Positioning the learning function as the engine for reskilling shows its strategic value.

Build for Speed With Intention

The pace of change is forcing learning to be faster, lighter and more woven into the work itself.

• In the flow of work: After years of hearing the phrase, it’s finally real: microcredentials, simulations, mixed reality labs and other immersive tools are now advanced enough to plug into most learning pathways and share data with your learning tech stack.
• Peer-to-peer sharing: Whether it’s a formal peer session or a quick TikTok-style demo, expertise spreads fast. Smart CLOs will leverage and curate that energy rather than trying to control it.
• Skills validation that keeps up: Performance-based assessments are rising because traditional credentials can’t keep pace. Cybersecurity teams have been using capture-the-flag models to prove capability for a while now, and you’ll see more functions embracing similar hands-on proof of skill.

Where Learning Is Headed

Learning in 2026 won’t live in long, linear programs. It will be practical, scenario driven and built into daily work. The pace of change means leaders and employees can no longer take days or weeks out of their normal routines to train on a new skill. They’ll learn constantly, try things, fail safely, reflect and share.

The best CLOs will blend reliable learning principles with smart use of new tools, grounding the shiny in the substantive.