How to Train Employees on AI in 2026: Fixing the Adoption Gap
Since 2024, there's been a massive surge in organizations training employees on AI. Yet by the end of 2025, much of that learning has evaporated into the ether of "L&D completion rates." It's not for lack of trying. UK workers are willing to learn, 89% say they want AI skills. The tools aren't the problem either; the beauty of LLMs is that they're easier to use than almost any enterprise software in history.
But somewhere between the workshop and the workflow, things fall apart.
People attend the session, nod along, maybe try a prompt or two, and then go back to doing things the old way. 97% of HR leaders say their organisations offer AI training, but only 39% of employees report having received any. That is a 58-point gap between what HR leadership believes is happening and what the workforce is actually experiencing.
This article breaks down why most AI training fails, what actually works based on real implementations, and exactly what we're doing differently at The Human Co. for the year ahead. You'll come away with a clear diagnosis of your own adoption gaps, concrete steps you can take immediately, and a framework to ensure your 2026 budget actually moves the needle.
Let's start with what's not working, and why.
The Four Reasons AI Training Falls Flat
Even when organizations do invest in training, they usually invest in the wrong things. The 58-point perception gap tells you something has gone badly wrong between intention and execution.
Here are the four specific traps organizations fall into when they try to train employees on AI:
1. The "One-and-Done" Workshop
The workshop model makes sense from an HR perspective: get everyone trained quickly, minimize disruption, and check the compliance box. But it ignores how human memory works.
Without immediate application, people forget what they've learned within weeks. I've watched this happen repeatedly. Someone attends a two-hour workshop on any given topic, feels energized, and tries a few things. But then work gets busy. A month later, the capability has evaporated.
2. Generic, Role-Agnostic Content
Sales, Finance, Operations, and HR all sitting through the exact same session on "Ethical AI Use."
It makes administrative sense. It's terrible for adoption.
A marketing manager doesn't need the same AI capabilities as a data analyst. When training is generic, people tune out. They sit through examples that don't relate to their reality and walk away thinking, "That was interesting, but not for me."
Skills England found that this kind of generic provision creates significant barriers, with many employers hesitating to commission AI training because of confusion over AI terminology—in some cases assuming it means coding and machine learning, in others expecting dashboards or compliance content, when their teams actually need practical, role-specific skills.
When training does not speak to a specific role, it is easily treated as general interest and quickly forgotten.
3. The "Menu" Problem
Most AI training starts with: "Here's how to use Copilot."
You learn the interface, how to write a prompt, maybe a few advanced features. It's the equivalent of teaching someone Excel by walking through every single menu option, one by one.
The problem? People learn the buttons, not the solutions.
Imagine you're an L&D manager. You train 200 people on "Prompt Engineering Basics." The content is solid. Yet three months later, usage is non-existent. When you ask why, the answer is consistent: "I don't know when I'd actually use this."
They learned how to operate the tool. They never learned what problems it could solve.
4. Measuring Inputs when you should be measuring outcomes
This one frustrates me the most because it's so common, and I'll admit, I've been guilty of it in my own career.
Organizations love to track completion rates. How many people attended? How many finished the module? These numbers look great on a dashboard. They keep the boss happy.
But completion rates measure whether training happened. They don't measure whether anything changed.
Meanwhile, UK employers’ training expenditure has fallen 10% in real terms since 2022, dropping from £59bn to £53bn and reaching its lowest level in more than a decade. Organizations are spending less, measuring inputs instead of outcomes, and then wondering why their AI adoption flatlines.
You are tracking attendance, but all you are really measuring is performativity.
The Underlying Problem
These failures share a common thread: they treat learning how to train employees on AI as a knowledge transfer problem. Learn the tool, check the box, move on.
But the real issue is deeper. Organizations are teaching people about AI when they should be redesigning workflows around AI. That's not a training problem—it's a change management problem.
Deloitte's CTO Bill Briggs recently shared a stat that illustrates this perfectly: 93% of AI budgets flow to technology—models, chips, software. Only 7% goes to the people, culture, workflow redesign, and training required to use it. But even that 7% is being spent wrong.
The technology is ready. The workflows aren't.
A Framework for Effective AI Upskilling
If the problem is treating AI as a training challenge when it's actually a workflow challenge, the solution isn't just better courses. It's a fundamental shift in how we approach capability building.
For decades, L&D professionals have chased the "Holy Grail" of Just-in-Time Learning—delivering the exact right knowledge at the exact moment a person needs to solve a problem. Generative AI is the closest we've ever come to fulfilling that promise.
The organizations seeing real returns in 2025 didn't just buy better software. They moved away from "Just-in-Case" learning (teaching everyone everything, hoping it sticks) to "Just-in-Time" capability building.
Based on successful implementations across the UK, here's a four-part framework for how to train employees on AI in a way that actually sticks.
1. Kill the "Workshop," Build the "Clinic"
The biggest waste of L&D budget is the mass-enrollment bootcamp where employees learn skills they won't use for three months. AI moves so fast that by the time you need the skill, the interface has changed.
Successful programs think long-term. Instead of a "Learning Day," think in terms of quarterly or annual support. Providers like MMC Learning have found success offering 12-month access to "AI Power Clinics"—drop-in sessions (60–90 minutes) where employees bring specific, real-time challenges.
Think of it as IT support, but for capability. An employee doesn't need a four-hour lecture on "The History of LLMs." They need 15 minutes on "Why is this specific client report hallucinating?"
The difference is sustained access versus a one-time event. When people know they can come back next week with a different problem, they actually experiment. When they know it's their only chance to learn, they play it safe and forget everything by Tuesday.
The Fix: Shift your budget from one-off events to sustained access. Create "office hours" where the agenda is set by the employees' current friction points, not an external curriculum.
2. Stop Teaching "Prompting." Start Teaching "Workflow Repair."
The most effective training doesn't start with the tool. It starts with the "grumble"—the part of the job everyone hates.
Analysis of 1,500 L&D conversations reveals that successful adoption rarely looks like a vendor's "ideal flow." It looks like messy, creative experimentation.
Here's what this looks like in practice:
The Manufacturing Example: One company didn't train engineers on "generative writing." They identified a bottleneck: engineers hated writing formal instructions. The solution? Train them to write messy, raw notes, then use AI to rewrite them into polished documentation.
The Legacy Content Example: Another team didn't learn "AI theory." They simply used the tools to modernize the tone of old training files, solving an immediate backlog problem.
In both cases, the "training" was invisible. It was wrapped inside a solution to a painful workflow problem.
This is the shift from teaching tools to teaching solutions. You're not asking "How do I use ChatGPT?" You're asking "How do I get this report done in half the time without losing quality?"
The Fix: Audit your workflows, not your skills. Find the friction. Train people to solve that specific friction. Diagnostic pilots reveal the actual skills gaps—like accuracy flagging or compliance checking—far better than a generic skills matrix.
3. Replace "Fear" with "Agency" (The NHS Model)
Here's a stat that matters: 42% of UK employees fear AI will replace some of their job functions. If your training ignores this, you're planting seeds in frozen ground.
The antidote to fear isn't cheerleading. It's agency.
The NHS offers a perfect case study. Their successful radiology training didn't just teach the tech; it explicitly covered ethical principles and emphasized that AI is there to augment clinicians, not replace them.
By teaching employees where the AI fails—and why a human is legally and ethically required to be in the loop—you flip the narrative. You aren't training them to be replaced; you're training them to be the safety net.
This approach addresses the psychological barriers head-on. 86% of employees question whether AI outputs are accurate. Instead of dismissing that concern, successful programs validate it. They show people how to critique AI outputs, how to spot hallucinations, and where human judgment is non-negotiable.
The Hard Truth: Some roles are at risk. Administration, accountancy, and coordination roles are being automated. You'll have employees who know, deep down, that AI threatens their core function.
You need to map out what that role looks like on the other side. Not vague promises, but specific skills that AI cannot replicate. And you must do this with the person affected—don't do this in isolation with your HRBP.
The Fix: Build psychological safety into the training design. Show people where AI fails. Teach them to be the quality control layer. Give them agency over how they use the tools, not just instructions to follow.
4. Build Governance That Enables, Not Blocks
Shadow AI is now normal. One recent study found that around four in ten employees have shared sensitive work information with AI tools without their employer’s knowledge. Another reported that 43% of workers admitted to pasting sensitive documents, including financial and client data, into AI tools.
This "Shadow AI" problem isn't a compliance issue. It's a symptom of broken governance.
When IT blocks the tools employees need, people don't stop using AI—they just stop asking permission. They use ChatGPT on their phones. They upload company data to unauthorized platforms. They create the exact security nightmare you were trying to avoid.
The solution isn't tighter restrictions. It's an AI Council.
This isn't a bureaucratic blocker; it's an enabler. By bringing Legal, IT, and Business Units into a single body, you create a "safe harbor" for experimentation. You move AI from being an "IT project" (which fails 80% of the time) to a business transformation project.
The AI Council sets guardrails, approves use cases, and creates pathways for employees to test tools safely. It replaces "Can we use this?" with "Here's how we use this responsibly."
The Fix: Stop banning tools and start creating approved pathways. Establish an AI Council with cross-functional representation. Make it easy to experiment within guardrails rather than forcing people underground.
How to Actually Measure Success
If you want meaningful learning, stop reporting on completion rates. AI implementation is behavior change. It's also risk reduction—which I know isn't half as exciting as innovation performativity, but it pays the bills.
The problem with traditional L&D metrics is they measure activity, not impact. You know how many people attended. You don't know if anything changed.
Here's how to measure what actually matters when you train employees on AI:
1. Audit Trails Over Quizzes
Most training ends with a knowledge check: "What are three best practices for prompt engineering?" The learner regurgitates what they just heard, passes, and promptly forgets.
Instead, require learners to submit the audit trail of their work—the prompt, the output, and their critique of it. This proves judgment, not just attendance.
For example: "Here's the prompt I used to draft this client proposal. Here's what it generated. Here's what I changed and why."
That submission tells you three things a quiz never will:
Can they spot when AI output is wrong?
Do they understand the limits of the tool?
Are they applying it to real work or just hypotheticals?
The Fix: Replace post-training quizzes with work artifacts. Make people show their process, not recite best practices.
2. Quality Metrics in Real Workflows
If you've trained your sales team to use AI for CRM entries, don't measure how many people completed the module. Measure the strategic depth of CRM entries compared to untrained peers.
Are the entries more detailed? More accurate? Do they include next steps and context that help the next person in the chain?
If you've trained finance to automate reporting, measure cycle time. How long does it take to produce a monthly report now versus before? What's the error rate?
These metrics tie training directly to business outcomes. They also reveal whether people are actually using what they learned or just going through the motions.
The Fix: Identify the business metric you're trying to move before you design the training. Then track that metric, not attendance.
3. Risk Reduction Metrics
Remember the Shadow AI problem? 43% of employees sharing sensitive data with unauthorized tools?
One of the clearest measures of successful AI training is whether that number goes down.
Track:
Reduction in unauthorized tool usage
Decrease in data breaches or near-misses
Increase in employees using approved platforms
Decline in IT helpdesk tickets related to AI confusion
These aren't vanity metrics. They're leading indicators that your governance and training are actually working together.
If people are still sneaking around IT to use ChatGPT on their phones, your training didn't address the real problem—which is that your approved tools are either too restrictive or too clunky.
The Fix: Measure adoption of approved tools and reduction in risky behavior. If Shadow AI persists, your training (or your governance) is missing the mark.
4. Behavioral Indicators Over Self-Reported Confidence
Post-training surveys love to ask: "On a scale of 1-10, how confident are you using AI tools?"
This metric is useless. 30% of employees admit to exaggerating their AI abilities because they feel insecure. You're measuring performance anxiety, not capability.
Better indicators:
How many people are bringing real problems to your AI Clinic sessions?
How many are submitting requests to your AI Council for new use cases?
How many are actively sharing prompts or workflows with colleagues?
These behaviors signal genuine adoption. Someone who's actually using AI will have questions. They'll hit friction points. They'll want to do more.
Silence after training isn't success—it's failure dressed up as compliance.
The Fix: Track active engagement signals—questions asked, use cases proposed, peer-to-peer sharing. These behaviors indicate people are actually experimenting.
The Bigger Picture: Stop Optimizing for the Wrong Outcome
MIT's 2025 study found that 95% of generative AI pilots fail to create measurable value. That's nearly double the failure rate of traditional IT projects.
The reason isn't that the technology doesn't work. It's that organizations measure the wrong things.
They measure training completion while usage stalls. They measure licenses purchased while workflows stay unchanged. They measure AI "readiness" while risk actually increases.
When you shift from measuring inputs to measuring outcomes—quality, speed, risk reduction, behavior change—you expose where the gaps actually are. And that's when you can fix them.
The Real Constraint Isn't Technology
We started this article with a stark reality: there's a 58-point gap between what leadership thinks is happening with AI training and what employees are actually experiencing.
That gap exists because organizations are solving the wrong problem.
The constraint in 2026 isn't technological capability—that gap has been solved. LLMs are easier to use than almost any enterprise software in history. The constraint is human readiness.
Not because people can't learn. UK workers are willing—89% say they want AI skills. But they're being taught buttons when they need solutions. They're attending workshops when they need clinics. They're being measured on attendance when the real question is whether anything changed.
The organizations that figure out how to train employees on AI effectively won't be the ones with the most expensive licenses. They'll be the ones that stopped treating this as a training problem and started treating it as a change management problem.
They'll be the ones that:
Built sustained capability programs instead of one-off workshops
Fixed workflows instead of teaching prompts
Addressed psychological barriers instead of pretending they don't exist
Measured risk reduction and behavior change instead of completion rates
The technology is ready. The question is: are your people?
Next Steps: Let's Build a Program That Actually Works
If you recognize your organization in the failures above—or if you're ready to finally close the adoption gap—we should talk.
At The Human Co., we don't do "tick-box" compliance training. We build the human infrastructure that allows your AI investment to actually pay off.
I'm Paul Thomas, and I help organizations design AI programs that work in the real world, not just on a slide deck.
Here's how we can start:
Not sure where your adoption gaps are?
I've built a 15-minute diagnostic audit that maps your current AI investment against the four failure modes we covered above. It'll show you exactly where you're losing value—and what to fix first.
[Take the diagnostic audit] or [book a 30-minute discussion of your results]
We can also help you:
Run a targeted "Workflow Clinic" for a specific team to solve one expensive problem they face daily
Design your AI Council structure to enable safe experimentation
Audit your current training spend to identify what's working and what's performative
Stop buying ingredients and start learning the recipe.
