Employee training is moving up HR’s agenda because AI is changing what each job involves faster than most learning programs can adjust. In HR Dive’s 2026 Identity of HR survey, 9% of HR professionals named training their organization’s top priority, up from 5% the year before.
Nine percent isn’t a majority. But a number that nearly doubles in a year, in a function that usually moves slowly, deserves a closer look. If you work in HR or L&D, this piece covers why the shift is happening, where most training plans fall short, and what you can do about it this quarter.
Why employee training jumped up HR’s priority list
Vishnu Shankar, chief data officer at talent intelligence company Draup, told HR Dive the rise isn’t about replacing people. His explanation: “AI is increasing role complexity faster than existing training programs were designed for.”
Read that twice. Nobody is claiming jobs vanish overnight. The claim is narrower, and we think more accurate: jobs are getting harder to do well.
Two other forces sit behind the number. Hiring has slowed, so employers have to develop the people already on payroll, a point made by Matt Poepsel of The Predictive Index. And outside hiring has lost its cost advantage. Shankar noted that labor-cost arbitrage has narrowed to below 20%, so buying skills on the market no longer saves much.
What AI role complexity looks like inside a team
“Role complexity” sounds abstract until you picture a working day.
Imagine a recruiter who used to screen 40 resumes by hand. With an AI screening tool, she screens none by hand, and her job got harder anyway. Now she has to judge whether the shortlist is biased, explain its logic to a hiring manager, catch the strong candidate with an unusual background the tool missed, and keep the candidate experience warm. Same title. Heavier judgment. (This is an illustration, not a case study.)
The same thing happens in finance, customer support, operations and marketing. The tool takes the routine step, and the person inherits every decision around it. Nobody rewrote the job description. Nobody built a course for it either.
The gap between using AI and being good at it
Usage is running ahead of skill. A Go1 survey of more than 2,000 L&D leaders and workers, reported by HR Dive, found 7 in 10 people use AI weekly, but only 14% call themselves advanced users.
The Cegos “Transformations, Skills and Learning” barometer for 2026 points the same way. It surveyed more than 5,500 employees and nearly 500 HR and training decision-makers across 11 countries in Europe, Asia and Latin America. Of the HR directors, 68% ranked AI and automation as the top transformations affecting skills over the next two years. Only 32% of employees had received any AI training.
The same study found that 91% of HR professionals call skills development strategic, against 78% of employees. We read that 13-point gap as a warning: what HR offers and what employees find useful aren’t lining up. (The Cegos sample isn’t India-specific, so treat it as a direction, not a local benchmark.)
Evan Metter, who leads HR transformation at KPMG U.S., calls the result a value realization problem. The return on any technology purchase depends almost entirely on whether people can use it.
What employee training should cover now
Technical training still leads. HR Dive reports budgets going to machine learning, AI and other tech-heavy skills. Money is also flowing to emotional intelligence, communication and collaboration.
We’d split the work into two buckets:
- Tool skills: writing good prompts, checking output, handling data safely.
- Judgment skills: knowing when to trust a result, when to escalate, and how to explain a decision a machine helped make.
Most companies underbuild the second bucket. Judgment doesn’t fit into a 45-minute module, so it gets skipped.
How to build an employee training plan for AI-changed roles
- Start with roles, not courses: Pick the four roles where AI has changed the work most. For each, write down which tasks moved to the tool and which decisions moved to the person. That list is your curriculum. What goes wrong: buying a generic course library and hoping it maps to your jobs.
- Ask employees before you design anything: Run a short, anonymous survey on which tasks feel harder and where people feel unsure. What goes wrong: asking managers what their teams need, then getting the manager’s guess.
- Train managers first: Managers decide whether people get time to learn. For a practitioner’s view on how leaders should approach this, read our conversation on AI, talent and human-centric work with Subramanyam Sreenivasaiah.
- Put learning inside the work: Metter points out that it’s hard to find the right trigger for learning in the flow of work, and AI may help. Practically, that means 15-minute sessions tied to a live task and peer demos. Our look at Bank of America’s employee development shows what a long-term internal growth path looks like.
- Measure behavior, not completion: A completion rate only proves people clicked. Track error rates, rework, time to full productivity in a changed role, and internal moves. What goes wrong: reporting “hours trained” to leadership and calling it impact.
Who this advice doesn’t fit
If you have fewer than 20 people and no role has really changed, a full program is overkill. Run the survey, set up peer learning and revisit in six months. And if leadership won’t protect time for learning, no plan will save you.
FAQs
Why is employee training a top HR priority in 2026?
AI is changing job content faster than existing programs were built to handle. Hiring has slowed, so companies must develop current staff, and outside talent no longer carries a clear cost advantage. In HR Dive’s survey, 9% named training the top priority, up from 5%.
How does AI increase role complexity?
AI takes over routine steps but leaves people with the judgment around them: checking outputs, spotting errors, explaining decisions and handling exceptions. The job title stays the same while the thinking required goes up.
What skills should employee training cover in the AI era?
Both technical and human skills. Technical means using AI tools, reading data and checking output quality. Human means communication, collaboration, emotional intelligence and decision-making. Budgets are moving toward both, according to HR Dive’s reporting.
How do you measure whether employee training works?
Track changes in work, not attendance: error and rework rates, time to reach full productivity in a changed role, internal moves, and manager ratings of confidence with new tools. Compare against a baseline taken before training starts.
Start with a baseline
A training plan built on guesses repeats the old problem at a larger scale. At Amazing Workplaces, we’ve surveyed over 60,000 employees, and one lesson holds: listening comes before building. Find out where roles have changed and where people feel stuck, then spend your training budget there.
