ATD’s 2026 State of the Industry report, sponsored by Zensai, found that 52% of organizations are considering embedding AI into their learning and development (L&D) programs. Only 26% have actually done it. That gap is understandable when the technology landscape shifts as fast as it has over the last couple of years.
But the distance between “considering” and “doing” is shorter than most L&D teams think. In this article, we break down the practical blockers keeping teams stuck, show what using AI for learning and development looks like day to day, make the case that AI frees your team to focus on the human work that actually matters, and lay out first steps you can take this quarter to move from consideration to action.
Why are most L&D teams stuck in “consideration” mode?
For every one company actively using AI in its L&D programs, there are two still stuck at consideration. That hesitation is understandable. But organizations that adopt it early for talent development will be better placed to close skill gaps, reduce admin workload, personalize learning paths, and prove L&D’s value to senior leaders.
Here are the major blockers:
Unclear use cases
Even among organizations that recognize AI’s potential, many aren’t sure where to start. Only 31% of respondents ranked using AI among their top three talent development priorities, and AI technical skills training actually fell from 55% to 45% between 2024 and 2025. The interest is there, but a clear picture of what AI does inside a learning program, day to day, is still missing for most teams.
Lack of internal skills
55% of organizations provide practical AI skills training, and only 45% provide technical training. That’s despite respondents rating bridging the employee skills gap as their number one priority.
We know that organizations recognize the importance of AI skills, as 74% plan to increase practical AI skills training. But fewer than half are actually delivering because L&D must effectively upskill everyone else while lacking confidence in their own AI-based capabilities.
Fear of getting it wrong
As an extension of the lack of internal skills, many L&D teams hesitate over AI implementation because they’re worried they’ll get it wrong. And 27% of respondents ranked “coping with the pace of change” among their top three talent development challenges, which compounds the problem. They don’t want to commit their organizations to an approach that might get proven sub-optimal in a few months’ time.
Budgetary pressure
Senior leaders are increasingly asking L&D teams to do more with less according to ATD’s findings. Despite an increase in dedicated learning hours, training investment per employee fell from $1,254 in 2024 to $846 in 2025. That’s a cut of nearly 33%, with direct learning expenditure falling from 2.9% to 2% of revenue.
On top of that, 25% of respondents expect their financial resources for talent development to decrease over the next six months, up from just 8% who said the same in 2024.
As a result, it’s no surprise that 27% of respondents cited “inadequate budget or resources” as one of their top three talent development challenges.
What embedding AI for learning actually looks like
If you’re not sure how to move from considering the use of AI in L&D to actually doing it, here are three practical ways to start:
Use AI for learning to automate your L&D admin
One of the main reasons L&D teams want to use AI is to reclaim the time they lose to repetitive admin.
L&D teams gain speed with AI: tasks that take hours manually can be completed in minutes. For learning management specifically, AI tools can automate enrollments, completions, grading, and certification.
Without AI, every new course becomes another layer of upkeep to manage. With these tools, however, your team can keep creating bespoke courses adapted to your current business needs without needing to worry about overburdening themselves with future administration.
Deliver personalized learning paths
Employees can only get so much value out of generic training. L&D teams with the most effective training policies tailor learning to specific roles and even individuals. That’s where AI-driven learning management gives L&D teams a real advantage, matching the right content to the right person without building every pathway by hand.
With AI, you can create individual or role-specific learning plans based on any number of criteria like skill gaps, internal promotion requirements, and even performance or onboarding conversations. Then, when an employee finishes their assigned learning, AI can recommend further content from your libraries based on employee needs and areas of interest.
Create adaptive, role-specific course content
According to ATD, 94% of organizations manage employee training using an in-house team. But building content from the ground up can be both resource-intensive and time-consuming, which makes the idea of new courses harder to sell to your C-suite.
However, with AI content creation tools, you can turn documents, videos, infographics, and other files into fully functioning courses complete with practical exercises.
With lower cost and greater time efficiency, you can build a large body of courses at speed, then either select the strongest or refine them all until they meet your standard. You’ll also be able to react to any new developments (internal or external) by creating new courses to update employee skills and keep your organization agile.
The pro-human case for moving forward
With nearly three quarters of organizations planning to increase practical AI skills training, there’s clearly a demand for it. The next step is to move from learning about AI to using AI for learning. We’ve talked about how these tools can automate learning management. The bigger payoff is what your team does with the time they get back.
Every minute your team regains is one they can invest in the parts of talent development that are fundamentally human: driving feedback processes and taking part in development conversations.
For instance, you might automate general learning management processes so you can take the time to directly look after new hires in the onboarding stage. By taking the time to talk with employees in their first few weeks, you uncover easily missed information that allows you to eliminate their personal blockers and increase their likelihood of sticking with the organization.
Why now is the right time to start with AI for learning
AI-driven learning is already changing how organizations train, and L&D teams that start now put themselves in a strong position. Even a small pilot gives your team the chance to reclaim time from manual processes and redirect it toward work that makes the biggest difference to your people.
That means timely, effective training tailored to each role and each person’s ambitions, giving employees the opportunities they’ve been waiting for. It also means a clear, data-backed view of the skills gaps in your business, which is essential for proving L&D’s value to senior leaders.
Using AI for learning: Take your first steps
If you don’t have any experience with using AI for learning management, it’s best to start small and work your way up. The first step we recommend is to automate some of your basic learning management processes.
Start with the basics, like enrollments and completions. Then, as you give AI more data, you can escalate to things like talent mapping.
We also recommend experimenting with AI course creation tools before you commit to them. Not only will this prepare you for more extensive course development down the line, it’ll also give you a sense of the time and resources needed to create training content in your new system. To explore the issue further, take a look at ATD’s full 2026 State of the Industry report.