Learning and development groups have a problem. The demand for training content keeps going up. New tools. New processes. New compliance requirements. New markets need localized versions of everything.
However, these demands are not being met by an increase in team size. More than 70 percent of L&D groups file their workload, growing by at least 25 percent year over year. That isn’t always a small growth. That is a full-size operational venture. The groups coping with it well have found a way to work differently.
This trend is also reflected in Smartcat’s 2026 Global Growth Report, which highlights how teams are turning to AI to manage growing content and localization demands at scale.
Here is what they’re doing.
Why Training Demands Keep Growing?
It isn’t simply one aspect of this growth. Numerous matters are happening at the same time.
Workforces Are More Dispersed Than Ever Before
Languages, time zones, and countries are all represented in the teams. A training program that once served one office now has to support twelve, bringing more versions, languages, and formats to manage at once.
Content Has a Shorter Shelf Life Now
Industries change fast. Regulations update. Products evolve. Software gets new features. Training content that was accurate six months ago may already be outdated.
It’s not just more content that L&D teams are creating. They are updating existing content faster than ever before.
New Channels Require New Formats
A written training document is not enough anymore. Video modules, interactive courses, quick reference guides, and formats that are mobile-friendly are all needed by teams.
Each piece of content needs to exist in multiple formats. Each format needs to work in multiple languages. The volume adds up fast.
How AI Is Helping L&D Teams Manage the Load?
AI has become a practical solution for L&D teams that are serious about keeping up. Not as a replacement for instructional designers. As a tool made for large-scale, repetitive tasks
Faster Content Creation
Drafting course content used to take days. The instructional designer looks over it, gives it context, changes the tone, and makes the structure better. The result is the same quality. The amount of time spent has significantly decreased.
Localization at Scale
This is where AI makes the biggest difference for global L&D teams. A training module built in English needs to run in ten languages.
AI translates all ten versions. Human reviewers check for accuracy and cultural fit. Courses that used to take months to localize now go out in weeks.
Updating Existing Content
When a process changes, every related training document needs updating. AI can process large volumes of existing content and flag what needs to change.
It can even draft the updated sections. Teams review and approve instead of starting from scratch every time.
What Smart L&D Teams Are Doing Differently?
Not every team using AI is getting the same results. The ones seeing real gains are doing a few things consistently.
They Build Reusable Content Systems
Instead of creating every training module from scratch, they build templates. AI fills them. Humans refine them. The process is repeatable. It scales without requiring more people every time the volume increases.
They Treat Localization as Part of the Workflow
Many L&D teams still treat translation as a separate project that happens after the main content is done. Smart teams build localization directly into the creation process. Content and translation happen in parallel. Launch timelines shrink dramatically.
They Measure What Matters
High-performing L&D teams track how long content takes to create. They track how often it needs updating. They track learner completion rates and feedback scores.
Practical Starting Points for Any L&D Team
- Identify your highest-volume, most repetitive content types
- Start using AI for first drafts on those specific pieces
- Build a simple review workflow around AI output
- Add localization to your standard production timeline
- Track time saved per project and share results internally
The Risk of Not Adapting
Some L&D teams are waiting. They want better tools. They want more certainty. But the workload is not waiting with them.
The Gap Keeps Growing
Even as their content needs a push, teams that don’t adopt AI continue to do the same amount of manual hard work. At some point, something fails. Either pleasant drops, closing dates get ignored, or humans burn out.
Conclusion
The quantity of includes carried by way of L&D teams has increased. That is not going to change. The way they carry it, however, can.
AI is not a silver bullet. The teams using it well are keeping up. The teams avoiding it are falling behind. The workload is the same either way.
Having the proper equipment to manipulate it makes the distinction. Start small, build over time, and you can close the gap.
