A practical guide for L&D leaders on using AI to improve knowledge access, skills development, proficiency, and workforce capability.
There’s no shortage of conversation about AI for frontline learning. But for L&D, the challenge isn’t finding possible use cases. It’s knowing where to start.
My advice? Start with the problem, not the AI.
Where are frontline employees losing time? Where is critical knowledge difficult to find? Where are people struggling to build or demonstrate the skills they need on the job?
That’s where AI for frontline learning becomes interesting. Not as a way to create more content or add another tool, but as a way to remove friction between “I need to know something” and “I know what to do.”
And you don’t need to transform everything at once.
Start with one frontline problem, one use case, and one outcome that matters. Then ask: Could AI genuinely make this better?
How can AI help frontline workers find the right knowledge faster?
AI can give frontline workers faster access to trusted knowledge at the moment they need it, without requiring them to search through libraries of SOPs, job aids, and training content.
Think about an employee on a factory floor who needs to complete a task they haven’t performed for several months.
The answer may already exist. But it could be buried in an SOP, a job aid, a training course, or somebody else’s head.
Using AI, an employee can ask a question in natural language and be directed towards relevant information, potentially in their preferred language.
That changes the role of AI from content creator to knowledge connector.
But one thing stuck out for me as foundational is: trust.
Tai Davidson, who leads Enterprise Learning & Development at Marvin, shared how frontline employees can use AI to find relevant information across thousands of job aids much faster, including accessing information in their own language.
“If you want people to actually use it, you need them to trust it.”
Tai Davidson, Enterprise Learning & Development, Marvin
That matters. If your SOPs contradict one another, your learning resources are outdated, or nobody knows who owns a process, putting AI in front of that information doesn’t solve the problem. It can simply make a knowledge problem spread faster.
So before asking how AI can surface your frontline knowledge, ask whether that knowledge is accurate, current, owned, and trusted.
Does AI for frontline learning mean creating more training?
No. One of the most valuable applications of AI for frontline learning may be making existing knowledge easier to access, adapt, and apply rather than simply creating more courses.
Dr. Reena Sanghvi, Senior Vice President at Aarti Industries, shared how her team has used AI to transform existing presentations into videos and shorter learning experiences. They have also used existing skill matrices to create question banks that managers can use when assessing proficiency.
To me, that raises a much more interesting question for L&D: How can AI for frontline learning make the knowledge we already have more useful at the moment somebody needs it?
Sometimes that will mean formal training. Sometimes it might mean a translated job aid, a short video, an answer to a specific question, or a quick refresher before someone performs a task.
But the key is better access to learning and knowledge, not more content.
Can AI replace hands-on frontline training?
No. AI can support frontline learning, but building real capability still requires people to practise, demonstrate, and receive feedback on their skills.
Getting the right answer and being able to do the job are not the same thing. This matters particularly in frontline environments where quality, safety, and production are at stake.
“They should be able to practice it to the extent that it becomes a muscle memory.”
Dr. Reena Sanghvi, Senior Vice President, Aarti Industries
This corresponds to how Tai Davidson described the rhythm of frontline learning as “tell, show, do, review.” Explain what needs to happen, demonstrate it, let someone perform it, and then review and reinforce their ability.
AI can strengthen that process. It can help with the “tell”. It can make the “show” more accessible. It can help someone prepare for or “reflect” on the “do”.
But it shouldn’t remove the “do”.
If course completion isn’t success, what should L&D measure?
L&D should measure whether AI for frontline learning helps people become capable faster, perform tasks independently, and apply their skills on the job, not simply how often employees use the technology.
L&D has traditionally been very good at measuring the things that are easy to count: completions, assessment scores, participation, and learning hours.
But businesses need people who can do the job, which requires more than completed training.
“Beyond just completion of a training element, we want a demonstrated ability to show it on the job.”
Erik Peterson, Director of Learning Excellence and Technology, GAF
At GAF, Erik Peterson and his team are exploring AI-generated questions and scenarios, including situations that may be difficult or unsafe to recreate regularly in real life. These can give frontline leaders another way to assess how someone thinks through a situation while the organization continues to capture evidence of demonstrated capability.
That helps shift the measurement conversation from “Did they finish the training?” to “Can they do the job?”
- Are new employees reaching proficiency faster?
- Can more employees perform critical tasks independently?
- Can people apply their knowledge when something unexpected happens?
- Are managers seeing evidence of improved capability?
- Can more employees move between tasks or roles when demand changes?
Those outcomes tell us far more about whether AI for frontline workers is actually creating value.
Can AI help capture knowledge before experienced workers leave?
Yes. AI for frontline learning can help organizations capture tacit knowledge from experienced workers and turn it into structured learning resources, job aids, and SOPs that others can use.
Every frontline organization has people who know things that aren’t written down.
An experienced operator hears a slightly different sound and knows something isn’t right. A construction worker spots a risk a new employee could easily miss. A supervisor knows which mistakes new starters are most likely to make.
That expertise is incredibly valuable, but much of it might never make its way into formal frontline training.
Tai Davidson described this as tacit knowledge and highlighted the risk of experienced employees leaving the business with that knowledge. One approach his team is exploring is recording an experienced worker performing a task while explaining what they’re doing. AI can then help identify steps, warnings, and practical tips and begin turning them into a job aid or video SOP, with operations, safety, and quality experts reviewing and validating the output.
That’s a powerful use of AI because the technology isn’t replacing the expert. It’s helping the organization preserve and scale their expertise.
How do we stop AI from becoming a shortcut to expertise?
AI should support learning without removing the thinking, practice, and problem-solving that help people build expertise.
This might be one of the most important questions L&D needs to consider.
If AI gives me the answer every time I encounter a problem, I might become very good at completing tasks with AI. But am I becoming better at the job?
Tai Davidson compared this with using GPS. A GPS can tell you every turn and get you to your destination, but that doesn’t necessarily mean you’ve learned the route.
The same risk exists with AI. Sometimes the best learning experience shouldn’t give someone the answer immediately. Maybe it gives them a hint. Maybe it asks another question. Maybe the employee needs to practise, make a mistake safely, or work through a problem before being shown the answer.
The goal of AI for frontline learning shouldn’t simply be to help someone finish today’s task. It should help us develop tomorrow’s experts.
So, where should L&D start with AI for frontline workers?
Start with one real frontline problem, make sure the knowledge behind it can be trusted, choose a measurable outcome, and test whether AI improves it.
You don’t need an organization-wide transformation program before you begin.
“Start small. And start with trust.”
Tai Davidson, Enterprise Learning & Development, Marvin
1. Where are people losing time?
Look for situations where frontline workers repeatedly search for information, wait for managers, or ask the same questions.
2. Is the underlying knowledge trustworthy?
Check whether the relevant SOPs, job aids, and learning materials are current, accurate, clearly owned, and validated.
3. Where could AI genuinely improve the experience?
Don’t add AI for the sake of adding AI. Identify the specific friction you want it to remove.
4. What outcome will tell you it worked?
Choose a measure connected to capability or performance, such as faster time to proficiency, greater workforce flexibility, faster access to trusted knowledge, or improved skill demonstration.
Dr. Reena Sanghvi offered complementary advice: identify the business problem first, determine what AI can handle and where human intervention is still required, then address one part of the problem at a time.
That’s the approach I’d advocate too. Don’t start by asking, “Where can we use AI?” Start with, “Where are our people struggling today, and could AI genuinely make that better?”
What is the real opportunity for AI in frontline learning?
The biggest opportunity for frontline learning AI is to help people access trusted knowledge, build skills, and reach proficiency with less friction.
AI for frontline learning can help someone find an answer faster. It can make knowledge more accessible across languages and locations. It can help turn existing content into more useful learning. It can help preserve expertise before it walks out the door. And it can give L&D and frontline leaders new ways to build and assess capability.
What matter is that people can do something better as a result.
So instead of asking, “How much AI are we using?” I think L&D leaders should be asking, “What can our people do now that they couldn’t do before?”
If we can answer that, we’re no longer talking about AI adoption for its own sake, but real progress that demonstrates added value to your business.
