Most AI upskilling programs are designed for knowledge workers. People who sit at desks, have laptops, and spend their days in Microsoft 365 or Google Workspace. And that’s fine, as far as it goes. But it leaves out a massive chunk of the workforce.

Frontline workers, the people in retail, healthcare, manufacturing, logistics, and hospitality, make up roughly 60% of Australian employees. They’re the ones who interact with customers, operate equipment, and keep organisations running at the ground level. And they’re being largely ignored by corporate AI training efforts.

Here’s how to fix that.

Why Frontline AI Training Is Different

Before designing anything, you need to understand the constraints:

No desk, no laptop. Most frontline workers interact with technology through a shared terminal, a tablet, or their personal phone. Training that requires a 14-inch laptop screen and a quiet meeting room doesn’t work.

Shift-based schedules. You can’t schedule a two-hour training block when someone is on a rotating roster. Even a 30-minute module is difficult if the shop floor needs staffing.

Different starting points. Digital literacy varies enormously. Some frontline workers are digital natives who can pick up new tools instantly. Others haven’t used a computer beyond basic data entry in years. One-size-fits-all doesn’t fit anyone.

Immediate relevance matters more. Knowledge workers might tolerate conceptual AI training because they can see potential applications in their work. Frontline workers need to see a concrete, specific connection to their daily tasks within the first five minutes, or you’ve lost them.

The Framework That Works

Based on work across retail, healthcare, and manufacturing organisations, here’s a four-part approach that’s producing results.

Part 1: Identify the Three to Five AI Touchpoints

Before building any training content, map the specific points where AI intersects (or will intersect) with frontline work in your organisation. Not theoretical possibilities. Actual, deployed or soon-to-be-deployed tools.

For a retail worker, that might be: AI-powered inventory management, automated customer inquiry routing, and predictive scheduling. For a warehouse worker: automated pick lists, voice-directed workflows, and predictive maintenance alerts on equipment.

Each of these touchpoints becomes a training module. Nothing else. Don’t teach frontline workers about large language models or neural networks. Teach them about the specific tools they’ll use tomorrow.

Part 2: Build Mobile-First Micro Modules

Each module should be:

• Five to seven minutes maximum. Not fifteen. Not ten. Five to seven.
• Accessible on a phone. Test it on a four-year-old Android device with a cracked screen, because that’s what many frontline workers carry.
• Video-led with captions. Show the tool in action, don’t just describe it. Always include captions for noisy work environments.
• Assessment-free for the first pass. Let people watch and absorb without the pressure of a quiz. Offer an optional knowledge check for those who want it.

Part 3: Embed Practice in the Workflow

This is where most programs fail. You can’t separate learning from doing when it comes to frontline workers. The training needs to happen on the job, not before the job.

Buddy system. Pair each learner with someone who’s already comfortable with the tool. Not a trainer from head office, a colleague who does the same job. Peer learning is the most effective method for frontline AI skills, and specialists in this space consistently recommend it as the primary learning mechanism for non-desk workers.

Guided first use. The first time someone uses a new AI tool, it shouldn’t be in a training simulation. It should be on a real task, with a buddy standing next to them. The emotional experience of “I did this for real” is far more powerful than any training module.

Error tolerance. Make it explicit that mistakes during the learning period are expected and consequence-free. Frontline workers are often in roles where errors have immediate visible consequences (wrong item picked, wrong customer interaction). If they’re afraid of getting it wrong, they’ll avoid using the new tool entirely.

Part 4: Measure What Matters

Forget completion rates. For frontline AI upskilling, track these:

• Tool adoption rate. What percentage of frontline staff are regularly using the AI tool after 30, 60, and 90 days?
• Time-to-competence. How long from first exposure to independent use without buddy support?
• Error rate trend. Are errors decreasing over time? A slight initial increase followed by a steady decrease is the healthy pattern.
• Voluntary feedback. Set up a simple mechanism (even a QR code on a break room poster) for frontline workers to share what’s working and what isn’t.

Common Pitfalls

Don’t outsource all training to the vendor. Vendor-provided training is designed to showcase features, not build competence in your specific context. Use vendor materials as a supplement, not the core.

Don’t assume managers can train their teams. Frontline managers are busy. If you expect them to deliver AI training on top of everything else, it won’t happen. Give them specific, time-boxed roles in the process (like “spend 10 minutes observing your team using the new tool and noting questions”) rather than making them the primary trainers.

Don’t ignore the anxiety. Frontline workers are the most likely to fear that AI will replace them. Address this directly and honestly. Explain what the AI does, what it doesn’t do, and why their role still matters. Skipping this conversation guarantees resistance.

Don’t launch during peak periods. If you’re in retail, don’t roll out AI training in December. Healthcare, don’t launch during flu season. Respect the rhythm of frontline work.

The Bottom Line

Frontline AI upskilling isn’t harder than knowledge worker training. It’s different. The constraints are real, but they’re solvable with the right approach: mobile-first content, embedded practice, peer learning, and honest communication about what AI means for their roles.

The organisations that get this right will have a genuine competitive advantage. The ones that ignore their frontline workforce will find that their AI investments deliver far less than promised.

Michelle Torres is an L&D strategist focused on workforce transformation and capability building.