When I ask manufacturing leaders about their AI challenges, skills consistently ranks near the top. Not just data scientists (though that’s a challenge too). The broader workforce—operators, technicians, supervisors—need to work alongside AI systems they don’t fully understand.

The skills gap is real. But so is the solution.

Over the past year, I’ve watched various training approaches succeed and fail across Australian manufacturers. Here’s what I’ve learned.

The skills your team actually needs

Let’s be clear about what we’re training for. Most manufacturing workers don’t need to become AI experts. They need to:

Work with AI-powered systems

Understand what the system is doing. Interpret its recommendations. Know when to trust it and when to question it.

A machine operator using a predictive maintenance system needs to understand what the alerts mean, how to respond, and how to provide feedback when the system is wrong.

Provide quality input

AI systems are only as good as their data. Workers need to understand why accurate data entry matters, how their inputs affect system performance, and what happens when data is missing or wrong.

Handle exceptions

AI systems don’t handle everything. Humans manage the edge cases, unusual situations, and decisions the AI isn’t designed for.

Knowing what the AI can and can’t do—and what to do when you’re in “can’t” territory—is a critical skill.

Troubleshoot basic issues

Not fixing the AI itself, but recognising when something’s not working correctly and knowing how to escalate or work around it.

Adapt as systems evolve

AI systems change over time. Workers need comfort with ongoing learning, not just one-time training.

Training approaches that work

Hands-on practice with real systems

Nothing beats actual experience. The most effective training programs get workers using AI systems early, even in limited ways.

One Sydney manufacturer introduced their predictive maintenance system by having operators review historical alerts. “Would you have agreed with this alert? What would you have done?” The discussion built understanding before they went live.

Paired learning

Matching workers who grasp new technology quickly with those who struggle. The peer teaching reinforces learning for both parties.

I’ve seen this work particularly well with older experienced workers paired with younger tech-comfortable staff. The experienced worker brings operational knowledge; the younger worker brings technology comfort. Both learn.

Problem-based scenarios

Rather than explaining how AI works in abstract terms, present specific problems.

“The system says this machine is likely to fail in three days. You’re the maintenance supervisor. What do you do?”

Working through realistic scenarios builds decision-making skills more effectively than lectures.

Incremental rollout

Start with AI suggestions displayed alongside current processes. Workers see what the AI recommends while making their own decisions. Gradually, as trust builds, shift more decisions to follow AI recommendations.

This approach trains workers while validating the system. If AI suggestions are consistently overridden, that’s useful information.

Continuous reinforcement

Training isn’t a one-time event. Brief refreshers, tip sheets, regular discussions about how the AI is performing—these keep skills current and surface issues.

One manufacturer has a five-minute “AI moment” at shift handovers. What did the system do well today? What did it get wrong? What did we learn?

Training approaches that fail

Vendor-led training that’s too technical

Vendors often explain their systems the way engineers understand them. That’s useless for operators who need to know what buttons to push and what alerts mean.

Insist on training designed for your actual users, not for technical staff.

One-time classroom sessions

A four-hour training session six months before go-live. Workers forget most of it before they touch the actual system.

Training needs to be close to when workers will use what they learn, and reinforced through practice.

Assuming younger workers will figure it out

Young workers are often comfortable with consumer technology. That doesn’t automatically translate to industrial AI systems with different interfaces and purposes.

I’ve seen assumptions that millennials “get technology” lead to inadequate training for everyone.

Ignoring resistance

Some workers don’t want AI in their workplace. Training them on button-pushing without addressing their concerns doesn’t work. They’ll find ways to avoid or undermine the system.

Effective training includes honest discussion about what AI means for their jobs and the genuine reasons for adoption.

Generic AI literacy courses

Broad courses about AI concepts and ChatGPT prompting. Interesting perhaps, but not relevant to operating specific industrial systems.

Training should be specific to the actual systems workers will use.

Addressing worker concerns

The elephant in the room: will AI take my job?

For most manufacturing roles, the honest answer is “not entirely, but your job will change.” That’s uncomfortable but addressable.

Be honest about intentions: If AI is being implemented partly to reduce headcount, workers will figure that out. Pretending otherwise destroys trust.

Emphasise augmentation: Most manufacturing AI augments workers rather than replacing them. Make this concrete with examples from your specific implementation.

Highlight new opportunities: AI often creates new roles or expands existing ones. Maintenance technicians who can work with predictive systems are more valuable, not less.

Involve workers in implementation: People support what they help create. Include operators and technicians in system design and rollout.

Commit to retraining: If roles do change, commit to helping workers transition. This matters for morale and for practical workforce management.

Building internal capability

Some manufacturers are developing internal trainers—experienced workers who deeply understand both operations and the AI systems.

These internal experts:

• Provide ongoing informal training
• Handle first-line questions
• Identify training needs
• Connect with vendors and external experts
• Champion the systems with skeptical colleagues

Investing in a few well-trained internal trainers often delivers better results than expensive external training programs.

External training resources

Several options exist for manufacturing AI training in Australia:

TAFEs: Some offer courses in industrial automation and data analytics. Quality varies significantly.

Vendor training: Usually focused on their specific products. Useful but narrow.

Industry associations: Ai Group and others offer manufacturing technology programs.

Universities: Short courses and certificates in industrial AI. Often more theoretical than practical.

Consulting firms: AI consultants Sydney and similar organisations offer training as part of implementation projects, which can be more practical than standalone courses.

The right choice depends on your specific needs and systems.

What to budget for training

Training costs are frequently underestimated in AI projects. Here’s what to budget:

Direct training costs: Course fees, materials, trainer time. Typically $500-$2,000 per worker for substantial programs.

Lost production time: Workers in training aren’t producing. Factor in this opportunity cost.

Internal coordination: Someone needs to plan and manage training. This takes time.

Ongoing reinforcement: Budget for refreshers and updates, not just initial training.

A rule of thumb: budget 15-20% of your AI implementation cost for training and change management. Skimp here and your technology investment won’t deliver its potential.

Measuring training effectiveness

How do you know if training worked?

System usage metrics: Are workers actually using the AI system as intended? Logging and analytics can reveal this.

Error rates: Are workers making fewer mistakes in working with the system over time?

Exception rates: How often are AI recommendations overridden? Is this decreasing as workers learn to trust the system (or stable because they’re correctly catching errors)?

Worker feedback: Do workers feel confident using the system? Regular surveys and conversations surface issues.

Productivity impact: Ultimately, is the AI delivering expected productivity improvements? Training problems often manifest as productivity shortfalls.

The real success factor

The manufacturers who do training best share a common characteristic: they treat it as an ongoing process, not a project.

Technology keeps evolving. Workers change. New applications get added. Training never really ends.

Building a culture of continuous learning—where workers expect to keep developing new skills throughout their careers—matters more than any specific training program.

That’s harder than running a course. But it’s what separates organisations that successfully adopt AI from those that struggle.

If you’re planning a manufacturing AI implementation, budget appropriately for training. Get AI consultants Melbourne or other specialists involved in training design if you’re not sure how to approach it. And commit to the ongoing investment that lasting capability requires.

The technology is the easy part. People are what make it work.