Every manufacturing AI conversation seems to focus on predictive maintenance or quality control. Those are valid applications. But there’s a use case that often delivers faster ROI with less complexity: energy management.

With Australian industrial electricity prices what they are, even modest improvements translate to real money.

The opportunity most manufacturers miss

A typical mid-size manufacturer spends $200,000-500,000 annually on energy. Many accept that cost as fixed—you run the machines, you pay the bill.

But energy consumption isn’t fixed. It varies based on:

• Which equipment runs when
• How processes are sequenced
• When you draw power (peak vs off-peak pricing)
• Equipment efficiency (which degrades over time)
• Compressed air leaks, HVAC inefficiency, and other waste

Most manufacturers don’t have visibility into these factors. They get a monthly bill and pay it. The waste is invisible.

What AI energy management actually does

At its core, AI energy management involves:

Monitoring: Collecting detailed data on energy consumption at the equipment level, not just the facility level.

Pattern analysis: Understanding how energy use varies with production schedules, weather, shift patterns, and other factors.

Anomaly detection: Identifying unusual consumption that might indicate problems (a failing motor drawing extra current, for instance).

Optimisation: Recommending or automatically implementing changes to reduce consumption while maintaining production.

Demand management: Shifting loads to avoid peak pricing and reduce demand charges.

Let me break down where the savings actually come from.

Savings source 1: Visibility alone

Before any fancy optimisation, just knowing where energy goes produces savings.

A metal fabricator in suburban Melbourne installed energy monitoring across their facility. They discovered their compressed air system—used maybe 30% of operating hours—was running 24/7 because nobody had thought to program it otherwise. Fix took half an hour. Savings: $18,000 per year.

Another client found a piece of equipment that had been left running overnight for years. Operators assumed someone else needed it. Nobody did. $8,000 per year.

These aren’t AI wins, strictly speaking. But they require the monitoring infrastructure that AI energy management needs anyway.

Savings source 2: Peak demand management

In most of Australia, industrial electricity pricing includes demand charges based on your peak usage in each billing period. One spike can affect costs for a month.

AI systems can learn your consumption patterns and predict when peaks will occur. Then they can:

• Delay non-critical loads temporarily
• Stagger equipment startup sequences
• Alert operators before peaks happen
• Automatically manage some equipment

A food processor reduced their demand charges by 22% through intelligent load management. They weren’t using less energy overall—just spreading it more evenly.

Savings source 3: Schedule optimisation

Many operations have flexibility in when things run. Energy prices vary through the day. AI can optimise schedules to take advantage of this.

One example: a plant with significant refrigeration load shifted their pull-down cycles (getting cold rooms to temperature) to early morning when prices are lowest. The product still reached temperature before production started. Savings: $45,000 per year.

This requires understanding both energy pricing and production constraints—exactly what AI is good at.

Savings source 4: Equipment efficiency degradation

Equipment gets less efficient over time. Bearings wear, filters clog, calibration drifts. This shows up as increased energy consumption before it causes outright failure.

AI systems can detect this efficiency degradation—a motor drawing 8% more current than it should, for example. This enables proactive maintenance that saves energy now and avoids failures later.

A chemical processor found a pump running at 67% efficiency (should have been 85%) through energy analysis. Refurbishment paid for itself in four months through energy savings alone.

Savings source 5: HVAC and compressed air

These utilities often represent 20-40% of manufacturing energy consumption, and they’re frequently poorly optimised.

AI can learn:

• What temperature set points actually maintain product quality
• When compressed air pressure can be reduced
• Which zones need conditioning and which don’t
• How to sequence cooling for maximum efficiency

A plastics manufacturer was running their entire facility at 18°C for a process that only needed cool temperatures in one section. Zoned control and AI-based setpoint optimisation cut their HVAC costs by 35%.

What this costs to implement

Basic energy monitoring (sub-meters on major equipment, gateway to collect data, simple dashboard) might cost $20,000-50,000 for a mid-size facility.

Adding AI-based analytics and optimisation layers on top increases that to $50,000-150,000 depending on complexity.

Typical payback: 12-24 months. Often faster if you find quick wins like the compressed air example.

The path to getting started

Step 1: Understand your current situation

Get your last 12 months of energy bills. What’s your baseline consumption? What are you paying per kWh? What are your demand charges?

Step 2: Identify major consumers

Even without sub-metering, you probably know your big energy users. Compressors, furnaces, motors, HVAC, lighting. Estimate their share of total consumption.

Step 3: Look for obvious waste

Walk the facility outside operating hours. What’s running that doesn’t need to be? Feel for compressed air leaks. Check that equipment shuts down when it should.

Step 4: Install basic monitoring

Start with your biggest energy consumers. Sub-meters aren’t expensive. Cloud-based dashboards make data accessible.

Step 5: Analyse patterns

Once you have data, patterns emerge. When do peaks occur? What drives consumption variation? Where are the anomalies?

Step 6: Consider AI-based optimisation

If the data shows significant opportunities, then invest in AI-based systems to capture them. By this point, you’ll have concrete ROI projections.

Who does this in Australia?

Several categories of providers:

Industrial automation vendors: Siemens, Schneider Electric, ABB all have energy management offerings that integrate with their broader platforms.

Specialised energy management companies: Firms focused specifically on industrial energy analytics and optimisation.

Electrical contractors with tech divisions: Some larger electrical contractors have moved into this space.

General AI consultants: Firms like Team400 that build custom AI solutions can develop tailored energy management systems.

The right choice depends on your existing infrastructure, scale, and in-house capabilities.

The bottom line

Energy management isn’t as sexy as predictive maintenance or computer vision. But it’s often the fastest path to AI ROI in manufacturing.

The technology is mature, the data requirements are modest, and the benefits are immediate and measurable. For manufacturers wondering where to start with AI, energy might be the answer.

At today’s Australian energy prices, can you afford not to look at this?