The conversation about smart lighting usually starts with occupancy sensors and daylight harvesting. Turn lights off when nobody’s there. Dim them when there’s enough natural light. These are proven strategies that have been delivering energy savings in commercial buildings for over a decade.

But there’s a new layer emerging that goes beyond these reactive approaches. AI-powered building management systems are starting to optimise lighting proactively — predicting occupancy patterns, learning individual space usage, coordinating lighting with HVAC and blinds systems, and making real-time adjustments that human-configured schedules can’t match.

It’s early days, but some Australian commercial buildings are already seeing 15-25% energy reductions beyond what conventional smart lighting controls deliver. Here’s what’s actually happening.

The Difference Between Smart and AI-Optimised

Conventional smart lighting operates on rules. If occupancy sensor detects no movement for 15 minutes, dim to 20%. If daylight sensor reads above 400 lux, reduce artificial lighting proportionally. If it’s after 7pm, switch to security lighting mode. These rules are effective, but they’re static. They don’t adapt to how the building is actually used.

AI-optimised lighting learns from data. The system monitors occupancy patterns, daylight availability, weather forecasts, calendar data, and energy pricing over weeks and months. It builds predictive models that anticipate what lighting will be needed where, and when.

The practical difference shows up in scenarios like these.

Monday mornings. A rule-based system waits for people to arrive, then responds. An AI system knows that this building fills up between 8:15 and 8:45 on Mondays, starting with Level 3 (the early starters) and reaching full occupancy on Level 6 by 9:15. It pre-conditions lighting 10 minutes before each floor’s typical arrival, so people walk into a properly lit space. And it knows that on public holiday weekends, the Monday arrival pattern shifts by 30-45 minutes.

Meeting rooms. A rule-based system turns lights on when someone walks in and off when they leave. An AI system knows that Conference Room B has back-to-back bookings from 10am to 2pm on Tuesdays, so it doesn’t dim between meetings when the 3-minute gap triggers the vacancy sensor. It also knows that Conference Room D is booked for 30 minutes but the meetings typically run 45, so it keeps lights on past the booking end time.

Seasonal adaptation. A rule-based daylight harvesting system responds to current light levels. An AI system uses weather forecast data to anticipate cloud cover and adjusts its response curves accordingly. If heavy cloud is forecast for the afternoon, the system pre-adjusts its dimming profiles so the transition from daylight to artificial lighting is smoother, avoiding the noticeable “pumping” effect that annoys occupants when daylight levels fluctuate rapidly.

What the Data Shows

A 12-storey commercial office building in Melbourne’s CBD retrofitted its existing DALI lighting control system with an AI optimisation layer in mid-2025. The building already had occupancy sensors and daylight harvesting — one company doing this well helped them implement the AI prediction layer on top of the existing infrastructure.

The results over six months:

• 12% reduction in lighting energy beyond what the existing smart controls were achieving
• Occupant comfort complaints dropped by 40% — fewer reports of lights being too dim, too bright, or switching on/off unexpectedly
• HVAC coordination savings — by coordinating lighting dimming with blind position and HVAC setpoints, the building achieved an additional 5% reduction in total energy use (not just lighting)

The energy savings alone represented approximately $18,000 per year for that building. Not transformative on its own, but meaningful in the context of a broader energy efficiency strategy.

Integration Is the Hard Part

The technology exists. The challenge is integration.

Most Australian commercial buildings have lighting control systems, BMS platforms, and HVAC systems from different vendors that don’t natively communicate with each other. Getting an AI optimisation layer to pull data from and send commands to all of these systems requires integration work that’s often more expensive than the AI software itself.

The BACnet/DALI bridge. The most common integration challenge is connecting DALI lighting controls (which speak DALI protocol) with the BMS (which typically speaks BACnet or Modbus). Purpose-built gateways exist, but configuration is fiddly and requires someone who understands both protocols.

Data quality. AI optimisation requires reliable sensor data. If your occupancy sensors are poorly positioned (covering corridors instead of workspaces), if your daylight sensors are behind blinds, or if your energy metering doesn’t disaggregate lighting from general power, the AI has bad inputs and produces bad outputs.

Legacy systems. Many Australian commercial buildings have lighting controls from the early 2010s that technically support DALI but don’t have the data logging or bidirectional communication capabilities that AI optimisation requires. In these cases, upgrading the control system is a prerequisite — an additional capital cost that affects the business case.

Is It Worth It for Your Building?

The honest answer depends on a few factors.

Building size. AI lighting optimisation makes more economic sense in larger buildings (above 5,000 sqm NLA) where the absolute energy savings justify the implementation cost. For smaller buildings, the percentage savings are similar but the dollar savings may not justify the investment.

Existing controls. If your building already has DALI controls and a modern BMS, adding an AI layer is a software investment of $20,000-50,000 plus integration costs. If you don’t have smart controls, you’re looking at a full control system upgrade first, which changes the economics significantly.

Occupancy patterns. AI optimisation delivers the most value in buildings with variable occupancy — shared workspaces, buildings with a mix of tenants, facilities with varying schedules. If your building is fully occupied 8am-6pm Monday-Friday with minimal variation, the optimisation headroom is limited.

Energy costs. At current Australian commercial electricity rates ($0.25-0.35/kWh depending on state and contract), the payback period for AI lighting optimisation in a suitable building is typically 2-4 years. As electricity prices continue to rise, payback periods will shorten.

The technology is moving fast. What requires expensive custom integration today will likely be available as a standard feature in next-generation lighting control platforms within 2-3 years. For building owners planning a lighting or BMS upgrade in the near term, specifying AI-readiness in the new system is a smart move — even if you don’t deploy the AI layer immediately.

For those with capable existing systems, the case for adding AI optimisation now is already strong. The energy savings are real, the occupant comfort improvements are measurable, and the technology is proven. It’s not theoretical anymore.