Making the business case for AI in energy management

For organisations considering AI in buildings and industrial operations, the useful starting question is specific: which energy or operating problem needs to be solved, and how will we know whether the investment has worked?

Customers’ questions about AI have become more practical over the past 18 months. Business leaders want to understand how it can improve productivity, strengthen decisions and reduce costs across operations, with outcomes they can measure.

Energy management is one place to examine that opportunity. Building systems, production equipment and energy controllers already generate information about performance. The challenge is to bring the relevant data together and use it to address a defined problem.

Establish the problem and the baseline

A useful business case might focus on reducing energy consumption during unoccupied periods, identifying equipment operating inefficiently or improving production scheduling. Each requires a clear baseline and an understanding of the operating conditions that affect the result.

The baseline should identify what is being measured, over what period and against which service or production requirement. A reduction in electricity use means little if it is explained by lower output or reduced occupancy rather than an improvement in performance.

Research discussed by UQ Business School highlights the importance of business alignment, data quality, resources and organisational processes in AI projects. These foundations deserve attention before organisations choose a tool or commit to scaling it.

For each proposal, teams should ask what AI adds beyond existing controls or conventional analysis. That comparison helps establish whether the additional complexity and cost are justified.

Connect the data needed for the decision

Building management platforms, industrial systems, energy controllers and enterprise software can each provide useful information. When that information remains isolated, teams may miss relationships between energy use and the way a site operates.

A factory, for example, could examine production requirements alongside electricity prices and equipment availability. This may help identify opportunities to adjust schedules, but any proposed change still needs to meet delivery commitments, quality requirements and operating limits.

Connecting data does not automatically make a decision better. Teams need to understand its quality, timing and meaning, and decide who is responsible for validating recommendations and authorising operational changes.

The aim is to give people a dependable basis for action. Start with the information needed for the chosen problem, rather than trying to consolidate every available data source before testing any value.

Use trials to test outcomes

In South Australia, SA Power Networks’ Energy Masters project is trialling flexible appliances and home energy management technology. The project aims to demonstrate benefits from coordinating household energy use and to share lessons about making those systems work together.

For organisations considering AI, a similar discipline is useful: define the operating model being tested and decide in advance what evidence would justify wider adoption.

Within Schneider Electric Pacific, our approach is to put proposed AI use cases through a business case that defines the problem, identifies data inputs, clarifies governance, forecasts benefits and establishes measures of success.

For an energy-management application, those measures should cover the outcome that matters to the site, such as energy use, cost or equipment availability. Evaluation also needs to account for integration, software, monitoring, training and ongoing support costs.

Scale what delivers a measurable benefit

Begin with a bounded use case and a clear owner. Test performance against the baseline, review unintended effects and determine whether the result remains useful under different operating conditions.

A reduction in energy cost is not necessarily a reduction in consumption or emissions. Report those outcomes separately and explain the assumptions used, so sustainability and financial benefits can be assessed on their own terms.

If the trial demonstrates value, the next decision is whether it can be repeated elsewhere. Differences in equipment, data and working practices may affect the cost and benefit at another site.

AI tools will continue to change. A durable business case rests on a well-defined operational need, dependable data and evidence that the application improves performance after its full costs are considered.

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