New Zealand businesses ramp up AI & automation push

New Zealand businesses are increasing investment in AI and automation to improve productivity, according to research commissioned by Schneider Electric. The survey found 61 per cent of business leaders see spending on automation and infrastructure as essential to staying competitive.

The findings point to a shift in priorities as companies face weak productivity growth and higher operating costs. Workflow automation is an active investment priority for 45 per cent of respondents, while 42 per cent are prioritising AI-driven efficiencies.

Almost half of those surveyed, 46 per cent, said they were confident AI would deliver a return on investment within the next three years. At the same time, implementation costs, shortages of internal expertise, and problems with data availability or integration were identified as the main barriers to using AI for infrastructure or sustainability work.

The results suggest many organisations have moved beyond early AI trials but have yet to embed the technology widely across their operations. That tension between ambition and execution was a central theme in the survey of 288 senior decision-makers across manufacturing, construction, technology, retail, healthcare and professional services.

Schneider Electric New Zealand country president Oliver Hill said businesses are now looking for practical returns rather than experimentation.

"Many New Zealand businesses are no longer experimenting with AI, they expect it to deliver practical results visible in their balance sheets," Hill said.

He said businesses were likely to see better results when projects were tied closely to defined operational issues.

"The challenge now is execution. The strongest outcomes will be reaped by businesses that start with a clear operational problem, use reliable data, measure the improvement and then scale what works."

Those issues often sit in day-to-day business functions rather than large technology overhauls. The survey identified energy management, maintenance, reporting and manual administrative work as areas where companies are seeking measurable gains.

Hill outlined some of the practical uses businesses are considering.

"That might mean reducing energy waste, improving maintenance, simplifying reporting or automating manual processes that are slowing people down. AI and automation are most valuable when they are tied to a real operational outcome."

Energy focus

Energy use emerged as one of the clearest areas for near-term deployment. The survey found 42 per cent of respondents plan to implement smart buildings to reduce energy consumption within the next three years, while 31 per cent are exploring or implementing AI for smart building control.

Other use cases under consideration include AI for emissions and sustainability reporting, energy forecasting, and predictive analytics and remote maintenance. Each was cited by 20 per cent of respondents.

For businesses under pressure to protect margins, such applications offer a more targeted approach than broad restructuring programmes. They also reflect a preference for projects that can draw value from existing assets, particularly in a tougher economic environment.

Hill said this is where companies can use AI and automation in immediate and specific ways.

"In the current environment, businesses are under pressure to protect margins, lift productivity and make better use of the assets they already have. That is where AI and automation can be useful now, not as a broad transformation ideal, but as a way to reduce inefficiency, improve energy performance and support better decisions."

Execution gap

The survey paints a picture of a market that sees clear commercial potential in AI but remains constrained by practical issues. Cost remains a hurdle for many businesses, especially where investment cases depend on integrating new systems with older infrastructure or first fixing fragmented data sets.

Skills are another constraint. A lack of internal expertise can slow deployment even where there is management support and budget, particularly for mid-sized organisations that may not have dedicated technical teams to evaluate, implement and maintain AI systems.

Data quality and access are also emerging as decisive factors. Businesses may identify worthwhile use cases, but inconsistent or siloed data can limit the reliability of AI tools and make it harder to demonstrate early returns.

The report suggests these obstacles are driving a more cautious approach to adoption. Rather than pursuing sweeping digital change, many companies appear to be choosing smaller projects with clearer operational goals and more visible payback.

Hill said this narrower focus is likely to produce better results than large-scale programmes.

"The biggest gains won't come from large transformation programmes, they'll come from solving specific operational problems and scaling what works. Businesses do not need to wait for perfect conditions to move forward. The opportunity is to make targeted improvements, prove the value, and build from there."

The research was prepared by Antenna and analysed by ASI researchers using an online quantitative survey of senior decision-makers from organisations ranging from small and medium-sized enterprises to large national companies.