AI adoption boosts productivity across New Zealand businesses
The survey, which covered 200 senior business leaders, indicated that 87 percent of New Zealand businesses now use some form of AI in their operations, compared to 66 percent in 2024 and 48 percent in 2023. Among larger organisations with over 200 employees, AI usage was found to be even higher at 92 percent.
Productivity gains reported
Productivity improvements were cited as the most common benefit of AI adoption. According to the study, 89 percent of AI users reported 'productivity gains'. Digging deeper, 20 percent of organisations said they achieved significant productivity gains - defined as 25 percent or more time saved or increased output - while a further 28 percent saw moderate gains between 10 and 25 percent, and 35 percent reported minor improvements.
Other reported benefits included enhanced decision-making and insights (42 percent), cost reduction (30 percent), staff enablement and retention (29 percent), and improved customer experience (26 percent).
The most widespread applications of AI in New Zealand organisations are automation of repetitive tasks (68 percent), data analytics and reporting (54 percent), workflow optimisation (51 percent), and customer or employee experience enhancement (32 percent). Sixteen percent of organisations surveyed said they are using AI to transform a core aspect of their operations or services. "The business case for AI is increasingly clear and it is encouraging to see New Zealand organisations capitalising on the benefits AI offers," says Datacom New Zealand MD Justin Gray.
Gray noted a shift in focus among organisations towards long-term preparedness, stating, "We're also seeing organisations starting to think in a more long-term way about AI, so they are having conversations with our team about data readiness, whether they have the right cloud environment to managing the increasing data demands, and about the interfaces between their existing applications and AI."
Within Datacom itself, Gray reported over 90 internal AI productivity tools have been integrated. The company has also restructured its digital engineering services to deploy a hybrid workforce of AI agents and human software engineers. This hybrid approach, he said, has enabled rebuilding of legacy systems within shorter timelines and delivered cost savings for customers ranging from 30 to 50 percent.
Challenges in scaling AI
Despite rising adoption rates, the research highlights challenges in scaling AI implementations beyond pilots and departmental use. While a third of respondents have deployed AI at the departmental level, only 12 percent have managed to scale it across their entire organisation. Eight percent reported using AI to transform core operations, and nearly half (46 percent) are still in an exploratory phase, using pilot projects to assess AI's potential.
Datacom Director of AI Lou Compagnone commented on the pace of change, stating, "We have seen significant progress in the past year, with some organisations moving from experimenting with genAI to rolling out agentic solutions in the space of 12 months."
He said many organisations face challenges moving from pilots to large-scale deployment. "There is a difference between being able to pilot AI and scale it successfully across your organisation. Creating a proof of concept with today's consumer AI tools is relatively straightforward, but productionising these solutions reveals critical challenges around data readiness, system integration, security and long-term maintainability."
Compagnone suggested effective scaling should focus on developing operational capability for AI: "That might look like setting up an 'AI Centre of Enablement' or an AI council that has cross-functional representation across the organisation, so they have visibility and coordination over their AI initiatives."
He added, "Success in AI implementation requires having a clear vision for the role AI will play in achieving business objectives, backed by a comprehensive AI strategy with clearly defined initiatives. This strategy should address key pillars such as optimisation of business functions, AI technical foundations, data governance, and talent development."
"Organisations that move beyond experimental projects to establish these strategic frameworks are the ones that will truly transform their operations with AI. Rather than isolated use cases, they create an ecosystem where AI solutions can be developed, deployed and managed at scale, with appropriate governance and measurable business outcomes."
Barriers and concerns
Barriers to broader AI adoption include lack of internal capability or skills (32 percent), issues with data quality or integration (22 percent), and uncertainty over governance or regulation (16 percent). Respondents also cited staff resistance and a lack of internal buy-in as obstacles.
Despite the increasing use of AI, skills training appears limited, with 46 percent of organisations providing AI training in the past six months and 10 percent in the past year. Another 28 percent are planning to provide training. Fifty-five percent of organisations indicated they want best-practice frameworks from the industry, while 40 percent are seeking external training support. Internally, 55 percent have an AI policy, but only 29 percent have formal ethics or safety guidelines in place.
Risks around AI are a notable concern, with 52 percent of leaders identifying "shadow AI" - the use of unapproved tools - as a problem. Other concerns included uncertainty about the implications of AI (80 percent) and loss of control (57 percent).
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