New data from Xero shows 45 percent of Kiwi small businesses see AI as the biggest opportunity since the internet, yet deeper adoption is being held back by a confidence gap driven by concerns around trust, privacy and lack of clear implementation pathways.

“They’re grappling with how and where to use it safely and confidently, how to move past the early adoption and unlock more meaningful value.”

Australian SMEs are hitting the same wall, with trust in AI decision-making remaining the single business barrier to adoption, according to data from the Australian Government National AI Centre. Its latest data shows that SME AI adoption rebounded to 44 percent in February – the strongest result in several months. But many businesses remain unsure how to stay in control once AI is introduced.

Belief high, execution shallow

According to Xero’s data almost two-thirds (61 percent) of Kiwi SMEs are already proactively using AI. But how they’re using it matters more than how many are using it – and the picture here is less impressive, with Xero saying much of the AI use is ‘only at a surface level’.

Most businesses are teaching themselves through experimentation, with 79 percent learning via trial and error and layering AI onto existing processes rather than redesigning workflows.

Bridget Snelling, Xero New Zealand country manager, says most SMEs aren’t questioning whether AI matters. “What they’re grappling with is how and where to use it safely and confidently, how to move past the early stages of adoption and unlock more meaningful value,” Snelling says.

In practice, that means AI is largely being used tactically – to draft emails, summarise documents or automate small tasks perhaps – but not strategically embedded into how the business actually runs.

That disconnect is reinforced by the barriers SMEs say are holding them back. The Xero data identifies a consistent set: Lack of time, concerns around data privacy and trust and – critically – the absence of a clear roadmap for implementation.

Asked what would help them feel more confident in deploying AI, the SMEs were clear: Practical training and workshops (47 percent), access to trusted, vetted tools built specifically for SMEs (46 percent) and real-world case studies showing how others are using AI (43 percent).

On the government side, SMEs were keen to see clear regulations and accountability standards (57 percent) and enforcement of strong data protections and standards (55 percent), with one in three also wanting the government to provide education and training resources.

A similar story for Australia

The confidence gap isn’t unique to New Zealand. Australian data points to the same pattern – growing uptake, but uneven and often ‘superficial’ use.

The National AI Centre figures show 43 percent of Australian SMEs have reported some level of AI adoption in the quarter to February 2026 – down from 45 percent the previous quarter.

The National AI Centre says while that figure might seem modest, among adopters the news is encouraging with a clear shift from surface level experimentation toward deeper integration, with broad adoption – where AI is embedded across multiple parts of the business – reaching its highest level (eight percent) in seven months. As businesses begin to see tangible benefits (Xero’s report points to significant time savings as a key benefit for Kiwi businesses adopting the technology), they tend to deepen their use – but getting to that point is proving a challenge.

Again, the survey identifies three distinct barriers which help explain why more than half of Australian SMEs are yet to meaningfully adopt AI, with trust the leading barrier at 65 percent. Relevance (54 percent) and cost and skills (20 percent) are also holding SMEs back according to the National AI Centre.

Among the non-adopting businesses, 54 percent believe AI isn’t relevant to their business, something the National AI Centre attributes to an absence of visible, relatable examples of what AI actually looks like in practice for a business like theirs.

The relevance gap is particularly pronounced in industries like construction and agriculture, where fewer than 30 percent of businesses are currently adopting AI. In contrast, the health, education and services sectors are leading adoption, with more than half of businesses in those sectors actively using AI.

“The difference isn’t capability, it is context. Businesses need to see themselves in the story of AI adoption,” the National AI Centre says.

Workers are using AI – and worrying about it

At the same time, the workforce is leaning into AI faster than organisations are formalising its use, creating new layers of risk.

Global research from GoTo (which wasn’t focused on SMEs) highlights a growing tension. While AI is boosting productivity, it’s also triggering concerns about overreliance and skills erosion. Half of employees say they already depend too heavily on AI, and 30 percent say they can’t function without it. Meanwhile, 39 percent believe that reliance is weakening their skill sets.

There’s also a sense of pressure with 60 percent of workers reporting feeling expected to use AI to improve productivity, even when they lack formal guidance or training.

The result is a workforce that is moving quickly, but not always confidently, and often without guardrails. That carries real consequences. Nearly one in four IT leaders surveyed in the GoTo report say AI has made mistakes that impacted customers, clients or their company’s bottom line. And 91 percent worry that AI could make a mistake that negatively affects their company.

Taken together, the Xero and GoTo findings point to a shift in the AI adoption conversation. It’s no longer about access to tools or even awareness of their potential. Businesses already have both. Instead the limiting factor is confidence – in the tools, the safeguards around them and in their own ability to apply them effectively.

Xero’s data breaks businesses down into four distinct ‘archetypes’ based on their beliefs influencing their AI adoption: Explorers, sceptics, trailblazers and pragmatists.

The company says for explorers, who are curious, practical and willing to try new tools but are largely experimental and self-guided, layering AI onto existing ways of working rather than rethinking processes, there’s opportunity to start baking AI tools in. “This could mean developing a deeper understanding of your business processes to better target AI deployment or more experimentation to reimagine workflows with an AI-first lens.”

For the sceptics, small mindset shifts could present opportunities.

“Whether it’s talking to other business owners about how they tailor AI implementation, or taking another look at your end-to-end business processes to identify specific opportunities that align to the capabilities on offer, knowledge and guardrails could be key to building confidence.”

For trailblazers it’s a case of avoiding being caught up chasing the newest, hyped up tools, and instead putting some effort towards mastering strategic integration of the existing tools. “The real opportunity isn’t just doing things faster or using as much AI as possible, but rather using a purpose-fit, context-aligned AI stack to realise even greater benefits within your business.”

And for the pragmatist? “View the time spent learning AI not as a ‘cost’ that takes you away from work, but as a ‘capital investment’ that buys back future time. Focusing on optimising the right processes could mean five hours spent today saves five hours every week – a worthy trade-off, indeed.”

From confidence gap to competitive advantage

There’s a throughline across both reports: SMEs, and indeed larger organisations too, aren’t failing to adopt AI because they don’t see the value. They’re struggling because they don’t know yet how to unlock it safely and consistently.

Businesses that close that confidence gap – by gaining the training, governance and clear use cases – will move from fragmented, task-level gains to sustained operational advantage.

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The Senate Education and Employment References Committee is examining the rise in the number of Australian university graduates struggling to find work, including whether graduates are being taught the skills employers are looking for and the state of the entry-level job market.

“Many graduates are not job-ready, with gaps in practical capability, digital and AI fluency, communication and professional judgement,”

The inquiry into Australian university graduates, launched in March, is considering a broad set of issues affecting graduate employability, including the quality of university education and alignment between graduate capabilities and workforce needs.

Early submissions indicate AI is influencing both how grads develop skills and how businesses structure entry-level roles.

Submissions note that AI is being used to automate entry-level work traditionally performed by university graduates, raising concerns about fewer job opportunities and weaker early-career training pathways.

One submitter told the inquiry that automation of routine tasks is reducing the number of roles available to graduates. In a written submission, ICT engineering graduate Nassar Zaytouni says AI is contributing to a ‘graduate employment crisis’ with businesses increasingly using AI to perform work previously undertaken by junior staff.

Zaytouni says when AI replaces lower-value tasks, new grads may lose the opportunity to develop practical skills in the workplace. “When universities fail to teach critical thinking and complex problem-solving, and AI automates the ‘low-value’ tasks… the window for on-the-job training vanishes,” he says.

The dilution of course content has ‘stripped’ graduates of their ‘competitive advantage’ – the rigor and diligence enabling them to contribute and offer insight as well as perform lower value tasks, he says.

The submission states that the removal of routine workplace tasks limits opportunities for graduates to develop practical experience, which has historically been part of early-career roles. It also raises concerns about the impact of AI on academic standards and graduate preparedness, with Zaytouni describing what he calls ‘the systemic dilution of academic standards and the resulting vulnerability of Australian graduates in an increasingly AI-driven economy.”

He’s called for changes to prioritise ‘high-order’ skills that are less likely to be replicated by AI systems, such as critical thinking and complex problem-solving.

Other submissions to the inquiry raise related concerns about the use of AI in university study and its potential impact on skills development.

One, from a postgraduate student, argues that the use of generative AI tools in coursework can have a ‘real, detrimental impact on learning and cognitive ability’ with implications for graduate preparedness in the workforce. It calls for restrictions on the use of AI technologies in academic work, stating that ‘AI usage should not be tolerated’ in coursework.

A submission from the Future Skills Organisation highlights a parallel issue, pointing to a gap between the skills universities are delivering and those sought by employers, particularly in technology-driven roles.

It says there is a ‘clear and growing mismatch between university outcomes, employer expectations and graduate capability’ across finance, technology and business occupations.

It says demand for skills in these sectors in increasing, particularly in technology-related roles, driven in part by the uptake of AI.

“The critical issue is not simply the volume of graduates but whether they are equipped with the skills employers actually need,” it says in a submission.

“Employers report that many graduates are not job-ready, with gaps in practical capability, digital and AI fluency, communication and professional judgement,” the FSO says. “As a result, employers are bearing the cost of developing workforce readiness post-hire.”

AI is accelerating the challenge, the FSO says, with baseline expectations now including the ability to use AI tools, assess outputs and apply judgement in digital environments. It notes too, a lack of consistent development of transferable, generalist skills such as communication, teamwork and adaptability.

The FSO is calling for AI literacy to be a core graduate capability across all university programs, with digital fluency, problem-solving, communication and teamwork included in generalist workplace skills. Aligning course design more closely to labour-market demand, expanding earn-while-you learn models and improving collaboration between universities, employers, vocational education and training providers and government to ensure more consistent, transferable, practical graduate skills are also recommended.

Independent senator Fatima Payman, who is leading the inquiry, has also made a submission which includes the recommendation that the Australian government introduce a standalone act governing AI, particularly with regard to its use in the workplace.

Submissions to the inquiry close in June.

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The annual awards, which celebrate the success of high-tech companies across a range of industries including software, electronics, telecommunications and agritech, saw Tait Communications, founded by Sir Angus Tait in 1969, take top honours as the PwC Hi-Tech Company of the Year.

“Tait is coming off a phenomenal year.”

The panel of international judges commented that while the company was founded 57 years ago the company continues to innovate on the global stage and move at speed.

“Tait is coming off a phenomenal year, passing the half billion-dollar revenue mark. It grew organically and by acquisition expanding into new countries and introduced technically advanced new products and it competes strongly on the world stage.”

The company, best known as a maker of radio communications equipment, has a customer list which includes the likes of the London Fire Brigade, Quebec’s Reseau de transport de Longuei urban transport organisation, Transport for London, Safework Australia, St John Ambulance, Silver Fern Farms, The São Paulo Military Police, and, for those rugby fans out there, The Crusaders.

The company started 2025 with the acquisition of Australia’s m-View, a video technology company specialising in safety and evidence offerings including body-worn cameras, in-car video tech and a digital evidence management platform and ended the year with its latest tech being rolled out in ambulances, fire appliances and police cars across New Zealand.

Other finalists for Company of the Year included Gallagher Group, Halter, Dawn Aerospace, Auror and Aroa Biosurgery.

Nearly 70 companies were vying for glory across 14 categories.

The gala dinner event, held at Auckland’s Spark Arena with a record 1300 attendees on hand, also saw agritech Hectre take two awards – claiming both the Most Innovative Agritech Solution and the Māori Company of the Year categories. It bet off BDX, Bovonic, TCS and Trackit to take the agritech category, while in the Māori Company category it claimed victory ahead of Alps2Ocean Foods, Bio Innovations and PAM. The company, which completed a $12m Series A raise earlier this year, was also a finalist in the Emerging Company category.  (For a more detailed breakdown on all the finalists and what exactly they do, head to our earlier story here.)

Several categories saw both winners and highly commended, including the Deep Tech Solution, won by Masco Technology which provides sensory integration for hardware companies, with multi-finalist Starboard Maritime Intelligence – provider of an AI-powered maritime intelligence platform – highly commended.

Meanwhile, Vend founder Vaughan Fergusson was recognised as the 2026 Flying Kiwi and inducted into the NZ Hi-Tech Hall of Fame. The accolade was not only for his entrepreneurial endeavours, but also for the major contribution he has made to the industry through his philanthropic contribution via the Pam Fergusson Charitable Trust and his support and mentoring of Kiwi tech founders.

Behind the glory of the awards, the night showcased something more significant: Kiwi tech companies are scaling internationally, doubling down on innovation and continuing to build globally competitive businesses from New Zealand.

The TIN 200 – which records the top 200 New Zealand tech companies – noted revenue from those companies of $20 billion in total revenue last year, up 9.9 percent on 2024 figures.

Marian Johnson, Hi-tech Trust chair, says the industry is continuing ‘exceptional growth as well as innovation and matching it with the best in the world’.

The full list of winners is:

PwC Hi-Tech Company of the Year: Tait Communications

Xero Hi-Tech Young Achiever: Lucy Turner – CTO and co-founder of VXT

Christchurch Airport Hi-Tech Solution for the Public Good

Winner: HARK by 800 Trust

Highly Commended: EVolocity

Consult Recruitment Best Contribution to the NZ Tech Sector: Oxygen Advisors

Datacom Hi-Tech Inspiring Individual: Tim Young – Smart Access

Fujitsu Most Innovative Deep Tech Solution

Winner: MACSO Technologies

Highly Commended: Starboard Maritime Intelligence

GreenMount and Poutama Trust Hi-Tech Kamupene Māori o te Tau – Māori Company of the Year: Hectre

Tait Communications Flying Kiwi: Vaughan Fergusson

NZX Most Innovative Hi-Tech Creative Technology Solution: Kitten Space Agency by RocketWerkz

Duncan Cotterill Most Innovative Hi-Tech Software Solution: Partly

Braemac Most Innovative Hi-Tech Manufacturer: Architectural Glass Products

Kiwibank Most Innovative Hi-Tech Solution For a More Sustainable Future

Winner: TCS

Highly Commended: NZ AutoTraps

NZTE Most Innovative Hi-Tech Agritech Solution: Hectre

2040 Ventures Hi-Tech Startup Company of the Year: Kara Technologies

ASX Hi-Tech Emerging Company of the Year: Calocurb

The post Hi-Tech Awards winners: scaling Kiwi tech appeared first on iStart keeping business informed on technology.

AppWrap May 2026

27.05 Today’s students may have grown up with tech, but they’ve just recorded their worst-ever results in national digital literacy tests, ABCnews reports. Just 37 percent of year 10 students and 50 percent of year 6 students were accessed as proficient – the lowest level for year 10s since testing began in 2005.

27.05 The Federal Court of Australia has called in CyberCX to test data and security controls of the platform used by VIQ Solutions court transcription service. The work follows February’s revelation that some transcription work had been subcontracted to an Indian company, in breach of contracts, and confirmation that there had been data privacy incidents in the performance of the services, ITnews reports.

22.05 Australian quantum computing startup Diraq has signed a $53m grant agreement with the US government to establish a supply chain in the US. Forbes Australia reports much or all of the grant funding will turn into equity making the US government an investor. Diraq is also eyeing up a US Defence deal worth up to $420m.

15.05 The Federal Budget 2026 is a mixed bag for tech companies with the end of capital gains tax discounts expected to have a major impact on founders of tech companies, while an R&D tax incentive and loss refundability changes could be a powerful funding strategy for startups, accounting and advisory services company WilliamBuck says. It also applauded the expansion of VC tax incentives.

13.05 New tax breaks for innovative companies, tax deductions for workers, more funding for CSIRO, an overhaul of the migration points test and changes to capital gains taxes all feature in this year’s Federal Budget. Up to $70m in AI Accelerator grants, tens of millions of dollars to expand AI use in government departments, $654m+ over four years, and $166m/year after that, to expand the Digital ID system and $357m for the ATO’s myID mobile app were among the funding allocated InformationAge reports. The CDR will also see expansion with an additional $62m allocated over two years, while cybersecurity, defence and space also saw boosts.

13.05 The parent company of the Canvas educational tool involved in a major global hack last week has struck a deal with the hackers to secure stolen student and school data, Reuters reports. All data was returned to the company, it says, and digital confirmation of data destruction along with a promise no Instructure customers will be extorted were received Instructure says.

12.05 Xero CEO Sukhinder Singh Cassidy has apologised to customers after the accounting platform was hit with five days of outages. Xero says it has contacted the ATO about deadlines which will be affected by the outages with the ATO ‘understanding’ of the situation, SmartCompany reports.

07.05 Legal practice management platform Smokeball has taken top spot as the best medium to large tech workplace in Australia’s Best Workplaces in Technology, followed by consulting and engineering company Mantel Group and cybersecurity company Crowdstrike. In the small category Macquarie Cloud Services took top for the second consecutive year followed by data and AI consultancy V2 AI and software company Corto, while in the micro category AI Technologies was followed by Adora and Causeis, Great Place to Work says.

07.05 Australia’s largest tech startup community, Fishburners, is in voluntary liquidation, with KPMG appointed administrator. InformationAge reports KPMG is facilitating a strategic restructuring of the organisation, and will be seeking expressions of interest from parties in the innovation and tech sectors. Fishburners will continue to trade as usual while the assessment is underway.

06.05 Australian education institutions are among those caught in a worldwide cyber security attack after they Canvas learning management system, developed by US company Instructure, was subjected to a hack. ABC News reports state schools in Queensland and Tasmania, universities in NSW and South Australia and TAFE in Tasmania are affected. Instructure says it doesn’t believe birthdates, passwords, government identifiers or financial information have been impacted. Names, locations of study, email addresses and messages between users are however believed to have been compromised.

04.05 NAB has changed its capitalised software policy, reducing the value and useful life of capitalised software assets thanks in part to AI enabling software to be built or replicated quickly and cheaply. ITnews reports the bank took a $1.3b hit to its underlying profit and $949m to its cash earnings for H1 FY2026 owing to the change.

AppWrap April 2026

28.04 Minister for Employment and Workplace Relations Amanda Rishworth says her department is currently undertaking gap analysis to identify how current frameworks and institutions are interacting with the adoption of AI. Speaking at the AFR Workforce Summit, Rishworth noted the tripartite AI Employment and Workplaces Forum, which meets for the first time this week, bringing together government, employers and unions to ‘build a common understanding’ and translate themes of trust, capability, transparency, safety and productivity into actions and outcomes in workplaces.

27.04 NAB is building an AI science team to guide how AI tools are deployed across the business and the workforce’s transition. TheAussieCorporate says the team will shape which tasks are automated and which new roles are created to support them. The work includes designing systems to safely handle customer data, helping make staff decisions and streamlining back office processes.

23.04 The Australian government has signed a MoU with Microsoft which is promising to continue investment in local AI capability and cloud computing and align with the governments Expectations for Data Centres and AI Infrastructure Developers. The Department of Industry, Science and Resources says the MoU – the second under the National AI Plan – also outlines Microsoft’s intention to collaborate with government on AI safety and workforce capability, support delivery of the APS AI plan and help the government understand future infrastructure needs.

22.04 The OAIC has found that InspectRealEstate’s rent-tech platform, 2Apply, collected excessive information and did so by unfair means. The OAIC says the platform has agreed to adapt its personal information collection practices on a without-admissions basis. The decision requires IRE to cease collecting personal information just as prospective renters’ gender, student status, citizen status and visa expiry and details of previous living history.

21.04 Identity verification company Persona has announced an integration with ConnectID, the Australian digital identity exchange created by Australian Payments Plus. BiometricUpdate says the integration keeps data private, providing age verification that relies on data a financial institution has already collected from a user.

22.04 The eSafety commissioner has given legally enforceable transparency notices to Roblox, Minecraft, Fortnite and Stream amid concerns online games are being used to spread violent propaganda and radicalise young people and by sexual predators to groom children. eSafety says the notices require the providers to explain how they are identifying, preventing and responding to these harms, along with bullying and online hate.

21.04 A NSW Treasury staff member has been charged after allegedly downloading more than 5,600 government documents containing ‘confidential commercial and financial information’. NSW Government says the information covers multiple departments and projects. Police believe all the data has been located and is now secure, with no external compromise to the Treasury’s system.

16.04 Apple has granted Australian law enforcement access to user notification data for the first time. InformationAge reports a transparency report shows four push token identifiers were sought across two of the requests, with one push token request granted.

16.04 South32 Aluminium is suing Siemens alleging missing code in a programmable logic controller led to a steam turbine generator overheating and being ‘effectively destroyed’, ITnews reports.

15.04 Travel giant Booking.com has notified an unknown number of customers about a data breach which has seen hackers steal customer data. BBC reports some customers have contacted it saying they have already started receiving suspicious messages. Booking.com has declined to say how many people are affected or in what regions.

09.04 Canva has acquired Australian AI tool Simtheory and Australian marketing automation company Ortto for undisclosed sums. Canva says the deals will take it from a design tool to an ‘end-to-end’ work system and strengthen its AI capabilities, 9news reports.

09.04 Bendigo and Adelaide Bank will cut its tech and business operations teams after signing two major tech deals with Infosys and Genpact outsourcing some tech and business management capability. The cuts will save the business at least $65 million yoy, by the 2028 financial year AFR reports. It adds hundreds of roles are likely to be affected.

03.04 Artemis II astronauts experienced Microsoft Outlook failures shortly after launch, with Mission Control called in to remotely troubleshoot the issue. Commander Reid Wiseman reported that two instances of Outlook were running simultaneously on his Surface Pro, leaving both unresponsive, Mashable reports.

02.04 The ATO has launched an in-app security feature enabling users to confirm, in real-time, that they are speaking with the real ATO not a fraudster. Almost 7,500 ATO impersonation scams were reported in July 2025 alone, with impersonation scams peaking during tax time, the ATO says.

01.04 SpaceX has filed confidentially for an IPO according to reports, which say the company is committed to debuting in June, with Elon Musk aiming to raise US$50b-$75b. The NYTimes reports SpaceX values itself at more than $1 trillion and would be one of the most valuable companies to reach the stock market.

01.04 Oracle is cutting thousands of jobs from its 162,000-strong workforce, with 10,000 believed to have lost their job so far. Senior engineers, architects, operations leaders, program managers and technical specialists are among those affected, the BBC reports. Around 10,000 people are believed to have lost their jobs so far. It is unknown if the cuts are related to Oracle’s heavy AI spend.

01.04 The Federal government has signed a new MoU with Anthropic to build on the national AI plan launched in late 2025.

AppWrap March 2026

31.03 eSafety has flagged Facebook, Instagram, Snapchat, TikTok and YouTube for compliance issues with the Australian Social Media Minimum Age obligation and is gathering evidence to inform potential enforcement action, it says.

26.03 Meta and Google have lost a landmark US case, with a Los Angeles jury finding the two companies negligent for designing social media platforms that are harmful to young people. Reuters reports the jury found Meta liable for damages of US$4.2 million, with Google liable for $1.8m in the case of a US woman who sued Meta and YouTube over her childhood addiction to social media.

25.03 Delaying public disclosure of serious cyberattacks on critical infrastructure operators ‘to prevent disclosure from compromising national security’ is one of five changes proposed in a Department of Home Affairs consultation paper for amended Security of Critical Infrastructure (Soci) rules. Other reforms in the paper include changes to make it easier for the government to restrict multiple entities from using high-risk vendors or technology and clarity around the threshold for issuing a direction.

25.03 Canva has acquired Melbourne-based digital out of home advertising company Doohly in a $30m deal which adds outdoor advertising to its offering. The deal is the design company’s third acquisition this year and comes just a month after it bought US startup Mango.AI and the UK’s Cavalry notes StartupDaily.

24.03 Silicon Quantum Computing has secured $20m from the National Reconstruction Fund to support scaling of its quantum processing units and Watermelon machine learning system, SmartCompany reports.

19.03 The X Money payments service, Elon Musk’s attempt to build a ‘everything app’ will enter early public access in April. Musk’s ambitions for the embedding payments into X are broader than most Western tech companies, notes PaymentsIndustryIntelligence, with Musk talking about a system that could eventually encompass savings, payments, securities and other financial activity, reducing reliance on traditional banking channels. At launch X Money is expected to offer core wallet and payment functions, including the ability to move funds within the platform.

18.03 An attempt by the CEO of US videogame publisher Unknown Worlds to wriggle out of paying a US$250m performance has suffered a setback after the court documents showed Changhan Kim asked ChatGPT how to avoid paying the bonus. InformationAge reports Kim engaged the chatbot to draft a corporate takeover strategy, which involved stymieing the release of the highly anticipated Subnautica 2. Following ‘guidance’ from ChatGPT he also posted public ‘critical messages’ on the company website.

17.03 Google has reportedly paused plans for a $20b AI and data centre hub, warning the federal government that high taxes could cause the country to miss out on investment. DatacentreDynamics reports the company has told government it is concerned that if it set up such a hub in Australia, the ATO would consider it a ‘permanent establishment’ exposing it to a 30 percent corporate tax rate. Google is currently exploring where to establish the major APAC hub.

16.03 AI disruption is causing Australian software companies to make big job cuts, with private tech companies expected to follow in the footsteps of Atlassian and WiseTech with redundancy rounds, valuation cuts and a bottleneck for public listings, AFR reports.

12.03 Atlassian is cutting around 1,600 jobs – or 10 percent of its workforce. The company says the move is to ‘self-fund further investment in AI and enterprise sales’, while strengthening its financial profile, Forbes reports. The retrenchment will cost between $225m-$236m.

11.03 Professional body CA ANZ says it will investigate the failure of an external exam delivery platform after more than 1,300 students were locked out of one exam and another 60 experienced delays of up to 40 minutes on a second exam. CA ANZ blamed a failure in an external exam platform operated by a third-party provider.

11.03 US medtech giant Stryker has been hit by a major cyberattack, with an Iran-linked hacking group claiming responsibility, saying it is in retaliation for the killing of more than 170 people – mainly schoolgirls – in a strike on a school, and warning it marks the beginning of a new chapter in cyber warfare, Al Jazeera reports. The group says it has seized 50TB of Stryker data in the attack which it says has erased data from 200,000 devices.

10.03 Anthropic is opening a Sydney office and hiring a local team. The company says Australia ranks fourth globally in Claude.ai usage, relative to population, with strong demand from local business. It says it plans to deepen engagement with Australian institutions and collaborate on projects that advance the country’s national interests and priority sectors. It is also exploring opportunities to expand compute capacity in Australia ‘given our longstanding belief that democracies should lead in AI development’. The company is currently suing the US government over its claims that Anthropic is a ‘supply chain risk’. The row erupted after Anthropic refused to all US military to have unfettered use to its AI tools.

05.03 Privacy Commissioner Carly Kind is warning Australian organisations that a recent ruling allowing Bunnings to use facial recognition is not a green light for the technology. Kind, who says she has not filed an appeal of the Administrative Review Tribunal finding, which overturned her ruling against the technology, says the decision shows the law allows for the balancing of competing interests – privacy vs public safety – but entities will need to conduct a detailed risk assessment specific to their circumstances before deploying the technology. “Retailers should view the decision as a useful case study, rather than a green light for deployment of biometric technologies,” she says.

AppWrap February 2026

27.02 Commonwealth Bank has called in the police and corporate regulator over concerns $1b in home loans were obtained fraudulently, including with AI-created documents. AFR reports the bank reported itself to police and the regulator after a review of the compliance practices and customer lending documents, which stepped up following revelations about the Penthouse Syndicate, which allegedly defrauded NAB of around $150m. Increasingly sophisticated AI has made it easier to create authentic looking false documents and to steal identities.

26.02 Nvidia has posted blockbuster quarterly results of US$68.1 billion – up 73 percent year on year and well above analysts’ forecasts. Revenue from the data centre division, which sells the chips used to train and run AI models was up 75 percent yoy to $62.3 billion, the company says.

25.02 Australian logistics software company WiseTech is cutting around 2,000 jobs – nearly a third of its global workforce in a restructure focused on using genAI to increase efficiency in software engineering and support, Reuters reports.

25.02 Australian Peter Williams has been jailed for seven years for selling critical software and information related to cybersecurity to a broker with ties to the Russian government. Williams was working in the US. 9News reports the tools were cyber-exploit components which can be used to identify weaknesses in tech systems or infect them with viruses.

24.02 The federal government has scrapped a permanent AI advisory board appointed to develop AI guardrails. ABCnews reports the body, funded in the 2024 Budget, was scrapped only months after spending $188,000 and more than a year whittling down a list of experts to a shortlist of 12 nominees. The government says it will instead establish an AI safety institute early this year.

25.02 Commonwealth Bank is spending $90 million to get staff ‘AI-ready’. The bank says the program will help employees ‘build skills, find new opportunities and get ahead of the changing nature of work’. AI learning has already been delivered to 30,000+ staff.

24.02 The ASD has publicly released its Azul open-source malware analysis tool to help safely handle and analyse malware. Azul is designed for large-scale malware analysis and is designed to be highly scalable and store tens of millions of samples, ASD says, adding it can turn common analysis steps into analysis plugins which can be used as part of an automated workflow and assist in identifying variants of a malware family more efficiently.

18.02 Xero is restructuring with 250 jobs on the line – including in Australia. The company is creating around 280 roles in Canada as part of the restructure, BusinessDesk reports [paywalled].

18.02 YouTube suffered an outage from around 11.50am AEDT, with a spokesperson for Google saying an issue with the recommendations system prevented videos appearing.

18.02 The federal opposition has unveiled a new-look shadow ministry under Angus Taylor, with Victorian senator Sarah Henderson appointed shadow communications and digital safety minister. ABCnews reports Casey MP Aaron Violi has been promoted to the outer shadow ministry with a broad portfolio including science, technology, cyber security and the digital economy. Deputy Liberal Leader Jane Hume will be shadow minister for employment and industrial relations as well as productivity and deregulation.

18.02 Australian company Terram Astra has set its sights on a US$10m seed capital raise for a new sovereign ground-based infrastructure platform designed to strengthen communications resilience, defence readiness and space safety across the region. Manufacturers’ Monthly reports Terram Astra’s first hub is located near Alice Springs.

17.02 Highly sensitive Australian court files have been accessed by a foreign entity based in India, an ABCnews investigation has revealed, with Greens senator David Shoebridge saying access to the cases by foreign entities is ‘a national security risk’. The investigation showed Canada’s VIQ Solutions, which employs transcribers in Australia, has also subcontracted work to an Indian company specialising in automated voice-to-text technology, in breach of the VIQ contract. Thousands of court files were accessed by e24 staff with Indian email addresses.

16.02 Federal government entities are failing to report cyber incidents to the Australian Signals Directorate. InformationAge reports just 35 percent of the 200 government entities said they reported at least half of all cybersecurity incidents observed on their networks.

11.02 Service NSW has started testing a new system enabling residents to verify who they are in a single step when interacting with state government services online. ITnews reports initially the digital ID system will be used with a limited number of services, starting with a state toll rebate scheme.

06.02 Australian-founded AI data centre startup Firmus Technologies has secured another $100m to expand its local data centres. The funding, from ASX-listed Maas Group means the company, which is Singapore-based, has raised more than $900m in less than five months, StartupDaily reports.

06.02 CommBank says it has become the first bank to disclose how it is ideating, developing, deploying and managing AI at an organisational level, including ‘practical examples’ of AI in action across the business. Those examples include AI to protect against fraud and scams, strengthen cyber security, enhance customer experience and detect abuse in transaction descriptions.

05.02 Atlassian’s revenue for the quarter to December 31 was up 23 percent to US$1.58 billion, with cloud revenue growing 26 percent providing the company with its first $1b cloud revenue quarter. However, net loss also grew from $38.2m to $42.6m. Operating loss was down from $57.5m to $47.7m the company says. CEO Mike Cannon-Brookes dubbed the quarter ‘fantastic’, adding: “We’re building a bloody great business.”

03.02 The federal government has rejected a bipartisan proposal which would have allowed a parliamentary committee to cancel large consultancy contracts, saying its own reforms are doing the job. A centralised register for conflicts of interest breaches has also been ruled out.

03.02 Australia’s STEM capability is facing a ‘severe crisis’ as a result of flatlining Commonwealth funded research, Science and Technology Australia says. In a pre-budget submission it says Stem research must stop being viewed as a cost and instead be seen as an investment. It says 47 percent of Stem professionals are considering leaving their current roles, with 33 percent planning to leave the sector altogether.

03.02 Xero says more than two million subscribers are using its full AI features, with more than 300,000 using its newer GenAI features. In a market update today the company claimed customers are saving around 22 hours a month using bank feeds and automated actions, with more than 12 percent using AI Insights.

03.02 Kiwi AI startup Teacher’s Buddy has raised $2.3m in trans-Tasman seed funding. The offering aims to reduce teacher workloads, helping with marking and student report writing and producing customised, differentiated curriculum-aligned teaching and assessment materials  The company says the funding was led by Auckland’s Soul Capital and Australia’s Giant Leap.

02.02 Victoria has set its sights on being Australia’s AI capital with the launch of an AI Mission Statement setting out the Allan government’s plans across six key pillars of investment attraction and adoption, data centres and digital infrastructure, local innovation, products and services, talent and workforce, ethical AI use and public sector adoption. The plan includes a $8.1 million Digital Jobs – AI Career Conversion program to safeguard jobs in industries at risk from AI, upskilling workers to transition into AI roles and become specialists in the technology.

AppWrap January 2026

27.01 Airwallex boss Jack Zhang says the fintech is at least two years away from a public listing, despite regular references to an IPO in 2026. Zhang’s comments, reported in StartupDaily, follow news last week that Austrac has ordered an audit of the company over AML/CTF concerns.

26.01 The APS plans a whole-of-government learning technology ecosystem following a co-design involving teams from 37 Commonwealth agencies. ITnews reports a core goal of the new system is to enable interoperability and integration across learning and development environments.

23.01 TikTok has finalised agreements with backers including Oracle, Silver Lake and Emirati company MGX to establish a US joint venture. Each of the three backers will hold a 15 percent stake in the company. ByteDance keeps a 19.9 percent share. The BBC reports the content recommendation algorithm has been licensed to Oracle – headed by Trump ally Larry Ellison – which already oversees US user data under a previous arrangement set up over security concerns. The deal will enable TikTok to continue operating in the US, but is likely to continue to be scrutinised, with some Democrats voicing concerns about the ties between Trump and TikTok’s new investor group could limit what gets shared on the platform.

22.01 Australia’s financial intelligence unit, Austrac has ordered an external auditor be appointed to assess whether fintech – and unicorn – Airwallex is complying with anti-money laundering and counter terrorism financing. Austrac says it has ‘concerns’ about potential non-compliance including that Airwallex’s transaction monitoring program has not been ‘attuned to the full range of risks it faces’ and the company hasn’t demonstrated an acceptable understanding of who its customers are and what reporting may be required. The auditor must report findings within 180 days of appointment.

20.91 AI projects to improve construction safety, enhance space safety and detect speech delays, are among the 174 projects winning funding in the latest round of Australia’s Economic Accelerator (AEA) Ignite program. More than $72.5m in funding was allocated. The funding is for projects that build capability I nationally important sectors aligned with the National Reconstruction Fund’s priority areas, AEA says.

20.01 Gilmour Space has become Australia’s first space tech unicorn after raising $217m in a Series E funding round – its largest funding round to date. SpaceNews reports the round was led by the federal government’s National Reconstruction Fund and retirement savings fund Hostplus along with several other investors.

20.01 A bill cracking down on hate speech has passed the House of Representatives and Senate at a special sitting late on Tuesday, a month after 15 were killed at Bondi Beach. It includes provision to ban groups deemed to spread hate, the BBC reports. The hate speech reforms were originally part of an omnibus bill along with gun reforms, but the two were split last week. The gun reform bill, which includes a buyback scheme, was also passed.

16.01 Social media companies removed access to around 4.7 million accounts for those under 16 in the first half of December, eSafety says.

16.01 Alphabet’s Waymo robotaxi business has held discussions with Chinese car maker Geely, and other electric car makers, as it looks to enter the Sydney market. AFR reports. Waymo is stepping up its efforts in Australia, with the appointment of a lobbyist and a search for an office now underway. It has yet to lodge an application to begin testing in the city.

13.01 Advertising is hitting the AI chatbots with ads starting to show up in Google’s ‘AI Mode’ in what the Washington Post says is likely to be just the beginning of more trial-and-error attempts this year. Google has been pushing a new type of ad in AI Mode to advertisers, OpenAI has also been looking at introducing advertising in ChatGPT and Perplexity attempted ads but pulled back last year – but has left the doors open to try again.

08.01 The DTA has warned against outright bans of IT suppliers and services companies for unethical behaviour warning such bans could introduce significant operational risks and unintended consequences. ITnews reports parliament is considering an exclusion regime preventing those engaging in unethical conduct from bidding for government work, in the wake of the PwC scandal. The DTA, however, says blanket bans could block access to tech advances or skilled resources.

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Six companies, spanning AI livestock systems, digital twin carbon tracking, circular agriculture, sustainable horticulture and on-farm energy generation, will be heading to New Zealand next month. They’re looking to ‘road test their technologies in real-world conditions, find industry partners and explore pathways to market entry’.

“[This] is a signal that the international innovation community sees New Zealand as a co-creator of solutions the world needs.”

The companies are arriving under Innovate UK’s Global Incubator Programme, delivered in New Zealand in partnership with agritech accelerator and investor Sprout Agritech. It marks the first time the program has been run in New Zealand, with the six-month initiative designed to connect UK startups with local industry, investors and research institutions and – crucially – to validate their technologies in real-world conditions.

For local companies, research institutions and investors, Sprout says the program provides a ‘direct pipeline to innovation’ which could otherwise take significantly longer to reach local shores.

New Zealand’s role as an advanced agricultural environment is key in bringing the program to the country. The country’s food and fibre sector generates NZ$59.9 billion in annual export revenue, contributing around 10 percent of GDP and organisers point to its diverse farming systems, strong science base and globally connected agribusiness sector as key factors positioning the country as a place where technologies can be tested under real commercial conditions.

Sprout Agritech’s network, spanning corporates including Fonterra, Gallagher, LIC, T&G Global and Zespri – also provides direct access to potential pilot partners and customers.

Sprout Agritech CEO Sandhya Sriram says the program acts as a two-way opportunity.

“New Zealand is one of the most sophisticated agricultural markets in the world… Partnering with Innovate UK to bring their best agritech companies here is a signal that the international innovation community sees New Zealand as a co-creator of solutions the world needs.”

It’s also being framed as an opportunity for New Zelaand to shape global agritech solutions while accelerating its own transition toward sustainable, high-value food systems.

Among those heading to New Zealand are x10NI, which provides a digital twin and MRV platform measuring carbon and nutrient flows across livestock supply chains, and Galebreaker, which optimises controlled-environment livestock housing combining automated ventilation, sensing and AI optimisation

Australian scale up opportunity

While the Kiwi bound contingent are about to hit our shores, with the first of two visits scheduled for June to coincide with Fieldays – the Southern Hemisphere’s largest agricultural event – and the second in October, applications for the Australian cohort, which will consist of up to eight companies, are still open. They’re due in market in October, followed by a second in February 2027, with the ‘acceleration program’ run in conjunction with Australia’s Farmers2Founders and Agrifood Futures.

Like New Zealand, Australia has a diverse and sophisticated agriculture sector – it’s on track to hit $100 billion by 2030 – while its multiple climatic zones and counter-seasonality offer the diverse environments required for testing solutions, making it a ‘perfect’ launchpad for UK businesses to connect, collaborate and scale, Innovate UK says.

The initiative is all part of a bolshy plan by Innovate UK to ensure high-potential UK agritech businesses capture market share and become industry giants – something that can only be done through international growth and scale. Agritech, which is estimated to currently be worth between £13 billion and £28.5 billion depending on how the market is defined, is one of six priority frontier industries within the UK government’s Advance Manufacturing Sector Plan.

A key part of the Innovate push is access to the R&D and innovation with demonstration and adoption.

The Australian program is explicitly focused on helping UK companies expand and commercialise, providing structured support that includes mentoring, workshops, market insight and direct connections with industry and investors. Participants also receive funded in-market visits and ongoing support from ‘innovation specialists’, with the goal of turning early validation into commercial traction.

Program documentation says examples of technologies and solutions of interest include robotics, automation, AI and sensor tech, increasing farm productivity and optimising soil fertility and management.

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Robotics, healthtech and fintech account for much of the remaining new entrants according to investing research platform BestBrokers, which pulled May data from Crunchbase, PitchBook and TechCrunch.

“Capital is now flowing heavily into the systems required to support automation, autonomous machines and large-scale AI deployment,”

Mirroring those global trends, Australia and New Zealand’s growing unicorn cohort is being built largely outside the artificial intelligence boom.

The raw data behind the report shows just one new unicorn for A/NZ so far this year: Australia’s Gilmour Space Technologies, with a most recent valuation of US$1 billion and lead investors including Blackbird, Hostplus Superannuation Fund and the National Reconstruction Fund Corporation.

It joins 12 other Australian unicorns across a diverse spread, weighted toward companies solving specific business or industry problems, particularly in fintech, work management and vertical platforms.

Biggest of all for Australia is Canva, with BestBrokers putting the company’s post money valuation at US$42 billion, ranking it the 15 largest unicorn globally. It’s followed by  Airwallex (with US$8 billion post money valuation it’s ranked 107 globally), which provides cross-border payments infrastructure.

Workplace focused platforms including HR’s Employment Hero (ranked 548 of the 1,757 unicorns globally) and Culture Amp (551), alongside workplace safety software specialist SafetyCulture (550) form part of the broader group of billion-dollar companies focused on software-driven business processes, built around practical business applications, rather than frontier AI models.

Australia’s other tech unicorns, according to BestBrokers; are Firmus Technologies (AI), blockchain gaming and NFT platform Immutable Systems, corporate learning company Go1, Latitude Financial Services, Judo Bank, Linktree and Deputy.

New Zealand makes the list thanks to agritech’s Halter officially gaining unicorn status in June 2025. As a public company, Xero is not regarded as a unicorn – a category reserved for privately held startups only. In fact, across A/NZ there were 40 tech and tech-enabled companies valued at over $1 billion in April 2026 according to Multiples– that includes the likes of Afterpay, Atlassian, REA Group and Seek.

Diversified mix

The BestBrokers’ report points to a diversified mix of industries contributing to new unicorn formation in 2026.

One in four newly minted unicorns is an AI company with the company saying these businesses are focused on building AI infrastructure and specialised tools built on large language models, reflecting how deeply AI has become integrated into the broader tech ecosystem.

Robotics took second place, accounting for 11.2 percent of new unicorns (and 11 companies as new unicorns). Leading the charge as the most valuable newly minted robotics companies are China’s Sudu Technology which specialises in R&D of general-purpose robot brains to enable robots to operate autonomously across diverse environments while learning and adapting, and Mind Robotics, a US industrial robotics company spun out of EV manufacturer Rivian in November 2025. Each are valued at $2 billion.

Healthtech accounted for 10.2 percent of the new unicorns, with 10 companies, spanning digital health platforms, AI-assisted diagnostics and data driven healthcare services, reaching unicorn status.

Seven fintechs also joined the club, with BestBrokers noting the fintech category is the most geographically distributed with companies from the US, UK, India, Bahrain and the Netherlands hitting the $1b mark, led by Rogo from the US and KreditBee from India.

Spacetech and securitytech (both 6.1 percent), cloud and infrastructure, cybersecurity and enterprise software (all 5.1 percent) and crypto currency and semiconductors (4.1 percent apiece) rounded out the list.

Paul Hoffman, BestBrokers data analyst, says while AI continues to dominate venture capital activity, investors are increasingly shifting their attention beyond foundational models and toward the infrastructure powering the broader AI economy, such as semiconductors, cloud infrastructure, robotics, defence tech and aerospace.

“Capital is now flowing heavily into the systems required to support automation, autonomous machines and large-scale AI deployment,” he says.

AI drives valuations

At the top end of the market, AI companies continue to command the highest valuations, with the world’s most valuable companies – including SpaceX post its xAI merger, Anthropic, OpenAI – heavily concentrated in AI and adjacent technologies.

These companies are attracting some of the largest funding rounds in private market history, pushing valuations into the hundreds of billions of dollars – or in SpaceX’s case $1.25 trillion. Anthropic comes in with a valuation of $900b, a massive step up from the earlier 2026 valuations of around $380b, and coming on the back of a major late-stage funding round. OpenAI ranks third with a $852b valuation following a record-setting $110 billion investment from Amazon, Nvidia and Softbank.

Meanwhile it’s a London-based startup, Ineffable Intelligence that takes the crown as the most valuable new unicorn in 2026. The company, which develops reinforcement learning-based ‘superlearners’ to discover knowledge through their own simulated experience, rather than relying on human-generated data, has attracted over $1.1 billion in venture funding, with a valuation of $5.1 billion.

Says BestBrokers’ Alan Goldberg: “Today’s venture market is rewarding companies building physical systems, compute infrastructure, and industrial-scale AI capabilities, suggesting the next decade of private market growth may look far more like advanced industry than traditional Silicon Valley software.”

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The new tech hub is the second State-side for CBA – it opened one in Seattle last March – and places ‘squads in residence’ – aka CommBank engineers and technologists – in what the bank describes as ‘one of the world’s most concentrated AI ecosystems’, working directly with partners such as OpenAI, AWS, Anthropic and Microsoft.

“The goal is to have them take away all the learning… and bring [it] back to Australia, to become force multipliers, and scale and compound that capability across our 10,000-plus technologists.”

Matt Comyn, CommBank CEO, says the hub will give the bank’s engineers and technologists access to world-class tools, expertise and partners.

The hub reflects a broader strategy for the bank: Proximity to where AI models are being built and structured pathways to transfer knowledge back to Australia’s engineering workforce.

“This is part of our commitment to working at the frontier of technology, alongside leading global partners,” Comyn says. “We’re investing significantly in our people and capability, because attracting and supporting the best talent means giving them access to the best tools, expertise and opportunities globally.”

Bringing San Francisco skills back

The San Francisco hub is designed as a rotation model. Engineers, product owners and data scientists travel in small squads for short residencies working alongside AI partners and observing how leading organisations are applying new tools and techniques.

For CommBank, the goal is not simply exposure, but replication. Martha McKeen, CommBank lead for AI powered engineering and international tech hubs, says the goal is for the cohorts to take away all the learning they can from the strategic partners, taking that back to Australia to become ‘force multipliers’ and scale and compound that capability across the bank’s technology workforce of more than 10,000 staff.

“Our tech hubs are all about creating incredible surfaces for learning,” McKeen says.

The model builds on earlier success in Seattle, where more than 100 engineers have already participated in similar rotations.

In Seattle, the first cohort back in early 2025 were focused on learning to fast-track adoption of agentic AI and genAI powered solutions to help small business banking customers manage their finances and run their businesses. A second cohort looked at modernising testing to respond to customer feedback faster.

Rodrigo Castillo, CommBank co-CIO central technology, says the new hub will help the bank continue to scale AI solutions, connecting internal teams with where new models are being created. “What we are looking for is connectivity between our teams and places where AI models are being created here in the heart of San Francisco.”

Learnings beyond the tools

For engineers taking part, the value is not limited to access to tools. CBA says it extends to exposure to how leading organisations structure work, iterate on products and integrate AI across the software development lifecycle.

George Beniac, CommBank crew lead, core foundations, says working with OpenAI in San Francisco has changed the way his learning squad is thinking about how it can use AI from planning to product design, to code development and deployment.

Product owner for AI powered engineering, Henry Chan, says the environment itself plays a role in shaping thinking.

“Very quickly, from the moment you land in San Francisco, you notice all the billboards are AI-related. You realise this environment is, by default, AI-powered. Your brain automatically switches.”

For Chan one of the things his team went to San Francisco to learn was how other leading companies were able to move so quickly and how they’ve altered or augmented their product delivery lifecycles over the last couple of years. (His insight: The importance of giving team members the freedom to experiment using AI to prototype, translating thoughts into real life use cases.)

Squads at the hub have also been looking at how AI is reshaping the way software is designed, built and maintained, with a focus on AI-powered engineering. “That is, giving our engineers the emerging best-of-breed, AI-powered engineering tools,” McKeen says. “These tools help them in their everyday workflows, and relieve the toil and burden of manual tasks and allows them to focus on what matters most.

The bank says more than 70 percent of its engineering teams are already using AI tools, with the new hub aimed at accelerating that adoption and embedding more consistent ways of working across teams.

Other Australian organisations are taking similar steps. Telstra partnered with professional services company Accenture last year to launch an AI ‘innovation and research hub’ in Silicon Valley, where Telstra works with partners including AWS, Databricks and Microsoft to build and test AI-based services. However, rather than flying in teams, the Telstra-Accenture joint venture appears to be more of a virtual offering, connecting with teams in Sydney, Melbourne and Bangalore and creating a ‘virtual door’ where teams can ideate, collaborate, build and test in a secure environment.

Australian-founded design platform Canva also expanded its presence in San Francisco last year, positioning the city as a key hub in its global operations and placing teams closer to talent, capital and early adopters.

CommBank’s hub model is built around turning short-term exposure into scaled capability. Engineers are expected to return and apply what they’ve learned across the organisation, embedding new approaches to prototyping, collaboration and AI-assisted development. For organisations across Australia and New Zealand, the key question is not access to new technology, but how effectively those practices can be distributed and embedded across teams.

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That’s the analogy Luke Ellery, a Gartner Sydney-based VP analyst, uses to describe how many organisations still purchase technology: Rigid requirements, locked in early, a process built around evaluation rather than learning with little room to adjust once the market responds.

“Risk‑tolerant leaders didn’t accept risk by default – they made better decisions.”

According to Gartner research, it’s also a pattern which comes with a high price. A Buyers Regret survey a couple of years ago found 79 percent of buyers regretted their most recent purchase – a figure Ellery says ‘blew me away’ and prompted new research to discover how to get a ‘high-quality deal’.

The answer wasn’t a better RFP or harder negotiations, but consistent use of five modern buying practices, he told attendees at Gartner’s IT Infrastructure, Operations and Cloud Strategies Conference this week. Organisations that applied all five achieved the high-quality deal nearly 60 percent of the time.

And the principles?

• Ensure the most senior-level sponsor is engaged throughout.
• Start with measurable and realistic business outcomes (and dump the detailed spreadsheet)
• Harness agile and lean procurement methods (again, dump the spreadsheet)
• Get thoughtful about risk tolerance – and this doesn’t mean being risk averse
• Build confidence in negotiating with vendors – and include some theatre where needed

Ellery acknowledges that some of the principles are already widely used. Sponsor engagement was already practiced by 75 percent of respondents. Fifty-five percent start with business outcomes, and just 30 percent use agile or risk tolerance approaches.

“But it’s about doing all of these practices consistently,” he says.

Getting agile

One of the biggest shifts Ellery called on businesses to embrace, was the move away from traditional, waterfall-style procurement, with its rigid, linear processes ad policy and compliance focus. He noted that while businesses are increasingly embracing agile business strategies, that needs to be coupled with an agile sourcing strategy.

He says the waterfall method assumes buyers already know what’s possible in the market and leaves little opportunity to adapt as insights emerge.

By contrast, agile buying approaches are framed as a learning and refining cycle. Teams start with business outcomes, engage the market to understand what solutions exist, evaluate what they learn, refine their thinking and repeat if necessary, or adopt a solution.

The car-buying analogy makes the point – you only learn what you want and what works by experiencing options and adjusting expectations.

Ellery notes that agile procurement shouldn’t be treated as a defined ‘process’. Instead, it’s a philosophy, similar to Lean, based around not being rigid.

And while many government departments believe the waterfall method is mandated, he urged them to double-check if that was truly the case. “Whenever I speak to the people who are responsible for policy, typically it is not. It’s just an interpretation. So it’s worth checking if it is the case or not,” he says.

Ellery says organisations should invest in skills and training for the procurement team to ensure they have agile approaches, then help the business with the learning and refining approach so they start getting excited about what they can learn from the market, in order to reap the 1.8x improvement in procurement seen through agile.

Risk tolerance: The biggest lever in better outcomes

If agile buying creates the room to learn, risk tolerance determines whether organisations take advantage of it.

Of the five practices highlighted in the research, risk tolerance delivered the highest impact on outcomes with a 2.1x improvement.

Crucially, Ellery stresses that risk-tolerant leaders are not those who ignore danger or accept risk by default. Instead, risk-tolerant leaders make better informed decisions by gathering more sources of information, understanding trade-offs and actively mitigating risks rather than avoiding them altogether.

Risk aversion, by contrast, can blind organisations to the opportunity. When teams avoid unfamiliar options because of uncertainty, they may miss better outcomes simply because they didn’t invest in understanding what the risks actually were.

Ellery says this mindset is reshaping organisational roles. He points to a growing trend, which began in financial services but is spreading more broadly, where IT sourcing, procurement and vendor management teams take on third-party risk management. These teams, he says, are already skilled at managing structured information and processes, making them well-placed to support better risk-informed decision-making.

Engagement, outcomes and negotiation confidence

Ensuring engaged leaders from the outset is already in use by the majority of companies and it’s a winning strategy, leading to 2x improvement for procurement. Ellery notes the outcomes depend on consistency, not symbolic involvement.

Similarly, shifting from detailed specifications to measurable business outcomes helps buyers and vendors alike focus on what success actually looks like. While 55 percent of organisations start with business outcomes, many still revert to long requirement lists that reflect today’s systems, rather than tomorrow’s needs.

He cites the example of a government entity which tried the same procurement three times, failing each time. Ellery was brought in to troubleshoot. One red flag: The organisation went to market with a spreadsheet of 2,000 requirements. When asked where it came from, the business analyst said they’d documented what the agency was doing today on the mainframe systems they were trying to replace.

“When a client sends me their user stories around what they want to achieve, I can see straight away what sort of solutions they need,” he says. “If they send detailed specifications, it’s going to be very difficult for me to work out.

“It’s easier to focus on the business outcomes and user stories, though sometimes it takes a bit of training your users, business analyst or the business owners around what a user story is – how we define it and what in a good example. But that’s a lot easier than coming up with a spreadsheet of requirements.”

Negotiation confidence also contributes, delivering a 1.4x improvement in outcomes. Here, confidence comes from preparation, knowledge and internal alignment, not from aggressive tactics or last-minute pressure.

A little theatre also won’t go amiss. Ellery trotted out a favourite story of the CFO who arrived late into a meeting with the vendor of an ERP solution they had fallen in love with but were having issues with the vendor not agreeing to three key aspects in the contract. She then walked out early when the vendor team continued with a sales pitch rather than addressing the issues. The vendor was escorted from the premises. Three days later they conceded on all points.

“It was all staged. The CFO turning up late, her walk out… Sometimes you need to use a little theatre to make sur the vendor knows your serious,” he says. (And if you want to hear more about negotiation theatre, read our story here.)

“We can all learn a bit around negotiation confidence and being better negotiators.”

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That was the message from governance, tech and legal experts during a New Zealand Privacy Week privacy and data security session focused on how organisations manage personal information in practice, particularly when resources are constrained and operational complexity is high.

“We wouldn’t ignore our finances. I think we need to think about privacy in the same way.”

While the session was aimed at not for profits, the privacy and data security issues raised – from access control to incident response – apply equally to other organisations.

Leadership and governance consultant Jo Cribb says boards frequently underestimate the extent and sensitivity of the personal information their organisations hold, including client case files, donor records, staff files and even information relating to health, children and family circumstances.

“From a governance and leadership perspective, you don’t need to be a technical expert. But we do need to ask the right questions and get assurance that actually, we are looking after the data and information incredibly well,” Cribb says.

She likens privacy oversight to financial oversight, noting that boards routinely scrutinise accounts and funding use, and should apply similar rigour to privacy and data – something that is often the engine enabling organisations to run and have impact.

“We’re really interested in terms of what’s happening with our accounts and how we’re using our resources. We wouldn’t ignore our finances. We need to think about privacy in the same way.”

Privacy she says, should not be ignored simply because organisations are busy keeping the lights on or operating with limited capacity.

The questions leadership expects tech to answer

From a governance perspective, Cribb outlined a set of questions boards should be asking –the same questions CIOs, heads of IT and security teams are being asked to answer:

• What personal information does the organisation hold?
• Where is that information stored?
• Who has access to it and why?
• Are staff and volunteers aware of their responsibilities?
• What happens in the first hours after a privacy or security incident?

Cribb says boards should be demanding visibility of privacy incidents and near misses and should receive regular reporting on privacy matters – with it on the agenda at least once, if not twice a year – rather than relying on ad-hoc updates when something has gone wrong.

Where everyday tech decisions create privacy risk

Anthony McMahon, Lancom chief customer officer, says many privacy incidents are not the result of sophisticated cyberattacks, but of common technology practices that organisations have normalised.

He highlighted shared log-ins as a well-intentioned practice which is creating privacy risks, particularly in organisations that rely on volunteers or have high turnover. Generic email accounts and shared system credentials make it difficult to control access when people leave and remove visibility over who has accessed information. While shared log-ins can feel convenient, they also create a single point of failure and significantly increase exposure.

He also warned about the use of spreadsheets to store sensitive personal information, particularly when access is broad or files are tied to individual user accounts, raising the potential for data to be lost when someone leaves and organisation and accounts are deleted. In other cases, information remains accessible to people long after their role has ended.

Louisa Joblin, principal at Moran Law, says she’s seen cases where spreadsheet data has been ‘weaponised’ by disgruntled former staff, creating ‘big issues’ for the organisations involved.

Minimum controls, maximum return

McMahon outlined a baseline set of technical controls organisations should be working toward. These included individual user accounts, multi-factor authentication, particularly for administrator accounts, and role-based access to ensure people can only see the information they need.

“Do people you’re bringing in need access to all your data or do they just need a very small subset? Don’t give them everything by default,” he says, noting it is a ‘simple mistake’ he sees a lot of organisations – not just not-for-profits – make.

He also discouraged attaching sensitive information directly to emails, instead recommending sharing links to files stored in systems that enforce access controls.

“That way, if it accidentally goes to the wrong person, I’ve already set the permissions. They could click on the link, but they’re not going to get access,” he says. “It takes it away from being a privacy breach to a simple ‘whoops, we sent an email to the wrong person’.

Best practice extends to areas including getting NFP licensing from the likes of Microsoft and Google, knowing who to call when something goes wrong – an issue he sees for commercial organisations as well as NFPs – and never using ‘free’ versions of tools such as ChatGPT, Copilot or Gemini. “The data you’re putting in there is being used to train the AI models themselves, so if don’t use the free ones. If you have to, don’t put any personal or sensitive data from your organisation in there.”

Policy as an operational control, not a checkbox

Joblin noted the need for privacy policies which reflect how organisations actually operate, rather than existing solely to meet compliance requirements.

“It’s not meant to be a tick box exercise where you just put up a policy on your website and say we do these things and now we’ve complied,” she says. “It’s actually a real opportunity to work through how you collect, handle, store, use and disclose. Let’s communicate that in a language that works for our organisation and communicates our brand and commitment to our people.”

From a governance perspective, Cribb encouraged boards and leadership teams to ensure policies are understood by all staff and volunteers and followed in practice, not just approved and filed. This includes asking whether staff and volunteers have been trained, and whether day-to-day behaviour aligns with written standards.

She also urged attendees to ensure their organisation has a privacy officer – “if you don’t have one, create one at your next board meeting” – and ensure you’re getting regular reports from them, including whether they’re getting the support and everything they need in order to implement the policy.

The post Getting privacy governance right at the board level appeared first on iStart keeping business informed on technology.

That’s according to Deloitte’s 2026 Global Technology Leadership Study, which surveyed more than 660 senior technology executives globally, with 31 percent APAC based.

“The gap between a tech leader’s expanded mandate and their organisation’s ability to execute is where competitive advantage can be won or lost.”

It says a new era of technology leadership is here, but warns that expectations have reset faster than organisational structures. While the tech leaders find themselves being held accountable for shaping strategy, leading change, building AI-ready teams, turning tech ambition into business results and driving enterprise-wide outcomes, many enterprises are still operating governance, funding and operating models designed for an earlier era of IT. That mismatch is leaving tech leaders caught between the ambition of an AI-driven world and the structural reality of legacy operating models, talent and budget.

While it might all sound like a headache in the making, the respondents aren’t despondent. In fact, more than seven in 10 say they’re inspired or determined about the future of their role, recognising that, while creating additional complexity, AI also creates an opportunity to step forward as ‘architects of enterprise advantage’, Deloitte says.

Driving outcomes, driving business

The report shows 79 percent of the tech leaders surveyed said driving measurable business outcomes was a top priority for 2026, reflecting a decisive move away from traditional operational metrics.

Within that shift, leaders identified growth, productivity and customer impact as key outcomes areas shaping how success is measured. These priorities now sit ahead of the traditional focus on infrastructure stability, availability and delivery performance that previously dominated tech leadership.

Other strategic priorities also featured prominently. Ensuring compliance with evolving digital regulations and embedding cybersecurity and digital resilience across the organisation were each cited by 77 percent of respondents as a key area of focus, highlighting how the scope of responsibility continues to widen, extending into regulatory readiness, enterprise risk and organisational resilience.

The results signal technology leadership’s central role in enterprise performance, governance and risk, rather than operating as a support function sitting alongside the business.

Deloitte frames the change as a move ‘from uptime to outcomes’ and a fundamental reset of the technology leadership mandate.

According to the study, AI adoption is a major driver. As the technology becomes embedded across business processes and decision-making, expectations around the role of technology leaders have expanding. Boards and CEOs are increasingly focused on how digital and AI investments translate into tangible business value, rather than whether technology has simply been deployed successfully.

“The era of the operational technologist is over,” says Deloitte Consulting’s Anjali Shaikh, managing director and leader of the global CIO and US tech executive programs. “This shift has been building for over a decade, and AI is the catalyst bringing that into focus. Today’s CIO isn’t just leading technology; they are being asked to redesign the very fabric of how the business runs.”

The survey also suggests this change is reshaping how performance is assessed across the technology leadership spectrum, and how leader are prioritising their agendas, which are consistently anchored around AI. Forty-six percent of CIOs say AI adoption and value realisation is the measure which will most define their success over the next two years, a result which underlines how closely the CIO role is now tied to turning AI initiatives into demonstratable business impact, rather than simply enabling adoption.

For CDAOs the priority is gaining tangible business value from data and AI assets, reflecting the growing emphasis on monetisation, insight and decision support. CISOs identified integration of security into AI initiatives as the key measure of success, highlighting the increasing convergence of cybersecurity, risk management and AI deployment. And for CTOs, AI-enabled automation and innovation velocity topped the list of measures defining success, signalling pressure to accelerate delivery while modernising platforms and architectures.

Despite the increasing focus on AI, the study shows leaders are not shedding their existing responsibilities. Tech executives remain accountable for a broad set of outcomes, including resilience, compliance and sustained enterprise value – on top of the expanded AI-related mandates.

Structural constraints

While the expectations placed on tech leaders continues to grow, the study makes clear that many organisations have yet to adjust the structures around them to support this shift.

Governance models, funding mechanisms and operating structures often remain rooted in an era where IT was primarily responsible for delivery and support. As a result, leaders are expected to take on broader strategic and transformational responsibilities without corresponding changes to the structural fragmentation from an expanding tech C-suite, constrained and misaligned funding models and outdated operating models that constrain them.

The gap between ambition and execution is a recurring theme, with Deloitte noting that while 81 percent of surveyed leaders say their current operating model can deploy and govern AI enterprise-wide, 75 percent also say their organisation must change its operating model within the next 12-18 months to drive greater value.

“This disconnect suggests that while organisations have established models to deploy and govern AI, they are still working through foundational questions around ownership, prioritisation, and integration needed to truly capture value,” Deloitte says.

“The gap between a tech leader’s expanded mandate and their organisation’s ability to execute is where competitive advantage can be won or lost,” Deloitte says. “The organisations that empower their leaders to close this gap – by redesigning how work gets done, how decisions are made and how value is created – can define the next decade.”

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New research from Teletrac Navman (those of a certain vintage may recall that Navman was a Kiwi company founded by Sir Peter Maire back in the 1980s) shows the issue is not a lack of technology investment. In fact, 84 percent of fleets have invested in tracking, telematics and integrated equipment platforms to some degree. The problem, Teletrac Navman construction solution specialist James French told iStart, is what happens next – or more accurately, what doesn’t, with many deployments remaining incomplete, creating pockets of insight, rather than organisation-wide intelligence.

“Without all the data… you can be profitable overall, but you’re leaving money in the dirt.”

The report, which is based on a quantitative online survey of 600 companies operating commercial vehicle fleets, including 200 across Australia and New Zealand, 75 percent are still relying on manual logs either as a primary tracking method or alongside digital systems. Fewer than one-third have fully implemented utilisation technology.

French says while most operators recognise utilisation is important – and believe equipment may be underutilised – the actual picture many businesses are working from is incomplete.

“The utilisation information is often incomplete,” he says, describing fleets where some equipment is tracked, while others aren’t, or where telematics is applied only to specific projects, rather than the entire fleet. The result is what he calls a partial utilisation picture – and decisions made on partial data rarely end well.

French says utilisation initiatives commonly stall because projects are left half finished. GPS might be rolled out to part of the fleet, but not all. Maintenance systems live somewhere else. Third-party equipment has its own tracking platform. Acquisitions add yet another layer of complexity, with multiple systems running side by side because no one wants to disrupt what the newly acquired business is comfortable using.

“You’ve got disparate systems,” he says. “GPS sits here, maintenance sits somewhere else, fuel lives somewhere else again.”

Without pulling those datasets together, organisations miss the opportunity to build a full picture of how assets are actually performing – and what they really cost to run.

French says once utilisation data is integrated with maintenance, fuel and financial systems, businesses can calculate their true total cost of ownership per machine, per hour. That’s where the money starts to surface – and sometimes, where uncomfortable truths appear.

“If it costs $250 an hour to run a machine and you’re charging $220 an hour, you’re losing money,” he says. “Without all the data, you don’t know it. You can be profitable overall, but you’re leaving money in the dirt.”

Idling away

One of the headline findings from the research is that equipment is sitting idle up to 50 percent of the time.

French admits calculating idle is fraught with technical challenges.

Basic GPS can tell operators where machines are and when engines are on or off. More accurate utilisation comes when systems are connected into engine data via CAN (controller area network) bus – the vehicle standard designed to allow microcontrollers and electronic control units such as the engine, brakes and lights, to communicate with each other – pulling details such as RPM to distinguish between productive work and an engine running without doing anything useful.

Machine-level integration also matters. A crane truck may be stationary with the engine running, but if the crane is operating, that’s productive use, not idle time. Without that visibility, utilisation data risks being misleading.

Manual logs are also fraught with issues. French points to a real-world productivity study he worked on in a previous role, comparing manually recorded data with machine-generated data.

“We found about a 15 percent difference,” he says.

Human error, transposed numbers, lost paperwork and best-guess reporting cause further issues in manual reporting. With digital systems, French says, the data becomes a single source of truth that can be audited, retrieved and compared across years.

The double-pay issue

Nowhere is the financial impact more visible than in equipment hoarding.

The report found that two-thirds of organisations admit assets are sometimes held onsite but unused, largely due to uncertainty around maintenance and scheduling. French says the behaviour is understandable, but costly.

“If I ring another site asking if they’ve got a 24‑tonne excavator, they’ll often say they’re flat out,” he says. “Sometimes that’s fear they won’t get it back. Sometimes it’s that they don’t actually know whether it’s being used or not.”

Without utilisation data, businesses turn to renting an additional machine – and double-paying.

“If I think it’s being used and I go and rent another machine, now I’m paying for one excavator sitting idle and another one working,” French says. “One is internal money. The rental is external money. That costs more.”

The fix does not require sophisticated analytics from day one. French says even fleet‑wide GPS coverage can immediately change behaviour.

“If I can ring a site and say, ‘I can see that machine hasn’t worked in a week – we’re sending a truck to move it,’ that saves money immediately.”

He cites the example of one client who reported saving roughly $30,000 in just 40 days by identifying idle plant and redeploying assets based on evidence rather than assumption.

For Australian companies, there’s another big win waiting: Fuel tax credits. He says many companies are leaving cash unclaimed simply because they can’t prove how, where and when equipment is used.

GPS and utilisation data can provide an accurate picture of when vehicles and equipment are operating on ‘non-gazetted’ roads where full fuel tax rebates can be claimed.

“GPS gives a very accurate picture of what percentage you’re off road,” he says, pointing to the ability to use geofencing and location data to distinguish between on‑road travel and off‑road work.

The distinction matters across a wide range of real-world scenarios, from trucks and light vehicles operating on job sites as opposed to driving to those sites, to concrete agitators running engines while unloading, washing and equipment once they enter a site. Forestry operations are another area French highlights, with vehicles transitioning from public roads onto private land and becoming eligible for different treatment.

“In Australia there are billions of dollars sitting in unclaimed fuel tax credits and this is one of the areas you make a quick gain.”

Where utilisation delivers fastest value

French says the first integration point for utilisation data should be maintenance. With live hour data, service intervals become predictable, automated and defensible – an operational and compliance win.

From there, fuel data becomes critical, particularly as costs rise and supply remains uncertain. Understanding where and how fuel is being consumed allows operators to narrow down why similar machines are costing more on one site than another – whether that’s environment, operator behaviour or simply the wrong machine for the job.

Critically, this feeds decision-makers beyond the fleet team.

“When it all feeds into a BI platform,” French says, “the CFO can see what machines cost, what they generate, and what happens if you remove assets that aren’t being used.”

At that point, utilisation stops being a reporting metric and starts behaving like what it actually is: One of the most powerful – and under‑leveraged – drivers of financial performance in asset‑heavy businesses.

The post Idle data, idle machines: The utilisation tech blind spot appeared first on iStart keeping business informed on technology.

Tuanz’s (Tech Users NZ) 2026 Digital Priorities Report, which is drawn from conversations with nearly 30 CIOs and CTOs, captures some of the frustration IT leaders are feeling at New Zealand, rich in innovation pockets, being constrained by fragmented execution, uneven adoption, rising technology costs and an ongoing shortage of specialist talent. The stagnation comes as other nations accelerate their digital transformation efforts, creating a widening innovation gap, Tuanz says.

“They’re not just going to pile money into new IT systems because it feels like the right thing to do.”

Craig Young, Tuanz chief executive, says the most striking finding in this year’s report was how leaders described the moment New Zealand is in.

“There was a real feeling that New Zealand is at a crossroads – a decision point where we have to decide to do something, or not,” Young told iStart.

That sense of drift is a change from previous years, which were focused more heavily on recovery and reflection following Covid-era disruption.

The plateau reflects a broader tightening of expectations around technology investment, particularly when it comes to AI. Young says AI continues to drive behaviour across vendors and organisations, but boards are no longer prepared to invest without clear evidence of value.

“Things are moving so fast, and AI is having such an impact, that boards are now saying: We need a return on our investment,” he says, acknowledging that is a good discipline to have. “They’re not just going to pile money into new IT systems because it feels like the right thing to do.

“AI might still be the flavour of the month, but CIOs are looking at whether they’re actually seeing value,” he says. “If they don’t see it, they’re prepared to sit where they are.”

The report found many organisations are moving away from building their own tools or running standalone experiments, and instead are prioritising AI capabilities embedded within the platforms they already use, including enterprise SaaS systems.

“What they’re looking to do is make the most of what they’ve already go,” Young says. “If the capability is built into SAP, Saleforce, Zoho, Xero or other services, that’s where the focus is going.”

Young says leaders broadly believe AI can deliver value, but only if it’s implemented properly.

“What we’re hearing is that it’s a mixed bag,” he says. “There are situations where people are finding real value. But it isn’t the hype it might have been a year ago.”

That has pushed digital leaders to get more disciplined, particularly around usage and licensing.

“If someone isn’t using a Copilot licence, take it off them and give it to someone else who will,” Young says. “They’re getting down to that level.”

At the same time, CIOs face pressure from suppliers whose products increasingly bundle AI features as part of standard upgrades, regardless of whether organisations are ready to use them.

“Providers say: You need to upgrade because this has AI in it,” he says. “But CIOs are asking: What’s the value right now.”

They’re waiting for real use cases, he says, adding “We need CIOs and digital leaders to find the use cases and the business cases for the stuff they’ve already go, but then start investigating what else they can invest in. It is a rock and a hard place for some of them.”

The report flags a shift away from broad experimentation toward targeted implementation and clearer business cases – a change Young sees as both necessary and overdue.

“We need to move away from experimentation to real implementation. That means getting beyond personal productivity and into areas where AI can actually automate workflows and drive productivity across the organisation.”

Cost pressures reshape priorities

The tightening of focus is occurring within a broader cost squeeze affecting local businesses. Rising cloud, licensing and infrastructure costs are forcing technology leaders to reassess earlier assumptions – including the long-held ‘cloud-first’ stance.

“Costs keep rising and organisations aren’t always getting the value they expected,” Young says. “So there’s more thinking around where things need to be in the cloud and where it might make sense to keep control over costs.”

He notes that New Zealand’s small market size leaves organisations particularly exposed to pricing decisions made by multinational providers, while global investment in AI-driven infrastructure continues to flow through to customer pricing.

“You’ve got massive investment going into cloud data centres and undersea cables,” he says. “That investment has to be paid for, and ultimately users pay for it.”

A crossroads, rather than a crisis

Despite the slowdown, Young is careful not to frame the findings as a failure of ambition or capability. New Zealand still has strong infrastructure and governance settings, and pockets of innovation continue to emerge.

The concern, he says, is what happens next.

“The key message is that we’re at a cross roads,” he says. “If we’re not careful, we’ll get left behind.”

The report calls for more deliberate choices – by government and by organisations – about how existing technology is used, how value is measured and how digital capability is built.

Among the recommendations for organisations to implement to drive adoption, stabilise their environments and build a resilient culture is a call for organisations to formally pause the deployment of new speculative tools and instead redirect IT and change management resources toward embedding existing platforms into daily workflows.

“By focusing exclusively on training, data cleanup and user adoption of current systems, businesses can reduce change fatigue, realise promised productivity gains and build a stable foundation capable of supporting advanced AI integrations in the future,” the report says.

On the talent side, Young says organisations need to look at different ways to bring people into their organisations. While interns and digital apprenticeships have been talked about ‘businesses need to actually start doing stuff like that now’, he says.

“We need to be bringing young New Zealanders into business – not waiting for them to graduate from university but looking at other pathways. Because if we train them properly, they will bring different perspective and views and will query things and bring a use of technology that perhaps us older people haven’t thought of.”

Because AI is automating the administrative tasks traditionally used to train junior staff, new frameworks must be created, pairing juniors with senior specialists and training heavily on critical thinking, data validation and AI prompt-auditing.

Young notes a positive move in the introduction of the electrotechnology, IT and creative Industry Standards Board to set qualifications, standards and training requirements for the sectors.

“Now it’s on us to make sure the industry is strong on what those vocational pathways look like. Now we’ve got to work and make it happen.”

The report also recommends strengthening national settings that digital leaders say are limiting progress. Top of the list is the introduction of mandatory, non-negotiable cyber security standards, reflecting concern that inconsistent security practices across organisations are exposing the broader economy to increasing risk.

Young says digital leaders feel minimum standards would lift the baseline across large and small organisations alike and reduce the burden on individual firms to assess security maturity on their own.

Beyond security, the report calls for stronger national coordination through mechanisms such as a digital clearing house to vet global technology vendors, accelerated progress on a secure national digital identity framework and recognition of data platforms as critical national infrastructure.

Young says these recommendations reflect a desire for clearer direction rather than heavy-handed intervention, with government uniquely positioned to set long-term priorities and provide certainty that extends beyond election cycles.

For enterprise leaders, the implication is clear. The pause in adoption is not an endpoint. It’s a moment where decisions made about investment, implementation and focus will determine whether New Zealand consolidates its digital foundations – or allows strategic drift to set in.

The post NZ digital leaders warn of ‘strategic drift’ appeared first on iStart keeping business informed on technology.

At the time, Bunnings was still a bricks and mortar retail defined by the big green sheds, weekend sausages, paper catalogues and deeply ingrained operational rhythms. It had no website, recalls Mike Schneider, Bunnings Group managing director, in Boston Consulting Group’s Business & Beyond podcast. Internally, the organisation had wrestled with whether digital even belonged in the model. Its website, while popular with about 20 million hits, was effectively just a digital catalogue and the company wouldn’t enter eCommerce in Australia until 2018 with the launch of ‘Special Orders Online’. It would be 2020 before it launched eCommerce in New Zealand.

“We are incredibly underestimated in how good our technology is.”

Deadline over comfort

The turning point came not from the technology team, but through leadership pressure.

Schneider recalls telling the market Bunnings was going online before the organisation felt ready, effectively forcing teams to work backwards from a public commitment. “As a leader, you sort of try different things, and in the end, I just told the market we were going online and didn’t tell my team,” he told the podcast, acknowledging the company was late to the online party.

It was a move he’d seen his predecessor do in order to enter a category. It was also, he admits, a move that wasn’t particularly popular with his team.

In 2017, Bunnings – or more specifically, Schneider – announced that its extended ‘special orders’ range – around 20,000 SKUs – would be available online by February 2018. “It was a bit of an ‘oh hell’ moment for the team because they had to work out how they were going to do it,” Schneider admits. The experience was clunky, imperfect and uncomfortable, but it proved two things: Bunnings could move faster than it thought, and online wasn’t the enemy.

By November 2019, a full online platform was live in Australia – just in time for Covid. Had it not been, Schneider says the business would have been exposed. (And, in a mark of how far the company had come digitally, when lockdowns hit and customers were craving their Bunnings sausage sizzles, it set up mobile payments so customers could snag their snag, contactless.)

The underestimated tech engine

Schneider says Bunnings’ technology capability is far more advanced than it is commonly perceived to be. “We are incredibly underestimated in how good our technology is.”

“We’ve gone from that kid that can’t run fast to the kid that’s actually in the elite sports team, and I think that’s actually deeply underestimated by many, many people.”

While externally the retailer may still be seen through a physical-retail lens, internally in now runs a fast-growing digital marketplace, a ‘hugely successful’ retail media arm enabling suppliers to reach customer customers across its digital and physical channels, advanced property development and sourcing systems and increasingly sophisticated digital operations.

He credits the company’s first CIO, Leah Balter, who was recruited to the role in 2018, and current CIO Genevieve Elliott, for the tech success.

“[They are] both world-class executives who have, and continue to, transform the organisation to the point where partners like Google, with the agentic Gemini commerce platform, have looked to us to partner as one of the early adopters.”

The company showed off its Buddy AI-powered shopping assistant, built using Gemini Enterprise for CX, at Google Cloud Next 2026 last month. Customers can tell Buddy what they’re looking to do – build a deck for example – and Buddy recommends the necessary equipment from decking to tools, and links to Bunnings’ how-to videos. Photos, including of a broken part or a handwritten shopping list, can be uploaded with Buddy visually identifying and locating items required.

The assistant has been rolled out in Australia across both the website and app, and will be available in New Zealand ‘later this year’, Bunnings says.

It is, Bunnings says, part of the company’s ‘accelerated use of data and AI to improve customer experiences, streamline work for teams and support long-term growth’.

Schneider frames digital as a capability layer, rather than a shiny channel – something to augment stores, suppliers, sourcing and customer experience, rather than replace them.

The shift reflects how far the retailer believes it has moved from its early digital hesitation into enterprise-grade technology execution.

That technology depth isn’t limited to eCommerce or customer-facing platforms. Schneider points to systems, processes and measures across the business designed to ensure disciplined execution and governance as Bunnings has scaled its digital operations.

Alongside its broader digital build-out, Bunnings has also deployed facial recognition technology as part of its in-store security operations.

The new industrial revolution

Schneider compares the current moment to the industrial revolution, saying he believes ‘AI will do to our generation what the industrial revolution did’.

He says Bunnings and parent company Wesfarmers are thinking deeply about how to use AI ‘properly’ for better customer and team experience, with ‘wise’ allocation of capital – and with the systems, processes and measures to do so behind the scenes.

“But there are going to be people who are worse off in the short term if it is not used properly. Just simply, ‘thank you so much for your service, we’ve not got an agent doing the work’ – that is going to happen.

“We are going to need to reimagine jobs and careers.”

He urged leaders to be clear on purpose and direction to ensure they make good choices.

“I’m an eternal optimist that things just get better and this become an enormous enabler of growth, productivity and job and life satisfaction.”

Schneider says there’s plenty of ‘really deep strategic thinking, planning and high-quality execution’ going into the Bunnings business – often through a lens of being ‘pretty casual’.

“A lot of successful Bunnings’ leaders don’t take themselves too seriously – but they are deadly serious about the work they do,” he says.

That casual exterior can mask how deliberate the machine underneath really is. Marketplace expansion, category adjacencies, retail media growth and digital platforms are all driven by disciplined capital allocation, systems and measures designed to scale without breaking trust.

And as for Bunnings’ technology being underestimated? That suits Schneider just fine – he says being underestimated has always been his natural preference.

Just a quick note for all our readers: This vendor case study was not a paid placement. We want to keep things transparent and share only authentic, fact-based stories about what real businesses are doing with real tech. If you’re a brand owner or an agent and have a genuine story to tell, iStart’s readers are eager to hear about it. Please don’t hesitate to get in touch if you’d like to share your experience.

Thanks for helping us showcase meaningful stories that inspire, educate and motivate businesses to invest in their productivity.

The post Inside Bunnings’ ‘underestimated’ tech engine appeared first on iStart keeping business informed on technology.

The gap isn’t ‘AI vs no AI’. Instead, it’s how AI is adopted and integrated, with 2degrees, which commissioned the Deloitte Access Economics report, warning that AI alone won’t lift productivity. Instead, the benefits depend on how effectively organisations adopt it, integrate it, and build the capability to use it well.

“AI itself will not lift productivity.”

OECD, NZ Productivity Commission and Stats NZ numbers consistently show New Zealand produces less output per hour worked than Australia and many comparable OECD economies, with recent growth driven more by people working longer hours than by efficiency gains. Things aren’t so hot for Australia, either. While it sits closer to the OECD middle on labour productivity, the golden era of labour productivity in the 1960s and 1970s are far from today’s reality.

A $400,000 signal, with conditions attached

The Deloitte Access Economics’ report, Productivity Propelled: The Impact of AI on Business Performance, claims to be the first New Zealand-specific attempt to quantify the link between AI adoption and firm-level productivity. Drawing on survey data collected in early 2026, it estimates the average SME using AI earned around $400,000 more in FY25 than similar companies that had not adopted the technology, with larger businesses seeing even greater uplifts.

However, the report is explicit that outcomes are neither automatic, nor evenly distributed. While 82 percent of organisations report using AI in some form, many remain early in their AI journey, relying on AI features embedded in existing software rather than standalone or deeply integrated systems.

Liza van der Merwe, Deloitte Access Economics lead partner at Deloitte New Zealand, says AI presents a significant opportunity, but only if businesses have the right foundations in place.

“Progress depends on building mindset, systems and skills in tandem,” van der Merwe says. “When these come together, businesses are far better placed to turn AI into real productivity gains.”

She says for many organisations the biggest gains are not from inventing new technologies, but from using what exists more effectively. “That means integrating AI into day-to-day operations, supported by the right infrastructure, processes and ways of working. Ambition alone isn’t enough – without the right systems and capability, businesses risk getting stuck in experimentation rather than delivering meaningful results.”

That risk is evident in New Zealand. MYOB’s survey of 500+ leaders and decision-makers at mid-sized businesses shows that while AI usage is increasing, a significant share are still not seeing commercial returns, critical gaps in operational readiness meaning most are leaving significant productivity and commercial gains uncaptured.

Two-thirds of the Kiwi mid-market companies said they have strong digital processes (66 percent) and good data foundations (68 percent), but workforce readiness (56 percent) and having proper governance in place (61 percent) and meaningful process autonomy (55 percent) were tracking behind.

Where AI is layered onto fragmented systems or manual processes, the gains tend to be incremental, with businesses tending to report time savings as the primary benefit.

In contrast, MYOB’s data shows that businesses embedding AI into core systems and processes, supported by stronger data, governance and workforce capability, are more likely to report impacts that move beyond time savings, including revenue growth, margin improvement and better decision-making at scale.

Australia shows what changes at scale

In Australia 81 percent of the more than 500 mid-market business leaders surveyed said AI has had a positive impact on productivity. That number rises to 93 percent among the most advanced organisations.

The strongest results were reported by businesses that had embedded AI into their core processes, rather than treating it as a bolt-on tool. Those organisations were also more likely to report improvements in output quality, revenue and profitability, not just efficiency.

MYOB frames this through five foundational pillars it sees as critical to unlocking AI’s value:

• Core processes – standardised and digitised workflows
• Data – quality and integration
• AI strategy – clear prioritisation and use cases
• AI governance – risk compliance and guardrails
• Workforce capability – skills and change capacity

Australian companies scoring well across these pillars are more likely to see compounding benefits from AI. Where pillars are weak or missing, returns are more limited, regardless of how much is spent on AI tools.

Alex Hooper, Oxford Economics Australia associate director, says with productivity such a central focus on both sides of the Tasman, the MYOB results showing 75 percent of Kiwi and 77 percent of Australian mid-sized businesses are reporting productivity benefits from AI is encouraging.

“However, the results also show a divergence in maturity across the two markets,” she says. “While New Zealand businesses show strong ambition, Australian firms are more likely to have translated that into deeper operational integration, suggesting a faster pathway to realising productivity and commercial benefits.

“Across both markets, businesses appear to be converging on a similar set of priorities for scaling AI and automation. Workforce skills, core system upgrades and data quality are coming through strongly, although New Zealand businesses are more likely to report a need for greater to improve workforce skills and training,” she explains.

The size of the prize

For Kiwi mid-sized businesses that have invested across more of the key pillars, MYOB says the commercial returns are already showing up in the numbers.

In addition to time saved (46 percent), almost a third (30 percent) of decision-makers polled believe AI has contributed to increased revenue or sales growth and 27 percent report improved profit margins, however the proportion enjoying such financial benefits grows with business size.

More than one-in-three (37 percent) of businesses with 100+ employees report improved profit margins, compared with just 11 percent among firms with 20-49 employees. The revenue gap is similarly sharp, with 36 percent of the largest mid-sized firms reporting revenue uplift versus just 16 percent of the smallest.

From experimentation to earnings

Taken together, the Deloitte Access Economics and MYOB findings point to the same conclusion from different angles. AI can deliver firm‑wide productivity and financial benefits, and Deloitte’s $400,000 figure underlines the scale of the opportunity.

But the returns come where it is tied to systems, processes and skills that scale. Without that groundwork, AI risks becoming another well‑intentioned investment delivering marginal gains, not the step‑change both economies are looking for.

The post Australian SMBs bank AI, NZ chasing appeared first on iStart keeping business informed on technology.

That’s according to Fonterra and Spark who are both selling the same idea in different uniforms: AI doesn’t need to transform everything overnight to make your P&L sing. At Spark, it’s as unglamourous – and as valuable – as taking two minutes out of a customer call and untangling the workflow knots that slow staff down. Over at Fonterra, AI is doing equally practical work: Monitoring butter packaging and pausing the line when faults show up, replacing spreadsheet-based production scheduling and pulling real-time IoT data from machinery across more than 100 plants into the cloud to support predictive maintenance.

“We’re using that database to pull research faster and to turn that into insights and innovation.”

Fonterra: From factory to office

Fonterra’s AI work spans both physical operations and corporate functions, with a focus on consistency, speed and decision support.

On the manufacturing side, the dairy co-operative, which processes around 22 billion letres of milk solids each season, is using AI at sites such as Clandeboye in South Canterbury to monitor butter packaging across multiple stages of production. When faults are detected, the system can pause the line, allowing staff to intervene early.

In production planning, AI has replaced spreadsheet-based scheduling, while real-time IoT data from machinery across more than 100 plants is streamed into Microsoft’s cloud to support predictive maintenance. The aim is to reduce downtime and disruption across a global manufacturing footprint.

At the same time Fonterra has embedded AI into everyday office work. The organisation was an early adopter of Microsoft 365 Copilot through the vendor’s early adoption programme, and identified hundreds of use cases across the business.

By February 2026, Fonterra says 35 percent of its global workforce was actively using AI tools, generating almost one million interactions in a single month across Copilot Chat, Copilot Studio agents, GitHub Copilot and the organisation’s own internal system, Co-op GPT.

The tools are being used to summarise meetings, capture actions, accelerate policy drafting and support internal decision-making.

Fonterra has also built a set of production AI agents using Copilot Studio in partnership with EY. These include an idea submission coach to improve the quality and consistency of investment proposals, an architecture assessment agent to support internal governance checks, and a technical accounting assessment agent designed to ensure financial assessments are audit-ready.

Skills, mindset and adoption at scale

Speaking at Microsoft’s AI Tour in Auckland last week, Fonterra CEO Miles Hurrell said the organisation’s AI work has been underpinned by a strong focus on upskilling its people and ensuring it has the right mindset around AI adoption. “There’s been a lot of work on how we get the right mindset, because you’ve got a range of views across an organisation,” he says.

Rather than positioning AI adoption as an IT-led initiative, Hurrell says Fonterra has taken a management-led approach. “We’ve gone with the approach of having it management-led as opposed to led from IT departments, and letting people explore and experiment in their own minds and share that knowledge,” he says.

One area where that approach is paying off is research and development. Hurrell says the company is using AI to improve outcomes at its Palmerston North research centre, which houses, decades of scientific work. With around 300 scientists, 120 PhDs and a century long legacy, the centre has a wealth of research documents. “We’re using that database to pull research faster and to turn that into insights and innovation,” he says.

Hurrell says the team is ‘hungry’ for the technology, but the organisation is focused on balancing experimentation with discipline. “It’s a matter of bringing the mindset and the skill set to ensure we don’t get too far ahead of ourselves, but experimenting and showing the benefit that we can create,” he says.

Spark: Fixing workflows first

At Spark the focus has been on understanding how work flows through the organisation and where it slows down or becomes more complex than it needs to be. Notes Spark CEO Jolie Hodson: “If you can remove some of that friction, you start to see a change not just in individual tasks, but in how the whole process comes together.”

The telecommunications and digital services provider signed a major strategic partnership with Microsoft in 2025, which included the country’s largest Microsoft Azure cloud agreement and one of its largest deployments of Microsoft Copilot.

The early impact has been felt in customer care. Speaking at the Microsoft AI Tour in Auckland, Hodson said Spark has more than 350 advisors now working with an AI system alongside them to help answer questions. Thousands of interactions are handled each day via the contact centres, and advisors don’t always have immediate access to the technical information needed to resolve enquiries on their own, requiring escalation to the operations team. Jolie says the system, a Copilot tool trained with technical and procedural knowledge call centre operators need, is deflecting 20,000 questions a month, or around 60 percent of issues, that would otherwise have gone to its back office.

The gains, however, are not limited to contact centres. Hodson says the organisation has deliberately spent time putting the right foundations in place before scaling its use of AI. “We spent quite a bit of time [on] the things you need to support the models – trust, privacy, the skills we need to build in our people,” she says. “With that you can then move at pace once the technology comes online.”

Spark is also using AI to accelerate its software delivery processes. “We are using it to test and change and still have human review inside that,” Hodson says, pointing to the use of AI to support development work without removing oversight.

She also pointed to broader ambitions for AI to lift productivity – a long term problem for Spark – and for the wider New Zealand Inc and Australia Inc. Hodson says AI is one way to lift productivity. “but standing back and waiting, hoping for something to change isn’t going to set us up to succeed.”

Moving from pilots to production

Across both organisations, the common theme is restraint paired with scale. Neither Spark nor Fonterra frame AI as a single transformational switch. Instead both talk about removing friction from existing processes, whether that friction sits in a customer interaction, a factory line, a governance workflow or research archive.

The signal is clear in how both organisations describe success: Measurable improvements in speed, consistency and throughput, grounded in skills, trust and governance rather than speculative pilots or one-off demonstrations.

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But recovery? That might be another story with only a third or organisations in a recent survey having a tested incident response or business continuity plan. The figures, contained in Datacom’s 2026 Cybersecurity Index point to a false sense of security big enough to drive a ransomware truck through.

“The figures point to a false sense of security big enough to drive a ransomware truck through.”

The Index draws on a survey of more than 700 security leaders across Australia and New Zealand. It found 73 percent of New Zealand respondents and 77 percent of Australian believe they have sufficient visibility across risks, vulnerabilities and compliance. They’re confident too (Australia: 70 percent; NZ: 78 percent), that they have the resources to deal with a cyber attack.

But Datacom has raised a red flag when it comes to organisational preparedness. The data shows only 30 percent of New Zealand and 32 percent of Australian organisations have a business continuity or cyber incident response plan in place. That gap between confidence and readiness is what Datacom dubs the region’s growing ‘resiliency gap’: Organisations have built world-class radar systems, but many still don’t have a safe runway to land the plane when things go wrong.

“Organisations have invested heavily in monitoring and detection, but they are falling short when it comes to recovery,” says Mark Hile, Datacom managing director, infrastructure products. “The priority now is not another dashboard but engineered resilience – from containment to stabilisation to rapid recovery.”

The Index shows most leaders expect to recovery from a major cyber incident within days, an optimism which flies in the face of real-world examples which routinely show recovery taking weeks or months. “Business leaders in particular underestimate recovery times, despite the fact IT leaders report an average of four weeks to reach a minimum level of operational recovery after an attack, according to global tech research firm Omdia,” the report says.

The problem is not a lack of alerts, but instead untested plans, unclear decision rights and an absence of operational muscle memory when cyber incidents become business crises.

“In 2026, maturity will be defined by resilience, not just detection,” the report says. “Visibility alone is no longer the differentiator; the ability to operate under high volume and to recover when defences fail is.”

As Datacom CISO Collin Penman puts it: “Detection is table stakes. Reporting is a must. Resilience is the differentiator.”

That resilience, he says, comes down to preparedness – something he says is a problem for A/NZ organisations, based on the survey data and an issue requiring a fundamental shift in how boards and business leaders make decisions in cybersecurity investment.

He notes the example of the 2025 ransomware attack on Jaguar Land Rover UK, which halted production for five weeks and took nearly five months for full recovery.

“What prolongs recovery is not a lack of alerts, but a lack of preparedness for disruption. Many organisations are well equipped to identify an incident, yet struggle once it impacts customers, services or revenue. At that point, untested continuity plans, unclear decision rights and cross functional dependencies slow progress,” the report notes.

The report suggests the threat landscape itself is well understood. Across both markets AI-enabled cyberattacks and phishing/social engineering consistently rank as the top concerns for organisations. These are closely followed by employee or user error, application and API attacks – particularly involving legacy systems – and data breaches involving extortion. Datacom notes AI-enabled threats are not necessarily new, but are acting as force multipliers, enabling attackers to move faster, scale more easily and compress attack timelines.

Cyber’s human problem

The same visibility trap is playing out inside security teams.

According to the Index, 43 percent of Kiwi and 36 percent of Australian organisations report signs of cyber burnout among their security or IT teams. While this is down form 2025 levels, Datacom warns that’s not because the work has eased, but because pressure has been recalibrated as ‘normal’.

Security teams are managing relentless alert volumes, AI-enabled attack acceleration and rising expectations from boards who often underestimate recovery realities. Responsibility for cybersecurity remains concentrated in IT, with over half of organisations in both markets still viewing cyber as primarily an IT or security function, rather than a shared executive responsibility.

“The drop in reported burnout is encouraging, but I don’t think the pressure has eased,” says Penman. “I think out people have recalibrated what they consider to be normal… no amount of hiring will outpace the volume and velocity of what we’re facing.”

Seeing everything, owning nothing

One of the potentially more uncomfortable findings in the Index is how poorly visibility translates into organisational action. Framework adoption remains fragmented, with no single framework surpassing 50 percent, even among organisations expressing high confidence.

Even where frameworks exist, Datacom notes they function more as ‘organising mechanisms than operational anchors’ with limited impact on real-world recovery capability.

Security teams are expected to manage incidents that cross cloud, identity, applications, supply chains and customer experience, often without clear authority to stabilise systems quickly. When response plans exist only on paper and have never been tested, recovery becomes improvisation under pressure. Says Penman: “A plan that’s never been tested isn’t a plan – it’s a document. Regular exercises build muscle memory, so response becomes automatic, coordinated and fast in the event of a cyber incident.”

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MIT Technology Review Insights’ report, Redefining the Future of Software Engineering, shows a surge of confidence that agentic AI – the advanced, autonomous AI systems capable of independently executing multi-step workflows – will dramatically accelerate software development. But DBT Labs’ 2026 State of Analytics Engineering Report, which tracks how AI is reshaping analytics and data platforms, highlights a quieter, but consequential trend: Infrastructure and integration costs are rising faster than budgets, particularly as AI becomes embedded across analytics and engineering workflows.

“This raises a central question for 2026: Whether validation, testing and oversight mechanisms are scaling at the same pace as AI-driven output.”

Together, the reports highlight that AI agents might be here, with adoption is accelerating, but the bill is landing not with licenses but with compute, orchestration and governance.

MIT Technology Review Insights surveyed 300 senior IT, software engineering and data leaders at large enterprises and found that 98 percent expect agentic AI to accelerate software delivery, with average speed gains of 37 percent anticipated over the next two years. More than half already rank agentic AI among their top three software investments, with that figure expected to rise to 84 percent within three years.

“2025 has marked a steep acceleration in how AI is used in software engineering, evolving from vibe coding experiments to more disciplined agentic engineering,” the report notes. Productivity no longer scales linearly with headcount. “It scales with how clearly we define the product vision, engineer context and onboard agents.”

Ambitions, too, are scaling quickly, escalating from assistance to autonomy, with agents managing product development and software development lifecycles. That’s a goal 72 percent aim to achieve in two years and 41 percent expect within 18 months.

The DBT Labs report shows the same momentum in analytics engineering. AI-assisted coding is now prioritised by 72 percent of analytics teams, and among leaders more than 77 percent emphasised AI for productivity gains.

“AI is no longer experimental inside data teams,” the report says. “Instead, it’s funded, embedded and actively reshaping analytics engineering workflows, influencing how code is written, how insights are generated and how teams invest in analytics and data infrastructure. What was once exploratory is now operational.”

Despite the optimism, the MIT report injects a note of caution: Most organisations don’t expect dramatic gains in the short term. The majority of respondents expect slight (14 percent) or at best moderate (52 percent) improvements from agentic AI over the next two years. Only nine percent expect the impact to be game-changing in that timeframe.

That detail reframes the adoption curve: Organisations are absorbing the full cost and complexity of integration before seeing transformational returns.

The DBT report reinforces this. AI’s primary impact today is operational – shortening cycle times, increasing throughput and expanding output volumes – rather than fundamentally changing what teams deliver.

The hidden cost of integration

The two reports align when it comes to the hidden costs, and a key challenge, involved.

In the MIT study, agent integration with existing enterprise applications and the ongoing cost of computing resources are tied as the biggest challenges to scaling agentic AI, each cited by 44 percent of respondents. Multi-agent orchestration across complex enterprise environments, often involving hundreds of APIs and third-party and home-grown applications, is a major constraint. Orchestration agents exist, but are not yet able to manage lifecycles end to end, partly because of that complexity.

The DBT Labs report shows the same pressure playing out in the analytics sphere. Fifty-seven percent of the 363 data practitioners and leaders surveyed reported increased data warehouse and compute spend as AI-drive workloads expand. Infrastructure demand is absorbing much of the AI investment, the report notes. Meanwhile, only 36 percent report increased team budgets.

While AI tools may be inexpensive to access, the reports point to the expense of operating AI at scale. Compute, storage, orchestration and observability costs quickly accumulate, especially when AI expands workload intensity by default.

Both reports also flag a growing imbalance between output and control.

DBT Labs notes that while 72 percent of teams prioritise AI-assisted coding, only 24 percent prioritise AI-assisted pipeline management, including testing, validation, observability and quality controls. “The signal is clear: Teams are leveraging AI to accelerate creation, but not to reinforce governance at the same pace.”

MIT’s research echoes this concern from a software engineering perspective. Reliability, governance and auditability are cited as gating factors to wider deployment, particularly in regulated or safety-critical environments. Most agentic systems today still require human oversight, which limits how quickly autonomy can scale.

DBT’s report found that trust in data remains a foundational concern as AI use expands. Poorly governed data doesn’t just undermine outcomes, it increases operational risk, forcing organisations to slow deployment or add safeguards after the fact.

“As AI expands what analytics teams can produce, concern is rising alongside adoption. This raises a central governance question for 2026: Whether validation, testing and oversight mechanisms are scaling at the same pace as AI-driven output.”

Or as one data engineer quoted in the report notes: “AI won’t fix a messy foundation. It just makes the lack of discipline much more visible.”

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The Autumn 2026 Young Company Finance report from NZ Growth Capital Partners and Angel Association New Zealand shows total startup investment rose to $754 million across 166 deals in 2025, a 61 percent year-on-year increase in total investment and a 14 percent rise in deal count. Australia recorded a similar pattern, albeit at far greater scale.

“Larger rounds are becoming more common, and that matters.”

The State of Australian Startup Funding 2025 report from Folklore Ventures and Cut Through Ventures found total funding was up 31 percent to $5.4 billion in 2025. As in New Zealand, the headline growth was driven by larger rounds, not a surge in deal numbers, which were down 20 percent year on year to 390.

Across both markets, the data points to a shift to more money flowing into later-stage, deployable technologies and less tolerance for long-horizon bets.

Execution, not experimentation

The most striking signal in New Zealand’s data is the stage profile of funding with nearly 49 percent of all successful rounds going to early expansion or expansion-stage companies. Those deals accounted for 83 percent of capital investment. The year saw 33 proof of concept deals recorded, with investment in PoC companies remaining largely unchanged.

“Larger rounds are becoming more common, and that matters. It signals that New Zealand companies are progressing further, staying competitive on a global stage and attracting the depth of funding required to scale,” says Angel Association of New Zealand chief executive Bridget Unsworth.

The report notes that New Zealand’s venture market is showing clear evidence of maturation at the scale-up end, with significantly larger follow-on rounds being raised by a small number of existing high-growth companies, with the market backing known performers. But the data also suggests a narrower pipeline of new companies entering large funding rounds, with the market not brining many new names into the $10 million-plus category.

But Unsworth warns that while larger funding round bring benefits, there’s a danger too. With funding heading to the later-stage rounds, there’s a natural risk that early-stage investment – ‘the lifeblood of the ecosystem’ – receives less attention and the pipeline of companies capable of reaching larger rounds will dry up.

It’s not a uniquely New Zealand challenge, with Australia’s data mirroring a similar concentration. The top 20 deals in 2025 accounted for 58 percent of total funding, with deal counts falling even as total capital rose. Median round sizes increased, at least marginally, at most stages.

AI leads, but fintech and healthtech are hot to trot too

AI featured prominently in both markets, but the nature of investment has shifted. The New Zealand report recorded seven AI-related deals – five rounds over $1 million and one large funding round of over $20 million – accounting for nine percent of all funding during H2 2025.

In Australia, 61 percent of total funding went to companies using AI somewhere in their technology stack. More than AU$1 billion went to AI-native startups and AI deals cleared at higher valuations than the rest of the ecosystem, the Australian report notes, with AI-at-the-core companies demanding higher valuations and attracting sharper investor competition, while non-AI deals were pricing within tighter, more disciplined bands.

“This bifurcation reinforces selectivity rather than broad-based hype,” the report says. “Investors report a willingness to pay up where AI meaningfully improves scale , defensibility or exit potential, while applying conservative benchmarks elsewhere.”

Quentin Wallace, cofounder and venture partner of Archangel Ventures, says the market is looking for ‘a synergy between strong growth and innovative products’.

The report says AI has acted as an accelerant, not because ‘AI’ sells, but because it compresses time. Startups can reach minimum viable product faster and more resource-efficiently, and iterate with minimal engineering effort, pulling companies into a fundable position earlier.

Both reports identify fintech and healthtech as consistent investment leaders.

In New Zealand, the Young Company Finance report notes fintech and healthtech recorded strong investment growth in H2 2025, with fintech increasing five percent year-on-year as a share of total deals to hit 12 percent of deals and 13 percent of funding. Five multi-million-dollar deals were logged during the half year, with total investment in the sector up 133 percent year-on-year.

Healthtech too, attracted ‘significant’ investment, accounting for 15 percent of total funding, with seven rounds exceeding $4 million, and overall investment in the sector up 89 percent year-on-year.

In Australia, fintech attracted $868 million, with biotech and medtech drawing $829 million, pushing them into second and third place, behind AI.

Climate-tech and clean-tech investment declined in New Zealand during 2025, a trend the report says reflects global funding patterns. Australia’s experience adds important context: Climate and deep tech investment continues, but later-stage rounds increasingly rely on government support or blended capital, rather than private venture capital alone.

Stepping away from the two reports – and from broader climate trends – agritech is entering a new phase of growth in New Zealand, with significant capital raises and a maturing ecosystem focused on scaling globally.

Brendan O’Connell, AgriTech New Zealand chief executive, says the scale of recent capital raises from companies like Halter, Hectre, Agovor, Scanabull and Scentian Bio ‘shows we’ve moved from promising to proven’.

In March, Halter raised NZ$377 million in series E funding, at a $3.43 billion valuation. Meanwhile, Hectre, which uses AI and computer vision to capture real-time data on fruit size, colour and quality before it enters the packhouse, raised $12 million in a series A round in February.

“Capital growth is a signal, but system strength is the goal,” says Young Company Finance. In practice, the data suggests that strength is increasingly defined by relevance. As capital concentrates across both markets, fewer companies are making it through, but those that do are arriving with the scale and backing buyers expect.

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Put another way, organisations are buying the AI sports car, but most employees still don’t have a driver’s licence.

“Organisations that treat AI literacy as a strategic priority, not a box-ticking exercise, will unlock meaningful productivity gains and long-term competitive advantage.”

AIQ measures the readiness of individuals, teams and organisations to adapt to, collaborate with, trust and generate business results from genAI and other forms of the technology. The Forrester report AIQ 2.0: Employees (Still) Aren’t Ready to Succeed with Workforce AI, finds that only 16 percent of employees achieved a high AIQ in 2025, up marginally from the 12 percent seen in 2024. That’s despite widespread deployment of generative AI tools such as Microsoft 365 Copilot, Google Workspace and enterprise copilots now embedded into daily workflows.

The disconnect matters. JP Gownder, Forrester VP, principal analyst, says the lack of AIQ is becoming a clear bottleneck to productivity and ROI. Employees with low AIQ are slower to adopt AI tools or use them incorrectly, increasing errors, rework and frustration. In many cases, the time and effort required to use AI outweighs the value generated, preventing organisations from reaching the productivity ‘crossover point’ where AI meaningfully improves work outcomes.

Forrester’s AIQ framework looks at four dimensions of employee readiness: Understanding AI, skills and training, confidence and motivation, and ethics, risk and privacy awareness.

While 68 percent of organisations report using generative AI in production applications, only a small minority of employees show a strong ability to work effectively with the tools, with AI skills ‘stagnating’ despite the widespread deployment.

“This slow progress indicates that enterprise AI rollouts are outpacing investments in employee capability, creating a readiness gap that limits productivity gains and increases operational risk,” the research firm says.

Training gaps a key constraint

Training is a major weak spot. Just over half of organisations provide AI training to non-technical staff and fewer than a quarter offer prompt engineering training, which Forrester dubs a ‘foundational skill’ for effectively using tools like Microsoft 365 Copilot and Google Workspace.

The report dubs a ‘mere’ four-point gain in the percentage of individual contributes who said they received formal training as a ‘shocking lack of progress’. “It’s clear that employers haven’t stepped up to enable their employees through learning initiatives.”

It notes that GenAI tools are probabilistic, rather than the deterministic computing we’re used to. Where with deterministic tools the same input produces the same output each time, following fixed rules and logic, with GenAI’s probalistic outcomes ‘we don’t know exactly what they’re going to say and do’, Forrester says. That changes how employees must work with technology, requiring them to understand that AI outputs are suggestions, rather than definitive answers and making the need to know when to question the output of AI ‘crucial’.

There’s also the human factor. Many employees still worry AI will replace their jobs, even though Forrester’s forecasts show limited job displacement to date. Poor communication and vague messaging about AI’s role are fuelling anxiety, reducing trust and quietly sabotaging adoption efforts.

Gaps in employee confidence and motivation are also highlighted. Only 37 percent of employees report feeling confident in adapting to AI-driven work and fewer than half are motivated to build AI-related skills.

Ethical readiness is another area of concern with just 44 percent of employees feeling confident using AI responsibly and ethically. Forrester warns these gaps increase organisational risk, particularly as AI is increasingly used in customer-facing, decision-making and regulated environments.

“Employers aren’t giving their people the skills, understanding or ethical grounding they need to succeed with AI,” Gownder says. “Our research shows most organisations are rolling out AI tools without investing in employees’ ability to use them effectively.”

Here’s how to close the AIQ gap

The good news is the research is equally clear on what works. High-performing organisations treat AI literacy as a capability to be built over time, not a one-off training exercise.

“To close the gap, businesses must move beyond surface-level training and build continuous, hands on learning that demystifies AI, addresses employee concerns and develops real capability,” Gownder says. “This isn’t about replacing workers – it’s about enabling them to work smarter with AI.”

Formal learning plays a surprisingly small role in raising AIQ, with linear training quickly forgotten, Forrester says. Organisations making progress are investing in continuous learning models that combine formal instruction with practical, hands-on experience. This allows employees to apply AI skills directly within day-to-day work and build capability over time. “Better is a weekly email with tips, videos and best practices form inside your organisation and ideally inside your division.”

Social learning – with weekly office hours with a subject matter expert – are also key. Approaches such as AI champions programmes, shared experimentation and peer support networks have been shown to be more effective than traditional training alone. These models help reinforce learning and support sustained adoption across teams. Middle managers need to signal with their attendance the behaviour they want to see from direct reports. Online discussion forums offering real-time help and testimonial videos of successes and failures from within your organisation have more legitimacy, Forrester says, than bland, online training.

Third, organisations need to ensure training reaches non-technical employees, who make up the majority of the workforce using generative AI tools. Practical training in areas such as prompt development and use-case application is critical to improving baseline AI capability.

Forrester also highlights the need to address employee concerns and build confidence. Clear communication about how AI will be used, what it will and will not automate, and where human judgement remains essential can reduce anxiety and support adoption.

Finally, the report stresses that ethical, risk and privacy considerations must be integrated into workforce AI education. Employees need guidance on responsible use, particularly as AI systems are embedded into sensitive and regulated workflows.

Forrester’s conclusion is clear: Technology deployment, as is so often the case, is not enough on its own. Without corresponding investment in employee readiness, organisations will struggle to realise the full benefits of workforce AI.

“The organisations that treat AI literacy as a strategic priority, not a box-ticking exercise, will be the ones that unlock meaningful productivity gains and long-term competitive advantage,” Gownder says.

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“We started using machine learning and predictive analytics about seven years ago, so this isn’t new for us” Bain tells iStart. “What people think of as AI now is really just the latest evolution of things we’ve been doing for a long time.”

“These projects aren’t really AI projects – they’re data projects.”

From the outset, Spark discovered that deploying AI – particularly where customer data is involved – raised complex questions that couldn’t be solved by technology alone.

“When you’re building models, it can be hard to explain how they choose. So the starting point for all of this was really strong ethical guidelines around how we use the technology.”

Rather than reacting case by case, Spark invested early in defining what it considered acceptable – and unacceptable – uses of AI. “What that looks like: What do we want to use this for, what data is acceptable, how is the data stored securely, and making sure that if a model does something we can explain why it did it.”

The principles were designed to be enduring, rather than technology-specific.

“The technology changes really quickly,” Bain notes. “But if we start from a foundation of what we believe is right and wrong as a business, and what we would never do – even if it was commercially attractive – then those principles still apply as the technology evolves.”

Over time, those principles were embedded operationally, supported by internal tools Spark developed to help teams assess proposed AI use cases before they’re deployed. “We’ve now got tools that allow our people to see the use case, understand what it’s being used for and the system will flag whether this is permissible.” The tool translates Spark’s ethical principles into practical decision-making, helping teams evaluate how data will be used, whether a human needs to remain in the loop and whether the use case aligns with published governance standards. Embedding these checks early reduces ambiguity and gives teams the confidence to move quickly once a project is cleared, Bain says.

He says this foundational work gave Spark a level of maturity before generative AI entered the mainstream.

“We already had a baseline. Our teams understood the effects, the principles and how they turn into actions.”

A Skinny test

That groundwork was tested with an ambitious project to clone a customer for an advertising campaign for Skinny – Spark’s low cost mobile and broadband brand – in what Bain says was a world-first.

“We’d never cloned a human before,” he says. “That’s a whole new level. Someone’s identity. Their voice. Their mannerisms and physical likeness.”

The technology made it possible to produce creative content faster and at a lower cost – and it feed into the Skinny brand – ‘cheeky and innovative’, using AI based on a real person, rather than a completely synthetic human to keep marketing costs low.

“We could put her in space. We could put her on the moon. We could test creative ideas really quickly and get to a finished product much faster than traditional filming.”

The visible outcome however, understated the work required behind the scenes.

“Above the waterlines is what you see,” Bain says. “Below the waterline was a huge amount of new thinking – particularly around security – because this is actually more sensitive than the personal information we usually hold.”

Skinny treated the AI likeness as requiring a higher standard of protection than typical customer data.

“Before we even captured the data, we had to decide who could access it, how many people, whether it could be downloaded, and where it could live. It could only exist in a very secure environment and could never leave it.”

Legal and privacy teams were involved throughout, running alongside the technical work.

“Some of these things you don’t realise until you hit them, so the practical steps evolved as we went,” Bain says. “Our legal and privacy teams were actively involved, making sure they were always a few steps ahead.

Liz Wright, the customer who was cloned, has remained involved throughout the campaign.

“She was more involved each time. We didn’t just do things and put them out there without her knowing, even where we could have done that legally. We didn’t think that was the responsible way to handle it.”

There are clear limits too, on usage. “We have rights for a defined period, and then we destroy the data. We don’t want to hold it for longer than we need to.”

Guardrails unlock speed

While the governance work was intensive upfront, Spark says it was this effort that now allows teams to move quickly.

“What we found was that we spend more time upfront evaluating the risks and making sure we have those covered. But once the guardrails are in place, teams can move very fast inside them.”

Bain argues that the common perception – that AI governance slows innovation – misunderstands where the real friction lies.

“Building the models is really quick these days,” he says. “The hard part is the guidelines and the ethical approach.”

Once teams understand how AI works and what the boundaries are, Bain says fear drops away. “When people understand the technology and the risks, they’re emotionally comfortable with it, that’s when they can really move.

Bain notes that AI governance must accommodate technologies that change continuously. “These models are doubling in capability every six months. You can’t imagine now what will be possible in two years.”

Rather than constantly rewriting rules, Spark applies the same principles to each new use case as it emerges.

“You need to govern it like a piece of technology that will constantly change,” Bain says. “Have broad principles, then apply the same framework every time.”

That approach, he says, creates clarity internally and avoids paralysis. “It means people are never ambiguous about where the business stands. And it lets them move quickly within those guidelines.

Hard won lessons

When asked which AI capability Bain is most proud of at Spark, he points not to the latest offering, but to its customer intelligence platform that uses hundreds of machine-learning models to understand individual customer preferences and predict what will be most relevant to them. “We now have hundreds of models running against our customer base that say things like: Heather loves music, she’s not price-sensitive, she prefers iPhones, she’s an early adopter,” Bain says. That insight is used across channels – emails, texts, retail stores and call centres – to personalise interactions in real time. “What we found was that we massively improved engagement with those communications.”

Adding that same intelligence helps frontline staff make more relevant recommendations when customers get in touch. “That’s a pretty cool thing – and it’s still pretty unique in the market today.”

Another internal AI tool is a system allowing staff to query financial and performance data using plain language, rather than writing manual SQL queries. Traditionally, Bain says, answering questions about revenue, sales or margins meant a slow loop between analysts, spreadsheets and follow-up requests. “We built an AI that could understand text, turn it into an SQL query, query the database and then create a multimodal report with graphs, tables and written insights.”

What once took days now happens in seconds or minutes, dramatically speeding up decision-making and allowing finance teams to focus on higher-value analysis rather than producing reports.

So what are Bain’s three key tips for companies finding their way with AI and data?

Get your data in order first Bain says data quality is the single biggest constraint on successful AI use. “These projects aren’t really AI projects – they’re data projects,” he says, adding that ‘the underlying data is 90 percent of the work’. Where data definitions are inconsistent or ambiguous, models become confused and unreliable. By contrast, when data is clear, structured and robust, Bain says AI solutions can be deployed very quickly and deliver real value.

Start now — the learning curve is unavoidable

Bain warns that delaying adoption doesn’t reduce complexity, it just postpones learning. “If you don’t get started now, there’s a learning curve – and that learning curve will be the same no matter when you start,” Bain says. He recommends beginning with low‑risk use cases so organisations can build confidence, understanding and capability before scaling more complex applications.

The technology is no longer the barrier — mindset is What used to be expensive and exclusive is now widely accessible. “What used to only be possible for a Spark is now available to small businesses – we’re using the same technologies,” Bain says. The real work is educating teams, demystifying how AI works, and putting appropriate guardrails in place. “By understanding it, you can mitigate the risks,” Spark says, adding that responsibility and confidence – not fear – are what allow businesses to move forward productively.

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