Amanda Williamson, director of Deloitte’s New Zealand Artificial Intelligence Institute, says most businesses are seeing productivity gains from AI use, but few can link that to revenue gains.

“Experiments do seem to be quite easy to do.”

“AI’s value is hitting the cost line, where it’s easy to see, but not the revenue line. So CFOs are not really clear on how to book it as growth,” she told iStart.

At the same time, research company Omdia says organisations are still approaching AI as a technology investment, rather than defining clear business outcomes – creating a ‘blind spot’ in measuring real returns.

One of the consistent themes across both sets of research is a disconnect between perceived and measurable value. On the ground, teams are using AI tools extensively and seeing benefits, particularly in areas like back office processing and customer interactions. But those gains are rarely tracked in a way that ties back to financial performance.

“We’re really bad at measuring AI right now,” Williamson says. “We’re not tracking value like we would other investments.”

The Omdia research, which included Australia and New Zealand and was conducted for Boomi, found 34 percent of organisations were unable to effectively measure the success of their AI initiatives.

The ROI blind spot

Michael Barnes, chief analyst enterprise IT Asia at Omdia, argues the problem starts with how organisations approach AI in the first place.

“There’s the erroneous assumption, or misguided belief, that somehow technology has value in and of itself,” he told iStart. “That’s simply not the case.”

AI is being treated as an extension of existing IT spend, rather than a strategic business transformation, meaning each new initiative adds complexity, rather than value.

He says organisations need to start with outcomes – something most are not doing.

“Experimentation with no clear end goals isn’t going to cut it. Think in terms of actual business outcomes and then work backwards towards the AI strategy.”

He admits it’s not easy. “It’s hard to engage the business, or have the business lead discussions with a focus on outcomes, when they don’t fully understand the capabilities of the technology.”

Without that outcomes-led approach, AI initiatives remain disconnected from the metrics that matter. And that, in turn, makes it difficult to justify investment and even harder to scale projects and reposition AI as a growth driver.

“Organisations need to have a better sense of outcomes in order to justify budgets for AI within particular business units,” Barnes says.

“The more different business units can clearly understand the value of AI and link that to their measurable outcomes, the more we will see AI budgets linked to those particular business units,” he says.

A/NZ decision makers were the most pragmatic and least enamoured of the potential value of AI and slightly more focused on the challenges that need to be overcome in Omdia’s research, with New Zealand decision makers being even more so than Australian respondents.

In growth markets such as the Philippines and Malaysia, there was a significantly higher expectation that technology would drive business growth or innovation.

That was also clear in the Deloitte research where just 23 percent of Kiwi CFOs saw application of technology, including AI, as a top three growth driver. That’s compared with 30 percent Apac-wide.

Acquiring new customers, increasing sales to existing customers, operational efficiencies, price increases and innovation all ranked ahead of technology for New Zealand CFOs.

Stuck in productivity mode

A key issue remains where organisations are focusing their AI efforts.

Many AI deployments are aimed at improving individual productivity – helping staff generate content, process information more quickly or automate small tasks. While useful, those gains don’t always translate into measurable business impact.

“Focusing on individual level productivity is only going to give us so much in terms of actually getting a return on investment from pilots and AI in general,” Williamson says. “We really need to focus on the whole of the workflow – where is AI really going to make a difference?”

Instead of a scattergun approach, putting efforts into many different ‘hobby projects’, she’s calling on local organisations to focus on where AI might really shift the dial.

“I’m seeing a lot of good effort and good time being spent on projects that you probably can tell upfront would not have been worth the time and effort. So get to the core of value.”

The inability to prove value is also showing up in the gap between experimentation and scale.

While AI pilots are widespread across Australia and New Zealand – covering everything from invoicing to customer-facing tools – many stall before delivering enterprise-wide impact.

“Experiments do seem to be quite easy to do,” Williamson notes. “But getting it into something that scales can be a real challenge because scaling forces you to confront clean data, a motivated team, a real workflow.”

The measurement gap is being reinforced by two practical challenges: Data and cost.

On the data side, both Williamson and Barnes point to long-standing weaknesses in data quality, integration and governance, that were often deprioritised in favour of more visible initiatives. As Barnes notes, AI has made data quality issues ‘very obvious, very quickly… up to board level’.

On the cost front, AI introduces a new level of unpredictability compared to traditional IT investments, with Williamson noting AI is opening up a whole new era of calculating costs. The shift – or tokenomics – makes it harder to build a reliable business case. (You can read more on tokenomics and how to address the issue in our earlier story.)

What needs to change

For CIOs, CTOs and CFOs the message from both Williamson and Barnes is consistent: The issue is not lack of capability, but lack of discipline.

Organisations need to define outcomes clearly and at a function level, with broad concepts like ‘productivity’ or ‘automation’ not sufficient.

“Outcomes for AI need to be context specific – they’re going to be very different by function,” Barnes says. “You can’t talk about process improvement or productivity improvements, that doesn’t necessarily mean anything in business terms. The question is always towards what end?

“Yes, you’ve improved productivity, but what does that mean for your staff, whether it’s your call centre or your fraud detection unit? What are you enabling staff to do that they can’t currently do – that needs to be thought through because in most cases, it isn’t simply a cost saving exercise.”

Measurement needs to be built from the outset, including clear baselines and post implementation tracking. “Measure what is the efficiency is without it and what it is afterwards,” says Williamson.

She says organisations also need to focus on end-to-end workflows, rather than isolated use cases, saying that’s where AI can begin to drive meaningful, measurable impact.

And finally, foundational issues, particularly around data, must be addressed if organisations want to move beyond experimentation.

The post AI value lost in translation appeared first on iStart keeping business informed on technology.

The admission relates to a February 2022 market update in which the ASX said the upgrade was ‘progressing well’ despite internal classifications at the time showing significant risks and unresolved issues.

“As market operator and a steward of critical market infrastructure, our words matter.”

Under the proposed resolution, agreed to by the ASX and ASIC (the Australian Securities and Investments Commission), the two organisations will ask the Federal Court to find the ASX breached provisions of the ASIC Act, impose the $20.5m penalty and order ASX to pay an additional $3m towards ASIC’s legal costs. The settlement is subject to court approval and will avoid a trial in proceedings first filed by ASIC in August 2024.

Other allegations of misleading statements – including claims by ASX that the project was ‘tracking to a published plan’ and ‘tracking to go-live in April 2023’ have been dropped as part of the settlement.

At the centre of the case is the Chess (clearing house electronic subregister system) replacement project, a long-running effort to modernise the clearing and settlement infrastructure underpinning Australia’s equities market, with a distributed ledger based platform. The system sits at the core of how trades are processed, settled and recorded, making it critical financial market infrastructure.

ASIC says the ASX misled the market by overstating the health of a project which, internally, was already experiencing significant delivery challenges. The ASX has since admitted that as early as December 2021 the project was no longer on the critical path required to meet its planned April 2023 go-live date. At the time of the February 2022 announcement publicly claiming the project was progressing well, the project was classified ‘red’ internally – denoting significant unresolved issues or risks – and had been at that status since December 2021. Industry test environments had been opened, and others were planned, but were unable to do all that the system had been scoped to do.

About six weeks later, in March 2022, the exchange disclosed that go-live would likely be delayed. The project, which began in 2016 with initial expectations of rollout around 2020, then moving to a planned go-live of April 2023, was subsequently paused and ultimately abandoned after six years work and $245-$250 million in spend.

A review by Accenture in 2022 was scathing, finding significant challenges and deficiencies in the project.

In 2023 the ASX offered $70m in incentives, including rebates on clearing and settlement fees, to encourage brokers to help redesign the system.

Market-wide implications

Sarah Court, ASIC chair, says the ASX’s admission of a misleading statement went to the accuracy of disclosures about a major technology initiative with market-wide implications.

“ASX has admitted to making a misleading statement in relation to critical market infrastructure at the centre of Australia’s financial system,” Court says.

She added that accurate and timely disclosures are fundamental to maintaining trust in Australia’s financial markets, particularly from entities that operate core market infrastructure.

David Clarke, ASX chair, says the settlement reflected the exchange’s responsibility to ensure the market can rely on its communications about major operational programs.

“The market must have confidence in what ASX says about its operations as these statements can be relied upon to make decisions,” he says.

“When we stopped the Chess project in November 2022 to reassess our whole approach, that tested market confidence in ASX and called into question the nature of statements previously made.

“As market operator and a steward of critical market infrastructure, our words matter. I am sorry ASX fell short.”

“Firmer footing” for new system

Clarke says the project is now on a ‘firmer footing’.

ASX interim CEO Darren Yip says Chess remains a ‘critical priority’.

“Just two months ago, the team successfully delivered release 1 of the new system, providing clearing services on a modern, cloud-aligned platform,” Yip says.

That system is based on Tata Consultancy Services’ BaNCS for Market Infrastructure and Quartz Gateway offerings.

Release 1 replaced the clearing component and introduced financial information exchange messaging for trade registration. Release 2, focusing on post-trade modernisation, is targeted to go-live in 2029 and will replace the settlement and subregister functionality, deliver improved corporate action functionality and make further enhancements to clearing. The introduction of global standard ISO20022 messaging interfaces will also be part of release 2.

The case highlights the regulatory scrutiny that can be applied to communications about large-scale tech transformation programs, particularly where those programs underpin core business or market operations.

The Chess replacement project’s scale, complexity and integration across market participants meant delays and performance issues had implications beyond the ASX itself, affecting brokers, investors and other market infrastructure participants.

ASIC says the February 2022 statement exposed market participants to the risk of financial harm, underscoring the extent to which disclosures about technology delivery timelines can affect planning, investment decisions and operational readiness across dependent organisations.

ASIC says it has obtained commitments from the ASX to strengthen oversight, governance and oversight of the replacement program.

The post Chess project failure lands ASX $23.5m penalty appeared first on iStart keeping business informed on technology.

For enterprise technology teams in Australia and New Zealand, the impact was immediate: Tools they expected to use were suddenly unavailable, and outside their control.

“There are real and valid concerns for enterprises in terms of stability.”

Anthropic was forced to ‘abruptly disable’ its Fable model – launched on June 9 – along with Mythos, for all customers after an export-control directive restricted access by foreign nationals over ‘national security concerns’ – reportedly over concerns of jailbreaking. That category was impractical to enforce technically according to Anthropic, leading to a full shutdown.

The shock goes beyond a single model release, highlighting the confused state of AI regulation and signalling a shift in how risk needs to be managed.

Sam Higgins, Forrester vice president, principal analyst, says the Anthropic shutdown fits a broader pattern he flagged several years ago, saying the 2020s were a decade of systemic risk, including geopolitical risk.

“Geopolitics was going to drive a wedge between tech markets,” he says, pointing to a longer-term trend toward fragmentation.

That fragmentation may now be accelerating, with traditional assumptions that allied counties would have continued access being challenged.

“There are regularly carve outs for Australia, New Zealand… I was surprised not to see them,” Higgins says, of the lack of exemptions in the export controls.

The result is that enterprise access to frontier AI is no longer guaranteed – even in aligned markets.

“If traditional allies are cut off we will start to look elsewhere,” Higgins says, suggesting diversification away from US providers.

The shutdown comes against a backdrop of tension between Anthropic and the Trump administration. The company very publicly disagreed with the Pentagon, refusing to allow the US military to use its models for domestic surveillance and fully autonomous weapons systems. It was blacklisted by the Department of Defense as a ‘supply chain risk’.

Higgins notes that ‘it seems odd’ that Anthropic seems to be targeted again. “This feels like strike two,” he notes.

While the underlying motivation for the shutdown might be unclear, the outcome is not.

“There are real and valid concerns for enterprises in terms of stability,” Higgins says, noting the impact of policy decisions that extend beyond technical issues.

Cybersecurity experts have also been quick to criticise the ban and shutdown, describing it is ‘dangerous’ and warning that it has taken the best models away from defenders while failing to meaningfully limit attackers.

In an open letter, they also argue the capabilities cited – such as identifying software vulnerabilities – are not unique to Anthropic’s models and can be replicated using other tools. Removing the capabilities from security teams could increase overall risk while adversaries continue to develop similar capabilities unchecked, they say.

Local impacts

The impact has landed quickly with regional businesses.

Amanda Williamson, director of the Deloitte Artificial Intelligence Institute told iStart the move is an inflection point for organisations. “It’s a very big deal,” she says.

“With a large technology vendor essentially switching off their AI… I think we’re about to see many leaders wake up to the huge dependence that we have on our fundamental systems.

“As we build more and more on AI, we need to be more resilient if we’re going to have our fundamental operations relying on it.”

She notes locally we’ve been working with two AI superpowers – China and the US, highlighting reliance on external providers. “This will help organisations really think about this and raise the question of AI sovereignty.”

The shutdown forces a rethink of that dependency, bringing the issue of AI sovereignty to the forefront, she says.

“The definition of sovereignty isn’t necessarily clear and consistent from an organisational standpoint,” Michael Barnes, Omdia chief analyst enterprise IT Asia, told iStart. “ What they’re thinking about is how to mitigate risk and ensure they are able to continue to operate and have access to whatever underlying capabilities – including models for AI – that they need.

“This announcement is a stark reminder that we need to be more aware of control, and that some of that access is outside their control – and that level of uncertainty is likely only to increase. This isn’t just a blip.”

Barnes says he’s ‘absolutely’ seen a growing concern among decision makers in New Zealand in particular. “We’ve had some conversations where it’s very much a case of we need to act now, we need to accelerate our level of access or even creation of our own models – things that give us an increased level of control, or if not control, then certainly visibility so we can mitigate risks when and if they happen.”

Other regions are already investing in alternatives. Higgins notes Singapore has made significant investments in their own frontier models pouring ‘truckloads’ of money into sovereign AI.

“This will drive some real thinking about what sovereignty means,” Williamson says.

A recent Forrester Sovereignty Forecast put Australia at the bottom end of the tech sovereignty index. Higgins believes New Zealand – which wasn’t included in the report – would be slightly lower than Australia, given the lack of hyperscale cloud capacity.

Reframing enterprise risk

Williamson says many organisations have simply been switching on the AI model. Provided in whatever technology stack they have access to.

“But there are other things which can be done like using open weight models.”

Those open weight models – essentially open source – changes the cost structure, but does require having some hardware.

“Now is a good moment to start exploring what that could look like and if there are certain use cases where it makes sense to switch out the model and approach and get CFOs and engineers to come up with smart plans for doing AI efficiently”, she says.

It’s not all up to business however. Higgins says governments need to step up too.

“This goes to the bigger question of do we really understand where we have agency as it relates to our sovereignty and where we should and shouldn’t invest? And if we’re not going to invest, what’s the economic continuity plan?

“This is a risk assessment at the end of the day. If you can’t control the hazard and remove the risk, then you need at mitigation strategy – and at the moment it doesn’t feel like governments on either side of the Tasman have any mitigation strategy in place.

“We should ask ourselves some hard questions about what arrangements we have in place with the US so that this doesn’t happen again,” he says, while acknowledging that governments are currently dealing with some unprecedented policy shifts by an often challenging administration to deal with. “And if we can’t guarantee that, what’s our Plan B? Every business has a business continuity plan. What’s our ECP – economic continuity plan? That’s what’s missing and voters should be demanding that from our governments.”

The post A/NZ’s Anthropic sovereignty wake-up call appeared first on iStart keeping business informed on technology.

AppWrap June 2026

23.06 The Australian Signals Directorate has joined other Five Eyes partners in warning government and corporate leaders that swift action is required to remain ahead of the increased risk from AI. The agencies are warning leaders to understand and assess risk, readiness and accountability, prioritise foundational cyber security practices and controls, empower cyber leaders with authority and resources and stay actively engaged. Their warning includes practical actions which while not new are now deemed ‘urgent’ by the agencies including reducing attack surfaces, accelerating patching and limiting who can access critical systems.

23.06 More than 4,000 Tasmanians have signed petitions opposing AI data centres, including 3,500 opposing Firmus Technologies planned Bell Bay facility, which will use up to 288 megawatts of power. PulseTasmania reports the facility will be the state’s second largest power user behind the aluminium smelter which draws 335 megawatts and employs more than 550 people. Firmus is also building a $2b data centre near Launceston, with a third flagged for Wesley Vale.

19.06 KPMG has been grilled by a parliamentary committee investigating whistleblower claims around the misuse of confidential client information to bid for new business. ABCnews reports senior KPMG execs fronted for the inquiry which heard about the alleged misuse of confidential information belonging to client Lendlease, its treatment of the whistleblower and a third KPMG confidentially breach involving client Optus.

18.06 The NSW government’s procurement board has issued a sweeping moratorium for all government agencies and departments on any new procurements involving KPMG. The ban comes amid ‘serious concerns’ about KPMG’s work practices. The company is embroiled in a series of allegations about misuse of confidential client information to bid for new business. An approval and reporting process for KPMG engagements has also been established in NSW.

18.06 The federal Department of Parliamentary Services has failed key cybersecurity benchmarks with protections still only ‘partly effective’ seven years after the network was breached and seven out of eight key cybersecurity controls falling short, InformationAge reports.

18.06 PsiQuantum has broken ground and started construction of its billion-dollar supercomputer facility in Melbourne. The company says it will build and deploy the world’s first utility-scale, fault-tolerant quantum computer at the Moreton Bay Central facility. It has received nearly $1b in federal and Queensland funding for the supercomputer.

17.06 The NSW Government has denied claims by ransomware group Nova that it has been breached, saying sample files provided are publicly available and historical information. CyberDaily reports Nova listed the state government as a victim on its darknet leak site, claiming to have exfiltrated sensitive data from the network.

17.06 A new Microsoft Teams feature enabling Teams to detect activity on a user’s device even when they are not actively using Teams has raised privacy concerns. Auckland University lecturer Alex Baird warns companies that the Privacy Act is clear on being thoughtful about why data is collected and using it only for that purpose. If used for checking teammates know where you are, that’s great he told RNZ. Collecting data for performance tracking however would be ‘a very different thing’.

16.06 Anthropic has disabled its new Claude Fable 5 and Mythos 5 models after a US Export Control order. Forbes reports the US Commerce Department mandated the suspension of access for all foreign nationals citing security and export control powers. The move followed reports of a jailbreak bypassing safety guardrails.

12.06 SpaceX has made the biggest stock market debut in history, making Elon Musk the world’s first trillionaire. Early trading saw share prices jump from the opening $150 a share by a double digit percentage, sending the company’s valuation above US$2tn, the Guardian reports.

11.07 Woolworths is moving IT jobs offshore to ‘remove complexity and drive efficiency’. Hundreds of jobs across the people, IT and finance teams are expected to be affected, ABCnews reports.

10.07 Tasmania’s Aurora Energy has kicked off an ERP modernisation program, replacing legacy systems with Microsoft Dynamics 365 finance, supply chain management and project operations, ITnews reports.

09.07 Anthropic has released a new version of its Claude Mythos AI model to the general public, but is restricting its use in sensitive areas. The Guardian reports Fable 5 is the first model to be made widely available from the company’s new Mythos class, unveiled in April but restricted to a small set of users over cybersecurity concerns. Anthropic says Fable 5 will be useful for writing and debugging software code, answering complex research questions and analysing images.

09.07 Perth Airport is deploying more than 70 IT and OT systems for its new terminal development. DXC, which has been selected as the master systems integrator for the program, says it includes a broad range of tech solutions including AI, cloud and cybersecurity across the new terminal for passenger processing systems, baggage tracking and reconciliation, advanced security screening integration, building management and automation, digital signage and wayfinding, public wifi and operational control systems.

04.06 Meta has accused Australia of violating a free trade agreement with the US by proposing a new 2.25% tax on tech giants who don’t have licensing deals with local media. The company claims the tax is broader than existing digital services taxes which have prompted US trade actions. Reuters reports a spokesperson for the Assistant Treasurer says the government remains committed to the change.

04.06 Australia ranks equal lowest out of 23 countries when it comes to AI sentiment according to an EY survey, which found while 51 percent of Australians like the idea of using AI to simplify life and support choices, 68 percent are worried about losing control over decisions made by AI on their behalf. ABCnews reports just one percent of Australians say they have complete trust that AI will be used responsibly.

03.06 The Department of Defence is using the Maven smart system from controversial company Palantir but says it is in a sandboxed environment and without AI functionality, ITnews reports. Defence has two active contracts with Palantir, including a $10.4m deal signed in February. It says it is using Maven to aggregate data ‘to inform targeting solutions’.

02.06 Florida has become the first US state to sue OpenAI, over the design and safety of its ChatGPT chatbot. The state is suing both OpenAI and CEO Sam Altman, alleging they know ChatGPT is not safe, especially for minors, CNN reports.

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.

The post Appwrap 2026: NSW govt KPMG ban, Tasmania’s data centre opposition and AI cyber concerns appeared first on iStart keeping business informed on technology.

That warning from the Gartner distinguished vice president, analyst, encapsulates a turning point for enterprise AI, and comes as the Linux Foundation moves to tackle AI token cost management and address rising AI costs.

“Get clear and understand those token costs and alternative methods organisations have to remedy the ills of the token cost burden.”

The Tokenomics Foundation, launched by the Linux Foundation this month, will focus on establishing open industry standards, benchmarks and best practices for the economics of AI infrastructure. Its launch comes as enterprises grapple with rising costs and uncertain returns from generative AI and agentic AI.

At the centre of that shift is a fundamental change in how AI is priced – and how organisations experience its costs.

Unlike traditional enterprise software, which is typically priced per user or per licence, AI is increasingly consumed on a usage basis. Costs are increasingly tied to how much compute is used, measured through tokens – the basic, fundamental unit of data that LLMs process and generate. The more complex a task, the more tokens it requires.

And while per-token costs fell heavily during 2023-2025, the Linux Foundation says they have now levelled off and new model token prices are rising, making AI the largest and fastest-growing line item on enterprise technology budgets.

Dr Amanda Williamson, Deloitte New Zealand AI Institute Director, told iStart AI is opening a whole new era of calculating costs and introducing a level of unpredictability unfamiliar to many enterprise buyers.

“What we’ve been observing around New Zealand is a lot of leaders have been rolling out AI tools, and AI is a tricky little beast because you roll it out and suddenly you get surprises with consumption costs around it,” she says.

“As usage continues, sometimes we have no idea how much it’s going to cost over the next six months or even the next six days because it’s based on usage as opposed to seats,” Williamson says.

This shift – commonly referred to as tokenomics – means organisations are no longer buying fixed capacity. Instead, they’re paying for every interaction, query and workflow processed by AI systems.

For some, that’s hurting. Uber’s CTO Praveen Neppalli Naga recently revealed that the company had exhausted its entire 2026 budget on AI coding tools in just four months, driven by ‘token-maxxing’, where engineers were encouraged to maximise usage. Another, unnamed, enterprise business reportedly accrued a US$500 million bill for Anthropic’s Claude AI in a single month after giving employees unrestricted access and uncapped usage of API tokens for agentic AI workflows.

The economics of AI are also being shaped by supply-side constraints.

Chandrasekaran notes that generative AI was a ‘consumerisation phenomenon’. Tools used in personal lives have moved into the enterprise. The companies making those tools have an incentive to continue to serve the consumer market, because it’s a big brand factor and a pull factor for them into the enterprise.

Vendors prioritised growth and adoption, offering relatively low-cost access in order to build user bases – and the all important data advantages: the more users, the more data to train models on, the better your model becomes

“We were getting access to AI often at a subsidised rate, and we were often able to just purchase it with a per seat approach,” Williamson says.

That, however, was the old world. Williamson notes a few weeks ago, things started to shift.

“There are a few factors that are making it really hard for them to continue to subsidise AI,” Chandrasekaran says.

Alongside the token maxing trend, he points to the rise of AI agents. Unlike AI chatbots, which are more asynchronous workflows, AI agents have more autonomy and are able to make sequential or parallel requests to AI models running in the back end – leading to AI models being hit with more requests from AI agents. Goldman Sachs has estimated agentic AI could see token use increase by over 24 times by 2030.

A scarcity of compute, with construction of many of the data centres planned to support the AI boom now delayed, has also driven the shift from surplus to scarcity, with direct implications for pricing.

Organisations that previously relied on predictable, low-cost access to AI are now facing the prospect of rising and variable costs tied to usage patterns, as vendors GitHub with AI Copilot, start transitioning to a token-based – or consumption – pricing and Anthropic and OpenAI move to pay-as-you-go models, charging business users for compute resources.

GitHub’s move prompted plenty of Reddit discussion and angst, with one user noting their bill would be going from US$25/month to US$750/month.

“Suddenly enterprises are worried because they don’t know what their downside is with the token-based pricing model because they don’t have tools, they don’t have instrumentation to really measure that token usage within the organisation,” Chandrasekaran says. “So there is a lot of worry, and rightfully so, around what the changing consumption pattern, the change in pricing model really means for long-term TCO of AI within the enterprise.”

Adds Williamson: “AI’s value is hitting the cost line… not the revenue line.”

While AI-driven productivity gains are widely acknowledged, many organisations are still struggling to quantify those gains in financial terms – particularly when it comes to revenue growth.

“Everyone’s using AI and feeling more productive, but very few can yet measure AI lifting revenue,” she adds.

As a result, AI is often being assessed through a cost lens, rather than as a driver of top-line growth.

At the same time, token-based pricing means those costs are becoming more visible—and, in many cases, harder to control.

For CFOs and technology leaders, this is creating a new area of focus: Managing token consumption.

Token usage can increase rapidly as organisations adopt more advanced AI capabilities, particularly those involving multiple chained interactions or automated workflows.

Even relatively simple use cases can drive higher-than-expected costs if usage scales across teams or business units, making cost visibility and measurement critical.

Engineering for efficiency

As token costs rise, organisations are being pushed to adopt more disciplined approaches to AI deployment.

One key lever is efficiency in model selection and system design.

“You don’t always need to use the best, most token-hungry AI models,” Williamson notes, highlighting the importance of matching model capability to use case.

“This is a great moment to start thinking about what models we’re using and when.”

Until now, most organisations’ approach has been to switch on the model provided in whatever tech stack they have access to, but Williamson notes use of openweight models – effectively opensource models you can get without paying normal fees – changes the cost structure, though they do require having hardware.

“There are certain use cases where it might actually make sense to switch out the model and switch out the approach and get the CFOs and engineers together to really come up with a smart plan for how to do AI efficiently,” she adds.

Optimising workflows – reducing unnecessary queries and improving system design – can have a significant impact on token usage.

Ultimately, tokenomics represents a shift in how organisations think about AI – from a relatively predictable tech investment to a dynamic, consumption-driven service, requiring a new level of financial discipline.

“Get clear and understand those token costs, how the world of token costs is shifting and alternative methods organisations have up their sleeve to be able to remedy the ills of the token cost burden,” Williamson says.

“Learn about token costs of AI and how to do it efficiently. There’s always going to be a cost-benefit analysis to decide to go forward or not with AI, and if the costs cannot be managed, then it’s going to be a non-starter.”

For many organisations, that means building closer alignment between finance and technology teams, improving measurement frameworks, and developing a clearer understanding of how AI usage translates into business value.

Standards emerge as costs rise

The Linux Foundation’s move to establish the Tokenomics Foundation reflects the growing importance of these issues.

As enterprises navigate shifting pricing models, rising infrastructure costs and evolving usage patterns, the need for standardisation and best practice is becoming more acute.

By focusing on benchmarks and economic frameworks, the initiative aims to bring greater transparency and consistency to how AI costs are measured and managed.

“Measuring and benchmarking token efficiency across different models and vendors is critical to how organisations make business decisions, but until now, there was no neutral home to develop the standards needed to measure token economics transparently across the entire supply chain,” says Jim Zemlin, Linux Foundation CEO. “The Tokenomics Foundation provides that neutral home, ensuring these standards remain open and community-driven.”

For enterprises, that could help reduce uncertainty—and provide a clearer path to scaling AI sustainably.

The post AI’s free lunch ends as token costs bite appeared first on iStart keeping business informed on technology.

Fei, a Gartner director analyst, says in many enterprise environments, agentic analytics can introduce additional cost, complexity and risk – particularly where decisions are routine, tightly regulated or based on immature data foundations.

“Bad insights become bad decisions at scale. And that’s a much bigger problem.”

Analytics and business intelligence platforms are increasingly integrating AI agents to transform how data is analysed and insights are delivered, and businesses across Australia and New Zealand are increasingly experimenting, often layering new capabilities onto existing data environments. Many already have well-established reporting platforms in place and are now looking to extract more value – particularly through natural language interaction and automated insights.

But Fei told attendees at this week’s Gartner Data and Analytics Summit in Sydney that the push to add agency to analytics workflows needs tighter discipline. She says AI agents fundamentally change how analytics operates, and that shift is not suited to every use case.

At its core, agentic analytics moves organisations away from the traditional linear BI workflows – where data is cleaned and transformed, queried and visualised in dashboards, step-by-step, before insight is interpreted manually – into systems where agents handle much of the heavy lifting. These agents can set multi-step processes, automating data preparation and generate insights with minimal human intervention and in a more conversational based interface.

“The biggest trend here is that Ai agents are going to proactively detect changes in context and adjust insights and actions,” she says, noting that the key thing about agents – differentiating them from traditional automation – is they are goal-driven, not task-focused.

That shift has clear appeal, but also introduces new requirements around governance, trust and data readiness that many organisations are still grappling with.

Where agents don’t belong

Fei was clear that agentic analytics isn’t a universal fix, and is ‘overkill’ in many cases and actively counterproductive.

“There are some situations where it makes sense to just adopt a traditional reporting tool or BI, rather than agentic analytics.”

Among those are highly regulated environments, which need strict compliance and auditability. Traditional analytics workflows remain better suited where decisions must be fully traceable, deterministic and auditable. In areas such as large financial reporting, organisations need to understand exactly how outputs are produced – something that can be challenging with systems built on large language models.

Similarly, routine business processes offer little justification for introducing agents, Fei says. Where the objective is simply to view static metrics or track recurring KPIs, without the need for adaptive decision, agentic analytics adds unnecessary overhead without delivering additional value.

“These are low ROI use cases,” Fei says, noting that agentic systems typically require significant investment across compute, integration and governance.

In these scenarios, organisations risk over-engineering solutions and introducing complexity where existing tools already meet business needs.

Data maturity still dictates success

Beyond use case selection, the success of agentic analytics hinges on the underlying data environment.

Fei is clear: Organisations with immature data foundations are unlikely to succeed. Instead, she recommends addressing data integration and quality issues before attempting to deploy AI agents at scale.

“AI won’t solve your data issues. It only amplifies them,” she says.

“If the data is immature itself and the overall integration readiness is low… you won’t be successful with agentic analytics.

That has implications for organisations attempting to build more advanced capabilities on top of existing reporting systems. While there is clear momentum to make data more accessible and interactive, the underlying governance, semantics and quality still need to be in place.

The cost of getting it wrong

Agentic analytics doesn’t just change how insights are produced, it changes where risk sits.

Fei notes once organisations introduce AI agents into analytics workflows, the stakes move beyond reporting errors. “The truth is AI will make bad analytics organisations fail faster.”

Impact will be amplified. “The impact is not just bad insights but bad decisions at scale. That’s a much bigger problem.”

She says that helps explain why 32 percent of respondents in a recent Gartner survey believe truly usable AI agents remain a distant reality. The same survey showed only 19 percent of those surveyed are using AI agents to some extent on a daily basis.

Avoiding agent washing

Fei also called out agent washing. “Not everything offered or claimed as an AI agent is indeed an AI agent.”

She says agent washing is emerging as a prevalent trend, with vendors rebranding legacy features as AI-powered or agentic capabilities, often without meaningful innovation. In some cases products rely on basic natural language queries, scripted workflows or pre-defined reports, while presenting them as autonomous systems.

The absence of key features such as multi-step reasoning, hypothesis testing or proactive recommendations, signals the tools are not agents.

That widespread agent washing is threatening to undermine customer trust and stall true innovation, and is creating market friction and pervasive AI fatigue among B2B buyers.

Fei urged organisations to adopt an evidence-based evaluation when it comes to agents, evaluating them on real-world scenarios, and implementing task-driven acceptance tests. That includes assessing how systems handle enterprise data volumes, complex integrations and governance requirements. She also recommended engaging in detailed roadmap discussions with vendors and urged prioritising explicit transparency features, such as code visibility, confidence scores and semantic grounding, when selecting agentic offerings.

The path forward

Rather than pursuing broad deployment, Fei points to a more structured, refined, AI adoption model.

The first step is defining where agentic analytics truly add value, focusing on use cases with clear decision logic, strong data foundations and a need for adaptive insights. She suggested picking three to five low-risk high-value use cases as starting points.

From there, Fei says organisations can take a stage approach, establishing cross-functional teams, testing shortlisted platforms, running proof of concepts with task-driven acceptance tests and ultimately scaling wins, raising autonomy where trust is earned and adopting multiagent platforms.

Fei’s underlying message is clear: Agentic analytics represents a significant evolution in how organisations interact with data – but it is not a replacement for all existing analytics practices.

“It’s not everywhere for all your use cases,” she says.

The post Where agentic analytics becomes costly overkill appeared first on iStart keeping business informed on technology.

The Hays Salary Guide FY26/27 shows a market caught in an uncomfortable middle: Pay rises are modest or non-existent for many workers, yet employers continue to report widespread skills shortages across industries.

Despite softer salary movement, organisations are still struggling to find the capabilities they need. Hays reports that 82 percent of organisations experienced skills shortages over the past year, up from 79 percent the year prior, with Australia (79 percent) feeling it harder than New Zealand (75 percent). Tech, however, isn’t feeling the pain as badly as many other sectors – Hays has the ‘technology’ category experiencing skills shortages seven percent lower than average, with ICT 14 percent lower than average and software and services three percent lower. Hurting the most is the engineering sector – it is +12 percent above average when it comes to shortages.

“Budgets may be tighter, but critical tech roles don’t wait.”

At the same time, the guide highlights that pay growth has been modest, with hirers reporting an average salary increase of 3.3 percent in New Zealand and 4.0 percent in Australia across all roles. Many (42 percent), however, are reporting either no increase, an increase below 2.4 percent or even a decrease.

The result is a market where demand remains strong, but pricing power is constrained. That’s forcing a shift in how organisations compete. Instead of relying purely on salary increases, employers are being pushed to look at broader workforce strategies, particularly in high-demand areas such as technology and digital.

Robert Half’s 2026 New Zealand Salary Guide reinforces the trend, noting that organisations are rethinking compensation strategies to attract professionals with the specialised skills needed to ‘maintain a competitive edge’ particularly as digital transformation continues to drive hiring decisions.

What roles are earning

While overall salary growth is muted, pay for technology roles, particularly specialised ones, is holding up.

The Hays salary guide includes detailed benchmarks across dozens of tech roles in Australia and New Zealand.

Technology salaries in New Zealand ranging from around $55,000 to $80,000 for entry-level service desk roles, while the same role in Australia garners from $51,000 to $80,000.

Those with SAP expertise can also command higher salaries, with the difference between Australia and New Zealand also more stark – and the differences between Australian states also more apparent. While an SAP Functional Consultant will see a ‘typical’ salary of $200,000 in NSW, in Queensland it drops to $160,000, and in Tasmania and NT its $145,000 and $150,000 respectively. Across the ditch, Kiwis in the same role will see a typical salary of $160,000.

More senior Dynamics 365 professionals are also commanding big salaries, with functional consultants on par with their SAP counterparts, as are Oracle and Salesforce functional consultants.

Australian AI engineers are seeing typical wages of $155,000 (Tasmania) to $180,000 (NSW and ACT), while their Kiwi counterparts are typically receiving $165,000 (Christchurch) to $180,000 (Auckland). Senior AI engineers meanwhile are commanding up to $250,000 in NSW. It’s the GenAI engineers, however, who are reaping the big returns, with typical ACT salaries $220,000 – and up to $285,000. NSW follows closely with a typical salary of $215,000 and a high end of the range of $280,000. In New Zealand, it’s much more muted, with Auckland genAI engineers commanding the highest typical salary of $170,000, and up to $210,000.

Data and analytics roles are also showing strength, as are cybersecurity roles. CISOs in Victoria are seeing between $200,000 to $350,000, with a typical salary of $280,000; NSW and ACT are seeing between $200,000 to $340,000, while New Zealand CISOs are seeing between $180,000 to $350,000.

Robert Half data meanwhile shows entry-level IT support roles sit around NZ$60,000 to $70,000, while more experienced systems administrators can command up to $95,000. More specialised roles attract significantly higher salaries. An AI engineer can command $120,000 to $160,000, with an AI tech lead, driving the design, implementation and delivery of AI solutions, commanding up to $220,000.

Hiring pressure and retention risks

Robert Half’s IT-focused data shows talent shortages, digital disruption and economic uncertainty are all shaping the tech hiring environment, with organisations recognising that skilled IT professionals are critical to maintaining operations and competitiveness.

Even in a more cautious market, hiring urgency remains a factor. A significant proportion of tech leaders say the need to fill roles quickly is influencing their willingness to increase salary offers during negotiations. In other words, budgets may be tighter, but critical tech roles don’t wait.

That tension is reflected in how organisations are allocating compensation. While overall salary growth is moderating – Robert Half puts increases for most employers at three to five percent – employers are still prepared to pay more for candidates with in-demand skills, particularly those tied to ‘rare or emerging skills’ particularly in AI, data engineering and automation. Those three areas are also seeing counteroffers becoming more frequent as companies compete for talent.

With salary budgets constrained, both Hays and Robert Half point to a broader shift in how organisations compete for talent. Non-monetary factors, including flexibility, training opportunities and career development, are becoming increasingly important in attracting and retaining skilled employees.

The rise of hybrid work also reflects this shift. Hays reports hybrid arrangements are now the norm for a large proportion of employees, signalling that flexibility is no longer a differentiator, but an expectation.

At the same time, employers remain willing to negotiate for candidates who bring specialised skills and can deliver immediate impact.

While a topline glance might suggest a relatively stable labour market, the data suggests pressure is building.

Hays’ findings show many professionals are feeling underpaid, though despite the rising cost of living, dissatisfaction scores are largely in line with those seen last year, with 29 percent feeling dissatisfied or very dissatisfied (seven percent) and 29 percent feeling neutral. Just nine percent feel very satisfied, with the most satisfied tending to be more senior, higher income earners who had had a greater salary increase in the past 12 months.

Among tech workers, that isn’t translating into increased job hopping, however, with IT managed service providers, ICT and technology workers among those most likely to have longer tenures.

AI demand rising faster than capability

Compounding the issue is the acceleration of AI adoption.

Hays data, which is drawn from surveys with more than 7000 people across Australia and New Zealand, shows that 60 percent of employees are already using AI at work, but only a minority (22 percent) have received formal training. That flies in the face of what employers are saying, with just 27 percent of employers reporting no AI training or support is provided.

That gap is creating a new layer of demand, not just for technical roles, but for employees with the practical capability to apply AI effectively. Hays notes that on the hiring side, no agreed standard exists for evidencing AI capability. “[Employers] are split: 69 percent point to portfolio or practical examples, 52 percent to internal assessment, 49 percent to professional references, while 43 percent say none of the listed credentials suffice. Forty percent are seeking university or formal academic qualifications.

“The competency question is open and that ambiguity is itself an opportunity to bring structure.”

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The deployment, built on Amazon Connect, has shaved more than two-and-a-half minutes off average calls for the 150+ year old company, one of New Zealand’s largest publicly listed general insurance companies, removing more than 796,000 minutes of customer time across sales, service and claims interactions.

“By bringing interactions into a single platform and integrating customer data, we’ve enabled real-time meaningful AI support for our frontline teams.”

But the more telling detail sits behind those numbers.

Paul Johnston, Tower CEO, told iStart the project centred on consolidating customer interactions into a single platform and combining that with integrating customer data to deliver AI support as conversations unfold.

“By bringing interactions into a single platform and integrated customer data, we’ve enabled real-time meaningful AI support for our frontline teams,” Johnston says.

That support is delivered during live calls, with real-time transcription across every call and AI-assisted guidance removing the need for agents to pause interactions to take notes or search for information, and there’s less chance of missing important details.

Rather than building bespoke AI capability, Tower leaned on what was already available within its platform, with Johnston saying most of the capabilities were delivered out-of-the box, with effort focused not on customisation, but integrating those tools into day-to-day operations.

The core of that work sat in data and knowledge.

“Our focus has been on strengthening our core knowledge base and integrating this seamlessly with the platform, ensuring agents receive accurate, up-to-date guidance,” Johnston says.

That meant ensuring information feeding AI responses was well-governed and current, so it could be surfaced in real-time to support customer conversations.

The deployment was designed to sit inside existing workflows rather than alongside them. Johnston says embedding AI into how work was already done reduced friction during rollout and allowed the system to evolve based on frontline use.

Real-time feedback loops

“We’ve been able to continually refine the platform based on what actually works best for our teams,” he says. Real-time feedback loops ensure continual improvements in responses. Involving frontline ‘change champions’ throughout also aided the process, he says.

As a result, Johnston says teams quickly saw benefits, with AI supporting conversations, reducing complexity and improving consistency in responses.

“Our ability to adapt and adopt has been a real strength, supported by a strong culture of innovation and a focus on bringing our people on the journey from day one.”

Alongside the reduction in handling times, he says employee and customer net promoter scores (NPS) have improved, while quality assurance coverage has increased.

The system is also generating a richer data set to inform future product and service decisions.

Monitoring is built into the deployment. Johnston says quality is assessed – and improved – using automated evaluation alongside agent feedback, providing real-time visibility and enabling issues to be identified and addressed as they emerge.

Across the project, Johnston points to a combination of strong data, well-governed knowledge and workforce engagement and empowerment as underpinning the outcome.

“A big part of our success has come from getting those foundations right, while continuing to build and improve over time.”

Johnston describes the work as part of a broader digital transformation, with further opportunities to expand how AI supports both customers and frontline teams.

“We see this as part of our ongoing digital transformation, with continued opportunities to scale and evolve how AI supports both out people and our customers,” he says.

“Looking ahead, we’re poised to invest further in technology and AI-enablement throughout FY26. This is a critical part of our strategy that will drive greater efficiencies and continue to enhance our customer experience over the medium- and long-term.”

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“Successful pilots are satisfying, but real value comes from end-to-end transformation,” the company warns – and most companies haven’t achieved that yet.

“Optimising a single activity, such as code generation, test creation or requirements drafting, simply shifts bottlenecks elsewhere.”

The warning comes as AI reshapes the fundamentals of software development. What began as incremental productivity gains from coding assistants is accelerating into something far more structural: A full redesign of how software is conceived, built and deployed. According to Bain & Company, AI is ‘creating a seismic shift in software development’ moving organisations from isolated use cases toward an integrated, AI-native development model.

At the centre of the shift is the move from AI-assisted to AI-led development. Today’s tools may still sit alongside developers, but Bain & Company argues the trajectory is clear with AI systems increasingly capable of executing entire workflows, not just individual tasks.

“This evolution isn’t incremental; it is redefining what’s possible,” the report says, pointing to the emergence of hybrid human-agent teams delivering dramatically higher output.

The scale of that change is already filtering through to executive expectations. In 2024, executives were forecasting productivity gains of 20-30 percent from AI in software development. Now those expectations have ‘surged’ with many now anticipating improvements of ‘five times to 10 times’ over the next several years, with exponential gains no longer framed as theoretical, but instead apparently becoming the benchmark organisations are planning for. By late 2026 more than half of the global executives surveyed for the report expect more than half of their engineering efforts to be agent assisted. By around March 2027, that number hits around 90 percent.

But while speed is increasing, reports suggest value is not automatically following. Bain & Company’s report, which is based on surveys of global executives and market research conducted across 2025 and 2026, makes it clear that faster coding alone won’t deliver those outcomes – and some companies are missing out on many of the productivity benefits.

“It’s not enough for engineering teams to deliver code five times faster; business teams must generate demand at the same pace, and operations must match that speed to deploy, scale, and support solutions in production,” the report says. Writing code faster in isolation, or indeed optimising a single activity, such as code generation, test creation or requirements drafting, simply shifts bottlenecks elsewhere.

The State of Engineering Excellence 2026 report from software platform Harness, reinforces that point. It found 89 percent of engineering leaders say productivity has improved with AI coding tools, but 81 percent say developers are spending more time on manual code review. The self-reported impact is ‘overwhelmingly positive – but the cost is accumulating in places organisations aren’t watching,’ Harness says, with nearly a third of the work now ‘invisible work’ – reviewing code, fixing bugs and context switching between tools.

This dynamic aligns with Bain & Company’s argument that traditional software development thinking, where improvements are made within isolated phases, is no longer sufficient.

“Rolling out lots of pilots may also feel like success, but pilots don’t necessarily translate into real usage or business impact. Without new workflows, measurement, and guardrails, companies are likely to see adoption plateau and only minimal new value,” Bain & Company says.

Instead it points to the rise of an ‘integrated AI development lifecycle’ which breaks down the traditional divide between product development (defining what to build based on customer needs, market opportunity and strategic roadmaps) and software development – two streams of work that often occur across different teams.

“AI shatters these boundaries, and it can define requirements, generate code, test, and iterate all within a more continuous flow.

“Companies are moving toward an AI development life cycle in which AI is embedded across the entire process and product and engineering operate as a more integrated system rather than sequential steps. Instead of product development defining the objective and engineering building it, AI-enabled teams continuously define, build, test and refine together.

 It’s a process, Bain & Company says, that requires a redesign of organisational structure with tech teams making adjustments to all levels of their engineering operations, with developers moving from code executors to agent architects and new roles created.

“The shift to an AI-led development life cycle is not just a technology upgrade; it’s a full-system transformation that rewires how organisations build, operate, and compete. “Companies that move decisively, redesigning workflows, redefining roles, and anchoring on measurable outcomes, will capture disproportionate value.”

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Unveiled at a company conference in London, the Meta Business Agent operates across WhatsApp, Messenger and Instagram, responding to customer enquiries, recommending products and booking appointments inside chat conversations. Meta says the agent can also qualify sales leads and complete transactions.

“The agent can also qualify sales leads and complete transactions, extending its messaging tools into revenue‑generating workflows.”

While Meta is dominant in online ads which account for the vast majority of its revenue, it has struggled when it comes to actually selling its products. The company’s messaging ecosystem, however, is already used as a communications layer for many businesses of all sizes.

Meta has famed the new offering as part of a broader enterprise push, with the company saying the goal is to provide businesses of any size with an always-available digital agent to handle customer engagement and operational tasks.

The company is also launching a ‘Business Agent Platform’ to allow organisations to build and customise their own AI agents, including integrations with third-party systems such as Shopify, Zendesk and Shopee.

“The platform provides larger businesses with enterprise-grade controls, guardrails and measurement built in so they can define rules and offer personalised experiences, starting with the messaging apps their customers already use,” Meta says.

The move also hints at Meta’s AI ambitions and desire to compete with the likes of OpenAI, Anthropic and Google with enterprise versions of their AI tools.

It also comes as competition intensifies in the market for enterprise AI agents. Companies including Google, OpenAI and AWS are developing tools designed to automate customer interactions and business workflows, targeting similar use cases in sales, support and operations.

The Business Agent places Meta directly into this category, with executives explicitly positioning it as an enterprise play, rather than an incremental update to existing messaging products. The launch builds on earlier chatbot capabilities, but extends them to enable automated actions, aligning with broader industry shifts towards ‘agentic’ AI systems that can take on tasks, rather than simply respond to queries.

Unlike some competitors, Meta’s strategy is to deploy the capabilities inside its existing messaging platforms, where businesses and consumers are already interacting at scale.

Meanwhile the Business Agent Platform places Meta in competition with vendors offering standalone AI agent frameworks and enterprise automation platforms. (As an aside, the Business Agent launch came just days after Meta floated the idea of becoming a hyperscaler and renting out its compute infrastructure.)

Chatting with customers, closing sales

A free test version of the Meta’s agent service was released in select markets, including Mexico and India, late last year under the ‘Business AI’ name.

At the launch of Business Agent, Meta said more than one million businesses were already using a Meta Business Agent – presumably the free test offering – on WhatsApp and Messenger.

The new Business Agent provides automated capabilities to allow companies to handle customer conversations at scale. It’s designed to answer questions specific to a business, make product recommendations from business catalogues, book appointments and qualify incoming leads, close sales and escalate more complex queries to human staff where required – with the business setting the parameters for when a team member will step in.

It’s also offering the ability to generate summaries of chats that occurred overnight and provide insights on threads.

Meta says the agent can be deployed ‘in minutes’ within its platforms or integrated directly into existing enterprise infrastructure and, while initially available for free, will be a paid subscription, believed to be a business-focused subscription tier for Meta One, the umbrella brand for Meta’s paid subscription offers, launched earlier this month. Larger enterprises are expected to be charged based on token usage, similar to existing pricing models for business messaging on WhatsApp.

While the initial rollout focuses on customer engagement and sales tasks, Meta is positioning the Business Agent as part of a broader toolkit for businesses.

It says it is developing additional features that would expand the agent’s role into areas such as conducting market research, surfacing product insights, connecting with tools to manage your calendar and providing competitive intelligence.

Challenges ahead

For enterprises, rolling out autonomous agents in customer-facing channels raises practical implementation questions, particularly around integration, governance and customer experience.

Meta says the agent can connect to third-party tools such as Shopify and Zendesk and can be embedded into existing business workflows.

For enterprises, deeper integration with systems such as CRM and ERP platforms would be required to align the agent with customer records, order management and service processes. Whether businesses are willing to connect those systems to Meta’s platform could be a factor for enterprise adoption, particularly given the sensitivity of enterprise data and existing system architectures.

While Meta brings scale through its messaging platforms, adoption at enterprise level will depend on how effectively those capabilities integrate with existing systems, operate within governance frameworks and deliver reliable customer experiences at volume.

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Within two weeks of launching the feature in the realestate.co.nz app, more than 60,000 searches had been clocked up. It’s still a tiny percentage of the company’s searches, but  Simon Hargraves, realestate.co.nz chief information officer, is optimistic.

“We had just finished a project to get our data into a good state. Without that, it would have been extremely difficult to do this work.”

He told iStart users are increasingly comfortable working with AI and expectations for search are changing. “They want to be able to express their search intent more naturally, rather than just using predefined filters or having to use exact keywords.”

The AI-image search gives users the ability to search ‘at a completely different level’ – searching what’s actually in photos.

What users are searching for, however, hasn’t changed in early searches. “We have a list of traditional keywords people were searching for and generally it’s following the same trend,” Hargraves says.

What has changed is how effectively results are being surfaced. Traditional keyword search relies on exact inputs from both sides, with agents required to include the right terms in listings and users having to guess and input the same wording. AI image search removes that dependency, Hargraves notes, with the AI system examining the photos and pulling out key features.

The system also changes how results are presented, with the platform able to surface the image where the feature has been detected, rather than returning a listing with no obvious visual confirmation.

The accuracy challenge

Hargraves says the feature itself was built and released quickly – one month to build and another month spent testing, tuning and getting feedback from the business. “The really challenging part was more the focus on accuracy and making sure the results aren’t frustrating for users because the way the semantic search works is it expands what the user is searching for to understand intent and then expands the search out from there.”

As an example, early on a search for wine cellars associated them with luxury properties – returning a broad range of luxury properties rather than those with wine cellars. Worse, searching for a property with an accessible ramp expanded out to include staircases.

“Those kind of cases are obviously going to be super frustrating to users when you’re getting the opposite of what you’ve actually search for. So we spent probably half the project time actually testing it internally, across the whole company and getting feedback from users of examples where it was wrong. Then we spent quite a bit of time tuning the system.

“Making sure the experience wasn’t frustrating was the most important part.”

Data does the heavy lifting

Behind the feature sits a significant data effort.

Realestate.co.nz had just completed an 18-month re-platforming and centralisation of all of its data into a single Snowflake platform, standardising definitions and consolidating multiple data sources across the business.

“That was a huge foundational piece that allowed us to build it very quickly.”

The work enabled the rapid development of AI features, but also highlighted a clear dependency.

“You have to have that data foundation in place… if you have bad data, then you’ll get bad results out from the AI.”

In image-based search, that extends beyond structured data to the quality of photos themselves – something the company doesn’t control.

“The AI performs much better when the features [are] clearly visible,” Hargraves says.

But he notes the higher the quality the images and the larger they are, the more cost to process via AI. “There are quite a few variables that we had to take into account and tune to try and get the right kind of cost versus quality.”

He deliberately frames the AI image search as an experiment. “The technology has evolved really rapidly and these kind of features are becoming easier and easier to implement so it makes sense to build the feature and release it to users and see if it’s used and useful.”

The company is less focused on initial uptake than on whether users will return. As Hargraves notes, if users don’t find it useful, they won’t come back to it again – a key test for the future of the system.

To manage expectations, it has been clearly labelled as a beta. “Because… with AI, it’s never going to be perfect.”

Beyond search

The company’s broader approach reflects a pragmatic view of AI. Internally it is being used across the engineering process, and teams are encouraged to experiment through hackathons and rapid prototyping.

“These tools are making it super easy and super quick to build out things and test them internally and build proof of concepts,” Hargraves says.

At the same time, decisions to productise those ideas need to be made carefully, he says, noting the challenge of ensuring projects show ROI and aren’t constantly ‘experiments’.

“You have to make sure projects make sense… and as with anything its prioritising what you have in your roadmap case by case with cost versus value.”

In that context, AI image search is seen as a logical extension of existing capabilities, rather than a standalone transformation.

Hargraves says the company is already working to merge AI image search with traditional keyword search into a single, unified experience.

Longer term, there is potential to expand search into other areas, including accessibility requirements and proximity to amenities, though that will depend on the availability and quality of additional data sources.

“Finding a good data source and being able to ingest it and surface it via AI search is going to be the challenge. But I think you’ll see a lot of those features coming over time. It’s just about getting data into a good place first because without that solid foundation you can’t build high quality AI experiences on top of it.”

Beyond real estate

So what’s Hargraves advice for any other companies considering similar tools?

“Getting the data into a good place is probably the most complex thing. We’re lucky because we had just finished a project to get our data into a good state. Without that, it would probably have been extremely difficult to do this work.”

The data needs to be high quality, well-structured and accessible and good governance around it is also required as well.

“Without that, it’s going to take a lot longer and there’s going to be much higher levels of complexity.”

And be prepared to spend time tuning. “The tuning and accuracy piece is going to be the most difficult part to get right from a product perspective,” Hargraves warns.

“With AI image search you don’t have so many of the concerns you’d have with AI in other areas, especially where you have chatbots and people interacting directly with the AI. AI search is an easier place to start but as we get more into potential AI features in future – chatbots and that kind of thing – it becomes more and more concerning from a risk perspective.”

On the cost front, he says there wasn’t ‘massive’ cost. The time spent tuning was the bigger cost, with the company now spending around US$1000 a month for processing all of its listing images.

“Costs are increasing in AI across the board though, so it’ll be interesting to see how it changes over time and it’s something we’re going to have to keep an eye on.”

Hargraves also acknowledges that for some companies, AI image search could surface unexpected content.

“With our platform, the photos have all been taken and verified by vendors and agents, so personally identifying information or anything sensitive is usually scrubbed from the photos anyway. On different platforms, you would definitely have concerns though,” he says.

Despite that, Hargraves says it’s important for all local companies to be experimenting with AI.

There are huge changes coming across every industry. There’s a massive shift in the way people are working in businesses and how they are interacting with AI and that’s changing their expectations of how they use other products too.”

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A study in the Journal of Service Management flags ‘AI complacency’ – a silent erosion of critical thinking that occurs when people stop questioning automated systems. Or, as defined by the report, an employee’s tendency to neglect validating AI-generated outputs, even when errors are present.

“The biggest risk of AI isn’t that it gets things wrong, but that nobody notices.”

The research is based on six experimental studies involving more than 1,300 participants and identifies a clear root cause: Lack of accountability for monitoring AI outputs. When responsibility for checking results is unclear, employees are significantly more likely to accept outputs at face value, even when errors are present. Over time, this leads to what the report authors – Khanh Bao Quang Le from Auckland University of Technology’s Department of Marketing and International Business and Werner Kunz from the University of Massachusetts Boston’s College of Management – describe as a diminished willingness to evaluate AI-generated outputs critically, along with an increase in work-related errors.

“The biggest risk of AI isn’t that it gets things wrong, but that nobody notices,” the pair say.

“It’s not about lacking technical skills; rather the machine’s fluent presentation creates a false sense of security. Over time, employees may fall into a ‘human-out-of-the-loop’ routine.”

The study suggests this behaviour reflects how organisations are deploying AI into workflows without clear ownership or oversight. Where monitoring is treated as optional, rather than embedded, complacency takes hold.

The way AI presents information compounds the issue. Outputs are typically fluent, structured and authoritative, even when incorrect. This aligns with well-documented ‘automation bias’ where users over-rely on automated systems and accept recommendations without sufficient scrutiny. The report notes that as AI become more deeply embedded in day-to-day work, that bias is strengthened by operational pressures for speed and efficiency, particularly in environments dealing with high workloads or complex tasks. Where tasks were cognitively demanding and employees were juggling multiple competing demands, complacency increased. Similar was seen when working in interdependent teams with people assuming ‘someone else will catch it’.

The shift was subtle but significant with employees moving from actively interrogating information to passively accepting it, and in some cases, stopping output reviews altogether.

High-stakes consequences

The consequences are already visible in high-stakes environments. Case in point, the Australian lawyer sanctioned in late 2025 after submitting documents containing AI-generated false citations. He admitted he didn’t verify the contents.

Similar patterns are emerging more broadly across organisations in Australia and New Zealand. A mid-2025 report from KPMG and the University of Melbourne found that AI is already in daily use in organisations but often without structured oversight or training. Globally, Trust, Attitudes and the Use of Artificial Intelligence found two in three reported relying on AI outputs without evaluating the information it provides, with other half saying they have made mistakes in their work due to AI. New Zealand and Australian respondents rated among the lowest for AI knowledge, efficacy and training.

The consequences aren’t limited inaccurate decision-making, with inadvertent exposure of sensitive information also a possibility, the KPMG/University of Melbourne report notes.

And consequences are limited to governance or security either, with cost control emerging as a risk area. In one widely reported case, a company spent US$500 million in a single month on Anthropic’s Claude after failing to set limits on employee usage. The overspend was reportedly linked to a lack of controls over how the tool was accessed and used (one CTO noted employees were using AI for things they could easily do themselves, such as checking the weather).

Local evidence suggests the problem runs deeper than individual behaviour. Ethos Advisory, which runs AI governance reviews, says most New Zealand organisations score poorly in the assessments, with AI policies often little more than documentation.

It notes typical gaps include no defined accountability for AI decisions with no defined processes for identifying accountability, investigating the failure or notifying affected parties.

Designing in oversight

Banning AI or requiring manual checks aren’t required, Le and Kunz say, instead, they call for a more nuanced approach. Among their practical options:

Build accountability into the process, not just the policy ensuring employees know that someone, somewhere, will ask them to explain what they reviewed and why they approved it.

Set outcome expectations so people know they’ll be held accountable for the outcome, not just the process – this, Le and Kunz says, makes users stay more alert.

Design for high-complexity, high-load environments specifically. “These are the conditions where complacency is most likely to develop.” Rotating oversight responsibility, adding review checkpoints or simply reducing simultaneous task demands during AI-assisted work can help close the gap.

Take team design seriously. Assign clear, individual ownership for AI monitoring, even in collaborative workflows, to avoid a situation where no one feels fully responsible for AI outputs.

“These measures are not meant to slow down innovation,” Le and Kunz say. “Rather they help ensure that AI fulfils its promise without eroding human judgement.”

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The automotive aftermarket parts supplier, which operates more than 500 stores and an 800-vehicle fleet across Australia and New Zealand, has digitised its delivery layer to address a core operational gap: The lack of visibility into what was happening once goods left the store.

“Last-mile delivery is where customer experience, operational efficiency and safety intersect.”

Arun Sen, general manager application systems for Repco parent company GPC Asia Pacific, told iStart delivery is one of the most visible and mission-critical parts of Repco’s operations, particularly for trade customers, where speed and reliability are essential, rather than optional.

“We recognised that last-mile delivery is where customer experience, operational efficiency and safety intersect,” Sen says.

Improving that layer of business would deliver immediate tangible benefits for the business, including better on-time performance, stronger driver safety outcomes and improved operational control.

“It was also an area where we could quickly move from manual or fragmented processes to real-time digital visibility, unlocking both short-term gains and long-term data value,” he adds.

At the centre of the shift is the introduction of real-time data capture across Repco’s delivery fleet. Previously, many operational processes were manual or fragmented, limiting the ability to track performance or intervene when issues arose. Now, using Zebra mobile computers running Repco’s Genuine Delivery application, the business is capturing live driver locations, digitised proof of delivery, vehicle usage metrics and device usage data to monitor compliance and ensure safe interactions – information that was not previously available in a structured or timely way.

The data is already changing how decisions are made. Instead of reviewing delivery performance retrospectively, Repco can now intervene while deliveries are still in progress. Live location data allows stores to instantly respond to customer enquiries about estimated arrival times, while central teams gain visibility into emerging delays or exceptions.

Over time, the accumulated data is also creating a new layer of operational intelligence. Route information trends can be analysed to identify recurring bottle necks in specific locations or time windows, while vehicle usage data provides a clearer picture of fleet utilisation across the network.

“With 800 vehicles, understanding utilisation is critical,” says Sen. “Previously, we had no visibility into how many trips a vehicle made. Soon, we’ll be able to see the full trip history and usage patterns. Store staff can see the driver’s real-time location and estimated time of arrival. If a customer calls, we can immediately tell them where the driver is and how long the delivery will take.

“We can analyse route performance trends, coach drivers based on objective safety data and identify recurring bottlenecks in specific locations or time windows.”

The shift from fragmented processes to standardised, digitised workflows was one of the key drivers behind the overhaul. Prior to rollout, proof of delivery, delivery confirmations and exception handling were not fully consistent across the operations, leading to inefficiencies and delays in data capture. Bringing those processes into a single system has create a more uniform data set, which in turn supports faster response times and more reliable performance tracking.

Safety has also been a core component of the transformation, but again the impact is driven through data, rather than devices alone. One of the key risks identified was the use of personal mobile phones by drivers – both for communication and navigation – which created safety and compliance concerns.

“We didn’t want people using their telephones while driving to find a route or take a phone call,” Sen says. “Reducing that risk was critical.”

By moving to a managed, standardised delivery platform, Repco can now monitor device usage and track compliance with safe interaction policies. The data provides an objective basis for managing driver behaviour, enabling coaching and intervention based on actual usage patterns rather than anecdotal reporting.

The new solution provides a single, streamlined platform for drivers, giving them everything they need to deliver items quickly and safely. Drivers can digitally view delivery lists, build efficient routes and add stops on the fly. Repco says this has significantly reduced the need for phone calls to drivers, and any necessary calls are now handled safely via hands-free Bluetooth on the Zebra TC5 devices.

The focus on measurable outcomes extends across the broader program. Repco is tracking delivery performance through on-time rates, completion times and exception resolution, alongside safety indicators and reductions in mobile phone usage while driving. Administrative time savings and the standardisation of proof-of-delivery processes are also being monitored as part of the efficiency gains.

Taken together, the metrics reflect a shift towards continuous optimisation, rather than one-off improvements. With consistent data flowing from the field, the delivery function becomes something that can be tuned and refined over time, rather than simply measured after the fact.

The implications extend beyond the delivery team. In a rapidly expanding eCommerce market, delivery has become a critical customer touchpoint. Real-time visibility allows Repco to respond instantly to delivery queries and address issues as they happen, tightening the link between operations and customer experience.

Just a quick note for all our readers: This 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.

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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

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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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