A customer-facing chatbot makes promises a business is ultimately forced to honour. A cyberattack destroys millions of euros’ of refrigerated stock. Sensitive discussions that once stayed inside a boardroom are automatically transcribed and stored on multiple machines increasing the potential for discoverable documents to be hacked. OpenAI agents ‘hack’ Australian sites.

None of those were part of anybody’s AI strategy. Yet all have become real-world examples of how AI can create consequences organisations never intended, from the Air Canada chatbot that invented a refund policy and left the airline liable for honouring it, to this week’s attack on the refrigeration systems of hundreds of French retailers, they demonstrate how increasingly sophisticated technology can amplify operational risk.

“You can outsource the model, but you can’t outsource the accountability.”

For governance specialist John Keogh, they highlight the growing challenge for organisations embracing AI: The biggest AI risks are often the ones nobody anticipated.

While much of the AI debate remains trapped between utopian promises and doomsday predictions, Keogh, partner at Sydney based advisory firm Centre for AI Governance, believes the reality is far less dramatic, and potentially far more important for business leaders.

“Some people think it’s the best thing that’s ever going to happen to us. Some people think all our jobs are going to disappear. Some people think the world’s going to end in three years,” he says.

“The answer is somewhere in between and balance, and balancing innovation with protection, is key.”

Accelerating risks, rather than creating new ones

One of Keogh’s central arguments is that AI should be viewed less as a standalone risk and more as an accelerant.

“It makes things happen a lot faster,” he says. And that acceleration applies to both opportunities and vulnerabilities.

The Australian Finance Industry Association has estimated the value created by AI and financial services by 2035 across areas including employee productivity, process automation, personalised marketing and customer experience to be around AU$60 billion. But, Keogh notes, those areas also represent some of the biggest areas of exposure if things go wrong.

The result is a balancing act many organisations are still struggling with.

Move too slowly and risk being overtaken by competitors. Move too quickly and risk creating problems that are difficult, expensive or impossible to unwind.

“It’s about balancing innovation and protection,” Keogh says. “If companies hunker down and don’t do stuff, they’ll get left behind.

What worries Keogh most are not necessarily the risks companies already know about, but the secondary effects they haven’t yet considered.

One example is the growing use of AI meeting assistants, recording tools and automated summaries.

Organisations may see them as productivity enhancers, but they can also create entirely new information management challenges.

“If there are a number of people on a call, that means there’s a transcript on pretty much everyone’s machine,” he says. “If any of that gets hacked and something sensitive has been discussed, then it’s in the ether.”

Board meetings and steering committee discussions often contain highly confidential – and discoverable – material. In the rush to automate note-taking and record keeping, organisations may inadvertently be increasing the number of locations where sensitive information is stored.

He notes the example of an Air Canada chatbot which told a customer bereavement discounts were available. The man paid the full fare but when he tried to get the bereavement discount was told they didn’t exist. In the ensuing Civil Resolution Tribunal case, Air Canada argued that it could not be held responsible for its chatbot’s autonomous communications, claiming the bot was a ‘separate legal entity’ responsible for its own actions. The tribunal rejected that argument and Air Canada was found liable.

“A lot of the unintended consequences are if it’s not programmed properly and the right checks and monitoring is not in place, then things can be promised that shouldn’t be. So you’re really exposing the organisation to things happening that you hadn’t accounted for,” he says.

The challenge with AI stuff is that if you make a mistake in the normal course of events in financial services – a change of policy or change a setting or credit scoring or something like that – it takes a while to push through the system. What AI does is it accelerates everything. So if there’s something wrong, it happens at scale. And a lot of that stuff’s difficult to undo once it happens.”It’s not just bespoke solutions serving up challenges either. Keogh says with vendors deploying AI within their tools organisations need to really understand their supplier base – and the potential exposure it brings.

“Often a very small supplier by spend can have a very significant impact on your organisation because of access to data and so on.”

Obligations apply

Despite the hype around AI, Keogh believes many organisations remain stuck in what McKinsey has described as ‘prototype purgatory’ – running pilots and experiments without moving significantly into production.

Others are waiting for regulators to provide clearer guidance.

But regulators are increasingly making it clear that existing obligations still apply, regardless of whether AI is involved.

That means organisations cannot assume responsibility shifts to technology providers.

Keogh points to the well-publicised Air Canada chatbot case as evidence that organisations cannot assume accountability rests with the technology itself. After the chatbot provided a customer with inaccurate information, the airline ultimately remained responsible for the outcome. The incident has become one of the most cited examples of what can happen when organisations assume AI-generated responses are someone else’s problem.

“The regulators and the AI vendors have made it clear that the responsibility for AI sits with the companies who deploy it,” he says.

Or, as Keogh puts it: “You can outsource the model, but you can’t outsource the accountability.”

Three steps

Rather than slowing AI adoption, Keogh says organisations should focus on governance, visibility and preparedness.

His first recommendation is understanding exactly where AI is being used across the organisation and which systems are touching customer data.

Second, executive teams need a shared understanding of both the opportunities and the risks.

Technology, security, legal, finance and operational leaders should be aligned rather than pursuing separate agendas, he says.

Third, organisations should ensure AI systems are properly tested before deployment and can be turned off quickly if necessary.

That capability becomes critical when AI systems behave unexpectedly. Keogh again references the Air Canada chatbot as an example of why organisations need effective monitoring and the ability to intervene quickly when things go wrong. “

You need to be able to make sure you can switch things off,” he says.

The ability to shut down a problematic system may prove just as important as the ability to launch one.

Underlying all of those recommendations is explainability.

If leaders cannot clearly explain how an AI-driven decision was reached, they may struggle to defend it to customers, regulators or shareholders when issues arise.

“If you can’t explain it at the kitchen table in simple English, then you won’t be able to defend it if something goes wrong,” Keogh says.

When energy giant Petronas was preparing for the AI era it deleted half its apps.

While much of the corporate world is busy bolting AI onto already cluttered technology estates, Petronas took a different approach, grabbing the pruning shares and decommissioning 50 percent of its apps. The discipline saw the Malaysian energy giant slash tech costs and embed a value framework designed to stop projects before they became expensive digital clutter. It also formed the beginning of a three stage journey to being digital.

“We poured a lot of money into IoT, analytics and stuff like that, but all it could do is just predict a machinery failure better.”

Shaharuddin Mustapha, Petronas Digital chief executive officer, admits the company is still only in the second ‘horizon’ of its plan: digitally enabled but still on the path to the ‘AI era’ and ‘being digital’.

Learning to say no

Speaking at Gartner’s IT Symposium on the Gold Coast recently, Mustafa compared the job of a modern digital leader to a game of Minesweeper – but Minesweeper where someone else is moving the mines, changing the grid and rewriting the rules while you’re asleep.

For Petronas, the challenge is bigger than technology.

The company operates in an industry where energy security, geopolitics, regulation, resilience and sovereignty can affect decisions as much as software architecture. Technology leaders can no longer focus solely on technical risk, he says. They must be able to explain risk in business terms, understand the value being pursued and decide how much control they are prepared to relinquish to vendors, governments and partners.

The changing environment led Petronas to a simple conclusion: Not every technology request deserves a yes.

“One of the principles that we put in was that the IT or digital organisation needs to have the ability to say no,” Mustafa says.

That’s not always an easy sell.

Before taking over Petronas Digital two years ago, Mustafa worked in the business itself, pushing for more capability, more investment and faster delivery.

“I was one of those people,” he admits. “I have the budget, just go do it. I want the cheap, good, fast.”

Then he moved to the other side of the table.

Suddenly, the reality of finite resources became impossible to ignore.

“We can’t do everything,” he says. “We cannot fulfil every single order.

The company responded by introducing an enterprise value framework to assess proposed initiatives. The key question became straightforward: what value will this create?

If the answer wasn’t compelling enough, the project didn’t proceed.

“We say no when the initiative creates no value,” Mustafa says.

Technology teams saying “no” was initially a shock to parts of the organisation, but Petronas sees that discipline as essential preparation for the next phase of transformation.

“The ability to say no is also a means for us to clean up our ecosystem,” he said.

That clean-up was the decommissioning of half of its applications because they were no longer needed. The company also undertook an extensive ‘cost compression’ program that reduced tech spend by around 20 percent, not through a major transformation program but by eliminating unnecessary services and negotiating more fit-for-purpose arrangements.

“We just took stuff out that we didn’t need,” he says.

The struggle for enterprise-wide value

Mustafa says the company is now in its digitally enabled stage. “A lot of platformisation happened here with the ERP, procurement systems and all that.”

Mustafa says Petronas invested heavily in cloud, cybersecurity, IoT and analytics long before today’s AI boom. But he’s open: Some initiatives delivered strong outcomes. Others generated isolated benefits that never translated into enterprise-wide value.

Case in point: Predictive maintenance. While the technology improved the ability to predict machinery failures, scaling that capability into broader economic value proved more difficult.

“We poured a lot of money into IoT, analytics and stuff like that, but all it could do is just predict a machinery failure better.” It never got to the point of scaling it the economic PNL of an asset.

On the flip side, one success story was optimisation of Petronas’ end-to-end liquified natural gas value chain, which consistently delivered results because it improved multiple parts of the business simultaneously, from production through to shipping and customer delivery.

“We’ve had the data coming in from the upstream production, sequenced to the onshore LNG liquefaction plants and then sequenced to the shipping, the marketing, and creative people to the end customer.”

Other cases, such as the predictive maintenance, were very task and use case based and couldn’t scale much beyond the initial use case.

“There have been many cases of value, but we haven’t got one that really scales up and is really pervasive,” he says.

The company is now looking – cautiously – at the value agents and AI can provide. “

We want to solve things in terms of the value of autonomy as opposed to the value of automation,” Mustafa says.

From manpower to outcomes

The company has also restructured how it delivers technology projects.

Petronas moved away from buying project manpower and shifted to outcome-based delivery models, forcing teams to define expected business value, workflows, data requirements and outcomes before work begins. According to Mustafa, the approach delivered an immediate reduction of around 30 percent in development costs.

The philosophy reflects the same underlying principle that drove the application clean-up: Technology should be judged on value, not activity.

For Australian and New Zealand CIOs facing mounting pressure to demonstrate AI progress, that may be the most important lesson from Petronas’ experience. The company is not arguing against AI. In fact, it sees significant potential in the next wave of autonomous systems. But before organisations start building an AI future, Mustafa’s message is clear: figure out which parts of the old technology estate deserve to come with you.

Sometimes the smartest technology investment isn’t a new platform. It’s hitting delete.

AI agents have moved from pilots and proof-of-concept into the enterprise mainstream.

At least, that’s according to new research from SailPoint, which found 79 percent of organisations now run AI agents in production environments. The findings are drawn from a survey of 340 senior identity, IT, cybersecurity and risk leaders globally, and suggest autonomous software has become part of day to day business operations.

Security teams recognise the risk of over-permissioned agents, but legacy ticketing workflows prevent them from auditing and scoping non-human entitlements dynamically at scale.

But, despite their apparent ubiquity, only two percent of organisations have deployed purpose-built tools designed to govern and secure those digital workers who are making autonomous decisions, querying critical databases and executing transactions on behalf of the business.

Nam Lam, SailPoint ANZ group vice president, says Australian and New Zealand organisations are embracing AI at speed, but many are managing AI agents using controls originally designed for human users.

“Australian and New Zealand organisations are rapidly embracing AI to drive innovation and efficiency, which is a testament to the region’s agility,” Lam says.

“However, our latest report reveals that 79% of organisations globally are managing non-human identities including AI agents with legacy identity security controls. This isn’t just a technical gap, it’s a significant business risk that leaves the door wide open for breaches.”

The Horizons of Identity Security 2026-2027 report found AI agents now represent 22 percent of all non-human accounts, underlining how rapidly autonomous systems are becoming part of enterprise operations.

Yet most organisations are still relying on technology never designed for that purpose. SailPoint says 85 percent continue to use either legacy identity platforms or platform-native controls, such as on-premises directories or manually maintained roles, rather than systems developed specifically for cloud, machine and AI-agent identities.

Built for people, not AI agents

That gap becomes clearer when organisations compare how they manage people versus software.

For decades, identity-management systems were built around employees. Staff joined the organisation, changed roles and eventually left. Access rights could be reviewed and updated through established processes.

AI agents operate differently.

They can work continuously, access multiple systems simultaneously and take action without direct human intervention. As their numbers grow, organisations are discovering the controls built for people don’t always translate neatly to autonomous systems.

While 87 percent of respondents rated their human identity-management capabilities as capable or better, fewer than half, 43 percent, said they had mature processes for managing agentic access. More than half, 54 percent, remain at the lowest maturity level for AI-agent identity security.

The report suggests many organisations are still struggling with the basics.

Only 15 percent of respondents said they could provision non-human access in real time. Another 15 percent reported mature capabilities for detecting shadow AI deployments, while just 43 percent said they felt highly prepared to provide evidence during an AI-related audit inquiry.

Lam says those challenges are particularly relevant in Australia and New Zealand, where many organisations are still building foundational identity-management capabilities.

“In Asia Pacific, with 37% of organisations still at the earliest stage of identity maturity, the immediate priority for ANZ leaders must be to extend their identity security strategy to discover, govern, and protect every single identity; human and non-human, because when something goes wrong, the tough questions will come from the board and local regulators.”

Practical headaches

The survey found organisations are less concerned about AI hallucinations than more conventional governance and security issues.

Data leakage and exposure topped the list of concerns, cited by 35 percent of respondents. Excessive permissions and access-control issues followed at 30 percent, while 27 percent pointed to autonomous actions occurring without human oversight. Hallucinations were cited by 17 percent.

Those concerns are also creating practical headaches for security teams.

Excessive permissions and privilege creep emerged as the most common challenge in managing AI-agent identities, followed by visibility and discovery issues and the ability to monitor and audit agent activity.

“Security teams recognise the risk of over-permissioned agents, but legacy ticketing workflows prevent them from auditing and scoping non-human entitlements dynamically at scale.”

The report also highlights what it terms a ‘pronounced awareness gap’ and a significant disconnect between leadership awareness and operational execution. “Across multiple survey measures, broad sentiment questions yielded high optimism, while granular technical questions exposed critical gaps,” the report says.

“This is not simply a matter of leaders being overly optimistic; it reveals a deeper awareness gap. For many leaders, ‘identity security’ still primarily means securing the human workforce, and their confidence reflects years of investment in maturing those legacy processes. The data shows this confidence is misplaced in the agentic era, exposing a fundamental lack of awareness of what ‘good’ now looks like.

“It is a strategy-execution gap where the ambition to secure the autonomous enterprise has outpaced the operational ability, and even the vocabulary, to do so.”

The Australian Taxation Office is preparing for a world where individual’s personal agents will handle tax returns, talking to other agents via the government ecosystem – though CIO Mark Sawade admits there are still plenty of hurdles to overcome first, not least the issue of ‘Know your agent’.

Sawade says the ATO has spent decades improving how citizens interact with services through websites, apps and online portals, but AI may fundamentally change that model.

“If we’re not thinking about how we get there and the types of building blocks we’ll need to put in place, it’ll be an incredibly long time before we’ll be ready.”

Speaking at the recent Gartner IT Symposium, he pointed to the transformation of individual tax returns as an example. In the 1990s, taxpayers collected a TaxPack from the post office and typically spent seven to eight hours completing a return. Today, you can download the ATO app with MyID or access through MyGov and file a return for simple tax affairs in roughly 12 minutes.

The next leap, he suggests, may remove even that interaction.

“We’ve often thought about it through the lens of create an app or create a portal or, in the myGov sense, how do we bring together the set of federal government services that can be accessed together”

“[But] we will be in a world where we’ll be eventually talking to our personal agent saying, ‘it’s now mid-July, can you please just fill out my income tax return?’,” and the personal agent will talk to one or more other agents through the government ecosystem.

Sawade says the agency wants to reduce the cost and effort involved in business tax compliance, with a long-term vision of making tax obligations increasingly seamless and automated.

“We focused on the way the individual interacts with our government services to try and make that increasingly more seamless and that gets a whole range of better outcomes, both for individuals, taxpayers, and the economy broadly. Our focus now is how do we do that for business and how do we get that same type of step change so that the cost of business in terms of complying with tax obligations is equivalently reduced and tax just happens [as a] far more seamless and automated process.”

The future could see AI agents managing routine interactions, collecting information, preparing submissions and interacting with government systems without human involvement in every step.

It’s a similar story across at Services Australia, where deputy CEO and chief customer officer Jonathan Thorpe says agents could potentially do a ‘fairly good job’ of organising government information around specific individual’s circumstances.

“Certainly we’re seeing – and I think anyone would attest to this in most industries – that increasingly customers are experimenting with agents in their everyday life.

“Increasingly, we’re going to have to think about what that means in the context of government service delivery. I think we’ll start to see it – perhaps we already are – in the simple way people bring content together, organise government information around their personal circumstances, and potentially an agent could do a fairly good job of that.”

The challenge of know your agent

But both Thorpe and Sawade are clear that identity, accountability and trust are central problems still needing to be solved.

“Where it starts to run into a lot of trouble really quickly is proving who someone is, or know your agent,” Thorpe says. “At the moment we don’t have the plumbing necessary for agentic identity, particularly in the consumer and citizen space.

“I think we’ll solve it, but it’s not here yet,” he says, adding its something he thinks about ‘a lot’ in the context of MyGov.

Another ‘big challenge to solve’ is the issue of liability and responsibility. If an agent submits incorrect information or misunderstands instructions, clarity will be needed around who is accountable for the outcome.

“What happens if you set up an agent [but] you didn’t quite set the appropriate guardrails? What’s government’s responsibility in that interaction?”

Sawade too, is clear that we’re still a long way from agent to agent communications through government systems, pointing to identity as a key issue to solve.

“But if we’re not thinking about how we get there and the types of building blocks we’ll need to put in place, it’ll be an incredibly long time before we’ll be ready to have government services that are safely, securely and transparently interacted with in that way.”

Humans at the helm

Both emphasised the need to focus on outcomes, governance and accountability before introducing advanced automation.

At the ATO, that means balancing enthusiasm for AI against the reality that the organisation manages one of Australia’s largest repositories of personal and business financial information. Sawade says its approach is deliberately measured and designed around strong controls, governance and human accountability.

The ATO has ‘tweaked’ the commonly used concept of keeping a ‘human in the loop’ and instead talks about keeping a ‘human at the helm’ reinforcing the idea that a person must remain accountable for outcomes even as AI capabilities expand.

Services Australia is taking a similar position, with Thorpe saying he doesn’t see AI as a replacement for people: “I see AI as an opportunity to evolve that service and make it better, not replace it,” he says, noting agentic AI’s best role may be in assisting staff.

“I don’t think its in front of our staff, I think it’s actually behind it – how AI can support public servants to do a better job and better inform the community about what obligations, payments and services are available to them.”

That could take the form of an agent assist providing the right information as prompts to someone to connect them to the right service.

“Some of these things we’re looking at now as we transform our contact centre, but I’m not seeing it as something you’d have in front of a customer service representative, particularly in the types of things that we do.”

In a separate session, ATO assistant commissioner, strategy, design and architecture Jimmy Tzimopoulos noted that while agentic AI in particular can achieve a lot of user cases ‘just because you can, doesn’t mean you should’.

“When we really assess the different use cases, we might find that 90, 95 percent of them could be achieved through some form of re-engineering of process and then driving business automation, then maybe some machine learning and then a bit of agentic at the end or at a component of it. But there’s a tendency to say, I’ll just get it all done by agentic.

“And I think that’s the thing for us that we’re learning about.”

He noted the need to enable people to learn that while they can achieve things with agentic, you don’t necessarily have to look at agentic from start to finish. “Let’s look at how we can compartmentalise it to really deliver the right solution that considers the commercial impact as well.”

“Anyone who is dependent on supply chains and the logistical risk [involved], needs to deploy agentic AI in order to reduce that risk. It’s as simple as that.”

It’s a bold statement, but according to Ilan Oshri, director of the Centre for Digital Enterprise at the University of Auckland Business School, local organisations are overlooking what could become agentic AI’s most valuable business application.

“Agentic AI in the supply chain should actually be positioned as a risk mitigation capability – that is where we’re going to see the biggest return.

While much of the discussion around agentic AI has focused on productivity gains, procurement automation and operational efficiency, Oshri argues businesses need to flip their thinking. For New Zealand and Australian organisations operating in an increasingly volatile global environment, the bigger opportunity may be using AI agents to continuously monitor, assess and respond to supply chain risk before disruptions reach the bottom line.

“Most companies think ‘let’s pilot agentic AI where we don’t have high risk. We’re saying you’ve got it wrong when it comes to procurement. You need to try it out,” he told iStart.

Oshri’s research into how agentic AI could transform procurement as part of global research for Boston Consulting Group, found organisations should start agentic AI deployments in areas with high transaction volumes and rich data environments, rather than low-risk experimental projects.

“Our findings are actually the opposite,” he says. “Where there are a high degree of transactions is where companies are actually supposed to start, simply because of the dependency on data in order to make agentic AI beneficial for the company.” That high data guides agentic AI to learn ‘really quickly’ reducing the level of errors significantly.

For local businesses however, Oshri believes the strongest use case is not procurement automation. Instead, he argues agentic AI should be viewed as a risk management capability.

“In the case of New Zealand, agentic AI in the supply chain should actually be positioned as a risk mitigation capability – that is where we’re going to see the biggest return, at least at this point in time.”

His argument reflects the realities of the smaller, geographically remote economies, heavily dependent on global trade, shipping routes and international suppliers.

From freight disruptions and geopolitical tensions to supplier issues and logistics bottlenecks, local organisations face risks that can emerge with little warning and quickly impact operations.

Using exporters as an example, he notes their need to get product to other countries. Freight costs are built into pricing, but what happens if emerging threats affect logistics, shipping costs or supply availability. Likewise, plenty of product is shipped to A/NZ to form part of products made here, and could benefit from similar agentic offerings.

“These kinds of agents basically do the scanning all the time and raise flags whenever they think a risk is going up.

Today, much of that monitoring is still performed manually or outsourced to specialist providers. Agentic AI could provide organisations with a constantly operating early-warning system capable of identifying risks – and taking action – before they become expensive disruptions.

“This is where possibly the highest benefit will be for companies.”

In the current environment, Oshri believes that capability is becoming increasingly important.

“Unfortunately, geopolitically, we are in the big surprises era at the moment,” he says.

While onboarding suppliers, managing contracts and automating routine procurement tasks all have value, he argues those applications are all ‘vanilla’ and relatively straightforward.

“You need to do that. But the big buck is the big surprises.”

Procurement should lead

The research also challenges traditional approaches to technology ownership. Historically, digital and IT teams have led major tech implementations across support functions. Agentic AI in procurement is different.

“We’re saying loud and clear that procurement needs to be the owner of the implementation of agentic AI,” Oshri says.

The reason, he argues, is simple: procurement teams understand the data they hold and the supplier relationships, business processes, contracts and operational risks in ways technology teams do not.

“Data scientists will make assumptions about the algorithm in terms of the ability to develop it and they will make assumptions about what data is needed in order to train it. That’s not the right approach to come up with an efficient AI-based solution.

“For this kind of technology, the approach should be completely different than what we have seen in ERP and what we have seen in RPA… It’s about do you understand what you have so the algorithm can actually behave according to the business process.

“It’s only procurement people who can answer that.”

An uncomfortable conversation about autonomy

Perhaps Oshri’s most controversial argument is that organisations also need to start thinking beyond human-supervised AI.

Current deployments typically are largely ‘human in command’, with some ‘human in the loop’. In either case people retain oversight of decisions and outcomes. Oshri believes many businesses are avoiding a more difficult discussion about autonomy.

“When we hear autonomous, I think many of us feel uncomfortable,” he says. “One reason for that is that we’re thinking if it is autonomous, that people just lost their jobs and it doesn’t sound right.”

He argues the transition towards greater autonomy is inevitable.

“As difficult as it is to think about, I think this is probably the future. We are moving towards that.”

Oshri says organisations should be honestly assessing which processes genuinely require human involvement and which could eventually be trusted to autonomous agents.

His view is that businesses often default to human-centred models because of concerns about the consequences, not necessarily because the technology is incapable.

“In many of the cases, we could have gone autonomous and we would have been actually providing outcomes that would not fall below the standards that we have built for human in the loop or human on the loop,” he says. “We are tilting towards the human-centred approach simply because we are struggling to cope with the consequences.”

It’s an argument likely to provoke debate among business leaders concerned about accountability, governance and workforce impacts. But Oshri believes the conversation is one organisations can no longer avoid.

For local businesses, the Oshri’s broader message is clear. The real value of agentic AI may not be found in making procurement processes a little faster or a little cheaper. Instead, it may lie in helping organisations anticipate disruption, manage uncertainty and build resilience in an era of increasingly unpredictable global supply chains.

And for companies exposed to those risks, Oshri’s advice is unequivocal: Deploy it.

What happens when critical knowledge sits with one person in a business and that person leaves?

For The Warehouse Group a missing shoe buyer cost the company millions, with declining sales, weaker profitability, stock markdowns and lost market share as product assortments went largely unchanged for two years.

“Where you have the real risk in the organisation is your people and processes – and that’s where the unstructured data lives.”

The experience convinced group chief executive Mark Stirton that one of the biggest risks facing organisations isn’t a lack of data, but a lack of accessible knowledge, and it’s helped shape the Kiwi retailer’s thinking about data, knowledge and emerging AI technologies.

Speaking at the recent Snowflake World Tour Auckland, Stirton outlined how the company is exploring what he calls a ‘digital bench’ and an ‘agentic university’ as part of a broader effort to make organisational knowledge easier to access and less dependent on individual employees.

Stirton was blunt that the retail game is changing. “If you don’t think its changing, I think you might be under a rock. And when the game’s changing, you’ve got to decide what are the new rules? And what is the new playbook you need to run these rules against?”

He described what he sees as the next major shift in business. The first wave gave organisations access to manufacturing, infrastructure and eCommerce capabilities on demand. The next, he says is ‘pay-as-you-go intelligence’.

“When you think about tokens and all these models, you’re getting intelligence at scale, which has never happened in the history of man.”

The belief is influencing how the company is preparing for the future.

Stirton says he has challenged leaders across the business to rethink how their departments operate, saying ’30 percent of it must change’ as the organisation prepares for changing business and operating models.

The catalyst isn’t a new technology – not even AI. Instead it’s that recognition that critical expertise often sits inside people’s heads.

In the footwear example, the absence of a buyer did not simply create a vacancy, it triggered a chain reaction as assortments were not refreshed and decisions were delayed and effects flowed on to other areas of the team and business.

“It’s the contagion of small risks. They don’t look big in isolation but when you add them up they start to look like a really destructive force.”

Building a digital bench…

The challenge sits behind one of the more unusual concepts discussed during the presentation – that of a ‘digital bench’.

Using a rugby analogy, Grayston suggested many companies are trying to deliver the same outcomes despite effectively playing short-handed. Businesses expect results even when expertise is missing, teams are understaffed or key knowledge exists only with a handful of employees.

The digital bench is his attempt to address that problem.

Rather than relying on individual employees to hold critical information, Stirton wants organisational knowledge to become more accessible across the enterprise. The goal is not to simply store information, but to make expertise available when people need it.

That ambition is shaping the retailer’s technology foundations.

The Warehouse is working with Snowflake on semantic layers and vector-based capabilities, while also experimenting with Cortex to surface insights from existing dashboards and data assets. Stirton describes this work as the ‘unglamorous’ foundations required before organisations can take advantage of future capabilities.

He argues that structured enterprise data is only part of the picture.

While information stored in systems such as ERP platforms can be managed and analysed, many organisational risks sit elsewhere.

“But where you have the real risk in the organisation is your people and processes – and that’s where the unstructured data lives and where most of the gaps start to form when you lose a person.”

…And an agentic university

That thinking has given rise to an even bigger concept: what Stirton calls an ‘agentic university’.

Universities, he noted, create knowledge, preserve knowledge and transmit knowledge. In his view, businesses increasingly need to do the same thing.

He described a future where employees can work alongside knowledge layers and context layers that help connect information from across the organisation. Rather than expertise being concentrated within individuals, it would become easier to access and share.

“There would be abundance of knowledge and those knowledge gaps where there’s no training or there’s no parts where knowledge is transferred, that starts to be on tap for someone and then connected brains start to connect the whole organisation.

“So if I’m a buyer and I want to ask a question on my supply chain, suddenly you’ve actually got a knowledge layer – ‘your professors with your long-term knowledge’ and a context layer that now connects with each other.”

Stirton, who admits he’s ‘a bit of a dreamer and what I call a metaphor person’ referred to the concept as a corporate ‘second brain’. The idea is that organisational memory, historical decisions, operational context and expertise become easier to draw upon regardless of where they originate.

Stirton is not approaching the challenge as a company-wide technology rollout, saying he’s avoiding the ‘spray and pray’ approach that leads to burning tokens.

He says the retailer is focusing on innovators and early adopters – the 2.5 percent who ‘statistically will always move the organisation faster because they’re the ones who are braver, more courageous and curious’  arguing that change rarely happens evenly across an organisation. These groups, he believes, can help accelerate adoption and encourage new ways of working across the wider business.

“Adoption will always be uneven in technology,” he says.

“I think the people with the highest desire to have a growth mindset will start to use the technology and the knowledge layer and the context layer at much more rapid speed.”

The result is a view of AI that differs from many enterprise discussions focused on productivity gains or automation.

Instead, Grayston’s presentation centred on something more fundamental: knowledge.

Who owns it. Where it lives. What happens when it disappears.

The footwear example offered a costly reminder of the consequences when critical expertise resides with one person. The digital bench and agentic university are attempts to ensure that knowledge becomes an organisational asset rather than an individual one.

“3,064. That’s the number of Power BI reports we have in our environment Mitre 10.”

The comment from Nastassia Subritzky elicited plenty of laughs at Snowflake’s World Tour Auckland, but the Mitre 10 head of data and insights admits she was ‘pretty alarmed’ when she did the count during the hardware chain’s migration to Fabric.

“Our human users are also getting trained by our agents.”

“This reporting sprawl, plus the seemingly infinite number of spreadsheets makes it difficult because you’ve got all these different reports and spreadsheets and they all tend to have slightly different numbers, so you end up spending so much time trying to reconcile between all of them,” Subritzky says.

The problem arrived at the worst possible time, with the co-op already in the middle of a major ERP transformation, moving from individually customised store data warehouses to a centrally managed SAP platform and data warehouse.

“The move to a centrally managed data warehouse has naturally resulted in many cries for help with how to actually analyse data in this new SAP world,” she says.

Stores were struggling to work out where to go find the analysis they previously had available. The challenge was compounded by Mitre 10’s cooperative structure, where independently owned stores have their own approaches to performance and operations.

And then there was Excel.

“Turns out people get really attached to their Excel spreadsheets, especially during times of change,” she says.

Breaking free from report sprawl

Rather than create report number 3,065, the retailer decided to do something different.

The goal wasn’t to replace reporting, but to reduce dependence on it through AI agents built on curated datasets. Using Snowflake CoWork, the company created agents that draw from the same underlying data as Power BI, ensuring answers remain tied to a common source of truth.

The governance layer was critical.

Many organisations are discovering that generative AI amplifies existing data problems. Mitre 10 started with carefully controlled datasets, guardrails and verified queries to prevent users from generating yet another collection of disconnected answers. The retailer also maintained control over which datasets agents could access.

“With Snowflake Co-work, we have complete control over what datasets the agents have access to, and naturally to help with some reconciliation issues they use the same base tables as our Power BI workbooks.

“Everything naturally ties back together which has been a fantastic win for manual work reduction because I don’t have to send my engineers out to reconcile yet another Excel spreadsheet.”

Trent: Good tech skills, no business sense

The first AI agent, Trent, is the merchandising team’s ‘general assistant’. He was rolled out to the team as part of a two month deadline-driven migration from a legacy SQL reporting platform.

“This deadline doesn’t sound too bad until you consider how many workbooks someone can connect to a very old SQL reporting server over the course of a decade,” Subritzky quips. “We thought given they would have to get used to a new way of working anyway, why not just chuck them in the deep end and give them something really, really different to worry about?”

Trent helps with common tasks, including looking at supplier performance, undertaking basic rent reviews and looking at product performance by store.

But launching the agent was just the start.

“We got basic Trent up and running in absolutely no time at all, but he was very much like an intern. Really good technical skills, but absolutely no business knowledge whatsoever.”

What followed looks remarkably similar to onboarding a new analyst.

The team reviewed logs, refined prompts, built verified queries and taught the agent how the business actually worked. Instructions were added to standardise responses, eliminate exaggeration and ensure consistent analytical outputs.

Trent’s creator and Subritzky reviewed logs created from the merchandising team’s enthusiastic testing of Trent, turning successful requests into verified queries. There was some fun along the way too, with easter eggs added for testers.

The effort paid off.

“Trent had gone from being a really enthusiastic intern to a competent junior analyst who sometimes surprises us with some quite cool solutions.”

Today the agent is embedded in the merch teams daily workflow and has helped staff focus their analysis rather than build ever-larger spreadsheets.

Agents training humans

The store pilot provided more insights.

Initially, staff asked relatively simple questions. Over time, as confidence grew and the results were verified, the complexity of those questions increased. Store operators moved from asking basic sales questions to exploring inventory optimisation, promotional effectiveness and slow-moving stock analysis.

One example involved a store asking which products should be included in a winter clearance programme for slow and obsolete stock. Instead of simply producing raw figures, the agent asked clarifying questions, identified priority categories and recommended potential actions.

“What we were finding here was they could actually genuinely chat away to it because my team had done all the hard work up front to get all of those table joins going, all of the Mitre 10 context, all the odd little language that you end up having in business.”

Just as interesting was what happened to the users.

Agents were instructed to push back on vague or incomplete requests and ask for more information when necessary.

“In the interest of the cost, the compute time, and user experience, we added in explicit instructions about what sort of prompts the agent should go back to the user with if they didn’t get enough information.”

If users keep asking ambiguous questions, the agent keeps prompting them for more information.

Over time, users learned to write better questions.

“So our human users are also getting trained by our agents.”

That improvement in data literacy surfaced elsewhere as well. Because the platform could return the SQL underpinning its answers, analysts began developing a stronger understanding of how data tables connected and how metrics were calculated. The result was not simply more automation, but more capable users.

Meanwhile, executives discovered a different benefit.

With access to mobile capabilities, senior leaders began using the agents directly during meetings. Questions that previously required analyst support could be answered immediately. In stores, managers started interrogating data from the shop floor, investigating anomalies as they encountered them rather than waiting to return to a laptop.

Most importantly, the initiative appears to be delivering against its original objective. During the pilots, stores participating in the programme requested fewer new Power BI reports and modifications. Users were finding answers themselves rather than generating more reporting demand.

As Mitre 10 looks ahead, the challenge is ensuring history doesn’t repeat itself.

The retailer is already receiving requests for additional AI agents and is wary of replacing one form of sprawl with another. “We’re working out how we can make the existing analysts flexible, what sort of combinations we can use so that we can reuse a lot of what we’ve got and only do new agents when it really makes sense or is a really separate use case.”

After all, nobody wants to spend next year cleaning up 3,064 agents.

Australia has taken its strongest step yet towards taxing revenue generated by offshore software providers, with the Australian Taxation Office expanding the scope of royalty withholding tax rules covering software and intellectual property arrangements.

The move was quickly followed by New Zealand’s equivalent, Inland Revenue, releasing draft guidance on how software payments, cloud services and SaaS subscriptions should be treated for tax purposes.

“The ATO takes a broader interpretation of when a royalty may arise in cloud-computing context.”

But while both countries are trying to update rules written long before the cloud era, they appear to be taking different approaches.

Under the ATO’s new ruling, TR 2026/2, some software and SaaS payments may be classified as royalties rather than ordinary commercial transactions, exposing a wider range of cross-border software arrangements to royalty withholding tax of between five percent and 15 percent. It’s a ruling aimed primarily at multinational digital giants such as Amazon, Microsoft, Meta, Google and Netflix, and distributors who structure intellectual property and revenue flows across multiple jurisdictions, and will see income earned from cloud and streaming services become taxable in Australia for the first time beyond GST.

By contrast, the Inland Revenue draft guidance, issued earlier this month and updating a framework dating back to 2003, largely maintains the view that most straightforward cloud software subscriptions do not give rise to royalty payments because customers are buying access to software, not the right to exploit the underlying IP. Consultation is currently underway on the draft interpretation guideline PUB00266.

“The ATO takes a broader interpretation of when a royalty may arise in cloud-computing context,” Deloitte New Zealand noted in a recent tax alert.

In both countries, global tech giants generate substantial revenue – billions in Australia’s case particularly – from local customers, yet the profits ultimately taxed in the country have often been significantly more modest after accounting for distribution and service fees and other cross-border payments within their corporate structures.

Chasing profits, not GST

For years, governments have focused on collecting GST from foreign suppliers of digital services. Australia and New Zealand both require many offshore providers to collect GST on products ranging from streaming subscriptions and digital advertising to cloud services and software licences.

GST, however, was only ever part of the solution.

When businesses buy cloud hosting, software subscriptions or online advertising, the GST paid is generally reclaimed through the normal input tax process. Governments collect the tax, but much of it is ultimately returned. The larger question has been how to tax the profits generated from those transactions when the underlying intellectual property is owned offshore.

That’s a debate that has simmered for years as the global tech giants generated billions in revenue while reporting far smaller local – taxable – profits.

The ATO’s new ruling is an attempt to address part of that issue as it targets billions in untaxed offshore software royalties.

A broader interpretation

The new ruling broadens the circumstances in which software-related payments can be treated as royalties for tax purposes. Jethro Byrne, Grant Thornton corporate tax partner, says the ATO has formed the view that Australian distributors ‘effectively posses the rights to on-sell or distribute software, which in its view is generally a royalty’.

According to the ATO, payments may qualify as royalties when they are made for the use of, or right to use, copyright or similar intellectual property rights. The ruling points to situations where software intermediaries reproduce, communicate, modify or adapt software, or otherwise exercise rights associated with copyright ownership. It also addresses modern software distribution and SaaS arrangements.

The ATO has also made clear that it is particularly concerned about cross-border arrangements that may reduce or avoid tax on profits connected with Australia – an issue that has also long vexed many countries, including New Zealand.

For multinational software vendors, distributors and cloud providers, the Australian moves raise the prospect of much closer examination of how software revenues are structured and reported. Professional advisers have already warned them to reassess software licensing arrangements, treaty positions, withholding tax obligations and transfer pricing documentation in light of the ruling.

US pushback

Australia’s tougher stance hasn’t been universally welcomed and has put it at odds with some of its biggest trading partners.

Previous drafts of the ruling drew criticism from the US Treasury and the US National Foreign Trade Council business association argues the ruling ignores recent Australian case law and ‘further erodes investor confidence as well as the overall business climate in the country’.

One of the more contentious aspects of the ATO’s position is that the ruling applies to payments made both before and after its publications. The ATO maintains the ruling reflects its longstanding interpretation of the law rather than a policy change, a position that could expose some existing arrangements to review.

Critics also argue that Australia’s interpretation of software royalties is pushing beyond internationally accepted practice.

Extensions ahead?

The Australian Institute of Company Directors meanwhile, is warning that while the ATO’s ruling targets software and tech, ‘it’s implications could soon reach almost all Australian industries’.

“While end-user companies aren’t directly impacted, boards should be aware the new tax will likely be built into the future pricing of their software subscriptions,” it says, while also warning that other industries with value tied to intangible assets could be future targets. It suggests sectors including pharmaceuticals, cosmetics, motor vehicles and beyond could see similar attention in future.

Suncorp’s Jonathan Rutter is blunt: “It can give a less than ChatGPT experience for the user,” he says of a Suncorp AI assistant. “But that’s important because we cannot have it giving the wrong information.”

While consumer AI tools compete to be more conversational and creative, businesses are clear that accuracy, governance and trust often matter more then delivering a flashy user experience.

“How do we not become a bottleneck and actually help the organisation move faster with deploying their initiatives?”

Speaking at Gartner’s IT Symposium/Xpo on the Gold Coast recently, Rutter – who is executive manager, programs and systems within Enterprise Risk at Suncorp – and AGL head of architecture – security and corporate technology Yaso Addanki, shared how they are embedding AI into their businesses, moving away from AI experimentation towards practical business applications designed to solve specific operational problems.

Stopping the bottleneck

At AGL, AI agents were built not as part of a grand transformation strategy, but because security reviews were slowing projects down. As AI initiatives multiplied across the business, security architects found themselves reviewing a growing number of projects, each requiring assessments, threat modelling and architecture reviews before work could proceed.

“We were thinking how do we not become a bottleneck and actually help the organisation move faster with deploying their initiatives?,” Addanki says.

“We had internal discussions and thought ‘how much time are we taking to turn around a security architecture review and how can we make it better and faster?’”

The discussions evolved into the development of an AI agent which uses threat modelling frameworks to generate security review documentation, providing architects with an initial draft they can refine, rather than creating assessments from scratch.

Initially deployed within the security architecture team, the agent was designed to reduce turnaround times and free specialists from routine work. But AGL sees a bigger opportunity ahead.

Rather than waiting for projects to reach security teams, the company is exploring how the technology could be made available directly to solution architects, allowing controls to be incorporated much earlier in the design process. The goal is to improve the organisation’s overall security posture while helping delivery teams move faster.

The project reflects a broader approach to AI across AGL. Addanki says the company is focusing on three key areas: Building enterprise AI and data platforms, leveraging AI capabilities already embedded in existing tech tools and orchestrating those capabilities into agents that support business workflows.

Customer service has been one of the beneficiaries. Within one AGL subsidiary, AI is used to generate summaries of customer interactions, recommend relevant knowledge articles, draft email responses and deliver more consistent service across teams. By removing the need for staff to manually document every interaction, customer service representatives can spend more time focusing on customers rather than admin.

Teaching AI to speak governance

Suncorp has taken a similar approach, focusing on solving specific business problems within risk management.

“We started with business problems first,” Rutter says. “Things that we would have looked to provide an enhancement solution for pre-AI. So these things were areas that we were going to address regardless.”

“We’ve got three areas we like to tackle: UX, efficiency and then the data quality,” Rutter says.

One project focused on incident reporting. Whenever an operational risk incident occurs, information needs to be captured accurately so teams can investigate causes, resolve issues and prevent similar incidents in future. While reports are usually submitted by risk specialists they can also be submitted by frontline employees who don’t have experience in lodging incident reports.

Suncorp’s AI-assisted incident process guides users through submissions, prompting them for missing information and helping categorise incidents correctly.

“If you haven’t provided enough, it’ll prompt you for what you’ve missed and it’ll categorise it and help it flow through our process effectively.”

The result is improved data quality from the outset, Rutter says, making downstream investigations and reporting more effective. “It’s [an area] where we will get efficiencies, but data quality is the driver.”

The company is also using AI to tackle another common enterprise problem: Dense policy documentation.

Risk and governance documents are often lengthy, technical and difficult for employees to navigate. Suncorp is using AI to simplify the language, reduce duplication and then serve that information up in a chatbot-style interface that enables staff to ask questions directly, rather than searching through extensive document libraries.

A third initiative applies AI to control testing and governance activities, helping automate administrative tasks that would otherwise consume significant amounts of specialist time. Again, the objective is not workforce reduction, but enabling experts to focus on work that requires human judgement.

Why enterprise AI can’t act like ChatGPT

Despite the growing use of AI, both organisations say the path hasn’t been without challenges – including the issue of trust. And here’s where Rutter’s comment’s about being a ‘less than ChatGPT experience’ come in.

“In the example of the policy consumption, one of the biggest risks we’ve got is the accuracy of information. It somebody has asked our assistant for support and it doesn’t give them the right answer back, we’re in a bad position.”

The company has built guardrails into its systems, including source references, hyperlinks back to supporting material and explanations showing how answers were generated. Inbuilt guardrails behind the scenes ensure the chatbot won’t hallucinate or go off topic.

The approach may create a more constrained experience than employees get from tools such as ChatGPT, but it ensures there’s no providing of wrong information.

“Something we’ve learned along the way is people will expect that [ChatGPT experience] and we’ve had to educate them that this is serving a specific purpose and there are controls in place.”

At AGL, meanwhile, security teams face a different challenge: Keeping pace with rapidly evolving models and vendor offerings. Understanding where models are deployed, where data is stored, whether information is used for training and what controls are needed around those environments has become an increasingly important part of AI governance, Addanki says.

“Sometimes it could become a black box because we don’t understand and some vendors might not be able to kind of reveal some of those details to us,” she says.

She notes that could see contractual controls becoming required.

Winning buy-in

Both organisations also highlighted the importance of proving value early. At Suncorp, proof-of-concept projects have played a key role in building stakeholder support and demonstrating how AI can address real business pain points. AGL similarly started by solving problems within its own security function before expanding further.

For both, success has come from applying AI to specific operational challenges, removing administrative burden and helping specialists focus on the work that matters most. As Richie Paul, IBM generative AI practice lead, who hosted the session, summed up: “Yasso and Jonathan give us insight into this idea of having multiple programs going in parallel and really, a thousand flowers truly will bloom, which will be the marker of enterprise-wide organisational change.”

“It’s the small things that could never really justify a business case on their own but ultimately do have an oversized impact on people’s day-in and day-out productivity that are providing the big wins,” says Stuart English of AI in ERP systems.

For English, CEO of Velocity Global, it’s not the AI moonshots dominating conference agendas which will bring real value to business, but instead the technology’s impact on the hundreds of small administrative tasks that quietly consume time across purchasing, inventory management, customer service and approvals. As businesses push for more AI capabilities from their ERP platforms, English says many are discovering the biggest opportunities are often hidden in the operational friction they’ve learned to live with.

English says demand for AI is no longer the issue.

“There’s often an impasse where a customer says, ‘just make us AI’, which is a little too broad and untethered to their world for it ever to be successful.”

“Almost all the customers we’ve sat down with are definitely seeing opportunity in AI,” he told iStart. But he notes “I don’t think that’s necessarily crystallised to specific use cases.”

Velocity Global, which implements and supports Pronto Software, works predominantly with organisations in manufacturing, logistics and distribution, typically employing between 50 and 250 staff. Across those sectors, English says interest in AI is widespread, particularly around productivity, forecasting, inventory management and customer service.

The challenge is turning that interest into something practical. Alongside issues with quality of data – “The system is only ever going to be as good as what you put into it” – he sees being clear on use cases as the biggest issue facing companies.

“There’s often an impasse where a customer says, ‘just make us AI’, which is kind of a little bit too broad and untethered to their world for it ever to be successful.”

Instead, he argues organisations need to approach AI much like any other technology investment by identifying where people spend time, where processes stall and where repetitive tasks create friction.

“Like any automation project, where is the time going? What’s the best bang for buck? What does the technology most align to in terms of driving efficiencies or productivity gains? And how do you leverage those gains to reinforce and build on the wins?

“You start with your low-hanging fruit.”

Early wins

English’s comments come on the back of Pronto’s latest AI roadmap announcement, which coincides with the 50th year of Pronto ERP in the A/NZ market. The announcement includes new and enhanced AI capabilities including natural language interaction, predictive inventory demand and fulfilment forecasting and voice enabled BI reporting and dashboards, across Pronto Xi. Other capabilities include machine learning predictions for service and maintenance, agentic assisted purchase order approval, automated inventory checks and stock recommendations, and intelligent B2B eCommerce agents.

English says addition of a more conversational element to getting data out of the ERP system is likely to be the biggest winner for customers near term.

“If you wanted to ask how much of a given stock item is in stock, you’d have to navigate through layers of menus. Now, there’ll be an app and you can say ‘tell me how much stock of X do I have? And it will do a live inquiry on your data and come back with an answer.”

That capability is as much about reducing friction as it is about introducing new technology.

“For your typical operational managers, it just makes finding answers quicker, which is a reduction in friction,” English says.

“The other big boon will be at the executive layer, where typically your CFOs, your CEOs, your COOs, they’re not in the system day in and day out.

“They’ll be asking a subordinate for that information. It provides a new interface to the system to surface up those answers in real time.

Another early use case courtesy of Pronto is purchase order approvals.

Today, approvals often require users to log into a system, navigate to the relevant screen and manually complete the transaction. In future, many of those interactions could be reduced to a simple response delivered through an AI-driven workflow.

“You’ll get a message saying, ‘Hey, you’ve got this PO’, and you can just respond yes, and then that goes back to actually do the mechanism of approving the order in the system without you needing to go through that step of logging in,” he says.

“That’s five minutes back in a day for a CFO per order.” Multiplied across teams, processes and departments, those efficiencies begin to add up.

“Pronto have done a great job because they’ve focused on actually getting some practical use, not just ticking a box.”

Into the future

Advances in AI-enabled ERP mean customer problems that weren’t practical or economical to solve with AI even 18 months ago are now far more realistic.

“Certainly for smaller customers, it gives access to automation that previously they would have required reasonably involved integration or automation projects to remove manual steps and change.

“Some of these AI agents and processes will enable that automation and remove that friction almost out-of-the-box.”

Longer term, the roadmap extends into agent-to-agent interactions, where systems communicate directly with customers, suppliers and other applications.

“The future vision is kind of agent-to-agent communication,” English says.

Yet despite the focus on new capabilities, he is adamant the role of ERP itself is not changing.

“ERP is still at the crux of the business. It’s still the core of business,” he says. “AI is just an augmentation to that to enable better ways of working and new ways of working.”

For organisations still trying to determine where AI belongs in their ERP strategy, English’s advice is to look beyond the hype and focus on the everyday frustrations that slow people down.

“There is a lot of hype around AI, but there are very tangible use cases where it provides productivity enhancement. And that is real.

“We’re seeing it all over the place.”