ADAPT Insider

ADAPT

This is ADAPT Insider. Proudly A/NZ first. For more than 15 years, ADAPT has empowered Australia and New Zealand’s executive community with trusted data, insights, and connections so leaders can make better decisions with confidence. Because this region is different. Our markets are unique. And the challenges facing enterprise leaders from legacy technology to transformation are only getting bigger. Each year, through in-depth research, benchmarking and executive-only events, ADAPT engages with over 2,000 senior leaders across the region’s most influential enterprise and government organisations. ADAPT Insider brings you inside those conversations. Real perspectives from technology and business leaders. Independent research grounded in local data. And practical intelligence you can actually use. This is not theory. It is insight for leaders driving modernisation. Welcome to ADAPT Insider. Built for A/NZ leaders. Backed by data. Designed to help you move with confidence.

  1. 4d ago

    Strong foundations and human adoption will decide who gets real value from AI, says Commonwealth Superannuation CTIO

    AI conversations are getting noisier, but the real divide is still familiar.Some organisations are strengthening core systems, simplifying complexity, and building the discipline to use new tools well. Others are layering AI over weak architecture, fragmented data, and unclear ownership, then wondering why the results feel shallow. In this ADAPT Insider conversation, Andrew Matuszczak, Chief Information and Transformation Officer at Commonwealth Superannuation Corporation, argues that AI will not rescue poor foundations or weak execution. For him, the more useful question is whether the business has done enough core transformation work to make AI worth adding in the first place. Key takeaways: Fix the core systems first, because AI will scale weakness just as quickly as it scales value.Treat AI adoption as a people challenge as much as a technology challenge, with practical training and use cases that make the value real.Use first principles, simple governance, and disciplined investment choices to avoid hype driven decisions. AI only becomes valuable when the foundations can carry itAndrew is blunt on this point. If the core systems are still monolithic, overly complex, and poorly integrated, AI will only amplify those weaknesses. That is why CSC has spent years doing the less glamorous work first, upgrading core platforms, improving its data stack, and simplifying how systems connect before trying to push AI deeper into customer and employee workflows. That approach matters because AI is being asked to sit on top of operational reality, not a demo environment. If the foundations are weak, the result is cosmetic change rather than meaningful improvement. If the foundations are strong, AI can start improving areas such as call centre interactions, knowledge access, and workflow support in ways that actually hold up. The harder problem is getting people to use it wellAndrew also argues that many organisations are still treating AI as a technology rollout when the bigger challenge is human adoption. Fear, uncertainty, and doubt still shape how people respond, from boards asking if they are being left behind, to employees wondering what the tools mean for their role. That is why CSC has focused heavily on practical, role based uplift rather than abstract AI messaging. Short training, relatable use cases, and internal champions are all part of building confidence. The aim is to help people understand how these tools improve the way they work, not just what the tools are called. In Andrew’s view, adoption will move faster when people can connect AI to everyday value instead of seeing it as another layer of technical change. Simplicity and discipline matter more than speedAndrew is also sceptical of the generic way AI is discussed. In his view, calling everything AI hides the real question, which is what problem the business is actually trying to solve. Different tools suit different needs, and that makes first principles thinking more useful than broad enthusiasm. That same discipline applies to governance and investment. Boards are pushing management teams to move faster, while also asking harder questions about risk, control, and exposure. The organisations that handle that tension best will be the ones that keep governance practical, make clear choices about where value sits, and avoid turning AI into another expensive layer of technical debt. Speed alone will not decide the winners. Clarity, simplicity, and execution will.

    Strong foundations and human adoption will decide who gets real value from AI, says Commonwealth Superannuation CTIO

  2. Jul 20

    Why healthcare AI must improve work without intensifying burnout, according to ADHA CDO

    Healthcare productivity is often measured too narrowly. Internal efficiency still matters, but in health the larger gains come from what the system enables for clinicians and patients. In this ADAPT Insider conversation, Peter O’Halloran, Chief Digital Officer at the Australian Digital Health Agency, argues that real value is created when clinicians have better information, unnecessary tests are avoided, and patients move through care with less delay and duplication. Key takeaways: Measure healthcare productivity by the value created for clinicians and patients, not only by internal efficiency gains.Build trust in AI through smaller use cases that show clear benefit and keep human oversight in place.Redesign work alongside automation so productivity gains improve care without intensifying burnout. Productivity in healthcare is created at the point of carePeter’s view is that the most meaningful productivity gains do not sit inside the agency that funds the work, but across the wider health system it helps improve. When clinicians can access the right information at the right time, they can avoid unnecessary pathology tests, reduce repeat visits, and make faster decisions about diagnosis and treatment. Those gains compound well beyond any internal efficiency measure because they improve care while also reducing waste across the system. That is why digital health has such a large upside. Better information-sharing improves what happens in front of the clinician and the patient as much as it improves the administration behind them. For a system under pressure from rising demand and limited workforce capacity, that is where productivity becomes most valuable. Trust sets the pace of AI adoption in healthcareHealthcare does not adopt AI on promise alone. The tolerance for failure is low, which means trust has to be built carefully. Peter makes the point that digital systems in health are often expected to be near flawless in ways that other tools are not. That creates a very different adoption environment from other sectors. The practical result is that AI has to prove itself through smaller, visible gains. One example is breast cancer screening, where AI can help prioritise higher risk scans for clinician review. That supports faster assessment and better outcomes, while keeping the clinician firmly in control of the final judgement. In his view, that is the right path for healthcare AI, start with uses that improve decision making and safety without asking clinicians to surrender trust in the process. Capacity gains only matter if the work is redesigned properlyThe conversation also sharpens the difference between automation and productivity. Freeing up time is helpful, but that does not automatically mean work improves. In some clinical settings, AI can push the most complex or high risk cases to the top of the list, which improves speed and clinical value. At the same time, it can increase the intensity of the workload if clinicians are left dealing only with the most demanding tasks all day. AI should help clinicians operate at the top of their scope, but in a way that is sustainable. If lower intensity work disappears and only the heaviest cognitive load remains, burnout becomes a real risk. The goal is not only to make clinicians faster. It is to structure work so that productivity gains support better care without eroding the human capacity needed to deliver it.

    Why healthcare AI must improve work without intensifying burnout, according to ADHA CDO

  3. Jul 13

    Construction productivity depends on shared data, says Built’s CDIO

    Construction productivity improves when project data stops sitting with individual players and starts working across the full build ecosystem. Construction has talked about productivity for years. The harder question is what actually shifts it. Kurt Brissett argues that the answer sits less in isolated tools and more in how data is structured, shared, and applied across the full project lifecycle. For Built, that means using digital engineering to reduce project risk, improve procurement and supply chain efficiency, and make collaboration stronger across a crowded ecosystem of clients, subcontractors, regulators, and government. Key takeaways: Construction teams get more value from digital tools when project data becomes a shared working environment, rather than staying trapped inside separate stakeholders or systems.3D models become far more useful when they are enriched with live construction data, giving teams a stronger basis for rehearsal, coordination, and earlier risk reduction.Industry productivity lifts when project insights are reused across bids and builds, helping teams benchmark performance, reduce rework, and improve sustainability over time. Shared project data makes construction decisions stronger Digital tools become more useful in construction when they give teams one place to work from, rather than another fragmented layer to manage. That is what turns technology from a nice capability into a delivery advantage. Kurt points to the three dimensional model as the heart of that shift. At Built, those models are increasingly being augmented with construction related data sets and used as a single source of truth for collaboration across internal teams, clients, and subcontractors. That gives teams a stronger basis for coordination and makes it easier to de risk elements of the build before they turn into larger schedule or cost issues. Better planning starts with earlier rehearsal and clearer visibility of risk A digital environment earns its place when it helps teams see issues sooner, rehearse more effectively, and make higher value decisions before work becomes expensive to unwind. That is where AI starts to have practical weight in construction. Kurt links that directly to scenario based rehearsals and design support. He explains that AI can help teams focus attention on the areas that matter most during construction, while richer modelling can improve sequencing, manage weather related risk, and reduce rework and material waste. In that sense, the value is not only operational. It also reaches cost control, schedule reliability, and sustainability outcomes across the life of the build. Industry productivity rises when data survives the project Construction will keep losing productivity if each project learns in isolation. The bigger opportunity comes when delivery data is carried forward, compared across jobs, and used to improve the next bid and the next build. That is one of Kurt’s clearest points. He says the sector has historically done a poor job of leveraging data because too much of it stays siloed within individual players. Built is trying to push further into a common digital environment where stakeholders can collaborate earlier, burn down risk, and work through cost planning and estimating more efficiently. Just as importantly, Kurt sees real value in benchmarking across past projects so teams can learn what worked, what did not, and apply those lessons to future tenders. That is the kind of feedback loop that can lift productivity across the wider industry, not just within a single project team.

    Construction productivity depends on shared data, says Built’s CDIO

  4. Jul 7

    Why most AI pilots fail after the demo, according to an ASX 30 transformation leader

    The pilot can make enterprise AI look further along than it really is. Once systems hit live workflows, organisations start dealing with messier user behaviour, weaker ownership, and economics that no longer match the original case. In a conversation with Anthony Saba, Partner & Managing Director, Transformation Services at ADAPT, Vijayan Seenisamy, Transformation Lead at an ASX 30 enterprise, argues that these failures are rarely about the model itself. They come from trying to scale AI inside operating structures that were built for a different kind of technology.   Key takeaways: AI pilots break down when production exposes weak ownership, messy workflows, and economics that were never tested properly.Enterprise AI creates value when leaders treat it as a business transformation, not a narrow technology program.Cost per successful outcome is a more useful measure than adoption or token volume when judging whether AI is working at scale.Shared ownership across technology, finance, and the business is what gives AI a better chance of surviving beyond the pilot.  Most AI programs are framed too narrowly Vijayan argues that many organisations get the framing wrong from the start. Once AI is labelled a technology transformation, responsibility narrows too quickly to the CIO or CTO. That misses the bigger issue. In his view, AI is a business transformation that affects workflows, leadership, workforce design, and operating model choices far more than most executive teams are prepared to admit. Technology may be essential, but it is only one part of the change. That is also why so many organisations are seeing a gap between executive expectation and financial outcome. Teams may have bought copilots, pushed adoption, and reported productivity gains, but the CFO still sees little movement in the P&L. The local wins are real, but they do not become enterprise value unless the business changes how work is structured around them.   Production is where the business case starts to unravel Vijayan describes a familiar pattern. The pilot is built in controlled conditions, with curated data, known prompts, and the builders close enough to keep things working. Production removes those protections. Real users behave differently, data gets messier, and systems start to wobble under conditions the demo never had to survive. He also points to unit economics as a major blind spot. If an agent costs more to complete a task than the human process it was supposed to replace, the business case weakens fast. That gets worse in multi agent systems, where orchestration overhead can erode the performance that individual agents appeared to have in isolation. What looked efficient in a pilot can become expensive and unstable once usage grows.   AI needs a different economic and ownership model A core argument in the conversation is that AI should be measured as a business asset, not as another software rollout. Vijayan says the most important metric is cost per successful outcome, meaning the cost of producing an outcome a user or stakeholder would actually accept. Token costs, task costs, and adoption rates are useful, but they do not tell leaders whether value is really being created. He also argues that ownership has to widen. Most AI initiatives still sit under a one signature model, usually with the CIO holding the budget and delivery burden. His alternative is a three signature model: the CIO for delivery feasibility, the CFO for unit economics, and a business leader for workflow change. That spreads accountability across the people who control whether AI can work in practice, not just in demo conditions.

    Why most AI pilots fail after the demo, according to an ASX 30 transformation leader

  5. Jun 30

    Cyber investment is won before it reaches the board, says Orica Australia’s CISO

    Boards need enough cyber detail to judge exposure, understand whether key controls are working, and decide where intervention or investment is required. Cyber lands better when it is framed around governance, business risk, and investment choices rather than technical complexity. Jamie Rossato, CISO at Orica Australia, shares a practical view on how security leaders build alignment with boards and executives by connecting cyber outcomes to risk appetite, operational priorities, and the cost of leaving gaps unresolved.  Key takeaways: Effective board engagement depends on clarity, brevity and aligning cyber discussions to fiduciary responsibility and risk appetite.Investment decisions are won at the executive layer, where operational ownership and funding decisions are shaped.A threat-led approach strengthens both security posture and compliance outcomes, making cyber spend easier to justify.  Board engagement demands clarity, not complexity Communicating with boards requires direct, concise messaging focused on controls, risks and outcomes, not technical detail. Boards operate under time pressure and broad accountability, so cyber leaders must translate security into governance, risk and fiduciary impact. Jamie emphasises avoiding overly dense materials, instead focusing on whether controls are effective, aligned to risk appetite, and what actions are in place to address gaps.   Executive alignment is where investment is won Cyber funding conversations are secured with executive management before reaching the board. Executives own implementation and operational risk, making them critical partners in shaping, supporting and advocating for cyber investment. Jamie notes that by demonstrating control effectiveness and value for money, leaders guide executives to recognise unaddressed risks, naturally building the case for further investment.   A threat-led strategy outperforms compliance-first approaches Focusing on real threats ensures security measures are meaningful, with compliance emerging as a by-product rather than the objective. Compliance alone can drive checkbox behaviour, whereas threat-led strategies prioritise actual risk reduction and resilience. Jamie explains that understanding internal and external threats allows organisations to meet regulatory requirements organically, while also making a stronger case for proactive investment, even when threats have not yet materialised. Cyber leaders create influence not by amplifying risk, but by demonstrating control, value and foresight in a language the business already understand.

    Cyber investment is won before it reaches the board, says Orica Australia’s CISO

  6. Jun 23

    Personalisation at scale raises the stakes on trust and governance, says Village Roadshow’s cyber lead

    Customer experience, data strategy, and resilience do not operate the same way across every business model. At Village Roadshow, the challenge is sharper because cinemas, theme parks, and film distribution each run on different customer expectations, planning cycles, and technology needs. Keyur Lavingia, Head of Cyber Security at Village Roadshow, outlined how the organisation is trying to tailor experience and innovation to each part of the business without creating fragmented governance or inconsistent trust.  Key takeaways: Tailor digital and operational decisions to each business model without letting governance drift.Data-driven personalisation is creating customer value, but it is also making trust and privacy more central to resilience.AI adoption is more likely to scale when organisations give employees simple, visible guardrails instead of heavy restrictions.Different business models create different resilience demands Village Roadshow operates across businesses with very different customer journeys. Cinema attendance is often spontaneous, while theme park visits are planned further in advance, with families saving and organising around a bigger commitment. Film distribution brings another operating model again, with its own systems, partners, and timing pressures. That changes how transformation has to be approached. A single digital model cannot be applied evenly across the group. The task is to adapt data, engagement, and operational decisions to each business without letting governance drift or the customer experience become disjointed. Personalisation gets more valuable as data responsibility rises With around 13 million customers each year and roughly 90% of interactions happening digitally, Village Roadshow relies heavily on data to shape customer experience. That creates obvious opportunities to personalise moments more effectively, whether through loyalty recognition, targeted rewards, or more relevant engagement. It also raises the stakes on trust. The more customer data an organisation uses to improve experience, the more carefully it has to handle privacy, consent, and protection. Trust becomes part of the experience customers have with the brand. AI adoption works better when guardrails are clear and usable Village Roadshow’s approach to AI has focused on making adoption easier rather than trying to control every use case upfront. The view is that AI only creates value when people actually use it, but that wider use still needs boundaries people can understand and follow. Keyur described an internal AI assessment model built around a traffic light system, green for approved use, amber for limited use, and red for blocked use. Designed with input from legal, business, and technology teams, the framework gives employees a simple way to understand what is acceptable without forcing them through heavy process each time. The goal is to let people use AI without exposing sensitive data or creating unnecessary risk.

    Personalisation at scale raises the stakes on trust and governance, says Village Roadshow’s cyber lead

  7. Jun 15

    AI creates more value when guardrails open it up to everyone, says EBOS Group’s Chief Data Officer

    As AI moves from specialist teams into everyday business use, data leaders have to focus less on scarcity and more on safe, scalable adoption across the organisation. Artak Amirbekyan, Chief Data Officer at EBOS Group, argues that this shift should not be resisted. Wider use is where the value comes from. The real task is making that use safe, deliberate, and broad enough that AI becomes part of everyday capability rather than a specialist function sitting off to the side. Key takeaways: AI creates more value when more people can use it, not fewer. The challenge is to widen access without losing control of risk, data, or accountability.Guardrails are what turn democratised AI from chaos into value. Restrict the wrong data, set clear rules, and open the tools up safely across the business.AI capability is becoming a baseline skill, not a specialist niche. People with AI fluency are gaining an advantage over people without it, which makes learning and adoption everyone’s responsibility.Cheaper AI changes who gets to create value AI becomes more powerful inside organisations when access stops being limited to specialists. As costs fall and tools become easier to use, more people can experiment, solve problems, and create value directly in their own roles. That is how Artak explains the current moment. He says large language models existed before tools like ChatGPT, but they were expensive, specialist, and largely inaccessible. Once the technology became cheaper and easier to use, adoption surged. For him, that is the pattern leaders should expect. When AI gets cheaper, more people use it, and that wider use is where much of the return starts to appear. Guardrails make wider AI use possible Democratised AI only works inside a business when the organisation is clear about where risk sits and what cannot be compromised. The answer is not to lock everything down. It is to put enough guardrails in place that more people can use the technology safely. Artak says companies need practical controls around what data can be shared, what must stay inside the organisation, and which protections need to sit around the models being used. He talks about restrictions on data leaving the business, firewalls, and protections such as indemnity requirements, but the broader point is more important. Guardrails should increase safe usage, not reduce it. In his view, organisations get more value from AI when they enable more people to use it within clear boundaries. AI fluency is becoming everyone’s responsibility AI capability is moving out of the specialist domain and into general business capability. That changes both workforce expectations and the role of technical experts. Artak argues that AI is now everyone’s domain because the people with AI skills are starting to outpace those without them. He also notes that this changes the shape of data and AI roles themselves. Where companies once searched for rare technical specialists who could both build models and speak to the business, they are now increasingly enabling business users to work with AI directly. In that environment, data scientists and machine learning engineers spend more time enabling use than building everything themselves. Learning how to work with AI is becoming part of staying effective at work, not a side topic for a small technical group. The interesting shift is not simply that AI has become mainstream. It is that AI is becoming cheap enough, broad enough, and useful enough that companies now need to govern widespread use, not specialist scarcity.

    AI creates more value when guardrails open it up to everyone, says EBOS Group’s Chief Data Officer

  8. Jun 2

    Business process owners should carry AI ownership, says the University of Sydney’s CDAO

    Enterprise AI programmes stall when they are treated as technical capability rather than organisational responsibility.AI adoption is a governance and ownership challenge. In conversation with ADAPT’s Head of Analytics & Insights, Gabby Fredkin, David Scott argues that lasting impact comes from aligning AI to strategic outcomes, business accountability and existing performance measures.  Key takeaways: AI strategy scales more effectively when domain priorities are aligned to a shared institutional direction instead of forced into a single blueprint.AI assurance builds momentum when it gives people confidence that use cases are being developed and deployed responsibly.Ownership needs to sit with business process owners, with value measured through the metrics the organisation already trusts.  Strategy works best when it follows the institution’s priorities Large organisations rarely run on a single operating reality. Different parts of the business have different objectives, stakeholders, and constraints, which means AI strategy has to hold together without flattening those differences. That is how David describes the University of Sydney’s approach. The university’s work is anchored to its broader Sydney in 2032 strategy, while data, analytics, and AI initiatives are directed towards specific institutional priorities such as student experience, research, and operational effectiveness. He is clear that this does not produce one standalone AI strategy. It produces a set of aligned strategies tied to the areas where the institution most needs improvement.   Assurance gives people the confidence to use AI well AI governance only becomes useful when it helps people believe the technology is being applied in ways that are responsible, credible, and fit for the setting they are working in. David points to the university’s investment in AI readiness and its AI assurance framework as an example of that. The framework draws on learnings from other organisations, then adapts them to the university’s own complexity. He also highlights Cogniti, a classroom tool that lets academic staff shape how agents engage with course content, as an example of where controls are designed into the use case itself. That matters because the scrutiny around AI governance is rising quickly, and confidence depends on visible assurance rather than generic policy statements.   Ownership and value both need to stay close to the work Enterprise AI becomes harder to sustain when ownership is separated from the process it is meant to improve. Local context shapes how agents are configured, how people use them, and whether outcomes hold up across different parts of the organisation. David is direct on that point. He says ownership should sit with the business process owner because they understand the local context and carry responsibility for the result. Data and technology teams still matter, but their role is to enable, support, and help industrialise what works. He also grounds value in the university’s existing metrics. The two he points to most clearly are student satisfaction and student success, including early intervention that has helped reduce student fail rates. That makes AI easier to evaluate because it is being measured through outcomes the organisation already recognises. AI strategy stays aligned to institutional priorities, assurance is built to create confidence, and ownership sits with the people closest to the outcome. In a complex organisation, that is what makes AI more likely to hold once it moves beyond early use cases.

    Business process owners should carry AI ownership, says the University of Sydney’s CDAO
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About

This is ADAPT Insider. Proudly A/NZ first. For more than 15 years, ADAPT has empowered Australia and New Zealand’s executive community with trusted data, insights, and connections so leaders can make better decisions with confidence. Because this region is different. Our markets are unique. And the challenges facing enterprise leaders from legacy technology to transformation are only getting bigger. Each year, through in-depth research, benchmarking and executive-only events, ADAPT engages with over 2,000 senior leaders across the region’s most influential enterprise and government organisations. ADAPT Insider brings you inside those conversations. Real perspectives from technology and business leaders. Independent research grounded in local data. And practical intelligence you can actually use. This is not theory. It is insight for leaders driving modernisation. Welcome to ADAPT Insider. Built for A/NZ leaders. Backed by data. Designed to help you move with confidence.

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  • Creator
    ADAPT
  • Years Active
    2026
  • Episodes
    16
  • Rating
    Clean
  • Copyright
    © ADAPT
  • Show Website
    ADAPT Insider

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