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. 3 days ago

    High-performing organisations connect AI to impact before efficiency, says Alyve CEO

    The organisations getting the most from AI are using it to expand what the business can do, where it can grow, and how quickly it can adapt. In this ADAPT Insider conversation, Mark Cameron, ADAPT Executive Advisor and CEO & Director at Alyve, argues that this shift starts with leadership. When leaders connect AI to mission and impact, the business gets a clearer reason to move. Key takeaways: High-performing organisations link AI to mission, growth, and impact, which gives the technology a stronger direction.Leadership alignment helps reduce friction and gives AI a clearer place in the business.Learning speed becomes a more durable advantage as organisations move towards adaptive operating models. Impact gives AI a stronger direction than efficiency alone Mark’s view is that high-performing organisations use AI to ask a bigger question than how to do the same work faster. They focus on what more the organisation could achieve with the technology in place. That creates room for growth, new value, and stronger strategic choices. That mindset also changes the business case. The strongest performers still see major productivity gains, but those gains come through a broader ambition around mission, growth, and impact. In practice, that gives teams a more meaningful reason to adopt AI and helps leaders connect the technology to something the business already cares about. Leadership alignment shapes whether AI gets traction Mark also points to alignment as the main difference between organisations making progress and those stuck in inertia. Boards, CEOs, finance leaders, product leaders, and risk leaders may all believe AI matters, but each can still pull in a different direction if the organisation has not agreed on what the technology is there to do. That creates friction, slows decisions, and turns AI into a collection of disconnected efforts. The organisations moving faster are the ones where leaders agree on purpose, direction, and what AI means for the business. That shared understanding reduces ambiguity and helps teams act with more confidence. Learning speed becomes the real advantage Mark’s broader argument is that AI pushes organisations away from rigid operating models and towards more adaptive ones. In that environment, advantage comes from how quickly an organisation can sense change, respond, learn, and improve. That is why he frames the future organisation as an intelligence-centred enterprise. The goal is not simply to automate tasks. It is to build stronger feedback loops between people, systems, and AI so the business can learn faster than competitors and act on that learning sooner.

    High-performing organisations connect AI to impact before efficiency, says Alyve CEO

  2. 24 Aug

    Better baselines and end-to-end measures improve AI ROI, says Teachers Mutual Bank’s CIO

    Many AI programs can show momentum, but not what actually changed for the business. In this ADAPT Insider conversation, Dan Chesterman, CIO at Teachers Mutual Bank Limited, argues that the issue usually starts earlier. Organisations need a clearer view of what they are trying to improve, how that process performs today, and which outcome would prove the investment worked.   Key takeaways: AI ROI becomes clearer when organisations start with a strong baseline and measure business improvement against it.Use cases, model choice, and governance need to match the decision or process being improved.End-to-end outcomes give a more useful picture of AI value than local productivity gains alone.  ROI starts with a baseline Dan’s view is that AI performance is hard to judge when the starting point is vague. If an organisation cannot describe current process performance, customer experience, or another measurable business outcome, it becomes much easier to celebrate implementation than improvement. That is why he puts so much weight on baselining. Once the current state is visible, AI can be measured against something real. That also makes it easier to separate activity metrics from business metrics. Token consumption, model usage, and go-live dates may show movement, but they do not show value on their own.   Clear use cases matter more than broad AI ambition Dan also points to overapplication as a common problem. AI can easily become a catch-all label for projects that have weak purpose, poor fit, or an inflated cost base. Organisations can end up applying more expensive models than the task requires and calling that progress. Start with the use case, the decision, or the workflow that needs to improve. Then choose the level of intelligence, cost, and governance that fits it. Some tasks need prediction. Some need precision. Some need human judgement at key points. Once that context is clear, AI becomes easier to apply with discipline.   End-to-end outcomes matter more than local gains Dan is equally clear that improving one step in a process does not guarantee a better result overall. A faster task can still create friction elsewhere, or save time that never becomes reusable because it disappears into other work. That is why he pushes leaders towards end-to-end measures. Completion rates, customer outcomes, process performance, and cost per successful outcome all give a better picture of whether AI is helping the system work better. In that sense, AI value comes from what changes across the journey, not from one isolated efficiency win.

    Better baselines and end-to-end measures improve AI ROI, says Teachers Mutual Bank’s CIO

  3. 16 Aug

    What leaders in disrupted industries need to redefine first, according to Southern Cross Media’s Director of Data and Growth

    Disruption puts pressure on more than revenue.It puts pressure on how people inside the business understand value, relevance, and their own future in it. In this ADAPT Insider conversation, Andrew Brain, Director of Data and Growth at Southern Cross Media, argues that leadership in disrupted industries starts with helping teams and stakeholders see where the next version of the business can grow, rather than anchoring everything to the part already under pressure. Key takeaways: Leadership in disrupted industries starts with helping teams and stakeholders see where new value can come from.Data and AI create more impact when they support forward decisions tied to audience, customer, or revenue outcomes.Capability building, sharper priorities, and modern tools help people stay engaged through change. Disruption changes the conversation leaders have to lead Andrew’s experience has been shaped by businesses moving through digitisation again and again, from publishing to out of home, audio, and streaming. A legacy model comes under pressure, people worry about what is being lost, and the business has to decide whether it will defend the old definition of value or broaden it. In media, that means moving the discussion beyond linear television alone and toward the value of total TV across broadcast and streaming.  For teams and stakeholders, that shift creates a more useful frame. It helps people understand where the business can grow, how new channels support the commercial model, and why the work still matters in a changing market. Data becomes valuable when it helps shape the next decision Andrew’s approach to data and AI starts with a simple question: what decision becomes possible when you stop only reporting what happened yesterday? At Seven, that meant using machine learning to forecast audience behaviour across devices and days ahead, then acting on those signals before audiences dropped away. That shift changed the value of the data function.Instead of producing hindsight, the team could help shape programming, re-engagement, audience growth, and advertising outcomes. The use cases then became easier to prioritise because they were connected to a commercial goal. For a broadcaster, that meant audience scale and revenue.  In other sectors, the shape may differ, but the principle holds.  Data matters more when it changes what the business does next. Capability and focus help people stay with the change Andrew is also clear that transformation depends on whether people can see the commercial value of their work and build skills that keep them relevant. That is why his teams were encouraged to challenge work that had no clear business outcome, focus on initiatives that could move the organisation forward, and learn through modern platforms that expanded their own career value. That matters in disrupted businesses because pressure can easily turn into anxiety or drift. Clarity around priorities, visible links to commercial outcomes, and investment in better tools all help give people a stronger reason to stay engaged. In that environment, transformation becomes easier to sustain because it creates both business momentum and individual growth.

    What leaders in disrupted industries need to redefine first, according to Southern Cross Media’s Director of Data and Growth

  4. 10 Aug

    Why retail teams need sharper trade offs between speed, cost, and quality

    In retail, speed only becomes useful when it helps move a business metric without creating a bigger operational problem somewhere else. Sakshee Kohli, Head of Technology - Store Customer Experiences at Coles, says technology earns its place when it helps move a real business number, gets into customers’ hands quickly without degrading experience, and stays useful beyond the first release. That makes the challenge less about chasing the newest tool and more about building systems that keep creating value over time. Key takeaways: Technology becomes more useful when teams start with the business metric that needs to move, whether that is loss, freshness, affordability, or personalisation.Speed only matters when quality, cost, and customer experience stay under control. That requires clearer trade offs around risk appetite, perfection, and release discipline.The strongest retail platforms are built to solve more than one problem. Reusable, extensible technology creates more long term value than one off fixes that become expensive to maintain. Business alignment is what makes retail technology useful Retail technology decisions improve when they start with the commercial problem, rather than the architecture or the tool. That changes the conversation straight away because it forces teams to focus on the number or outcome they are trying to shift. That is the lens Sakshee applies to business engagement. She explains that retail teams are trying to reduce skip scans, improve freshness, protect affordability, or create more relevant customer experiences. In that context, AI, computer vision, and other technologies matter only insofar as they help solve that problem. The value comes from aligning around the shared business goal first, then deciding how technology, process, and workforce change each contribute to the outcome. Speed creates value only when quality and cost stay in balance Moving fast is easy to talk about and much harder to do well at scale. In retail, there is a difference between deploying quickly inside engineering and releasing quickly into the hands of customers without damaging their experience. Sakshee makes that distinction clearly. She tracks both deployment speed and release speed, but ties them back to confidence, quality, and cost. That is why simpler architectures and stronger quality engineering matter in her model. They help teams move faster without creating downstream friction. She is also clear that perfection can become a distraction. Safety, security, and compliance are non negotiable, but beyond that, teams need a sharper view of what truly has to be perfect on day one and what can be managed through process as the product matures. In practice, that is where risk appetite becomes a useful operating discipline rather than an abstract leadership phrase. Future ready retail technology has to outlive the first use case Large retailers do not get much value from solving one problem once. The bigger payoff comes from building technology that can evolve, scale, and unlock value repeatedly across the business. That is why Sakshee talks about creating for the future. She argues that solving only for today can create a longer term cost if the platform lacks scalability or extensibility. That mindset matters even more in a complex retail environment with diverse systems, store infrastructure, and many teams contributing to change at once. Her example of skip scan technology shows the point well. The business problem was loss, but the challenge was to get the capability live at scale across 550 stores, at speed, in a way that could keep supporting future value. Looking ahead, she expects AI to play a larger role in targeted use cases such as loss analysis and consumer behaviour, while lifecycle management and technology sustainability remain critical operational pressures. The underlying lesson is that future readiness comes from building technology that keeps paying back, rather than technology that solves one problem and leaves the business with another.

    Why retail teams need sharper trade offs between speed, cost, and quality

  5. 4 Aug

    How Australian government agencies can get more from AI through smaller bets than big programs

    Government is under pressure to do more with less, and AI is now part of that conversation. That does not mean moving fast. In government, the cost of getting AI wrong is higher, which makes caution a strength rather than a weakness. In this ADAPT Insider conversation with Anthony Saba, Managing Director – Transformation Services at ADAPT, David Heacock, ADAPT Executive Advisor, former Chief Digital Innovation Officer and BCG Partner, argues that public sector leaders are right to take a measured approach. The gap between vendor hype and real-world performance is still too wide, and in government that gap can damage trust, disrupt services, and create harm.  Key takeaways: Move carefully where the risk of AI failure is high.Treat governance and decision-making as the main delivery challenge, not the technology alone.Break large programs into smaller, outcome-driven pieces and build more capability internally. Government is right to be cautious on AI He argues that AI should be introduced carefully in public sector settings. Unlike traditional systems, generative AI is probabilistic, which means it does not always produce the same answer in the same way. That creates a different risk profile, especially in government environments where decisions can affect people’s lives. This is why a slower pace makes sense. Government does not need to lead the market on AI adoption. It needs to understand where the tools are useful, where precision matters, and where experimentation is safe. The bigger problem is organisational frictionHe is clear that technology is no longer the main constraint. The harder problem sits in governance, decision-making, and organisational design. Faster tools do not remove those bottlenecks. They hit them sooner. He points to executive choke points, where delivery teams move quickly but decisions still stall further up the chain. He also warns that uncertainty often leads to more governance, more scrutiny, and more delay. In practice, that usually makes delivery slower rather than safer. Smaller internal bets will work better than large programsHe also argues for a different delivery model. Rather than packaging large problems into single vendor-led programs, agencies should break work into smaller pieces with tighter accountability and clearer outcomes. He sees strong potential in building more capability internally and focusing on use cases that sit between personal productivity tools and large enterprise systems. Areas like policy analysis, legislative synthesis, and information-heavy work are a better fit for small, targeted AI projects that can prove value early without creating unnecessary risk.

    How Australian government agencies can get more from AI through smaller bets than big programs

  6. 27 Jul

    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

  7. 20 Jul

    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

  8. 13 Jul

    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
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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
    21
  • Rating
    Clean
  • Copyright
    © ADAPT
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    ADAPT Insider