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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. 4h ago

    Australia’s AI ambitions depend on trusted data centre infrastructure, says Data Centres Australia CEO

    ADAPT Insider

    Australia’s AI ambitions depend on trusted data centre infrastructure, says Data Centres Australia CEO

  2. Oct 5

    Incremental AI wins turn infrastructure investment into business value

    Most AI initiatives stall when ambition outruns clarity. Dean Nelson outlines how leaders can reduce that risk by applying first principles, proving value through targeted use cases, then using each result to guide the next investment. His perspective links practical AI adoption to the infrastructure choices required for sovereignty, sustainability and public trust. Key takeaways: Define the outcome before choosing the technology: A narrow operational problem gives teams a shared target, makes progress measurable and creates a credible path to scale.Design for control from the outset: Private models, secure data environments and locally hosted infrastructure allow organisations to pursue AI without surrendering sovereignty.Earn confidence through visible proof: Clear evidence of efficiency, sustainability and community value helps digital infrastructure retain public trust.Start with one business problemThe strongest AI programs begin with a specific operational constraint rather than a broad transformation mandate. Dean says many pilots fail because teams select the technology before agreeing on the result the organisation needs. A first-principles approach brings business, technology and security leaders around one measurable objective. It also creates a practical sequence: solve the immediate problem, validate the outcome, then expand from evidence. Dean points to an organisation in Mexico that built a secure private AI environment for field teams. By focusing on one defined need, it achieved meaningful productivity gains without making enterprise-wide transformation the starting condition.  Build infrastructure that keeps AI under controlAI workloads are changing the physical design of data centres. GPU-dense environments require new cooling methods, with liquid cooling and closed-loop water systems supporting greater computing density while reducing water use. The same infrastructure decisions shape sovereignty. Dean argues that governments and enterprises need locally hosted capacity, private models and Australian-controlled digital capabilities so critical data and services remain within national control. Sustainable AI therefore depends on architecture that can support intensive workloads without weakening security, operational resilience or accountability. Public trust determines whether AI can scaleTechnical progress alone will not secure the future of AI infrastructure. Communities increasingly scrutinise the environmental impact of data centres, yet the industry often struggles to explain improvements in renewable energy, efficient cooling and resource use. Dean calls for open engagement backed by tangible local benefits. A stronger public narrative must show how infrastructure supports economic activity, improves efficiency and responds to legitimate community concerns. Inside the organisation, that trust starts with disciplined governance. Leaders should manage workloads deliberately, use AI to strengthen oversight where appropriate, then measure progress against business outcomes rather than market hype.

    Incremental AI wins turn infrastructure investment into business value

  3. Sep 28

    The real AI shift is the repricing of cognition, says Wisely Co-Founder

    For the first time, organisations can measure and buy certain kinds of cognitive work at scale. Once thinking becomes measurable through tokens, the economics underneath a business start to shift. Work that once sat inside roles, teams, and service models can now be broken apart, reassessed, and redistributed across people and agents. In this ADAPT Insider conversation, Mark Pesce, ADAPT Executive Advisor and Co-Founder at Wisely AI, argues that this is why the current moment is bigger than another wave of software. Businesses are not simply adopting a new tool.  They are being pushed to rethink the cost of work, the design of processes, and the basis of competitive advantage.  Key takeaways: AI is becoming infrastructure, which means the real shift is organisational design, not tool selection.Cognition can now be priced more directly, forcing businesses to rethink where cost sits and where value is created.The biggest gains will go to organisations that redesign workflows around agents and feedback loops instead of optimising old processes. AI is becoming part of the infrastructure Mark’s view is that AI will disappear into the background in the same way electricity did. It will stop being treated as a standalone capability and become embedded across products, workflows, and decisions. That is why he uses electrification as the comparison. Businesses that once bolted electricity onto old factory designs were eventually overtaken by those that rebuilt around distributed power. He sees the same pattern emerging with AI. The larger gains will go to organisations that redesign around agents rather than leaving AI in a central team or treating it as a side feature. The real pressure is economic Mark is less interested in AI as a novelty than in what it does to the economics of cognition. Once organisations can buy certain kinds of thinking directly, they have to revisit where expensive human effort is still essential, where agents can take on part of that load, and how that changes their margins and operating model. That is where he sees the real disruption. Many businesses are still running on assumptions formed before this shift began. The bigger risk is discovering too late that the business is still priced and organised for a world that no longer exists. Redesign will matter more than optimisation Mark is clear that the biggest gains will not come from adding AI to existing workflows and hoping for incremental improvement. The larger opportunity comes from redesigning those workflows around agents and feedback loops. He points to software engineering as an example, where agents are already being used to detect issues, fix code, test changes, and redeploy continuously. In that kind of setup, the work does not disappear, but it changes. Humans move up a level, from executing repetitive tasks into designing, guiding, and improving the loop itself.

    The real AI shift is the repricing of cognition, says Wisely Co-Founder

  4. Sep 21

    Productivity gains create little value without a plan for saved time, says Work Futurist Dom Price

    Saving time creates little value when organisations have no deliberate use for the capacity released. In this ADAPT Insider episode, Dom Price, Work Futurist and Visionary at Be Luminous, examines why conventional productivity metrics reveal too little about the conditions that sustain performance. He considers how organisations can redirect saved time, protect long-term workforce capability and build the confidence to experiment amid constant change. Key takeaways: Productivity gains create value when organisations direct released capacity towards meaningful, higher-value work.Workforce decisions should account for the experience, context and institutional knowledge that employees develop over time.Small, safe experiments help teams act on new information, adjust direction and share useful lessons across the organisation.Saved time requires a deliberate destination Productivity, sales and GDP remain common measures of performance. These lagging indicators reveal little about whether people have the environment, tools, trust and space to produce meaningful work. Dom points to organisations that achieved significant productivity gains, then filled the released capacity with more meetings. Without a clear next-best use for that time, greater efficiency changed the volume of activity without improving its value. Leaders can assess the conditions behind performance alongside the final output. Discretionary effort, curiosity and human connection reveal whether people feel engaged enough to contribute beyond the minimum expected of them. Automation decisions shape future capability AI can improve work by augmenting employees and redirecting their attention towards higher-value contributions. Removing roles without considering the talent pipeline risks leaving organisations without the experience required for future leadership and specialist positions. Graduate roles illustrate the long-term value at stake. Early-career employees may initially cost more than they contribute, yet those roles build context, experience and institutional knowledge over time. Dom points to IKEA’s decision to redeploy employees affected by automation into new roles. Their existing expertise remained inside the organisation and supported a different source of value. Workforce planning should account for the capabilities an organisation will require in the future, including the pathways through which employees develop them. Learning velocity helps organisations adapt Many organisations were designed around stability, efficiency and five-year planning. Constant disruption has reduced the value of waiting for certainty before acting. Dom describes learning velocity as the ability to absorb new information, adjust direction and test ideas without punishing people for changing course. Leaders can select one manageable opportunity each quarter, test it at low cost and share the result. A small experiment gives teams evidence they can act on while limiting the consequences of failure. Leading by example gives employees permission to try, learn and adapt. Repeated experimentation builds practical knowledge faster than waiting for a perfect business case.

    Productivity gains create little value without a plan for saved time, says Work Futurist Dom Price

  5. Sep 14

    What enterprise AI looks like when leadership and governance are built in

    DBS built AI into the bank through leadership ownership, digital and data foundations, and governance built into the workflow. In this ADAPT Insider conversation, Sanjoy Sen, MD – Group Head of Consumer Bank at DBS Bank (Singapore), shares how that shift helped move AI from isolated use cases into broader operating change across the bank. Key takeaways: Leadership ownership and organisation-wide experimentation help AI move beyond isolated use cases.Digital, data, and workflow foundations make AI more usable across the enterprise.Human accountability and embedded governance help AI earn trust as it spreads.  Leadership ownership helps AI spread across the organisation Sanjoy’s view is that enterprise AI starts with leadership mandate and organisation-wide adoption. A central team can support the work, but adoption grows when leaders across the business understand AI as part of their role and teams are encouraged to experiment with it directly. At DBS, that meant creating a culture where employees could test ideas using data, see what worked, and scale successful ideas quickly. That approach gave AI a place inside the operating rhythm of the bank rather than leaving it in a separate innovation stream. Strong foundations make enterprise AI usable Sanjoy is equally clear on the role of foundations.AI needs digital readiness, usable workflows, and a real-time data environment to support it. DBS had already invested in cloud-based architecture, straight-through processing, and a stronger data platform before pushing AI more broadly across the organisation. Those foundations made it easier to apply AI across underwriting, customer journeys, and service operations in a way that could support real decisions and real execution. Without that groundwork, AI stays fragmented and localised.  With it, AI can move across the business with much more consistency. Governance and accountability shape trust at scale DBS also keeps human judgement and governance inside the design. Sanjoy describes three principles that guide this: human in the loop for key processes, human-centred design in the workflow, and human accountability for the final decision. That sits alongside the bank’s PURE framework, where AI use has to be purposeful, unsurprising, respectful, and explainable. Together, those principles help keep trust close to the work itself. AI can support the analysis, improve speed, and surface better insights, while people remain accountable for judgement, customer impact, and regulatory confidence.

    What enterprise AI looks like when leadership and governance are built in

  6. Sep 7

    Why AI strategies fail when leaders delegate them, according to Pathfindr CEO

    AI strategies often stall because the people expected to lead them are too far from the technology itself. When AI gets handed off to a project team or parked inside the technology function, business leaders stay disconnected from where it actually creates value. In this ADAPT Insider conversation, Dawid Naude, CEO at Pathfindr, talks about how AI behaves less like a fixed enterprise system and more like a capability leaders have to use, test, and learn from directly. Key takeaways: Leaders need direct experience with AI if they expect it to create value across the business.The most useful AI applications often emerge through hands-on experimentation rather than narrow upfront use cases.Cost, data, and oversight still matter, but they need to be managed in ways that support adoption rather than delay it.  Leaders have to make AI their own Dawid’s point is that AI cannot be treated like another transformation program that gets delegated and reviewed at a distance. Even a well-written strategy can fail if the leaders expected to act on it do not feel that it belongs to them. That is what changed in Pathfindr’s own work. Earlier strategy engagements often produced strong recommendations but weak follow-through. The shift came when AI stopped being the strategy itself and became an input into each leader’s own strategy. Once leaders started using the tools directly, they could see where AI helped them think, test ideas, and solve problems in ways that felt practical rather than abstract. AI value shows up through use Dawid also pushes back on the way many organisations still frame AI through narrow use cases. His view is that AI behaves more like a general capability, where the highest value often only becomes clear once people start using it seriously. That is why he compares it to tools like Excel or the internet. The value did not come from defining every use in advance but from putting the capability in people’s hands and seeing where it unlocked better ways of working. In AI, that means leaders using it to prototype ideas, pressure test assumptions, and work through more complex problems than a standard business case would surface. Cost, data, and oversight need better judgementHe is equally clear that none of this removes the need for judgement.  Token costs can rise quickly, so organisations have to link usage back to value. Data still matters, but it should improve alongside adoption rather than hold it up. And human oversight does not disappear, it changes shape as people and AI work back and forth across a task rather than through one static checkpoint. The practical challenge is to sequence all of this properly. Organisations need enough freedom to learn where AI is useful, enough discipline to stop waste, and enough human judgement to know where oversight still matters most.

    Why AI strategies fail when leaders delegate them, according to Pathfindr CEO

  7. Aug 31

    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

  8. Aug 24

    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
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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.

Information

  • Creator
    ADAPT
  • Years Active
    2026
  • Episodes
    27
  • Rating
    Clean
  • Copyright
    © ADAPT
  • Show Website
    ADAPT Insider

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