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. 3d ago

    Enterprise AI requires an operating model built for uncertainty

    Enterprise AI pilots often succeed under controlled conditions, then break when messy data, edge cases and dependent systems enter the journey. In this first episode of ADAPT Insider’s AI Economics Series, Escaping the Pilot Trap and the Demo God Curse, Vijayan Seenisamy, Author of The Pilot Trap | Enterprise Agentic AI Delivery, joins ADAPT’s Byron Connolly to explore why organisations manage probabilistic AI through operating models designed for deterministic software. The gap surfaces when a successful demonstration reaches production. AI systems change with data, context and user behaviour, requiring continuous monitoring, clear ownership and business cases that account for reliability across the entire journey. Key takeaways: Treat AI readiness as a continuous management responsibility rather than a gate the organisation passes once.Test the messy end-to-end journey instead of relying on an individual agent or carefully curated demonstration.Fund experiments as experiments and assign one owner to the reliability of the entire chain. AI requires an operating model built for change Many enterprise AI experiments run through operating models designed for traditional software. Code is tested, defects are fixed and the system is expected to behave consistently in production. AI behaves differently. Its performance shifts as data, context and user behaviour change. Vijayan describes the distinction as construction versus weather. A building can be completed and signed off. An AI system requires ongoing observation, correction and governance. Without that operating discipline, a pilot may perform well in controlled conditions while remaining unprepared for production. A perfect demo proves potential, not resilience A polished demonstration can create false confidence when its conditions are mistaken for evidence of production readiness. Demo data is clean, prompts follow a happy path and the environment is arranged to help the agent succeed. Production introduces messy data, unscripted questions, edge cases and failures in surrounding systems. The environment has become honest. Before approving the business case, executives should ask what the test actually covered. A curated demonstration shows potential. Production readiness requires evidence that the system can perform under real operating conditions. Reliability belongs to the entire chain Multi-agent journeys expose how quickly reliability falls across connected steps. Five agents with 92% reliability each produce an end-to-end success rate of about 66% when every agent must perform correctly in sequence. Each component can pass its own test while the customer journey still fails around one time in three. Measuring agents individually conceals the reliability of the outcome experienced by the customer. The business case should price that end-to-end result and assign one person accountability for the entire journey. Five owners focused on five agents leave no one responsible for whether the chain works.

    Enterprise AI requires an operating model built for uncertainty

  2. 5d ago

    Safe experimentation belongs at the centre of technology leadership

    Control through prohibition cannot keep pace when employees can access AI tools and build solutions themselves. In this ADAPT Insider episode, Tracey Evans, Group CIO at Virtus Health, examines how technology teams can create safe environments for experimentation while maintaining clear security, regulatory and compliance guardrails. She also discusses how business ownership strengthens delivery and how new interfaces can improve the experience of legacy systems.   Key takeaways: Safe environments give employees room to test AI while maintaining clear controls around security, data and compliance.Business sponsors should remain involved from problem definition through to production, supported by technology teams.Securing legacy systems and building modern interfaces over them can improve access to information without wholesale replacement.Guardrails give experimentation a safe path The democratisation of AI allows employees to test tools and create solutions without waiting for a technology team to direct every decision. Tracey’s approach at Virtus Health gives clinicians and business teams controlled environments for learning and experimentation. Security, regulatory and compliance guardrails establish clear boundaries around what teams can test. Technology teams work alongside users to understand the problem before introducing a solution. This positions technology leaders as educators and guardians who help employees apply new tools responsibly. Safe pathways allow useful ideas to develop while reducing the risk that unmanaged experimentation reaches sensitive data or production systems. Business ownership strengthens delivery Technology delivers stronger outcomes when the people closest to the problem help shape the response. At Virtus Health, each business problem requires a business sponsor and a team that works alongside technology throughout delivery. This shifts technology away from a back-office order-taking role and connects it directly to frontline priorities. A digital consent program across 46 clinics demonstrates the model. Clinical leaders helped define, standardise and refine the process, while the technology team supplied the capability and guardrails required to digitise it. Virtus Health digitalised 26 consent processes within 12 months. Clinical stakeholders also became champions for the change because they had shaped the program from problem identification through to production. Legacy systems can support a modern experience Legacy ERP, electronic medical record and laboratory systems remain central to operations at Virtus Health. Replacing every underlying platform would introduce significant cost and operational risk. Tracey focuses on securing existing databases and building new interfaces over them. AI-enabled capabilities can then give clinicians and operational teams more useful ways to access the information those systems contain. This approach modernises the user experience while preserving stable systems of record. It also gives technology leaders a growing role in educating users about security, data governance, APIs and the limitations of AI-generated solutions. Clear technical guidance helps teams turn early experimentation into solutions that are safe, scalable and ready for production.

    Safe experimentation belongs at the centre of technology leadership

  3. Oct 20

    Production readiness determines whether AI pilots scale, says Harris Farm Markets’ Head of Infra

    AI pilots can create false confidence. Controlled data, prompts and supervision often make early results look stronger than the systems that eventually reach production. Leonard Rawat, Head of Infrastructure, Security and Operations at Harris Farm Markets, explains why infrastructure choices must start with the use case. He examines what changes as token demand grows, why guardrails belong in the architecture and how internal capability turns experimentation into practical value. Key takeaways: The use case must determine the infrastructure. Organisations should define the business problem before selecting a platform, model or deployment approach.Production readiness starts during the pilot. Security guardrails, cost controls and token optimisation must be designed before workloads scale.Business context has to be built internally. Training and sandboxed experimentation help employees turn operational knowledge into useful AI solutions.Production exposes the weaknesses pilots concealMany AI projects perform well in pilots because the data is clean, prompts are controlled and users receive close supervision. Those conditions disappear when systems enter day-to-day operations. Leonard says organisations should plan for scale from the outset. Token consumption can rise quickly as experimentation develops into agentic workflows or automation, increasing pressure on the platform and its operating cost. The AI platform must align with data location, expected demand and the original business case. Infrastructure choices determine whether a promising pilot can deliver reliable value in production. Guardrails have to be part of the architectureAI infrastructure extends beyond keeping systems available. It must account for cloud spend, token usage, security exposure and the wider ecosystem surrounding each model. Leonard argues that clear controls are needed at the infrastructure layer before deployment expands. Sandboxed environments give teams room to test ideas without exposing production systems, while token optimisation helps contain costs as usage increases. Embedding these controls into the architecture supports confident scaling. Without them, risk and expenditure can grow faster than the value created by the technology. Internal capability turns business context into valueAt Harris Farm Markets, employees across marketing, finance, procurement and warehouse functions can use sandboxed environments, training and accessible development tools to build prototypes safely. Leonard points to practical applications ranging from Shopify workflows to security vendor mapping. People closest to an operational problem can test a solution in a controlled environment, shortening the distance between an idea and a useful outcome. Organisations cannot buy their own business context. They need people who understand the work, supported by the tools and governance required to apply that knowledge.

    Production readiness determines whether AI pilots scale, says Harris Farm Markets’ Head of Infra

  4. Oct 13

    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

  5. 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

  6. 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

  7. 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

  8. 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
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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
    30
  • Rating
    Clean
  • Copyright
    © ADAPT
  • Show Website
    ADAPT Insider

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