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.

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.

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 judgement

He 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.

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.

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.

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.

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.

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 friction

He 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 programs

He 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.

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 it

Andrew 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 well

Andrew 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 speed

Andrew 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.

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 care

Peter’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 healthcare

Healthcare 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 properly

The 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.

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.

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.

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.

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.

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.

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.

According to Dr Jon Whittle, ADAPT Advisor & former Managing Director of Data61 at CSIRO, AI must be a purpose led transformation.

He argues that sustainable impact comes from aligning AI to organisational intent, leadership capability, and human outcomes rather than narrowly defined productivity gains.

Key takeaways:

• Efficiency is not value. AI delivers lasting impact only when aligned to purpose, not just productivity metrics.
• Leadership sets the ceiling. CEOs and boards must define the why of AI or risk fragmented, low impact adoption.
• Culture scales AI faster than technology. Change management, governance, and human outcomes determine success.

Productivity is a weak primary driver for AI

Task efficiency is easy to see.

Organisational value is harder to earn. AI can save time at the individual level while failing to lift performance across the business, because that time is often absorbed by more meetings, more tasks, or higher expectations.

That is why productivity on its own is a weak organising principle for AI strategy.

Jon makes this point directly, arguing that the evidence so far shows personal productivity gains do not necessarily aggregate to the organisational level, and can even contribute to burnout when leaders respond by pushing more work into the space AI creates.

Purpose-led AI unlocks different use cases and outcomes

The quality of AI use cases depends on the quality of the question leaders ask at the start.

Organisations that begin with speed and savings tend to optimise existing activity.

Organisations that begin with purpose tend to identify barriers that are stopping them from delivering what they exist to do.

Jon argues that this shift has to come from the CEO and board. His dental practice example shows the difference clearly.

An efficiency lens focuses on getting more patients through the chair. A purpose lens focuses on reducing fear, increasing early intervention, and improving health outcomes.

As he puts it, the use cases become “quite different” once purpose comes first.

AI adoption is a leadership and culture challenge

Most organisations do not stall on AI because the models are weak.

They stall because the organisation is not aligned strongly enough to absorb the change.

Governance, stakeholder management, executive backing, and change management determine whether AI moves beyond experimentation or gets trapped in committees and theatre.

Jon is explicit that adopting AI is “fundamentally not a technical problem” and instead goes back to culture and change management.

He also warns that committees without a clear sense of purpose and top down buy in will just keep spinning the wheels, and that leaders in charge of AI need fluency across technology, governance and ethics, and business rather than depth in only one of those areas.

Simon Kriss uses that breakdown to explain why AI scale in Australia is being held back less by tooling than by trust, governance maturity, and cost discipline.

 
Key takeaways:

• AI fails when treated as a plug‑in: Governance, risk and operating models must change with the technology.
• Token cost is the new cloud bill shock: AI economics demand active financial controls long before scale.
• Selective sovereignty creates advantage: Cultural alignment reduces cost, increases trust and keeps value local.

Trust collapses when AI is forced into old business models

Most organisations do not stall because the technology is missing.

They stall because finance, risk, and governance are still trying to judge AI through models built for more predictable systems.

Simon says corporate Australia has been “very, very, very slow to adopt” because “we simply don’t” trust AI, even though “we know the tech” and “we get it.”

He then points to two practical blockers that show up when organisations try to move from proof of concept to production: the CFO cannot see where the ROI comes from, and the chief risk officer struggles to apply the same risk logic to very different use cases, like summarising phone calls versus helping synthesise a human drug.

His conclusion is direct: organisations “can’t just shoehorn AI into an existing business model” because the business model, the risk model, and the governance model all need to change with the technology.

AI economics replace cloud economics as the next leadership shock

The cost problem changes shape once AI moves beyond contained pilots and into agent driven work.

Simon separates simple exploitation use cases, where the ROI calculation looks like any other business case, from exploratory and agentic use cases, where the cost profile becomes much harder to predict.

He says the issue intensifies with agents because they do not complete a task once, they iterate, backtrack, and retry, sometimes “20 or 30 times,” which can blow out token consumption rapidly.

In his example, something that started at “$28 per month for five people in proof of concept” can turn into a “half $1 million bill” once lag billing catches up.

He is also clear that the market is still “figuring it out,” so leaders cannot assume AI cost controls are already mature in the same way cloud FinOps became.

Sovereignty shifts from risk control to economic advantage

Sovereignty becomes more valuable when leaders stop treating it as a narrow hosting question and start treating it as a control and value capture question across inference, models, and cultural fit.

Simon says sovereignty is “about protectionism more than isolationism” and argues that organisations still mostly see it as a way of controlling risk, even though it is starting to become “an economic enabler.”

He points to dependence on offshore inference, the US Cloud Act, limited control over how foreign foundation models are built, and the fact that token spend continues to flow offshore.

He also gives a more operational argument for local relevance: a chatbot that better understands Australian language and nuance will use fewer tokens and create better customer loyalty because it reaches understanding faster.

In his view, that makes selective sovereignty a practical commercial decision as much as a governance one.

That is the challenge CommBank faced.

With 50,000 employees and responsible AI established as a long-term strategic priority, the bank offers a useful example of what it takes to move AI beyond isolated capability and into enterprise-wide execution and impact.

Jen French explains how CommBank is approaching that shift through leader led adoption, responsible AI frameworks, practical skill building, and reviewing adoption data to understand where confidence is genuinely taking hold.

Key takeaways:

• AI scales through people first. Widespread capability comes from education, access and confidence, not just specialist teams.
• Responsible AI enables trust and speed. Strong, embedded governance makes safe scaling possible.
• Leadership drives adoption. When leaders walk the talk, AI becomes part of the organisation’s culture.

Scaling AI through people, not just platforms

CommBank's AI journey began nearly a decade ago with machine‑learning‑driven customer engagement, initially built by specialist technical teams.

What quickly became clear, however, was that value only scaled when the broader workforce understood and used AI.

Rather than limiting AI to technical experts, CommBank focused on equipping employees with AI assistants and foundational education: what AI is, how it’s already used across the bank, and how individuals can apply it safely in their day‑to‑day work.

This democratisation of AI capability required a mindset shift, not just new tools.

Guardrails that enable, not restrict

Empowering tens of thousands of employees naturally raises risk, but CommBank addressed this by embedding responsible AI principles directly into how work gets done.

A group‑wide Responsible AI Framework, supported by clear policies, standards, and toolkits, ensures AI use is ethical, secure, and compliant with regulation.

Importantly, governance is not treated as bureaucracy: legal, risk, compliance, HR and technology teams are brought together early to shape AI solutions before they are deployed, whether for customer experiences or internal productivity.

Leadership, momentum and measuring value

Leadership visibility has been critical, with senior executives actively role‑modelling AI use to normalise adoption and build trust.

Usage data helps to understand how AI tools are being used and where confidence is growing, while a strong learning culture allows teams to iterate on what works and adjust what doesn’t.

Rather than relying on one‑off business cases, CommBank sustains momentum through continuous learning, shared ownership, and embedding AI into everyday operating rhythms.

This is a #paidpartnership with Commonwealth Bank.

It is being driven from the ground up by students, academics, and researchers already testing where AI helps and where it starts to distort learning.

Kerry Holling, Interim CIO at the University of Sydney, explains how that pressure is changing governance, teaching, and trust across the institution.

Key takeaways:

• Student behaviour is forcing universities to move faster on AI governance, with guardrails that protect privacy, sovereignty, and research integrity without slowing useful experimentation.
• Assessment design is becoming a bigger challenge than tool access, as universities work out how to test real understanding in an AI enabled learning environment.
• Trust in university AI depends on balance, with enough freedom to support research and learning, and enough control to protect rigour, accountability, and public confidence.

Universities need guardrails that people will actually use

AI governance in universities has to work in the real world. If the controls are too rigid, staff and students will route around them.

If they are too loose, privacy, data sovereignty, and research integrity are exposed.

That is why the University of Sydney has focused on practical guardrails developed jointly across IT and Legal, with self assessment tools that help staff judge use cases without turning governance into a bottleneck.

Kerry’s point is that balance matters more in a university environment because academic work depends on openness and experimentation, while the institution still has to protect sensitive data, intellectual property, and research quality.

The goal is to create enough structure to support safe use without shutting down the value AI can bring to research, teaching, and operations.

The bigger teaching challenge is no longer access to AI, it is assessment

The hard question for universities is no longer whether students will use AI. 

The real issue is whether assessment still measures understanding, judgement, and learning in an environment where AI can generate convincing outputs quickly.

That is where Kerry sees the pressure building.

He argues that AI can improve learning when it helps students deepen their understanding, but weak assessment design will invite shortcuts instead.

The stronger institutional response is to rethink how knowledge is tested so students still have to demonstrate real comprehension.

He also points to examples where AI is improving access to teaching rather than undermining it.

One University of Sydney academic built Cogniti to replicate parts of one to one support at scale, and Kerry says it is now used by more than 5,000 academics to help develop curriculum material and provide more personalised tuition to students.

For him, that is what useful AI in education looks like, expanding learning support in places where human access is naturally limited.

Trust grows when AI is used to augment people, not displace judgement

Universities will get more value from AI when they treat it as a tool for augmentation, not as a substitute for human thinking, academic rigour, or institutional accountability.

Kerry is optimistic about AI’s potential, but he is careful about how far that optimism should go.

He supports AI for personal productivity and sees genuine value in tools that accelerate research, improve learning outcomes, and reduce friction in university work.

At the same time, he is wary of over-dependence, concerned about the concentration of power in large technology companies, and clear that institutions should be selective about how they deploy AI.

He describes it as something that should be used with respect, not submission, and that framing matters.

The trust challenge in higher education is not only about policy.

It is also about making sure AI strengthens human capability, protects academic judgement, and earns confidence as adoption becomes more embedded.

In this ADAPT Insider podcast episode, Daniela Polit, Public Sector Transformation Executive, outlines a clear test for government AI. It should help solve complex public problems, reduce friction for citizens, and improve services at scale, while operating within guardrails that protect sovereignty, accountability, and trust.

Key takeaways:

• AI should only be used when it clearly improves a public outcome, whether that means faster service, less friction, better inclusivity, or more efficient processing.
• Trust depends on keeping sovereignty, transparency, and human accountability intact, with AI used inside closed environments and final decisions always staying with people.
• Safer AI adoption comes from matching governance, training, and oversight to the level of public risk, rather than applying the same approach everywhere.

Public value has to come before the technology

Strong AI strategies begin with the problem being solved.

In government, that means asking whether a tool can genuinely improve a service, shorten wait times, reduce bureaucracy, or make support easier to access for citizens.

That is the lens Daniela applies throughout the conversation.

She describes AI as a way to solve complex public sector problems, especially where service delivery involves scale, complexity, and large volumes of information.

The value, in her view, comes from helping people deal with government faster and with less friction, whether that means reducing unnecessary touchpoints, improving transparency, or tailoring services more effectively across very different citizen needs.

If AI can clearly improve the outcome, it has a case. If the likely value is marginal and the risk is higher, it should not be forced in.

Trust holds when sovereignty and accountability are protected

Public sector AI needs trust built into where models run, how data is handled, and who remains responsible for the outcome.

Daniela makes that standard explicit.

She says government models are hosted in closed internal environments, with the same rules and authorisations that already apply to public data carried through into AI use.

She is equally clear that accountability stays with a person.

A tool can support a decision, accelerate a process, or structure information more effectively, but it cannot replace the accountable decision maker.

That combination, sovereignty, transparency, and human oversight, is what allows AI to be used in sensitive environments while preserving public confidence.

Safer scaling depends on risk based governance and training

AI becomes easier to scale when governance gives teams a clear way to assess value, manage risk, and move suitable use cases forward with confidence.

That is how Daniela describes the public sector approach.

She points to frameworks and assurance checks that run from ideation through implementation, testing, and evaluation, with policies evolving as use cases become more complex.

She also makes clear that training should reflect the level of public impact.

More structured education and tighter oversight are used for public facing or higher risk applications, while lower risk internal tools can be handled more flexibly.

Even in a large and federated system where collaboration across agencies is still often informal, that discipline creates a stronger filter around value, accountability, and safe deployment.

Governance, trust, and accountability have to mature alongside the technology.

CareSuper CTO Simon Reiter talks about how the fund is balancing experimentation with regulatory obligations, internal adoption, and the controls needed to move AI safely into day to day operations.

Key takeaways:

• Governance has to come before scale, with clear policies, standards, and risk controls in place before AI moves into production.
• AI adoption depends as much on trust and change management as it does on the technology itself.
• Production ready AI needs strong foundations, especially clean data, connected systems, and clear identity controls.

Governance has to come before scale

Regulated organisations cannot treat AI as a tool first and a governance problem later.

The controls need to be built early so promising use cases can move into production safely.

Simon says CareSuper is running two streams in parallel, one focused on governance, ethics, standards, and regulatory alignment, and another focused on practical use cases from across the business.

That creates a clearer path from pilot to production, while filtering out ideas that are really process issues rather than AI opportunities.

AI adoption moves faster when people trust the change

Adoption does not come from rollout alone.

People need to understand where AI fits, what it improves, and why it is being introduced.

Simon says CareSuper invested heavily in change management before scaling usage across the organisation.

Executives, general managers, and technology teams went through training supported by internal sessions and practical examples, helping staff build confidence and see AI as a way to remove low value work while keeping humans in the loop.

Data, integration, and identity will decide what reaches production

The hard part is not running a pilot.

It is building the foundations that make AI safe and reliable once it starts acting across systems and workflows.

He points to three essentials: data quality, integration capability, and identity management.

Poor data can still produce convincing outputs, weak integration limits operational value, and unclear identity makes it harder to trace, audit, and govern agent activity.

That is why CareSuper has invested in stronger identity controls and a central asset register to link agents to accountable owners and ongoing review.

ADAPT Executive Advisors Claudine Ogilvie, CEO at HivePix and former CIO at Jetstar, Mark Cameron, CEO and Director at Alyve, and Brett Raven, Fractional CTO and CIO at The Consulting CIO, examine how leaders are reworking governance, decision making, and accountability as AI becomes embedded across the business.

Key takeaways:

• Boards are moving from broad AI guardrails to clearer risk appetite. Leaders are being forced to define where experimentation is encouraged, where tighter controls apply, and how much downside the organisation is willing to tolerate.
• The strongest AI programs start with a business problem. Fear of missing out is still driving poor sequencing, with many organisations choosing tools first and searching for value later.
• Agentic AI is turning governance into an operating model issue. As AI takes action across workflows, leaders need clearer ownership, stronger controls, and explicit accountability for outcomes.

Boards need clearer risk appetite as AI moves deeper into the business

AI is becoming a board level issue because it is starting to shape business value, operating models, and enterprise risk.

Broad guardrails were a useful starting point, but they are no longer enough.

Claudine argues that boards are now moving towards clearer policy and more explicit risk appetite. That matters because leaders need to define where experimentation is encouraged, where tighter control is needed, and how much downside the organisation is prepared to tolerate.

As AI becomes more embedded in the business, governance has to become more specific and more actionable.

The fastest way to waste AI investment is to start with the tool

Many organisations are still approaching AI backwards, choosing the platform first and searching for the use case later.

Brett warns that this approach is usually driven by fear of missing out rather than clear strategy.

His argument is simple, start with the business problem, define the value to be created, then assess whether AI is the right fit.

That discipline is what separates scattered experimentation from meaningful adoption.

Agentic AI is turning governance into an operating model issue

Once AI starts acting across workflows, governance becomes much more than a policy discussion. It becomes a question of ownership, access, supervision, and performance.

Mark says leaders need to focus less on chasing every technical update and more on how AI is changing the structure of work itself.

In practice, that means defining the role an agent is performing, the boundaries around its actions, and who remains accountable for its outcomes.

As agentic AI spreads, organisations will need stronger controls and much clearer operating discipline.

The organisations that move fastest will be the ones with the strongest discipline

As AI moves from experimentation into execution, the organisations that pull ahead will be the ones that treat governance, ownership, and operating discipline as core parts of adoption, rather than problems to solve later.

Andrew Dome, Chief Digital Information Officer at Uniting, explains how the organisation is using AI to reduce documentation friction, keep people in the loop, and build towards safer care outcomes.

Key Takeaways:

• The strongest frontline AI use cases remove admin where care happens, giving staff more time back in the day without taking people out of the process.
• Trust grows faster when AI is introduced with clear guardrails, visible consent, and human oversight built into the workflow.
• A practical AI use case becomes far more valuable when it proves repeatable enough to scale internally and relevant enough for others to want it too. 

Frontline AI works when it removes admin at the point of care

The most valuable AI use cases in aged care sit inside frontline workflows, where they can remove admin at the point of service and return time directly to care.

Andrew says their Azure/ChatGPT 5.0-powered AI assistant “Buddy” was designed to reduce documentation friction for frontline teams, especially in home and community care.

Staff can use it to capture notes through voice transcription straight after visits instead of losing time later to manual write ups.

For Uniting, that turns AI into a practical care workflow tool rather than another layer of system complexity.

He also points to a stronger sign that Buddy has moved beyond experimentation.

Other aged care providers have shown interest in adopting it as a software as a service platform, suggesting the tool is solving a repeatable frontline problem rather than a one off internal need.

Trust depends on visible guardrails and human oversight

AI in care settings cannot sit in the background as an invisible layer.

People need to understand what it is doing, where it is being used, and what remains under human control.

In residential care, that means staff explaining when AI transcription is being used, checking that clients are comfortable, and showing the output so it can be reviewed.

Andrew argues that trust grows faster when consent, visibility, and quality assurance are built into the workflow itself.

If AI transcription is being used in front of a client, staff explain what is happening, ask if the client is comfortable, and show the output so it can be checked.

That makes the lesson broader than responsible AI as a slogan.

In care environments, trust grows when consent, visibility, and quality assurance are built into the workflow itself.

The next gains will come from safer and more responsive care

With AI already reducing admin safely, the next opportunity is to extend its value into earlier action and more consistent care.

Andrew links that next step to Uniting’s work on AI supported service interactions and fall prevention, where the aim is to help staff respond sooner, reduce risk, and strengthen continuity of care.