Globalisation is a relatively recent phenomenon. And in the early 2000s, something subtle but consequential began to unfold inside Western organisations. Standing in line for coffee in the Sydney CBD, shoulder to shoulder with the bankers of George Street and Martin Place, it was easy to assume that everything was working as designed. The offices were full. The deals were flowing. The machinery of capitalism looked solid.

But what began as a quiet experiment in cost reduction soon exposed a far deeper truth. When organisations attempted to move work beyond their walls, they discovered they didn’t actually understand how that work got done. Processes lived in people’s heads. Decisions relied on proximity. Exceptions were handled by habit rather than design. Basically, the first wave of globalisation forced organisations to confront the fact that they didn’t truly know themselves.

Business Process Outsourcing and Offshoring was supposed to be about labour arbitrage. Lower wages. Time zone leverage and scale. Large banks were among the earliest adopters, not out of experimentation but necessity. When margins are defended at the second decimal place and operational efficiency compounds at scale, even small reductions in unit cost become strategically meaningful.

What it became instead was a forced confrontation with organisational reality. Processes that lived in people’s heads had to be written down. Exceptions had to be named. Decisions had to be justified. Handoffs had to be explained to someone who wasn’t sitting three desks away. And for a while, progress was slow. Painfully slow.

Executives underestimated the sheer volume of documentation required. BPM diagrams multiplied. SOPs sprawled. Edge cases turned out to be the rule rather than the exception. And there was a deeper discomfort too. Not just with process discipline, but with trust. Trusting distant teams. Trusting unfamiliar cultures. Trusting companies most leaders had never even heard of.

Even as late as 2005-06, there was still a surprising lack of clarity about what was unfolding. At IBM’s Australia–New Zealand Sales Kick-Off on the Gold Coast, I was asked to stand in front of more than 300 sales and account executives and explain who the “Indian outsourcers” were and why they mattered. But then, almost without warning, the floodgates opened.

Once processes were explicit, portable, and measurable, they stopped being local. Work that had once been anchored to physical offices began to flow. Entire delivery models shifted. Cities like Bangalore, Pune, and Hyderabad didn’t rise because of cheap labour alone. They rose because execution itself had become transportable.

The result was profound. A generation of global technology firms emerged. Economies rebalanced. New talent ecosystems formed. The centre of gravity shifted and it never fully shifted back.

I saw that transition up close. In 2004, I travelled to Manila to close the local META Group office near Makati City. At the time, Bonifacio Global City literally did not exist. Other than in developer brochures. The land between Makati and the old military reservation was sparse, quiet, and largely undeveloped. It was a place passed by, not a place you went to.

Less than a decade later, that same ground was unrecognisable. Where there had been open land, there were now dense clusters of glass and steel high-rise offices, residential towers, and global delivery centres operating around the clock. Bonifacio had become a city in its own right and one of Asia’s most important hubs for global outsourced execution. That is the scale of change BPO set in motion.

I’ve been thinking a lot about that period lately and am increasingly convinced that while the delivery platform has changed, we are otherwise watching the same tectonic shift occur all over again.

Last time, the logic of optimisation drove execution to the only places where cheap labour at scale still existed, and entire cities rose to serve that demand. This time, the same logic applies, but the city won’t be physical.

Agentic AI is currently in its “this looks promising but feels messy” phase. A lot of early pilots have stalled. Proofs of concept have struggled to scale. Leaders have complained that the tools are impressive but unreliable. Teams have quietly discovered that their processes aren’t nearly as clean as they thought they were. Decisions that felt obvious have turned out to be contextual. Exceptions have exploded.

I think the disconnect is less about technology and more about readiness. Many vendors are aggressively marketing Level 5–6 agentic capabilities to organisations that are still struggling to define their Level 1–2 foundations. The result is that sales and delivery organisations end up aligned to the pace of marketing rather than the maturity of the customer. And while the two can coexist, operations cannot move at the speed of marketing. That was never the intent.

But this hasn’t been a failure of AI. It’s exactly the same organisational reckoning we saw during the first BPO and offshoring wave. In the same way that you can’t outsource chaos, you can’t delegate incoherence to an agent.

Before anything meaningful can happen, work has to be made legible. Inputs defined. Outcomes bounded. Authority clarified. Escalation paths designed. Controls articulated. Metrics agreed. The real work, once again, isn’t automation. It’s self-knowledge. That’s something we’re all impatient for.

That’s why progress feels slower than the headlines suggest. The technology has been ahead of the organisations adopting it. Exactly as it was twenty years ago. But history suggests this phase doesn’t last forever.

That was the hidden force behind offshoring. Documentation didn’t just enable delivery. It shifted the centre of execution. Once work could move, it did. Talent followed. Capital followed. Capability clustered.

Agentic AI creates the same condition, but with a far more radical implication. Execution itself is becoming abstract. That is not hyperbolic. Let’s ponder that for a minute. Agents literally don’t sit in buildings. They don’t sleep. They don’t resign. They don’t require visas or commute times. They consume tokens, operate within guardrails, and escalate when designed to do so. They can also be insourced, outsourced, federated, shared, or rented. Geography becomes optional. Time becomes elastic.

But this is also where the analogy with BPO stops being clever and starts being uncomfortable. In the 2000s, organisations had to decide whether to build captive centres offshore or outsource to third parties. Some did both.

Today, the choice reappears in a new form. Do you run agents internally, tightly coupled to your data, architecture, and governance? Or do you consume agentic capacity as a service, trusting external platforms to execute parts of your organisation on your behalf? If the decision feels tactical, that is a mistake. It isn’t. Because just as before, the early choices will harden into operating models that last decades.

Contemporary BPO cities and offshore delivery hubs came into existence because of density. Skills, services, capital, and coordination concentrated until something self-reinforcing emerged. Once enough capability clustered, those cities stopped being back offices and began to function as innovation engines.

Over time, some went further still. As talent deepened and decision-making followed execution, these centres began to assume strategic importance, shaping roadmaps, influencing investment, and increasingly determining where growth would come from. What started as delivery hubs evolved into centres of gravity in their own right. We’re seeing this occur today in the way large global technology vendors are balancing India as a strategic execution and innovation centre, with Singapore increasingly acting as a regional coordination and administrative hub.

Agentic ecosystems are already showing the same tendencies. Common agent frameworks. Shared orchestration layers. Specialised agent roles. Reusable process patterns. Governance primitives. Observability tooling. Token optimisation. Cost-to-serve models. These are the streets, utilities, and institutions of a new kind of city.

It won’t have a skyline. It won’t appear on a map. But it will concentrate execution, capability, and influence in ways that are no less real for being invisible. We’re already seeing early versions of this in the wild. Platforms like ServiceNow describe it as a control tower. The place where work, agents, workflows, and decisions are orchestrated rather than directly executed. Accenture talks about agent-led delivery factories embedded inside client operating models. IBM, drawing on decades of systems integration and automation, frames it as cognitive operations.

The language differs, but the struggle is the same. That is, to become the de facto regulatory authority for digital workforces setting the rules, enforcing the controls, and deciding how autonomous execution is allowed to become.

Digital agentic cities won’t live entirely in the cloud or inside legacy systems. They will span them coordinating execution across environments that were never designed to be seen as a single place. For that to work, they must be interoperable by design and capable of managing multiple digital labour forces within a single, regulated operating environment. Not one workforce, not one platform, but many (human, automated, and agentic) governed together inside the complexity of a contemporary enterprise.

Organisations that align themselves early will find execution cheaper, faster, and more adaptable. Those that don’t will struggle to compete with firms whose workforce can scale at machine speed. The uncomfortable truth is that these cities are being built regardless, and without a central authority, what emerges isn’t innovation, but sprawl.

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Much of the public debate about AI has to this point fixated on human displacement. That’s understandable, but I also think it misses the deeper pattern.

BPO didn’t eliminate work. It reorganised it. Yes, it did hollow out organisations that couldn’t define themselves clearly but it also elevated those that could orchestrate complexity across distance. Agentic AI will do the same.

It will punish organisations that rely on tacit knowledge, heroics, and informal coordination. It will expose unclear decision rights. It will surface governance gaps that have been tolerated for years. It will make ambiguity very expensive. But it will also reward those that understand their own architecture, both technical and organisational.

This is why AI strategies that start with tools and finish with dashboards feel fundamentally incomplete. The challenge isn’t intelligence or automation. It’s whether an organisation can describe itself clearly enough to be executed by something other than habit. That’s why this is a leadership moment. CEOs don’t need to understand AI, but they absolutely need to understand the organisational consequences of deploying it.

What is undeniable is that the offshoring boom reshaped global technology dominance because it changed where execution lived. Agentic AI will reshape it again by changing what execution is.

The slow burn we’re seeing now, the frustration, the false starts, the documentation fatigue, is not a sign that this moment is overhyped. It’s a sign that we’re still early. Confidence should come though from the knowledge we have lived through it before. And we came out stronger. For those early in their careers, that means experiencing a period of uncertainty where the rules are shifting faster than the narratives explaining them. Because the very shape of work is changing underneath us all.

But once the legibility problem is solved at scale, and once agents become trusted participants in organisational workflows rather than novelties, the shift will feel sudden. It always does. And by the time it’s obvious to everyone, the agentic city will already exist.

And perhaps that is the quiet unease sitting underneath all of this. Not that we are entering a simulation, but that we are finally seeing how simulated our organisations already were. Roles, approvals, escalation paths, and incentives are all abstractions we agreed to treat as real because humans were inside them.

Agentic AI doesn’t invent a new world. It just removes the camouflage that humna-in-the-loop has always provided. And when execution no longer requires presence, belief, or habit, what remains is structure. Some organisations will recognise themselves in that reflection. Others will discover that what they thought was a living system was mostly theatre.

The city of digital agents will keep growing either way. The only real questions left will be who will govern it, whether you helped build it, and whether your work quietly moved there without you.

Above all else, there is a quiet irony here. The captive centres and offshore providers that industrialised execution during the BPO era are likely to be among the first organisations forced to confront an agentic future. I don’t think it is a case that what made them indispensable last time makes them replaceable first this time.

Rather, after spending two decades breaking human work into repeatable systems and delivering it at scale, it is that they now find themselves confronting the same optimisation logic they once helped their clients apply, only this time where that same work can be executed by something else entirely.

For them, the change may be as great, where the implications of agentic execution are not theoretical. They are immediate, unavoidable, and impossible to ignore. Paid subscribers can read on for a closer look at the ten Australian organisations and five technology vendors I’ll be watching in 2026 as they shape the early contours of the agentic decade.

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This week I thought I’d do something a little different.

I don’t usually write product reviews. But every so often, a product moves out of the realm of technology and becomes something closer to a lived experience.

We’ve all heard the stories about brilliant products plagued by persistent problems. Range Rovers are the perfect example. They’re stunning to look at and a genuine design icon. Yet behind all that elegance sits a well-earned reputation for unpredictable electrical faults and the occasional roadside moment where prestige quietly gives way to frustration. My cousin’s electronic key once opened his neighbour’s garage doors, which tells you everything you need to know.

And that, unfortunately, is where the Surface Laptop 6 for Business1 now sits for me.

It is, without question, a stunning device. The industrial design is exceptional. It’s thin, modern, understated, and precisely the kind of aesthetic that signals “premium” to match it’s AUD$4,000+ price tag. I should know. I am about to receive my third one since May. Not because I wanted a collection. Because the first two failed.

Across the last several months, I’ve experienced a pattern of issues that would be excusable if they were isolated. But they weren’t. And when the same behaviour appears across multiple units, it stops being an anomaly and starts revealing something more fundamental than cosmetic flaws or unlucky hardware.

Although I don’t typically write about end-user devices (most of my work sits in strategy, roadmaps, and broader technology portfolio decisions), I’m often asked for advice on laptop selections and desktop fleet approaches as part of those consulting and advisory engagements. When a device begins to materially affect my own work and patience to this degree, it becomes something worth sharing, if only to inform the organisations I support when they ask the inevitable question: “What laptop should we standardise on?”

The Surface Laptop 6 is a masterclass in how design can oversell capability. Or even mask engineering compromises. From the outside, it presents as a modern device. The kind confident executives love and IT teams quietly tolerate. But the aesthetic promise collapsed for me within a few weeks. The longer I used it, the more I began to see the competing behaviours operating just beneath the surface, working against the very experience the device was supposed to deliver. I had early buyers remorse.

A laptop designed for business must do two things exceptionally well. It must start consistently, and it must stay stable. That’s the baseline. The two units I’ve had so far (the original purchase and the first warranty replacement) have done neither. I now await the third with dread.

What makes this more disappointing is that I have always welcomed the promise of Microsoft hardware in the enterprise. Years ago, when I was Head of Technology at one of Australia’s capital city councils, I even pursued a Surface Pro rollout. The vision for a modern, flexible device ecosystem tightly integrated with the Microsoft platform was compelling. But the reality, from an ITSM and operating environment perspective, was simply too unstable at the time, and we ultimately standardised on HP EliteBooks instead.

So when I returned to the Surface line with the Surface Laptop 6 for Business, I did so with genuine optimism that the longstanding challenges had been resolved. I had also seen a few positive reviews from analyst colleagues that had attended events in Singapore for the launch of the new line, and thought it time to try again. Instead, I found myself facing the same fundamental reliability issues only now in a device positioned, priced, and marketed specifically for business use.

So what’s the problem? Well I am not a hardware engineer. But the event logs (which I’ve kept for verfication) tell a remarkably consistent story. It’s clear that, in this device, form won the argument over function at Microsoft HQ. And the consequences for my lived experience have been equally clear. The beautiful design introduces a significant thermal constraint that affects the entire behaviour of the system. In other words, this isn’t an isolated defect. It appears to be a platform issue.

To make a laptop this thin, light, and quiet (in other words, beautiful) something has to give. And in this model, what gives is cooling capacity. The thermal solution is physically elegant, but thermodynamically insufficient. In practical terms, when you place a modern Intel processor inside a chassis designed to prioritise silence and aesthetics over airflow, the physics eventually catch up. And catch up they do. Because this device runs hot.

It hits its thermal limits early and often. And once it does, the rest of the system begins to behave like it’s trying to move through soft sand. The CPU frequency collapses. Windows services time out. Start-up slows. Authentication fails. Drivers initialise late. Applications stutter. The entire system becomes unstable under basic workloads.

At one point I found myself browsing for gaming-laptop cooling mats and ventilation stands, which is precisely the kind of improvised workaround you’d expect from someone trying to cool a budget gaming tower, not a premium business laptop.

It is pretty obvious that none of this is ideal in a cloud-based working environment. Form me that meant critical shut downs during Zoom calls, Teams meetings, and pdocast sessions. But not just that . Collaborative documents and browser-heavy workloads sometimes trigger some instability. When that happens, the 6 can lock up or trigger a critical shutdown because of cascading thermal events. Your productivity doesn’t slow. It simply stops. As does the user’s trust.

This isn’t conjecture. It’s observable, measurable, repeatable and leads to one of the more ironic realities of the Surface Laptop 6 experience. The specs are are technically impressive but practically irrelevant.

What is the purpose of a 3.x GHz processor if it spends most of its operational life throttled down to around 1.8? On paper, the device delivers modern performance. In practice, the thermal envelope is so tight that the processor rarely operates in its advertised range. It’s the hardware equivalent of owning a high-performance car that looks incredible in the brochure but spends most of its driving life stuck in second gear. Not because of the engine, but because the cooling system can’t keep up. Kind of like a 1980’s Volvo. At some point, you stop blaming the engine and start questioning the design of the car.

The knock-on challenges from the hardware are consequential. Especially in the interplay between temperature, power management, and Windows boot sequencing.

When the 6 is already running hot during startup, the firmware steps in to protect the hardware. It throttles aggressively, delays power delivery, and forces the CPU to operate well below its intended baseline simply to keep temperatures under control. In practical terms, it takes a machine that is marketed as a thoroughbred and turns it into a donkey before it’s even out of the gate.

Windows, meanwhile, is trying to do what Windows must do. That’s a good thing. It has to authenticate the user (me), start essential services, attach to the network, initialise drivers, and bring the system to a point where you can actually begin working.

But with the heat problem, the result is a cascading failure pattern that became easy to recognise with uncomfortable familiarity. Before long, I was far too well-versed in hard-reset protocols and scrambling to remember where my BitLocker recovery key lived just to unlock the device and start again. This is not the workflow anyone imagines when buying a premium business laptop, yet it has become an oddly regular part of my 2025.

And, of course, the outcome is predictable. System services fail not because they are inherently unreliable, but because the OS is trying to sprint while the firmware has already decided it’s protecting a donkey. But you can’t simply flog a donkey to be fast out of the gate in the Melbourne Cup, and no amount of beautiful industrial design changes the physics underneath.

This forces the errors that show up in the system event logs. Not because Windows is misconfigured. Not because the laptop is faulty. But because the device is unable to maintain stable thermal and power conditions while starting.

What makes this more problematic is Microsoft’s positioning. This isn’t marketed as a lifestyle product. It’s not a student laptop. In fact by comaprison, my engineering student daughter’s ASUS laptop is highly performative at less than half the price. But the Surface 6 is explicitly sold in the business cateogry. And that carries certain expectations.

At its most basic that expectation is for reliability, consistency and predictable behaviour under load. Businesses do value appearance, but they value reliability more. And no amount of industrial polish compensates for a machine that can’t deliver dependable daily work.

For me, the real cost of these issues isn’t the technical noise but the practical impact. The disrupted meetings. The repeated troubleshooting. The replacement logistics (OMG!!) that should never be necessary, and a noticeable erosion of trust in the brand. At the end of the day, this device hasn’t supported my work. It has interrupted it. It has become a point of failure in what should be a straightforward, hybrid, remote-friendly working environment.

I blame myself. The Surface Laptop 6 is a beautiful machine. But its beauty cannot compensate for its instability. I’m now waiting for my third unit in under a year. That fact alone should tell the story.

During the first warranty replacement, a series of follow-up exchanges (messages, emails, and a Teams call) were arranged through a connection with the analyst relations team. In those conversations, I was offered some additional support due to the recurrence of issues, including assistance with the return process and a spare device (a smaller, lower-spec model) to use as a safeguard if the problems resurfaced.

The warranty replacement arrived. The spare never did. And when I followed up, the conversation simply stopped. It was an unfortunate experience and one that sits awkwardly alongside the “for Business” positioning of the device.

I really wanted the Surface Laptop 6 to be the perfect blend of form and function, and a dependable part of my daily workflow. Instead, it has become an ongoing case study in how technology can fail quietly, subtly, and consistently when the design brief is not aligned with operational reality.

I’ve deliberately waited months before publishing this to ensure it wasn’t written in frustration, though there has certainly been enough of that. We are where we are. What’s left is and a genuine desire to share my lived experience with others.

And with the warranty valid until 19 May 2027, I’m not entirely confident this will be the end of the story.

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That has meant the focus has always been firstly on maintaining traditional vendor-customer relationships as the foundation of the business model, and then selling big solutions to meet their needs. Partners always operate in close proximity, often in a dominant advisory capacity.

When the Enterprise Becomes the Platform

What happens to the model when major enterprises are no longer just consumers of technology? What happens when they become creators, platform providers, and digital product companies in their own right?

Consider how Commonwealth Bank is leading the shift with its Banking-as-a-Service (BaaS) offering, opening its scalable digital platform to FinTechs and smaller institutions right across its ecosystem.

Conside how Coles is leveraging AI and analytics not just to optimise logistics and customer engagement, but potentially to commercialise those capabilities as platforms.

Consider how Woodside is developing proprietary mining optimisation software that could extend well beyond its own operations.

These are not isolated examples.

The technology landscape is evolving rapidly. We can see it in every facet of our lives. But what is truly remarkable, and less visible to the naked eye, is that enterprise innovation is now keeping pace. For perhaps the first time, the business environment is evolving as fast, if not faster, than the technology itself.

Traditional enterprises are stepping into roles once reserved for software vendors and cloud-native startups. They’re not just buying and using technology. They are building it, architecting it as platforms, and in many cases, commercialising it.

Commonwealth Bank’s BaaS model, Coles’ emerging AI-driven logistics platforms, and Woodside’s proprietary mining optimisation tools all point to the same shift. Large enterprises are becoming technology companies (vendors) in their own right.

I’ve been working on this piece for six months and there is a lot to unpack, so it’s a bit longer than my usual posts. But every part felt worth including. If you’re short on time, here are the five key takeaways upfront. Feel free to read on if any of them catch your interest.

1. Control is shifting. The power to set enterprise architecture will rest with those who own the platforms and run the infrastructure, not with traditional SIs or PaaS vendors.

2. Large enterprises, think banks, retailers, and industrial giants won’t just use technology, they will build and monetise their own platforms, reshaping ecosystems from the inside out.

3. The era of broad, bespoke integration is ending. SIs will either go deeper with fewer platforms, focusing on orchestration, governance, and optimisation, or face accelerating irrelevance. Hypercompetition to specialise will benefit the enterprise customer.

4. Automation and PaaS maturity will quickly reduce the inefficiencies that once justified high-margin labour models. Consulting must shift from arbitrage and headcount to outcomes.

5. Selling alone won’t be enough. To stay relevant, PaaS vendors must treat enterprise clients as strategic partners and embed themselves to co-create in platform-led innovation.

The Illusion of Fintech Disruption

If you are still with me, let’s take a small step back.

Walk into any fintech conference today and you’ll hear the same hype. “Revolutionary advisor platforms!” “Seamless digital experiences!” “Data-driven insights!”

I really do shrink from marketing that seems intent on repeating the mistakes of past tech and finance cycles marked by overblown promises and underwhelming outcomes. As historical tech-bubbles (and the occasional banking royal commission) reminds us, hype without substance catches up eventually.

So let’s do better. Calm down, Fintech. Because beyond the marketing noise, the real transformation isn’t happening in the spotlight, it’s unfolding deeper inside the system, where the structures, incentives, and architectural control are actually shifting.

For financial advisors, the rise of “wrap” solutions, basically slick portals, integrated dashboards, and API-fed ecosystems, feels like a step change. But for anyone in technology, this is not innovation. It’s financial services, particularly the mid-market, finally catching up to what enterprise software has been doing for decades.

Much of this shift has been driven by regulatory changes, rather than a deep rethinking of banking. In Australia, reforms like the Future of Financial Advice (FoFA) legislation banned conflicted remuneration, including trailing commissions, forcing financial advisors to provide transparent, fee-for-service models.

Similarly, the Design and Distribution Obligations (DDO) and Financial Accountability Regime (FAR) have further tightened compliance, requiring advisors and institutions to modernise their data management and client engagement processes.

These regulations have left the industry with no choice but to adopt platforms that standardise workflows, improve compliance tracking, and enhance client reporting. Not because of an organic push for innovation, but because staying manual became untenable, if not illegal.

But these new solutions are not reimagining banking. They are simply repackaging existing processes with a more polished user experience and greater regulatory alignment. That’s okay. Not every transformation needs to be disruptive. Sometimes, being forced to modernise is still progress.

The glossy side of fintech has become a distraction from the real transformation happening in financial services. While the industry fixates on sleek user interfaces and digital wrappers, the true disruptors like embedded finance, decentralised models, and AI-driven decision-making remain on the fringes. These are the hard, structural shifts, yet they receive far less attention than they deserve.

That got me thinking. If the most meaningful change isn’t coming from the fintech darlings or wealth platforms, then where is it happening? The answer is within the largest banks themselves. And the scale of that change isn’t incremental, it’s tectonic to the capital structures that underpin the technology industry.

Banks, Retailers, and Miners Are Redefining the Stack

Commonwealth Bank is no longer just a financial services provider. It has become a technology giant. It is not simply adopting technology. It is building and owning platforms, digital ecosystems, and data-driven services that extend well beyond banking. The largest financial institutions are no longer just buyers of technology. They are now major technology vendors in their own right, developing in-house capabilities at a scale that once belonged exclusively to traditional software providers.

This shift demands a fundamental rethink of how PaaS providers engage with banks. For decades, banks were treated as customers. They were buyers of technology to support operations. But that dynamic is breaking down. As banks become technology creators in their own right, the traditional vendor-customer model no longer fits. It’s a 20th-century relic about as relevant today as a political long lunch at Machiavelli’s on Clarence Street.

Selling $10 million worth of licensing and services to a bank might seem impressive, but in the grand scheme of this new world order, it’s a rounding error. Somewhere inside that same bank, someone is working on a platform, a marketplace, and an industry-wide infrastructure play worth billions. So the real question for PaaS providers isn’t how to sell to banks. It is now how to stay relevant as banks become the platform builders themselves.

The opportunity is no longer about selling technology to banks, it’s about realigning with them as strategic partners. As banks (and retailers, and miners, and telcos, and yes, even SI partners) increasingly develop their own platforms, PaaS providers must get sharper about where true external innovation is still needed, and where banks are simply rebranding internal solutions.

I also don’t think the future is about competing with banks. That winner take all mentality is also a 20th century trope. It is about knowing when to collaborate, and when to respectfully get out of the way.

Some may argue that the evolution of banks, retailers, and miners into technology players mirrors what has already happened between tech companies and telco-service providers. After all, software vendors have long partnered with telecommunications firms to deliver everything from cloud infrastructure to enterprise applications. But this comparison oversimplifies the reality of what’s unfolding.

Telcos were always first cousins to IT. Their core business has always been infrastructure-heavy and tech-adjacent. Their networks, data centres, and connectivity services positioned them naturally as platform enablers for enterprise IT. When Telstra began expanding its business beyond networks into software and services, it wasn’t reinventing itself, it was simply extending its existing role in the digital economy. The same applies to all the other global telco giants.

However, banks, retailers, and resource companies are fundamentally different. Their core businesses were never inherently about technology in the same way telcos have always been.

Commonwealth Bank is not just layering technology onto banking. It is becoming a platform provider in its own right, allowing third parties to build financial services on top of its infrastructure. Coles is not just using AI for internal efficiencies. It is developing commercial-grade analytics platforms that could be monetised beyond its own operations. Woodside is not simply automating mining. It is creating proprietary software solutions that might soon be licensable across the energy sector.

There’s also a stark difference in execution and impact. Telcos, for all their elegant narratives about transformation, from network fibre and towers to business applications, have rarely delivered at scale.

While Telstra has successfully repositioned itself as a digital services provider, it has not fundamentally disrupted or reshaped industries in the way banking, retail, and mining players are now achieving.

The imperative here is greater, sharper, and far more financially significant. There are billions of dollars at stake, not just in driving operational efficiencies but in redefining their entire industry positioning.

Am I being clear enough? This is not an adjacent business expansion. It is a strategic repositioning of their role within the technology ecosystem.

For PaaS providers, this moment represents both a threat and a strategic opening. Their largest enterprise customers are no longer just buying technology. They are becoming platform builders themselves. The real opportunity lies in reframing these enterprises not as customers, but as strategic partners. To stay relevant, PaaS vendors must embed themselves within enterprise-led innovation ecosystems, opening up partner models that were once reserved for ISVs and developers.

But this is more than just a natural expansion of enterprise IT capabilities. It is a structural reordering of the tech landscape. It challenges who gets to define architecture, set integration rules, and lead ecosystems. In this new reality, many PaaS vendors and ISVs have been slow to adjust, still operating as if they alone control the playbook. That era is ending.

What all this means is not that enterprises are taking control of their technology architecture for the first time, but that they are reclaiming it. In the early days of enterprise IT, large organisations invested heavily in defining their own architectures, simply because they had no choice. Technology was fragmented, and no integrator or platform vendor could offer a complete solution. Over time, as global SIs and powerful PaaS ecosystems emerged, much of that architectural power was outsourced. Enterprises became buyers of pre-packaged roadmaps, rather than authors of their own.

Now, as the platform landscape matures and composability becomes real, enterprises are once again positioned to take ownership. But doing so requires more than just tooling. It demands that organisations actively reassert architectural authority to shape their own standards, integration logic, governance models, and platform priorities. They must unlearn the habit of deferring to vendors and rediscover their capacity to lead.

So let’s say Banking-as-a-Service (BaaS) is representative of the broader shift in how enterprises and platform providers collaborate. These initiatives aren’t just about scalable infrastructure. They depend on full-stack PaaS capabilities, from workflow automation to data orchestration and seamless third-party integration. Without a solid platform layer, the complexity of managing these ecosystems would become a barrier to growth, scale, and innovation.

In this model, PaaS providers are not just infrastructure vendors. They are still critical enablers of enterprise-led innovation, supplying the backbone that allows businesses to scale, integrate diverse technologies, and create entirely new ecosystems. By embedding themselves in these transformation journeys, PaaS providers don’t just stay relevant but become indispensable, ensuring that even enterprise-driven platforms continue to rely on their capabilities.

Rather than viewing Commonwealth Bank’s BaaS model as a competitive threat, a PaaS provider could see itself as the silent enabler, powering the bank’s expansion while securing its own recurring revenue and strategic influence. In this scenario, Commonwealth Bank is no longer just a customer. It’s a partner.

This shift demands a rethink.

PaaS providers would have to consider how to integrate mega-enterprises into their partner programs, treating them not as competitors, but as co-creators, ensuring they remain essential allies in the new platform economy. To do that they need to overcome the next roadblock. The existing SI Platinum Partners.

The emergence of partner-style enterprises as technology providers themselves challenges the traditional role of platinum-level partners like Accenture, Deloitte, and Tech Mahindra. These firms have long positioned themselves as integration specialists, managing platform customisation, implementation, and large-scale operations for enterprise clients.

But as more businesses pivot to take greater control of their own technology ecosystems, defining their own architectures and leveraging PaaS models, the influence of these integrators is shifting.

Their role can no longer be just about implementing and integrating. It will have to be about navigating a new power dynamic where enterprises and hyperscalers increasingly dictate the rules. This transformation is driven by a fundamental shift in power.

The End of 20th Century Style SI Dominance

For a generation now, system integrators have played a central role in shaping enterprise technology stacks, selecting platforms, and overseeing integrations. We all know that their influence is significant and extends beyond technical expertise to business considerations, often favouring technologies where they have certified resources, strong vendor partnerships, or commercial incentives. This allows them to steer architectural decisions under the guise of best practice, even when those choices are influenced as much by internal resourcing strategies as by client needs.

With PaaS I think some of that control will slip away. Very large enterprises will no longer just be technology consumers. As platform providers themselves, they will increasingly seek to set their own API standards, security models, and service governance. Meanwhile, hyperscalers and major PaaS vendors, that have solidified their dominance through the broad acceptance of “hybrid”, will increase in influence as to how ecosystems will function.

It’s a subtle shift, easy to overlook, but it marks a major realignment of architectural authority. The power to define enterprise technology architecture is no longer likely to sit with traditional system integrators, many of whom never fully moved into strategic business consulting. Instead, that influence is consolidating around two dominant forces: platform-owning enterprises and hyperscalers. The idea being that these players now have the leverage to define the architecture because they own the platforms or run the infrastructure.

For SIs, this will mean more than just a shift in technical responsibilities. It will require a structural change in how they operate. No longer able to freely choose technology stacks based on internal resource availability, they will have to go deeper with fewer platforms, embedding themselves within select ecosystems rather than maintaining broad, generalised capabilities.

The old model of playing across multiple vendors will need to give way to a more focused approach, where their value comes from helping enterprises optimise and orchestrate within more defined technology environments.

At the same time, the nature of integration itself has changed. The challenge is no longer just about stitching systems together. It is about ensuring enterprises can function as platform businesses. This requires expertise in platform governance, interoperability, and monetisation, helping companies manage developer ecosystems, ensure regulatory compliance, and continuously evolving their services.

Instead of defining the architecture, SIs will have to be better at enabling enterprises to operate their own platforms effectively, balancing the strategic priorities of their clients with the overarching influence of hyperscalers. It opens the door for a new kind of centre of excellence more focused on the extrinsic ecosystem and not internal service delivery.

This shift consolidates power at the platform level, leaving SIs with a choice to adapt by becoming orchestrators and governance enablers within these ecosystems, or risk being sidelined as more enterprises take ownership of their technology destiny.

Those that fail to evolve will find themselves with diminishing influence, as enterprises will need less help in building technology stacks and more help to navigate, optimise, and extract value from an ecosystem they no longer control.

One of the bottom lines here is that the shift to enterprise-owned platforms and hyperscaler-controlled ecosystems caps the ability of SIs to dictate technology choices and inflate costs through complexity. This also breaks the labour arbitrage model that has sustained SI profitability for decades.

The ability to charge ever-increasing day rates was never just about technical skill. It relied on exploiting inefficiencies. As IT environments grew more complex, system integrators became more indispensable. But that model is breaking down.

AI and automation are clearly replacing low-value integration work. Tasks that once required large offshore teams, like manual API integration, testing, and configuration, are now handled by AI-powered platforms. Enterprises need fewer billable hours when low-code tools, automated workflows, and managed services do most of the work.

At the same time, standardised PaaS offerings have reduced the need for bespoke engineering. Enterprises no longer have to solely depend on SIs to stitch together custom middleware or bridge fragmented systems. Instead, they can adopt pre-integrated, API-first platforms that simplify implementation. Rather than designing the architecture, integrators are now expected to work within an existing framework, which will limit their ability to sell high-margin services.

Meanwhile, many enterprises are bringing this capability in-house. As banks, retailers, and industrial companies evolve into platform builders, they will rely less on external integrators. They are already hiring their own architects and engineers, which directly undercuts the traditional SI model.

This shift marks the end of the unchecked day rate. Enterprises will still pay for real expertise, but not for bloated engagements that exist only to navigate unnecessary complexity. System integrators that fail to evolve beyond staff augmentation and manual build work will face falling margins and shrinking relevance.

As enterprise architectures consolidate around standard PaaS models, the space for expensive customisation narrows. SIs must either reinvent their role, by focusing on orchestration, governance, and continuous optimisation, or become commoditised service providers with little pricing power.

The change is structural. Day rates will come under pressure, and commercial models must shift from time-and-materials to outcome-based pricing, subscription-style services, or embedded roles within hyperscaler ecosystems. The industry can no longer justify escalating costs without delivering proportional value.

This is a long-overdue reckoning.

The combination of automation, standardisation, and enterprise self-sufficiency is ending a cycle of inefficiency that has lasted too long. And that’s a necessary step if the integration market is to take its next leap forward.

There is, however, a twist emerging within parts of the system integration industry. Certainly one that warrants scrutiny. As AI becomes central to enterprise transformation, some SIs are developing their own agentic AI solutions and proprietary automation platforms. On the surface, this looks like a bold shift toward innovation. But in practice, it often recreates the very dynamics that once made the integration market so inefficient.

These in-house AI tools are rarely open, modular, or portable. Instead, they introduce bespoke layers that only the SI can maintain, effectively rebuilding a dependency model under a new name. They are sold as accelerators but function more like anchors, keeping the SI embedded in long-term support arrangements under the guise of continuous innovation.

This approach doesn’t empower the enterprise. It reinscribes the same service-heavy economics that AI, low-code, and platform standardisation were supposed to dismantle. It’s a clever play, but it risks undermining trust. Enterprises that genuinely seek autonomy and platform maturity will see through it.

If SIs want to remain relevant in a world of composable infrastructure and AI-native operations, they must let go of the urge to control and instead focus on orchestration, governance, and enablement. Anything less is just another form of lock-in, only this time, disguised as intelligence.

A Reckoning, and a Choice

As I’ve said before, I’m not an advocate for the destruction of capital. PaaS providers should avoid forcing a break between enterprises and system integrators. Instead, they must take a balanced approach to integrating mega-enterprises into their ecosystems. One that respects both the shifting role of the enterprise and the continued relevance of trusted implementation partners.

This starts with creating more flexible partnership models that recognise large enterprises not only as customers, but as platform providers and ecosystem orchestrators in their own right. These organisations are no longer passive buyers of technology. They are actively shaping the environments in which PaaS vendors and integrators now operate.

For system integrators, this marks an inflection point. The market no longer rewards high-margin consulting engagements driven by labour arbitrage. Enterprises are focused on value, not volume, and AI is automating much of the routine integration work that once sustained the SI business model.

To stay relevant, integrators must evolve from selling labour to enabling scale. Their future lies in managing governance, compliance, and ecosystem complexity across multi-platform environments, not in staffing technical teams to complete repetitive tasks. There is very little that can’t be done today on PaaS that previously required armies of custom developers.

PaaS vendors must also acknowledge this transition and adjust their partner strategies accordingly. Rather than relying solely on traditional integrators to drive adoption, they need to invest in new forms of collaboration that prioritise platform enablement over implementation. This will require clearer role definitions to avoid conflict, ensuring that enterprises, PaaS providers, and integration partners each bring distinct value to the table.

A core part of this strategy will involve joint go-to-market efforts, where enterprises, PaaS vendors, and partners co-develop scalable solutions and unlock shared commercial opportunities. What may appear at first as a threat to traditional vendors is in fact a new kind of opportunity. The goal for organisations like Commonwealth Bank or Suncorp isn’t to displace existing partners, but to broaden the ecosystem and create room for shared growth. Dominance will be redefined. As it always is.

What’s needed now is not just a response to change, but leadership in a new era defined by enterprise-driven platforms. PaaS providers that can embrace and orchestrate this shift will shape the next phase of the platform economy and secure their place at the centre of the next wave of innovation.

Did you make it all the way to the end? Sorry, I know it was long, but these complex global inflection points only come along once every generation…

Following a sweeping leadership reset and a visible pivot toward platform ecosystems like ServiceNow, Microsoft, SAP, and AWS, the company is clearly repositioning itself. The departure of long-time regional CEO Graeme Beardsell, Enable’s Bruce Hara, and Chief Strategy Officer Tim White marks the end of an era defined by relationship-driven growth and platform acquisition.

In their place, incoming CEO Peter Grassi and his new executive team signal a shift in tone. It is more assertive and more sharply aligned with what they’re calling the “next tech curve.” The language is also bold: sovereign capabilities, cleared personnel, cyber-first foundations, AI frontiers, quantum futures. The defence credentials are solid, the multi-industry case studies credible, and the partner ecosystem increasingly focused.

This is a strategy in transition. Fujitsu is leaning hard into platform-enabled digital transformation, particularly in sectors where sovereign control and operational resilience are non-negotiable.

But if it wants to lead the next curve, not just the current one, it must develop the business capabilities to match its technical ones.

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Right now, Fujitsu’s value proposition is largely executional. The emphasis is on integration, security, and trusted delivery at scale. Their message at their Executive Analyst Day made that clear. There's a heavy leaning into Uvance, their global transformation umbrella, and confidence in their ability to implement complex technical solutions.

And fair enough. In a world of air-gapped clouds and decentralised service networks, Fujitsu has earned the right to be taken seriously. But something is missing.

For all the talk of transformation, there’s little evidence that Fujitsu is engaging in business architecture or value chain redesign. Uvance supports DX, yes, but who, inside Fujitsu, is helping customers rethink how they operate, not just what tech stack they run? Where are the playbooks, the operating model frameworks, the transformation accelerators?

This is the difference between being trusted and being transformative.

Trusted partners implement platforms. Transformative ones shape strategy. Accenture, Deloitte, even ServiceNow itself. These players are positioning themselves as business model orchestrators, not just delivery arms. Fujitsu, by contrast, is still largely describing itself in terms of technical competence.

And while that may be enough in the short term, especially in government, it does also act as a delimiter on long-term relevance in the private sector boardroom.

If Fujitsu wants to lead the next curve, it needs to close the distance between platform delivery and business value realisation. That means investing in strategy-to-execution frameworks. It means building more narratives that start with outcomes (I did see a few), not deployments. And it means developing a business transformation capability that can sit confidently at the same table as the CIO, COO, and CFO.

Until then, Fujitsu will remain what it already is. A highly capable, secure, and reliable technology partner. These are classic, and still valuable, 20th century traits. Fujitsu is a safe pair of hands with a great culture. When the problem is well-defined, the scope is large, and operational certainty is paramount, they’re exactly who you’d want on the shortlist.

But they are also a paradox not unfamiliar to many Japanese firms. The same traits that make them trusted (precision, discipline, reliability, and long-term orientation), can sometimes inhibit adaptability or radical innovation and make them appear cautious in an era hungry for imaginative disruption. The result is that they have not always been the first partner you call when the problem isn’t clear, and the future isn’t written. Dependable doesn’t always mean visionary in times of uncertainty. And in an era defined by reinvention, clients need both.

And yet, credit where it’s due.

Fujitsu made some brave calls at their Executive Analyst Day, particularly in rationalising their platform partnerships and signalling a clear commitment to where they believe value will be created.

We need look no further than their exit from the mainframe business as a proof point that they are laser focused on executing with clarity and conviction. That same discipline is likely to serve them well as they double down on ServiceNow, Microsoft, and their sovereign cloud offerings.

They may not be the loudest voice in the room, but Fujitsu has long been one of the most dependable. So if this new leadership team can build on that foundation, while strengthening their business design and transformation capability, then they’re well positioned to take a meaningful role in shaping the next chapter of regional enterprise transformation.

Disclosure: I attended the Fujitsu Oceania Executive Analyst Summit in June 2025 as a guest.

It sounds technical. But the implications are deeply strategic.

This isn’t just about upgrading automation tools. It actually signals a fundamental shift in how we think about workforce design, employee capability development, and the operating model of the modern workplace.

Shortly after the ServiceNow and NVIDIA announcement, Kore.ai, a not-yet-public, but significant company financnially backed by NVIDIA, also unveiled its enterprise-grade multi-agent orchestration framework.

Add that to ServiceNow’s March Yokohama release, and it becomes clear that the AI conversation has moved well beyond simple task automation. We’re now seeing the emergence of full-scale AI workforce coordination.

Then came Accenture.

At a May advisor day, it unveiled its own multi-agent orchestration engine, built around a growing catalogue of specialised digital workers. If there was any lingering doubt, this should end it. The shift isn’t just about automating tasks, it’s about rearchitecting the workforce itself.

If you are still trying to make sense of what’s really happening you are not alone. The daily drumbeat of AI announcements is disorienting.

But here’s how I’ve started to make sense of it. This isn’t just about more intelligent tools. It’s about a new operating model for the workplace. And the common thread running through many of these moves, NVIDIA, is providing a surprisingly useful reference point.

Once known for powering AI through its GPUs, NVIDIA is now embedding itself into the platforms where AI agents will live and operate. Rather than building all the agents itself (though it is doing that too), NVIDIA is laying the tracks they’ll run on. It’s positioning itself as the infrastructure, orchestration, and execution layer for the next era of enterprise work. Think of it like Intel, or even Windows, for AI agents.

In a future where every enterprise manages hundreds (if not thousands) of AI agents, from finance assistants to customer service bots to procurement specialists to marketing gurus, someone needs to power the ecosystem. NVIDIA wants to be that AI Workforce Operating System.

So that’s the important backstory. But here’s the real headline.

In an agentic workforce, why are we still training humans for roles that no longer make sense? That is no longer a question for futurists. It’s a strategic imperative for every executive team because AI has moved from being a tool applied to work, to becoming a participant within the work.

Microsoft now allows AI agents built in Copilot Studio or Azure AI to be assigned managed identities within Entra Active Directory. That effectively gives them permissions, access controls, and security protocols just like human employees.

That’s not workflow automation. That’s workforce integration.

Think about the people in your business. And the unfilled positions, and the ceaseless requests for addition headcount. For decades, we’ve worked within the natural constraints of human capital. We hire, we train, we try to retain. We’ve built complex onboarding and performance frameworks, hoping each employee stays long enough to return that investment.

And when they don’t, we explain it away with generational or cultural clichés. Millennials lack loyalty. Gen Z wants purpose. But these are distractions. The real problem isn’t about who is leaving. It’s about why we keep designing roles that only work if people stay. If we can even get them in the first place.

There’s a structural mismatch between the time, effort, and cost required to build human capability and the speed, precision, and resilience that today’s organisations demand. Part of that is because constant disruption is a key part of modern business.

Take frontline public service roles, like those at Services Australia1. Proficiency takes years. Yet turnover is constant. Staff leave each month. Many actively look for a way out. Not because they aren’t capable, but because the roles have become unsustainable. They are emotionally demanding, under-resourced, and burdened with unrealistic KPIs. The employee liabilities are significant.

My wife, a qualified lawyer and grief counsellor, once worked for a major government-funded training and employment provider. The policy and services onboarding was brutal. The emotional toll was heavy. The turnover? Predictably high.

In my own time in city government, I saw the same pattern. Our call centre staff handling parking disputes needed deep policy and legislative knowledge. But above all, they needed resilience. Their wellbeing was tested daily.

These are all examples of where we are asking humans to fill roles that no longer align with the way work, or life, actually functions.

This isn’t an argument to replace people with AI. It’s about facing the fact that some roles have become incompatible with human sustainability. AI isn’t here to displace. I think it is a critical tool that is here to preserve what matters, by taking on what no longer does.

So do we just keep training? Keep rehiring? Keep burning money? The question is why? Not philosophically. Operationally.

Why continue investing finite resources training people for roles they’re statistically unlikely to stay in? Once, there was no alternative. But now, AI agents can do that work without sick days, without churn, and with total consistency. This isn’t a rhetorical question anymore. It’s a strategic decision.

This doesn’t mean removing humans from the equation. In many roles, you can’t. But it does mean reallocating people to where they add the most value and using AI agents for structured, repeatable, emotionally draining tasks that don’t retain people anyway. That’s the opportunity we keep missing. AI isn’t a replacement. It’s a reallocation engine.

And the platforms enabling this transition like ServiceNow, Kore.ai, Salesforce, Microsoft, NVIDIA, Accenture aren’t just selling software. They’re building the infrastructure for a new kind of workforce called the Agentic Workforce.

Some agents assist. Others act autonomously. They’re not dumb bots. They’re skilled digital teammates. And, if we’re honest, they’re already better than humans at a lot of things. They don’t get tired. They don’t resign. And they learn fast. Really fast.

This is why system integrators like Accenture and Infosys are pivoting. Their whole labour-arbitrage model used to scale by hiring. Now it will scale by orchestration.

Practically though it is not just about the global BPOs and SIs and MSPs feeding the global hunger for outsourcing and skilled labour. Every workforce plan now needs a new line item: Agentic Entities (ID-assigned).

Microsoft’s Entra Agent ID makes this real. Agents now get unique, managed identities. They’re secure, auditable and governed under policy. They are not rogue bots. They’re recognised colleagues managed like staff, not software.

It also addresses the next looming threat which is agent sprawl, the unchecked spread of AI agents without governance. Entra Agent ID offers a framework to contain that risk. In short, you don’t just deploy agents, you manage them. That changes everything.

So tear up your legacy training programs. Rethink your workforce assumptions. The platforms, data pipelines, and orchestration layers now being built are equivalent to laying rail during the industrial revolution. By comparison, your HR onboarding playbook is probably still riding horseback.

For decades, we’ve designed learning programs to shrink the gap between new hires and peak performers. But in the age of AI agents, performance isn’t something you build over time. It’s something you configure, deploy, and tune.

So this isn’t about whether AI will replace jobs. That’s a distraction. The better question is:

Why are we still training humans for roles they were never meant to stay in, when agentic AI is ready to work today?

Because the real shift isn’t about humans versus AI. It’s about creating teams where both thrive. Where humans focus on what only they can do. And AI takes on the work we never should have asked people to do in the first place.

This isn’t the future of work. It is already here. It is time to let go of the old assumptions. It is time to redesign your operating model. To modernise your core. To choose your hybrid platform architecture (HYPA) wisely.

The real leaders in this new era won’t be the ones who resist AI. They’ll be the ones who strike a balance by blending the best of human and agent to build something stronger than either could alone.

Today, we’re in the midst of another confidence recession. But this one is different. It wasn’t triggered by collapse. It was triggered by acceleration.

Artificial intelligence, especially the rise of AI agents and orchestration platforms, has created a paradox. The faster the innovation, the deeper the uncertainty.

Leaders aren’t hesitating because they lack tools. They’re hesitating because they lack clarity. And in enterprise technology, clarity is the currency of progress.

This AI moment is a generational inflection point. One of the few exponential leaps that only comes every few decades. And yet, instead of sparking confident action, it has created hesitation at scale. Here is the way out.

A New Kind of Recession

This isn’t a classic market downturn. And it is not even about cost-cutting or capital constraints. It’s about strategic disorientation.

Sure there is a flood of AI jargon contributing to the confidence recession. But executives don’t actually need to master the language of AI anymore than they have the nuanced language of any other breakthrough technology of the last 30 years.

That only has the effect of placing the burden of technical fluency above strategic clarity. Rather, execs need need to understand how to lead through technological uncertainty.

In past shifts like cloud, mobile, and digital, leaders didn’t wait until they understood every protocol and platform. That is not their role. They moved with conviction once the direction was clear. AI is no different.

Yet behind closed doors, many CIOs, CTOs, and boards are still grappling with the “What AI solution?” question. But that’s the wrong question. That’s a technical question. The leadership questions sound different:

• What kind of business architecture do we need to succeed in the era of AI agents?

• Which partners can we trust to scale responsibly and adapt continuously?

• How do we lead through a transformation that no one can fully explain yet?

The danger isn’t that we’re moving too fast. It’s that decisions are being made in isolation without a clear framework, without shared direction, and without the connective tissue that links leadership, architecture, and execution.

The First Pillar of Recovery: Leadership

In any confidence recession, the temptation is to wait. To sit still until the fog lifts. But in this one, waiting is riskier than moving. The AI shift is destabilising the whole value chain, and waiting isn’t safe for anyone.

It’s risky for enterprise buyers, who may miss a narrowing window to gain strategic advantage before AI maturity becomes table stakes. It’s risky for vendors, walking the tightrope between accelerating innovation and preserving platform integrity. And it’s risky for partners, especially in a global outsourced economy, where large systems integrators and managed service providers are still trying to redefine their role in a world increasingly shaped by platform-native architectures.

This moment doesn’t call for reckless speed. But it does call for once in a generation courageous leadership. The kind of outrageous courage that can hold two truths at once: the need to experiment quickly, and the discipline to scale carefully.

Take the large systems integrator Accenture as an example. As the services provider that most frequently and lucratively rings the till for the largest technology vendors, they are deeply embedded across Microsoft, Salesforce, ServiceNow, and down into the hyperscaler layer. Strategically, they’ve hedged well. They are quite literraly everywhere. But they are also a major AI player themselves, building their own AI orchestration platforms, control towers, and agent layers.

From Accenture’s perspective, this is brilliant. They can’t lose. But from the market’s perspective, across the whole AI value chain, it does add to the fog of ambiguity.

If you’re a client, are you being advised or directed? Is your SI partner independently helping you choose the AI platform that’s right for your enterprise? Or are they subtly nudging you toward one they’ve architected? Independent, but ultimately hybrid and proprietary?

AI hesitancy and this kind of ambiguity ultimately serves the systems integrators. But I’d argue it doesn’t serve the long-term interests of their ecosystem partners or the enterprise clients themselves. Because it means someone else is choosing the architecture.

This kind of structural misalignment isn’t unique to Accenture. They are simply the most visible example. Across the entire ecosystem, vendors, partners, and enterprises are moving fast, often reactively. But mostly in parallel. There's energy, but little alignment. No shared rhythm. No real intersection. Just motion.

And that’s precisely why leadership matters so much right now.

Not leadership that waits for the market to stabilise. But leadership that brings internal clarity despite external noise.

The role of the digital leader today is to bring coherence where the ecosystem refuses to. To align vision, architecture, and execution. To stop asking, “Which AI tool should we use?” and start asking, “What kind of system are we building, and who are we building it with?”

In many cases, it’s service providers like Accenture, not the platform vendors themselves, who are shaping enterprise architecture decisions. Their influence, scale, and strategic positioning often mean they have more say in how platforms like ServiceNow are implemented than the vendors do. That should matter to the AI ISVs and should change the way partnerships are structured.

And that is why the most effective leaders, be they tech CEOs, business CEOs, or partner CEOs, aren’t those chasing every new AI announcement. They’re the ones who can clearly articulate where their organisation is in its journey, resist the pressure to follow the crowd, and set a deliberate, sustainable cadence between experimentation and scale.

Because in the AI era, leadership isn’t about certainty. It’s about momentum with intent. It’s about moving forward even when the map is blurry and building clarity where none yet exists.

The Second Pillar of Clarity: Architecture

AI is exposing the fragility of traditional enterprise systems. Many organisations are trying to “bolt on” intelligence to architectures that were never designed for real-time orchestration, agent-based automation, or dynamic workflow augmentation. And it shows.

A CoPilot layered on top of a fragmented backend doesn’t transform anything. It merely decorates dysfunction.

That’s why the AI conversation must be grounded in architectural thinking. Not “what features can we deploy?” but “what kind of system are we building?”

True AI-readiness isn’t just about model integration. It’s about embracing composability, observability, and governance at the core. It’s about creating a system that can flex, scale, and adapt as AI capabilities continue to evolve.

When architecture becomes the lens, the path forward becomes clearer. It stops being a question of which vendor has the most AI features and becomes a matter of which platforms align with the long-term operating model.

The Third Pillar of Trust: Ecosystem Alignment

No enterprise can navigate this shift alone. But the ecosystem designed to support transformation is not just lagging, it’s fracturing.

What no one wants to say out loud is this: the ecosystem isn’t misaligned by accident. It’s competing. It’s at war with itself.

Platform vendors are racing to declare themselves the center of gravity. Every week, someone new is “AI-native” or “enterprise-ready.” System integrators, meanwhile, are still operating with legacy delivery models while quietly building their own control layers, orchestration platforms, and agent frameworks. Many service providers don’t know whether to double down on existing partnerships or hedge their bets elsewhere.

This isn’t just disjointed. It’s geo-technical. A strategic contest to dominate the AI operating model for the next decade.

The traditional buyer-vendor-partner triangle is breaking down. Not because of resistance, but because the incentives are no longer aligned. What’s needed now isn’t more transactions. It’s co-architected transformation where platform selection, implementation, and scaling are treated as a shared journey, not a delivery contract.

The lack of ecosystem alignment is one of the root causes of the AI confidence recession. Vendors overpromise. Partners underdeliver. Buyers grow cautious. Trust erodes. Momentum fades. Transformation stalls.

Confidence cannot be restored in isolation. It only emerges from coordinated movement and right now, that movement is lacking.

The AI confidence recession won’t resolve itself. And it won’t be fixed by the next breakthrough model or killer app.

Because the real challenge isn’t technological. It’s organisational. It’s leadership. We are actually witnessing a leadership reckoning. That’s the uncomfortable truth. AI is going to expose business leadership more than technical leadership. It will reveal who can lead through ambiguity, who can architect alignment across silos, and who can build the structures that let answers emerge over time, rather than waiting passively for clarity.

The defining enterprise architecture of the 21st century won’t be built for stability. It will be built for disruption. And it must include AI.

But the pivot to that kind of architecture won’t come from technical mastery. It will come from business leaders who are willing to lead before the answers are clear.

At an organisational level, confidence doesn’t return when the fog lifts. It’s too late then.

It returns as soon as leaders step into the fog with vision, with structure, and with the resolve to align architecture, people, and partners around forward momentum.

But the fundamental assumptions underpinning this model no longer hold true. That era is ending.

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The IT services industry is undergoing a seismic shift. It will not be incremental but foundational. It is not just that system integrators are being displaced. Rather, the function of systems integration is being redefined. Traditional SI-driven custom integration work is giving way to pre-integrated platform ecosystems, where technology service providers act as orchestrators and platform aggregators (PAs) rather than builders of bespoke solutions.

Unlike their predecessors, these firms will not need to rely (solely) on vast armies of developers and offshore coding factories. Instead, they will focus on assembling domain-specific workflows within powerful Platform-as-a-Service (PaaS) ecosystems. This will vastly reduce the need for large-scale custom development and shift the value proposition from labour-intensive software projects to AI-driven automation and preconfigured business processes.

The implications, especially in Asia Pacific, are considerable.

The collapse of the traditional SI model begins with the breakdown of its underlying economic logic. Labour arbitrage, the idea that a company's competitive advantage comes from shifting work to lower-cost regions, no longer holds the same power in a world where intelligent automation and configurable platforms can do much of the heavy lifting.

Yes, it is still cheaper for an Australian or Singaporean government or multinational to outsource to India or Vietnam, but the equation has changed. The unit cost savings are shrinking, and more importantly, the long-term liabilities are growing. Traditional SI-driven projects often function like long-term debt on an organisation's technology balance sheet. They provide short-term gains but accumulating structural burdens in the form of rigid architectures, high maintenance costs, and expensive future upgrades.

Just as financial analysts scrutinise the sustainability of long-term debt, organisations must now assess whether their technology investments are creating enduring value or merely deferring future costs. The appeal of SI-driven custom integration, once viewed as an asset, is now being reevaluated as a liability. And one that many organisations are eager to retire.

This transformation represents more than just a shift in tooling. It is a fundamental change in how organisations account for technology investment and risk. The previous assumption that large-scale development teams were necessary to build and maintain business systems has been eroded by the rise of low-code and no-code solutions, which allow organisations to create sophisticated enterprise applications without needing to write and maintain millions of lines of bespoke code.

This reallocation of technical debt is akin to moving from long-term liabilities to flexible, on-demand financing models where software adapts to business needs without locking organisations into costly, multi-year development cycles. AI-powered automation has accelerated this trend, removing the need for manual integration and maintenance work that once justified high-value outsourcing contracts.

Where once companies had no choice but to borrow against their future agility, by signing long-term SI contracts to fund the development and upkeep of rigid, custom-built systems, today, they can deploy modular, self-optimising platforms that reduce both cost and complexity. The result is a more liquid, adaptive technology portfolio, one that shifts from heavy debt financing (high-cost, long-term SI contracts) to pay-as-you-go efficiency (automated, configurable platforms).

For traditional SIs, this is an existential problem. Their business model was built on enterprises treating technology as a high-cost, capital-intensive investment. Our generation was defined by solutions that required deep technical expertise, complex integrations, and long-term support contracts. But as more businesses shift towards subscription-based, AI-augmented, and low-code solutions, the justification for these legacy contracts is collapsing. The SI model, once an asset on the enterprise balance sheet, is becoming a liability. And it is one that fewer organisations will be willing to carry.

Isn’t PaaS Just the New ERP?

For years, the core work of SIs has not been about building entire ERP systems but rather about designing and maintaining the business applications that operate between, around, and on top of ERPs and other enterprise platforms. Their role centered on constructing workflows that automated business processes and linked various enterprise applications, ensuring that disparate systems could function together in a cohesive manner.

They were responsible for developing system-to-system integrations, connecting ERP suites such as SAP, Oracle, and JD Edwards with other business-critical applications to enable seamless data flow. Additionally, they played a key role in bridging workflows across multiple systems, ensuring that complex business processes functioned end-to-end across fragmented IT environments.

Beyond the back-end logic, SIs also built and maintained the presentation layer, designing the user interfaces and experience frameworks that allowed customers and employees to interact with these workflows and business applications efficiently (see The Portal Wars Are Back).

Historically, these elements were heavily custom-built. SIs and their customers relied on a combination of bespoke middleware, custom logic, and lengthy DevOps pipelines to manage and update services. Their value came from building and maintaining these complex, tightly coupled integrations, which required constant updates and support.

Yet even when SIs were "integrating" solutions, they often did so through stack-based development, rather than creating true plug-and-play interoperability. This meant that, over time, many organisations accumulated an increasingly brittle patchwork of customised software deployments, requiring continuous SI intervention for upgrades, maintenance, and security patches.

This entire model is now being upended by PaaS.

The need for custom development to integrate systems and automate workflows is rapidly disappearing as organisations transition to platform-native solutions that reduce complexity and increase agility. Instead of writing bespoke code to link different enterprise applications, businesses can now configure and orchestrate very complex workflows directly within PaaS environments. These platforms provide built-in automation capabilities that replace traditional development-heavy approaches with configurable logic and low-code process modelling.

At the same time, system-to-system integrations no longer require custom middleware or extensive development effort. Leading PaaS ecosystems now offer prebuilt connectors, API-driven automation, and plug-and-play integrations, allowing applications to communicate seamlessly without the need for complex coding. The reliance on dedicated SI teams to build and maintain these connections is being significantly reduced. Modern platforms are now capable of providing what we always wanted with ERP but could never have: native interoperability across business applications.

This is a fundamental shift. It is not simply about replacing ERP systems with PaaS, nor is it about writing entirely new enterprise applications within PaaS environments. Rather, it is about recognising that most “business applications” in the SI sense, things like workflows, system integrations, and UI layers, can now be built and maintained directly within PaaS ecosystems without traditional custom development.

This shift breaks the traditional SI model in multiple ways. First, it removes the need for bespoke integrations, eliminating one of the primary sources of SI revenue. Second, it makes ongoing maintenance easier and cheaper, reducing the necessity of long-term support contracts. Third, it shifts the skill set from software development to platform configuration, further reducing demand for the large offshore development teams that have historically been the backbone of SI operations.

For customers, this means faster development cycles, shorter project timelines, and lower overall costs.

By eliminating custom development, solutions can be deployed more quickly, reducing the complexity and risk traditionally associated with large-scale implementations. The ability to configure rather than code also accelerates testing and deployment, making it easier to iterate and refine solutions without prolonged development phases. Additionally, the reduced reliance on large offshore teams translates to lower operational expenses, while the simplification of maintenance reduces the need for costly long-term SI contracts. Ultimately, customers benefit from a more agile, cost-effective approach that gives them greater control over their technology investments, enabling them to adapt to business needs with far less friction.

With this shift will come a fundamental restructuring of the IT services industry.

System integrators have long operated on a linear growth model, where scaling required expanding headcount to take on more projects, driving revenue through billable hours. However, as automation and platform configuration replace custom development, this model no longer scales.

More importantly, it is becoming increasingly unappealing as a long-term investment. Industry analysts are already questioning its viability, and financial analysts will soon follow. The SI model has been sustained by an 8–10% profit margin, but maintaining that in a growth environment is becoming increasingly dependent on cannibalising a labour arbitrage market that is visibly collapsing.

As automation, AI, and platform-based workflows replace the need for large offshore teams, the SI business model is beginning to look like a legacy approach, much like what happened to traditional BPOs when SaaS and automation eroded their relevance. As revenue growth slows and margins come under pressure, the SI model will struggle to maintain investor confidence, leaving its long-term prospects in serious doubt.

Changes to the Partner Model

Another major shift is in the nature of vendor relationships. Traditionally, SIs maintained broad partnerships across a wide range of independent software vendors (ISVs), often working with dozens of platforms and technologies. However, platform aggregators (PAs) will need to operate differently, prioritising deep, strategic relationships with a select group of core PaaS providers rather than spreading their expertise too thin.

Despite this shift, PaaS providers continue to treat their long-term SI partners the same way they always have, without recognising the need for a different approach. The largest SIs should, at a minimum, be as technically proficient as the best PaaS employees themselves, yet too often, they remain mono-dimensional in both their understanding of the platform and their go-to-market strategies. To succeed, the PaaS leaders in this space must rethink their partner programs, ensuring that the next generation of service providers is fully aligned with the evolving needs of the ecosystem.

Unlike the cursory accreditation programmes of the past, successful PAs will invest heavily in platform-specific expertise, ensuring their teams have deep technical skills and high-level certifications such as Certified Technical Architect (CTA) and Certified Master Architect (CMA). The ability to configure, optimise, and extend PaaS capabilities should now be the primary differentiator in the IT services market.

The rise of embedded AI within PaaS further accelerates this transformation. As AI-driven workflow automation, predictive analytics, and machine learning become standard features of modern platforms, the need for traditional manual development continues to erode.

This shift does more than just improve efficiency. It fundamentally reshapes the nature of IT service delivery. The PA firms that thrive in this new environment will be those that embrace AI-enhanced automation and focus on delivering business value through intelligent platform configuration rather than relying on labour-intensive development.

For the global SIs that have long equated dominance with sheer headcount, this is an existential challenge. In the old model, size mattered. More people meant more capability, more influence, and more revenue. But in an economy where automation replaces manual effort, size without intelligence becomes a liability. The largest firms can no longer be considered the biggest silverbacks in the jungle. They are simply the slowest-moving targets.

Those that fail to adapt will not just struggle. They will be actively penalised by an economic model that rewards agility, intelligence, and efficiency over brute scale.

Case Study: Coforge

Many SIs are beginning to contemplate this shift. Some are even demonstrating early success. Coforge provides an interesting case study.

Coforge1 itself may not have always had a clear answer to what has driven its success over the years. When asked, their response has often been simply, "customers love us." While that may be true, the more important question is why? And that’s something they haven’t been able to fully articulate, at least to me.

While I wouldn’t say they deliberately set out to adopt the platform aggregator model, their recent progress, whether by design or adaptation, suggests they are naturally evolving toward a delivery approach that better aligns with the changing technology landscape.

Their strong presence in industries such as banking, financial services, insurance, transportation, and logistics reflects this shift. These sectors have seen traditional system integration approaches struggle to keep pace with evolving client demands, yet Coforge has managed to carve out a highly competitive edge. Their success doesn’t fit neatly into conventional IT services models, but when viewed through the lens of platform aggregation, their trajectory starts to make far more sense.

They have developed strategic partnerships with key PaaS providers, including Salesforce, Pega, ServiceNow, Duck Creek, Appian, AWS, Microsoft, and Google, aligning their services with the major workflow and hyperscaler platforms that are driving enterprise transformation. This alignment enables them to deliver solutions that integrate more seamlessly into modern enterprise ecosystems.

Beyond their technical strategy, Coforge’s real strength lies in its client-centric approach and focus on long-term value. They have built deep, enduring relationships with major clients such as British Airways, Virgin Australia, Arnott’s, and Coles. Not by competing on headcount or pricing but by delivering preconfigured, industry-specific solutions that drive faster, more scalable outcomes.

By leveraging prebuilt workflows within established PaaS ecosystems, they are minimising the need for extensive custom development while enhancing the efficiency and agility of their clients’ technology operations. This approach not only accelerates implementation but also reduces long-term maintenance burdens, making their services more sustainable and cost-effective in a rapidly evolving digital landscape.

And that, I believe, is the real differentiator. The challenge, however, is that Coforge invests little in marketing, which sometimes leads to a lack of clarity in its go-to-market messaging.

When messaging is developed entirely from within the company, without external validation, it can become insular and difficult to communicate effectively. Great technical and solution architects don’t always translate into great marketing strategists, and that gap is evident in how Coforge presents itself to the market. So at this point they are still positioning themselves as a challenger SI.

To really lock in on this model they have some work to do. And that has implications for them and their partners and their customers. In the past, a firm like Coforge may have used Salesforce for CRM, ServiceNow for ITSM, and Pega and Appian for general workflow automation. But today, each of these platforms are expanding their ambitions, positioning themselves as broader enterprise-wide solutions that challenge traditional ERP vendors like SAP.

Salesforce is no longer just a CRM. With Financial Services Cloud, Public Sector Cloud, and MuleSoft, it is making a serious play into ERP-like territory. ServiceNow, once seen purely as an ITSM tool, is aggressively expanding into customer service, HR, and enterprise-wide workflow automation into every corner of the enterprise.

For Coforge, simply being a PaaS partner is no longer enough. Service providers will have to make strategic decisions about which PaaS vendors they go deepest with, forming exclusive alliances that shape the new IT services landscape.

It is now incumbent on PaaS vendors to ensure that their platform becomes the foundation of choice for platform aggregators, offering incentives and building stronger partner ecosystems that make their solutions indispensable in this evolving market.

The bottom-line on all this: the future of IT services belongs to Platform Aggregators (PAs), not System Integrators (SIs).

This transformation is not a trend. It is a fundamental redefinition of how technology services are delivered. The days of labour-intensive, custom-coded solutions are numbered, replaced by a world where AI-driven automation, prebuilt workflows, and deep PaaS specialisation define success.

Organisations, and that includes end-user customer organisations, still operating under the SI model must start to adapt or risk irrelevance in a rapidly moving industry.