Intelligence is Commoditising - by Chris Jon

Introduction: Navigating the AI Tidal Wave – The Commoditisation Pattern Demanding a New Strategy

Prepare yourselves. The market landscape is undergoing a seismic, irreversible transformation, driven by the relentless march of Artificial Intelligence. This isn’t just another technology cycle; it’s a fundamental reshaping of value, a pattern breaking the very foundations upon which businesses have traditionally built their competitive advantage. At the heart of this disruption lies the accelerating commoditisation of intelligence and the subsequent commoditisation of product.

For decades, expertise – human intelligence – has been a premium asset, a key differentiator for individuals and organisations. Knowledge workers, from bookkeepers to customer support agents and even early-stage consultants, commanded value based on their specialised understanding and ability to perform complex tasks. But the rise of advanced AI capabilities, particularly Large Language Models (LLMs) and the emergent power of AI agents, is democratising these skills at an unprecedented pace. Tasks previously requiring significant human effort and tacit knowledge are becoming increasingly automatable, inexpensive, and widely accessible. The intelligence that once lived “between your ears” is now being codified, packaged, and made available through AI, lowering the barrier to entry across numerous domains. This isn’t just reducing costs; it’s fundamentally eroding the unique value of routine or easily codified knowledge work.

Concurrently, this commoditisation of intelligence is creating a powerful commoditisation of product. Software products, particularly those built on underlying AI capabilities that are rapidly becoming generic or easily replicated by foundational models from major labs like OpenAI and Google, are facing an existential threat. Today’s cutting-edge feature can be tomorrow’s easily copied function. This rapid obsolescence is making investments in AI-centric products increasingly risky, prompting Venture Capitalists to become hesitant to fund ventures built on potentially temporary technological advantages. The traditional model of building a fixed software product, defending its feature set, and relying on long development roadmaps is being swept away by a faster, more fluid reality.

Think of it as a rising tide, a tsunami of commoditisation that threatens to drown traditional business models. Organisations that continue to operate based on the old patterns – valuing intelligence purely on headcount or scaling through fixed product offerings – risk being left behind, or worse, rendered obsolete. The ability to perform tasks is becoming democratised, but the strategic insight to leverage this democratisation and navigate the turbulent waters is becoming the new high ground.

This challenging, yet exhilarating, landscape demands a strategic response. Senior leadership teams must recognise this pattern immediately and understand that the path to resilience lies not in resisting the tide, but in learning to surf it. It requires a fundamental cognitive shift, a move away from selling fixed “products” and towards offering dynamic, outcomes-focused “services” that leverage AI as an accelerator and enabler. It necessitates focusing on strategic advisory, helping enterprises define their AI roadmap and integrate capabilities, rather than just implementing specific tools. Crucially, it elevates the importance of uniquely human attributes like trust, domain-specific tacit knowledge, adaptability, and the ability to manage complex change management – skills that AI can augment to create “superhumans”, but cannot fully replace.

This essay will delve deeper into this pattern of commoditisation, exploring its nuances, the strategic shift required to reach “higher ground” through a services-led model, the enduring and evolving value of human expertise in an AI-augmented world, how enterprises can build resilience, and the crucial non-technical factors like trust, literacy, and change management needed for successful navigation. By understanding these dynamics, senior leadership can position their organisations not just to survive, but to thrive in the age of AI commoditisation.

Navigating the Commoditisation Floodwaters: The Strategic Shift from Products to Services in the Age of AI

The pattern emerging from this AI-driven commoditisation is clear: relying on selling fixed products or hourly intelligence is a path increasingly fraught with peril. The strategic imperative, the essential pivot for resilience and growth, is a fundamental shift from a product-centric model to one based on delivering high-value services that leverage AI as a powerful accelerator.

Why this dramatic shift? The sources paint a stark picture. The breathtaking pace of innovation from major AI research labs like OpenAI and Google means that a cutting-edge product built today on a specific AI capability can be rendered obsolete – effectively “blown away” or “wiped out” – in a matter of months by the next foundational model release. This rapid obsolescence creates immense technological debt risk for enterprises investing in such products. Venture Capitalists, sensing this shift and the diminishing lifespan of product-based advantages, are already becoming hesitant to fund ventures vulnerable to this quick AI obsolescence, increasingly favouring services businesses which are seen as more likely to maintain their advantage for longer.

The “product house” approach pulls a business into an exhausting cycle of constant development, support, and trying to build ever-higher moats against the rising tide of AI capabilities. The regulations and support overheads for product provision are also substantial. In contrast, a service model sidesteps many of these pitfalls by reframing the value proposition entirely. Instead of selling a static tool, you sell the outcome, the capability, and the strategic guidance to achieve business objectives in a fluid AI landscape. The consulting firm, not the client, bears the primary responsibility for staying current with the ever-evolving AI technology.

This service-led model offers a suite of compelling, high-value benefits that directly address the pain points faced by enterprises navigating the AI revolution.

1. Mitigating Obsolescence Risk for the Client: By delivering AI capabilities as part of a service – perhaps using AI agents like Umbra, Nebula, or DocGen as “accelerators” or “service components” – the service provider takes on the burden of adapting to new AI models and advancements. The client doesn’t buy a product that might be obsolete in six months; they buy a promise of continuous value and capability, regardless of the underlying AI version. This sells “resiliency and future-proofing strategy” and the essential ability to “pivot and swivel” as the market changes. The service provider acts as the client’s guide, helping them anticipate and react to the floodwaters, pulling them to “higher ground”.
2. Addressing Enterprise Complexity and Integration Hell: Large enterprises typically operate with complex, fragmented system landscapes – think the historical state of SAP with disparate systems. Simply providing an AI product leaves the client with the daunting task of integrating it across these silos. A service approach includes the crucial expertise to navigate this complexity. This is where technologies like the Model Context Protocol (MCP) become vital. MCP acts as a translation layer, providing a “unified language” for AI agents to access data and functions across various enterprise tools via a single integration point, dramatically improving the “time to market” for agent capabilities and making initiatives “maintainable”. Agent-to-Agent Protocol (A2A) is another crucial piece, enabling collaboration between agents from different vendors (e.g., ServiceNow and Microsoft Copilot) to deliver higher value than siloed systems. The service provider brings the knowledge of these architectural components and the skill to integrate them seamlessly.
3. Filling the Internal Talent and Expertise Gap: Enterprises across the board face a significant shortage of individuals with the skills needed to understand, deploy, and manage AI agents effectively. Handing over a product does little to solve this internal deficit. Strategic consulting services, on the other hand, offer “strategic enablement” and “consulting and enablement”, actively helping to “upskill the organisations themselves” and build AI literacy. This involves training operational teams to interact effectively with “agentic AI”, helping bridge the gap between business needs and AI systems, and providing expertise in areas like prompt engineering and AI management. It’s not just about the tech; it’s about enabling the human element to leverage it.
4. Providing Strategic Guidance Beyond Technical Deployment: Many enterprises are uncertain about how and where to best apply AI for maximum business value. They need help defining their AI roadmap and understanding the strategic implications. A service-based approach goes far beyond the technical “realisation” pillar to include crucial “advisory” and strategic consulting. This involves identifying “white space” opportunities where AI can create new value, aligning AI initiatives with overarching strategic goals, and providing the foresight needed to navigate the revolution effectively. The service sells the strategic vision and guidance, not just the implemented tool.
5. Addressing Specific Functional Pain Points: The modular nature of service offerings allows firms to market specific AI agent capabilities as solutions to concrete business problems. For instance, in cybersecurity, AI agents delivered as a service can automate alert triage, manage false positives in vulnerability scanning, and assist with managing complex, ephemeral infrastructure. These capabilities directly tackle the human pain points of analyst fatigue, inefficiency, and high turnover. In data management, an agent service can solve the problem of finding and accessing data within vast data lakes like SAP’s “Octopus” by providing automated discovery. The example of the automated testing company illustrates how a service could offer AI-driven testing capabilities across any SaaS platform (ServiceNow, Salesforce, SAP, Oracle) by providing the AI agent as a service outcome, rather than being tied to a product designed only for one ecosystem.
6. Building Trust and Navigating Organisational Change: AI adoption isn’t just a technical challenge; it’s deeply human, requiring trust and managing significant organisational change. A product-first sale can feel transactional. A service-based model, especially one with a strong “people-centric approach” and focus on strategic enablement, fosters trust by positioning the firm as a “trusted advisor” who understands the client’s unique needs and the human element of change. It helps overcome resistance and assures clients that solutions are reliable and align with their culture and processes.

This strategic shift requires a cognitive leap, moving away from the traditional consulting model of “headcount equals unit of work”. Scaling services in the AI age demands focusing on value delivered, potentially through monthly retainer or subscription models, where AI acts as an internal leverage point to deliver multiple units of output income from one unit of consultative effort. The “products” or tools developed become “just a result or realization of the services that you are rendering,” not the core offering itself.

In essence, navigating the AI commoditisation floodwaters means abandoning the sinking ground of static products and hourly billing for commoditised skills. The higher ground is in providing dynamic, adaptable services that guide clients through the chaos, leveraging AI not as the end product, but as a powerful engine within a broader strategic advisory, integration, and enablement offering. It’s about selling resiliency, strategic foresight, and the expertise to harness the commoditised power of AI for the client’s unique advantage.

Having detailed the “why” and “what” of the strategic shift, a crucial next step for our analysis would be to explore the “who”. Essay Topic 3 delves into the evolving value of human expertise and the ‘New AI Talent Stack’. Understanding which human skills remain paramount and how individuals and organisations can cultivate them is essential for complementing this service-led approach and truly achieving “superhuman” capabilities.

The Evolving Value of Human Expertise: Tacit Knowledge, Tribal Knowledge, and the New AI Talent Stack in a Commoditised World

The world is facing a seismic shift. Large Language Models (LLMs) and AI agents are unleashing a “tidal wave” or “floodwaters” that are relentlessly driving down the value of traditional knowledge work and certain software products. This isn’t just incremental change; it’s a fundamental “commoditization of intelligence” that is making previously specialized tasks accessible to non-experts. Tasks that were once the exclusive domain of highly paid professionals – the “easily deterministic” work, the tedious, mundane tasks – are being digitized and replicated at near-zero marginal cost. This creates a precarious situation for those whose value is solely tied to these commoditized skills.

But amidst this rising tide, there is “higher ground”. Not all human expertise can be easily replicated or commoditized. This is where tacit knowledge and tribal knowledge become paramount. This is the deep, nuanced understanding built through years of experience, the intuitive grasp of complex situations, the collective wisdom held within teams and organizations. It’s the subtle cues in a negotiation, the unspoken dynamics of a market, the ingrained understanding of a specific industry’s intricacies that isn’t written down in manuals or readily available online. This “higher ground” is difficult for current AI systems or even digital twins to fully access or replicate. It’s the unique human context that AI, for now, fundamentally lacks.

Navigating this landscape isn’t about resisting the wave; it’s about learning to surf it. It requires a strategic, personal transformation – the development of the “New AI Talent Stack”. This isn’t a technical stack of databases and servers; it’s a personal framework of capabilities, a human operating system for the AI age. It’s the answer to the “who” question in this evolving strategic picture.

At the core of this new stack lies Domain Expertise. This is your “bread and butter”, the deep well of knowledge and experience in a specific field like HR, cybersecurity, finance, or consulting. Your existing understanding of the nuances, the unwritten rules, the critical context within your domain is more valuable than ever when combined with AI. An HR expert who understands HR becomes exponentially more powerful when they know how to wield AI to enhance HR functions. This combination creates the potential for “human plus AI equals superhuman” productivity and value, pushing individuals onto that coveted “higher ground”.

Layered upon this indispensable foundation is AI Fluency (or AI Literacy). This is about understanding what AI is, how it works, its capabilities, and critically, its limitations. It’s not necessarily about being a deep AI researcher or coder, but about knowing how to effectively interact with AI agents, treating them perhaps like “genius six-year-olds” that need clear, structured instructions. This literacy is becoming as fundamental as spreadsheet literacy was in a previous era – an expected entry on a CV. It means understanding when an AI system might “hallucinate” or hit a “task length barrier” and how to guide it effectively.

Building this fluency requires active Experimentation with AI Tools. You have to get your hands dirty, try out the tools relevant to your domain, and learn by doing. This practical application is crucial for discovering how AI can truly augment your specific expertise.

Crucially, navigating this rapidly changing frontier is not a solo journey. It demands Building a Learning Community, your “Tribe”. The pace of AI innovation is too fast to learn in isolation. Finding or creating a group of peers with whom you can share knowledge, experiences, and learn collaboratively is essential for continuous adaptation. Collaboration, both with other humans and effectively with AI agents, becomes a cornerstone skill.

This leads to perhaps the most critical personal attributes in the new AI talent stack: Adaptability, Curiosity, and Resilience. The ability to learn continuously, embrace change, experiment, grow, and share that learning is paramount. Hiring practices are shifting to seek these traits beyond traditional credentials and keywords. Organizations need people who are fueled by a desire to learn and experiment, individuals who can learn from failure and pivot quickly.

Furthermore, as AI handles more deterministic tasks, human roles, even technical ones, will demand enhanced Soft Skills and Collaboration. Learning how to manage and collaborate effectively with AI agents – thinking of them less as complex systems and more as intelligent assistants or interns requiring clear guidance – becomes vital. This underscores the increasing importance of the “human element” and change management in AI adoption.

Finally, this new stack requires a shift in perspective regarding systems. Beyond traditional technical architecture, individuals need to understand Process Architecture – how to design and embed AI effectively within business workflows. This is about thinking structurally about how humans and AI interact within processes to achieve outcomes.

For organizations, building this New AI Talent Stack across the workforce is key to resilience. Instead of expensive “ship and replace” strategies, the focus should be on a “micro AI build” – empowering existing employees across all departments (HR, legal, cyber, etc.) to become fluent in using AI to augment their specific roles. This requires adjusting hiring to prioritize domain expertise augmented by AI fluency, and seeking candidates with adaptability, curiosity, and collaborative skills. It also highlights the need for “bridge roles” that connect business functions with AI capabilities. Consultants play a vital role in providing this “strategic enablement” and helping organizations cultivate AI literacy internally.

The New AI Talent Stack is the human-centric strategy for not just surviving but thriving in the age of commoditized intelligence. By doubling down on unique domain expertise, embracing AI fluency, cultivating adaptability, and fostering collaboration, individuals can elevate themselves to the “higher ground,” creating resilience and unlocking opportunities in “white space” – those new frontiers that emerge when the old patterns are broken. This is how we move towards truly “superhuman” capabilities.

A critical next step in this analysis is to explore the organizational challenges in implementing this widespread upskilling and “micro AI build” strategy and how effective change management can overcome resistance, particularly from departments like HR and Legal who might perceive AI as a threat.

Enterprise Resilience in the Face of Commoditisation: Challenges, Opportunities, and the Role of Strategic Consulting

Forget the incremental tweaks; we’re staring down a seismic shift where the floodwaters of AI commoditization are fundamentally reshaping the terrain for large enterprises. It’s not a gentle rise; it’s a relentless tide driving down the value of traditional knowledge work and making existing software products vulnerable to rapid obsolescence. This isn’t just abstract theory; it’s an existential threat to the established patterns of business.

For the large enterprise, sitting atop decades of accumulated systems and processes, these rising waters present immense challenges. One major pain point is the looming threat of technological debt. Investing millions in proprietary AI tools today feels increasingly risky when foundational models from major labs could “blow away” that investment in months. You build something, and tomorrow, OpenAI or Google makes the underlying capability a free API call. This creates a paralysis of potential obsolescence.

Furthermore, large enterprises are complex beasts, often with fragmented system landscapes built over years. Scaling AI solutions beyond isolated departmental pilots is incredibly difficult. Simply “shipping” an AI agent and hoping it integrates seamlessly is a pipe dream. You need the expertise to connect these new AI capabilities, perhaps leveraging concepts like MCP servers, to the disparate data sources and legacy systems that actually run the business.

Then there’s the internal talent gap. Enterprises lack the sheer number of people who understand not just AI technology, but how to effectively use it to augment their specific domain expertise across the entire organization. You can’t just hire a few AI researchers; you need HR experts, cybersecurity analysts, and supply chain managers who are fluent in leveraging AI within their world. This lack of pervasive AI literacy is a critical barrier. As Greg puts it, AI is “10% technology, 90% people”.

Compounding these issues is the simple fact of uncertainty. Senior leadership teams often “don’t know what they don’t know” about AI. They struggle to identify where and how to best apply AI for maximum business value, fearing disruption and the unknown. This can lead to a cautious, slow pace of adoption, particularly evident in markets like Europe compared to the US. This creates the risk of being “too soon” with an offering, but also the greater risk of waiting too long and becoming the “frog in water that’s the stove’s been turned on”.

This is precisely where strategic consulting must become the indispensable partner – the “trusted advisor” who helps enterprises navigate these turbulent waters and find the “higher ground”. It’s not about selling them a black-boxed product; it’s about providing a continuous service that offers resilience and strategic foresight in a constantly changing landscape.

The core value proposition of this new breed of strategic consultant is to help enterprises build resilience and get to that higher ground. How? By focusing on capabilities that cut through the commoditization:

1. Identifying White Space: Consultants help enterprises look beyond the obvious and find the “white space” where AI can be leveraged to create new opportunities, disrupt existing markets, or solve previously intractable problems. This requires understanding both the deep domain expertise of the enterprise and the bleeding edge of AI capabilities.
2. Implementing Secure and Resilient AI Architecture: Navigating commoditization means not just adopting AI, but doing so securely and compliantly. This could involve guiding the enterprise to implement private LLM instances for sensitive data, similar to SAP’s approach with a private ChatGPT on Azure. It means treating AI agents like humans within the existing security architecture, granting role-based access and ensuring compliance.
3. Enabling the Internal Team - The “Micro AI Build”: You can’t simply replace the workforce; it’s “way too expensive”. Resilience comes from enabling the existing employees across all departments to become AI fluent – fostering a “micro AI build” capability throughout the organization. This is about helping employees build their own “New AI Talent Stack” by augmenting their domain expertise with AI literacy. The consultant provides the “strategic enablement,” teaching the organization how to effectively interact with and manage AI agents, ultimately striving for that “human plus AI equals superhuman” state.
4. Providing the “Pivot and Swivel” Capability: The pace of AI innovation is such that any product or solution is at risk of obsolescence. Enterprises need a partner who stays on the bleeding edge, constantly monitoring the incoming “floodwaters” and advising on when and how to “pivot and swivel” to incorporate new capabilities or abandon old ones. This ongoing strategic guidance is the real value, not the one-time implementation of a tool.

Importantly, this strategic guidance must address the fundamental human challenges. Trust is foundational; clients need assurance that the consultant understands their unique context and will guide them through the complexities safely. Building AI literacy is crucial, just as spreadsheet literacy became essential in a previous era. And above all, addressing change management – the fact that “people don’t change” – is paramount to overcome resistance and ensure successful adoption across potentially skeptical departments like HR, Legal, and Cyber.

Enterprises face significant challenges from the commoditization of intelligence and product, but they are not without agency. By embracing strategic AI consulting, focusing on building internal AI fluency, addressing human and organizational factors, and actively seeking opportunities in the newly created “white space,” they can build resilience and navigate these disruptive changes to emerge stronger.

A critical next step for senior leadership would be to conduct an internal assessment of their current AI literacy and identify key domain experts who could become the early adopters and champions of a “micro AI build” strategy across the organization.

Beyond the Tech: Trust, Literacy, and Change Management as Crucial Factors in Navigating AI Commoditisation

Right, let’s cut through the usual tech-hype blather and get down to what really matters when enterprises are staring down the barrel of AI commoditization. Forget the endless parade of shiny new AI models and platforms for a moment; while the technology is undoubtedly the engine, the sources make it abundantly, brilliantly clear that the actual leverage points, the non-commoditizable bedrock for weathering this storm, lie beyond the circuit boards and algorithms. This isn’t about buying a better hammer; it’s about understanding the materials, training the builders, and managing the site effectively. The absolute, utterly crucial factors for enterprise leaders navigating this landscape are Trust, Literacy, and Change Management. These aren’t peripherals; they are the very foundations upon which any sustainable enterprise AI strategy must be built.

Think of it like this: the AI itself – the large language models, the agents, the fancy protocols like MCP or A2A – they’re akin to incredibly powerful, potentially unruly tools. But without Trust between you and your partners, you haven’t got a reliable hand to help you pick the right tool for the right job, or frankly, to help you navigate the mess when it goes wrong. Without widespread AI Literacy, your workforce, from the top floor down, are just poking these powerful tools with a stick, not really understanding what they do, what their limits are, or how they can genuinely augment human capability. And without effective Change Management, you’re building a state-of-the-art factory but failing to train your workforce on how to use the machinery, manage the inevitable disruption, or even get them through the front door without a riot. This, my friend, is the real game for enterprise leadership.

First off, Trust. In a world where the ‘intelligence’ itself is becoming a cheap commodity, and any given AI product’s unique selling proposition could vanish overnight, who you partner with and whether you trust them is paramount. Large enterprises, bless their cotton socks, move like supertankers – slowly, deliberately, and with immense caution. This is particularly true in places like Europe compared to the States, where regulatory landscapes and inherent risk aversion slow things down considerably. Introducing AI touches sensitive data, core business processes, and the very structure of work itself. Clients need a “trusted advisor,” not just some bloke trying to flog them a black box solution. They need partners who deeply understand their specific, often unique, pain points and industry nuances, and who can guide them through the genuine complexities, risks, and ethical considerations of AI adoption. This is why secure architectures, perhaps involving private LLM instances hosted within their own environments, become non-negotiable, particularly in regulated sectors. Building that trust, demonstrating genuine expertise and reliability beyond just the tech, is a core, non-commoditizable service offering. Consultants aren’t just selling tools; they’re selling the capability to “pivot and swivel” when the technology inevitably changes. This strategic guidance, informed by deep experience and knowledge of how to navigate complex enterprise environments, is what large organisations are increasingly looking for.

Secondly, and you absolutely cannot skimp on this, is AI Literacy. The sources paint a clear, often painful, picture: there’s a significant gap in understanding AI across enterprises, glaringly so at the senior leadership level. Leaders frequently “don’t know what they don’t know,” making strategic decisions about AI adoption difficult and potentially misguided. AI literacy isn’t about coding or understanding the mathematical matrices behind LLMs. It’s about grasping what AI is, what it can do, its current, very real limitations (like hallucinating or hitting the task length barrier, although LLMs are bad at maths, they excel at things like summarising and case studies), and its broader implications. This lack of pervasive understanding is a colossal blocker to scaling AI initiatives beyond isolated, often pointless, pilots. How can an organisation effectively uncover “white space” opportunities or fundamentally rethink processes using AI agents if its people, from the C-suite down, don’t understand how to interact with these agents or even what questions to ask?. The analogy to spreadsheet literacy is spot on. Just as understanding Excel became a basic expectation, AI literacy – knowing how to query, constrain, and manage AI, perhaps treating it like a six-year-old genius that needs careful instruction and correction – will become an essential skill on CVs and resumes. Consultants absolutely must pivot to being educators, providing “strategic enablement” and fostering a “micro AI build” capability within the client organisation, teaching people to weave AI fluency into their existing domain expertise. This involves helping people adapt to a new “cognitive shift,” learning to interact with agentic AI as a team member or even being led by it.

And finally, the perennial beast: Change Management. As was rightly pointed out, AI is perhaps “10% technology, 90% people”. And while the tech gallops along at breakneck speed, “people don’t change” anywhere near as easily. Rolling out AI isn’t just introducing a new tool; it demands a fundamental “cognitive shift” in how work is conceived and executed. This goes deep, potentially challenging entrenched processes, established roles, and even HR structures. Resistance is a natural, predictable response. Key departments like HR, Legal, and Cyber Security are not being obstructive for the sake of it; they have entirely valid concerns about data privacy, regulatory compliance, security risks (like prompt injection), and the profound impact on job roles and organisational structure. A purely technical consulting approach that ignores these human and organisational hurdles is, frankly, doomed. Effective strategic consulting must actively incorporate change management, building enthusiasm, equipping leaders to guide their teams through the disruption, and framing the adoption of AI not as a threat of redundancy but as an opportunity to achieve a “human plus AI equals superhuman” state. Lessons from past, often painful, large-scale tech rollouts like SAP or ACP highlight the critical, often overlooked, role of change management. The ability to manage this transition, address concerns, and demonstrate tangible value outcomes is indispensable.

So, bottom line: for large enterprises navigating the choppy waters of intelligence and product commoditization, building resilience isn’t found by chasing the next algorithm release. It’s achieved by establishing deep Trust with knowledgeable, reliable partners who understand regulated markets and scale, cultivating widespread AI Literacy so the workforce can actually leverage the technology safely and effectively, understanding its nuances and limitations, and proactively, expertly managing Change to overcome human resistance and integrate AI seamlessly into the very fabric of the business. Strategic consulting’s critical, enduring value proposition lies precisely in providing this essential, non-technical guidance, enabling enterprises to look beyond the “cool bubble” of the tech itself, identify where the true value lies, and build the foundational human capabilities needed to harness AI for long-term success. This is the higher ground for both enterprises and the consultancies serving them.

A cracking next step, therefore, would be to develop a robust framework designed specifically to assess your enterprise’s current maturity and readiness across these three non-technical pillars: Trust (e.g., evaluating existing partner relationships, internal security posture regarding AI, compliance readiness), AI Literacy (e.g., benchmarking understanding levels across different employee groups, identifying critical skill gaps from leadership down), and Change Management Readiness (e.g., reviewing existing processes for technological adoption, assessing leadership preparedness to guide teams, identifying potential areas of significant resistance within key departments like HR, Legal, and Cyber). This diagnostic would provide a clear, actionable roadmap, highlighting where the most critical foundational work is needed before significant technical AI investments can truly pay off and avoid becoming just another piece of commoditized technology.

Case Studies in Commoditisation and Resilience: Lessons from SAP and Emerging AI-First Businesses

Right then, let’s talk brass tacks for enterprise leaders staring into the maelstrom of AI. The whispers you’re hearing about “commoditisation” aren’t just noise; they are the sound of the fundamental ground shifting beneath you. Intelligence itself is becoming cheap and widely available, and as a direct consequence, the value of traditional software products built on that intelligence is evaporating faster than dew in a desert. This isn’t a gentle tide; it’s a bloody floodwater rising. The question for you, as leaders, isn’t if this is happening, but how the devil you build resilience and find the highest ground in this new landscape. Forget abstract theories for a moment; let’s look at the battle scars and breakthrough plays happening right now, drawing lessons from a corporate titan like SAP and the lean, mean AI-first machines emerging in the market. These case studies aren’t just anecdotes; they are blueprints for survival and strategic advantage.

First up, let’s turn our gaze to a proper behemoth: SAP. They’ve faced fragmentation and the need for fundamental shifts before, but the AI wave presents a new kind of challenge. They recognised that their existing system landscape was, frankly, fragmented, necessitating a strategic shift towards cloud and centralisation, famously embracing ServiceNow for internal operations and customer service management. This was their initial move to rationalise and modernise. But here’s where it gets interesting in the context of AI commoditisation: they didn’t just adopt platforms; they started applying AI and Machine Learning (ML) in practical ways, achieving tangible results like an 80% reduction in alert noise, freeing up valuable human capital from tedious, easily commoditised tasks. They also used Natural Language Processing (NLP) to gain deeper insights from incident data. This wasn’t just playing with shiny new toys; this was using AI to tackle specific, painful inefficiencies where human effort was drowning in data.

But perhaps the most pattern-breaking move by SAP, born directly out of confronting the limitations of existing, potentially commoditised AI offerings (like ServiceNow’s native Now LLM, which was found to be significantly less capable than alternatives), was the implementation of a private ChatGPT instance in Azure. Think about that for a second. A massive enterprise, facing regulated data requirements (like German council regulations), couldn’t rely on a vendor’s potentially limited or non-compliant LLM. Instead of waiting or accepting the status quo, they became their own blueprint. By hosting a powerful LLM within their own secure Azure environment and implementing data anonymisation, they took control of the core AI capability needed to handle sensitive, regulated data. This wasn’t just an IT project; it was a strategic play to mitigate the risk of commoditisation on their own terms, ensuring they had a reliable, secure AI foundation for critical, data-sensitive applications. The SAP case demonstrates that resilience for a large enterprise involves rationalising systems, applying AI for efficiency gains, and, crucially, potentially building or controlling foundational AI capabilities when standard offerings aren’t sufficient or compliant. It’s about getting ahead by controlling the non-commoditised layers.

Now, let’s pivot entirely and look at the other end of the spectrum: the “AI-first” businesses that are fundamentally built around leveraging AI from the ground up. Names like Shopify, Klarna, Duolingo, and Artesan are cropping up as examples. These companies aren’t just adding AI to existing processes; they’re embedding it so deeply into their operations that they are actively disrupting traditional job markets. Artesan, for instance, is cited as using AI agents instead of hiring traditional sales lead representatives. This isn’t theoretical; it’s happening. These “AI-first” companies are demonstrating another path to navigating commoditisation: by embracing the democratisation of skills enabled by AI and rethinking core business functions from the perspective of “human plus AI equals superhuman”. They are finding the white space in disrupted markets and leveraging AI to operate with unprecedented efficiency and scale, effectively building their entire model on the new commodified bedrock. This shows that resilience can also mean shedding old models entirely and building anew with AI as the core engine.

On the flip side, there’s a cautionary tale woven through the sources, particularly for companies operating in areas susceptible to rapid AI advancement. Consider the example of the automated testing company. Their core value proposition lay in automated testing of SaaS products. A perfectly valid product in a previous era. But here’s the rub: as Large Language Models and AI agents get better at understanding interfaces, generating code, and performing tasks based on natural language instructions, their core technology is at risk of rapid obsolescence. The fear isn’t just a competing startup; it’s that Google or OpenAI could release a foundational model or a new agent capability tomorrow that fundamentally undermines their specific product offering. This illustrates the chaotic, unpredictable nature of product commoditisation driven by advancements from major research labs. Venture Capitalists are already becoming hesitant to fund products in these vulnerable areas. For companies in this position, the lesson is stark: relying solely on a product built on an easily replicable or rapidly evolving AI capability is a precarious strategy. Resilience here means urgently finding a way to “pivot and swivel” – perhaps by leveraging the democratisation of skills AI enables to diversify offerings, or by shifting from selling a fixed product to offering a service that adapts as the underlying tech changes.

So, what are the essential takeaways from these contrasting cases for you, the enterprise leader?

1. Commoditisation is Real and Varied: It’s not a single event but a process impacting knowledge work and products differently. Basic tasks and products built on today’s AI are most vulnerable.
2. Resilience Requires Proactive, Multi-faceted Strategies: There’s no one-size-fits-all answer. For large, regulated entities, it might mean strategic centralisation, applying AI for efficiency, and potentially taking control of core AI infrastructure like SAP did with their private LLM. For others, it might mean fundamentally restructuring as an “AI-first” organisation, leveraging democratised skills to disrupt traditional markets.
3. Beware the Product Trap: Building a business solely on a proprietary AI product is increasingly risky in a world of rapid LLM advancement. The value shifts to the service of navigating this change and integrating adapting capabilities.
4. The Game is Strategic, Not Just Technical: SAP’s move wasn’t just tech; it was governance and compliance. AI-first companies aren’t just coding; they’re fundamentally rethinking business models. Surviving commoditisation is about strategic positioning and adaptability.

The floodwaters are rising, no doubt about it. But these case studies show that it is possible to find higher ground – whether by fortifying your position and taking control of critical capabilities like SAP, or by becoming one of the disruptive forces yourself, built for speed and adaptability, like the AI-first players. The pattern that’s breaking is the old stability; the new pattern is constant, AI-driven evolution. Your job is to lead your enterprise through this disruption, building resilience not just in your tech stack, but in your strategy, your structure, and your people’s ability to adapt.

A cracking next step for you would be to conduct a focused internal audit. Based on the SAP example, identify critical enterprise processes involving highly regulated or sensitive data. Then, evaluate your current AI capabilities and potential roadmap against the need for secure, potentially private AI infrastructure that gives you control over that data, assessing the risks and limitations of relying solely on vendor-managed solutions in these specific areas.

The Geographic and Market Variations in Experiencing and Responding to AI Commoditisation

The commoditisation of intelligence and product driven by AI isn’t some abstract future threat; it’s here, and it’s a tidal wave reshaping the enterprise landscape. Intelligence itself is getting cheap, and consequently, products built purely on easily replicated intelligence are facing the abyss. But here’s the pattern that’s not uniform: this flood isn’t rising at the same pace or impacting everyone in the same way. Its velocity and effects vary dramatically depending on where you are geographically and which specific market sector you inhabit. Understanding these geographic and market variations isn’t just academic; it’s absolutely critical for senior leaders trying to find the highest ground and build genuine resilience in this chaotic era.

First off, let’s talk geography. The sources paint a clear picture: the speed of AI adoption and, consequently, the felt impact of commoditisation differs significantly between regions. America, for instance, is perceived as being more open and further ahead in embracing new AI technologies. They’re potentially experiencing the initial swell of commoditisation impacting certain knowledge work and product categories sooner. Europe, on the other hand, is described as more cautious, even “hiding there” or looking on with “scare”. This slower pace isn’t just a matter of cultural temperament; regulatory environments play a massive role. German council regulations, for example, created complexities around sensitive data that required a major enterprise like SAP to go as far as implementing a private ChatGPT instance in Azure, rather than relying on a vendor’s native, less compliant LLM. These stringent data privacy and governance requirements can slow down the adoption of certain AI tools and services, meaning the pressures of commoditisation manifest differently, often demanding more bespoke, secure solutions rather than plug-and-play products. This geographical variance means enterprises aren’t all facing the same immediate pressure; some have a bit more time to adapt, while others are already knee-deep in the rising waters.

Beyond the map, the impact of commoditisation is deeply uneven across different market sectors and types of work. The AI tide hits some shores much harder and faster than others. The sources highlight that basic knowledge work – roles like customer service, bookkeepers, and low-level auditors – are among the first to feel the full force of intelligence commoditisation. The cost of tasks like bookkeeping could plummet from thousands of pounds a year to merely the cost of an OpenAI license. This is the lowest-hanging fruit for AI-driven efficiency, directly disrupting established roles and business models built on these tasks.

However, move “higher up the hill,” and the ground feels firmer, at least for now. Strategic consulting, complex financial analysis, and tasks heavily reliant on tacit knowledge – the kind of wisdom built over years of experience that’s hard to write down or for a “genius six-year-old” AI agent to replicate – offer a degree of temporary refuge. Even within a profession like accounting, while basic bookkeeping becomes commoditised, the strategic advisory component, especially from an AI-savvy accountant who can navigate changing regulations and leverage new tools, remains valuable and might even command a premium. The value shifts from executing repeatable processes to providing nuanced judgment and strategic guidance in a volatile landscape.

Specific industries also face unique challenges and opportunities. In cybersecurity, for instance, AI agents can address critical pain points like alert triage fatigue and managing false positives in vulnerability scanning, which are essentially tasks where humans are “drowning in data”. These are ripe for AI-driven commoditisation of the task, freeing up human analysts from “toil”. Yet, more complex cybersecurity activities like correlating data across disparate systems to spot insider threats or performing autonomous remediation require higher levels of AI capability and trust, placing them further out on the commoditisation curve.

The therapy industry is another example cited as facing potential disruption and commoditisation from AI-powered applications. For businesses in this sector, navigating commoditisation might involve embracing these new AI tools, not as replacements, but as complements or gateways (e.g., an AI app leading users to human therapists) to find resilience. The pattern breaking here involves redefining the service itself in the face of technological change.

Product companies, particularly VC-backed startups, face a different, often more chaotic form of commoditisation driven by the rapid innovation from major research labs like OpenAI and Google. Products built on specific, proprietary AI capabilities developed a few years ago are at high risk of being quickly superseded by advancements from these hyperscalers. An automated testing company, for example, built its value proposition on a type of automation that new, more capable AI agents could potentially replicate or bypass entirely with simple instructions. This makes investors hesitant and forces such companies into a precarious position, potentially needing to pivot rapidly or shift from selling a fixed product to offering an adaptive service. Conversely, “AI-first” businesses like Artesan are emerging by fundamentally embedding AI into their operations from the ground up, potentially operating with disruptive efficiency and rethinking traditional job functions entirely. This represents a strategy of building resilience by embracing the new commoditised core rather than fighting against it.

The interplay of these geographic and market variations means that senior leaders cannot adopt a one-size-fits-all approach to AI and commoditisation. An enterprise in a highly regulated European market operating in a critical infrastructure sector will face different challenges and require different strategies for resilience than an enterprise in the US operating in a less regulated, faster-moving consumer service sector. The former might need to focus on secure, private AI infrastructure and building internal expertise to navigate compliance, as seen with SAP. The latter might need to prioritize speed, identifying and leveraging new AI capabilities to stay ahead of market disruption and competitive pressures.

Ultimately, navigating AI commoditisation requires a deep understanding of where your organization sits within this complex, uneven landscape. It means recognizing that while the broad trend is clear, the specific threats and opportunities are highly localized by geography and industry. Strategic consulting becomes invaluable here, helping enterprises cut through the hype, assess their specific exposure to commoditisation based on their market and location, and identify the “white space” or “higher ground” where value can still be captured and maintained. The pattern to break is the assumption of uniform impact; the new pattern is tailored resilience built on nuanced understanding and proactive adaptation.

A critical next step for any senior leadership team is to conduct a granular assessment of how AI commoditisation is specifically impacting their key markets and core business functions, considering both geographic regulatory landscapes and the specific nature of the knowledge work and products involved, similar to the detailed pain point analysis suggested for cybersecurity or data management.