Beyond the Intelligent Delusion: Why AI’s Promise Still Depends on the Unfashionable Discipline of Product and Platform Thinking

In boardrooms around the world, a new sense of certainty is spreading: artificial intelligence—once limited to small experiments and pilot projects—has now pushed its way into the centre of corporate growth strategies. According to The Global Intelligent Delusion Report by Emergn(*), 77% of senior leaders expect new AI solutions to generate real business value within the next 12 months, almost matching the 81% who still rely on traditional efficiency gains. Remarkably, not a single respondent believes returns will take more than two years. In today’s digital transformation race, patience is no longer part of the plan. Yet amid this optimism sits an uncomfortable truth. Many firms remain trapped in what Emergn dubs the “intelligent delusion”: the belief that AI tools alone deliver transformation. They do not. Early wins are real enough, but scaling those wins—turning isolated sparks of automation into sustained enterprise outcomes—continues to elude most organisations.

AI, it turns out, is not necessarily a technology problem. It is an operating-model challenge.

The rise of product power—and the limits of ambition

The survey data chronicles a quiet restructuring of corporate governance. Product management, once relegated to backlog hygiene and feature delivery, is suddenly ascendant. Sixty-six percent of executives say product management will be critical to company strategy over the next year, up from 27% in the previous cycle. Meanwhile, 44% of organisations hired a Chief Product Officer in the last 12 months, and 88% increased investment in product roles, tools and processes.

This shift is not ideological. It is financial. Leaders report that investment in product teams is driven primarily by two pressures: to ensure AI is implemented successfully (42%) and to integrate AI/ML capabilities directly into products and workflows (41%). AI, in other words, has become a forcing mechanism: either organisations learn to ship value like product-led firms, or they face ballooning expectations with diminishing returns. Yet ambition outruns capability. A striking 57% say expectations for AI outpace their organisation’s ability to deliver; 34% report projects taking longer than anticipated; 29% say AI has yet to live up to its promise; and 31% confess to multi-month delays in digital programmes due to inadequate training.

Even management confidence is wavering. Some 68% percent believe their managers lack sufficient skills, and 32% feel they haven’t received adequate training to keep up with AI-driven change. The result is predictable: fatigue, uneven execution and a widening gap between aspiration and operational reality.

The human bottleneck

Despite the hype, AI still relies heavily on human stewardship. The hardest skills to recruit for? Data and AI fluency (48%), followed closely by critical thinking and analytics (36%) and cross-functional leadership (27%). Far from replacing product roles, AI is amplifying them: 81% say AI makes the product manager’s job easier, but 27% also report rising stress as expectations escalate. Product teams are asked to do more with more. They must interpret ill-defined AI mandates from boards, govern models with opaque behaviour, craft value hypotheses and integrate new capabilities into legacy architectures. Without a strong operating model—the connective tissue linking AI ambition to customer, financial and operational outcomes—even the most sophisticated AI tools remain ornamental. And this is where many firms stumble. They have clarified definitions of AI (85%) and translated those definitions into targeted solutions (71%).

Yet clarity has not yielded consistency. Execution remains patchy, paced unevenly, and undermined by insufficient training, weak governance and the absence of a coherent product-centric framework.

The architecture that AI forgot

AI’s promise depends less on algorithms than on integration: integrating AI into workflows, into customer journeys, into operational systems and into decision-making cycles. The report underscores that AI’s value materialises only when paired with clear problem framing, disciplined experimentation and cross-functional product governance.

But here lies the rub: most enterprises are simply not architected for this. Their technology estates are sprawling, heterogenous, and entangled with decades of legacy systems. Their workflows cross boundaries AI cannot navigate without orchestration. Their data lives in silos that resist training and inference at scale.

This is why the most successful organisations are quietly investing not only in AI but in platforms—structured frameworks that standardise the messy middle between data, systems, people and processes. Enterprise-grade low-code environments such as Mendix exemplify this shift. They give firms a common framework through which AI capabilities can be operationalised across teams and systems:

• accelerated application development to operationalise AI insights quickly;

• workflow orchestration to embed intelligence into end-to-end processes;

• standardised integration layers to bridge modern SaaS with ageing core systems;

• governance models that align product teams, compliance, data stewardship and DevOps;

• reusable components that convert experimentation into repeatable enterprise patterns.

In other words, they address the very execution gap that Emergn’s report identifies. AI succeeds not when a model works, but when an organisation can repeatedly turn models into products, and products into outcomes.

The beginning of the post-tool era

If the last decade was defined by the accumulation of AI tools, the next will be defined by the ability to embed them—reliably, responsibly and repeatedly. What Emergn calls the “intelligent delusion” is simply the misunderstanding of where value sits. Tools generate potential; operating models and platforms convert it.

The firms that will lead the next wave of productivity are those that treat AI as part of a structured system, not an accessory. They will not rely on isolated proofs of concept, nor expect AI to compensate for organisational shortcomings. Instead, they will make three hard but necessary bets:

1. They will invest in product leadership and cross-functional capability building, recognising that human judgement—not model sophistication—is the ultimate bottleneck.

2. They will adopt integrated platforms that standardise orchestration, governance and delivery, reducing the tax of legacy environments and fragmented tooling.

3. They will create operating models that tie AI funding to measurable customer, operational and financial outcomes, reinforcing discipline over enthusiasm.

The data makes one conclusion inescapable: the technology is ready. The question is whether enterprises are.

AI will not transform organisations until organisations transform the way they deliver AI. And that begins not with another pilot, but with the unglamorous, long-overdue work of building the structures required to make intelligence—artificial or otherwise—truly scale.

(*) "The Global Intelligent Delusion Report" was produced by Emergn. The study draws on responses from over 700 business leaders across North America, Europe, and Asia, giving it strong cross-industry and cross-regional relevance. Its findings capture how senior executives are framing AI’s near-term value and the organisational capabilities required to realise it.