Overnight, AI became the hottest technology in decades. Eager to capitalise on the technology’s potential to deliver differentiated capabilities that could elevate their entire business, enterprise executives instituted corporate mandates to develop AI-driven applications that would change the game.
The results were immediate. Today, just under 90% of all enterprises use AI to support at least one corporate function, according to consulting firm McKinsey and Company, with 56% of all organisations applying the technology across three or more discrete tasks. This pace of adoption is unprecedented. Stanford University’s 2026 AI Index Report said 53% of the global population was using generative AI within three years of widespread availability. For comparison’s sake, it was 12 years before the personal computer reached 40% of the population.
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The appeal is obvious: executives see AI’s promise as an engine to accelerate cost savings and operational efficiencies while delivering data-driven analytics that produce better outcomes and rapid revenue expansion. On paper, the technology is a game changer, the kind that has corporations pressing lines of business to quickly develop and deploy AI-driven applications to realise fast returns. But the reality, particularly given the nascent nature of the technology and the relatively limited experience of most organisations in developing and deploying it, presents a much more complicated reality.
Figures like the 95% AI project failure rate quoted by the Massachusetts Institute of Technology (MIT) in the research university’s 2025 study of 300 public AI implementations accounting for a $30bn to $40bn total investment raise widespread concerns about poor outcomes and an imminent AI bubble implosion. Broader studies show that while less drastic, early AI results have delivered less than stellar results. IBM Institute for Business Value (IBV) research reported that the 1,250 IT executives surveyed in the summer of 2026 saw a return on their AI investment of 17%. The result: nearly two-thirds of all AI projects fell short of corporate goals. Separate IBM research pointed to internal technical oversight and management issues often beyond IT’s control, can cut into 20% of AI returns. These issues range from disjointed processes, erratic measurement, and limited visibility and high upfront costs.
The source of much of this friction is trying to apply AI project budgets across multiple departments where priorities and end goals may be wildly different. The simple guidance for enterprises, particularly those with limited AI experience and even lower success rates, is to allocate resources to smaller, more focused AI projects initially. The goals should closely align with the unit’s requirements and should, by their nature, be easier to monitor and measure and need fewer resources. Enterprises can leverage learnings from these individual engagements over time, which they can then scale for larger inter-departmental projects.
As with any cutting-edge technology investment, failures are bound to happen more frequently than any organisations want to see. The key is to strike the right balance of adequate resource allocation and manageable implementations with clearly established governance over monitoring, management and overarching objectives.
