Summary Bullets:

• Concerns about missing the AI boat have many corporate executives pushing AI application development mandates.

• However, research indicates that far too few organizations are realizing the kind of expected dividends in terms of cost savings, revenue generation, productivity gains, and innovation. What can they do differently to make the most of their investments?

To say artificial intelligence (AI) has become ubiquitous is almost an understatement. Just under 90% of all enterprises today use AI to support at least one corporate function, according to consulting firm McKinsey and Company, with 56% of all organizations applying the technology across three or more discrete tasks. This pace of adoption is unprecedented. Stanford University’s 2026 AI Index Report says 53% of the global population had been using generative AI (GenAI) within three years of widespread availability. For comparison’s sake, it was 12 years before the personal computer reached 40% of the population.

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 realize fast returns. But the reality, particularly given the nascent nature of the technology and the relatively limited experience of most organizations 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 $30 billion to $40 billion 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 reports that the 1,250 IT executives surveyed in mid-2026 had seen 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 points to internal technical oversight and management issues often beyond IT’s control, which can cut into 20% of AI returns. These issues range from disjointed processes, erratic measurement, 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 organizations 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.