Why Most AI Projects Will Fail — And How to Find the Companies That Won't

The current AI landscape is shifting from speculative experimentation to a rigorous demand for measurable business outcomes and return on investment. While companies initially prioritized AI strategy to satisfy board pressure, the focus has pivoted toward financial accountability as infrastructure costs for frontier models skyrocket. Steve Lucas, CEO of Boomi, emphasizes that data infrastructure—the essential energy powering AI—represents the most durable investment opportunity rather than the models themselves. Organizations must prioritize data quality and trust, as AI adoption remains contingent on human reliability. Furthermore, the long-term sustainability of current AI business models depends on moving beyond consumer-level usage toward deep enterprise integration. Ultimately, while AI holds transformative potential for fields like healthcare, its immediate corporate value hinges on demonstrating efficiency through concrete, data-driven results rather than serving as a convenient justification for workforce reductions.