AI can cost more than human workers now

As AI model costs continue to rise due to increased compute requirements and infrastructure investments, organizations may find AI solutions more expensive than traditional human workforces for certain use cases, fundamentally challenging the ROI assumptions underlying many AI initiatives. IT leaders must conduct rigorous cost-benefit analyses of AI implementations, accounting for total cost of ownership including compute, licensing, maintenance, and integration expenses, rather than assuming AI automatically delivers cost savings. This shift requires a more strategic, use-case-specific approach to AI adoption where business value and competitive advantage—not just cost reduction—become the primary drivers of investment decisions.