AI Is Now an Enterprise Resource. So Why Are We Still Managing It Like Software?

For decades, companies have tracked technology spending in straightforward terms: how much software licenses cost, how much infrastructure they consume, and what their IT teams manage. However, the arrival of Generative AI has fundamentally changed the technological landscape, demanding new ways of managing its impact on businesses.

Most companies continue to treat AI as a software expense, but AI is increasingly performing core functions, not merely supporting existing processes. The real question is no longer "how much are we spending on AI?" but "what outcomes is it producing, and who owns them?"

Enterprise AI spending is projected to reach $2.59 trillion by 2026, up 47% year-over-year. Yet, only 39% of organizations can attribute any EBIT impact to their AI usage. The problem isn't rising AI costs per se; it's the lack of visibility into what AI is actually doing, who consumes the most resources, and whether any automation delivers measurable value.

Traditional software governance models built for stable, predictable objects are insufficient for AI, which often changes behavior depending on the model, prompt, data, and context.

A new governance framework is needed—one focused on outcome ownership rather than software ownership. This framework should include five key principles: visibility into where AI is used across workflows and teams; robust evaluation of AI-driven outcomes; clear attribution of costs to specific business functions; proactive management of decision-making capacity as AI increasingly performs tasks without human involvement; and continuous adaptation to the evolving nature of AI technologies.

By addressing these areas, companies can better manage the new operational capacity AI brings and ensure that investments produce tangible, accountable results.