The Missing Link To AI ROI: Connecting AI Consumption To Business Outcomes

Jesse Stockall is Chief Architect, Flexera.

Enterprise AI is entering a new phase. Organizations are moving beyond experimentation and embedding AI into products and business processes. As adoption accelerates, leaders are finding AI costs easier to understand than its value. New AI applications are appearing faster than traditional governance and budgeting approaches can adapt. The challenge is not simply tracking AI spend. It is understanding what that spend produces.

What has stood out to me as organizations navigate this shift is how quickly the conversation is moving from what AI costs to what that investment actually produces. Thirty-six percent of leaders report overspending on AI applications and 14% cite wasted AI spend, according to Flexera research. Yet spend visibility is only the foundation. To demonstrate ROI, leaders must connect AI consumption to the work performed, the quality of the result and the business outcome achieved.

That requires a progression from discovery to outcome attribution, to continuous optimization and control.​

Step 1: Discover What You Have

During the AI experimentation phase, organizations adopted a flurry of new tools, ballooning their technology stacks. As a result, leaders no longer have one clear place to see it all.

Traditional management processes assumed more predictable software, infrastructure and ownership. AI disrupts that model as it appears across compute, applications, agents, workflows and data infrastructure. An agent may reason, invoke tools, call multiple models, access data and trigger downstream actions to complete a task. One business outcome can create costs across several systems.

Organizations should begin by inventorying every AI application, API, model, embedded AI feature and agent in use. When the data remains siloed, each team sees only one part of the investment and no one can assess the economics of the complete system.