The substantial cost savings that many businesses expected artificial intelligence to deliver have yet to materialize. In fact, 4 in 10 companies have seen cost reductions of 10% or less, a new survey from Bain & Co. found, while only 4% globally have achieved savings greater than 30 percent.
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Nevertheless, a small group of companies is reaching their savings targets by treating data access, governance and process redesign as CEO-level problems, rather than IT problems. Bain & Co. shared several recommendations to help businesses maximize AI savings:
- Pay down workflow debt before deploying AI. The single most costly mistake in AI deployment is automating a broken process. The question to ask before any AI program is approved is not “Where can we apply AI?” but “If we were designing this process from scratch today, what would it look like?” Only then should the technology conversation begin.
- Validate the investment case and name a governance owner before programs launch. Before approving the next wave of AI spending, CFOs should audit actual returns from prior automation programs, not projected returns. CEOs must answer one question their IT function cannot answer for them: “Who is personally accountable when an AI agent makes a consequential wrong decision in production?” Accountability must be established in advance.
- Use AI to solve the data problem. Imperfect data infrastructure is the most cited reason to defer AI investment, but also the least valid one. The more productive posture is to sequence AI investments to start where the data is already bound and accessible, and to use AI itself to improve how data flows through the organization. The fastest path to value is often automating one repeatable, high-value workflow where humans currently are pulling data manually, consolidating spreadsheets and producing reports, and replacing that entire sequence with AI.
- Redesign the operating model, not just the process. Deploying AI agents without changing how people work around them almost guarantees that an organization will underdeliver on the business case. The organizations capturing transformational savings have leaders who recognize that the human operating model is as important to redesign as the process itself.
- Measure outcomes at the enterprise level, not the program level. Programs will always optimize for what they were designed to measure, typically cost and hours saved. But what matters for the enterprise is whether AI investment is producing better decisions, faster responses and stronger customer outcomes. If those metrics aren’t on the CEO’s dashboard, programs will keep delivering the wrong things efficiently, and the value gap will persist regardless of how much the budget grows.
“The turning point for most companies is not finding the best AI technology,” the survey report concluded. “It’s the moment when leaders decide—before the next budget cycle, before the next vendor pitch, before the next program launch—that they have a personal responsibility to create the organizational conditions for AI success. The window to make that decision ahead of the competition is still open, but it’s narrowing faster than many executive teams realize.”
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