For decades, technology leaders have wrestled with the same frustrating paradox: a business case loaded with compelling numbers that still can’t get funded. Joe Batista, founder of M37 Advisory and a veteran of more than 650 enterprise engagements, including services providers such as Dell Technologies and Hewlett Packard Enterprise, contends that the challenge is rarely the budgetary math — it’s that the math is answering the wrong question.
Batista calls it the “color of money” problem. Every executive sponsor sees value through a different lens — cost reduction, revenue growth, risk mitigation, regulatory compliance — and a business case that doesn’t speak the language of the person holding the budget is dead on arrival. In the Q&A below, Batista explains how CXOs and enterprise technology leaders can reframe their investment arguments, navigate tighter budget cycles, and apply the same discipline to today’s wave of AI and autonomous systems spending.
When you talk about the “color of money,” what do you mean?
Joe Batista: When I say “color of money,” I’m getting at the idea that value looks different depending on who you’re talking to. A CFO, a business unit leader, and a risk officer can look at the same project and see three very different payoffs. One cares about operating costs and cash flow, another about revenue growth in a specific product line, another about avoiding regulatory trouble or a major breach. My point is that there is no generic ROI that works for everyone. If you show brilliant savings in a budget your sponsor doesn’t own, or you solve a risk problem your audience doesn’t feel responsible for, then you’ve picked the wrong color of money for that conversation.
Why do so many IT business cases fail this “color of money” test?
Joe Batista: A lot of business cases are engineered from the IT point of view instead of the buyer’s point of view. You’ll see a beautiful stack of numbers about infrastructure savings, or power and cooling, or utilization, and they’re all technically correct—but they don’t map to how the sponsor gets measured. Early in my career, I learned that the hard way. I walked into a big account with a very thorough analysis of data center and facilities savings, and the executive looked at me and basically said, “That’s not my budget, so I don’t really care.” That’s the classic failure mode: the math is fine, but it’s answering a question the decision-maker isn’t asking.
How should CIOs and IT leaders figure out which “color of money” matters on a given initiative?
Joe Batista: You have to do some interviewing before you do any modeling. I always want to know whose budget is actually on the line, what metrics are on that person’s scorecard, and how they would need to explain this project to their own leadership or the board. I also want to know if this is something they’re doing because they’re forced to—say, regulatory pressure or an obvious risk exposure—or because they think it would be nice to improve how things run. Once you have that picture, the right “color” tends to reveal itself. You stop talking in abstract ROI and start talking in the specific language that matters to that sponsor.
How does this shape which projects get funded and which don’t, especially with tighter budget cycles?
Joe Batista: With budget cycles compressing, more projects are being asked to show results in a year or less. That doesn’t mean every single thing has to hit a twelve‑month return, but it does mean you can’t be vague about value anymore. If something is a “need to have”—for example, it keeps you on the right side of regulators or it closes a serious risk gap—it can justify a longer payback because not doing it is simply not an option. But if something is a “nice to have,” it has to prove, very clearly, that it moves the needle in the color that matters to the sponsor, and it has to do that fairly quickly. That mix of urgency, value type, and time horizon is what decides whether a project gets greenlit, piloted in a smaller way, or left on the cutting-room floor.
How do you apply this “color of money” lens to AI and agentic or autonomous systems?
Joe Batista: With AI, I start from the same place: what outcome are we really chasing, and in what color? Are we trying to reduce a particular category of cost, grow a specific revenue stream, lower a clearly defined risk, or meet a concrete compliance obligation? Once I know that, I can ask whether AI or autonomous systems are actually the right tools to get us there and how we’d prove that they did. If someone walks in and says, “We need agents” or “We need AI” without being able to say which metric this will move and how fast, then to me that’s not an investment case; it’s a science project. The technology only matters when it’s tied to the color of money that the organization—and the sponsor—actually care about.
