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Remember when cybersecurity was an IT problem?
Then breaches started hitting revenue. Headlines. Stock prices. Suddenly every board meeting had a cyber risk slide. CISOs got invited to the table, and security budgets stopped being an afterthought.
AI is doing the same thing to cloud economics.
Remember the pitch? No more buying hardware upfront. No more amortization schedules. No more guessing what capacity you'd need in three years and praying you got it right.
Cloud meant pay for what you use. Scale up when you need it, scale down when you don't. Convert CapEx to OpEx. Let the infrastructure flex with the business, not the other way around.
And it worked, mostly. Building product got easier. More product got built. Revenues rose. Boards saw the top line move and said "great, keep going" and stopped paying attention to the details. Which cloud, how it was architected, what got spent on what. None of that mattered at the board level. Cloud was a math problem, not a strategy one.
Today, the question isn't "are we making more than we spend on cloud?" It's "how do we deploy AI to move the business forward?" We've been hearing AI will change business. Now we're watching it happen. And that means cloud decisions aren't math problems anymore. They're directly tied to customer outcomes, margins, and competitive position.
Take a company deploying an AI support agent. Model choice affects answer quality and cost. Prompt design and tool use change token volume and latency. Guardrails affect resolution rate and escalations. These aren't infrastructure decisions. They're product decisions that shape margin, retention, and support cost.
When cloud was just infrastructure, getting something wrong meant an awkward budget conversation. Maybe you overpaid for capacity you didn't use. Maybe you had to scramble during a traffic spike. Annoying, but contained.
With AI, mistakes hit customers directly. Provision too little and your chatbot throttles during peak hours. Pick the wrong model and response quality tanks. Cut costs in the wrong place, and your LLM starts hallucinating more.
Traditional FinOps optimizes for spend. FinOps for AI must account for both efficiency and outcomes. Miss that distinction and you end up cutting response length to save tokens while escalations spike, downshifting models while quality drops, setting budget caps while reliability breaks at peak. Cheap AI that customers don't trust is expensive.
If you can't show it on a dashboard, you can't govern it. But the starting point matters. Don't start with "AI spend." Start with "cost per outcome."
For the support agent example, cost per resolved ticket is the number that matters. Pair it with deflection rate, escalation rate, and the CSAT delta between AI-resolved and human-resolved. This is where spend connects to business value or doesn't.
You also need a forecast view: AI spend vs. budget on a weekly basis, a 30 and 90-day projection, and an indicator showing whether usage growth is outpacing revenue growth. That last one is your early warning for margin erosion. If usage is compounding faster than revenue, you may be in trouble.
Then there's the ownership view. Which teams and products are responsible for spend. Where the levers are: caching, routing, model tiering, prompt efficiency. This is where finance and engineering stop debating the bill and start fixing things together.
The support agent is one use case. But AI is showing up everywhere: in how companies sell, build, and operate. The organizations deploying it are moving faster and doing more with less. This is just scratching the surface.
Cloud used to be math. AI makes it strategy. And just like cyber became a board priority when companies realized breaches could tank a quarter, cloud economics is becoming a board priority as AI becomes central to how businesses compete.
The only question is whether your company figures it out before or after the board starts asking.
If you're building AI into your product, this is worth getting right early. We can help.
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