Don't Cut Technology Spending. Fix It.
Updated: 1 hour ago
Joaquin "Jack" Fagundo, writing from Parkland, FL, on why optimization beats across the board cuts, and where AI helps and hurts.
When budgets tighten, the easiest instruction to give is a flat cut across every line. It's also the instruction most likely to damage the business. In my experience running large technology budgets, the savings that last come from optimization, not reduction.
The difference is worth spelling out. Reduction removes spend. It's fast and visible, and it often quietly reverses within a year when the work it supported comes back. Optimization improves the return on what you spend. It takes longer, but it changes how the organization buys, uses and retires technology, and that change sticks.
Where AI earns its keep
AI is good at finding waste. Usage analytics spot idle cloud resources, oversized instances and unused licenses much faster than a manual review. Models built on consumption history give finance and engineering a shared view of where costs are heading, which settles a lot of arguments before they start. Routine work like scheduling non production environments, rightsizing and storage tiering can run continuously instead of once a quarter. And AI tools can compare contract terms against actual use before a renewal negotiation instead of after it.
Where AI adds to the bill
AI is also a new line item, and it can grow quickly. Inference, GPU capacity and data platforms add up, and the recurring bill comes from running models every day, not from training them once. AI workloads deserve exactly the same discipline as the rest of the estate.
Cutting spend is a decision. Optimizing spend is a capability.
The order I'd do it in
Get visibility first by tagging and allocating cloud and software spend to the teams that consume it. Give every major cost line both a business owner and a technical owner. Rightsize, retire and consolidate before you cut anything. Automate the guardrails, so budgets, alerts and policies act without waiting for a monthly review. And let teams reinvest part of what they save; the ones who see savings fund their own priorities tend to keep finding more.
The leaders who build that capability, with AI and FinOps doing the heavy lifting, end up spending less and moving faster at the same time. For practical starting points, see There's Money Hiding in Your Technology Budget and Follow the Megawatts.
Joaquin "Jack" Fagundo writes about data centers, cloud and the business of AI from Parkland, FL. He has spent 20+ years leading enterprise infrastructure and advises companies through JFagundo Consulting Services. Read more about Jack or find him on LinkedIn.
