The Boring Phase of AI Is the One That Pays
Updated: 54 minutes ago
Joaquin "Jack" Fagundo, writing from Parkland, FL, on why AI's move from demos to daily operations is where the real economic impact shows up.
The first wave of generative AI was all demos and pilots. Impressive, sometimes thrilling, and mostly disconnected from how work actually got done. The phase we're in now is quieter, and I think it matters more. AI is becoming part of ordinary work, and that shift from experiment to operation is where the economic impact will finally show up.
"Stabilizing" is easy to misread. It doesn't mean slowing down; investment in AI infrastructure keeps rising, as I laid out in Follow the Megawatts. What's stabilizing is the pattern of use. Companies are moving from scattered experiments to a smaller set of use cases that are funded, measured and supported in production.
I. Where it shows up first
The early payback comes in high volume, repeatable work, which makes customer service and operations the obvious starting point. Software development is next, as coding assistants change how engineering teams plan and staff their work. Knowledge work like drafting, research and analysis is being redesigned around AI tools. And the infrastructure buildout itself, the data centers and the power behind them, has become a visible part of the economy.
II. The questions that haven't gone away
Productivity gains show up when the work is redesigned, not when a tool gets bolted onto the old process. Roles tend to change before they disappear, so training and redeployment plans need to start early. Data protection, model risk and accountability need owners, the same as any other critical system. And energy has quietly become part of AI strategy, because AI's growth depends on power. I looked at the first hard numbers on AI and job cuts in The Loudest AI Warnings Are Coming From Inside the Industry.
Productivity gains show up when the work is redesigned, not when a tool gets bolted onto the old process.
III. What I tell executives
Fund a few use cases well, because depth beats a long list of pilots that never reach production. Measure business outcomes such as cycle time, cost per transaction and customer satisfaction, not model metrics alone. Put governance in place before you scale rather than after an incident forces it. And know where your AI workloads will run and how much capacity they'll need, because that answer takes longer to get than most people expect.
The headlines will keep swinging between hype and doubt. The real story is steadier than either. The organizations that turn AI into dependable operations are the ones that will capture most of the value.
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.
