AI-Built Systems — Shipping Faster with AI
January 24, 2026
AI-Discoverable Products and Services — Can AI Actually Find You?
January 28, 2026
This is part of the blog series, “A practical AI strategy framework — Beyond the hype”.
In the earlier posts, I talked about AI-powered experiences and AI-built systems — how AI changes what we ship and how fast we ship it. This post looks at something less visible, but very important: AI-run operations.
AI-run operations is where AI takes on the behind-the-scenes work that keeps your business running. And most companies are sitting on far more opportunity here than they realize.
This started years ago with AIOps in IT — monitoring systems, detecting anomalies, correlating alerts, and helping teams respond to incidents faster. That foundation still matters, but the scope has expanded dramatically. Today, AI-run operations cut across technology, support, customer service, sales, and HR — essentially any operational function where patterns repeat and decisions slow teams down.
If you’re a digital product company, technology operations are a big part of this. AI can detect anomalies before they become outages, route incidents based on past resolution patterns, or even generate runbooks from previous incidents. Google’s SRE teams have written extensively about this evolution — moving from alert floods to intelligent, context-aware operations that reduce toil.
On the business side, the same pattern shows up everywhere. AI can triage support tickets, surface customer churn risk, flag sales conversations that need intervention, or highlight operational bottlenecks that we humans overlook. This is why tools like AI-enhanced customer support platforms or conversational intelligence in sales feel so compelling — they operate continuously, and learn automatically. McKinsey has pointed out that AI investments in Operations are paying back faster than ever, because operational decisions are frequent, repetitive, and data-rich.
The ROI here is real. Downtime is expensive. Slow support erodes trust. Reactive operations burn out your best people. I’ve seen teams cut incident response times by ~20% simply by letting AI route issues based on who actually solved similar problems before. That’s not magic — that’s AI making obvious connections at a scale humans can’t, once the foundation is in place. And that foundation is where many companies stumble.
Many companies buy AI operations tools and expect intelligence to magically appear. But…
AI can’t detect issues if your telemetry is inconsistent.
AI can’t triage tickets if your categories are a mess.
AI can’t automate responses if there are no clear playbooks to begin with.
The teams that win with AI-run operations do the unglamorous work first. They clean up data. They standardize processes. They create feedback loops so AI learns from real outcomes, not assumptions. Forbes makes similar assertions when they talk about why 95% of AI Pilots fail.
And just like with AI-built systems, humans aren’t being removed here — their role is evolving. Operations teams move from reacting to alerts to overseeing systems, tuning decisions, and preventing problems before customers ever notice. AI-run operations aren’t about replacing ops teams. They’re about giving them leverage.
In my next blog, I’ll look at AI-Discoverable Products and Services — can AI actually find you? Happy exploring — and may your AI-run operations surface issues before customers feel them 🖖
