6-minute read | Case Studies, AI Strategy
I want to tell you about a company that almost didn’t make it to their AI implementation at all.
Not because of budget. Not because of leadership resistance. Because six months into the project, with the infrastructure work still unfinished, a senior stakeholder walked into a steering committee meeting and said the words that kill more AI initiatives than anything else:
“Can’t we just skip ahead to the good stuff?”
They couldn’t. And the reason why and what happened when they didn’t is worth understanding before you start your own AI journey.
Where They Started
When I first engaged with this mid-sized manufacturing client, they had all the hallmarks of a company ready to invest in AI and not yet ready to succeed with it.
Strong leadership team. Clear appetite for change. A list of AI use cases that would have impressed any vendor on the market. And underneath all of it, an IT infrastructure that had been patched, extended, and worked around for the better part of fifteen years.
Their operations teams were running manual processes for incident detection. Technicians physically reviewing logs, cross-referencing systems that didn’t talk to each other, making judgment calls based on experience rather than data. When something broke, the average time to identify the root cause was measured in hours. Sometimes days.
The cost wasn’t just in downtime. It was in the 24/7 staffing required to maintain a level of vigilance that a well-designed system should handle automatically. It was in the senior engineers burning their expertise on repetitive triage instead of actual problem-solving. It was in the decisions that never got made because nobody had clean enough data to make them confidently.
They knew all of this. That’s why they called us.
The Conversation That Changed the Scope
My first site visit wasn’t about AI. It was about plumbing.
I spent two days mapping their data flows; where information originated, where it traveled, where it stopped, and where it disappeared entirely. What I found was a picture most businesses would recognize: a patchwork of systems that worked well in isolation and created invisible friction the moment you needed them to work together.
Three separate monitoring platforms with no unified view. Alert data that lived in one system, historical performance data in another, and the institutional knowledge about what those alerts actually meant sitting in the heads of five engineers who’d been there for a decade.
There was no malicious design here. Every one of those systems was purchased to solve a real problem. But nobody had ever stepped back and asked: what happens when we need all of this to feed a single, coherent intelligence?
I had to tell the client something they didn’t particularly want to hear: before we could build anything with AI, we needed to spend real time and real money on the foundation. Unified data architecture. API integration across platforms. A governance layer that defined what the data meant, who owned it, and how it would be maintained.
The reaction in that first meeting was what I expected. Silence, then pushback, then the familiar question: can’t we just skip ahead?
The answer was no. And here is why that turned out to be the right call.
What the Foundation Work Actually Looked Like
Over the next several months, we built the infrastructure that the AI would eventually run on. This is the work nobody puts on a press release, but it’s the work that determines whether your investment returns a dollar or costs you two.
We consolidated the three monitoring platforms into a unified observability layer with a single data schema; one definition of what an alert meant, one place where historical patterns lived, one source of truth for every downstream decision.
We built the API integrations that let systems talk to each other in real time rather than through manual data exports. We established data governance standards; written policies, assigned ownership, defined retention and quality rules, so the AI would have something reliable to learn from.
We documented the institutional knowledge. Those five senior engineers who carried everything in their heads? We spent weeks systematically extracting their pattern recognition and encoding it into the training data. That step alone was one of the most valuable things we did.
And throughout all of it, we kept the steering committee informed with a 24-month Azure AI roadmap that showed exactly where every dollar was going, what it was building toward, and what the milestones looked like on the way there. When that stakeholder asked to skip ahead, we pulled up the roadmap and showed them exactly what “skipping ahead” would cost in terms of the outcomes they actually cared about.
They stayed the course.
The Results
When we deployed the anomaly detection system, it had something most AI implementations don’t: genuinely good data to work with. Clean, structured, historically rich, and representative of how the environment actually behaved.
The difference was measurable almost immediately.
Incident detection improved by 38%. Problems that previously required a human to notice, investigate, and escalate were being surfaced automatically with context, with historical pattern matches, and with a recommended action, before most technicians would have even opened their monitoring dashboard.
The cost impact over the following year exceeded $2 million. Some of that was direct; reduced overtime, fewer escalations to senior engineers, lower third-party support costs. More of it was indirect: decisions that got made faster because the data behind them was trustworthy, capacity that got freed up for higher-value work, and a reduction in the kind of slow-moving operational debt that compounds quietly until it becomes a crisis.
The five engineers who used to spend their days on triage are now working on the next phase of the AI roadmap. That’s probably the outcome I’m most proud of.
What This Story Is Really About
I tell this story not to describe a technology project, but to describe a decision.
The decision to do the hard, unsexy, expensive infrastructure work before the exciting AI work. The decision to stay on that path when the pressure to shortcut it was real. The decision to treat AI not as a product you purchase but as a capability you build on a foundation that has to be able to hold it.
That decision is available to every business. It doesn’t require a massive budget or a Fortune 500 pedigree. It requires being honest about where you actually are before you commit to where you want to go.
Most AI projects that fail weren’t doomed by bad technology. They were doomed by that one question. Can’t we just skip ahead? and a leadership team that didn’t have anyone in the room to say no with a good enough reason.
That’s what we do at Summit AI Business Solutions. We’re the people in the room with the reason.
If you’re planning an AI initiative and want an honest assessment of whether your foundation is ready to support it, that’s exactly the conversation we’re built for.
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Russell Love is the Founder & CEO of Summit AI Business Solutions, based in Browns Summit, NC. With 20+ years of enterprise transformation experience at IBM and Kyndryl, Russell has led AI and infrastructure programs for some of the world’s largest organizations — and now brings that same discipline to businesses of every size.