Everything I Know About AI, I Learned in the Boiler Room
When I was younger, working in Facilities, I was given a piece of advice that changed my career:
"Learn every department in this organization. Understand not just what they do, but why they do it."
I took that to heart. Since then, I have spent my career quietly learning. I’ve watched how the loading dock's timing impacts the sales floor’s inventory. I’ve seen how a single HVAC fluctuation can disrupt a data center's uptime. I learned that in facilities, you are the silent engine of the entire organization.
Today, as we stand at the edge of the AI era, I realize that implementing Artificial Intelligence is exactly like managing a complex facility. If you don't understand the "plumbing" of your data and the "wiring" of your human departments, your AI strategy will inevitably leak.
Improving Outside Our Preferences
We often prefer to optimize the departments we find "exciting." But in facilities, you learn that ignoring a "boring" boiler room can shut down the whole building.
AI works the same way. The biggest gains aren't always in high-level strategy; they are in the unglamorous corners of the business—automated maintenance logs, energy optimization, or predictive vendor management. To find these, you have to look outside your comfort zone and understand the entire organization's needs.
Why Data Must Capture Gut Feelings—Not Erase Them
Many of us have built success on instinct and "secret sauce" logic that isn't documented. While that's a competitive advantage, it's also a digital bottleneck. The goal of an AI department shouldn't be to replace your intuition, but to operationalize it.
Recent industry research suggests the stakes of this transition are higher than ever:
- The Productivity Multiplier: IDC projects that AI solutions and services will generate an estimated $22.3 trillion in global economic impact by 2030, with models suggesting roughly $4.90 in economic value for every dollar invested in AI.
- Executive ROI: Recent industry research from Google Cloud reports that around 74% of executives saw measurable return on investment from at least one generative AI use case within a year of implementation.
- Adoption vs. Scaling: While AI adoption is now common—with McKinsey reporting that about 88% of organizations use AI in at least one function—most organizations are still working to scale AI strategies broadly across the enterprise.
Navigating the "Legal Trap" of the Digital Breadcrumb
For those of us used to "instinct-based" decisions, AI presents a unique risk: the permanent record. In facilities, a verbal instruction on the floor used to leave no trail. Today, every AI prompt is a discoverable record.
To scale safely, my approach focuses on:
- Private AI Frameworks: Keeping proprietary operational secrets off public training models.
- Human-in-the-Loop Governance: Using AI to surface the data, but relying on the "Facility Manager's Instinct" to make the final call.
- Cross-Departmental Literacy: Building teams that understand the entire organization's workflow, not just the code.
The Path Forward
AI is the new infrastructure of business. It’s not meant to replace the human element; it’s meant to ensure the organization runs more efficiently than ever before.
I am currently exploring leadership roles where operational literacy and responsible AI deployment intersect. If your organization is looking to bridge the gap between "the boiler room" and "the boardroom," let’s connect.