Case study · AI transformation
From Experiment to Margin
How a mid-market industrial distributor built a real AI operating model in 12 weeks
Most companies don't have an AI problem. They have an execution problem.
Awareness is high. The tools are everywhere — Microsoft 365 Copilot, Copilot Studio, Azure AI. Executive interest is real. Yet in most mid-market organizations, AI still shows up as scattered pilots and individual experiments that never quite change how the business runs. The gap isn't curiosity. It's the absence of an operating model that turns AI from a stack of demos into measurable business outcomes.
We recently spent twelve weeks closing exactly that gap with a mid-market industrial distributor in the metals sector — an operationally intensive, multi-site business serving customers nationwide, with the kind of deep operational discipline where structured AI adoption either sticks or stalls. Here is how the engagement worked, what came out of it, and what we learned.
A proven model, not a one-off project
We don't treat AI transformation as a single initiative with a finish line. We treat it as an operating capability that moves through four stages — TRY, BUILD, SCALE, and OPTIMIZE. TRY proves value in real workflows. BUILD redesigns the highest-impact ones. SCALE embeds AI across the enterprise. OPTIMIZE ties every workflow back to the P&L. The point of the model is sequence: each stage earns the right to the next, so the organization is never betting on a transformation it hasn't yet seen work.
This client came to us at the start of that journey — strong operationally, curious about AI, and wary of the pilot-purgatory so many companies get stuck in.
Twelve weeks from mandate to working agents
We opened with an AI Executive Workshop to secure alignment from the CEO through the leadership team, and to frame AI as a cross-functional priority rather than an IT project. That distinction mattered more than any technical decision that followed. From there, we identified the workflow pain points worth attacking first.
Next we stood up a cross-functional AI Council, drawing “AI Champions” from each department. The Council — not a central IT team — owns workflow identification, agent design, and peer-to-peer training, on a quarterly release cadence tied to its reviews. Putting ownership inside the operating teams is what makes adoption scale.
Over the engagement we deployed seven pilot agents across five functions — Sales, Operations, Finance, Procurement, and HR. They ranged from inventory-shortage detection and days-sales-outstanding monitoring to purchase-order verification against mill confirmations, natural-language querying of sales data, customer prioritization, and an HR policy assistant. Each was vetted by IT for security, built with the operating team that would use it, and owned by the Council. We deliberately started with low-hanging fruit: simple wins that build confidence and momentum.
The result: 5x usage and a culture of experimentation
Usage grew fivefold over the engagement. Functional training — organized by team, not by tool — was aligned to each quarterly agent rollout, creating a repeatable adoption cycle rather than a one-time launch. But the most durable outcome wasn't any single agent; it was a culture of experimentation backed by the governance to sustain it. The experiment is proven. The board's question is no longer whether AI works — it's how it reaches the P&L.
What we learned
- Top-down prioritization is the single biggest unlock for adoption speed. Leaders need a simple, true story: AI works here, in our workflows, for our people.
- Structure beats enthusiasm. Adoption is a process, not an event, and the quarterly cadence is the engine. Enthusiasm alone doesn't scale.
- The best opportunities live deep in the organization. Solutions emerge from operating teams; a cross-functional Council is what makes them scalable, and peer-led training drives adoption faster than any mandate.
- IT enables, but shouldn't gate. IT leadership is essential for security and scale, but workflow ownership belongs to the teams doing the work.
- Every agent needs a workflow owner. Agents that aren't owned don't survive past the pilot.
Where it goes next: from adoption to the P&L
With adoption momentum established, the next phase moves from usage to impact. SCALE maintains the machinery — quarterly agent releases, ongoing training, usage measurement, and Council governance — while OPTIMIZE compounds it, tuning agents to KPIs and to the cost of compute. We've begun mapping each agent to a measurable business outcome, with days sales outstanding, procurement accuracy, and sales velocity as early lead indicators.
Redesigning workflows is a key success factor for the companies seeing the most value.
McKinsey, The State of AI in 2025
That matches what we see on the ground: value doesn't come from buying AI. It comes from rebuilding the work around it — and measuring the result.
The takeaway
The mid-market doesn't need more AI education, or another pilot that goes nowhere. It needs a repeatable way to find where AI matters, deploy it into the right workflows, drive adoption across teams, and measure whether the business is actually improving. That's the operating model we bring — and in twelve weeks, it took one industrial distributor from scattered experiments to a proven foundation for measurable margin.