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By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions AI adoption in mid-market rarely fails because the models are wrong. It fails because the people who were supposed to use the models never do. We spent 14 weeks with a $215M specialty hardware distributor rebuilding what "adoption" actually meant inside their walls, and the number that finally moved was not model accuracy. It was daily active use by the people who touch orders. The Client and the Hole

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions Most Copilot for mid-market rollouts we inherit look the same. Someone bought M365 Copilot licenses for the whole company, sent a getting-started email, and expected productivity. Six months later the licenses show 12 percent active use, IT gets asked why the answers are wrong, and someone drafts a memo about pausing the program. We were brought into a $295M industrial parts distributor last spring afte

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions Most mid-market distributors we meet have already run one or two AI experiments. A ChatGPT pilot in customer service. A Copilot rollout that nobody used after week three. A vendor demo that promised "predictive parts ordering" and never made it past the sandbox. When these fail, the reflex is to blame the technology or the company. Both are usually wrong. What failed was scoping. The team ran an AI proof o

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions AI transformation for distributors gets sold as a growth story. Buy the platform, plug in the agents, watch the funnel light up. That framing collapses on contact with a real mid-market distributor. Most of the businesses we work with are not stagnating because they cannot acquire customers. They are stagnating because their operating environment has quietly stopped functioning as a system. Orders live i

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions An AI operating system for mid-market is not a platform you buy. It is an organizing principle that decides which systems own which decisions, where knowledge lives, and where AI is allowed to help. This case study walks through a 22-week build at a $410M commercial equipment distributor where we rebuilt the operating backbone on M365 first, added three domain systems on top, exposed a customer-facing po

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions A boutique AI consulting firm wins mid-market work the same way a good general contractor wins residential renovation work. Not by having more people. By showing up with the right people, refusing scope that does not fit, and staying on the job until the client can run the system without us. This case study walks through an engagement at a $195M specialty hardware distributor where two national systems i

Case study of what AI automation consulting looks like at a $280M industrial parts distributor. 18-week engagement, 3 workflows: invoice matching, quote-to-order, slow-moving inventory. Fixed hourly. Handoff to one internal Ops Analyst.

Case study from a $420M Agricultural Equipment Distributor. Over 18 weeks we built AI process automation across warranty adjudication, parts kitting, and dealer order routing. Warranty cycle 4.2 days to 1.1 days, first-pass approval 71% to 86%, dispatcher time cut roughly in half.

Case study from a $310M regional Building Supply Distributor after 14 months trying to hire an AI architect full-time. We ran a 36-week fractional AI architect engagement, ~40% of full-time cost, shipped 3 workstreams, left 6 named owners behind.

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions Most LLM implementation projects we walk into died at pilot. A vendor sold the exec team on a chatbot, the sales team never used it, and now the CFO wants to know what the $180K spend produced. We get called after that happens. This post is a case study on doing it right the second time, at a Commercial Equipment Distributor where LLM implementation was tied to a real revenue metric: quote turnaround tim

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions Most AI strategy consulting engagements we get called into skip the boring layer and go straight to the model. The client wants a copilot, an agent, a chatbot. What they actually need first is a knowledge foundation that any model can retrieve from without hallucinating. We built one for a specialty hardware distributor over 12 weeks, and this is what the sequence looked like in practice. The client is

When an industrial parts distributor asked us for RAG architecture consulting, they had already tried the retail version of the story. They had bought a chat interface, pointed it at a document library, and shipped it to their inside sales team in six weeks. Ninety days later the tool was quietly dead. Reps did not trust its answers. Half the responses cited the wrong document. The remaining half hallucinated part numbers that did not exist in the catalog.

When an agricultural equipment distributor signed our enterprise AI integration services engagement, they did not need another dashboard. They had eleven of them. What leadership actually needed was a predictive decision layer that combined finance, supply chain, dealer behavior, and operational data into one place a chief of staff could query in plain language, in under sixty seconds, before any executive meeting.

Phase 4 case study. We sequenced a 24-month modular cloud ERP rollout for a $180M building supply distributor using Dynamics 365. Three waves: Finance/CRM, then OMS/WMS, then TMS. 11 months in, all 6 branches live.

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions When a $140M commercial equipment distributor asked us to help them deploy AI agents across their operation, the executive team had already watched two pilots stall inside the previous year. Both followed the same pattern: a vendor demo, a wide-scope agent build covering ten departments, six months of integration work, and a launch that no one used. The agents could answer questions about the company. Th

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions When a specialty hardware distributor asked us to lead their AI implementation, the leadership team came in with a list of vendor pitches. Copilots for sales. An AI agent for purchasing. A generative model bolted onto their ERP. Each pitch had a polished deck, a reference customer, and a six-figure annual price tag. We declined to start with any of them. The first phase of credible AI implementation co

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions Mid-market distributors spend a lot of money on AI tools that produce nothing because the data underneath them is fragmented across five systems that do not talk to each other. We have walked into enough of these to recognize the pattern within an hour: a legacy ERP doing 60% of the work, a CRM that nobody trusts, a separate warehouse system, freight booked manually in spreadsheets, and finance running i

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions Most mid-market distributors we talk to about agentic automation are skipping three layers of work. They want copilots in the C-suite before they have documented processes, before they have a knowledge management system, and before their operational data is structured well enough for a model to reason against it. That gap is where pilots die. We engaged with an agricultural equipment distributor runnin

AI readiness consulting case study: how we documented and costed ~140 workflows for a mid-market building supply distributor before any AI build, recovering 10-15 admin hours/week and exposing the true cost of every recurring process.

By Ed Hitchcock, Enterprise AI Systems Architect, SupplyTech Solutions The Problem We Walked Into A mid-market commercial equipment distributor came to us with a familiar pattern. Three decades of operational knowledge lived in legacy on-prem file shares, departmental OneDrive accounts, individual desktops, email threads, and a small constellation of departmental SharePoint sites that had been spun up without a shared metadata model. Roughly 40% of the documents employees