For mid-market manufacturers & retailers

Your AI pilot is still a pilot.

Almost every mid-market manufacturer ran an "AI initiative" in the last year. Most are still demos — impressive in a meeting, untouched in operations. The model was never the problem. Shipping it into the real workflow, and getting people to actually use it, is.

I was VP of AI at a $250M furniture manufacturer. I shipped agents into real operations — and watched nine of ten "AI projects" die in pilot. This is the playbook for the tenth: five high-ROI agents, live and in use, in 30 days.

~95%of enterprise GenAI pilots deliver no measurable P&L impact — and MIT found the bottleneck is adoption and integration, not the model.

Why the 90% stall

It's a chatbot, not a workflow. A general assistant nobody's required to use. The 5% embed the agent inside an existing job, so using it is the path of least resistance.

No success metric. "Explore AI" isn't a goal. Without an hours-saved or error-rate number, there's nothing to defend at budget time.

No production-readiness. No evals, no human-in-the-loop on high-stakes steps, no guardrails — so one bad output kills trust and the project.

No owner, no adoption plan. It's a science project on the side of someone's desk, not an operational tool with a champion.

The 5 agents worth building first

High-frequency, document-heavy, low-ambiguity workflows — where agents earn trust fast in a manufacturing/retail ops setting.

AGENT 01

Supplier-doc intelligence

RAG over supplier specs, POs, certs, datasheets. "What's the lead time / spec / compliance status on X?" answered in seconds instead of an email chain.

Saves: hours/week of purchasing & eng lookups

AGENT 02

Order & quote hygiene

Reviews incoming orders/quotes for wrong configs, pricing errors, missing fields — flags them before they hit the floor and become a rework cost.

Cuts: costly downstream errors

AGENT 03

Ops / QBR prep

Pulls from ERP + BI to draft the weekly ops review and flag exceptions — late jobs, margin slips, at-risk orders — so the meeting starts at the answer.

Saves: a day of analyst prep

AGENT 04

Order-status & service triage

Handles "where's my order," tier-1 customer questions, and routes the rest with context — off the CSR's plate, with a human in the loop on anything sensitive.

Deflects: routine ticket volume

AGENT 05

Demand & inventory Q&A

Natural-language over planning/inventory data. "What's at risk of stockout next month? What's overstocked?" — answers without waiting on a report.

Speeds: planning decisions

THE POINT

Pick one, ship it, then repeat

You don't need an "AI strategy." You need one agent live and used by Friday, a number on the board, then the next. Momentum beats roadmaps.

The 30-day path out of pilot

Scope. Pick one workflow above. Write the success metric (hours saved / errors caught) before any building.
Build. Wire the data, build the agent, test against real historical cases — not toy prompts.
Ship with guardrails. Human-in-the-loop on high-stakes steps, evals on real cases, embedded in the actual tool people already use.
Measure & expand. Track adoption + the metric, fix what drags, then start agent #2. The engine is now repeatable.

What the 5% do that the 95% don't

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Embed in the workflow — the agent lives where the work already happens, not in a separate app.

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Evals on real cases — measured accuracy on your actual data before it touches a user.

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Human-in-the-loop where the cost of a mistake is real — trust is the whole game.

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One business metric + one owner — defensible at budget time, championed day to day.

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Ship narrow, then widen — a working agent beats a grand platform every time.

See it on your own workflow — free.

Send me one workflow your team wishes ran itself. I'll build a working agent on it and screen-record the result — so you see exactly what "out of pilot" looks like before deciding anything.

Book a 15-min call →

Jason · ex-VP of AI, $250M furniture manufacturer · AI agents shipped into real manufacturing ops