Three days to know where AI actually pays off in your plant.
A working sprint for manufacturers — not a slideware assessment. Your team brings the manual work that eats their week; you leave with a ranked portfolio of AI agents, a business case for each, a build-ready runbook for your first agent, and a 90-day plan your own leadership has signed.
Why manufacturing AI stalls
The board wants an AI story. The floor wants its hours back. In between sit ERPs that don't talk, data living in spreadsheets, and a market full of vendors selling the same generic workshop. Most initiatives die one of three ways:
Starting with the technology
A platform gets picked before a problem does. Licenses get bought, a chatbot gets demoed, and six months later nobody can name the business number it moved.
Starting too big
An eighteen-month transformation program that has to fix all the data before anything ships. It dies in the cleanup phase, and takes the appetite for AI with it.
Starting without owners
An IT-led pilot the business never asked for. It works in the demo, nobody adopts it, and it joins the agent graveyard.
The sprint inverts all three: start from the work your people actually do, size every idea against the data you actually have, and make your own team the owners — before a single platform decision is made.
Grow the plant.
Not the headcount.
Demand is soft, and the growth targets didn't move. Every manufacturer we talk to is being asked to do more with the team they have — while their most experienced people edge toward retirement. AI agents are how a stretched workforce handles more: not by replacing people, but by taking the manual work off their desks. The question isn't whether to start. It's where — and that's what the sprint answers.
Where agents pay off in manufacturing
Four places the hours and margin actually leak — each one a pattern we've scoped, scored, and built agents against inside real plants.
Ship more with the same team
Throughput lost to manual coordination.
Shipping paperwork — load sheets, schedules, carrier docs — assembled by hand from the ERP, spreadsheets, and a routing tool, against a hard daily cutoff.
Estimates for custom and configure-to-order work are built by hand — dug out of old spreadsheets and memory, human mistakes included. Those errors flow straight into the schedule: 20 units planned, 16 built.
Stop the margin leaks
Money lost to stale and disconnected data.
Inventory reports take hours to build, so sales quotes from stale numbers — and the same unit gets sold twice.
Pricing across thousands of SKUs maintained entirely by hand — margin erosion nobody can see until the quarter closes.
Invoice coding and approval done line by line, even though most vendors bill in consistent patterns.
Keep the knowledge
Expertise that retires with its owner.
Veterans with decades on your floor who diagnose a machine by its sound — and no manual that captures what they know.
Specs, substitutions, and process decisions scattered across documents and memory — every answer costs an expert an interruption.
Give your experts their week back
Skilled people consumed by repetitive asks.
A capable IT team burning its days on password resets and "how do I do this in the ERP" questions.
A one- or two-person legal team carrying the entire contract load, on the other side's paper.
The Monday reporting ritual: the same KPI pack rebuilt by hand from half a dozen spreadsheets, every week.
Figures are from real plants we've run this sprint in — your numbers will differ. Finding them is Day 1.
Three days. Your people. Their pain points.
No slideware audit. You nominate one champion per function — the people who actually do the work — and we facilitate. The decisions are theirs, which is why the roadmap survives after we leave.
Find where the hours go
- Pre-work review: your team's own AI-readiness scores and pain inventory, collected before we arrive.
- Process & data walk-through: map the manual work, the systems it touches, and where the data actually lives.
- Gap analysis: get past the symptoms to the root causes — and an honest read on your data's readiness.
Turn pain into candidates
- Use-case generation: typically 20+ candidate agents surfaced across functions.
- Value-vs-effort scoring: every idea ranked on business impact and data readiness — in the open, with your team holding the pens.
- Live working session: we demonstrate your top use case running as a real agent — and if your environment is ready, we start building in it, together.
Leave with a signed plan
- Consolidation: the long list becomes a short portfolio of agents, each with a one-page business case and a measurable KPI.
- 90-day roadmap: quick wins first, sequenced by data readiness — including what shouldn't be an AI project at all.
- Executive sign-off: your champions present the portfolio to your leadership. They own it; we support it.
The deliverables
Agent one-pagers
Each selected agent documented: the problem, the systems it touches, success criteria, KPIs, and effort size — ready to hand to any builder, including your own team.
Value / effort matrix
The full scored inventory of every idea raised — including the ones that didn't make the cut, and why.
90-day roadmap
Sequenced, owner-assigned, executive-signed. First agents live in month one, not quarter three.
First-agent build runbook
Step-by-step instructions to stand up your first agent — platform, connections, guardrails, test criteria. If your environment is ready during the sprint, we start the build together, and whatever we build stays with you.
Data readiness read
An honest assessment of which use cases your data supports today — and what the gaps will cost you if left alone.
Platform recommendation
Microsoft, Anthropic, AWS, OpenAI, Google — each use case matched to the right platform and model, with running costs. Never picked by default.
Proven inside a top-10 North American manufacturer
A ~$1B, family-owned manufacturer facing a 43% market downturn — while opening new plants and targeting 50% growth without proportional hiring. Ten functional champions, one sprint, one signed decision.
"Drive the technology — don't let the technology drive you."
Client leadership, Day 1 of the sprint
Our ground rules
Augmentation, not replacement
The goal is your existing team handling more — not fewer people. That's the only version of AI a stretched workforce will actually adopt.
Your stack, not ours
The agent's logic lives in your workflow, not in a vendor. We've built the same designs on Microsoft Copilot, Claude, AWS, and OpenAI — so we start from what you already license and connect to the ERP and files you have. Nothing gets ripped out, and no platform locks you in.
Read-only until trust is earned
First agents observe, draft, and recommend under least-privilege access. Autonomy is increased only after measured performance — your IT and legal teams will thank us.
We tell you when it's not AI
Some of your worst reporting pain is a data-plumbing fix, not an agent. We'll say so — that honesty is why the roadmap holds up.
Your people own the result
Champions present the portfolio to their own executives and lead adoption in their functions. We facilitate, build, and train — you keep the capability.
Find your first agent.
A 30-minute scoping call. Tell us how your plant runs; we'll tell you which two or three of your processes are agent-ready — before you spend anything. And if the honest answer is "fix your data first," we'll tell you that too.
Book a scoping call