AI operations for industrial, logistics, and manufacturing teams
Case study: AI program inside a public logistics company
A public logistics company of thousands, with an engineering team of ten. Seven AI and automation workstreams across finance and operations, so AI is how the company gets work done, not a side project sitting with engineering.
01The situation
What the operator was working with
A logistics operator needed AI that could survive real enterprise constraints, and a path to becoming an AI leader in its industry. Finance and operations already sat on an ERP and on documents. Buying ran through a multi-person approval chain. Thousands of people worked there. Engineering was ten people.
The pressures were the ordinary ones at this scale.
- Purchasing and accounting processes carrying high document volume and manual, repetitive steps.
- The same ERP and process questions asked repeatedly, of whoever happened to know.
- Manual data transforms moving between systems and spreadsheets.
- Internal AI prototypes with no owner to turn them into something supported, and a ten-person engineering team that could not productionize every one.
- People were scared to use AI, so internal usage stayed low.
- Leadership wanting a clear AI use policy before anyone rolled anything wider.
They needed a partner who could help them transform the business into an AI leader in their industry.
02What we built
Seven workstreams under one engagement
Governance runs underneath the automation rather than arriving after it. The ten-person engineering team is no longer the bottleneck on every prototype.
- 01AI use policyDraft, review with stakeholders, and socialize an acceptable-use and tooling policy, so wider adoption is visible rather than shadow use.
- 02Accounting workflow automationManual, repetitive accounting and finance operations steps replaced with an owned automation, with human checkpoints and named go-live owners.
- 03Purchase order automationManual PO triage and processing reduced with assisted workflows and human checkpoints.
- 04ERP AI assistantNatural-language help with ERP-adjacent questions and procedures, for finance and operations users.
- 05Document processing automationIngest, classify, and extract from high-volume operations and finance documents.
- 06Data transformationsReliable transforms between systems, exports, and operational datasets.
- 07Productionizing internal AI prototypesInternal experiments moved into owned, supported tools with somebody accountable for them.
03Results
What the work made possible
- 01Prototypes from anyone, not only engineeringThousands of people, an engineering team of ten. Anyone in the company can now productionize an AI prototype, and use it to make their own job more efficient, or someone else's across the team or the company.
- 02From scared to heavy useInternal AI usage was low because people were scared to use it. With a policy, and live examples of AI that augments the work, they use it.
- 03AI as a force multiplierThe business is transforming. AI is no longer a side experiment sitting with ten engineers. It is how the company gets work done.
Let’s talk
Schedule a free consultation →Made in America
Anvil Labs LLC
AI operations for industrial, logistics, and manufacturing teams.
anvil.industries