Fractional AI Leadership — Manufacturing Decision Systems
The problem
Solid plumbing, no product coherence
A manufacturing data integration company had built solid plumbing — factory data flowing in from ERPs, MES systems, sensor feeds — but no product coherence. Multiple data connectors existed. No clear product existed. The engineering team was building features without a prioritization framework, and the company was positioning itself as "data integration" in a market that doesn't pay for pipes. It pays for decisions.
They needed AI leadership to transform data infrastructure into decision intelligence. They didn't need (or want) a full-time hire.
What AI leadership meant
Three work buckets, one outcome
System Definition
Mapped every data source to potential decision outputs. Ranked use cases by customer willingness-to-pay and technical feasibility. Killed three feature initiatives that would have consumed engineering cycles without moving revenue.
- -Data source → decision output mapping
- -Use case prioritization framework
- -Product strategy document and prioritized roadmap
Workflow & Product Design
Worked with the engineering team to design the schema that normalizes disparate factory sources into a single queryable layer. Defined MVP scope: production scheduling, quality trends, maintenance prioritization.
- -Unified data model across MES/ERP/sensors
- -MVP scope: 3 high-value manufacturing decisions
- -API contracts and decision output formats
Productization
Repositioned from "data integration platform" to "decision intelligence for mid-market manufacturers." Target: 100-500 employee manufacturers who have data but lack the engineering team to build their own analytics.
- -Go-to-market positioning shift
- -Sales narrative and competitive framing
- -Prioritization framework for ongoing product decisions
What changed
From pipes to decisions
Positioning
"We integrate factory data" → "We tell manufacturers what to do next"
Product
Three concrete decision outputs: scheduling optimization, quality trend detection, maintenance prioritization
Framework
Prioritization framework and product principles documented for team to continue independently
Why this matters
Product strategy for AI and data products requires domain depth. Generic product management produces generic products. We brought manufacturing operations knowledge, data architecture experience, and the product discipline to say no to features that felt productive but weren't.
This is what Fractional AI Leadership looks like in practice.
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