[insight]
AI in Production. Not Just in Pilots.
The Problem
Fewer than 1 in 4 pharma AI pilots reach production. That is not a technology failure.
The gap between a pilot that impressed a board and AI that changed how your plant operates is structural. It shows up in fragmented data, undefined success criteria, governance built as an afterthought, and plant floor teams who were never in the room when the model was designed.
The window for unstructured AI experimentation in regulated manufacturing is closing. Roche committed $1.1B to AI production infrastructure. The FDA and EMA published joint AI validation principles in 2024. The manufacturers who have already built governed, production-grade AI will not wait.
How We Work
Phase 1: Assessment (4–6 weeks)
Map every data source across your production environment.
Identify which AI use cases are production-ready in 90 days.
Define success in operational terms — dollars, hours, yield points.
Build the governance and validation framework before the first model runs.
Phase 2: Production Build (8–10 weeks)
One use case, built to production standards — not demo standards.
Two-week sprints, aligned to your board review timeline.
Plant floor leadership in the room from sprint one.
Change control and validation documentation built in parallel.
Phase 3: Governed Scale
Expand to 2–3 additional use cases on the existing infrastructure.
Operations team owns the models — not dependent on external teams.
What We Deliver
A PE-backed contract manufacturer needed board-ready AI results in two review cycles. Their data was fragmented across ERP, SCADA, sensors, and freight systems. We started with a data assessment — identified which use cases were viable and which weren't — then built two production models in 10 weeks. Both were running on the plant floor before the board meeting. The ROI dashboard ran on operational data, not projections.
The Pyramid of Adoption
Every durable AI deployment in manufacturing is built on five layers. We build them in order.
Scale: Enterprise-wide deployment.
Adoption: Workforce integration, daily use.
Strategy: Prioritized roadmap tied to KPIs.
Governance: Validation, change control, ownership.
Data Layer: Unified, production-ready pipelines.
What Makes This Different
McKinsey-level insight at a fraction of the cost and time
Delivery aligned to board review cycles, not IT project timelines
Validation and regulatory documentation built in, not retrofitted
Plant floor adoption designed in — not announced after go-live
Agile 2-week sprints; meaningful delivery within two board meetings
Ready to move from pilot to production?
Start with a 4–6 week assessment. Walk out with a prioritized use case roadmap, a data readiness evaluation, and an ROI framework. No slide decks.
