Most AI pilots at mid-market manufacturers stall for the same reason. The model is fine. The data underneath it is not ready.
This session covers what has to be true before AI gives you an answer you would actually act on. We start with a real failure: two fields, ship date and shipped date, that meant different things in two systems and turned into a missed customer commitment with a dollar figure attached.
From there we walk the sequence. Define the process before you automate it. Connect and standardize data across your operational systems. Build a semantic model so a term means the same thing in every plant and every report. Then let dashboards answer the common questions and put AI on the long tail where it earns its keep.
Bring a number your team argues about.
We will trace it live.
WHAT YOU WILL LEARN
- Why definitional drift, not model quality, is what kills most manufacturing AI pilots
- How to define a process well enough that automating it is safe with well defined data definitions
- What connecting and standardizing across operational systems actually requires
- How a semantic model keeps terms consistent across systems, plants, and reports
- Where dashboards belong and where AI belongs, and why the split matters for cost
WHO SHOULD ATTEND
COOs, VP Operations, plant managers, IT and operations leadership, and finance partners at discrete manufacturers. Useful for anyone who owns a number that gets argued about in a monthly review. No technical background required.
ABOUT HUMAGINED
Humagined is an AI and data consultancy working with mid-market discrete manufacturers. Our thesis is Standard Work Before AI Work. Most AI programs fail on definitional problems rather than technical ones, so we start by making systems agree on what terms mean before anything gets automated. Delivery runs on a four-stage model: Assess, Architect, Adopt, Own. We build as well as advise, with Snowflake and Databricks partnerships and an ISO/IEC 42001 certified approach to AI governance.
