---
title: "General Motors Proactive Governance Approach"
url: "https://atlan.com/regovern-watch-center/gm-proactive-governance/"
description: "Sherri Adame from GM explains how they built governance that scales globally"
keywords: "atlan, data governance, regovern 2025, regovern watch center"
---

> Atlan is hosting Context Conference, bringing together the leaders and builders at the frontier of giving AI the context it needs to understand their business. It runs online on October 28, 2026, from 11:00 AM to 2:00 PM ET. Atlan co-founder Prukalpa Sankar opens and closes the day. Leaders from AstraZeneca, BNY and Verizon share why they invest in context and what they get from it. Registrants get early access to The AI Context Gap, a new study from MIT Technology Review Insights. Register: https://atlan.com/context-conference/

General Motors Proactive Governance Approach is a video from Re:Govern 2025, published in Atlan's Re:Govern Watch Center. Sherri Adame from GM explains how they built governance that scales globally

Watch it at https://atlan.com/regovern-watch-center/gm-proactive-governance/ (free; the page asks for an email after two minutes of viewing).

## Speakers

- **Sherri Adame**, Enterprise Data Governance Leader, General Motors. Sherri transformed GM's governance from "hunting teams down" to having teams seek her out. Through shift-left automation, data contracts, and transparent metrics, she's proven governance is a product that delivers value - now leading GM's evolution from traditional manufacturing to AI-powered, software-defined vehicles.

## Companies

- **General Motors**: America's largest automaker transitioning from manufacturing to software. GM is investing $10.9B in Databricks and Google Cloud to power its mission of software-defined vehicles targeting zero crashes, zero emissions, zero congestion by 2035.

## What's inside

General Motors proactive governance approach combines global processes with technical automation across six core systems. Starting with governance and AI policies that define ownership and accountability, they built a stewardship community by domain, then deployed technical solutions that automatically collect metadata from GitHub, enforce quality checks for AI legislation, and enable stewards to verify classifications through intuitive workflows.

Key takeaways:

- Proactive governance starts with clear policies defining roles, ownership, and expectations before deploying technology
- Domain-based stewardship communities act as custodians who define business rules and data management standards
- Automated metadata collection from GitHub repos, YML files, and model cards feeds into centralized catalogs at scale
- AI legislation requires minimum data quality standards - GM automated ~20 essential checks across all AI/ML datasets
- Data classification utilities use ML to predict classifications, then stewards verify through catalog workflows to reach 98% coverage

## Related

- [Contracts, Context, and Cars: General Motors' Governance Blueprint for the Agentic AI Era (session recording)](https://atlan.com/regovern-watch-center/contracts-context-and-cars-general-motors/)
- [How General Motors Gamified Metadata Enrichment](https://atlan.com/regovern-watch-center/gm-metadata-enrichment-gamification/)
- [Inside General Motors Metadata Lakehouse](https://atlan.com/regovern-watch-center/gm-metadata-lakehouse/)
- [All Re:Govern 2025 sessions and highlights: the Re:Govern Watch Center](https://atlan.com/regovern-watch-center/)
- [Book a demo of Atlan](https://atlan.com/forms/talk-to-sales-contact/)