Strong technical foundation with a deep understanding of AI.
Responsible AI governance knowledge is preferred; ethics and process can be learned.
Strategic mindset with operational execution capabilities.
Effective communicator who can educate and influence teams across the organization.
Own all MGS AI use cases and collaborate across all business segments.
Run the impact assessment process for every AI use case. All use cases must go through this team via a defined intake process. Each segment has its own contact who also assesses use cases.
Conduct audits and ensure teams (use case owners, data analytics) understand governance. Help identify and mitigate risks.
Navigate leadership teams and present governance processes. Participate in roadshows to promote AI governance across the company. Responsible for ensuring ~150K employees understand the AI process.
Evolving governance model based on Responsible AI frameworks (e.g., RAI governance from effem.com). Five main phases: pre-assessment, impact, etc. The process is continuously evolving.
Expansion from GenAI to broader AI domains, including genetic AI. Lower-risk use cases may be governed by individual segments.
Business breadth is crucial: Must understand where use cases originate and be empowered to say no. Needs to be senior and capable of making tough decisions.
Primarily Azure/Microsoft. Some teams are shifting toward Google (e.g., snacking). Kellanova acquisition brings AWS into the mix.
Fast-paced, dynamic environment. Governance tools are essential. Use cases span multiple layers: Middleware (possibly Max Teams). Build/buy/existing SaaS vendors. Platforms like SAP with add-on widgets that require assessment. Must understand both platform architecture and how AI integrates across layers.
Sr. AI Governance Manager • Newark, NJ, United States
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