Director, AI Data

Position Overview

AI Data & Knowledge Director – Global AI & Knowledge Organization. This leader owns the strategy, design, and operations of the data and knowledge layer underpinning all AI tools, connecting firm‑wide structured systems (EDW, Salesforce, Workday) and unstructured knowledge stores (SharePoint, engagement repositories). Reporting to the Chief AI & Knowledge Officer, the role advises Business Units on integration approaches tailored to their unique data and security contexts.

• Lead strategy, design, build and operations of the AI data layer across structured and unstructured sources, championing in‑place access over unnecessary data movement.
• Establish requirements for governed pipelines connecting enterprise systems (EDW, Salesforce, Workday, SharePoint, ERP) to AI consumption layers; make informed build‑buy‑partner decisions in partnership with the engineering lead.
• Define outcomes and standards for data quality, metadata management, and lineage tracking; hold the engineering team accountable to those standards.

• Own the strategy for making firm knowledge AI‑accessible—SharePoint, document libraries, engagement deliverables, and BU content stores—via federated indexing and retrieval rather than bulk extraction.
• Own the retrieval backend for AI search—the index scope, permission inheritance model, and data quality requirements—in partnership with the Apps team who owns the user‑facing search experience.
• Define success criteria and requirements for how firm knowledge surfaces in AI tools, including chunking strategies, embedding pipelines and index refresh processes; partner with engineering on technical implementation of retrieval pipelines and with BU content owners on taxonomy and relevance requirements.

• Lead the knowledge management tech stack as part of this practice—knowledge tech sits within the data layer, making knowledge architecture a first‑class responsibility of this role.
• Define the strategy for knowledge graph adoption—modeling firm knowledge as relationships rather than retrieving documents—and partner with engineering to design and implement.
• Partner with subject‑matter experts, global KM and BU knowledge leads to develop taxonomies and metadata standards that make firm knowledge findable, reusable, and trustworthy at scale.
• Define the strategy for connecting unstructured knowledge (engagement deliverables, practitioner expertise) to structured retrieval—enabling contextual AI responses grounded in A&M’s institutional knowledge.

• Define requirements for a permission‑aware data access model reflecting the firm’s complex multi‑BU structure; partner with Information Security and engineering to implement.
• Define data classification standards, access tiers, and audit controls in collaboration with Information Security and enterprise data governance; navigate conflicting access requirements across BUs.
• Ensure governance and security controls are embedded into data layer architecture by engineering teams, in support of the CoE’s Responsible AI framework.

• Serve as strategic owner for integrations with firm‑wide systems; lead engineering team to develop reusable integration patterns and standards for the CoE tool portfolio.
• Advise BUs on connecting proprietary datasets and SharePoint content to CoE AI tools—data readiness, security constraints, and governance requirements—without requiring BUs to surrender data ownership.
• Partner with the Apps, Marketplace, and BU leads to define how the data layer enables end‑to‑end AI use cases—translating combined capabilities of each practice into a coherent picture of what’s possible, then working with each team to define their specific contribution to making it work.

• Lead and grow a team of data engineers, software engineers, AI engineers, and knowledge tech professionals: goal‑setting, performance management, and mentorship.
• Partner with the CoE Tech Lead on engineering standards, delivery processes, staffing, and capacity planning.
• Partner with the team’s most senior engineer…