Process Improvement, Vice President

The AOP Knowledge Lead is responsible for building and managing the enterprise-wide process knowledge capability that underpins AI-driven automation and agent-based systems within the Agentic AI Program. This role focuses on transforming Agent Operating Procedures (AOPs) into structured, reusable, and machine-executable knowledge assets within a Knowledge Graph, enabling scalable and consistent deployment of automation and AI agent use cases across Global Delivery. The role acts as the owner and steward of AOP knowledge, ensuring processes are standardized, enriched with subject matter expertise, and governed to support operational efficiency, control, auditability, and long-term scalability. It is explicitly designed around the four-step knowledge-led AOP creation methodology (LLM-generated best-practice draft → SME enrichment → consolidation & iteration → governance & sign-off) that forms the strategic foundation of the Agentic AI Program. The VP-level AOP Knowledge Lead plays a critical role in converting expert knowledge into agent-ready process graphs that form the digital knowledge backbone for AI-driven operations across Global Delivery. Impact This role enables the organization to: Establish a standardized process knowledge foundation Accelerate deployment of AI and automation solutions within the Agentic AI Program Improve operational efficiency and consistency across functions Build a sustainable, scalable capability supporting long-term transformation Key Responsibilities 1. AOP Standardization and Knowledge Engineering Develop and implement the standard methodology for AOP design and documentation Convert AOPs into structured, machine-readable process representations consumable by AI agents Define and maintain AOP templates, taxonomies, and metadata standards, capturing actions, decision criteria, data checks, and resolution options Drive process simplification and standardization, removing duplication and inefficiencies, and ensuring AOPs reflect target-state operating models rather than as-is processes 2. Knowledge Graph Enablement Translate AOPs into knowledge graph-compatible structures for reuse across workflows and AI use cases Ensure process knowledge is modular, reusable, scalable, and interoperable across multiple agentic use cases Partner with Knowledge Graph specialists, engineering and AI teams to ensure integration of AOP knowledge into automation and agent-based systems, supporting end-to-end AI-driven workflows Define standards for how process knowledge supports decisioning, automation, and agent execution 3. SME Knowledge Integration Lead SME workshops and knowledge elicitation sessions to capture and embed institutional expertise (client nuances, system quirks, edge cases, hard-won judgment) into AOPs Consolidate cross-functional knowledge into a single structured capability Ensure consistency in process logic, definitions, and decision frameworks across operational teams Collaborate with LLM & Prompt Specialists to refine prompt strategy and improve output quality of LLM-generated AOP drafts 4. Governance, Quality and Controls Define and implement quality standards for AOPs and knowledge assets Establish governance for ownership, versioning, lifecycle management, and change control Ensure knowledge assets support auditability, traceability, and regulatory compliance Embed process controls and decision points to support automation and AI execution, and prepare AOPs for Business Service Owner approval prior to encoding into the Knowledge Graph 5. Value Delivery Enable scalable AI and automation deployment through reusable AOP knowledge within the Agentic AI Program Support measurable reduction in process design duplication Support measurable reduction in time to implement automation and AI use cases Drive standardization across business functions Track and report on AOP development progress, quality metrics, and adoption rates 6. Collaboration and Stakeholder Engagement Partner with Product