Role Summary

The Senior Manager, AI Control Tower & Agentic AI Governance will serve as the central operating and governance lead for Lenovo’s AI Control Tower and enterprise-scale agentic AI initiatives. This role ensures that new AI agents are business-justified, well-governed, properly supported by data and knowledge assets, and operationally scalable.

The individual will coordinate across DTIT, business units, segments, and functional teams to manage agentic AI intake, governance, prioritization, lifecycle oversight, knowledge base strategy, change management, and value realization. The role is critical to helping Lenovo scale agentic AI safely and efficiently while preventing fragmentation, duplication, and unmanaged AI sprawl.

Key Responsibilities

AI Control Tower & Agentic AI Governance

• Operate and evolve the AI Control Tower as the central coordination point for enterprise agentic AI initiatives.

• Establish and manage governance frameworks for AI agent intake, prioritization, risk assessment, lifecycle management, and change control.

• Coordinate with DTIT to ensure AI agents align with enterprise architecture, security, compliance, and operating model standards.

• Track and manage the enterprise portfolio of AI agents, including status, dependencies, reuse opportunities, and value realization.

Business Engagement & Enablement

• Engage business units, segments, and functions to support AI agent design, development, piloting, testing, and adoption.

• Define training and enablement needs for AI agent deployment.

• Ensure clear business ownership, success metrics, adoption readiness, and ongoing stakeholder alignment.

• Act as the primary interface between business stakeholders and DTIT for agentic AI initiatives.

Business Case & Value Validation

• Partner with business sponsors to validate business cases for new AI agents, including value hypotheses, KPIs, and scale assumptions.

• Ensure proposed AI agents address clearly defined, high-value use cases.

• Assess duplication risk and identify reuse opportunities across the enterprise AI portfolio.

• Track realized value and feed insights back into Control Tower prioritization.

Data & Knowledge Base Strategy

• Lead engagement with business teams on data sourcing strategy for new AI agents.

• Own intake and governance for new product or domain-specific knowledge bases, as well as changes, enhancements, and retirement of existing knowledge bases.

• Coordinate with DTIT on knowledge base setup, seeding, chunking, curation, validation, maintenance, updates, and version control.

• Serve as the business-facing owner for knowledge asset quality, relevance, and fitness for AI use.

Legacy Data Rationalization

• Lead structured efforts to inventory and rationalize legacy unstructured data stores.

• Consolidate content into domain-specific product knowledge bases.

• Eliminate duplication, stale content, and unmanaged repositories.

• Drive standards for content ownership, stewardship, and ongoing governance.

Key Interfaces

• DTIT teams, including architecture, AI platforms, security, data, and change management

• Business unit and segment leadership

• Product, sales, services, and operations teams

• AI platform, knowledge management, and governance functions

Required Qualifications

• Business degree with MIS or Computer Science background

• 10+ years of experience in enterprise transformation, digital platforms, data/AI governance, and product operations

• Direct experience working across business units and central IT organizations

• Strong background in governance, operating models, and portfolio management

• Demonstrated ability to translate business needs into structured AI, data, and knowledge requirements

• Proven track record of managing AI projects through the AI solution lifecycle

• Strong executive communication and stakeholder management skills

Preferred Qualifications

• Computer Science, MIS, Business Analytics, or AI/ML certification

• 3+ years of experience with agentic AI, AI platforms, or AI-enabled workflows

• Knowledge of knowledge bases, unstructured data management, and content lifecycle governance

• Familiarity with change management frameworks in large, global organizations

• Experience rationalizing fragmented data or content ecosystems

Critical Success Factors

• Ability to balance speed and control in AI deployment

• Strong systems thinking across technology, data, knowledge, and operating model dimensions

• Comfort operating in ambiguity while creating structure

• Enterprise-first mindset with business credibility