Senior Manager, AI Portfolio and Enablement

Company Overview

​At Motorola Solutions, we believe that everything starts with our people. We’re a global close-knit community, united by the relentless pursuit to help keep people safer everywhere. Our critical communications, video security and command center technologies support public safety agencies and enterprises alike, enabling the coordination that’s critical for safer communities, safer schools, safer hospitals and safer businesses. Connect with a career that matters, and help us build a safer future.

Department Overview

Department Description
Our IT organization has a critical role in driving extraordinary business results. Through a strong partnership with other areas of our business, we bring innovative thinking to every conversation and deliver with integrity. We’re looking for people who bring great ideas and who make our partners’ ideas better. Intellectually curious advisors (not order takers) who focus on outcomes to creatively solve business problems. People who not only embrace change, but who accelerate it.

Job Description

The AI Center of Excellence (CoE) is chartered to drive self-service enablement and the upskilling of AI talent across the enterprise. It serves as a central hub to enable business functions to share best practices, promote cross-team learning, and is responsible for delivering high-value AI use cases that create efficiencies, and deliver insights to support critical decision-making across the organization.

We are seeking a highly experienced and strategic AI Leader to manage the intake, development, and scaling of the enterprise AI use-case portfolio. This role is critical to translating the CoE's mission into tangible business value by ensuring all AI initiatives are aligned with the overall enterprise strategy, follow governance standards, and drive measurable performance improvements.

Key Responsibilities

1. Portfolio Strategy & Development

• Manage the intake and qualification process for new AI requests, providing critical challenge to existing business leaders on feasibility, value alignment, and technical approach.

• Design and implement the AI strategy roadmap, specifically defining the value realization model, associated KPIs, and calculations for enterprise use-case delivery.

• Serve as the primary CoE liaison to platform teams, driving the technical requirements for integrating AI/ML models into existing platforms, tools, and architecture.

• Collaborate with the business to shape and develop compelling AI use cases from ideation through deployment.

• Establish and manage a robust use-case intake funnel, including initial scoring mechanisms to assess potential business value, technical feasibility, and alignment with the CoE's current Model Development roadmap.

• Develop and maintain the enterprise-wide LLM (Large Language Model) usage policy, working with the Data Science Platform & Security team to ensure responsible scaling of generative AI capabilities.

2. Governance, Standards & Management

• Develop the core templates and data models for use-case reporting and performance metrics, establishing the single source of truth for AI value delivery.

• Abide by and enforce policies and standards of data usage as defined by Governance and Operations teams.

• Integrate AI and ML capabilities into standard reporting as needs arise, enabling the CoE to track and report on its value.

• Manage the high level of change impact expected due to standing up non-existent capabilities and scaling existing ones across the organization.

3. Learning, Sharing & Enablement

• Champion and facilitate training and sharing of best practices across functions, including prompt engineering, model building, deployment, and monitoring practices/methodologies.

• Lead AI communities that facilitate cross-team learning, knowledge sharing, and the creation of practice process creation and management.

• Partner with HR, business teams, and functional teams to understand workforce and talent demands and support the creation of materials/assets to upskill & close talent/skill gaps.

4. Inter-Team Collaboration

• Provide guidance to Data Products & Platforms teams to build data infrastructure that enables core AI capabilities across the enterprise.

• Evangelize policies and standards set by Governance &