VP, AI Center of Excellence (CoE), Enterprise Systems

Labcorp•Durham, NC

1d•Hybrid

About The Position

The VP, AI Center of Excellence (CoE), Enterprise Systems is responsible for operationalizing Labcorp’s enterprise AI-first strategy and driving the delivery of AI initiatives across Diagnostics, Central Laboratory Services, and Early Development business units. This role owns the end-to-end AI delivery portfolio, partnering with senior business and IT stakeholders to implement scalable AI solutions that deliver measurable business outcomes. Reporting to the SVP, Enterprise Systems, this position leads AI architecture, delivery, and governance within Enterprise Systems while collaborating closely with Enterprise AI Architecture, Security, Infrastructure, and delivery teams. The role ensures AI solutions align with enterprise standards, drives adoption across the organization, and builds high-performing teams that enable long-term competitive advantage through AI.

Requirements

High school diploma with 19 or more years of experience in enterprise technology delivery, data, analytics, or AI initiatives; or Associate degree with 17 or more years of experience; or Bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a technical field with 15 or more years of experience; or Master’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a technical field with 13 or more years of experience
15 or more years of experience leading enterprise technology delivery initiatives, including data, analytics, or AI programs
5 or more years of experience building and leading teams of AI architects, engineers, or data professionals
10 or more years of experience owning end‑to‑end solution delivery lifecycles, including ideation, design, development, deployment, and lifecycle management
10 or more years of experience managing vendor relationships and delivering solutions using services‑based, outcome‑based, or staff augmentation engagement models
Strategic and Business Acumen: Ability to define and execute enterprise‑level strategies aligned with corporate objectives. Understanding of financial and operational drivers to support AI investment prioritization and decision‑making. Knowledge of healthcare or life sciences industry dynamics and regulatory environments.
AI and Emerging Technology Expertise: Understanding of AI and machine learning concepts, including generative AI, large language models, natural language processing, and predictive analytics. Familiarity with data ecosystems, model lifecycle management, and AI platforms. Awareness of responsible AI principles, explainability, fairness, and compliance considerations.
Leadership and Influence: Ability to build relationships with executive stakeholders and influence enterprise decision‑making. Experience leading cross‑functional teams in matrixed environments. Strong executive communication and storytelling capabilities.
Innovation and Transformation: Ability to identify and prioritize high‑impact AI use cases. Experience developing business cases, roadmaps, and implementation strategies. Ability to assess feasibility, scalability, and risk of emerging technologies.
Organizational Change and Enablement: Experience driving enterprise adoption of new technologies and ways of working. Ability to support organizational transformation and talent development initiatives. Ability to operate in evolving, innovation‑driven environments.
External Ecosystem Engagement: Understanding of AI vendor landscape, startups, and research ecosystems. Ability to build partnerships that accelerate enterprise AI capabilities.

Nice To Haves

Master’s degree in Business Administration, Data Science, Artificial Intelligence, or a technical discipline
10 or more years of experience in healthcare, diagnostics, life sciences, or biotechnology environments
5 or more years of experience working with AI or machine learning technologies, including generative AI, natural language processing, or predictive analytics
5 or more years of experience working with enterprise platforms such as AWS, Databricks, Snowflake, or similar cloud and data ecosystems
5 or more years of experience applying responsible AI governance frameworks, ethical AI principles, or regulatory compliance standards
5 or more years of experience collaborating with AI vendors, startups, research institutions, or innovation partners

Responsibilities

Provide strategic and operational leadership for the AI Center of Excellence (CoE) within Enterprise Systems.
Lead execution of transformational and operational AI initiatives aligned to enterprise and business objectives.
Partner with senior business and IT stakeholders to translate business needs into scalable AI solutions.
Lead and manage a team of AI Architects and AI Engineers, driving engineering excellence and delivery consistency.
Own the end‑to‑end AI delivery lifecycle, including ideation, intake management, use case identification, evaluation, prioritization, design, development, deployment, and lifecycle management.
Establish structured intake, evaluation, and prioritization frameworks aligned to enterprise priorities and business value.
Partner with Enterprise AI Architecture and IT teams to ensure alignment with enterprise tools, standards, and best practices.
Ensure AI solutions comply with enterprise architecture, security, governance, and technology standards.
Oversee architecture, design, and implementation of AI solutions to ensure scalability, performance, and reliability.
Coordinate with Project Delivery Managers and Delivery CoE teams to ensure integration of AI and non‑AI solution components.
Collaborate with development and QA teams on solution components such as UI/UX and data engineering.
Build, mentor, and develop high‑performing AI teams, including defining performance goals and development plans.
Lead talent strategy for AI teams, including recruitment, retention, and career development.
Provide executive oversight of vendor relationships across consulting, services, and delivery partners.
Manage multiple delivery engagement models, including services‑based, outcome‑based, and staff augmentation.
Ensure vendor‑delivered solutions align with enterprise standards, timelines, and expected outcomes.
Drive delivery excellence through scalable processes, reuse of AI capabilities, and continuous improvement initiatives.
Provide executive reporting, risk management, and issue resolution for AI initiatives.
Act as an enterprise AI advocate, driving adoption, change management, and integration of AI into business processes.