Vice President, Applied AI & Architecture - US Commercial

Job description

WHY THIS ROLE MATTERSRealizing the value of AI across the CMO requires more than individual products and use cases. It requires a clear technical vision for how AI, data, platforms, applications, and business workflows work together across the organization. This role ensures that AI investments are technically sound, reusable, integrated, and aligned with Pfizer’s enterprise environment. By establishing a coherent applied AI architecture and providing technical leadership across major transformation initiatives, the Vice President will accelerate delivery while reducing duplication, fragmentation, and long-term technical debt. CANDIDATE PROFILEThis is a senior technical leadership role for an executive who combines deep applied AI expertise with enterprise architecture, business judgment, and the ability to influence across organizational boundaries. · Applied AI expertise: Deep knowledge of modern AI, including generative AI, agentic systems, machine learning, retrieval architectures, evaluation, observability, and human-agent collaboration. · Enterprise architecture: The ability to connect AI, data, platforms, applications, integrations, and workflows into a coherent enterprise architecture. · Transformation fluency: Experience translating business transformation priorities into technical strategies, solution designs, and capability roadmaps. · Executive influence: The ability to establish technical direction and decision authority across teams that do not report directly to this role. This leader is not expected to be the primary engineering-delivery executive or portfolio-delivery owner. They must remain close enough to the technology to challenge assumptions, evaluate designs, resolve complex technical decisions, and maintain credibility with senior architects and engineers. ROLE RESPONSIBILITIES1. Define the Applied AI Strategy· Define and evolve the applied AI strategy supporting CMO and Commercial priorities. · Translate business and transformation ambitions into a clear technical direction and capability roadmap. · Identify where AI can create differentiated value and where traditional technology or process solutions are more appropriate. · Establish principles for using generative AI, agentic systems, machine learning, intelligent automation, and human-in-the-loop models. · Advise senior leaders on emerging AI capabilities, limitations, risks, and strategic implications. 2. Own the Target AI Architecture· Define the target architecture connecting AI capabilities, commercial data, enterprise platforms, applications, workflows, and user experiences. · Establish the architectural patterns required to build scalable, secure, reusable, and interoperable AI solutions. · Ensure major initiatives contribute to a coherent technical ecosystem rather than creating stand-alone tools. · Guide architecture across the AI forward evolution of the commercial org, transformation of existing capabilities around commercial strategy, and other priority initiatives. · Maintain clear architectural boundaries, integration principles, and technical decision rights. 3. Provide Technical Direction for the AI forward future· Serve as the senior applied AI and architecture leader for the AI evolution workstreams · Translate the vision and product priorities into an integrated AI and technology architecture. · Define how AI capabilities connect with commercial data, analytics, enterprise platforms, customer workflows, and digital experiences. · Establish reusable architecture and solution patterns that can support multiple products, brands, functions, and markets. · Partner with business, product, data, Digital, and engineering leaders to ensure technical choices enable the intended business outcomes. 4. Shape AI Architecture for internal process and strategy Transformation· Define the applied AI architecture supporting internal process transformation and broader Commercial Transformation priorities. · Translate future-state workflows into technical designs that appropriately combine people, AI agents, data, applications, controls, and escalation paths. · Identify shared capabilities and reusable components across transformation programs. · Ensure that transformation roadmaps account for architectural dependencies, enterprise integration, data readiness, and operational requirements. · Provide technical guidance as initiatives move from early concept through design, engineering, and deployment. 5. Establish Technical Decision Frameworks· Create clear frameworks for evaluating AI use cases, technologies, models, vendors, platforms, and architectural approaches. · Establish principles for build, buy, partner, integrate, reuse, and retire decisions. · Lead resolution of the most complex cross-portfolio technical decisions and trade-offs. · Balance speed, business value, technical quality, risk, reuse, flexibility, and long-term sustainability. · Provide clear recommendations when priorities or technical approaches conflict. 6. Set Applied AI Standards· Define standards for AI solution architecture, agent design, model selection, retrieval, evaluation, observability, interoperability, and human oversight. · Establish what good looks like for production-grade applied AI solutions. · Ensure standards support innovation while remaining practical for teams responsible for product and engineering delivery. · Partner with engineering and Digital leaders to align applied AI standards with enterprise architecture, security, software engineering, and operational requirements. · Regularly update standards as technology, regulation, and Pfizer’s enterprise capabilities evolve. 7. Drive Reuse and Technical Coherence· Identify common AI capabilities, services, components, and patterns that should be shared across the portfolio. · Prevent unnecessary duplication and isolated technical solutions. · Promote modularity and reuse without creating excessive centralization or slowing delivery. · Clarify which capabilities should be enterprise-wide, domain-based, product-specific, or locally configurable. · Monitor architectural drift and recommend corrective action where solutions diverge from the intended direction. 8. Provide Technical Oversight of Strategic Initiatives· Conduct architecture and technical-design reviews for the most significant AI initiatives. · Challenge assumptions and validate that proposed solutions are technically feasible, scalable, responsible, and aligned with enterprise standards. · Surface technical dependencies and risks early enough to influence delivery decisions. · Provide escalation support for complex architecture, integration, model-performance, and solution-design challenges. · Confirm that technical designs remain connected to the business outcomes they are intended to enable. 9. Partner Across Transformation, Product, and Engineering· Partner with the VP, Transformation & Delivery to translate portfolio priorities into technical strategies and architectures. · Partner with the VP, AI Applications to ensure engineering teams have clear architectural direction and reusable technical patterns. · Partner with product leaders to align product vision, user needs, technical feasibility, and platform capabilities. · Partner with Data Strategy and Digital leaders on data architecture, enterprise platforms, integration, security, and technology standards. · Create clear handoffs and decision rights across architecture, product, transformation, and engineering teams. 10. Enable Responsible AI by Design· Embed responsible AI, privacy, security, regulatory, legal, compliance, and quality considerations into solution architecture. · Define appropriate human oversight, evaluation, monitoring, traceability, and escalation requirements. · Apply proportionate architectural controls based on the purpose, data, users, and potential impact of each