Director, Applied AI (Horizon Scanning)

Director, Applied AI (Horizon Scanning) The Director, Applied AI (Horizon Scanning) is responsible for identifying, evaluating, and translating emerging AI technologies, capabilities, vendors, and industry trends into actionable opportunities for Pfizer's U.S. Commercial organization. The role serves as the connection point between external AI innovation and U.S. Commercial strategy, ensuring that evolving technologies are assessed through the lens of commercial priorities, customer engagement, marketing, market access, patient solutions, commercial operations, and future business transformation. Reporting to the Senior Director, Strategy, this leader is responsible for horizon scanning, external technology intelligence, capability assessment, technology scouting, rapid evaluation, and future-state capability planning. The role helps ensure U.S. Commercial remains at the forefront of AI innovation while maintaining focus on business value, technical feasibility, enterprise readiness, and responsible adoption. The Director partners across AI Partnerships & Ecosystem, Product Management, AI Applications, Data Strategy, Transformation & Delivery, Governance, and Digital to convert external signals into a prioritized pipeline of opportunities, experiments, and roadmap recommendations. When an opportunity requires external engagement, the Director provides the technical and strategic assessment and transitions the opportunity to the AI Partnerships & Ecosystem Lead for partner engagement, coordination, and ongoing relationship management. AI is evolving faster than traditional business and technology planning cycles. New foundation models, agent frameworks, industry-specific solutions, and commercial use cases emerge continuously. To maintain leadership in AI-enabled customer engagement and commercial operations, U.S. Commercial requires a dedicated capability focused on separating meaningful innovation from short-term hype, identifying strategic opportunities early, and determining where to invest, experiment, partner, incubate, or scale. This role ensures U.S. Commercial is not simply reacting to AI change but proactively shaping its future AI roadmap and external innovation agenda. U.S. Commercial AI Horizon Scanning • Continuously monitor emerging AI technologies, vendors, startups, research, industry developments, and competitive activity relevant to U.S. Commercial. • Track advances across generative AI, agentic systems, multimodal AI, intelligent automation, knowledge systems, customer engagement technology, commercial analytics, and AI infrastructure. • Maintain a structured horizon-scanning framework that categorizes developments by strategic relevance, maturity, feasibility, and potential business impact. • Produce concise executive updates on significant developments and their implications for U.S. Commercial. • Identify signals that should influence future Commercial Engine, product, architecture, and transformation roadmaps. AI Innovation Assessment • Evaluate emerging AI technologies and capabilities against U.S. Commercial priorities. • Assess technical feasibility, scalability, differentiation, data requirements, integration considerations, and enterprise readiness. • Distinguish meaningful commercial opportunities from short-term market hype. • Create repeatable evaluation frameworks for AI products, vendors, models, platforms, and partnerships. • Recommend whether opportunities should be monitored, tested, partnered, incubated, scaled, or retired. U.S. Commercial Opportunity Identification • Identify emerging AI capabilities that can create value across customer engagement, marketing, market access, patient solutions, insights and analytics, commercial operations, field enablement, and the Commercial Engine. • Translate external AI developments into potential U.S. Commercial use cases and future-state capabilities. • Develop concept papers, opportunity briefs, and strategic recommendations for senior leaders. • Partner with Product Management and Transformation teams to shape future investment and experimentation priorities. • Connect emerging technologies to U.S. Commercial strategy and transformation roadmaps. External Innovation and Ecosystem Assessment • Maintain intelligence on the external AI ecosystem, including startups, technology companies, academic institutions, research organizations, and industry innovators. • Identify emerging capabilities that may warrant further evaluation or partnership. • Lead the technical and strategic assessment of external capabilities for U.S. Commercial, including strategic fit, differentiation, feasibility, enterprise readiness, and potential value. • Partner with the AI Partnerships & Ecosystem Lead to determine the appropriate engagement and partnership pathway. • Provide clear recommendations on whether opportunities should be monitored, tested, partnered, incubated, scaled, or retired. • Transition approved partnership opportunities to the AI Partnerships & Ecosystem Lead for engagement, coordination, contracting support, and ongoing relationship management. • Monitor competitor AI developments and industry adoption trends. • Maintain a prioritized pipeline of external technology opportunities aligned to U.S. Commercial strategy. Rapid Experimentation and Proof of Value • Design and oversee focused proofs of concept for promising technologies and external capabilities. • Define learning objectives, evaluation criteria, success measures, and time-bound decision points. • Partner with AI Applications, Product, Data, and business teams to evaluate technical and commercial viability. • Document findings, implications, and clear recommendations. • Support recommendations to scale, partner, incubate, monitor, or retire opportunities, with partner engagement led by the AI Partnerships & Ecosystem Lead. Innovation Portfolio Management • Maintain a transparent pipeline of emerging AI opportunities across near-term, emerging, and transformative horizons. • Prioritize opportunities based on strategic value, commercial relevance, technical feasibility, scalability, differentiation, and risk. • Apply a consistent intake and disposition model across external opportunities. • Track progress from initial signal through evaluation, experimentation, partnership recommendation, roadmap integration, or closure. • Provide portfolio insights that support U.S. Commercial investment decisions. AI Trend Analysis and Thought Leadership • Synthesize research, market intelligence, industry developments, conference insights, and analyst perspectives into clear U.S. Commercial implications. • Develop executive-ready materials that explain why developments matter, not only what has changed. • Publish periodic AI outlooks and horizon-scanning updates. • Serve as a credible internal thought leader on emerging AI capabilities and their commercial relevance. • Help leaders distinguish durable opportunities from transient trends. Responsible AI and Enterprise Readiness • Assess emerging technologies through security, privacy, legal, regulatory, compliance, quality, and Responsible AI lenses. • Identify implementation constraints and risks early in the evaluation process. • Partner with Governance, Risk, Architecture, Digital, and other relevant teams to evaluate enterprise readiness. • Ensure recommendations account for Pfizer standards, technical dependencies, and responsible adoption requirements. • Build risk and governance considerations into experimentation and partnership recommendations from the outset. Cross-Functional Partnership and Influence • Partner with the Vice President, Applied AI & Architecture to translate external signals into future-state technical strategy and architecture. • Partner with the AI Partnerships & Ecosystem Lead to connect broader external ecosystem activity with U.S. Commercial priorities and create clear handoffs for approved partner engagement. • Partner with AI Applications and Product teams to create effective handoffs from exploration into product and engineering evaluation. • Partner with Transformation & Delivery to connect validated opportunities with implementation and adoption pathways. • Communicate complex technical and market developments clearly to business and executive audiences. Success Measures • Quality and relevance of horizon-scanning insights and strategic recommendations. • External technology opportunities converted into pilots, partnership recommendations, product concepts, or roadmap priorities. • Speed and rigor of opportunity evaluation and disposition. • Adoption of recommendations in U.S. Commercial planning and investment decisions. • Influence on future Commercial Engine and applied AI priorities. • Quality of external capability assessments and partnership recommendations advanced to the AI Partnerships & Ecosystem Lead. • Clarity and transparency of the innovation opportunity pipeline.