Marketing Manager, AI Technology Specialist

Job description

Career CategorySales & Marketing OperationsJob DescriptionRole summary The AIN Marketing Manager, AI Technology Specialist will serve as a franchise partner to the U.S. Oncology Business Unit, helping teams translate business challenges into practical, responsible, and scalable AI-enabled solutions. Working closely with U.S. brand marketers, Marketing Operations, analytics, field teams, Data and AI, Information Systems, Legal, Regulatory, Compliance, and other cross-functional partners, this individual will identify opportunities to apply AI where it can meaningfully improve efficiency, decision-making, execution quality, and business impact. The role will support the design, development, testing, and adoption of approved generative AI solutions, AI-enabled development tools and workflows, AI-assisted insight generation and synthesis, and workflow automation. The individual will also establish and lead the measurement of AI initiatives across the OBU, ensuring solutions are evaluated against clearly defined adoption, efficiency, quality, and business-impact measures. This role does not replace brand, technology, analytics, or compliance ownership. It serves as the connective point that translates OBU business needs into execution-ready AI use cases and works with the appropriate partners to develop, govern, scale, and sustain those solutions. By simplifying work and accelerating decisions, the role will help the OBU operate with greater direction, speed, and purpose in service of patients. Core deliverables will include a prioritized AI use-case dashboard, solution business cases, pilot plans, measurement dashboards, governance documentation, and concise leadership readouts. Key responsibilities AI opportunity identification and prioritization Partner across OBU franchise and brand functions to identify business challenges that may be addressed through AI, automation, or technology-enabled process improvement. Conduct discovery sessions and workflow assessments to understand user needs, pain points, current processes, data requirements, and desired outcomes. Translate business problems into clear AI use cases, functional requirements, user stories, solution concepts, and measurable success criteria. Maintain an integrated pipeline of OBU AI opportunities and support prioritization based on business value, user impact, feasibility, risk, scalability, and potential for cross-franchise reuse. Identify common needs across brands and functions and recommend shared solutions rather than duplicative, brand-specific builds where appropriate. Develop clear business cases and pilot recommendations that define the problem, proposed solution, expected value, resource requirements, risks, and measurement approach. Help distinguish where AI is the appropriate solution and where process simplification, standardization, or a non-AI technology solution may better address the need. AI solution design and development Design, configure, test, and improve approved generative AI assistants that support approved OBU business processes and user needs. Develop and coordinate AI-enabled development tools and workflows that automate repeatable tasks, improve access to information, support data preparation, or simplify operational processes. Build practical prototypes using approved AI, automation, analytics, and low-code or no-code platforms. Partner with Data and AI, Information Systems, engineering, analytics, and other technical teams to move validated prototypes into secure, sustainable, and scalable solutions. Develop reusable solution components, prompt frameworks, templates, workflow patterns, and documentation that can be adapted across brands and functions. Establish clear testing and acceptance criteria, including output quality, accuracy, usability, reliability, security, and required human review. Ensure solutions are grounded in approved sources and include appropriate traceability, quality checks, and human-in-the-loop controls. Monitor solution performance after deployment and make iterative improvements based on user feedback, usage patterns, output evaluations, and changing business needs. AI-assisted insights and synthesis Develop AI-enabled approaches to support the synthesis of approved customer, market, campaign, field, operational, and performance information. Help teams organize large volumes of information, identify recurring themes, summarize findings, and surface potential questions or opportunities for further analysis. Partner with analytics and insights teams to ensure AI-assisted outputs supplement—not replace—appropriate analytical methods, business judgment, and subject-matter expertise. Establish review processes that ensure insights are traceable to source material, appropriately contextualized, and validated before they are used to inform decisions. Identify opportunities to streamline recurring insight-generation activities, including report synthesis, meeting preparation, trend summaries, and consolidation of cross-functional inputs. Support the development of tools that enable teams to access and apply existing knowledge more efficiently while respecting data-access, privacy, and compliance requirements. Workflow automation and operational efficiency Identify repetitive, manual, or fragmented workflows across the OBU that could benefit from automation or AI-enabled support. Design solutions that simplify processes, reduce unnecessary handoffs, improve consistency, and return capacity to higher-value work. Support automation across areas such as planning, reporting, content operations, project management, meeting preparation, knowledge management, and administrative workflows. Work with process owners to document current-state and future-state workflows, clarify roles and handoffs, and define how new tools will be incorporated into day-to-day operations. Ensure automation efforts address the underlying process rather than adding technology to inefficient or unclear ways of working. Track dependencies, risks, decisions, and implementation milestones for AI and automation initiatives. Develop operating guides, standard procedures, user documentation, and support models for solutions that move into ongoing use. Experimentation and solution validation Design and support structured pilots and experiments to evaluate AI use cases before broader deployment. Define test populations, baseline measures, success criteria, evaluation methods, review checkpoints, and scale-or-stop decisions. Support comparative testing of prompts, workflows, solution designs, and user experiences to determine which approaches deliver the strongest results. Coordinate feedback sessions and usability testing with marketers and other intended users. Document findings, limitations, risks, and lessons learned so that successful approaches can be replicated across the OBU. Help teams move from isolated experimentation to repeatable, governed, and measurable execution models. Maintain a balanced experimentation portfolio that creates space for innovation while focusing resources on use cases with meaningful business potential. AI measurement and value realization Establish and lead a consistent measurement framework for AI initiatives across the OBU. Define baseline performance before implementation so that efficiency, quality, adoption, and impact can be measured credibly. Develop initiative-level KPIs and reporting plans that may include: User adoption and active usage Time saved or capacity returned Process and review-cycle time Output quality and accuracy Error and rework reduction User satisfaction and confidence Cost avoidance or productivity improvement Solution reuse across brands and functions Improvement in decision-making or execution outcomes Build and maintain dashboards or scorecards that provide visibility into the OBU AI portfolio, initiative status, adoption, realized value, risks, and next actions. Partner with Finance, analytics, technology, and bu