Associate Director, Commercial AI – Business Value Delivery

Job Description

Associate Director — Commercial AI – Business Value Delivery, Digital Human Health

Job Description

This role sits within the Commercial AI vertical of Digital Human Health (DHH), which is responsible for building and scaling AI capabilities across cross-functional teams and divisions. The team operates as an embedded strategic partner to the commercial organization, providing thought leadership and delivering AI-powered solutions to marketing and cross-functional stakeholders. It shapes demand by translating business priorities into scalable AI products and agent-driven solutions that enhance decision-making and drive measurable business impact.

Role Overview

As the Associate Director – Commercial AI – Business Value Delivery, this role is accountable for shaping and delivering AI and Agentic AI solutions across Commercial functions and DHH value teams (Marketing, Market Access, Field Force Effectiveness, Omnichannel, and Forecasting).

This role combines strong commercial analytics leadership with AI/GenAI technical literacy to frame business problems, build value cases, align stakeholders, and drive adoption—partnering closely with product, data science, engineering, IT, and risk/control teams.

The role’s primary mandate is measurable value realization: defining success metrics, enabling scalable deployments, and ensuring solutions are governed, safe, and fit for enterprise use.

Key Responsibilities

• ·       Frame business problems as clear decision statements with defined value hypotheses, success metrics (ROI, KPIs), and value realization cadence.
• ·       Lead commercial analytics strategy across enterprise use cases for agentification (segmentation, NBE, forecasting, omnichannel, marketing effectiveness, field insights, HCP journey) using a business-first, evidence-led approach.
• ·       Translate priorities into a value-driven AI roadmap—sequenced by impact, feasibility, and risk—with defined scope, success criteria, and adoption plans.
• ·       Define AI product vision including problem statement, user personas, workflows, guardrails, and success metrics (operational + business impact).
• ·       Own stakeholder alignment—influence senior leaders, manage trade-offs, and drive cross-functional/global accountability.
• ·       Drive adoption and change management through operating model design (incl. human-in-the-loop), training, communication, and sustained usage tracking.
• ·       Establish portfolio governance for Commercial AI (intake, prioritization, value-based sequencing, delivery checkpoints, risk/compliance alignment).
• ·       Ensure measurable value delivery via early metric definition, MVP/pilot validation, post-launch tracking, and continuous improvement loops.
• ·       Scale and standardize capabilities by building reusable assets, playbooks, and cross-market solutions.
• ·       Mentor and lead engineers/data scientists: design reviews, coding standards, coaching, hiring input, and building a high-performing delivery culture.

Technical Expertise

• Partner with tech leads and engineering to shape AI/Agentic AI solution approaches (LLM workflows, RAG patterns, tool/function calling, orchestration; multi-agent patterns only when appropriate and governed).
• Convert business needs into well-formed requirements and user stories; ensure the design supports reliability, scalability, maintainability, and cost controls (e.g., performance/latency expectations and run-cost awareness).
• Ensure enterprise deployment practices are applied: LLMOps/MLOps, CI/CD, monitoring/observability, evaluation frameworks, drift/quality gates, and operational readiness for production.
• Establish guardrails and responsible AI controls: hallucination mitigation, data privacy-by-design, security considerations, auditability, model risk awareness, documentation, and compliance alignment for a regulated environment.
• Review and challenge analytical and GenAI approaches (internal and vendor) for rigor, transparency, explainability, and compliance; drive corrective actions where needed.
• Support vendor/partner evaluation (build vs buy), including technical due diligence, proof-of-concept design, and recommendations based on time-to-value, scalability, ownership, and risk.

Education Requirements

Bachelors/Masters (or equivalent) in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Statistics, Engineering, or a related quantitative discipline with …