Senior Director, AI Strategy, Governance and Transformation

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 115,000 colleagues serve people in more than 160 countries.

Executive Summary

The Senior Director AI Strategy, Governance & Transformation, converts enterprise AI ambition into prioritized use cases, governed delivery, and realized business value. This leader owns the AI portfolio, resourcing and prioritization—from idea intake and prioritization to Responsible AI oversight, operational and change management, adoption, and benefits tracking—ensuring AI becomes a repeatable engine for growth, productivity, and risk‑aware innovation.

This role will partner with Corporate and Division leaders, and AI Engineering teams to align platform capabilities with sponsored, high‑impact use cases, while embedding Responsible AI standards and measurable outcomes across the lifecycle.

Strategic Mandate

• Build and lead an enterprise AI portfolio office, a new organization focused on AI governance, transformation, process reengineering, portfolio management and change management.
• Identify & prioritize enterprise AI/GenAI use cases tied to strategic objectives and P&L.
• Build and run a unified AI portfolio with clear stage‑gates, funding, and ownership.
• Develop a process excellence mindset and capability to ensure processes are AI ready.
• Embed Responsible AI (RAI), risk controls, and regulatory compliance by design.
• Drive adoption, change management, and value realization across business units.
• Establish an AI operating model that scales: intake → experiment → pilot → production → value.

Key Responsibilities

1) Enterprise Use‑Case Strategy & Portfolio

• Stand up and lead an enterprise AI Portfolio Office (APO) that manages demand intake, evaluation, and cross‑BU prioritization. Run regular portfolio reviews with IT, Finance and Business Unit Leaders to ensure strategic and investment alignment.
• Build a 12–24‑month AI roadmap that sequences lighthouse use cases alongside platform enablers; define rubic scores across value, feasibility, risk and data readiness.

2) Governance and Responsible AI

• Partner with existing data use and global compliance councils to operationalize Responsible AI standards (fairness, transparency, privacy, safety, explainability, human oversight) across the lifecycle.
• Oversee model risk classification , AI risk registers, human‑in‑the‑loop controls, documentation and audit readiness across the lifecycle.
• Govern third‑party AI/LLM providers and data usage in partnership with Legal, Procurement, and Security.
• Establish stage gate governance with clear exit criteria, investment thresholds and benefits tracking (discovery – pilot – scale)

3) Process Transformation & Adoption

• Lead process reengineering and design to make workflows AI ready—defining new roles, policy guardrails, controls, and exception handling.
• Build and execute a change management and enablement engine (communications, training, playbooks, competency development) to drive sustained adoption.
• Partner with R&D to build highly technical training programs and HR/L&D to build enterprise AI fluency programs for executives, product owners, engineers, and end‑users.

4) Value Realization & Performance Management

• Design and implement benefits tracking —from baseline to post‑deployment value capture (revenue, cost, risk, CX).
• Publish a quarterly AI Value Dashboard (adoption, time‑to‑value, ROI, control effectiveness, incident reporting, model performance).
• Continuously improve through post‑implementation reviews and portfolio rebalancing.

5) Organizational Leadership and Influence

• Develop, and retain key talent to enable adoption of AI; foster a culture of excellence, accountability, and continuous learning
• Work in partnership with business and IT to ensure executive sponsorship and product ownership for each use case; clarify OKRs, KPIs, and benefit hypotheses up front.
• Run a cross‑functional steering forum with Technology, Risk/Compliance, Legal, Security, to unblock, align, and accelerate.

6) Partnership with AI Engineering (Operating Model)

• Co‑own AI release readiness (security, privacy, resilience, monitoring) and handoffs from experiment → production → run.
• Align platform roadmaps with prioritized use cases; ensure reusability via APIs, shared services, templates, guardrails, standardized tooling.

Success Profile (12–24 Months)

• A governed AI portfolio is in place with clear sponsorship, funding, and stage‑gates; 3–5 lighthouse use cases scaled enterprise‑wide.
• Responsible AI embedded in intake, build, and run; positive outcomes in internal audit/external exams .
• Adoption and value realization metrics established and tracked
• A durable AI operating model (APO, governance forums, dashboards, playbooks) used across business units.
• High trust and alignment between business, risk, and engineering , with demonstrable acceleration and fewer production frictions.
• Process re-engineering is a key enabler for all AI transformation efforts.

Ideal Candidate Profile

Experience

• 15+ years in strategy, transformation, product/portfolio leadership, or risk/governance roles within complex regulated industries ( financial services, automotive, or large‑scale technology) ; 7+ years working with AI/ML initiatives.
• Built and led an enterprise portfolio office or transformation program with measurable outcomes (multi‑BU scale).
• Hands‑on experience with Responsible AI/Risk , regulatory engagement, and audit‑ready controls in complex environments.
• Proven track record driving adoption and realizing benefits for AI‑enabled process change.

Technical & Governance Acumen

• Working knowledge of AI/ML lifecycles , GenAI (LLMs, RAG, prompt orchestration), data governance, and model monitoring.
• Familiarity with MLOps concepts and platform‑led delivery; comfortable partnering deeply with engineering leaders.
• Proficient in risk/control frameworks , documentation (model cards, data sheets), and policy design .

Leadership & Influence

• Credible with executives and regulators; clear, concise communicator able to translate complex risk/tech topics into business outcomes.
• Inclusive leader who builds high performing teams both direct reports and across a matrix of teams.
• Builder‑operator mindset: strategic framing with bias for execution and measurable results.

Education

• Bachelor’s degree required (Business, Computer Science, Engineering, Data/Analytics, or related).
• Advanced degree (MBA, MS in Data/AI with tech focus) or relevant certifications in governance/risk/compliance preferred.

The base pay for this position is $190,000.00 – $380,000.00. In specific locations, the pay range may vary from the range posted.