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AI Enablement and Adoption Lead, Strategy and Operations, Specialty Care GBU

Job in Cambridge, Middlesex County, Massachusetts, 02140, USA
Listing for: BioSpace
Part Time position
Listed on 2026-07-17
Job specializations:
  • IT/Tech
    Change Management, AI Business & Operations, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 180000 - 230000 USD Yearly USD 180000.00 230000.00 YEAR
Job Description & How to Apply Below

Job Title: AI Enablement and Adoption Lead, Strategy and Operations, Specialty Care GBU

Location: Cambridge, MA or Morristown, NJ (Hybrid - 3 days a week in office; 20% travel expected)

About The Job

The AI Enablement & Adoption Lead brings AI-enabled ways of working to the Specialty Care GBUs senior leadership team and the organizations they run. You will accelerate adoption of the AI systems and platforms already available to the business, build the leadership teams practical fluency, and change how senior leaders and their teams work day to day moving AI from something people are interested into something they use by habit.

Reporting to the Global Head of Strategy & Operations, Specialty Care GBU, this is a senior individual contributor role. It carries the direct sponsorship of the EVP, Specialty Care GBU, giving the work a clear mandate from the top of the business unit. You will partner closely with the Digital, Data, and AI functions that build and govern the platforms the GBU relies on.

Success comes through credibility and influence rather than formal authority earning the confidence of senior leaders and helping them adopt new, AI-enabled ways of working.

About Sanofi

Were an R&D-driven, AI-powered biopharma company committed to improving peoples lives and delivering compelling growth. Our deep understanding of the immune system and innovative pipeline enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve peoples lives.

Main Responsibilities Executive Enablement & Reverse Mentoring
  • Build the AI fluency of the Specialty Care GBU leadership team through hands-on coaching, reverse mentoring, and working sessions shaped around how each leader actually spends their time.
  • Sit alongside senior leaders on real work strategy reviews, planning cycles, board and ExCo pre-reads and show where AI changes the task, rather than demonstrating tools in the abstract.
  • Translate what good looks like elsewhere, in other business units and other industries, into practical habits the leadership team can adopt now.
Adoption of AI Platforms & Ways of Working
  • Drive real, sustained adoption of the AI systems and platforms already approved for use across the GBU, closing the gap between what is available and what is actually used.
  • Redesign recurring leadership and team workflows reporting, analysis, meeting preparation, synthesis around AI, and embed the new approach so it holds after the initial push.
  • Identify high-value use cases across the leadership agenda, prioritize them, and carry them from pilot to routine use.
  • Stay current on new and emerging AI tools and features, judge what is genuinely useful for the leadership team, and turn that into practical application.
  • Partner with the Digital, Data, and AI functions to bring enterprise capabilities into the GBU, and feed back what leaders need from those platforms.
AI Culture & Community
  • Set the tone for a confident, curious, and responsible AI culture across the leadership team and the teams they lead.
  • Build and run an internal network of practitioners and champions so good practice spreads without depending on a single person.
  • Create practical, lightweight resources prompts, playbooks, worked examples calibrated to senior pharma users rather than generic training.
Responsible & Compliant Use
  • Ensure new ways of working respect the GBUs regulatory, privacy, data-protection, and information-security obligations; partner with Legal, Compliance, Privacy, and the AI governance function to keep adoption inside the lines.
  • Build leaders judgment about where AI helps, where it must not be used, and how to check its output particularly around regulated content, confidential information, and personal or patient data.
  • Escalate gaps in policy or guardrails rather than working around them.
Measurement & Impact
  • Define what adoption and value mean for this work and track usage, time saved, quality, and decisions improved, connecting activity to outcomes leaders care about.
  • Report progress and impact to the Head of Strategy & Operations and the leadership team.
  • Capture what works and reuse it to…
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