Job description:
Drive enterprise adoption of agentic AI — orchestrate the people, process, and communication side of rollout so that engineering, business, and risk stakeholders embrace and effectively use the platform.
Required Qualifications:
• 7–10 years in change management, digital transformation, or enterprise technology adoption.
• Demonstrated experience leading adoption for large-scale AI, automation, or intelligent workflow deployments.
• Working knowledge of agentic AI — multi-agent systems, LLM-powered tools, human-in-the-loop design.
• PMP a plus.
• Exceptional executive communication and cross-functional stakeholder management skills.
Roles/Responsibilities:
• Develop AI adoption roadmaps covering stakeholder sequencing, change impact assessments, and go-live milestones.
• Conduct workflow assessments to identify resistance patterns and role-level impacts before deployment.
• Facilitate executive briefings and build internal AI champion networks across business units.
• Design and deliver role-specific training programs, enablement assets, and sandbox learning environments.
• Track adoption KPIs (usage rates, productivity deltas, confidence scores) and translate insights into targeted interventions.
• Embed responsible AI principles — transparency, accountability, human oversight — into every rollout design.
• Collaborate with IT, legal, and compliance to ensure deployments operate within governance guardrails.
Key role:
Build and execute the adoption strategy: stakeholder mapping, persona analysis, value-realization plans, success metrics.
Run change-impact assessments across squads, business units, and operating teams; identify and address resistance.
Partner with HR, L&D, Risk, Legal, and Compliance on responsible-AI policy rollout, acceptable-use guidelines, and audit readiness.
Define and track adoption KPIs (active users, tasks automated, time saved, defect deflection) and present to executive sponsors.
Establish feedback loops between users and the engineering team; manage the intake of feature requests, defects, and risks.
Lead rollout waves: pilot MVP → define entry/exit criteria, comms plans, and rollback playbooks.
Strong understanding of how AI/LLM/agentic tools change knowledge-worker and developer workflows.
Excellent executive communication, facilitation, and stakeholder-management skills.
Comfort with responsible AI principles, model risk, and enterprise compliance frameworks.
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