AI Implementation Strategist

Job Description

The AI Deployment Strategist role reports to the Head of AI Deployment and serves as their primary partner in translating AI capabilities into trusted, repeatable workflows across the firm. This hands-on role owns deployment and adoption for assigned initiatives, working with business teams to decompose workflows into engineering requirements, deliver training, and measure business impact within the broader strategy established by the Head of AI Deployment.

Key Responsibilities

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• Partner with the Head of AI Deployment to execute the firmwide AI enablement and adoption roadmap, applying established engagement models, standards, and success measures.
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• Embed with investment teams to identify workflow optimization opportunities, translate feedback to engineering team, define adoption milestones, and manage expectations.
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• Deliver workshops, demonstrations, office hours, learning programs, and targeted coaching; support executive briefings and broader education initiatives.
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• Develop practical playbooks, prompt and workflow guidance, role-based training materials, and reusable communications aligned with responsible-use standards.
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• Coordinate launches and rollout activities with AI Product, Engineering, Data, Technology Support, Cybersecurity, Legal, Compliance, Human Resources, and business champions.
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• Support and coordinate the AI champion network, equipping champions with materials and guidance to reinforce standards, collect feedback, and encourage adoption within their teams.
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• Track active use, proficiency, satisfaction, workflow change, time savings, quality, and business value; identify adoption barriers and implement targeted improvements.
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• Apply established support and escalation processes, help resolve user issues, and ensure employees understand solution capabilities, limitations, risks, and appropriate human oversight.
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• Manage day-to-day deployment schedules, content, communications, and partner coordination for assigned initiatives; track resource needs and escalate delivery risks to the Head of AI Deployment.
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• Translate user feedback and observed workflow requirements into actionable recommendations for AI Product and Engineering, and share lessons learned to improve future deployments.
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