Director of AI

Responsibilities

• Conduct a rapid audit of current AI usage and operational workflows across the organization, identifying the highest-leverage opportunities for improvement.
• Build a prioritized roadmap of AI implementation opportunities ranked by time savings, output quality, and strategic impact.
• Identify recurring bottlenecks in research, reporting, communications, and decision-making that are strong candidates for AI-assisted automation or augmentation.
• Design, test, and iterate on prompts and prompt chains that deliver reliable, high-quality outputs for specific business functions (research synthesis, executive briefings, partner outreach, content drafts, etc.).
• Build and document repeatable AI workflows for core operating functions including reporting, analysis, planning, and communications.
• Develop systematic evaluation criteria for AI output quality and reliability, so the organization knows when to trust AI outputs and when to verify.
• Maintain a living prompt library organized by function, with version history and performance notes.
• Implement AI-driven automation for recurring processes including reporting pipelines, research summaries, meeting prep, and post-meeting follow-through.
• Integrate AI tooling across the organization’s existing stack (Slack, Notion, Monday.com, Google Workspace, etc.) to reduce manual handoffs and eliminate repetitive work.
• Evaluate and recommend new AI tools and platforms, with a clear framework for build vs. buy vs. configure decisions.
• Identify where agentic workflows can replace multi-step manual processes and scope those builds.
• Enablement & Organizational Adoption:
• Train founders, leadership, and cross-functional teams on AI tools, workflows, and best practices, meeting each team where they are and making adoption feel obvious, not obligatory.
• Create and maintain the organization’s AI playbook: a living, searchable reference that codifies best practices, approved prompts, and role-specific workflows.
• Create lightweight documentation and training materials that allow any team member to self-serve on core AI workflows without needing ongoing support.
• Measurement & Iteration:
• Track and report on key AI adoption metrics: hours saved on recurring work, output quality and consistency, team workflow adoption rates, and number of processes meaningfully improved or automated.
• Run a continuous improvement loop, ship, measure, learn, and iterate on all AI workflows based on real usage data and team feedback.
• Report directly to founders on AI impact and the organizational adoption roadmap.

Qualifications

• Professional experience with deep, hands-on daily usage of AI tools in real work contexts - you’ve built systems that demonstrably improve the quality and speed of your own work, and you can show them.
• Hands-on experience with agentic frameworks.
• Proven track record of implementing AI workflows in real organizations with measurable impact - not pilots, but production systems that teams actually use.
• Strong prompt engineering skills: you understand how to structure context, chain reasoning, control output format, and evaluate reliability across models (GPT, Claude, Gemini, etc.).
• Systems thinker: you see workflows as interconnected processes, not isolated tasks, and you design solutions that scale beyond the immediate problem.
• Exceptional communicator with a track record of translating complex AI concepts into clear, practical guidance for non-technical stakeholders, including C-suite.
• Background in operations, product, strategy, or engineering - you understand how organizations work and where friction lives.
• High ownership mentality: you don’t wait for direction and you don’t declare victory until it’s in production and being used.
• Comfort working in a fast-moving, ambiguous environment where priorities shift and execution speed is a competitive advantage.

Bonus Points

• Engineering background or scripting proficiency (Python, JavaScript) - the ability to build lightweight integrations rather than relying solely on no-code tooling.
• Experience in crypto, Web3, or high-growth tech environments where the pace of change is a constant.
• Previous experience building internal AI capability at a startup or foundation, where you owned the function from scratch.