AI Enablement Project Director

1. Find and Prioritize High-Value Problems

• Embed with business teams and frontline users to understand their workflows, decisions, data, constraints, and pain points.
• Separate root causes from symptoms and determine whether the right answer is process redesign, better data, automation, AI, or a combination of these.
• Prioritize opportunities based on business value, user reach, risk, feasibility, time-to-value, and reuse potential.
• Translate ambiguous needs into clear problem statements, target outcomes, success metrics, and executable plans.

2. Build and Deliver Solutions

• Define the minimum valuable product and rapidly validate it with real users and data.
• Personally prototype solutions such as copilots, agents, workflow automations, and internal tools.
• For larger or higher-risk initiatives, partner with Engineering and Data teams on architecture, integrations, security, reliability, evaluation, and production readiness.
• Own delivery from discovery and prototype through launch, adoption, measurement, iteration, and handover.

3. Drive Adoption and Business Impact

• Design solutions with users and adapt workflows based on real operational behavior.
• Lead pilots, enablement, training, and change management required for sustained adoption.
• Track outcomes such as time saved, cycle-time reduction, quality improvement, risk reduction, cost avoided, and active usage.
• Ensure every initiative has a business owner, an adoption plan, and a sustainable operating model.

4. Productize and Scale What Works

• Identify recurring workflows, data needs, integrations, and governance requirements across teams.
• Convert successful deployments into reusable components, templates, reference architectures, or configurable products.
• Decide when a need should remain bespoke, use shared components, or become a standardized product or platform capability.
• Build practical playbooks and standards for opportunity assessment, prototyping, evaluation, productionization, and impact measurement.
• Feed field learnings into shared Product and Engineering roadmaps, reducing duplicated and one-off solutions.

• 4+ years of experience across product management, technical delivery, solutions engineering, enterprise transformation, consulting, or software/data engineering.
• Demonstrated ability to uncover high-value problems through workflow observation, data analysis, and stakeholder engagement.
• Hands-on experience building and shipping AI, data, or automation-enabled solutions used in real operational workflows.
• Strong product judgment, including prioritization, MVP definition, trade-off decisions, rapid validation, and avoidance of over-engineering.
• Sufficient technical depth to prototype independently and work effectively with Engineering on data, integrations, architecture, security, evaluation, and reliability.
• Working knowledge of LLMs, RAG, agents, tool use, prompt design, evaluation, observability, and human-in-the-loop systems.
• Experience turning bespoke business needs into reusable products, shared services, or operating standards.
• Strong communication and stakeholder-management skills across frontline users, technical teams, and senior executives.
• High ownership and comfort operating in ambiguous, fast-changing environments.