I led the development of a strategic framework to improve enterprise AI adoption and maximize the value of underutilized AI tools across the organization. After identifying low adoption rates among existing enterprise Copilot license holders, I proposed expanding access beyond leadership teams to include high-impact knowledge workers and early adopters within the organization. This shifted the committee’s approach from role-based distribution to value-based enablement.

To operationalize this initiative, I designed a scoring model and prioritization framework that evaluated employee goals, job functions, and workflow needs to determine whether individuals would benefit most from ChatGPT Enterprise, Microsoft Copilot, both platforms, or neither. I also created supporting adoption mechanisms, including a usage survey to gather behavioral insights and a microlearning resource that guided employees in identifying AI use cases aligned to their individual goals and responsibilities.

I collaborated closely with the Senior Director of Product Management and executive stakeholders to align licensing decisions with organizational objectives, responsible AI practices, and long-term scalability. The framework became the foundation for future enterprise AI license allocation and contributed to increased adoption among strategically selected individual contributors and AI power users.

As the Talent Development and Human Resources representative on the committee, I led the design and implementation of the organization’s AI learning and enablement strategy. My work focused on building scalable learning pathways that would support responsible AI adoption while improving employee readiness, confidence, and practical application across functions.

I partnered with Talent Development leadership, Product Management, and Marketing to evaluate and rank existing AI learning resources available through platforms such as Skillsoft and Coursera. Together, we developed persona-based learning pathways aligned to employee roles, business priorities, and skill levels. I also created a centralized AI learning hub within the company’s internal learning platform to organize resources, microlearning content, and future enablement materials.

In addition to designing learning infrastructure, I worked to connect AI education directly to operational goals by identifying practical use cases for ChatGPT and Microsoft Copilot tied to employee objectives and workflows. This strategy helped establish a more structured and sustainable approach to organizational AI enablement while supporting broader digital transformation efforts across the enterprise.

I partnered with the Senior Director of Product Management to support the development and training of AI agents designed to leverage institutional knowledge and improve organizational access to information. This initiative focused on transforming existing learning and operational content into structured knowledge assets that could support scalable AI-powered assistance and decision-making.

My role involved reviewing instructional design materials, identifying high-value artifacts appropriate for AI training, and helping establish governance processes to ensure content accuracy and sustainability. I also contributed to the development of a stakeholder ownership model that identified subject matter experts responsible for maintaining and validating information used to train the agents.

By helping bridge instructional design, knowledge management, and AI implementation, I contributed to the organization’s broader strategy of embedding AI into operational workflows in a responsible and sustainable way. This work laid foundational infrastructure for future AI-driven support systems while reinforcing governance, trust, and long-term maintainability.