Global enterprise marketing organization · AI adoption infrastructure
How can marketing teams turn AI experiments into shared capability?
Tamara Franklin's enterprise AI adoption work connected use-case evaluation, tool discovery, reusable skills, enablement, and supported learning.
Project experience of Tamara Franklin
The problem
AI experiments were growing across teams, but the ideas, tools, and lessons were difficult to evaluate, discover, and reuse.
The approach
The work connected use-case evaluation with tool discovery and reusable AI skills. This addressed both sides of the problem: deciding which ideas deserved attention and making useful capabilities easier for teams to find and reuse.
Launch enablement, supported learning, and cross-functional build programs formed part of the same adoption system. Strategy, hands-on building, and learning were connected so that teams had a path beyond isolated experimentation.
The outcome
The project created adoption infrastructure built to scale, connecting strategy, hands-on building, enablement, and adoption so useful experiments could become shared team practices.
Practical lessons for marketing leaders
These lessons interpret the project for teams planning their own work.
- Evaluate AI opportunities and design for reuse together. A useful experiment needs a way to become discoverable and repeatable.
- Include enablement and supported learning in the operating model. Publishing a tool alone does not establish a team practice.
- Define adoption and business measures before expanding your own rollout.
About this project record
The organization is anonymized to protect internal information. This summary reflects Tamara Franklin’s professional project record. Quantitative adoption and business outcomes are not published for this work.