AI adoption systems

Turning fast-moving AI capabilities into practical guidance and repeatable workflows.

AI adoption usually fails in the gap between excitement and operational clarity. Teams know the tools are powerful. What's missing is structure.

At Foster, I've helped translate emerging AI capabilities into practical marketing, communications, and education systems: internal adoption guides, reviewer bots, AI-assisted narrative workflows, and HeyGen-enabled informational workshops. The focus has never been novelty. It's been AI as an operating layer that improves clarity, increases capacity, and creates faster feedback loops.


The challenge

AI tools were evolving faster than shared understanding, governance, and production workflows. Without structure, experimentation becomes fragmented and hard to evaluate. The work required practical systems that could answer: what use cases are worth pursuing, what needs human review, how do we preserve accuracy and trust, how do we scale educational content without scaling production burden.

My role

I led development of AI-enabled marketing and education workflows across strategy, content, review, and implementation. That meant identifying high-value use cases, building reviewer workflows, and translating experimentation into repeatable systems.


The work

Adoption guidance

Internal resources on where AI creates value, how to use it responsibly, and how to evaluate outputs before publication. The goal was moving AI from abstract possibility to practical application.

Reviewer workflows

AI-enabled reviewer tools to improve consistency, quality, and speed: a faster first-pass layer, not a replacement for editorial judgment.

Educational media

A repeatable HeyGen-enabled video workflow for admissions workshops, covering use-case selection, scripting, production, and stakeholder alignment.

Feedback loops

Analytics and audience behavior used to refine content strategy and prioritize higher-impact education moments.


Produced HeyGen-enabled admissions workshops that increased watch time, inquiry quality, and conversion. Reduced production time through AI-assisted scripting and review. Built a faster test-and-learn model for educational content across the team.

New capabilities only create value when people understand them, trust them, and know how to use them. This work gave teams reusable systems, not one-off experiments.