Enterprise AI Adoption
Enterprise AI Adoption Across 10 Global Brands
How I helped move AI from “a tool people had access to” to workflows people actually used.
- employees
- 3,500+
- global brands
- 10
- manual tasks eliminated
- 2,000+/mo
- recurring manual work saved
- 50+ hrs/mo
The problem
Giving employees access to generative AI did not automatically create business value.
The organization needed to answer more practical questions: Where could AI meaningfully improve day-to-day work? Which use cases were worth prioritizing? How could repetitive workflows be redesigned rather than simply sped up? How should teams be trained? How could leadership understand adoption and business impact? Where did human review still matter?
The work required more than tool rollout. It required a repeatable system for identifying use cases, building solutions, changing behavior, and measuring what happened afterward.
My role
I served as a project manager and AI subject-matter expert for the global AI initiative.
My work included partnering with executives and functional teams, identifying and prioritizing practical AI opportunities, mapping workflows, designing AI-enabled processes and automations, building with tools including ChatGPT, Claude, Zapier, and related automation platforms, leading workshops and enablement, developing adoption and reporting frameworks, and communicating progress, utilization, impact, and roadmap recommendations to leadership.
How I approached it
Start with work, not with AI
Instead of asking “Where can we use AI?”, I focused conversations on repetitive tasks, bottlenecks, handoffs, high-volume administrative work, information trapped in unstructured text, decisions that needed better context, and work employees disliked doing.
Separate automation from augmentation
Some workflows could be heavily automated. Others required a human reviewer because judgment mattered, source data was inconsistent, system/API constraints existed, or the cost of an incorrect output was too high.
The goal was not maximum automation. It was the right automation.
Build proof before scale
Useful workflows were prototyped quickly, tested with the people who would use them, and refined before broader rollout.
Treat adoption as part of the product
Training, examples, templates, communication, and workflow design were treated as part of the solution — not as work that happened after the “technical” project was finished.
Measure what changed
Reporting focused on utilization, repeat usage, automated task volume, time saved, operational friction removed, and future opportunities.
The system / workflow
Outcome
The program supported AI adoption across approximately 3,500+ employees and 10 global brands.
Automation workflows eliminated more than 2,000 manual tasks per month, with documented workflows saving 50+ hours of recurring manual work per month.
More important than the tooling itself, the program established a repeatable model for moving from business problem → use case → workflow design → implementation → enablement → measurement.
What I learned
The technology was rarely the hardest part. The hardest part was changing how people worked. AI implementation succeeds when workflow design, human behavior, technical feasibility, and business value are treated as one problem.