Strategy
Treat early adoption as a learning problem, not only a launch problem. We initially made the feature free so accounts could test it before price became a barrier.
Amazon WorkSpaces / Product launch
Launching WorkSpaces for AI agents.
I led the go-to-market work, then helped developers try the feature through partner activation, hands-on workshops, and community pre-briefings.
01 / CONTEXT
The customer problem.
Enterprises depend on desktop applications that are difficult to modernize or connect through APIs. WorkSpaces for AI agents provides a managed environment where agents can operate those applications.
02 / OWNERSHIP
My contribution.
I brought Amazon WorkSpaces for AI agents to market. My work spanned positioning and messaging, sales enablement, executive thought leadership, analyst and press narratives, partner activation, and early customer advocacy. To support adoption, we focused on developer-oriented partners, AWS Summits, and third-party developer events. Community pre-briefings let developers walk through the feature, give feedback, and explore proof-of-concept use cases.
- Enterprise positioning and messaging
- Sales enablement and executive thought leadership
- Analyst and press narratives, partner activation, and early advocacy
- Developer community pre-briefings and feedback
- Partner activation and hands-on developer workshops
03 / PROJECT APPROACH
Strategy, execution, and results.
Execution
I brought Product, developer-oriented partners, the AWS developer community, and the field into one adoption motion: pre-briefings, hands-on workshops, third-party developer events, and proof-of-concept conversations.
Results
We monitored adopting accounts, session hours, and MCP-related usage to distinguish curiosity from active testing. Exact figures are confidential.
03 / PUBLIC WORK
Explore the work.
The feature was initially free to lower the barrier to testing. We tracked adopting accounts, session hours, and MCP-related usage to understand engagement beyond launch interest. Exact adoption figures are confidential. The product page below documents the offering; these program details describe my work.
Product page ↗Shows the offering’s enterprise value proposition, benefits, and use cases.