Enterprise AI maturity assessment
Assess the current state of the business and its digital maturity, identify critical gaps and viable use cases, and avoid discovering only after investment that the necessary foundations are missing.
About
About
I’m Rikka Chen, a product manager and consultant focused on enterprise AI and digitalization.
For more than a decade, I have worked on enterprise software, industry platforms, data modeling and governance, and complex business systems across manufacturing, medical equipment, and retail. I now focus more closely on putting AI into real use—especially this question: when agents begin to enter real organizations, how should the business world be represented, organized, and restructured so that people, systems, and agents can understand, decide, and act together?
I am skilled at identifying the underlying issue within complex business and product requirements, designing long-term AI and digitalization paths from an enterprise-wide perspective, and working closely with teams on the ground to guide key decisions, refine solutions, and adjust implementation as needed. My aim is to turn project-level experience into lasting business and system capabilities. I currently focus on AI-native architecture, complex business modeling, Agent Harness, and related original methods.
Contact
I welcome conversations about work opportunities and collaboration.
Email: cs@rikkalab.com
Services
Identify the real problem before choosing the model, system, or implementation path
Start from the enterprise as a whole to assess the prerequisites for AI implementation, the long-term architecture, and the right place to begin—so that individual projects gradually become durable enterprise capabilities.
AI-native architecture & blueprint
Design an enterprise-wide AI-native architecture that reorganizes the relationships among business objects, data, responsibilities, office automation (OA) and enterprise resource planning (ERP) systems, and agents, then define a phased roadmap for implementation and evolution.
Long-term advisory
Starting from the premise that requirements continue to emerge through business change, modeling, and actual use, stay involved in key decisions, solution evolution, and architectural correction so that each local initiative becomes a foundation for the next stage.
Why must requirements be modeled continuously?Focused collaborative development
Work together on a real business problem through business modeling, prototyping, MVP validation, and production implementation, using project results to validate the business definition, data foundations, and approach to applying AI.