This talk is a case presentation about using generative AI and graph languages to come up rapidly with complex enterprise designs. We are using a repository based enterprise architecture tool and EDGY, an open source visual language, to feed GPT4 with context-rich queries. The resulting maps and models are … wrong.

But they have proven to be inspiring or even triggering for conversations across a diverse stakeholder community, and shortcut our way to a set of correct and useful models that inform design decisions. Moreover they can highlight blind spots and interrelationships previously unknown and thereby enrich the design process with minimal effort.

Takeaways

• Recognising blank page moments in complex challenges
• How to embed context and an ad hoc Training in an LLM prompt
• How to make generate a web of coherent maps such as Journey, JTBD, Organization, Process Maps that cover a complete design related to a given challenge
• How to use these maps and how not to use them when co-creating with others
• When to keep tackling the blank page yourself instead