Summary
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
Key Insights
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Designers often face the 'blank page' problem when tackling complex enterprise domains without sufficient research or domain knowledge.
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Enterprise design requires making the invisible 'dark matter'—complex social, technical, and political systems—visible and understandable.
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EDGY is an open source graph language that models enterprise complexity across three facets: customer experience, enterprise architecture, and organizational identity.
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AI, particularly GPT-4, can augment designers by consuming structured repositories as context, reducing reliance on generic internet knowledge.
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Using AI in enterprise design is more effective as augmentation of human creativity rather than automation or replacement.
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Three AI collaboration modes identified are the 'jester' for provocative incorrect ideas, the 'sidekick' that integrates research context, and the 'matrix' that transforms knowledge across enterprise perspectives.
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AI-generated diagrams are imperfect but valuable as starting points to defeat the blank page and stimulate discussion.
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Inputting domain-specific research and insights into the AI prompt improves the relevance and quality of generated task maps.
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Biases can be intentionally introduced in AI prompts to explore specific enterprise perspectives, such as organization design or brand identity.
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Enterprise design must bridge silos by fostering knowledge sharing and cross-team collaboration, which AI and graph languages can facilitate.
Notable Quotes
"Designers are thrown into environments with experts that have decades of experience, and we have to come up with something relevant really quickly."
"The blank page can be terrifying because you don’t want stakeholders to laugh or say you got it all wrong."
"Behind simple tasks, there’s often big complexity hiding, especially in enterprise environments."
"We think the problem of 'dark matter' in enterprises is about making invisible things visible and bridging silos."
"AI is better seen as augmentation to make us more efficient, not automation to replace us."
"We use our model repository as context to help AI generate answers based on our specific enterprise knowledge rather than general knowledge."
"The jester mode throws something in your face, maybe wrong or uncomfortable, but at least it moves the conversation forward."
"AI-generated diagrams are usually not perfect, but they offer a better starting point than a blank page."
"If you say, ‘let’s look from an organization design perspective,’ AI reproduces the bias of that perspective in its output."
"Enterprise design is about sharing knowledge, analyzing it, improving it, and then consuming it to create better outcomes."
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