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Limited model controls (temperature, context window)

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Forced or constrained tool usage (e.g., MS Copilot)

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Unclear data privacy and protection boundaries

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Uncertainty about regulation and compliance

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Each group adds 1 🚀bold idea + 1 🍃easy win

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Please discuss and respond to the following aspects in separate posts below:

1. 🔍 Root causes
What are the underlying causes of this frustration?

2. 🌀 Agile practices affected
Which agile practices, e.g. planning, reviews, retros, etc. are impacted?

3. 📉 Technical/Knowledge gaps
What limitations prevent AI from being effective?

4. 🔬 Research or Tool Ideas
What tools, frameworks, or research directions could address this frustration?

💡 Instructions for each Group:

• Add posts per aspect by clicking "+" under your group name (e.g. one post titled 'Root causes').

• Keep answers focused. Use bullet points if helpful.

• After your discussion, each group add one 🚀 Moonshot idea and one 🍃 Low-Hanging Fruit to the last column.

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AI used poorly in learning and team settings

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AI integration doesn't yield valuable outcomes

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Lack of prompting skills or best practices

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Missing or insufficient context in prompts

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Closed system, small model for testing and better understanding of AI impact

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Governance moonshot - create your own llm

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Governance moonshot - create your own llm

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Commit to training, develop a coworker culture with ai

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Shadow agents company paralleling projects. For comparative analysis to tell you what you should do.