Problem Statement
Organizations need a structured way to design AI adoption workshops tailored to their analytics teams’ skill levels, tools, and goals. This workflow generates a complete, customized workshop package including curriculum, hands-on labs, facilitator scripts, and post-workshop implementation checklists. The goal is to ensure practical, adoption-focused training that aligns with real organizational examples and leads to measurable outcomes.
Steps
Ingest Workshop Requirements
INGESTCollect audience skill levels, tools in use, time available, adoption goals, and organizational use-case examples. This forms the foundation for a tailored analytics-focused AI workshop.
Assess Audience and Context
EXECUTEAnalyze inputs to determine participant readiness, tool access constraints, and complexity appropriate for labs and curriculum. Identify gaps or ambiguities that require conservative assumptions.
Generate Curriculum and Learning Objectives
EXECUTEDraft a balanced theoretical and practical curriculum aligned with adoption goals and available workshop time, ensuring progressive difficulty and room for Q&A.
Create Hands-on Labs with Real Examples
EXECUTEDevelop datasets, exercises, and guided tasks that derive directly from the organization’s analytics workflows and use cases, ensuring practical relevance and tool compatibility.
Generate Facilitation Script and Implementation Checklist
EXECUTEProduce a facilitator script with timestamps, engagement notes, and Q&A blocks. Generate a post-workshop implementation checklist that supports sustainable adoption of AI in analytics workloads.
Prompt Configurations
Workshop Design Prompt
You are an educational designer creating an AI-in-analytics adoption workshop. Use the provided audience skill level, tools in use, time available, adoption goals, and organizational examples to generate: (1) a balanced curriculum with clear learning objectives, (2) hands-on labs using realistic datasets based on the examples, (3) a facilitator script with timing and Q&A, and (4) a post-workshop implementation checklist. Maintain an educational, practical, and adoption-focused tone. Ensure exercises are progressively challenging, use only the provided tools, do not invent tools that the organization has not mentioned, and keep all materials accessible and realistic. Do not fabricate organizational data — only generalize from the types of examples provided.
Example Inputs/Outputs
Audience skill level: intermediate analysts. Tools in use: Snowflake, dbt, Looker. Time: 3 hours. Adoption goals: use AI to streamline data quality checks and dashboard creation. Example org use cases: automated anomaly detection on dbt tests, summarizing insights from Looker dashboards.
Curriculum includes: introduction to AI in analytics workflows; guided demo on AI-assisted dbt test interpretation; applied exercise generating Looker dashboard summaries; 20 minutes Q&A. Labs include anonymized Snowflake sample tables and dbt test logs. Facilitator script includes timeboxing for each module and sample questions to prompt discussion. Implementation checklist includes steps to integrate AI prompts into existing QA workflows and guidance for measuring adoption success.