Generative AI is reshaping business intelligence, turning dashboards into dialogues and static reports into dynamic, co-piloted decisions. This masterclass will teach you how generative BI changes the way data teams design, deliver, and govern insights across the enterprise. Scott Clendaniel will cut through the hype to define what “generative business intelligence” actually looks like in production: conversational analytics, AI-assisted data modeling, auto-generated narratives, and personalized insight delivery embedded in everyday workflows. 

You will learn how large language models sit alongside your warehouse, semantic layer, and existing BI platforms; where they add real value; and where traditional BI is still the better tool. The course will also address core concerns around data quality, trust, security, and governance, including prompt governance, guardrails, and human-in-the-loop review. 

Through demonstrations, patterns, and discussion, you will leave with a clear roadmap for introducing generative BI into your organization in a safe, measurable, and strategically aligned way. You will be equipped with language and frameworks to engage business stakeholders, data leaders, and IT on what to do first, what to avoid, and how to show ROI.  

If you are responsible for the next generation of your analytics and BI stack, this masterclass will help you design and lead your generative BI journey.  

 You Will Learn:

• Understand what generative business intelligence is (and is not) in the context of modern data and analytics architectures. 
• Identify high-value generative BI use cases such as conversational analytics, auto-generated narratives, and AI co-pilots for reports and dashboards. 
• Map how large language models integrate with warehouses, data lakes, semantic layers, and existing BI tools. 
• Design prompt patterns, guardrails, and feedback loops that improve accuracy and trust in generative BI experiences. 
• Apply governance practices for generative BI, including security, privacy, compliance, and responsible AI considerations. 
• Evaluate vendor capabilities and marketing claims using a clear reference architecture and decision framework. 
• Plan a phased generative BI roadmap, from small experiments and pilots to broader enterprise rollout. 
• Define metrics and instrumentation to measure adoption, quality, and business impact of generative BI solutions. 
• Engage business stakeholders with clear value propositions, change management tactics, and training strategies for generative BI. 
• Anticipate organizational and cultural shifts as BI moves from static content delivery to AI-augmented decision workflows. 

Geared To:

• Director/head of business intelligence or analytics 
• Data and analytics product owner or BI platform owner 
• Chief data officer (CDO) or head of data and AI 
• Analytics or BI solution architect