This week on The Data Stack Show, John chats with Paul Blankley, Founder and CTO of Zenlytic, live from Denver! Paul and John discuss the rapid evolution of AI in business intelligence, highlighting how AI is transforming data analysis and decision-making. Paul also explores the potential of AI as an "employee" that can handle complex analytical tasks, from unstructured data processing to proactive monitoring. Key insights include the increasing capabilities of AI in symbolic tasks like coding, the importance of providing business context to AI models, and the future of BI tools that can flexibly interact with both structured and unstructured data. Paul emphasizes that the next generation of AI tools will move beyond traditional dashboards, offering more intelligent, context-aware insights that can help businesses make more informed decisions. It’s an exciting conversation you won’t want to miss.
Highlights from this week’s conversation include:
- Welcoming Paul Back and Industry Changes (1:03)
- AI Model Progress and Superhuman Domains (2:01)
- AI as an Employee: Context and Capabilities (4:04)
- Model Selection and User Experience (7:37)
- AI as a McKinsey Consultant: Decision-Making (10:18)
- Structured vs. Unstructured Data Platforms (12:55)
- MCP Servers and the Future of BI Interfaces (16:00)
- Value of UI and Multimodal BI Experiences (18:38)
- Pitfalls of DIY Data Pipelines and Governance (22:14)
- Text-to-SQL, Semantic Layers, and Trust (28:10)
- Democratizing Semantic Models and Personalization (33:22)
- Inefficiency in Analytics and Analyst Workflows (35:07)
- Reasoning and Intelligence in Monitoring (37:20)
- Roadmap: Proactive AI by 2026 (39:53)
- Limitations of BI Incumbents, Future Outlooks and Parting Thoughts (41:15)
The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it’s needed to power smarter decisions and better customer experiences. Each week, we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.
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