2026 Talks
What Happens to BI in an AI-First World?
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- Analytics & Data Sci
Are dashboards dead? Will BI tools disappear? Will analytics move entirely to chat? In this session, we’ll run a thought experiment: in a world where AI is exceptionally good, what do organizations actually need from data - and what does the data stack look like to support it? This exercise reveals a new operating model for analytics: much of the technical work becomes automated, and data teams shift toward managing systems of AI agents that generate and refine data products. A similar shift is already underway in software engineering, where teams are building self-reinforcing feedback loops that continuously improve software. Drawing on real examples from our team’s development workflows, we’ll explore how data teams can apply these patterns to analytics - and how they can prepare for an AI-first future while maintaining strong governance and security.
Co-founder
Sean Hughes
Evidence
Sean is Co-founder of Evidence, a platform for building AI-powered reporting systems with markdown and SQL. Evidence started as an open source project and is now used in production by hundreds of organizations, including Fortune 500 companies and government agencies. Prior to Evidence, Sean and his co-founder Adam built the data science team at a $4B private equity firm, developing the data infrastructure and analytics tools used by investment teams and portfolio companies. Through this work, Sean formed a strong point of view: data teams should work more like software teams, delivering data products with the quality and usability people expect from great software.
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