Building AI Analysts: Why Safe Experimentation Is Everything скачать в хорошем качестве

Building AI Analysts: Why Safe Experimentation Is Everything

Building AI analysts isn't just a technical challenge, it's a cultural and governance one. In this clip from Atlan's Great Data Debate, practitioners discuss what actually works when building AI analysts: creating safe environments for experimentation, establishing regular demo culture where teams share AI wins, and ensuring data is queryable with strong governance and semantic definitions. The most valuable insight? Half the AI implementations that succeed come from unexpected places, not from leadership directives, but from individual team members experimenting with AI in their daily work. The key enablers: centralized data access, strong governance guardrails, and clear semantic layers that let both humans and agents extract value safely. Key Takeaways Bi-weekly AI demo culture drives 50%+ of unexpected innovations — Teams sharing AI experiments that """"made their lives easier"""" surface use cases leadership wouldn't have predicted, creating organic adoption momentum. Safe experimentation requires three foundations: centralized queryable data, strong governance, and clear semantic definitions — Without this triad, teams either move too slowly (over-governed) or create ungoverned AI chaos. The best AI implementations come from practitioners, not directives — When data scientists and product teams have access to governed, well-defined data, they organically build AI tools that solve real workflow problems. Semantic layers enable both human and agent consumption — Clear business definitions and metadata make data accessible to LLMs and AI agents, not just human analysts, unlocking agentic workflows at scale. "It's safe to try different things" is the unlock — Organizations that create psychological safety plus technical governance see dramatically higher AI adoption and ROI than those optimizing purely for control. This is part of Atlan's Great Data Debate series, featuring practitioners solving real AI governance and implementation challenges.