From AI Experiments to Enterprise Outcomes

Most enterprises have moved past asking whether they can use AI. The real challenge now is far more difficult: How can you make AI deliver repeatable, governed, business-reliable outcomes at scale? 

Early AI experiments and single-step workflows often succeed in controlled environments, but they break down when organizations try to operationalize them across teams, systems, and decision cycles. The gap isn’t about model capability – it’s about the absence of shared semantics, governance, and architectural discipline.

In this session, Andrew Brust (Blue Badge Insights) and Matt Fuller (Starburst) outline what it takes to move from AI experimentation to production-grade, enterprise-ready AI systems. They examine the architectural, semantic, and governance foundations required to make AI trustworthy, observable, and aligned with business intent. 

This session covers:

• Why semantic layers and data products are now essential execution infrastructure for AI
• How shared business semantics  – grounded in governed, structured data – reduce non-deterministic behavior in LLMs
• Why multi-step and multi-agent workflows fail without consistent meaning and governance
• The coordination challenges – including control, observability, and governance – introduced by agentic systems
• How standards like Model Context Protocol (MCP) and Open Semantic Interchange (OSI) standardize access to data, tools, and shared context
• How open, federated architectures enable scale, safety, and autonomy without vendor lock-in

Attendees will leave with a clear framework for turning AI initiatives into scalable, governed, outcome-driven systems – not just demos.