Why enterprise AI can't answer high value business questions—and how to change that

A customer support workflow fails not because data is missing, but because existing systems aren’t set up to do ad-hoc, multi-source data fetches and joins in real time.

In theory, enterprise AI should be able to answer high-value business questions. In practice, it can’t. It’s common for disconnected tools, brittle pipelines, and rigid logic to fall apart under real-world conditions.

In this talk at The 2024 AI Conference, Snow Leopard founder and CEO Deepti Srivastava shares why enterprise data complexity, not model reasoning, is the real barrier to effective AI. Drawing on her experience as the lead PM for Google Spanner, she introduces Snow Leopard’s approach: retrieving the right business data at query time, directly from source systems, without preloading or transformation. The result is a simpler, more scalable foundation for building agents and assistants that can actually deliver answers that matter.

[02:17] Root cause: missing joins across systems at runtime
A customer support workflow fails not because data is missing, but because existing systems aren’t set up to do ad-hoc, multi-source data fetches and joins in real time. Siloed data and fixed APIs force human-in-the-loop workflows unnecessarily where automation could improve efficiency, productivity, and customer experience.

[05:27] AI agents fail when business logic is baked into ETL
Most current systems encode logic into pipelines—meaning any shift in schema, source, or decision flow requires reengineering. This tightly couples business logic to data movement.

[07:13] RAG turns structured data into lossy approximations
Deepti critiques RAG-based retrieval for structured data: embeddings flatten semantics, introduce fuzzy matching, and discard schema integrity, making it unusable for high-trust or transactional use cases.

[09:02] Why agentic frameworks don’t generalize
Even sophisticated function-calling agents depend on predefined APIs and context windows. They don't handle ad hoc retrieval across changing schemas or evolving enterprise systems without heavy orchestration.

[14:00] Snow Leopard’s design: building intelligence about the data rather than into the data pipelines
Snow Leopard queries each data source —OLTP databases, data warehouses, API based systems, vector stores —natively at runtime, without the need for ETL, pre-defined pipelines, data pre-loading or transformations. The system routes intelligently at runtime and builds the exact query needed to fetch data from a specific data source.

[16:09] Benefits: increased query precision, reduced system coupling
Querying in place preserves data fidelity, reduces latency, and eliminates the need to model or pre-define every possible user query path. It also reduces maintenance cost.

[17:02] Unlocking applications that require dynamic joins
From support and inventory to account intelligence, Snow Leopard enables AI agents to execute cross-system queries without brittle connectors or upfront orchestration—bringing new categories of enterprise questions into scope.

Sign up for early access to Snow Leopard: www.snowleopard.ai