The enterprise AI problem nobody talks about
AI adoption is no longer about if; it’s about how. Enterprises everywhere are experimenting with large language models (LLMs) and conversational interfaces. But once those experiments move beyond a pilot or proof-of-concept, a recurring problem appears: vendor lock-in.
It’s tempting to choose the “easy button” – a single vendor’s ecosystem that promises a neat, packaged solution. But that path often means trading flexibility for convenience. You’re bound to one platform’s logic, integrations, and pricing model.
Fast-forward six months: leadership asks to pivot, or a new tool suddenly outpaces your chosen vendor. You’ve got teams building parallel dashboards, redefining metrics in multiple places, and questioning whether the answers they’re getting are trustworthy.
Enterprises don’t just want conversational AI. They want it to work across the tools they already use, with governance and consistency built in.
Why open semantics matter
This is where open semantics come in. Instead of ripping out your stack or surrendering to a single vendor’s approach, open semantics create a neutral layer that separates how a question is asked from how the answer is governed and delivered.
With an open semantic protocol, enterprises can:
- Support multiple LLMs without rewriting business logic.
- Keep consistent definitions across departments and platforms.
- Avoid lock-in with one database, one LLM, or one cloud provider.
Think of it as future-proofing your AI strategy. With open semantics, a chatbot in Slack today can evolve into an assistant in Google Meet tomorrow, all while respecting the same rules, governance, and logic that keep your business aligned.
Vendor lock-in in practice: where it hurts most
Lock-in doesn’t just show up in pricing or contracts. It shows up in how fast (or slow) your teams can adapt.
- A finance team defines “revenue” one way in a Snowflake-native chatbot. The sales team, running on a Databricks interface, gets a slightly different definition. Both are “right” according to their platform, but now the executive team has two conflicting answers to the same question.
- An operations manager wants to experiment with a new LLM for cost reasons. But the existing AI interface is tied tightly to a specific model. Switching isn’t a matter of swapping out a connection. It’s a rebuild.
- A marketing analyst asks a question in Slack and gets an answer. The same question asked during a Google Meet with leadership comes back differently, because the tools don’t share a semantic backbone.
These examples aren’t hypothetical. They’re the exact friction points slowing down enterprise AI adoption today.
The role of the semantic layer
The semantic layer isn’t new to the data world, but in the AI era, its importance has multiplied. It provides the bridge between questions and governed answers.
When natural language queries (NLQ) flow through a semantic layer:
- Trust is enforced. Every answer is based on approved, governed business logic.
- Consistency is maintained. Definitions remain the same, no matter the model or interface.
- Security is respected. Access rules and permissions travel with the query, not the interface.
Without a semantic layer, conversational AI risks becoming another case of “shadow IT”: tools that work in demos, but fall apart under enterprise complexity.
From chatbots to assistants
Most conversational AI projects today stop at the “chatbot” stage: useful for basic queries, but not fully integrated into workflows.
The next frontier is building a company-wide assistant. One that:
- Lives inside the collaboration tools employees already use (Slack, Google Meet, Teams).
- Connects to the right datasets automatically.
- Enforces governance and security by default.
- Moves beyond generic tasks like summarization, into acting like a true team member.
The difference is subtle but important: a chatbot answers questions. An assistant helps get work done.
Open semantics and protocols like MCP make this possible. They allow enterprises to connect multiple LLMs and agents into workflows that act with the same context and governance as a human employee would.
A forward-looking view
Enterprises shouldn’t have to choose between two extremes:
- Relying on proprietary, vendor-tied solutions that create long-term risk.
- Or stitching together custom projects from scratch, burning resources to reinvent the wheel.
Open semantics offer a third path, one where conversational AI is flexible, governed, and built for scale.
At Distillery, we’ve seen firsthand how teams can evolve from one-off chatbot demos to enterprise-ready assistants by embracing open approaches. It’s not about starting over. It’s about extending what you already have responsibly, and in a way that grows with your organization.
Want to go deeper?
On September 24 at 2PM ET / 11AM PT, Distillery’s Emanuel Paz (Head of Data) and Francisco Maurici (Head of Web) will join AtScale’s CTO Dave Mariani for a webinar: Building Trusted NLQ Experiences with the MCP Protocol.
We’ll share how enterprises are using MCP + semantic layers to:
- Escape vendor lock-in
- Enable NLQ across Slack, Google Meet, ChatGPT, and more
- Enforce governance and consistency in conversational AI
- Take the leap from demo to production-ready assistants