Why chatbot demos impress, but rarely scale
For many enterprises, AI exploration begins with a proof-of-concept. An LLM queries a dataset in plain English, the answers look sharp, and the demo sparks excitement about what’s possible.
But when the same approach is applied to enterprise-scale data, new challenges surface:
- Inconsistent answers depending on which tool or model is used.
- Different definitions of the same metric across departments.
- Gaps in security and permissions when governance isn’t enforced.
This is the gap between what looks promising in a controlled demo and what it takes to make conversational AI work in production.
The leadership challenge: scaling AI responsibly
Scaling NLQ isn’t just a technical hurdle, it’s also a leadership challenge. Decision makers are under pressure to show progress in AI adoption, but “progress” means more than running pilots. It means making sure:
- Every answer can be trusted – no surprises when executives compare notes.
- Governance is built in – security and compliance are never optional.
- Flexibility is preserved – so the organization isn’t tied to one vendor or LLM.
Without these safeguards, pilots often lose momentum and fail to earn lasting trust across the business.
What changes between demo and production
Many chatbot pilots show quick results but struggle to scale because they’re optimized for simplicity, not enterprise complexity:
- The dataset is small, so accuracy isn’t tested.
- The questions are narrow, so governance isn’t stressed.
- The model is fixed, so consistency isn’t challenged.
When broader data, stricter governance, and multi-LLM needs enter the picture, leaders quickly see that what worked in the pilot doesn’t translate one-to-one into production.
How DistillGenie evolved into an MCP-powered approach
At Distillery, we started with DistillGenie – a Slack + Databricks integration built with AtScale’s semantic layer. It was a first step: making conversational AI useful inside the tools employees already rely on.
But Slack was only the beginning. Enterprises don’t operate in a single tool, and they don’t want to be locked into a single model. That’s why we extended the project into an MCP-powered approach:
- MCP (Model Context Protocol) enables interoperability between LLMs, chat tools, and the semantic layer.
- Governance is enforced centrally, so definitions and security rules stay consistent.
- The same trusted answer can be delivered in Slack, Google Meet, or a custom enterprise assistant.
For leaders, the takeaway is clear: MCP + semantic governance = enterprise-ready AI, without vendor lock-in.
What leaders should be asking their teams
You don’t need to know every detail of how NLQ is engineered. But you should be asking your teams the right questions:
- How are we ensuring governance across models and interfaces?
- What’s our plan to stay flexible if we want to change tools or LLMs?
- How do we keep definitions consistent across departments?
- Are we building toward an assistant that truly helps employees, or just another chatbot pilot?
Clear answers to these questions are what move AI adoption from early experiments to production-ready systems.
Looking ahead
Moving from demo to production isn’t about chasing the latest model. It’s about creating a foundation that enforces trust, consistency, and flexibility from the start.
At Distillery, our work with AtScale has shown that enterprises don’t need to rip and replace their stack to make progress. They need the right architecture – one that combines open semantics, a semantic layer, and a flexible approach that adapts as their needs evolve.
Want to see it in action?
On September 24 at 2PM ET / 11AM PT, Distillery’s Francisco Maurici (Head of Web) and Emanuel Paz (Head of Data) will join AtScale’s Dave Mariani for a live webinar: Building Trusted NLQ Experiences with the MCP Protocol.
We’ll share:
- Why enterprises are embracing open semantics
- How MCP enables governed NLQ across multiple LLMs and tools
- What it takes to move from chatbot demo to enterprise-ready AI