How to Set Up Your Conversational AI Agent

The Ease of Using Conversational AI for Quick Business Insights

Using conversational AI to interact directly with your business data – asking plain questions, getting instant answers – sounds very appealing.

Who wouldn't want to ask their online assistant, "What were our top 5 best-selling products last month?" or "Show me the customer acquisition cost trend over the past year," and just get the information needed without navigating complex menus or waiting for a report? 

It promises to make data less intimidating and speed things up considerably. You might even think setting this up is as simple as activating a new cloud service and pointing it towards your company's data repository, perhaps a data warehouse like Google BigQuery.

Bridging the Context Gap

While the underlying AI technology is powerful and available, getting a conversational AI tool to be genuinely useful, reliable, and trustworthy with your specific business data takes more effort than just activating a feature. 

The significant challenge lies in bridging the gap between what the general AI model knows (a lot about language, but little about your company) and the specific nuances of your business operations. 

Think of a standard AI model like a very capable new hire on their first day: intelligent, but unfamiliar with internal terminology like 'Project Hurricane”' meaning the Q3 revenue initiative, or knowing that the cust_rev column is outdated and everyone actually uses customer_annual_spend. 

When this basic conversational AI tool accesses your database, it just sees tables and columns. It does not understand your internal jargon, specific calculation rules, or the implicit knowledge your human analysts have. 

It doesn't know that 'ARR' requires a specific calculation involving three different tables, or that the real customer master data resides in crm_prod_final_v3, not the other similarly named tables.

Without this deep context, the AI is likely operating without full understanding, potentially misinterpreting questions, using incorrect data, performing flawed calculations, or providing answers that are misleading in practice.

Key Steps for Setting Up Conversational AI Agent

To make it work well, things need to be set up properly. This process requires careful configuration and usually involves collaboration between people who understand the business well and those with technical expertise. Generally, you'll need a few key components. 

Ensuring Your Data is Ready

First, your data needs to be in adequate shape and accessible. This usually means organising it in a database or data warehouse (like Google BigQuery). 

While perfect data is rare, the cleaner, better organized, and more accessible your data is, the smoother the setup process will be. Data quality principles still apply; inaccurate input will lead to inaccurate output, even with advanced AI. 

Choosing the Right AI Engine

Second, you need the AI engine itself. This is the core technology doing the work – understanding language, figuring out queries. You obtain this from AI platforms (like Google's Vertex AI) that provide the models and tools to build upon.

Adding Business Context

Third – and this is critically important – you must add business context. This is the crucial step where you teach the AI how your business operates.

What do your acronyms mean?

How is 'Customer Lifetime Value' actually calculated here?

Which table holds the official sales numbers?

This vital layer of understanding usually doesn't live in the database itself; you need to provide it externally.

Defining Logic

One common method to do this is using data modelling or semantic layer tools. 

For example, tools like Dataform allow data engineers and analysts to centrally define business logic using just SQL. Within a single repository, they can create table definitions, configure dependencies between data sources, add descriptive comments to columns, and even set up data quality checks. This approach can often be adopted incrementally without disrupting existing workflows.

Because Dataform open source and can be used locally, it offers flexibility and avoids vendor lock-in, which is beneficial for more complex setups. Other platforms, like Looker, also offer features for building semantic models. 

Regardless of the specific tool, the goal is the same: create reusable definitions for metrics (e.g., "This is how we calculate 'Net Revenue'"), specify calculations, document fields, identify authoritative tables, and define relationships. 

Essentially, you build the AI's instruction manual for your business. This semantic layer acts as a translator, helping the conversational AI understand the meaning behind user requests, not just the literal words, and ensures everyone gets consistent answers based on the same defined rules. You define 'Active User' once, correctly, and the AI uses that definition every time.

Leveraging Existing BI Semantic Layers (like Looker)

Alternatively, if your company has already invested significantly in a BI tool like Looker and built out a robust semantic model (like LookML), you might be able to leverage that existing BI knowledge for your conversational AI. 

Instead of recreating definitions, you could potentially configure the conversational AI agent to tap into that existing BI layer for definitions and logic. This can save time and ensures the AI uses the same trusted logic as your existing dashboards. Whichever tools you use, the objective is the same: give the AI explicit, unambiguous instructions about your data's meaning and structure.

Building the User Interface

Finally, people need a way to chat with the AI. This means a user interface (UI) – usually a chat window. It could be a standalone application, embedded in your company portal, or part of another tool. Making this UI intuitive and easy to use is important for adoption; if it's difficult to use, people may not engage with it, regardless of the AI's capabilities. 

In summary, creating a helpful conversational AI assistant takes time and effort. It involves connecting the AI to your data and, crucially, teaching the AI about your specific business. This setup requires both business knowledge and technical skills to turn a general AI into a reliable and accurate tool for your team.