Conversational BI Implementation in Looker: Step-by-Step Guide

Before getting into setup or tools, it’s worth understanding how Conversational BI actually works when someone types a question and expects an answer.

At a practical level, the flow is straightforward:

-  A business user asks a question in plain language.

- That question is interpreted by a conversational layer.

- A structured query is generated against Looker.

- Looker runs the query using its existing semantic model.

- The result is shaped into a readable response and sent back

Most real-world implementations separate this into four layers:

• Chat or UI Interface The entry point of the system. This is the front-end surface (Slack, Microsoft Teams, or a custom web dashboard) where the user inputs their query and receives the final visualization or answer.

  
  

• Optional Memory or Caching Layer Before processing a new request, the system checks this layer to see if the answer was recently generated or if there is ongoing "state" (like a previous filter applied) that needs to be carried forward into the new query.

  
  

• Conversational Agent Layer The "brain" of the operation. It uses Natural Language Processing (NLP) to perform intent classification and entity recognition. It determines if the user is asking for a specific metric (e.g., "Gross Margin") and validates if the request is possible within the current data scope.

  
  

• Looker API Interaction Layer The final execution stage. It translates the validated intent into Looker-native code (like LookML dimensions and measures), sends the request via API, and retrieves the structured data to be sent back down through the layers to the user.

Steps for Conversational BI Implementation

Step 1: Prepare Looker for Conversation-BI Analytics

When we say "Prepare Looker for Conversation-Ready Analytics," we aren't just talking about fixing broken joins. We are talking about contextual clarity. Here is why the "artificial" approach fails and what actually works:

• The Intuition Gap: A human analyst knows that "Sales" usually means "Gross Revenue" unless specified otherwise. An AI has zero intuition. If you have five different fields named "Total_Amount," the AI is essentially flipping a coin.

  
  

• The Vocabulary Problem: Users don't talk like database schemas. They don't ask for count_distinct_order_id; they ask, "How many customers did we have?" Your LookML needs to bridge that gap using clear labels and descriptions that match business speak.

  
  

Preparation usually involves revisiting fundamentals:

• Field names that reflect business language, not raw schema

• Metrics that have one clear meaning, not multiple interpretations

• Joins that are intentional and predictable

• Date fields that behave consistently across explores

This is why teams often discover modeling issues only after introducing Conversational BI. 

You can read more about how AI-driven analytics in Looker simplifies complex data models for non-technical users.

Step 2: Define the Conversational Entry Point

Once the data layer is ready, the next question is practical: where will people actually ask questions?

In most organizations, there are three realistic options:

• Inside Looker itself

• Inside chat tools like Slack

• Inside internal applications through embedding

Chat-based entry points tend to see higher adoption, not because they are more advanced, but because they remove friction. People already ask questions there. No new habit is required.

This decision is less about technology and more about behavior. If users need to leave their flow to ask a question, many simply won’t. Conversational BI works best when it fits naturally into how teams already communicate.

Step 3: Translate Natural Language Into Looker Queries

This is where most of the real complexity lives.

When someone asks, “What were last month’s weekly signups?”, the system has to make several decisions:

• What's the average customer lifetime value across different age groups? 

• Show me the purchase frequency for each age segment 

• What product categories do high-value customers ( lifetime value > 1000) prefer?

• What's the profit margin on these categories? 

• Which marketing channels are most effective at acquiring high-LTV customers?

None of this is obvious without guardrails.

Strong implementations are intentionally restrictive:

• Only approved explores can be queried

• Queries are bounded to prevent runaway costs

• Generated queries are validated before execution

To the user, the experience feels open and flexible. Underneath, it is tightly controlled. That balance is what keeps the system trustworthy and predictable.

The stakes for getting your semantic model right are high. Google recently highlighted that Suzano, the world's largest pulp manufacturer, used Gemini Pro to build an AI agent that translates natural language into SQL. Because their data was structured correctly, they saw a 95% reduction in query time for 50,000 employees (Google, 2026). Without a conversation-ready model, that kind of speed and accuracy simply isn't possible."

Step 4: Add Context, Memory, and Reuse

This is the point where Conversational BI stops feeling transactional and starts feeling usable.

Real users rarely ask a single, perfectly phrased question. They build understanding step by step:

• Compare this to last month

• Break it down by region

• Why did it change?

Maintaining conversational context allows the system to:

• Carry filters forward

• Understand references like “this” or “that”

• Keep answers logically consistent

Many teams also introduce caching at this stage. If the same question is asked repeatedly, there’s no reason to recompute it every time. Faster responses matter, not just for performance, but for confidence. When answers come back quickly and consistently, users trust the system more.

Step 5: Secure Access and Respect Governance

This is not optional. And it’s where shortcuts cause real damage.

Conversational BI must respect the same access rules as Looker itself. If a user does not have permission to see data in Looker, they should not see it through a conversational interface either.

Good implementations:

• Use Looker permissions as the source of truth

• Enforce row-level and explore-level security

• Treat chat access and data access as separate concerns

One of Looker’s strengths is that governance is already embedded in the semantic layer. Conversational BI should sit on top of that foundation, not try to work around it. Our Conversational Analytics implementation framework follows a phased approach to ensure these queries remain governed and

secure.

Step 6: Test With Real Business Questions (Not Demo Prompts)

Before rolling this out broadly, teams should collect actual questions from business users. Testing should focus on:

• How the system behaves with unclear phrasing

• Where responses become misleading

• When the agent should refuse to answer

Healthy signals:

• Clarifying follow-up questions

• Conservative answers when intent is unclear

Warning signs:

• Overconfident responses

• Silent failures

• Different interpretations of similar questions

This phase usually takes longer than expected. It’s also where adoption is won or lost.

Common Failure Patterns (And How to Avoid Them)

Conversational BI struggles when teams assume it will compensate for poor data foundations or unclear ownership.

It tends to fail when:

• It’s treated as a magic layer

• Data quality issues are ignored

• Access is opened too broadly

• No one monitors how it’s used

It succeeds when expectations are grounded, scope is controlled, and reliability is valued over flashiness.

Measuring Success: Adoption Over Accuracy Alone

More meaningful indicators include:

• How often users ask questions

• Whether they ask follow-ups

• A visible drop in ad-hoc analyst requests

• Faster answers to everyday business questions

How SquareShift Helps Teams Implement Conversational BI in Looker

This is where SquareShift typically comes in. Most teams don’t struggle with the idea of Conversational BI, they struggle with execution. SquareShift helps by starting at the right layer: fixing semantic models, tightening governance, and making data conversation-ready before any agent is introduced. From there, we design and implement conversational workflows that sit cleanly on top of Looker, whether inside Looker, chat tools like Slack, or embedded applications. We bridge the gap between having data and actually using it. By making analytics as easy as a chat, we free your analysts for high-value work and ensure business leaders get the insights they need in seconds, not days.

FAQs

 What are the primary steps involved in a Conversational BI implementation?

The implementation involves preparing Looker for analytics, defining entry points, translating natural language, adding context/

memory, securing access, and testing with real questions.

What is the most complex part of the implementation process?

The real complexity lies in translating natural language into specific Looker queries, such as defining date ranges and choosing the correct measures.

How do we ensure the system understands our specific business terminology?

You must bridge the "Intuition Gap" by updating LookML with clear labels and descriptions that reflect business language rather than raw database schemas.

 How does SquareShift help teams avoid common implementation failures?

SquareShift ensures success by fixing semantic models and tightening governance before introducing an agent, preventing the system from being treated as a "magic layer".

How do we ensure the implementation doesn't lead to runaway costs? 

A strong implementation uses guardrails to bound queries and ensures that generated queries are validated before they are executed against the database.

How can SquareShift accelerate the time-to-insight for our business leaders? 

SquareShift designs conversational workflows that sit cleanly on top of Looker, allowing leaders to get insights in seconds and freeing analysts for high-value work.