Use case type
Uses natural-language interfaces to query data, generate insights, and explain results from analytical systems. It addresses the need for faster access to business data without requiring specialized query or dashboard skills.
18
18
9
5 mo
Adoption over time
By case publish month · completed months only
18 cases documented across 31 months (Jan 24 – Jul 26), peaking at 7 in June 2026.
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
Company examples
10 shown from 18 use cases
Telenor Norway, a telecommunications operator in Norway, moved from a centralized legacy BI model to self-service conversational analytics on Google Cloud.Using Looker and LookML, the company lets non-technical users ask questions in natural language, drill into audiences, and support targeted actions.
Insurancemarket, a Greek insurance aggregator, partnered with AWS partner Chaos Gears to build Voxflow, an automated call analytics system.The solution uses Amazon Bedrock, Amazon S3, AWS Lambda, and Amazon EventBridge to transcribe and analyze customer calls as they arrive.It provides near-real-time quality evaluation, sentiment analysis, structured reports, and follow-up workflows for supervisors and agents.
Subskribe used Looker’s embedded analytics platform and LookML semantic model to deliver accurate, persona-based financial reporting to customers.The implementation automated data delivery, reduced engineering overhead, improved support efficiency, and created an AI-ready foundation for future conversational analytics.
Fluent Commerce built Fluent Analytics using Looker and LookML to give retailers trusted, embedded conversational analytics for fulfillment operations.The system standardizes 100+ KPIs, grounds agents in a semantic layer, and lets store and warehouse staff ask natural-language questions in the UI.It reduced complex data customization from a six-month engineering project to ad hoc business queries and reached 33% customer adoption shortly after rollout.
GumGum, an ad tech company, needed a unified BI layer to centralize data silos, keep KPI definitions consistent, and remove analyst bottlenecks for more than 2,000 external clients.It implemented Looker and LookML as the primary BI and semantic layer, embedded dashboards into a self-service portal, and used row-level security and CI/CD practices to scale access safely.The company is also exploring Gemini in Looker and Model Context Protocol for conversational analytics and agentic data access.
Lighthouse, a global hospitality technology company, needed to move from prescriptive custom reporting toward flexible self-service analytics for hotel chains and management companies.The company used Looker with LookML semantic modeling and embedded dashboards to launch BI Pro, a premium analytics tier with custom report building, visual metric galleries, templates, and personalized scheduled reporting.Lighthouse positioned the platform as AI-ready and conversational-analytics-ready, with the semantic layer serving as the trusted metrics foundation for future natural-language insights.
Google Cloud Support centralized fragmented support BI in Looker on Google Cloud with BigQuery and Gemini Enterprise conversational analytics.The team shifted to governed semantic metrics and self-service conversational analytics to reduce BI bottlenecks, improve consistency, and speed decision-making for about 5,000 monthly active users.
Chaplin is an open source solution for enterprise operations teams that turns AWS Health notifications into self-service analytics through AI agents exposed via the Model Context Protocol (MCP).It centralizes event ingestion from multiple AWS accounts into Amazon S3 and Amazon DynamoDB, then lets users ask natural-language questions in MCP-compatible assistants to get precise counts, contextual impact analysis, and remediation guidance.
OtherThe Stakeholder Company (TSC) built Genie, a cloud-native intelligence platform that unifies global media signals and proprietary stakeholder data for Fortune 500 and other clients.The platform uses BigQuery as a primary data warehouse and vector store, Gemini in Vertex AI for conversational reasoning, and GKE-based microservices to deliver a secure AI co-pilot for strategic dialogue with data.The system links real-time signals across 95 countries and millions of stakeholder data points to support evidence-based decision making.
Global engineering and consulting firm Tetra Tech consolidated 100+ ERP systems into Oracle E-Business Suite hosted on Oracle Cloud Infrastructure and then built an interactive analytics platform on Oracle Autonomous AI Lakehouse and OCI Generative AI so business users could access complex finance and project performance metrics on demand.Using Oracle Digital Assistant with text, chat, and voice interfaces, the platform lets finance and project teams ask nuanced questions in natural language, minimizes information delays and inconsistencies, and provides secure access to metrics across projects, customers, and regions.Oracle APEX and Oracle Analytics support role-based reporting, while the company is also planning AI agents for routine tasks such as transaction error fixing and time-entry streamlining.
Common questions
