AI forbusiness intelligence

Integrating artificial intelligence into business intelligence workflows so business users can self-serve data insights using advanced text-to-SQL (also known as RAG for structured data)

  • Overview
  • What is AI for business intelligence?
  • What are the benefits of using AI for business intelligence?
  • Where are some general use cases for leveraging AI for business intelligence?
  • How can departments use AI for business intelligence?
  • How Snowflake helps with AI for business intelligence
  • Customers
  • AI for Business Intelligence Resources

Overview

Business intelligence (BI) is an engine for powering strategic decision-making — providing organizations with technology and processes for collecting, organizing, analyzing and visualizing their data in a systematic way. 

Artificial intelligence augments BI so any user can analyze data without needing to be proficient in writing SQL, the language of analytics and databases where data is stored. Instead, users can interact with conversational interfaces that streamline data exploration, visualization and reporting — democratizing data insights for everyone.

What are the benefits of using AI for business intelligence?

AI-augmented business intelligence can help organizations become more efficient and make more data-driven decisions across the board. Here are a few key benefits:

  • Empower business users to self-service data analysis through conversational interfaces, no SQL knowledge required
  • More flexible querying: Tralist--blue-bulletsditional dashboards are rigid and require developers to make changes in order to ask new questions. AI allows for more flexibility so users can ask questions of large data sets without as many additional resources. 
  • Faster complex analysis: Users can ask questions, receive answers quickly and then ask follow-up questions instead of having to wait between cycles for reports. This fast self-service iteration helps derive data-driven insights for decision-making.  
  • Automated data extraction, preparation and cleansing: By using outputs of text-to-SQL, the underlying technology that powers AI for BI, teams can use generated SQL queries to prepare and clean data faster to provide curated tables for business users to get answers they can trust.
  • AI-powered dashboards: AI can generate helpful summaries and craft narratives from data visualizations, making it easier and faster to figure out what to do based on that interpreted data. Additionally, AI models can forecast future trends and outcomes based on historical data and current indicators.

What are some general use cases for leveraging AI for business intelligence?

With the rapid advancement of gen AI, BI augmentation has become a helpful tool within many AI-powered systems. Here are a few things it can be used for across teams:

  • Democratizing insights: Reducing reliance on analysts and other SQL developers for getting answers to ad hoc questions users — especially non-technical users — might have that aren’t available on dashboards or other, more rigid self-service analytical tools
  • Empowering more data-driven decision-making: By having real-time insights and answers for their questions — and being able to iterate on those questions with “what if” scenarios or similar lines of questioning, business users can become more data-driven with their decision-making without having to become technical experts
  • Customer 360: Enabling greater interactivity and self-service analysis of customer data and extracting structured data and insights from unstructured customer data sources
  • Customer behavioral insights: AI can produce business intelligence by monitoring competitor actions, pricing and customer survey results and identifying potential gaps to compete more effectively.

How can departments use AI for business intelligence?

AI for BI can also be helpful across industries, in particular in certain departments within an organization. Here are just a few examples: 

Supply chain operations

 AI-augmented BI solutions enhance supply chain operations by detecting data anomalies in supply chain data to identify issues early, analyzing alternative data sources like satellite imagery to predict bottlenecks, and improving demand forecasting and logistics coordination based on historical data.

Marketing

A natural language interface can empower more marketers to be data-driven, and customer 360 data can help provide better understanding for campaign performance and product sentiment.

Data analytics

Gen AI can help automate the generation of SQL code, which can both reduce data analyst coding time and allow non-technical users to access more insights.  It can also validate and correct SQL code to improve quality and efficiency. Text-to-SQL capabilities can generate customer analysis, build reports or track KPIs from text without the need for complex SQL queries, helping power and disseminate rich BI data sets across the organization.

Business operations

Enable teams leading projects with ad hoc analytics via applications they use every day — without encountering road blocks like waiting for data teams to provide custom reports. 

Sales

Provide an interface for your sales team to manage pipeline and analyze competitor impact on sales. 

How Snowflake helps with AI for business intelligence

The Snowflake AI Data Cloud provides you with the data and AI infrastructure and services  required to build and run AI-augmented business intelligence solutions with capabilities such as Snowflake Copilot and Snowflake Cortex Analyst.

Reliable business intelligence, ready when you actually need it

Talk to your enterprise (structured) data using Cortex Analyst2: Self serve answers from analytical tables such as sales transactions without writing any SQL with Cortex Analyst text-to-SQL service. With industry-leading accuracy of ~90%, Cortex Analyst provides the right foundation to generate answers business users can trust providing the foundation for RAG on structured data. With its convenient and scalable REST API, data teams can integrate Cortex Analyst into any business application. Learn how Linqto successfully accelerates conversational app development with Snowflake.

Accelerate development for analysts and data engineers with Snowflake Copilot

Simply ask Copilot your data questions in plain English, and it will generate the corresponding SQL queries for you, help you clean up SQL queries to run more efficiently or assist with finding the right data and documentation for development flow — all without you needing to write complex queries. This comprehensive functionality streamlines your workflow and enables high-quality data analyses.

Augment historical reporting with predictive insights using Snowflake ML

Develop and deploy predictive AI models such as forecast, churn prediction or asset failure prediction using a Snowflake ML complete solution for custom model development that includes Notebooks, Feature Store and Model Registry. 

How Snowflake customers are using AI for Business Intelligence

Real Snowflake customers are saving time for their teams, boosting productivity and cutting costs by using AI for BI in Snowflake.