BARC Spotlight
GenAI in Business Intelligence and Analytics
Kevin Petrie, August 2024
Generative AI (GenAI) is revolutionizing business intelligence and analytics (BIA) by enabling teams to analyze and prepare data using natural language. This boosts productivity and supports sophisticated use cases.
GenAI's capabilities extend beyond natural language processing, converting human commands into SQL queries and describing findings. This BARC Spotlight draws on a survey of 238 data leaders to explore the adoption status, benefits, risks, and best practices associated with GenAI in BIA.
Download: GenAI in Business Intelligence and Analytics
Mgmt Summary
GenAI holds significant promise for transforming business intelligence and analytics by enhancing productivity and enabling more sophisticated analyses. While the technology is still in the early stages of adoption, organizations that strategically implement GenAI, address associated risks, and strengthen their data culture can leverage its full potential to drive data-driven decision-making and operational efficiency.
Adoption of GenAI in BI
The adoption of GenAI in BIA is in its early stages, with only a small percentage of companies implementing it fully. Approximately 29% of respondents are discussing GenAI, 9% are evaluating, and 22% are experimenting with it. Only 9% are in the implementation phase, with 6% in partial operational use and 3% in full operational use. Advanced users of AI/ML are more likely to adopt GenAI, leveraging their established data science programs. Over one-third of respondents expect GenAI to moderately improve their BIA use in the next 12-18 months, with data engineers and business users being the most optimistic.
Benefits & Risks
The primary benefits of GenAI for BIA include faster time to insight, reduced workload, enhanced user interaction, simplified BIA processes, and expanded self-service capabilities. These benefits help organizations become more data-driven and democratize data consumption. However, risks such as data privacy, skills gaps, compliance, data quality, and bias are significant concerns. Early adopters find these risks manageable due to their maturity in traditional AI/ML governance.