Here’s what AI likely means for traditional BI and analytics tools

Is artificial intelligence leading to the decline or the rebirth of business intelligence? 

Front-end business intelligence and data analytics tools ruled the markets for years. Now, AI is changing all that. Accordingly, major BI vendors are transitioning to “AI” companies. What do end-users need to know about the future of BI and data analytics in the AI era? 

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Established BI vendors have gotten the message, with new tools that go well beyond reporting and pretty graphs. For example, Qlik, long a leading BI vendor, is conducting what it calls a global “AI Reality Tour,” described recently by industry speaker and author Dez Blanchfield. The vendor’s move into natural language processing (NLP) — via generative AI — enables users to interact with data using everyday language, he noted. Additional capabilities “extend to data visualization, presenting complex data in an easy-to-understand format.”

Industry observers agree that the rise of AI — particularly large language models — is greatly expanding the capabilities and reach of BI and data analytics tools. “LLMs are transforming data analytics by enabling the integration of structured and unstructured data,” said Chida Sadayappan, managing director of Deloitte Consulting. They enhance data interpretation, improve decision-making, and automate processes, allowing organizations to derive deeper insights and create more value from their data.”

BI and analytics tools are here to stay, but their technology foundation is changing — moving to an AI stack on the cloud. “Every layer of the current data stack will be reimagined and reinvented,” said Jitendra Putcha, executive vice president with LTIMindtree. “This includes moving from extract, transform, and load [ETL] methodologies to AI-driven data processing. In addition, user analysis will move from SQL- and Python-based queries to conversational analytics with natural language processing.”  

This means changes in the roles of coders, who “will become designers adopting no-code and conversational mode to build applications using Copilots and Studios, replacing integrated development environments,” said Quang Trinh, business development manager at Axis Communications. “We will move from static reports to dynamic data products, providing real-time, actionable insights embedded directly into workflows to drive decision-making at every level.”    

The natural language processing capabilities inherent in LLMs also are easing “data analytics by translating natural language into database queries and creating data visualizations,” said Trinh.

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This means greater opportunities for creativity among end-users, he continued. “LLMs such as Claude, for example, can generate code for data visualization when it’s connected to a customer’s database. LLMs are crossing over to images and videos to assist in analyzing images and videos, but also generating new images and videos from data it has learned from.”  

Sadayappan agreed that these tools are evolving “to offer more interactive and user-friendly experiences. With advancements in GenAI, users can now ask questions in natural English and receive detailed, descriptive answers, making them more accessible and effective for users.”    

Data analytics “democratization” — long sought as the Holy Grail of authentically data-driven enterprises — may finally be closer to reality. The rise of conversational AI through NLP means a new user interface — no need for formally structuring queries. “Insights creation and consumption will become conversational,” Putcha pointed out. “Systems will adapt to humans. Business users will interact with data in natural language, asking questions and getting insights without SQL or Python skills.”

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At the same time, as with many technology advancements, success with AI-fueled business analytics tools hinges on data quality — the classic conundrum of garbage in, garbage out. “Many organizations will need to invest in data cleaning and validation as they integrate siloed systems into one platform,” said Trihn. “Employee training must emphasize trust-but-verify principles for ethical and effective AI tool usage in their organization.”

The main challenge with AI-driven business intelligence “is integrating data from various sources, which often exist in silos,” said Sadayappan. “Modern data intelligence platforms with LLMs help by facilitating seamless integration, enhancing data quality, and automating insights, thus providing a comprehensive view of customers and operations.”