Enterprises urged to keep BI as AI spending soars

Analysts are urging companies to continue investing in business intelligence (BI) and core data work as Gartner forecasts global AI spending will reach $2.52 trillion this year. The forecast includes infrastructure such as data centers and spending by hyperscalers and AI vendors and represents a roughly 44% year-over-year increase. That sum far exceeds estimates of the BI market at about $40 billion and the wider data and analytics market at $175 billion.

Speakers at a recent data and analytics summit said much of the expected AI investment will rely on existing data and analytics capabilities. They advised organizations to strengthen data quality, tracking and governance so AI outputs can be used reliably in business processes.

Carlie Idoine, a vice president analyst at Gartner, said “I don’t think AI is going to kill BI.” She added that AI will extend BI capabilities and that metrics, data lineage, observability and governance are as important for AI projects as they are for traditional analytics.

Gartner research found poor-quality data cost businesses an average of $12.9 million a year as of 2020. The figure does not account for costs tied to wrong decisions or reputational harm that could arise when AI systems use flawed data.

Levent Ergin, chief strategist for agentic AI, regulatory compliance and sustainability at Informatica by Salesforce, said organizations need trusted, connected and well-governed data whether they are producing BI reports, forecasting demand or using AI agents to automate decisions. He said a single, trusted data foundation can improve reporting consistency and speed insight delivery across an enterprise.

Analysts described BI as providing analytic authority by combining systems of record into a single version of the truth. Idoine noted that many employees work daily with established analytics workflows and that large language models do not yet produce the operational reports companies use to run their businesses.

Julian Sun, a Gartner vice president, said users want tools that can answer complex questions by pulling context stored in multiple places. He described a future in which BI and AI capabilities converge on a single platform that can deliver context-rich answers while maintaining consistent results.

Vendors and consultants said analytics work prepares data for AI applications. Ash Gawthorp, co-founder and CTO at Ten10, said many organizations have data but lack trust and connectivity across systems. Chris Riche-Webber, vice president of BI and analytics at SmartRecruiters, said traditional machine learning remains in use for measurable tasks such as forecasting, matching, optimization and classification.

Analysts recommended companies invest in clear metrics, lineage tracking, observability and governance before scaling AI initiatives. They said those practices can reduce costly data errors and improve the reliability of AI-driven decisions as AI spending rises.