How to build a BI strategy for the AI era

AI is reshaping business intelligence by enabling employees to query data, create dashboards, and build analytical tools through natural-language applications, often outside traditional governed BI environments, which can create risks around data exposure, inconsistent metrics, and untraceable analysis. An effective AI-era BI strategy should begin with high-value business decisions rather than tools or datasets, then balance flexibility with governance through four areas: defining who can ask questions and where, determining what users can build and which assets should be maintained, establishing trusted sources and review processes for AI-generated answers, and managing the combined costs of licenses, AI usage, warehouse compute, and ongoing governance. Governance should cover the full analytics workflow, including access controls, credentials, auditing, ownership, sensitive data protections, and AI integrations such as Model Context Protocol connections. Organizations can implement this incrementally by prioritizing trusted datasets, improving metadata and business documentation, reusing existing semantic models, enabling self-service in prepared domains, and using observability to identify gaps in agent context. Success should be measured through faster access to reliable answers, increased self-service and reuse, improved answer quality, and business outcomes, with the central goal of making governed analytics as convenient as ungoverned alternatives.

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