Secoda AI is powering the next era of business intelligence

The surge in Secoda AI adoption reveals something profound about how enterprises are truly embracing AI. With 76% of usage concentrated in business intelligence and analytics, we're seeing organizations move beyond AI experimentation to workflows that fundamentally change how decisions are made.

What the numbers tell us

The usage patterns emerging from Secoda AI deployments paint a clear picture of enterprise AI maturity over this year:

• Business Intelligence and Analytics: (76%) This overwhelming concentration shows that enterprises are fundamentally shifting how they use data platforms. Rather than relying on Secoda AI primarily for traditional cataloging and governance work, organizations are leveraging it as an analytical engine to generate insights, reports, and visualizations to drive decision-making. This represents a clear move past the hype cycle toward AI applications that directly impact business outcomes. The breakdown looks like this:‍
  
  • Data questions and analysis – 45%: Everyday exploration and ad-hoc queries that allow teams to conversationally engage with their data.‍
  • SQL generation – 19%: Using AI to draft SQL queries, improve performance, and validate query logic, accelerating the workflow for both technical and business users.‍
  • Visualization and reporting – 11%: Automated generation of reports and charts to understand and explore trends.‍
  • Metrics, glossary, and definition retrieval – 1%: Support for consistent metric definitions and business glossary usage.‍
• Governance and Cataloging: (10%) Foundational workflows that enable AI and analytics at scale. They account for a smaller share of direct AI usage since they’re primarily handled by workspace admins, who represent a smaller fraction of overall Secoda AI users compared to platform users focused on analysis and data questions. The most popular use cases include automating documentation, implementing suggested glossary terms, and generating descriptions.‍
• Data Preparation and Enrichment: (3%) Include transformation and enhancement processes that convert raw enterprise data into formats optimized for LLM consumption‍
• Data Quality and Observability: (4%) Quality assurance workflows, like generating suggestions for new monitors and alerts, featuring automated monitoring systems and data asset scoring that underpin confident decision-making‍
• Platform, Integration, and Operations: (3%) Backend operations including workspace administration and rule based automations‍
• Data Onboarding and Collaboration: (2%) Supporting capabilities that democratize data access and empower non-technical users to participate in data-driven decision making‍
• ML and Model Management: (2%) Specialized functions to improve ML model performance

What is most striking about this distribution is how it reflects the maturation of enterprise AI thinking. Organizations are no longer asking "How do we implement AI?" but rather "How do we use AI to make better decisions faster?"

Riding the perfect wave

Throughout 2025, we've observed a fascinating alignment between industry AI adoption phases and Secoda AI's product evolution. In early 2025, as AI tools became mainstream and enterprises recognized data governance as crucial for AI readiness, Secoda AI introduced enhanced AI options and custom roles that addressed exactly these foundational needs.This wasn't coincidental timing as it reflected a deep understanding of how enterprise users would want to interact with intelligent data systems as AI became more conversational and accessible.

Most significantly, this summer’s introduction of Secoda AI agents and automation blocks arrived precisely as enterprise AI reached its production inflection point. With hundreds of millions of people using consumer AI applications daily, enterprises needed platforms that could scale sophisticated analytical capabilities across their organizations.

Why business intelligence dominates

The 76% concentration in business intelligence applications highlights Secoda’s role in helping organizations close the gap between curiosity and insight.

Traditional business intelligence required users to know what questions to ask and how to structure queries. AI-enhanced BI through platforms like Secoda AI allows organizations to explore data conversationally, uncover hidden patterns, and generate insights that would be practically impossible through conventional analytical approaches.

This shift toward analytical augmentation over operational automation demonstrates sophisticated enterprise AI strategy. Organizations are recognizing that competitive advantage comes from better decisions, not just more efficient processes.

Looking ahead

What Secoda AI's adoption patterns reveal extends beyond any single platform. We're witnessing the emergence of a new category: AI-native business intelligence that fundamentally changes how organizations interact with their data assets.

The concentration of usage in analytical applications suggests that enterprises have learned to identify AI's highest-value applications. Rather than trying to apply AI everywhere, successful organizations are focusing on areas where AI can multiply human intelligence and accelerate insight generation.

This represents exactly the kind of thoughtful AI adoption we at Secoda have been advocating for, which is to give everyone the ability to use data.

The transformation we're observing through platforms like Secoda AI represents the maturation of enterprise AI from experimental technology to essential business infrastructure. The future belongs to organizations that can effectively combine human insight with AI-enhanced analytical capabilities.

If you’d like a concise, visual recap of these trends, you can grab the usage summary PDF here.