AI Isn’t the Problem. Your Data Model Is.
Why enterprise AI struggles to deliver reliable answers—and why the next analytics revolution will be about meaning, not models.
Artificial intelligence is changing how organizations interact with data. Business users increasingly expect to ask questions directly and receive meaningful answers in real time. But most enterprise data environments were never designed for AI systems that interpret business questions directly.
In this white paper, Emma McGrattan, CTO at Actian, explores why many AI analytics and AI analyst initiatives struggle—not because of model limitations, but because enterprise data lacks consistent meaning, context, and semantic structure.
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AI Is Exposing Problems That Already Existed
For years, dashboards and analysts acted as a translation layer between business questions and enterprise data. AI removes that layer.
When AI systems attempt to answer business questions directly, they encounter fragmented definitions, inconsistent metrics, unclear ownership, and missing context. The result is not intelligent automation. It is automated ambiguity.
Without semantic consistency and contextual meaning, AI systems do not scale analysis—they scale uncertainty.
Lo que aprenderá
- Why dashboards are reaching their limits: Understand why traditional BI systems were designed for monitoring, not answering dynamic business questions.
- Why AI struggles with enterprise data: Learn how inconsistent definitions, missing context, and semantic ambiguity undermine trust in AI-generated insights.
- What the next phase of analytics requires: Explore why semantic consistency, metadata, and governed data products are becoming foundational for AI-ready analytics.
- Why AI will not replace BI, but will expose its limits: Discover why organizations that succeed with AI will not replace BI, but rethink how analytics systems answer questions.
- The rise of AI analysts: Understand why a new generation of AI-powered analytics systems depends on semantically governed, context-aware data environments.
Sobre la autora
Emma McGrattan
Directora de Tecnología, Actian
Emma McGrattan es directora de tecnología de Actian, donde dirige la visión tecnológica y la estrategia de ingeniería de las plataformas de datos e inteligencia artificial de la empresa. Cuenta con más de tres décadas de experiencia en el diseño y la gestión de bases de datos y sistemas de gestión de datos de misión crítica, abarcando la evolución desde las arquitecturas relacionales tradicionales hasta las plataformas de datos modernas impulsadas por la inteligencia artificial.
Emma is a recognized industry speaker and practitioner known for her pragmatic approach to emerging technologies. Her work focuses on the architectural foundations required to make AI systems reliable in production, including data quality, observability, governance, metadata, and integration. Rather than emphasizing tools in isolation, she advocates designing data platforms that are resilient, explainable, and suited to enterprise-scale AI workloads.
