The enterprise data landscape has reached a critical juncture. Despite decades of investment in data infrastructure, most organizations still struggle to transform raw information into actionable insights that drive faster, smarter decisions. MIT research reveals that 95% of enterprise AI initiatives fail to deliver meaningful business value – a symptom of deeper structural problems in how we approach data.

The core issue? Organizations have assembled complex, fragmented architectures of stitched-together tools that make extracting intelligence from core business systems slow, expensive, and limited. Traditional reporting remains backward-looking, narrowly scoped, and often obsolete by the time it reaches decision-makers. As enterprises rush to implement AI solutions, they’re attempting to bolt artificial intelligence onto these broken foundations.

What is Decision Intelligence?
Decision intelligence represents a fundamental shift from passive analytics to proactive, data-driven decision-making. It’s a systematic approach that integrates insights from artificial intelligence, analytics, business rules, and process automation. Rather than relying on intuition-based choices, decision intelligence empowers organizations to make decisions backed by trusted historical data, augmented with
advanced technologies like machine learning and AI.

The Decision Intelligence Lifecycle: Connect, Understand, Act
Effective decision intelligence requires addressing three critical capabilities:

Connect: Organizations need seamless access to live, detailed data from core business systems without the delays and degradation caused by complex ETL processes. Traditional approaches create pipeline dependencies that introduce latency and lose data fidelity through aggregation, undermining the foundation for accurate AI-driven decisions.

Understand: AI systems require semantic layers that deliver contextual intelligence—understanding not just what the data says, but what it means in a business context. This contextual understanding is essential for successful AI implementation and enables systems to interpret data within the framework of
business rules and objectives.

Act: The ultimate goal isn’t just analysis, but enabling agentic workflows where AI can understand business context, recommend actions, and execute decisions while maintaining human oversight for governance and security. This moves organizations from passive reporting to proactive decision-making.