Enterprise AI deployments are stalling, and the bottleneck is not model quality or agent sophistication. It is data infrastructure. Organizations have invested heavily in streaming platforms, data lakes, and analytical pipelines that move data quickly but cannot deliver authoritative, transactionally consistent state at the moment a decision must be made. AI agents querying stale batch data produce hallucinated outputs and inconsistent recommendations. The result is a growing gap between agentic AI in pilot and agentic AI in production.
This analyst report from Stratola Chief Analyst Dinesh Chandrasekhar examines why data product maturity has become the single strongest predictor of AI success in the enterprise. Drawing on the 2026 Actian Survey of Enterprise Data Leaders, Deloitte’s State of AI in the Enterprise 2026, and IDC market projections, the report introduces Decision Products as the fourth and most critical tier of data product maturity, the layer that converts streaming infrastructure into real-time, authoritative decisions. Organizations with company-wide data product maturity are scaling AI at 3.4x the rate of those without it. The report explains why, and what it takes to close the gap.
At the core of the analysis is a structural distinction between analytical data products, which inform decisions, and Decision Products, which make the system act. Decision Products deliver sub-10 millisecond, ACID-compliant outcomes directly tied to operational and revenue results. They are what fraud prevention systems, 5G policy enforcers, and dynamic pricing engines require to function correctly under sustained load. Most current platform vendors, including Databricks, Snowflake, and Microsoft Fabric, address the first three tiers of data product maturity but leave the fourth almost entirely unaddressed. Volt Active Data is positioned to fill that gap, collapsing the five-layer real-time stack into a single execution environment with ingest-to-action latency measured in single-digit milliseconds.
If your AI systems perform well in development but degrade in production, if your agents are reasoning from yesterday’s data rather than right now’s truth, or if your streaming infrastructure has not yet translated into reliable decision authority, this report shows where the architectural gap is and what a Decision Product approach looks like in practice. Read the full report to understand what separates AI at scale from AI that stays stuck in pilot.