Bridging the AI Production Gap: How Observability Unlocks Enterprise AI Success

Enterprises have poured time and capital into AI pilots, yet too many initiatives stall before production because classical monitoring can’t see or explain AI behavior. This report defines the “AI production gap” and shows why AI-native, multilayer observability—spanning application, agentic, model, data, and infrastructure layers—is now essential to move from experiments to reliable, scalable outcomes.

Futurum Research data shows organizations are rapidly prioritizing observability to operationalize AI at scale. Drivers include competitive pressure, the shift of AI into mission-critical roles, escalating financial/reputational risk, and mounting governance requirements. The report details how traditional APM misses AI-specific risks such as hallucination cascades, semantic drift, tool misuse by agents, and multi-agent coordination failures—and how AI observability closes these gaps.

In Bridging the AI Production Gap: How Observability Unlocks Enterprise AI Success, developed in partnership with Dynatrace, Futurum Research maps the market landscape, highlight adoption trends, and provide a pragmatic implementation guide—covering vendor selection criteria, phased rollouts, and success metrics that quantify operational efficiency, risk mitigation, business impact, and strategic value.

In this market brief, you will learn:

• Why AI pilots stall—and the specific operational risks unique to agentic systems.
  
• The multilayer model for AI observability (application, agentic, model, data, infrastructure).
  
• What buyers are adding next: cloud, API, security monitoring, and AIOps (see chart on page 6).
  
• How to evaluate platforms: integration breadth, scalability, roadmap fit, time-to-value.
  
• A phased adoption playbook and KPI framework to prove ROI and resilience.

Download Bridging the AI Production Gap: How Observability Unlocks Enterprise AI Success to accelerate your path from pilot to production with confidence.