As AI adoption accelerates, organizations are discovering that the real challenge is no longer building models—it’s running them reliably at scale. This webinar explores the growing “production AI gap,” where promising prototypes fail to translate into consistent, real-world outcomes. Attendees will gain a clearer understanding of why inference—not training—is becoming the defining layer of AI, and what it takes to operationalize AI systems in environments shaped by unpredictable demand, latency constraints, and increasing architectural complexity.

Through an analyst-led discussion featuring Futurum Group’s Nick Patience alongside industry practitioners, the session will unpack the infrastructure realities behind modern AI workloads. From cold starts and traffic spikes to observability gaps and cost unpredictability, the conversation will focus on the systemic challenges that emerge in production—and how organizations can better align infrastructure decisions with business outcomes. The webinar is designed for technical and business leaders navigating the transition from experimentation to deployment, offering a grounded view of what breaks, why it breaks, and how to think about solving for it.

Attendees should join to better understand how inference requirements are evolving across use cases—from traditional model serving to emerging agentic workflows—and why many existing environments fall short under production conditions. The session will also highlight how different types of organizations, from AI-native startups to large enterprises and ISVs, are approaching this shift, and what common patterns are emerging in successful deployments.

Top reasons to attend include: (1) gaining a market-level perspective on the shift to inference-driven AI, (2) understanding the root causes behind production failures and performance gaps, (3) learning how real-world AI systems behave under scale and stress, (4) exploring how infrastructure choices impact reliability, latency, and cost, and (5) hearing firsthand insights from both an independent analyst and practitioners operating AI in production environments.

Key Takeaways:

- Inference—not training—is now the primary driver of real business value in AI, and requires a fundamentally different approach to infrastructure and operations.

- The biggest barrier to successful AI adoption is the “production gap,” where systems that perform well in testing break down under real-world conditions like scale, latency, and variability.

- Modern AI workloads—especially agentic and multi-step systems—introduce new challenges such as bursty demand, observability blind spots, and unpredictable costs that traditional environments aren’t designed to handle.

- Bridging the gap between experimentation and production requires rethinking how performance, reliability, and control are managed across the full AI stack.