AI Inference: Enterprise Infrastructure and Strategic Imperatives
Updated
In our latest Market Report, AI Inference: Enterprise Infrastructure and Strategic Imperatives, completed in partnership with Lenovo, Futurum Research examines the growth of AI inference, the shift toward hybrid/edge deployment, and the infrastructure choices enterprises must make to run inference…
Futurum's Nick Patience,
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Nick Patience, The Futurum Group, "AI Inference: Enterprise Infrastructure and Strategic Imperatives," January 27, 2026. https://preview.erikbethke.com/research-reports/ai-inference-enterprise-infrastructure-and-strategic-imperatives/

Artificial intelligence has entered its production phase. While foundation-model training gets the headlines, the real economic value is created through inferencing—deploying trained models to make predictions, generate responses, and drive day-to-day business decisions across the enterprise.
As organizations move from pilots to production, inference infrastructure becomes a strategic choice, not a tactical one. Enterprises must balance latency, cost-per-inference, power density, and data sovereignty across cloud, on-premises, and edge deployments—while avoiding “bill shock,” performance bottlenecks, and operational fragility.
In our latest Market Report, AI Inference: Enterprise Infrastructure and Strategic Imperatives, completed in partnership with Lenovo, Futurum Research examines the rapid growth of AI inference, the shift toward hybrid and edge architectures, and the technical requirements for building reliable, cost-efficient inference at scale—along with a practical framework for evaluating solutions.
In this report, you will learn:
- Why AI inference infrastructure is projected to grow from $5.0B in 2024 to $48.8B by 2030—and what that means for enterprise investment priorities
- How and why hybrid and edge inference are accelerating (65% CAGR), reshaping how organizations place and operate inference workloads
- The most common business, operational, and technical bottlenecks (e.g., cost management, talent gaps, memory bandwidth saturation, and Time to First Token requirements)
- What “specialized” inference infrastructure looks like across compute, memory, networking, cooling/power, and the software stack (optimization, runtimes, orchestration, observability)
- How to evaluate AI inference solutions using a consistent framework (performance validation, scalability, TCO, flexibility, and security/compliance)
To learn more about the AI inference infrastructure market outlook, hybrid/edge shift, and the technical requirements for scaling inference, download AI Inference: Enterprise Infrastructure and Strategic Imperatives today.
Published by Futurum.
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