For a Solid Return on Investment with AI, Consider the Many Ways to Purpose-Fit Your Infrastructure

This white paper examines why many enterprises struggle to achieve strong returns on AI investments and outlines how infrastructure choices directly impact AI success. Drawing on IDC survey data, it highlights cost, complexity, and misalignment between use cases and infrastructure as key barriers, while proposing a practical, purpose-fit approach to AI deployment.

• Over 50% of enterprises report that fewer than half of their AI projects deliver measurable business outcomes, with specialized infrastructure costs as the top concern.
• Different AI approaches — from traditional machine learning to generative and agentic AI — require very different infrastructure, ranging from CPUs and workstations to GPU-based datacenters.
• Seven core factors, including model type, data volume, accuracy, time to value, and query performance, determine AI infrastructure cost and ROI.

The paper concludes that organizations can improve AI ROI by involving IT early, carefully selecting how models are built or sourced, and aligning infrastructure with real workload needs. By adopting a spectrum-based, purpose-fit infrastructure strategy and leveraging flexible vendor ecosystems such as Supermicro and AMD, enterprises can better control costs, scale effectively, and achieve meaningful business outcomes from AI.