The Infrastructure Imperative

Most organizations are investing heavily in AI and losing money on it. Gartner found that 72% of CIOs report their organizations are breaking even or losing money on AI investments. McKinsey found that only 6% of companies are generating meaningful business impact. The gap isn't a model problem or a talent problem. It's an infrastructure problem.

The Infrastructure Imperative is a research report from EverOps that draws on findings from McKinsey, Gartner, IDC, Google's DORA program, the Cloud Native Computing Foundation, Salesforce, and more than a dozen other primary sources. It makes a direct case: the organizations pulling ahead on AI aren't choosing better tools. They're building better foundations first.

The report covers:

Why most AI investments stall. Nearly two-thirds of organizations remain stuck in experimentation and have not begun scaling AI. The blockers are consistent: fragmented infrastructure, siloed observability, inconsistent deployment patterns, and data that AI tools can't reliably reason about.

What infrastructure readiness actually requires. Consolidated Kubernetes environments, unified observability with OpenTelemetry, standardized CI/CD pipelines, and disciplined FinOps aren't separate initiatives from AI strategy. They are the AI strategy's foundation.

The fragmentation problem. Salesforce's 2025 MuleSoft Connectivity Benchmark Report found that 90% of enterprise IT leaders say data silos are creating active business challenges, and that organizations manage an average of 897 applications. When environments are fragmented, AI tools cannot reason across the stack and cannot be trusted with autonomous action.

The productivity math. Teams using AI coding tools report 15%+ velocity gains and 40-55% more code per week. But DORA's research makes clear those gains don't translate to organizational output without strong platforms underneath them. Infrastructure quality determines whether individual productivity becomes organizational performance.

Two trajectories. The report maps what happens to organizations that build the foundation first versus those that invest in AI tools without addressing fragmentation. McKinsey's data shows these paths are already diverging and compounding.

A self-assessment. Eight questions technology leaders can use to evaluate their organization's AI infrastructure readiness right now, each mapped to a specific capability the research identifies as a prerequisite for scale.

This report is for CTOs, VPs of Engineering, and platform leaders at SaaS, fintech, and consumer technology companies who are under pressure to show AI ROI and want to understand why the infrastructure layer is where that pressure gets resolved.