The race toward AI maturity is on, but most enterprises are running uphill. According to new research from S&P Global Market Intelligence and Vultr, more than half of organizations expect to reach the "Transformational" stage of AI maturity by 2027 — a phase defined by widespread, embedded AI use across business operations. Yet as AI embeds deeper into real-time systems and mission-critical workflows, the gap between ambition and operational readiness is becoming harder to ignore.
The latest AI Maturity Report makes one thing clear: reaching the transformational stage isn’t just about building better models. It’s about reengineering the infrastructure that supports them.
Today’s AI workloads push the limits of compute, storage, and orchestration. For IT operations leaders and platform teams, the barriers are increasingly systemic: GPU shortages, security gaps, observability blind spots, and rigid cloud architectures that weren’t built for dynamic AI deployment at scale.
Still, some organizations are getting it right. The most AI-mature enterprises are rethinking how they design and scale infrastructure. These high-performing companies are significantly more likely to improve customer satisfaction (78% vs 58%), revenue (76% vs 51%), and operational efficiency.
What’s standing in the way of AI maturity, and how are leading organizations getting past it? These four infrastructure hurdles could be slowing your progress. Here's what you need to know to clear them and move forward with confidence.
Hurdle 1: Infrastructure bottlenecks limit real-time AI performance
According to the report, 54% of enterprises say their compute resources are inadequate for real-time inference. About half report that storage throughput and data locality are also creating friction. These constraints directly impact the ability to operationalize AI in high-throughput, latency-sensitive environments.
The limitations are often architectural. Infrastructure built for web apps or batch processing can’t match the performance demands of dynamic inference. Lag from data bottlenecks, inconsistent compute tiers, and lack of proximity to end users can all degrade outcomes.
How mature organizations jump ahead: High-maturity enterprises approach infrastructure as a performance enabler, and invest accordingly. The majority dedicate a substantial share of their IT budgets to cloud and AI, with most allocating over 40% to cloud resources alone. As a result, they’re running more models — on average 16% more than their peers — and seeing greater returns on innovation.
To meet performance demands, mature teams are also increasingly turning to composable infrastructure to better support performance demands, compute, and edge deployment. Many are moving away from traditional hyperscalers in favor of GPU-optimized environments and open-source models tailored to specific use cases. Observability, orchestration, and proximity to users are treated as design requirements.
Hurdle 2: Operational complexity delays scalable deployment
As organizations advance along the AI maturity curve, the real challenge becomes operationalizing models reliably and at scale. The average number of models in production grew by nearly 24% in the past year alone. At the transformational stage, that number grew by 38%, exceeding 220 models.
More models mean more infrastructure to manage — and more chances for things to break. Teams face pressure to streamline how they build, test, and monitor AI systems — often wrestling with manual processes and fragmented observability.
How mature organizations jump ahead: Leading teams scale effectively by leveraging composable infrastructure. Transformational-stage organizations are 2.6x more likely to use open-source models, with 67% tuning them in-house. They rely on standardized, declarative infrastructure — often through Kubernetes and Infrastructure-as-Code templates — to make deployments repeatable and observable.
By treating orchestration and monitoring as core infrastructure functions, they reduce time-to-deploy and accelerate iteration cycles. The result? Faster experimentation and stronger alignment between infrastructure and AI teams.
Hurdle 3: Security and compliance gaps slow production deployment
Even the best models won’t reach production if they can’t meet enterprise security and compliance requirements — and 45% of organizations cite these concerns as a top constraint. The risks are especially acute in regulated industries, where legal uncertainty and audit readiness can delay or derail deployment.
Security and compliance challenges often stem from fragmented infrastructure and opaque vendor practices. As inference workloads grow, teams struggle to verify data controls, trace model decisions, and document compliance.
How mature organizations jump ahead: Transformational-stage companies treat security and compliance as architectural imperatives. When selecting AI cloud partners, 83% of mature organizations rate security and compliance as a top priority. They also prioritize transparency, financial stability, and open ecosystems — factors that support long-term compliance and minimize lock-in.
Operationally, these teams build with guardrails in place: infrastructure-as-code templates with baked-in policies, audit trails, and regionally-aligned deployment strategies. By embedding governance into platform design, they unlock speed without sacrificing trust.
Hurdle 4: Overreliance on hyperscalers undermines AI flexibility
As organizations scale AI investments, the limitations of hyperscaler architecture surface. Vendor lock-in, opaque pricing, underutilized compute, and inflexible service tiers make it difficult to optimize for performance or cost. Only 18% of organizations plan to leverage hyperscalers for future AI projects, while 30% say they’ll turn to alternative or "neocloud" providers.
This pivot reflects a broader shift toward modular environments supporting open-source tooling and distributed deployments. For many, this isn’t just about economics; it’s about regaining control and minimizing risk.
How mature organizations jump ahead: Transformational-stage organizations are leading the neocloud shift. When choosing AI infrastructure partners, they prioritize open ecosystems (83%), transparency (81%), and financial stability (84%). These preferences enable greater flexibility in deployment and optimization.
By diversifying infrastructure and embracing composable building blocks, high-performing teams avoid the one-size-fits-all trap, designing architectures that reflect AI deployment realities.
AI maturity is a systems challenge
AI maturity isn’t just a technical achievement. It’s an operational discipline. The organizations pulling ahead aren't winning because they've built the biggest models or adopted the flashiest tools. They're winning because they've built infrastructure that can keep up.
From scalable inference to compliant deployment and multicloud architecture, transformational-stage companies are solving for AI at production scale. They’re investing in the systems and strategies that turn innovation into impact — showing what’s possible when infrastructure evolves with ambition.