How AI Is Forcing an IT Infrastructure Rethink

As AI adoption accelerates, organizations are discovering that many legacy systems aren’t built to support the demands of modern AI workloads. According to JumpCloud’s Q1 2025 IT Trends Report, AI tools and cloud infrastructure are now top spending priorities, second only to cybersecurity—signaling a fundamental shift in how IT leaders think about architecture and modernization.

Legacy Systems Struggle to Keep Up with AI Demands

AI workloads require more flexible, scalable, and data-centric environments than many traditional IT systems can provide. The demands of model training and inference (everything from high-throughput data pipelines to adaptable compute power) are pushing organizations toward cloud-native and hybrid approaches. While on-premises infrastructure remains necessary for compliance-heavy or latency-sensitive workloads, it’s increasingly supplemented by cloud environments that offer the agility AI needs.

Legacy systems with rigid data structures or siloed security frameworks often can’t keep up. In response, IT leaders are migrating core services, such as identity and directory platforms, to cloud-based solutions that can better support distributed, AI-powered operations. The goal is not to eliminate on-prem but to enable seamless integration across platforms prioritizing adaptability and performance.

See also: Groups Focus on Infrastructure for AI and High-Performance Workloads

AI-Specific Security Challenges Prompt Modernization

Security is also under pressure to evolve. Traditional cybersecurity frameworks were not designed to handle risks like adversarial AI attacks, data poisoning, or bias in automated decision-making. While nearly half of IT teams are increasing cybersecurity investments, the report notes a growing need for frameworks that can manage AI-specific vulnerabilities through model oversight, explainability standards, and dynamic access control.

Centralized identity, access, and device management are emerging as critical to this transition. Unified systems reduce complexity while providing the data foundation AI needs for automation and anomaly detection. Without integration, siloed systems slow response times and limit visibility—critical drawbacks in an AI-driven environment.

In short, modernizing the IT stack for AI isn’t about ripping everything out. It’s about identifying what’s no longer serving, reinforcing what works, and building for a future where intelligence at scale is the norm.