Not each system is constructed for the AI period. As new fashions and workloads push the boundaries of what legacy environments can help, IT leaders are being compelled to ask: Which components of our stack are prepared for this, and which of them are holding us again?

Within the Q1 2025 IT Tendencies Report from JumpCloud, IT decision-makers named AI-related instruments (42%) and cloud infrastructure (40%) among the many prime spending priorities, second solely to cybersecurity. That convergence indicators one thing crucial: IT leaders aren’t simply deploying AI—they’re rethinking the infrastructure that helps it.

How AI is reshaping IT structure

AI workloads include distinctive infrastructure calls for, from high-volume information pipelines to scalable compute environments. That is prompting a pivot towards extra versatile, cloud-native architectures that may adapt as AI programs develop extra advanced and resource-intensive.

Legacy programs, significantly these with inflexible information constructions or restricted scalability, pose actual obstacles. On-premises infrastructure typically can’t maintain tempo with the computational calls for of coaching and working AI fashions, whereas siloed or outdated safety frameworks might not account for brand spanking new assault surfaces launched by machine studying.

To construct for AI, IT leaders have to suppose when it comes to adaptability and integration—designing environments that help the information gravity, mannequin coaching, and dynamic entry wants of AI-driven operations.

The transfer to the cloud is effectively underway, however that doesn’t imply the top of on-prem infrastructure. Hybrid approaches are more and more widespread, significantly for organizations with delicate information or latency-sensitive functions. The secret’s seamless integration throughout environments.

Workloads that contain delicate PII or require strict compliance oversight might keep on-prem, whereas much less regulated or compute-heavy operations transfer to the cloud. As one strategy, organizations are beginning to transition foundational companies—like id or listing platforms—away from legacy infrastructure towards cloud-based alternate options that higher help AI-scale operations.

Updating safety and compliance for an AI world

Safety and compliance frameworks are additionally being examined. Conventional approaches weren’t designed for the complexities launched by AI, together with adversarial manipulation, information poisoning, and algorithmic bias.

Whereas 48 p.c of IT groups report elevated funding in cybersecurity, the actual problem is evolving these frameworks to account for AI-specific dangers. This contains establishing protocols for mannequin monitoring, explainability, and entry management that replicate how AI operates in dynamic environments.

Unification is changing into a core requirement. Consolidating id, entry, and machine administration doesn’t simply scale back software sprawl—it creates a centralized, data-rich basis that AI can use to speed up decision-making and automation.

When these core programs function in silos, it’s tougher to establish anomalies, implement coverage, or reply rapidly to threats. A unified stack helps IT groups keep visibility and management, whilst AI programs introduce new forms of interactions and entry requests.

Rebuilding the IT stack for an AI-first world isn’t a teardown—it’s an evolution. It means re-evaluating which legacy programs are holding you again, embracing hybrid flexibility, and constructing a safe basis that’s designed for intelligence at scale.

Fascinated with studying extra about how your friends are occupied with AI and different crucial IT developments? Obtain JumpCloud’s full report here.