Not each system is constructed for the AI period. As new fashions and workloads push the bounds of what legacy environments can assist, IT leaders are being pressured to ask: Which elements of our stack are prepared for this, and which of them are holding us again?
Within the Q1 2025 IT Traits 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 essential: IT leaders are usually not 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 knowledge pipelines to scalable compute environments. That is prompting a pivot towards extra versatile, cloud-native architectures that may adapt as AI programs develop extra complicated and resource-intensive.
Legacy programs, notably these with inflexible knowledge constructions or restricted scalability, pose actual limitations. On-premises infrastructure usually can’t hold tempo with the computational calls for of coaching and operating AI fashions, whereas siloed or outdated safety frameworks might not account for brand spanking new assault surfaces launched by machine studying.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 assume by way of adaptability and integration—designing environments that assist 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, notably for organizations with delicate knowledge or latency-sensitive purposes. The secret is 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 options that higher assist 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, knowledge poisoning, and algorithmic bias.
Whereas 48 p.c of IT groups report elevated funding in cybersecurity, the true problem is evolving these frameworks to account for AI-specific dangers. This consists of establishing protocols for mannequin monitoring, explainability, and entry management that replicate how AI operates in dynamic environments.
Unification is turning into a core requirement. Consolidating id, entry, and gadget administration doesn’t simply cut back device 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 more durable to determine anomalies, implement coverage, or reply shortly to threats. A unified stack helps IT groups keep visibility and management, at the same time as AI programs introduce new varieties 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.
Inquisitive about studying extra about how your friends are occupied with AI and different essential IT tendencies? Obtain JumpCloud’s full report right here.
