The advent of AI is forcing IT leaders to reassess their infrastructure from the ground up. According to the Q1 2025 IT Trends Report from JumpCloud, IT decision-makers have identified AI-related tools (42%) and cloud infrastructure (40%) as top spending priorities, just behind cybersecurity. This convergence indicates that IT leaders are not merely deploying AI but are rethinking the underlying infrastructure.
The Impact of AI on IT Architecture
AI workloads come with unique demands, including high-volume data pipelines and scalable compute environments. This is driving a shift towards more adaptable, cloud-native architectures that can evolve as AI systems become increasingly complex. Legacy systems, particularly those with rigid structures or limited scalability, are significant obstacles. On-premises infrastructure often struggles to keep up with the computational needs of AI model training and execution, while outdated security frameworks may fail to address new vulnerabilities introduced by machine learning.
To build for AI, IT leaders must focus on adaptability and integration, designing environments that support the data requirements, model training, and dynamic access needs of AI-driven operations. The transition to the cloud is ongoing, but it doesn’t signify the end of on-prem infrastructure. Instead, hybrid approaches are becoming more prevalent, especially for organizations handling sensitive data or latency-sensitive applications. The key lies in achieving seamless integration across environments.
Updating Security and Compliance for AI
Security and compliance frameworks are also being put to the test. Traditional methods were not designed to handle AI’s complexities, such as adversarial manipulation and algorithmic bias. While 48% of IT teams report increased cybersecurity investments, the real challenge is adapting these frameworks to address AI-specific risks. This involves establishing protocols for model monitoring, explainability, and access control that reflect AI’s dynamic nature.
Unification is becoming essential. Consolidating identity, access, and device management reduces tool sprawl and creates a centralized foundation that AI can leverage to accelerate decision-making and automation. A unified stack enables IT teams to maintain visibility and control, even as AI introduces new interactions and access requests.
Rebuilding the IT stack for an AI-first world is an evolution, not a revolution. It involves re-evaluating legacy systems, embracing hybrid flexibility, and building a secure foundation designed for intelligence at scale. For more insights into how IT peers are approaching AI and other critical trends, you can download JumpCloud’s full report.
