Bottom line: Enterprise IT architectures prove non-scalable for productive AI workloads and require fundamental modernization.
An NTT DATA study documents that existing IT architectures in enterprises are not designed for productive AI deployment. For CTOs, this becomes a central infrastructure challenge.
The NTT DATA study identifies a structural mismatch between the requirements of modern AI applications and existing IT infrastructures in enterprise environments. Many organizations have optimized their systems over years for traditional workloads — legacy architectures, monolithic applications, classical database setups — and are now hitting capacity limits when they want to productively integrate Claude, other large language models, or specialized AI pipelines.
Concretely, bottlenecks emerge in several areas: computing resource requirements for model inference do not scale linearly with traditional server setups. Data flows to AI systems require real-time processing instead of batch processing. Security and compliance models are designed for deterministic systems, not probabilistic AI outputs. Network latency and storage I/O become critical factors that overload classic three-tier architectures.
For CTOs, this means no short-term patch solution: fundamental decisions about containerization, microservices, GPU resource management, data architecture (data lakes, feature stores), and AI-specific observability systems are on the agenda. Hybrid approaches — on-premises legacy alongside cloud-native AI stacks — require new integration and governance patterns, for which many organizations lack both the architecture and the specialized personnel.
Source: itwelt.at · Published 17 June 2026
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