The bottom line: Production-ready AI integration demands fundamental modernization of IT architectures, not merely incremental adjustments.
A recent study by NTT DATA demonstrates that increased deployment of language models such as Claude and other AI systems exposes fundamental problems in established enterprise infrastructure. Existing architectures are not aligned with the requirements of AI-powered workloads.
NTT DATA has documented in an investigation that conventional IT architectures exhibit significant gaps when integrating AI models into production environments. In particular, scalability, latency requirements, and the throughput of AI inferences are insufficiently supported in traditional systems.
The challenge directly affects CTOs and their teams: models such as Claude require dedicated compute resources, optimized data flows, and specialized monitoring stacks. At the same time, the security architecture must be extended to address API calls, prompt injections, and data protection when passing data to external AI services.
For enterprises, this concretely means that a pure lift-and-shift strategy does not work. Instead, architectural adaptations are necessary: containerization of AI workloads, implementation of caching layers for frequently accessed requests, redesign of databases for higher throughput, and integration of specialized orchestration tools.
Source: itwelt.at · Published 17 June 2026
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