In a nutshell: Without systematic, automated quality management for AI processes, the transition from pilot to production fails.
Many organizations launch AI projects but fail to move them beyond the pilot phase. The reason is a lack of confidence in result quality, data protection, IT security, and the impartiality of AI systems.
The gap between AI hype and operational reality becomes apparent in enterprise environments: while artificial intelligence is technically available, its actual use in core business processes remains limited. Organizations initiate pilot projects but often hesitate to scale them across the enterprise.
The central obstacle lies not in the technology itself, but in trust issues. CTOs and executives are reluctant to deploy AI systems in production as long as uncertainty exists about the reliability of results. Additionally, there are concerns regarding data protection, IT security, and the risk of biased outputs caused by bias in training or inference data.
To overcome this hurdle, organizations need a comprehensive quality management system specifically designed for AI processes. Such a system must be scalable and, ideally, function in an automated manner to consistently verify that a model meets the required reliability, compliance, and fairness standards even under production conditions.
Source: itwelt.at · Published 8 June 2026
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