The key point: AI projects fail when backends are incompletely digitalized, as fragmented data and manual processes render AI systems ineffective.
Organizations that focus primarily on digitalizing frontends while their backend processes remain analog or manual in nature jeopardize their AI transformation. This window-dressing digitalization leads to media breaks and data silos that AI systems cannot effectively leverage.
The phenomenon of window-dressing digitalization is widespread in many enterprises: websites, chatbots and apps present a digital facade, yet behind them manual or analog-driven processes persist unchanged. For users, this creates a seamless digital experience on the surface, but internal workflows often still follow established, non-digitalized procedures.
This decoupling of frontend and backend creates systematic problems: media breaks between digital and analog interfaces, delays in data management, and isolated data repositories that are not available across departments. In the context of artificial intelligence, this incompleteness becomes a strategic competitive risk. AI systems require high-quality, integrated data as a foundation – if the consistent data infrastructure in the backend is lacking, even modern AI tools cannot fully realize their potential.
CTOs must therefore evaluate digitalization projects holistically: the value of an AI initiative depends directly on the completeness of the underlying data architecture, not merely on the visible functionality of the application.
Source: itwelt.at · Published 27 July 2026
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