The gist: Sustainable AI success does not result from isolated tool implementation, but from systematic observation of actual employee workflows and identification of bottlenecks before development.
The failure of expensive AI projects in mid-sized companies rarely stems from the models themselves, but from their implementation across existing structures. An approach called “AI Shadowing” demands instead: First analyze human behavior and real workflows, then develop AI solutions.
The Principle and the Starting Point
In AI Shadowing, employees are observed in their day-to-day work and it is documented what they actually do – not what process documents say. In service environments, often 80 percent of daily time is spent on a few, repetitive tasks: manual data transfers between systems, time-consuming information searches, or repeated customer inquiries for data that should actually be available. The key insight: As long as such system breaks exist, even the best AI creates no real added value.
Systematic Analysis Before Implementation
Practical implementation begins with six guiding questions: Where is data manually transferred between systems? Where is more than 15 minutes spent daily searching for information? Where do errors arise due to lack of dual control? Which reports are regularly created in a similar way? Where do customers wait for automatable answers? Where is knowledge lost when employees leave? Recurring answers from different departments mark the first, reliable use case.
Mid-Market Practice Example
An elevator control manufacturer had a classic scaling problem: as more systems came online, technical support inquiries increased significantly. The company first implemented an AI chatbot for frequent customer inquiries. However, analysis of actual bottlenecks revealed: the constraint was not in customer contact, but in internal knowledge search. Manuals, project documentation, and product configurations existed in different formats and barely searchable structures. The solution was an AI agent that made technical documentation so accessible that on-site support technicians could get answers in seconds instead of minutes.
Infrastructure and Data Quality as Prerequisites
To sustainably implement such use cases, on one hand an environment for safely testing multiple models is needed – such as central platforms with access to 50+ AI models in secure cloud infrastructure. On the other hand, data quality issues often emerge: knowledge databases that have grown organically over years are designed for human readers, not machine processing. Articles covering five different topics create challenges for AI systems in precise answer generation.
Source: www.it-daily.net · Published 12 July 2026
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