The Point: While 54.5 % of German companies already use AI, regulated industries in particular lack governance structures for autonomous systems – the costs of this gap quickly exceed the costs of structured, controlled implementation.
German companies are increasingly deploying AI, yet especially in regulated industries there is a lack of clear governance structures for autonomous systems. The McKinsey 2026 AI Trust Maturity Survey shows that only about 30 % of organizations achieve maturity level 3 or higher in the areas of strategy, governance and controls – while the costs of inefficient processes through hesitation grow daily.
According to a study published in June by the ifo Institut, 54.5 % of German companies are already using AI in their business processes, up from 40.9 % in the previous year. Another 16 % are planning implementation, 21.6 % are still in discussion. However, this growth is halting, as companies are uncertain where AI creates measurable value, what ROI to expect, and whether autonomous systems can be trusted.
But waiting incurs concrete costs: service teams answer repetitive questions manually again and again, claims processors examine documents by hand, compliance teams compile information across fragmented systems, retail teams respond too slowly to customer demand, and healthcare staff spend time on routine coordination instead of patient support. These inefficiencies lead to longer response times, higher operating costs, slower customer processing and reduced capacity in already overburdened teams.
Regulated industries are particularly exposed. They must ensure accountability, transparency and auditability – and this is precisely where weaknesses emerge: According to the McKinsey 2026 AI Trust Maturity Survey, only around 30 % of organizations achieve maturity level 3 or higher in governance for agentic AI, even though AI systems are becoming increasingly autonomous and integrating into central workflows. Without clear governance, companies risk fragmented intelligence layers working independently alongside each other – with consequences such as duplicate logic, inconsistent decision-making and difficulties in compliance evidence.
Successful implementation therefore requires vertical specialization and integrated accountability. An AI agent in banking must understand onboarding, fraud, compliance and account workflows. An agent in insurance must master claims processes and policy administration. The alternative – allowing bolder competitors to reshape their workflows around AI-driven processes while your own processes remain inefficient – is increasingly becoming a competitive risk.
Source: www.it-daily.net · Published 28 July 2026
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