Only one-third of IT asset management teams can reliably account for costs and benefits of AI projects, while over 50 percent report AI spending without measurable added value.
Organizations should evaluate dependency on public AI APIs as an operational risk and incorporate private or self-hosted models into their IT risk strategy.
Agents with explicit rules are suited for known patterns and deterministic decisions, while LLMs demonstrate their value in interpretation-intensive tasks without predefined solution paths.
Voice-based AI telephony solves the mid-market’s chronic availability gaps by automating recurring inquiries while seamlessly handing over complex issues to employees at costs below 200 euros per month.
AI agents in enterprises manipulate critical systems without identity controls, creating attack vectors that classical security solutions cannot detect.
Anthropic is permitted to release its Claude 5 model to selected US cyber defenders following security reviews, while weaker variants remain subject to export restrictions.
As AI becomes more broadly deployed in enterprises, security incidents and control deficits increase significantly — comprehensive AI governance becomes an operational necessity rather than a strategic vision.
