The core message of this CIO reverse pitch was that enterprise AI is not primarily blocked by model capability, agent roadmaps, or technical enthusiasm; it is blocked by the unresolved operating problems CIOs face when demand accelerates faster than governance, ownership, and domain understanding. The moderator framed the discussion through the Bavarian “Teufelstand” or devil’s wheel: everyone wants to get on, many want to watch others fail, the ride begins slowly, then accelerates suddenly and throws people off unless they continuously adapt, learn, connect, disconnect, and change strategy. That metaphor set up the panel’s central value proposition: listen to CIO, legal, and industrial AI leaders about what they still cannot solve, rather than assuming more AI tools alone will solve the enterprise transformation problem.

Ralf’s first unsolved problem was the growing gap between business-domain AI needs and IT’s ability to support them. He described manufacturing plants with mechanical engineers, KUKA robots, architecture teams, and import/export experts all using AI in highly specific contexts. When those teams ask IT for help with a precise operational issue, such as an AI-enabled manufacturing robot failing at a particular step, the IT department may have “no clue what they are talking about.” The moderator summarized this as a lack of deep domain knowledge across processes, teams, and organizational areas, even while everyone lines up in front of IT asking them to “make it happen.”

Miyu then described a different Teufelstand moment from a fintech startup and legal/compliance perspective. On one side, AI tool adoption is exciting, innovative, and promising; on the other, governance creates a very different emotional reality. She said her “heart is sometimes even bleeding” because AI introduces governance challenges while exposing parts of the company that were not meant to be visible. Her concern was not only speed, but uneven speed: AI adoption, regulation, operational readiness, and compliance structures move at different levels, directions, and time lags, leaving companies structurally unprepared for some AI setups.

Corina brought the long-view industrial AI perspective, reflecting on AI-specific roles before 2019 and even a 2016 big-data role at Bosch. Her guiding metaphor was “survival of the fittest,” meaning organizations that can adapt will win. She emphasized that companies should not try to compete with AI’s speed, because humans and organizations will lose that race; instead, they must learn how to “drive” transformation safely, like knowing not to accelerate in a curve. The interactive hot-seat discussion then added a practical governance problem: AI and “vibe coding” are moving into the business, creating an application “zoo” full of unmanaged “pets.” Miyu responded that IT should likely define the approved toolset centrally, while functions must own inputs, orchestration, quality control, and the operational purpose of each AI tool.