Kerry Brown has led change management programmes at scale for years. Her recent comments on what makes AI different from previous technology waves cut to something we hear in almost every senior hiring conversation: the trust question. Previous transformations asked employees to learn new tools. AI transformations ask employees to trust outputs they cannot fully verify.
That distinction reshapes how companies staff these projects. The classic change management hire was someone who could run communications, manage stakeholder buy-in, and sequence training. The AI-era change management hire needs to do all of that plus design feedback loops that let frontline workers flag when the system gets it wrong. Brown describes asking herself whether she had automated her own role. The answer, as she found it, is that the human layer carries more weight, and the work inside that layer shifts toward governance and trust architecture.
Clients now ask us for process mining or automation programme leads who can sit between the data science team and the business users and translate in both directions. A role we filled last month in Munich carried an explicit requirement: experience designing override protocols for AI-driven decisions. The hiring manager said her last two automation projects stalled because frontline teams did not trust the outputs and had no structured way to challenge them.
Senior professionals with pure technical delivery backgrounds should consider how they would describe the trust architecture they built around their last AI deployment. The companies moving fastest on process automation are staffing these roles with people who have run programmes where the technology made autonomous decisions and the humans had to learn when to override. That capability now sits at the top of hiring specifications we take across the DACH region.
Prompted by reporting from Diginomica.