The rise of the Agentic Enterprise: AI’s role in business transformation

Although AI has moved fast, many organisations haven’t. Most leaders we speak to aren’t short on ideas, proofs of concept, or vendor demos. The challenge is turning AI into something repeatable — a capability you can trust, scale, and steer without creating new risks or bottlenecks.

That gap is the starting point for this conversation between Romano Roth, Prof. Dr. Oliver Gassmann, and moderator Prof. Dr. Naomi Haefner. They explore what agentic AI actually means for the enterprise, where it works today, and what organisations need to change before they can scale it.

The discussion surfaces a productive tension. Oliver, drawing on his research article "The Non-Human Enterprise", pushes the idea of fully autonomous, agent-driven organisations as a radical thought experiment. Romano argues that real enterprises are socio-technical systems that need human-led steering, feedback loops and continuous adaptation. Where they converge is just as revealing as where they disagree.

Romano Roth is the Global Chief of Cybernetic Transformation and a Partner at Zühlke. Romano shapes Zühlke’s global strategy for cybernetic transformation, platform engineering and AI integration.

Prof. Dr. Oliver Gassmann is a Professor of Technology Management at University of St. Gallen and Vice Chairman and Partner at Zühlke. He brings deep expertise in innovation management and the leadership required to make transformation stick.

Prof. Dr. Naomi Haefner is an Assistant Professor of Technology Management at University of St. Gallen. Naomi researches how emerging technologies reshape organisations, decision-making and management practices.

Oliver defines the agentic enterprise as the ultimate vision of a fully automated organisation — AI agents with described goals and tools that plan, execute and collaborate, much like the “dark factories” we already see in manufacturing. He deliberately pushes this to its extreme saying, 'If we would design a global insurance company on a green field with only five people, how would it look?' 

Romano brings the counterpoint. While you could build an agentic enterprise for an online shop that sells black socks, a global insurance provider is a different matter entirely. Real enterprises are complex socio-technical systems with feedback loops, regulatory constraints and human judgement at their core. Today’s AI agents reliably handle five to seven steps autonomously, but then a human needs to step in.  

This tension between radical automation as a thought experiment and pragmatic, human-led transformation runs through the entire discussion and is what makes it valuable.

Oliver shares a compelling example from the telecom industry. Autonomous network agents monitor radio access networks and respond to unplanned events — a festival, a traffic jam, a demonstration — in real time:  

• An event agent scrapes social media data every 15 minutes to detect gatherings.  
• A remediation agent automatically re-segments the network.  
• A guardian agent monitors performance KPIs and rolls back changes if they don’t improve the situation.

This innovative approach means that what previously took a team of engineers one hour now happens autonomously in eight minutes and with better performance.  

These are what both speakers call “agentic islands”: bounded, well-understood domains where automation delivers clear, measurable gains. The challenge is connecting these islands into end-to-end value streams.

Both speakers are blunt about why so many organisations struggle: they add AI onto existing processes and expect magical results.

According to Romano, the current focus on generative AI tools (which vendor, which LLM) distracts from the real work. 'Go away from the tool itself. Think about identifying processes and bottlenecks.' His recommended method is value stream mapping, a technique from the Toyota Production System. This means mapping every step, and analysing process time, lead time, percentage complete and correcting it; and only then designing the future state. Sometimes that future doesn’t even involve AI.

As Oliver points out, most European companies still think of hardware-era strategy cycles. The automotive industry plans around SOP minus three — three years before starting production. But AI frameworks change every three to four months and inference costs drop by 900x per year. Strategy as a plan has to be replaced with creating strategic flexibility.