80 INTERVIEW The enterprise AI tipping point Lindsay James speaks with ServiceNow’s Chad Scheller about why AI agents are moving from experimentation into the enterprise – and why governance, accountability and better processes will determine whether they deliver For years, enterprises have added more and more technology to increasingly complicated processes. But as AI agents become capable of taking action rather than simply providing assistance, Chad Scheller, ServiceNow’s area vice president for global Microsoft business, believes it’s time to ask a more fundamental question: why does the process exist in the first place? What changes have you seen that give you confidence enterprises are now ready for the shift to agentic AI? The question has changed. A year ago, customers asked me whether the technology worked. Now they ask who is accountable when it does. I had a chief information officer (CIO) ask me how he would attest to an agent’s actions in an audit and who owns the identity that the agent runs under. Nobody builds an audit trail for a science project. That is a deployment question. The other change is where the money sits. Agentic AI moved from the innovation budget to the operating budget. Once something is in the run budget, it has to work, be supported and have somebody’s name on it. And frankly, the models stopped being the hard part. The bottleneck moved to data, permissions and process. Those are problems enterprises already know how to solve. Where are you seeing the strongest business cases for the use of autonomous AI today? IT and employee service, by a wide margin. High volume, well documented, already measured and contained risk. Rolls-Royce is the example I keep coming back to. It has achieved roughly 54 per cent help desk deflection and about 5,000 hours given back. That is not a demo, that is a profit and loss statement. Customer service operations are right behind it, and the back office is catching up fast in finance and procurement. The pattern is consistent. The work is repetitive, the source of truth is already digital and somebody already tracks cycle time. If those three things are true, agents work. Where I tell people to slow down is anything requiring novel judgment in a process nobody has ever measured. You will not be able to prove you won. Are enterprises making a mistake by putting AI into existing processes? Should they instead be asking whether those processes should exist? Yes, that is a real mistake, and it is everywhere. Most enterprise processes exist because software used to be dumb. Four approval steps because we could not trust a system to check three fields. If you point an agent at that process, you have automated the tax instead of removing it. The right question is what this process would look like if it were designed today, then build for that. What is preventing enterprises from scaling AI agents right now? The biggest barrier to scale is sprawl. Every function is standing up agents right now. Marketing has some, engineering has
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