SPOC - ServiceNow Partner

Choosing a #ServiceNow partner is one of the bigger calls a client makes, and I've sat in enough of those conversations to know the checklist is rarely the hard part. The question that matters most comes before it: does this partner understand where you want to take your business? I wrote down the questions I'd ask to find out, including a few for the age of AI.

If a regulator asked you to justify one specific AI decision from six months ago, could you? Not a report. Not a trend line. That one decision: the data that went in, the model version that produced it, who supervised it, and what happened next. Most teams I talk to can't do this yet. They've built reporting. They haven't built proof. I wrote about the layer that closes this gap, what I call the Evidence Trail in the Controlled Autonomy model. It's built around a simple idea: every meaningful AI decision gets its own record, called a Decision Object, holding the input data, output, model version, guardrails, human sign-off, and the control it ties back to. In the article, I also get into why human oversight has to leave an actual trace instead of just existing as a policy statement, and why not every decision needs the same depth of evidence. Full piece is linked below. Curious where others are on this.

I’ve written a new article about a challenge I often see in ServiceNow environments: a CMDB can contain thousands of Configuration Items and still fail to provide clear answers when they matter most. The article explores why more data does not always mean more control, and how service context, ownership, and maintained relationships can make a CMDB useful in daily operations.

A single #RiskPolicy applied uniformly to all #AI systems fails to recognize their inherent differences. For instance, a chatbot operates fundamentally differently from a system responsible for making credit decisions. Therefore, these systems should not be governed, documented, or reviewed in the same manner or on the same schedule. I explored the implications of categorizing risk class as a property of the system itself, which can be stored in the record. #ControlledAutonomy #ServiceNow #AICT

AI governance should not start when an audit arrives. With the AI Act Audit-Ready Framework for ServiceNow, governance becomes part of daily operations. Register AI systems, assign ownership, manage risks, maintain evidence, and monitor their status continuously within ServiceNow. 🔗 Discover more: https://lnkd.in/eCgweign #ServiceNow #AI #FutureReadyWorkflows

Can you see every AI system operating across your organization and prove who is responsible for it? The AI Act Audit-Ready Framework for ServiceNow brings AI systems, owners, risks, controls, and evidence into one operating model. Build governance into everyday workflows and stay ready to demonstrate control when it matters. 🔗 Discover more: https://lnkd.in/eCgweign #ServiceNow #AI #FutureReadyWorkflows