Wolf&Grayson EditorialGenerative AI is reshaping how enterprises think about IT service management, and the implications are proving to be more structural than technological. From this point forward, the technology is better understood as GenAI. For Richie Adetimehin, an AI Advisory and Transformation Delivery Consultant, the core opportunity is not automation at scale, but a fundamental shift in how service organizations define value. GenAI, he argues, should be used to reorient ITSM around outcomes, trust, and decision quality rather than the ability to get work done faster.
“My view has actually shifted from process first to outcome first,” Adetimehin says. “Technology does not transform operations. Operating models do.” That distinction is increasingly relevant as enterprises rush to embed GenAI into Service Management platforms. As many organizations are discovering, when data quality is poor or ownership is unclear, AI risks amplifying issues rather than resolving them.”
At ServiceNow, a cloud-based digital workflow platform widely used by large enterprises, Richie helps organizations translate ServiceNow investments, including Now Assist and agentic workflows, into measurable outcomes across service management, setting the foundation for the shifts explored throughout this article.
From process control to outcome clarity
Traditional IT service management has long been optimized for control. Standardization, governance, and repeatability defined success. GenAI introduces a different capability altogether: speed of understanding. Models can summarize context, identify patterns, and suggest next steps in seconds. That capability changes what service teams can focus on, but only if the foundation is right.
“If your data is messed up or ownership is unclear, AI just scales the confusion faster,” Adetimehin says. The strongest transformations he has led begin with clarity on outcomes rather than workflows. Leaders must define what success looks like, which work is genuinely repetitive, and where humans need to stay in the loop to manage risk and judgment.
This reframing turns ITSM from a system designed to manage tickets into a system designed to deliver results. It also challenges organizations to stop measuring success solely by activity and start measuring it by impact.
Designing for trust before automation
One of the most persistent misconceptions about GenAI in IT operations is that it can be plugged in and expected to perform. “People think GenAI is a tool you just plug in and play, when it is really a capability you have to train through clean data, curated knowledge, and strong guardrails,” Adetimehin says.
Instead of automating everything possible, organizations need to design for trust. That often means starting with assistive use cases that support human decision-makers, such as AI-generated incident summaries that help agents work faster and with better context.
“You earn the right to automate before you start automating,” Adetimehin says. Proving value, quality, and governance at each stage creates the confidence required to expand autonomy responsibly.
Where GenAI delivers real ITSM gains
Across large enterprises, Adetimehin sees three applications of GenAI consistently improving IT service outcomes. The first is faster incident resolution through contextual triage. AI can summarize incidents, surface related cases, highlight probable causes, and propose next steps. That reduces the time agents spend reading and increases the time spent resolving issues, directly improving mean time to resolution.
The second is keeping knowledge alive. Knowledge bases often decay because documentation is treated as an afterthought. GenAI can draft knowledge articles from resolved incidents and route them for human review. “That is how you stop self-service from becoming a graveyard,” Adetimehin says. Human validation preserves accuracy while AI ensures knowledge keeps pace with reality.
The third is improving the user experience through conversational service. Employees want outcomes. Well-designed AI interactions can guide users to the right request, ask the right questions upfront, and reduce back-and-forth or unnecessary escalation. Even routine requests, such as access resets, become faster and less frustrating when AI gathers information that agents previously had to chase down.
From systems of record to systems of action
Looking ahead, Adetimehin believes the next frontier is systems of action that predict, recommend, and in some cases execute within defined risk boundaries. “ITSM will not just log tasks. It will predict based on cognitive signals,” he says.
That shift is already redefining the role of the IT professional. Ticket handling is giving way to service design, exception management, and advisory work. The professionals who thrive will be those who combine operational judgment with AI fluency.
Service excellence, in this model, is no longer defined by speed alone. It is measured by anticipation, transparency, consistency, and the ability to prevent issues rather than simply reacting to them.
Human-centered transformation matters
Despite the sophistication of GenAI, Adetimehin emphasizes that successful transformation remains overwhelmingly human. His rule of thumb is 10 percent technology, 20 percent data, and 70 percent people, process, and culture.
Organizations must also account for where AI can hallucinate or introduce bias. That requires human-in-the-loop, human-on-the-loop, and human-in-control approaches, supported by strong ethical foundations and governance.
“Human-centered transformation is going to become more important than before,” Adetimehin says.
As enterprises race to adopt GenAI, the differentiator will not be who deploys it fastest, but who designs service management around trust, outcomes, and people.
Follow Richie Adetimehin on LinkedIn or visit their website.
