NowAssist gives ServiceNow customers a broad set of AI capabilities across ITSM and ITOM. Breadth is also the trap: organisations that try to switch on everything, or that lead with the most impressive demo, tend to end up with AI that is technically live and practically ignored.
This guide sets out how NowBench helps customers choose practical first use cases, and the principles that keep AI valuable once it is deployed.
Start with user and operational outcomes, not features
The productive question is not which AI features do we have, but where do people lose time and where do experiences disappoint. Long resolution notes nobody writes well. Knowledge that exists but is not found. Cases that take minutes to understand before work can start. Repeated requests that follow the same shape every time.
Each of those is a candidate use case with a measurable before and after. Feature led adoption has no before and after, which is why it is so hard to defend later.
Selection criteria that hold up
- Frequency. The scenario happens often enough for improvement to compound.
- Existing workflow. The use case sits inside a ServiceNow workflow people already use, rather than requiring a new experience.
- Data readiness. The knowledge, case history or operational data the AI depends on actually exists and is reasonably healthy.
- Measurability. You can state what better looks like in numbers before you start.
- Human control. Where a decision carries accountability, a person reviews the AI output rather than the AI acting alone.
Common practical first use cases in ITSM
For ITSM, the strongest early candidates tend to be assistive rather than autonomous: case and incident summarisation so agents understand context quickly, suggested resolutions drawn from similar past cases, knowledge generation from resolved work, and AI assisted search that actually finds the right article.
Virtual Agent belongs in this list with a caveat. It works when it is designed around the channels people already use, including portals, Microsoft Teams and the service desk, and around the requests that genuinely repeat. Introducing a new conversational experience without understanding current channel adoption is how chatbots end up unused.
Common practical first use cases in ITOM
On the operations side, AI value concentrates where signal volume overwhelms people: alert summarisation and grouping, probable cause suggestions that draw on service context, and recommended remediation for well understood fault patterns.
The prerequisite is an event pipeline worth automating. AI applied to noisy, contextless monitoring amplifies the noise. Organisations further behind on Event Management foundations should usually fix those first, then add AI where it compounds the improvement.
Build in stages and measure adoption
The pattern that works is a small number of use cases delivered as minimum viable capability, measured honestly, then expanded. Measurement should cover adoption as well as output quality: an AI capability that agents bypass is a design signal, not a user failure.
Governance completes the picture: clarity about where AI output is reviewed, how quality is monitored and who owns each use case. Keeping humans in control is not a limitation on value. It is what makes sustained value possible.