Use-case assessment
Candidate use cases scored on effort, data readiness, risk and likely benefit, with a recommended first wave and an explicit "not yet" list.
ServiceNow AI Solutions
Now Assist and Generative AI on ServiceNow
Pick the generative AI use cases that are worth doing, configure them with guardrails, and get people to actually use them.
What it is
Now Assist is ServiceNow’s generative AI capability embedded in platform workflows — summarizing, drafting, searching and assisting across areas like ITSM, HR service delivery and customer service. Exact features, supported workflows and licensing depend on your release and entitlement.
The technology is the easy part. The hard parts are choosing use cases where a summary or a draft genuinely saves time, setting guardrails so output does not leak sensitive data or invent answers, and persuading agents and employees to change how they work.
We handle all three, and we will tell you when a candidate use case is not worth enabling. Turning on every available feature is a reliable way to erode trust in all of them.
Who it is for
- Service desk and HR leaders under pressure to cut handle time
- Platform teams holding a license entitlement they have not deployed
- Organizations that enabled generative features and saw little adoption
- Teams needing guardrails defined before anything touches customer-facing text
What is included
The deliverables below are the default scope. We adjust them to your situation during scoping and confirm the final list in writing.
Guardrail design
Data scoping, role restrictions, prompt and output controls, disclosure to end users, and what a human must still review.
Configuration and tuning
Features enabled for the chosen workflows, tested against representative records rather than clean demo data.
Knowledge readiness fixes
The knowledge-base and content cleanup that generative features depend on — retired duplicates, filled gaps, clearer ownership.
Adoption plan
Enablement for agents and employees, in-workflow guidance, and a feedback path that routes bad output to someone who can fix it.
Measurement baseline
Handle time, deflection, reopen rate and satisfaction captured before launch so the change can be attributed.
Process and timeline
Indicative timings for a typical engagement of this type. Your scope, environment and availability of stakeholders will move them.
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Assess and select
Workshops, data checks and a prioritized use-case shortlist.
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Design guardrails
Agreed with security and privacy before configuration begins.
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Configure and test
Build, then test with real historical records and a pilot group of users who will say when output is wrong.
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Pilot and tune
Limited rollout, measure against baseline, tune or withdraw.
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Scale and enable
Broader rollout with training and an ongoing review cadence.
Outcomes we aim for
These are the goals we design the engagement around. They are objectives, not guarantees, and the result depends on decisions and effort on your side as much as ours.
- A first wave of use cases chosen for value rather than availability
- Measurable change against a pre-launch baseline, framed as a goal not a promise
- Guardrails documented and agreed by security and privacy
- A feedback loop that catches poor output before users stop trusting the feature
A note on accuracy. ServiceNow releases change quickly. Treat the capability descriptions here as a general guide and confirm current features, licensing and prerequisites against the official ServiceNow product documentation for your release.
Questions
About now assist and generative ai on servicenow
Which use case should we start with?
It depends on your data, but incident and case summarization is often a reasonable first step: the benefit is immediate, the output is reviewed by a human, and the blast radius of a bad summary is small. We confirm that against your own volumes rather than assuming it.
Will generative AI expose sensitive data?
That depends entirely on how you scope it. Which records a feature can read, which roles can invoke it, and what gets logged are all configuration and design decisions. We make them explicitly, with security in the room, and write them down.
What if the output is wrong?
Assume some of it will be. That is why we keep a human in the loop for anything customer-facing or consequential, and why we build a path for users to flag bad output. Use cases where nobody would notice a wrong answer are use cases we recommend against.
Is now assist and generative ai on servicenow the right next step?
Tell us the symptom rather than the solution. If a different service fits better — or none of ours does — we will say so.