- Post History
- Subscribe to RSS Feed
- Mark as New
- Mark as Read
- Bookmark
- Subscribe
- Printer Friendly Page
- Report Inappropriate Content
3 hours ago - edited 3 hours ago
The Final Installment of the “Inside the AI SDLC at ServiceNow” Series
Transitioning AI from development into production is a pivotal step in the lifecycle. The release phase is where validated features are prepared, reviewed, and made available to customers. But successful deployment doesn’t end there, monitoring and maintenance ensure that what’s delivered remains reliable, compliant, and valuable long after launch.
In this post, we’ll explore how ServiceNow manages the release of GenAI features, the safeguards built into the process, and the practices that sustain performance at scale.
The Release: More Than a Launch
Release is about more than publishing code. It’s a structured process that confirms every requirement has been met before customers gain access. Teams prepare documentation, support materials, and operational readiness assets, while engineering ensures certification requirements are complete.
Only once these steps are fulfilled does the Release Program Manager publish the feature to the ServiceNow Store, timed to a scheduled release date for customers. This disciplined approach is designed for predictability, trust, and smooth adoption.
Definition of Done: Raising the Bar for GenAI
Before release, every feature must meet a comprehensive Definition of Done (DoD). For GenAI, that means following both the standard Store release requirements and additional AI-specific safeguards:
- Experience Quality Review (EQF): Design leadership validates user experience quality before implementation.
- Legal Review: Legal teams review features for alignment with applicable regulatory and contractual considerations.
- High-Risk AI Approvals: If a feature involves high-risk AI, mitigation tasks are completed and reviewed at senior levels.
- Model Quality Validation: Central QE ensures model performance meets or exceeds defined thresholds with no regressions.
- Golden Dataset: A reference dataset is created and validated to benchmark model behavior consistently.
When all criteria are satisfied, the Store release manager provides final signoff, confirming readiness for certification and deployment.
Customer Access: Delivering Value Seamlessly
Once released, AI functionality is packaged as part of Now Assist applications on the ServiceNow Store. Customers simply install the application plugin on their instance to unlock access to both out-of-the-box skills and business unit–specific AI capabilities.
The process feels seamless to end users, but behind the scenes it reflects months of preparation, review, and testing to ensure reliability from day one.
Monitoring and Maintenance: Safeguarding Performance Post-Release
Deployment is not the end of the journey; it’s the beginning of sustained oversight. ServiceNow uses multiple layers of observability to track and improve AI features over time:
- AI Control Tower: A single source of truth for all AI system components, skills, models, prompts, data, and approvals, ensuring traceability and governance.
- Early Adopter Validation (EAV): Selected customers engage with new features early, providing real-world feedback and surfacing issues before broad rollout.
- Product Dashboards: Performance, quality, risk, and usage metrics are continuously tracked. Product Managers monitor thresholds quarterly, with clear remediation workflows if issues arise.
If defects are identified, they’re addressed with hotfixes or patch releases. Insights from monitoring also inform future enhancements, feeding back into the development cycle.
Sustaining Responsible AI at Scale
A successful release is more than a milestone, it’s the start of a living relationship between customers and the AI features they depend on. By combining rigorous pre-release standards with proactive post-release monitoring, ServiceNow is committed to maintaining its GenAI capabilities in a manner that is transparent, reliable, and aligned with customer needs.
From design signoffs to golden datasets to continuous monitoring, every step of the journey reflects a commitment to deploying AI responsibly and sustainably at scale.
Conclusion: AI at Scale, Built Responsibly
The journey from model and feature ideation to production use is long, but for good reason. ServiceNow’s AI development lifecycle ensures that models are not only powerful, but also safe, transparent, and aligned with business needs.
From scoping and tuning to alignment and deployment, every step is designed to balance innovation with responsibility.
Thanks for following along in this series. If you missed the earlier posts, be sure to check them out:
