At a Glance
A global enterprise-tech leader built a Generative AI–powered HR Service accelerator to handle employee relocation queries. Persistent stepped in post-development to stabilize it in a live environment, assume full operational ownership and prepare the accelerator to scale into broader HR processes. What started as a functional prototype became an enterprise-ready capability.
The Challenge
The accelerator was functionally complete, but moving from a controlled build to production exposed real-world gaps. Employees depended on it, yet the model lacked operational rigor.
Persistent Stepped in to Face Four Critical Challenges Head-on:
- No prior development involvement: Architecture and dependencies had to be absorbed solely from handover documentation and code.
- High-stakes deployment: Being live on a public platform meant downtime or bugs could immediately affect end users.
- Ongoing maintenance demands: Without a dedicated monitoring and triage team, updates lagged and user frustration grew.
- Scalability needs: The client wanted to extend into onboarding, travel and benefits—so operations had to be both stable and flexible.
Together, these challenges highlighted the gap between innovation and enterprise readiness—a gap Persistent was asked to close.
The Solution: An Engineering-led Operating Model
Persistent approached the engagement in four dimensions, building stability first, scale second.
- Rapid knowledge transition involved reviewing repositories, documentation and architectural artifacts, alongside functional walkthroughs with the client’s AI team.
- DevOps-based operations added automated pipelines for deployment and rollback, regression test suites to validate code and structured SLAs with triage to prioritize issues.
- Engineering-led maintenance meant replicating bugs in staging, diagnosing root causes and redeploying fixes with version tracking to prevent recurrence.
- Finally, future-proofing modularized workflows, adapting approval logic to enterprise hierarchies and identifying connectors for integration with internal systems of record.
This progression transformed the accelerator from a handover artifact into a continuously evolving platform.
According to Deloitte, 25% of enterprises using GenAI will deploy AI agents in 2025, rising to 50% by 2027. For this client, operational maturity—not experimentation—was the path to that scale.
How it Works: A Closed-Loop Lifecycle
The accelerator’s lifecycle under Persistent followed a continuous improvement loop:
- Code intake and deployment: Validate handover code, baseline functional testing and production deployment.
- Continuous monitoring and triage: Log and prioritize user-reported issues, replicate bugs in staging before resolution.
- Bug fixes and enhancements: Implement changes, regression-test for stability and redeploy with version tracking.
- Optimization and improvements: Monitor performance, suggest enhancements and update documentation for feature evolution.
This closed-loop system ensured that every fix and enhancement strengthened the enterprise fabric.
McKinsey finds that organizations embedding continuous improvement practices can boost productivity by 25% or more. Persistent’s model turned each fix into an upgrade and each enhancement into a step toward enterprise scale.
Outcomes: Stability Today, Scalability Tomorrow
The results of this operating model were felt quickly across the enterprise:
- Operational continuity without disruption: The accelerator ran reliably in a live, public environment. Updates and monitoring occurred in the background, avoiding service interruptions and building user trust.
- Accelerated issue resolution and responsiveness: SLA-driven triage shortened the time to identify, replicate and resolve issues. Problems that once required escalation were handled faster, restoring functionality quickly.
- Reduced load on internal teams: By absorbing day-to-day maintenance, Persistent freed the client’s AI and development teams from operational overhead, allowing them to focus on innovation.
- A scalable foundation for broader HR automation: The stabilized relocation use case became the springboard for expansion into onboarding, travel, benefits and compliance. Modular workflows and adaptable approval logic meant new HR functions could be added without costly rebuilds, while connectors enabled integration with systems like Workday and SAP SuccessFactors.
- Improved governance, visibility and compliance: Version-controlled logs, automated pipelines and updated documentation provided transparency and traceability, ensuring the accelerator could scale confidently even in regulated environments.
Closing Ownership Snapshot
What began as a relocation query bot became an enterprise-grade HR Service accelerator under Persistent’s stewardship. By stepping in after development, Persistent ensured stability, responsiveness and scalability—turning a standalone tool into a foundation for intelligent automation across HR.
Turn GenAI pilots into production-grade platforms. Scale beyond innovation—engineer for enterprise continuity.
