Delivering Enterprise Case Management with ML and Data Integration
I recently led the delivery of an initiative that tackled the management of customer warranty cases by integrating Palantir Foundry with Salesforce to create an machine learning-powered case management system. The goal was simple but ambitious — leverage machine learning to classify cases, prioritize work, and automate write-backs, so that teams could resolve issues faster while improving overall data quality.
From Complexity to Clarity
The project required orchestrating a complex integration of data pipelines and enterprise systems. We were connecting two major platforms while layering in AI-driven features — all under the pressure of business-critical timelines.
I served as both Project Manager and AI Product Manager, bridging the gap between execution and product vision. On the delivery side, I oversaw milestones, dependencies, and compliance. On the product side, I defined requirements for case clustering and prioritization, and translated business rules into automated workflows that Palantir’s AI could support.
The Challenges We Faced
No project of this scope is without obstacles. Among the most pressing were:
- Integration Complexity: Connectivity issues demanded escalation across international teams, compounded by multi-factor authentication hurdles.
- Technical Constraints: Salesforce struggled with historical data loads, forcing us to restructure pipelines.
- Team Continuity: High turnover among vendor staff created knowledge gaps and threatened consistency.
- Process Deviations: We had to adapt when the deployment pathway bypassed standard AMS handover procedures.
- Scope Creep: Stakeholders pushed for additional features beyond the original design, which required careful balancing to keep delivery on track.
The Approach
To manage these challenges, we broke delivery into phased milestones, starting with foundational technical issues before progressing to production deployment. Key steps included:
- Implementing incremental data loads to relieve memory pressure.
- Designing scheduled write-backs for real-time Salesforce updates.
- Facilitating cross-team meetings to maintain alignment despite turnover.
- Positioning the application as a continuous improvement platform, allowing iterative deployment of new features.
What We Achieved
The result was a fully functional ML-enabled case management platform with tangible impact:
- Automated case clustering and prioritization.
- Streamlined write-backs from Palantir to Salesforce.
- Comprehensive documentation and hybrid support models that enabled agile enhancements while preparing for AMS transition.
Lessons Learned
Reflecting on this project, three themes stand out:
- Flexibility Matters: AI/ML initiatives rarely follow a perfect playbook — process agility is essential.
- Fix Problems Early: Tackling connectivity and memory issues upfront avoided downstream delays.
- Communicate Relentlessly: Frequent alignment and thorough documentation were vital in overcoming turnover and integration challenges.
Closing Thoughts
ML-driven case management is about more than automation — it’s about creating systems that scale, adapt, and deliver real business value. This project was a reminder that when product vision and disciplined delivery work hand-in-hand, even the most complex enterprise environments can be transformed.