There’s a moment right before a virtual session starts when you wonder if anyone is going to show up.
That was the vibe at the beginning of our AI Service and Warranty Lifecycle (AI-SWLM Think Tank) Virtual Showcase Series kickoff. A few early “hellos,” and then the room started filling quickly—OEMs, parts manufacturers, truck and automotive leaders, retail, insurance, and extended warranty stakeholders.
By the time we got underway, there was representation from 12 different OEMs, along with a broad mix of partners and experts who work in warranty and aftercare operations every day.
The same pressure points—across every segment
Once the discussion opened, the themes came quickly and consistently.
Across organizations, leaders described:
- Not enough capacity to review everything
- Heavy manual effort across claims workflows
- Too many disconnected systems
- Persistent fraud and abuse concerns, especially post-COVID
- Increasing complexity due to regional and regulatory differences
That last point stood out. Rules and requirements vary not just by country, but often by region or state. Flexibility in workflows and decision logic is no longer optional—it’s structural.
Where the “leak” actually happens
The conversation then shifted to a central question: how do you fix the leak in warranty—reducing cost and reclaiming margin across the full claims lifecycle?
Abhi Rele, who leads Manufacturing Product efforts at ServiceNow, outlined what they consistently see across OEM environments:
- Claims intake issues (missing information, incorrect coding, mismatched parts)
- High rework rates early in the process
- Slow adjudication cycles driven by queues and escalations
- Limited visibility into where value is being lost
- Weak linkage between warranty execution, supplier recovery, and quality feedback loops
The pattern is clear: leakage is not a single point problem—it’s systemic across intake, validation, decisioning, and recovery.
Fragmentation as the core challenge
Jerome Clerc, IT Domain Manager for Aftersales – Warranty & Recall Campaign at Stellantis, brought a perspective that resonated strongly across the room.
The core issue is fragmentation.
Following major mergers, Stellantis operates multiple IT landscapes across warranty, pre-approval, case management, and recall processes. The result:
- Inconsistent dealer experiences, especially for multi-brand dealers
- Higher risk of processing errors
- Duplicated capabilities across siloed systems
- Reporting complexity for leadership
- Increased IT cost and slower change cycles
He also emphasized a growing pressure point: duplicate claims and Unsubstantiated Claims are not edge cases—they are operational realities that need scalable solutions.
What the audience confirmed
A quick audience pulse check reinforced the same picture.
When asked where warranty leakage shows up most, responses clustered around:
- Delayed or missed supplier recovery
- Weak connection between warranty and quality data
- Fragmented systems (often 4–6+ across the lifecycle)
- Lack of cross-functional alignment
- Data readiness gaps blocking improvement efforts
Even when teams know what needs fixing, structural barriers slow execution.
Workflows + AI, not AI alone
From the ServiceNow perspective, Abhi framed the approach as workflows plus AI—not AI in isolation.
The goal is straightforward:
- Connect fragmented data sources
- Bring teams into a single operational workflow
- Reduce manual triage through AI-assisted processing
- Improve speed and consistency of decisions
This spans:
- Dealer-facing submission and status workflows
- Adjudicator workspaces (queues, approvals, returns)
- Structured tasking across functions
- Reduced dependency on email and spreadsheets
Stellantis: starting with consolidation, not transformation
Jerome outlined a pragmatic roadmap already underway:
- Unified dealer portal
- Standardized warranty claim forms across brands
- Extension into recall campaign processes
The focus is on consistency first—then intelligence.
AI is being introduced step-by-step to:
- Support assessors
- Accelerate rule application
- Reduce turnaround time
- Maintain governance and control
The emphasis was deliberate: reduce risk, deliver value early, and scale only after foundations stabilize.
Anomalous Claims and what happens after the flag
Unsubstantiated Claims generated strong interest across the group.
ServiceNow shared configurable rule sets that can flag:
- Duplicate claims
- High-frequency claim patterns
- Suspicious or reused attachments and imagery
But the key shift is not detection—it’s actionability.
Once flagged, a claim can:
- Route for review
- Request additional documentation
- Be held for inspection
- Or proceed through controlled escalation paths
As confidence builds, more of this can be automated—while preserving auditability and governance.
The supplier recovery gap
One of the most candid points in the discussion was around supplier recovery.
Across participants, confidence in recovery processes remains low.
Common challenges include:
- Coding errors that break recovery paths
- Rework required before recovery can even begin
- Disconnected systems limiting traceability
- Significant manual effort spent “cleaning” claims
The net effect: time is spent correcting process issues instead of preventing them.
What actually works: start small and make it visible
The most grounded takeaway from the session was not about technology—it was about sequencing.
Start with clarity:
- Establish a baseline before optimizing
- Connect existing data before adding complexity
- Pick one high-impact use case (intake, triage, fraud flagging, dealer communication)
- Prove measurable value before scaling
And critically, define ownership early. AI-enabled warranty processes still require clear accountability for decisions, exceptions, and governance across business and IT.
Closing thought
This was only the kickoff in a broader series.
More AI case studies are coming in the weeks ahead, with deeper working sessions planned leading into Detroit later this year.
What stood out most was not a single capability or system—it was shared reality:
Warranty is a margin battlefield.
And the path forward is not more systems, but better workflows, cleaner data, and AI applied where it actually removes friction—without breaking trust, control, or accountability.
Click here to download the recording.
Join us for our 2 remaining Virtual AI-SWLM Think Tank Showcases:
May 12 – Toyota & Pegasystems Case Study: From Warning Signs To Action: Warranty Early Warning Detection
June 9 C.A.R.S. Protection Plus & Circuitry.ai Case Study: Smarter Warranty Decisions: How AI Drives Claims Accuracy & Reduces Costs
See how OEMs are applying AI in real operations. Explore the AI-SWLM Think Tank and request to participate.
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