Automating Warranty Claims with AI

The Challenge

A leading global manufacturer of heavy trucks and buses was running a warranty claims process that was consuming significant manual effort while delivering inconsistent results. Warranty and service claims were reviewed manually, and the prioritization logic was largely intuitive. The wrong claims were being flagged for review, and a high proportion of manually reviewed claims ended up being approved regardless — meaning the effort invested was not translating into effective risk control. Incorrect claims were slipping through, driving up warranty costs and eroding margins.

At the same time, service claim checking was being handled locally within individual National Sales Companies across the globe. There was no central oversight, no consistent methodology, and no scalable way to improve quality across the network.

When the organization launched a broader transformation program aimed at enhancing efficiency and customer focus, warranty claim automation became one of its most strategically significant workstreams. The goal was not just to reduce manual effort but to fundamentally improve the quality of decisions being made at scale — across 20 countries.

The Approach

Building the Machine Learning Foundation

A machine learning model was developed and trained on historical claims data, incorporating variables such as claim type, vehicle model and age, and service history. The model was designed not as a binary decision tool but as a staged automation system. In the first stage it identified risk levels within incoming claims. In later stages it would generate approval recommendations, and ultimately, for claims where the model's confidence exceeded defined thresholds, make autonomous decisions without human review.

When change2target became involved, a proof of concept for the first stage already existed. The challenge was defining the architecture and requirements for the progression from risk flagging through to autonomous decision making. With expertise spanning both machine learning implementation and after sales process requirements, change2target guided the client through each stage of that development, coordinating between business departments and the IT team responsible for integrating the models into the existing technology landscape.

Centralizing Service Claim Checking

While warranty claims were already reviewed by a central team, service claims remained decentralized, checked locally by National Sales Companies with their own processes, expertise, and standards. Centralizing this function required more than a structural decision. The knowledge held within local teams had to be systematically transferred to a central organization that did not yet exist in its new form.

Through structured workshops and working sessions with stakeholders across multiple countries, roles and responsibilities were redefined, competency requirements were established, and the procedural changes needed to make centralization work in practice were agreed and implemented. This included preparing both the central team and the National Sales Companies for a fundamentally different way of collaborating.

Managing the Human Side of Automation

Introducing AI into an established organization always surfaces the same underlying questions, even when they go unspoken: what happens to my role, can I keep up, and will my experience still matter? This project was no different. Claim checkers who had built their expertise over years were now being asked to become auditors of a machine learning model — a genuinely different skill set requiring a genuinely different mindset.

A significant portion of the project was devoted to making this transition work. Communication was structured and sustained throughout. Roles were redefined clearly, with transparency about what would change and what would not. Coaching was provided to help individuals and teams build confidence in the new system and develop the auditing capabilities the new model required. Resistance was addressed directly rather than managed around.

Maintaining Operational Focus

Throughout the transformation, a continuous focus was maintained on achieving the operational results defined in the original business case. Key performance indicators were established at the outset, tracked throughout, and used to drive decision making. Where adjustments were needed, they were made promptly. The program management structure, which change2target led across all workstreams, ensured that the technical, organizational, and operational dimensions of the program remained aligned with each other and with the client's strategic objectives.

The Results

All operational targets defined in the business case were achieved. The machine learning model enabled the warranty team to concentrate its attention on high-risk claims with a genuine probability of being denied, rather than working through a broad and undifferentiated queue. Manual effort in claim checking was significantly reduced. Consistency in check quality improved materially. Risk mitigation in the handling of critical claims was strengthened. And overall warranty costs were reduced as a result of more accurate identification and rejection of incorrect claims.

The program was rolled out across 20 countries and National Sales Companies worldwide, bringing a previously fragmented service claim process under a unified, centralized operating model for the first time.

Beyond the measurable outcomes, the organization emerged from the project with meaningfully expanded internal capability. Data scientists were coached in the methodology required to set up and continuously improve machine learning models. The auditor role was redefined and operationalized. A governance structure for ongoing model management was established. The client did not just implement a tool — it built the organizational capacity to own and evolve it.

Key Takeaway

Automating a high-stakes process like warranty claim checking is not primarily a technology challenge. The model is the starting point. The harder work is designing the staged automation logic, centralizing the processes that need to be centralized, transferring the institutional knowledge that has built up in local teams, and preparing people for roles that did not exist the year before.

For manufacturing executives considering AI-driven automation in after sales or claims management, the lesson is consistent: organizations that treat the human and organizational dimensions of implementation with the same rigor as the technical dimensions are the ones that achieve sustainable results. The technology performs to its potential only when the operating model around it has been rebuilt to match.