Reducing back office workload by up to 30% with AI and intelligent automation

Challenge

A large automotive group wanted to identify where artificial intelligence and automation could generate measurable efficiency across its back office operations.

The company had hundreds of employees across administrative, financial, and operational teams. The discovery mapped dozens of processes covering approximately 90% of total operational activity.

The process analysis showed that operational effort was heavily concentrated in repetitive, information-intensive activities. Data integration and processing, validation and quality control, and workflow management represented more than 85% of total effort.

This created a clear opportunity to automate data processing and validation, improve workflows, and introduce AI into activities requiring analysis and decision support. The key question was where these technologies could deliver the highest business impact while remaining feasible to implement.

Solution

LTPlabs conducted a structured discovery across the teams, connecting operational pain points with opportunities for AI, automation, data, and platform improvements.

The assessment resulted in 13 prioritised use cases, evaluated according to expected business impact and implementation complexity. These initiatives covered AI-enabled operations, including automatic data extraction from core platforms and automatic validation of long documents, data and AI enablement.

Several priority use cases illustrate how the operating model could evolve:

1. AI-assisted warranty validation

The proposed warranty assistant integrates with the existing environments and uses three specialist agents for eligibility, document, and technical validation.

The agents analyse warranty information using brand process documentation, maintenance histories, warranty records, and information from the existing warranty process management platform.

The solution is designed to generate compliance scores, identify required corrections, suggest corrective actions according to criticality, and produce detailed reports. Critical cases remain subject to human validation.

2. Intelligent payment processing

The intelligent payments platform introduces automated controls into financial operations, including IBAN validation, mandatory data checks, alerts for inconsistencies, configurable approval workflows, automatic prioritisation of critical requests, and real-time operational monitoring.

It also has integrations that support the consultation of pending invoices and accounting movements. A common platform is designed to support operations across the organisation.

3. Reusable AI and automation architecture

The roadmap also defines a reusable hybrid automation architecture combining browser automation and Generative AI.

A common technology layer provides connectivity with internal systems and external brand portals, centralised authentication and session management, and shared logging and monitoring.

This architecture provides the foundation for additional use cases without rebuilding the underlying technology for every process.

The main goal of this application is a multi-automotive brand agent designed to extract information from multiple portals, structure the collected data, consolidate it, and make it available for operational workflows, including planned outbound calls and personalised customer emails.

Results

The assessment identified significant potential to redesign the automotive group’s back office operating model.

Across the prioritised initiatives, the estimated impact corresponds to a reduction of 20% to 30% of the current employee operation.

At the upper end of the identified opportunity, the potential impact corresponds to approximately €1.6 million.