AI in Customer Support: Cutting Service Costs by Up to 30 Percent

08/24/2026 | Blog

As product portfolios expand, service workloads are surging across many companies. AI-based customer support breaks this cycle. The technology offers high cost efficiency, especially for mid-market businesses, and unlocks new growth, sales, and marketing opportunities. A McKinsey analysis highlights this potential: AI-assisted interactions cut service costs by up to 30 percent while simultaneously boosting revenue by 5 to 8 percent.

AI Relieves Service Structures and Drives Sales

In many service teams, manually handling recurring standard queries ties up valuable working hours. This is precisely where AI systems come in. They take over routine requests, access product data directly, and free up specialists for cases that require personal touch or detailed technical expertise.

The impact shows up primarily in two key areas:

Direct cost reduction:Recurring issues are answered automatically. As a result, service teams gain capacity for complex cases where personal guidance and expert context are vital.
Additional revenue potential:Rapid responses at the right moment prevent abandoned purchases. At the same time, intelligent systems identify real-time cross-selling opportunities—such as accessories, spare parts, updates, or complementary components—generating actionable leads for sales and marketing.

Whether consumer goods, smart home components, smart meters, building automation, or electrical installation systems: products are becoming increasingly connected and complex. This puts added pressure on service, product management, and digital business models. AI supports service operations not just selectively, but across the entire product lifecycle. Companies streamline their processes while delivering a more reliable customer experience.

Leveraging Regulations and Product Data as a Knowledge Base

For AI to perform reliably in support, it requires structured access to product data, usage data, and technical documentation.

This creates a direct synergy with current EU regulations. Manufacturers will soon need to comply with mandates like the Digital Product Passport (DPP), the Cyber Resilience Act (CRA), the Ecodesign Regulation (ESPR), the Battery Regulation, the Construction Products Regulation, and the proposed European Product Act (EPA).

With deep market expertise, M&M aligns data architectures with these requirements at an early stage. This enables companies to fulfill compliance obligations while establishing a robust knowledge base for AI support—turning regulatory mandates into direct economic value.

Implementing AI into Service Processes

The crucial question for a successful project setup is: Where in the support workflow do recurring tasks exist that can be meaningfully automated, and how can product data be optimally leveraged? This analysis reveals which use cases make economic sense, what data quality is required, and where AI can deliver tangible relief within the service process.

Source: McKinsey https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/next-best-experience-how-ai-can-power-every-customer-interaction

About the author

Roger Faust is a Senior Sales Manager at M&M Software and supports companies in the consumer goods industry with projects involving software, data, and AI. His focus is on helping organizations leverage data and AI to improve process efficiency and drive sustainable growth.

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