Servitization in Manufacturing: Creating Customer Value Beyond the Product

Leaders are increasingly confronted with the practical realities of designing, delivering, and measuring services that create tangible, outcome-driven value for their customers.

For high-tech, capital-intensive sectors, success is no longer defined solely by product performance or uptime. Instead, companies are differentiating themselves through service models that optimize customer outcomes, predict needs proactively, and provide actionable insights across the entire lifecycle of a product. This requires a new level of integration across engineering, service delivery, operations, and customer success functions, as well as the strategic use of AI and data to support decision-making and personalization.

In a recent episode of Copperberg Conversations, Umayal Palaniappan, Service & Transformation Leader with extensive experience at Rolls-Royce and other industrial organizations, explains how servitization can fundamentally reconfigure the organization to prioritize customer outcomes, operational agility, and sustainable value creation.

1. AI as a Catalyst for Smarter Services

Artificial intelligence has been a part of industrial operations for years, especially in predictive maintenance, total care contracts, and operational optimization. The recent rise of generative AI and large language models has created an entirely new layer of opportunity for industrial service transformation. Unlike traditional AI, which focuses on analytics and optimization, generative AI can support smarter decision-making, knowledge sharing, and service design. It can help organizations to shift from reactive, product-focused maintenance to proactive, outcome-driven services, enhancing both customer satisfaction and operational cost efficiency.

However, AI alone is not enough. Palaniappan emphasizes that AI must be embedded into organizational structures and operating models to generate value. Implementing a tool without adjusting how people work, how decisions are made, and how services are delivered leads to underutilization.

“Yes, it’s interesting. Yes, it’s super cool, it has a lot of potential, but it’s not a magic wand that’s going to solve all your problems.”

Teams must understand how AI augments their roles. Without that understanding, staff may ignore AI insights or fail to integrate them into daily decision-making. According to Palaniappan, change programs account for roughly half of any AI implementation’s success, so the technology is only one component of effective servitization.

2. Scaling Servitization Initiatives

Transitioning from product-centric operations to a fully service-led model is rarely straightforward. Many organizations start with pilots or small-scale initiatives, but success requires a clear strategy from vision to value for both the customer and the organization. Palaniappan emphasizes that servitization is fundamentally a business model transformation, not just a technological deployment.

“It has to be a clear route to value for the customer and also then route to value internally for the organization offering the service.”

Defining what value means is essential. Companies must understand not just what they consider valuable, but what the customer truly values. This can include uptime, operational efficiency, cost predictability, or insights that help the customer optimize their own operations. Services that only benefit the provider or are misaligned with customer outcomes fail to gain adoption.

Pilots are important to test assumptions and refine offerings, but they must be designed with cross-functional teams. Engineering cannot design services in isolation, and service teams cannot dictate product requirements independently. Successful pilots include:

• Full value-chain collaboration: Engineering, service delivery, operations, finance, and customer success teams working together to understand all aspects of the service.
• Agile iteration: Pilots as learning vehicles. Fast feedback loops enable organizations to identify flaws, test solutions, and optimize offerings before scaling.
• Outcome alignment: Every pilot is measured against clear metrics tied to customer outcomes, not just internal KPIs or operational efficiency.

Modularity is equally important. Instead of building one-off services for each customer, companies can design modular offerings that can be combined to meet different customer needs. For instance, one client may need modules focused on predictive maintenance, another on usage analytics, and another on spare parts optimization. Modular design allows personalization without creating multiple siloed operations, making scaling feasible while maintaining efficiency.

3. Overcoming Cultural Barriers to Collaboration

Even the most advanced technology and well-designed service models cannot succeed without cultural alignment and effective change management. Engineering-driven companies, in particular, face challenges in cross-functional collaboration because teams struggle to understand each other’s priorities and constraints. Engineers may underestimate the complexities of delivering ongoing services, while service teams may not fully grasp the technical challenges of product design or maintenance.

Palaniappan explains that these gaps are natural differences in “lived experience” across functions. She also references Kotter’s framework “Our Iceberg Is Melting”, noting that some embrace change immediately while others are more skeptical:

“Some of the penguins believed that the iceberg was melting, some of them didn’t believe in it at all. And so, how do you glue people together? There are steps. You create a sense of urgency, and then everyone likely bands together, some more reluctantly than others. But then you work towards how you solve that.”

4. Defining Customer Value and Measuring Success

Value is defined by what enables the customer to succeed. For example, predictive maintenance schedules, operational insights, or modular support offerings only deliver real value if they align with the customer’s desired outcomes. This requires close engagement, iterative feedback, and co-creation of service models.

“For the customer, your product is just a tool in them achieving their outcomes. So that is where the value lies, and that’s where you understand what it is that the customer thinks is important, and you are a subset of their outcome.”

To put this into practice, organizations need:

• Modular service offerings: Services designed as configurable modules that can be combined to meet the unique needs of each customer. One client may prioritize predictive analytics, another real-time support, and another inventory optimization. Modular design enables personalization at scale without creating operational complexity.
• Embedded feedback loops: Continuous communication channels with customers ensure that services remain aligned with evolving needs. Feedback can take the form of product insights, service usage patterns, or outcome assessments. This enables rapid adjustment and prevents the service from becoming misaligned with the customer’s objectives.
• Outcome-focused KPIs: Metrics must reflect both customer and organizational value. Examples include operational uptime, cost efficiency, customer satisfaction, and the financial contribution of service offerings to the business. KPIs should be prioritized based on what matters most to the customer, ensuring that measurement drives meaningful action.
• Scalable personalization: By combining modular design with outcome measurement, companies can scale offerings across multiple clients while still tailoring services to individual priorities.

By adopting this framework, organizations can become partners in the customer’s operational success. Services are designed not for the convenience of the provider but to maximize the customer’s performance, efficiency, and business outcomes.

Key Takeaways

Servitization is not merely an operational shift, but a strategic transformation that impacts culture, technology, and organizational design. In 2026, leaders who succeed in servitization:

• Integrate AI strategically to enhance services and customer outcomes.
• Pilot, iterate, and scale initiatives with a clear route to value.
• Address cultural barriers to cross-functional collaboration.
• Define and measure customer value to guide service design and operational decisions.

As Palaniappan said, “Servitization succeeds when product becomes a subset of service.”

Organizations that internalize and cultivate this mindset will differentiate themselves, delivering superior outcomes, service excellence, and long-term competitive advantage.