Servitization beyond the product.

Article by: Roberto Sala, Researcher at the Università degli Studi di Bergamo and member of the CELS research group.
Giuditta Pezzotta, Associate Professor at the Università degli Studi di Bergamo – Marco Ardolino, Researcher at the Department of Mechanical and Industrial Engineering of the Università degli Studi di Brescia – Federico Adrodegari, Associate Professor at the Università degli Studi di Brescia, active in the field of industrial engineering.

Industrial servitization is not merely a transformation of the business model. It is a transformation of work. When companies move from selling products to the ongoing management of integrated solutions over time, internal organization, required skills and the relationship between people and technologies change profoundly.

Value creation no longer ends with the delivery of the asset, but requires the ability to manage complex systemsthroughout the entire life cycle, integrating data, digital services and technical expertise.

This shift redefines roles, responsibilities and decision-making processes: hybrid professional profiles emerge, capable of combining engineering knowledge, data analysis and relational skills, while organizational structures become more cross-functional and oriented toward continuous collaboration with the customer.

The reflections presented here are based on experience developed within the MICS project – Line 7.1 “Pay-per-X” – and on the research activities of the ASAP Interuniversity Research Centre, which in recent years has analysed the evolution of servitization models and the integration of artificial intelligence, service and work organization in European manufacturing.

From predictive maintenance to Operations intelligence

The first phase of digital servitization was characterized by the introduction of IoT technologies and remote monitoring tools. Sensors, connectivity and cloud platforms made it possible to collect data on asset operating conditions and anticipate failures through predictive models. Today, we are entering a more advanced phase. Predicting a malfunction is no longer enough: information must be integrated into structured decision-making processes. The objective is not only to reduce downtime, but to optimize the entire service life cycle systemically.

There is increasing reference to Operations intelligence: a set of capabilities that transform operational data into concrete actions. This means dynamically planning technical interventions, managing priorities in real time, supporting field technicians with digital assistants, optimizing service team routing and, in some cases, adapting pricing to actual usage conditions.

The value does not lie in the data itself, but in the ability to continuously manage contractualized performance. In an outcome-based model, every operational decision has a direct economic impact. Artificial Intelligence (AI) therefore becomes a management support tool, not merely an analytical one.

However, this evolution entails a significant change in the role of people. The decision-maker does not disappear, but operates in a context increasingly mediated by algorithmic systems. The challenge is not to replace human experience, but to integrate it into human-in-the-loop processes, in which responsibility and control remain clearly defined.

When the service includes autonomous systems

A further evolutionary step concerns the integration of autonomous physical components within servitized models. Collaborative robots, autonomous mobile systems and intelligent devices are no longer confined to the production line: they become part of the service offering.

In contexts characterized by high risk, geographical distance or shortages of specialist skills, the use of autonomous systems can reduce intervention times, increase safety and ensure greater operational continuity. Consider maintenance in hostile environments, inspections in hard-to-reach areas or interventions under emergency conditions.

This introduces a new dimension into the design of outcome-based models. If part of the service is delivered through autonomous systems, the distribution of responsibilities, risk management and cost structure must be reconsidered. The initial investment may be significant, but the potential scalability is high.

Robotics, in this sense, is not merely a production technology. It becomes an enabler of technology-mediated service models, in which the combination of human operators, digital systems and autonomous physical devices creates new organizational configurations.

This scenario raises important questions: how is safety guaranteed? Who is responsible in the event of an error? How can the technician’s experience be integrated with the capabilities of the autonomous system? The answer cannot be purely technological. It is a matter of governance and organizational design.

Accountability and new grey areas in decision-making

The extensive introduction of advanced Analytics and AI into servitized processes inevitably opens up new grey areas in terms of responsibility. If an algorithm suggests a decision that affects the performance or safety of a plant, who is accountable?

In traditional models, the chain of responsibility is relatively linear. In data-driven models, however, the decision may be the result of a complex interaction between automated systems, human inputs and contractual rules.

This issue is particularly relevant in outcome-based contracts, where performance is directly linked to economic obligations. The need for transparency, traceability and explainability of algorithmic decisions therefore becomes not only an ethical requirement, but also a contractual one.

Moreover, rising compliance costs and regulatory complexity may widen the differences between large companies and SMEs. Organizations with greater resources are better equipped to develop sophisticated governance systems, while smaller firms risk finding themselves in a subordinate position within ecosystems.

The digital transformation of servitization is not neutral: it redefines power balances and requires careful design of responsibilities.

Hybrid skills and cultural transformation

One of the most critical issues concerns skills. Performance-based servitized models require professional profiles capable of integrating technical, digital, contractual and organizational knowledge. The maintenance technician must understand data analysis logic; the data analyst must understand how plants operate; the sales manager must be able to negotiate contracts linked to complex SLAs; management must integrate economic, operational and environmental dimensions into performance assessment.

It is not simply a matter of adding new professional profiles, but of encouraging cross-pollination between roles: servitization breaks down traditional functional barriers between production, service, IT and sales.

This transformation also requires a cultural shift. In a sales-based model, success is measured in volumes and short-term margins. By contrast, if we base our business model on performance, success depends on the quality of the relationship over time. Transparency, data sharing and collaborative risk management become central elements. For many organizations, the greatest challenge is not technological, but one of identity.

The risk of polarization and the European challenge

If not properly governed, digital servitization can lead to market polarization. A small number of ecosystem orchestrators, equipped with advanced data infrastructures and proprietary platforms, could concentrate an increasing share of value. Around them, a multitude of subordinate suppliers could risk operating with compressed margins and reduced strategic autonomy.

For the European industrial system, characterized by a strong presence of highly specialized SMEs, this scenario represents a concrete risk. The challenge is to build interoperable models, shared standards and data infrastructures that avoid excessive dependencies.

Global competition will not be played out solely on product quality or technological sophistication, but on the ability to orchestrate complex ecosystems while maintaining a balance between innovation, responsibility and inclusion.

Beyond experimentation: making the model scalable

The pioneering phase is now over. The technologies exist, contractual models have been tested, and AI and robotics applications are increasingly mature. The real issue is scalability. Making an outcome-based model scalable means equipping oneself with shared metrics, reliable monitoring systems, sustainable pricing mechanisms and robust data governance. It also means investing in continuous training and rethinking professional development pathways.

Servitization is neither an IT project nor an isolated initiative of the service department. It is a strategic transformation that spans the entire organization. Over the next decade, European manufacturing will face a choice: use AI and robotics as tactical tools for efficiency, or integrate them into a systemic vision focused on performance, sustainability and trust.

The difference between these two paths will determine not only the competitiveness of companies, but also the quality of industrial work and Europe’s ability to maintain a central role in global value chains.