Servitization and Gen AI

The evolution of servitization in the era of generative Artificial Intelligence

In today’s market, product quality is no longer the sole differentiator. Global competition and evolving customer expectations are driving companies toward a radical transformation: no longer merely selling "pieces of iron," but rather selling solutions that integrate software and services. This transition has a precise name: Servitization.

What is servitization?

The term "servitization" was coined in 1988 by Sandra Vandermerwe and Juan Rada in their article "Servitization of Business: Adding Value by Adding Services." Even then, the authors identified a crucial trend: the need for companies to add value to their core offerings through services in order to distinguish themselves from the competition.

However, it was Baines’ definition (2009) that gave the concept its contemporary form: "The innovation of an organisation’s capabilities and processes to better create mutual value through a shift from selling product to selling Product-Service Systems."

This entails moving from a production-based model to a service-based one, integrating service logic into every stage of the value chain, from design to post-sales.

Despite being studied for nearly forty years, servitization has sparked renewed interest recently, driven by the acceleration of digital technologies. Some experts use the term "Digital Servitization" to describe the "transition toward intelligent systems that integrate product, service, and software," defined as Product-Service Systems.

The typologies: The three levels of servitization

The implementation of servitization varies significantly between companies. However, it is possible to identify three progressive levels of maturity, each characterized by an intensification of the relationship with the customer and an increasing assumption of risk by the supplier.

• Base Services (Value-added Manufacturer): Attention remains focused on the physical product, but the manufacturer begins to view itself as a service provider as well. These services maintain a complementary role to the main offering and include activities such as installation, spare parts supply, and warranty management. The supporting technological infrastructure is still elementary.

• Intermediate Services (Value-added Manufacturer): At this level, the company assumes responsibility for maintaining the product's operating conditions. This category includes repairs, direct technical assistance, and condition monitoring. Supporting technologies evolve significantly: interconnected devices, IoT sensors, and predictive algorithms emerge. Without adequate technological infrastructure, delivering these services would be impractical.



• Advanced Services (Full-service provider/Leader): This represents a substantial paradigm shift. The company provides "outcomes" rather than products. Examples include Pay-Per-Use contracts, Availability Contracts (guaranteeing operational availability), and integrated solutions requiring a comprehensive redefinition of the company's value proposition. This marks the shift from selling a single product to offering an ecosystem of support services.

As one progresses along this maturity scale, the customer abandons the one-off purchase of machinery (CapEx) to adopt a model based on recurring fees tied to usage (OpEx) or performance guaranteed by the supplier. Consequently, the manufacturer's revenue depends directly on achieving measurable results rather than simply selling products.

Enabling factors for servitization

As highlighted by the different implementation levels, servitization requires a solid organizational and technological foundation to progress toward more advanced forms. It is crucial to consider that corporate evolution and business model transformation must proceed in parallel with daily operational management, which includes meeting existing contractual commitments and achieving revenue targets.

Since servitization affects the entire value chain, proceeding in progressive steps is fundamental to ensuring sustainability and allowing the company to learn through direct experience (trial & error), thereby identifying the most suitable path.



Beyond organizational and financial limits, technological support must also be considered; it must evolve alongside service implementation. While technologies in the early phase were solely oriented toward product manufacturing, software systems must now manage ongoing relationships with customers, keeping track of interaction history.

To ensure success, specific levers must be pulled:

• Digital Readiness and Data: The ability to collect and analyze data is the fuel of servitization. Without adequate IT infrastructure and a data culture, offering advanced services like predictive maintenance is impossible.
• The Role of KIBS (Knowledge-Intensive Business Services): Servitization, especially the "digital" kind, requires skills that manufacturers often do not possess internally (cybersecurity, data analytics, IoT, AI). The ability to create an ecosystem with specialized partners (KIBS) therefore becomes a strategic asset.
• Internal Commitment: Strong sponsorship from top management is required. Without clear leadership, servitization risks remaining a tactical experiment rather than a corporate strategy.
• Targeted Hiring Programs: It is necessary to bridge the skills gap by hiring hybrid profiles capable of translating technical data into business value.

How to advance on the servitization journey?

The step-by-step progression to transform a traditional product-based model into a value- and service-oriented model begins with the Ideation phase. Here, the company clearly defines business requirements, identifies the most relevant use cases, and establishes the KPIs that will guide the entire project roadmap.

In the Discovery phase, a PoC (Proof of Concept) or MVP (Minimum Viable Product) is developed to validate the technical feasibility and potential value of the solution, monitoring early performance metrics.

Once the model is validated through Testing, the Scalability phase involves integrating the solution into corporate systems, extending it to other use cases, and engaging teams through training and change management.

Finally, in the Support phase, the company guarantees reliable performance over time through constant monitoring, updates, and continuous alignment of the solution with evolving business objectives.

This structured path allows organizations to implement servitization sustainably and effectively, maximizing value for both the company and its customers.

Opportunities for manufacturers

Adopting an "as-a-service" model radically transforms the manufacturer's role, opening the door to new competitive levers.

The first tangible benefit is financial stability: by replacing one-off sales with recurring fees (subscriptions, pay-per-use, or outcome-based models), companies can generate predictable cash flows and stabilize growth. This approach lowers entry barriers for customers, facilitating the transition from capital expenditures (CapEx) to operating expenses (OpEx) and offering unprecedented operational flexibility. The customer, relieved of maintenance and management burdens, pays for the effective use of the asset, not its possession.

Servitization evolves the customer relationship into a lasting partnership. Access to usage data allows for hyper-personalized solutions: for example, a manufacturer in the HoReCa (Hotel, Restaurant, Catering) sector can propose connected machines optimized for a restaurant's specific volumes, including maintenance and consumables in a single fee. In the wellness sector, this translates to spa beds or cabins paid for based on the number of treatments performed, with performance reports sent to sports centers to maximize efficiency.

Finally, this model offers a clear competitive advantage over those competing solely on price or technical specifications. By enabling circular economy practices such as recovery, refurbishment, and reuse ("certified refurbished"), servitization not only meets environmental sustainability goals but extends the product lifecycle, creating continuous value for the entire ecosystem.

New pricing models

Servitization opens the door to recurring and predictable revenue streams, decoupling turnover from the pure sale of physical units. The most recurring pricing models are:

1. Subscription: The customer pays a fixed recurring fee for access to the product and included maintenance services. This eliminates the initial investment and guarantees stable revenue (ARR - Annual Recurring Revenue) for the supplier.
2. Pay-Per-Use (Equipment-as-a-Service): The cost is aligned with actual consumption, but the manufacturer must be able to accurately monitor billing metrics. Payment is based on operating hours, units produced, or kilometers driven. This model requires precise IoT telemetry for billing. Notable examples include "Pay-per-wash" in professional appliances (renting a dishwasher along with soap supply) or "heat-as-a-service" offers gaining ground in the energy sector, where the customer buys the heating service rather than the energy or the equipment itself.
3. Leasing and Rental: Leasing and rental contracts allow manufacturers to facilitate market penetration by eliminating the need for significant initial investments from customers; clients pay only operating installments.

• In Leasing, a financial company buys the equipment on behalf of potential clients and transfers the right of use for a defined period, with the ultimate goal of transferring ownership. Generally, maintenance and insurance remain the lessee's responsibility, as if they were already the owner.
• Rental, on the other hand, is a pure service contract. Customers pay a fee exclusively for the use of the equipment, without any intention of purchase. They bear no burdens related to ownership (obsolescence, breakdowns) precisely because the fee is often "all-inclusive": maintenance, assistance, and insurance remain the provider's responsibility.

1. Outcome-Based: This is the most advanced frontier. The provider is paid based on the achievement of specific KPIs (e.g., energy savings, increased productivity, zero downtime). Here, operational risk is entirely borne by the provider, who becomes a true strategic partner.
   

Transforming AI into value: overcoming implementation errors

Although manufacturing companies have correctly identified Artificial Intelligence as the ideal catalyst to accelerate servitization and the shift to outcome-based models, implementation often faces a high failure rate due to fundamental strategic errors.

The gap between theoretical potential and real value stems from the tendency to treat AI and services as disjointed silos, neglecting critical prerequisites such as solid data governance, cross-functional team alignment, and the preemptive definition of clear monetization logic. To transform current experiments into concrete and scalable profits, manufacturers must overcome "technological haste" and adopt a structured approach that integrates data infrastructure, business strategy, and operational processes from the start.

Here are the most common errors blocking AI implementation for servitization:

• Misalignment between AI and Business: A critical error is managing AI and servitization in separate compartments, locking the potential of both into unproductive silos lacking a clear ROI. Real value emerges only by integrating the two strategies: AI data must directly feed business models.
• Data Silos: Often, production, maintenance, and CRM data do not communicate with each other. AI needs unified and clean data to function. Without solid data governance, algorithms cannot establish cause-and-effect links. Thus, data preparation becomes a prerequisite for project scalability.
• Lack of a Monetization Model: It is wrongly assumed that the customer will pay more simply because the service is "digital." Without a clear value proposition and a defined pricing model, digital functionalities remain costs, not revenue.
• Internal Team Misalignment: To move from a product-centric to an outcome-centric approach, investing in people is necessary: workshops and training on AI and data culture are essential to align all teams with business goals and valorize individual contributions.

Servitization represents a profound transformation requiring a paradigm shift—from a simple product-price exchange to a continuous relationship based on value and results. In this context, Artificial Intelligence offers extraordinary potential, but it cannot be treated as an isolated intervention.

To generate real impact, AI must be embedded within a strategic journey that aligns data, processes, skills, and business models. Only a gradual, structured approach supported by a long-term vision allows manufacturers to leverage AI as a tool to renew their offerings, transform customer relationships, and consolidate a mature and scalable servitization model. Technology accelerates the journey, but it is strategy that makes it possible.