Author Copperberg Editorial Team | *This article was developed using a combination of human expertise and AI-assisted writing. The concept, structure, and editorial direction were defined by our team, while elements of the text were generated with the support of advanced language tools. All content has been reviewed, refined, and approved by humans to ensure accuracy, clarity, and relevance.

Customers can no longer tolerate critical equipment going down, yet traditional models built around reacting to failure are reaching their limits. Contracts are shifting, installed bases are becoming more complex and partially connected, and expectations for 24/7 availability, sustainability, and outcome-based value are rapidly rising.

At the Parts & After Sales Business Platform 2026 – Power of 50, Freddy Guerrero Lyons of Bosch Home Comfort underlined that sustainable growth in equipment sales is now inseparable from the maturity of the aftermarket, particularly spare parts availability, process discipline, and the intelligent use of data.

From Cost Centre to Strategic Asset

Across industrial sectors, aftermarket is still often treated as an operational necessity rather than a strategic lever, and in many organisations it remains positioned more as a cost centre than a growth driver.

A different perspective is emerging in which aftermarket is reframed as:

• A direct contributor to revenue and margin, but also a stabiliser of broader commercial performance through its impact on uptime, retention, and brand trust.
• A core part of the overall value proposition, where product credibility depends on lifecycle support rather than the initial sale alone.
• A source of structural resilience, helping smooth demand cycles and strengthen long-term customer relationships.

This shift is less about operational improvements in isolation and more about how leadership chooses to define and communicate value. Where aftermarket is integrated into the core business narrative, supported by clear financial visibility and executive ownership, it is more likely to attract sustained investment and deliver outsized impact.

Without that strategic repositioning, aftermarket tends to remain under-recognised, despite its disproportionate influence on customer loyalty and long-term profitability.

Building an Aftermarket Strategy Around Reality, Not Assumptions

Three foundational disciplines need to be in place before introducing advanced tools or AI: understanding the business context, defining clear responsibilities, and rigorously measuring impact.

There is no universal aftermarket model. Different industries, and even different regions within the same sector, operate under distinct service realities. In some markets, repair remains the default approach, while in others replacement is increasingly preferred. These differences directly shape spare parts strategies, inventory planning, and service design.

After-sales responsibilities must be clearly structured and aligned around a coherent system. This typically includes:

• Treating field technicians as key sources of insight that inform both product and service decisions.
• Systematically capturing field feedback to challenge assumptions about how products are actually used across different environments.
• Maintaining strong documentation and information flows to ensure accurate, accessible technical and parts data across the organisation.

When these elements are managed as an integrated system, organisations develop a more realistic understanding of product performance in practice. Spare parts and service strategies can then be based on actual usage patterns rather than internal assumptions, improving both accuracy and responsiveness.

Designing the Spare Parts Lifecycle as a Cross-Functional System

Spare parts management is often framed as the responsibility of after-sales alone. In practice, it is more effective to treat it as a cross-functional system spanning quality, purchasing, logistics, and technical functions.

The spare parts lifecycle is managed end-to-end, by identifying critical components, forecasting demand based on installed base and sales data, defining stocking policies, and designing delivery models across different markets. These decisions are continuously refined using feedback on field performance, reliability trends, and evolving regulatory requirements.

Two principles are central:

• Shared ownership with clear roles. After-sales typically coordinates the strategy, but quality analyses failure patterns, purchasing manages supplier relationships, logistics ensures availability and lead times, and technical teams provide insight into real field requirements.
• Process discipline over urgency-driven execution. High service levels are achieved through structured workflows, clear responsibilities, and integrated systems rather than ad hoc responses. This ensures that documentation, inventory positioning, ordering, and transport options operate as a single coordinated flow.

This enables a reliable service promise even in critical cases, where rapid delivery is required to maintain uptime. Decisions are guided by long-term customer retention and market position, rather than short-term cost optimisation.

Customer Trust Is Built in the Aftermarket, Not in the Showroom

The perceived quality of HVAC manufacturers is ultimately judged when something goes wrong. Sales teams may win the first order, but after-sales determines whether there is a second, third, and fourth.

The sequence is straightforward but powerful:

• Fast and reliable spare parts availability enables quick repairs.
• Quick repairs increase end-user trust in both the equipment and the brand.
• Trust drives repeat business, recommendations, and willingness to adopt new technologies from the same supplier.

Without a strong after-sales backbone, equipment sales strategies are likely to fail over time. This is particularly acute in climate control, where failures directly affect comfort, safety, and sometimes health. A heat pump breakdown at -10°C or a cooling failure at 42°C is not a minor inconvenience, but a crisis for the end-user.

Organisations that internalise this connection between aftermarket performance and commercial success are more inclined to invest in spare parts infrastructure, digital tools, and field support. Those that continue to see after-sales as a necessary cost will likely face erosion in customer loyalty, even if their products are technically competitive.

AI as Amplifier, Not Silver Bullet

Predictive maintenance and AI have become standard themes in industrial transformation, yet failure rates remain high. Many initiatives never progress beyond proof of concept, and even fewer deliver sustained operational value.

A recurring issue is that projects are often shaped by technological ambition rather than clearly defined operational problems. They are launched without a full understanding of the end-to-end system required to act on predictions, and they are frequently implemented in isolation from the processes, people, and constraints that determine whether insights can actually be used.

In practice, the value of AI depends on the ability of the organisation to respond. A forecast only matters if it triggers timely action across technicians, spare parts availability, logistics, decision-making authority, and customer communication.

AI is not a standalone capability but an amplifier of existing operational maturity. Where service structures, workflows, and responsibilities are well aligned, it can significantly enhance responsiveness and decision quality. Where they are fragmented, it adds little beyond visibility.

Its real strength lies in detecting subtle patterns in complex, continuous data that are difficult to capture through traditional threshold-based monitoring. By analysing the evolution of signals over time rather than isolated values, it becomes possible to identify early signs of degradation that would otherwise remain hidden.

However, predictive insight only becomes meaningful when it is embedded in a system capable of acting on it. Without that integration, even accurate predictions have limited commercial or operational impact.

Avoiding the Common Pitfalls in AI-Enabled Aftermarket

For organisations starting to implement AI in spare parts and predictive maintenance, several pitfalls emerge from this case:

• Underestimating the importance of end-to-end process design. AI is sometimes treated as a discrete IT project, separate from service operations. Without mapping the full journey from data capture to technician action and part replacement, even accurate predictions remain unused.
• Confusing experimentation with implementation. Proof-of-concept exercises may demonstrate technical feasibility but often ignore scalability, integration with existing systems, or the cost-to-serve implications. Many projects fail in the transition from pilot to business-as-usual because these elements were never addressed.
• Investing in tools without infrastructure. Companies can invest in advanced analytics while their field service teams still lack connectivity, mobile access to documentation, or basic visibility into parts availability.
• Focusing on technology before mindset. AI is often positioned as the solution rather than an amplifier of well-designed processes. Without a shared internal belief in aftermarket as strategic and a willingness to adapt working methods, technology either remains underused or creates new complexity without adding value.

Organisations that navigate these pitfalls tend to first identify which concrete problems in service and spare parts data can be solved at scale. From there, they design the ecosystem required to act on those insights, and only then deploy the technology.

Mindset, Structure, and Technology: A Coherent Blueprint

Mindset is non-negotiable. Aftermarket teams cannot drive transformation alone. Senior management must explicitly recognise service and spare parts as value creators, not merely cost drivers. This recognition should be backed by KPIs, investment decisions, and organisational design.

Structure and process discipline come before digitalisation. Clarity on who owns which part of the after-sales value chain, how data flows, how decisions are made, and how performance is measured is more important than adding new tools.

Well-chosen tools can dramatically improve speed, accuracy, and customer experience. But they cannot define strategy, replace cross-functional collaboration, or fix cultural misalignment. Technology makes a good system better.

For industrial leaders in HVAC and beyond, competitive advantage is increasingly determined not only by the efficiency of production or the quality of products, but by the resilience, intelligence, and responsiveness of the aftermarket ecosystem that sustains them. Those who treat spare parts, field support, and AI as central elements of their business architecture are positioning themselves to capture both near-term revenue and long-term customer loyalty.