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.

By this time of year, 2027 is finding its way into conversations across the service industry. Leaders are looking at budgets, technology investments, workforce needs, and the changes they expect to see in the years ahead, while speculations about what service will look like in 2027 are starting to spread around.

Instead of adding another speculation to the pile, we spoke to the people who are dealing with service concerns every day and asked what they are seeing in their own organizations, what is getting their attention, and what they think will be most relevant in the year ahead.

In Service at the Core — Copperberg 2027, we draw from the perspectives of 100 service leaders across industries, looking at the forces they expect to shape service and which conversations they believe deserve more time, ahead of the Aftermarket Business Platform — Power of 100, coming in October 2027, in Berlin.

Some of the findings in our white paper confirm what we might expect. Others are more revealing because they highlight a difference between what the industry talks about, what organizations invest in, and what service leaders believe they need to understand better.

AI Is Leading the Conversation, but It Is Overdiscussed

AI in service operations was identified by 47% of respondents as one of the forces most likely to impact service by 2027, followed by:

• Agentic AI and automation at 41%;
• Customer success and lifecycle management at 40%;
• Installed base intelligence at 38%;
• Service-led business models at 32%.

AI is clearly part of the service landscape, but the survey also shows that it is one of the topics respondents feel has been discussed ad nauseam.

Many feel the conversation has reached a point where questions about AI’s potential have become redundant, while the stage beyond the pilot is still an unsolved mystery. Service leaders now want to know where AI actually improves performance, which applications can be scaled across an installed base, and if improvements can be measured in operational or commercial terms.

A predictive AI model may identify a developing failure, for example, but the benefit depends on whether the organization can schedule the work, locate the part, assign the right technician, and intervene before the customer is disrupted.

Likewise, producing an answer with generative or agentic AI is one thing, but giving the customer the right answer, with the right technical context, and at the right point in the service journey, is another. So, after several years of discussing what AI might make possible, service leaders are zooming in on what it can actually do inside the operation.

Service Is Getting Closer to the Commercial Core

Customer success and lifecycle management was identified by 40% of respondents as a force likely to have an impact by 2027, while service-led business models reached 32%.

Because service can now draw on information from the asset throughout its life, the relationship with the customer does not have to end with the equipment sale or wait for the next repair. Connected equipment can support remote diagnostics and predictive maintenance, while software updates, upgrades, spare parts, technical support, and service agreements create additional reasons to stay engaged with the customer. However, while this creates more commercial possibilities, it also puts more responsibility on the service organization.

If a company sells uptime rather than maintenance hours, for example, it has to decide what uptime means, how it will be measured, what happens when availability drops below the agreed level, and how much operational capacity is required to support that promise. Pricing and contract design have to account for the risk being taken on, while finance and service operations need a shared understanding of what the agreement is likely to cost to deliver.

Service is becoming a commercial core essential, but service leaders still need to work out the business model around this development.

The Workforce Problem Is Also an Operations Problem

Talent scarcity was the most frequently identified challenge, ranking ahead of data quality, AI, and economic volatility. At the same time, only 6% of respondents said field service operations was receiving the most internal attention, making it the lowest of the 12 options. Workforce planning, scheduling, and route optimization received even less interest when respondents were asked which technology areas they most wanted to learn about. Only 4% selected it, placing it last among 21 categories.

“Remove AI from the discussion and the most persistent operational concern is people. When respondents were asked openly about their biggest challenge, talent scarcity and knowledge transfer appeared again and again. The issue crosses sectors, geographies, and service models.”

– Lisa Hellqvist, Managing Director & Co-founder, Copperberg

In the operational reality many service organizations function, these numbers are paradoxical. Installed bases continue to grow, experienced technicians are retiring, newer employees need to acquire years of accumulated product knowledge, and customers expect faster responses even when the available workforce is under pressure.

Having enough technicians is only part of the problem. The organization also needs to know where those technicians are needed, which jobs require particular expertise, how much time they spend travelling, how quickly new employees can become productive, and how much service demand the existing workforce can realistically absorb.

“Strategic attention is concentrating on the outcomes organizations want – growth, profitability, customer experience, and AI enabled productivity. The enabling disciplines receive less visibility: people, parts, data, and sustainable lifecycle practices. That imbalance deserves scrutiny because execution depends on them.”

– Lisa Hellqvist, Managing Director & Co-founder, Copperberg

Workforce effectiveness is thus a capacity issue. Technology can help by supporting remote diagnostics, self-service, AI-guided troubleshooting, or better scheduling, but those tools still have to fit into an operation with finite people, time, and expertise who ultimately determine how much of the promised service the organization can actually deliver.

Service Leaders Want More Intelligence, but Intelligence Still Has to Reach the Operation

The priorities receiving the most internal attention today are:

• Service profitability at 37%;
• Service growth at 35%;
• Customer experience at 32%;
• Service transformation at 30%;
• AI and automation at 28%.

The technologies leaders want to learn more about point in a similar direction:

• AI and Generative AI at 43%;
• Service intelligence and decision support platforms at 38%;
• Predictive analytics and failure prediction at 37%.

Organizations are therefore investing resources in better information, better decisions, and better economics. But information is only as valuable as the changes it produces on the ground. A decision-support platform can recommend an intervention, but someone still has to decide whether the intervention should happen and have the authority and resources to make it happen.

The ability to see what is happening across the installed base is becoming more advanced. However, leveraging that information for planning, inventory, technicians, and customer communication requires the right people and processes to act on it.

The Conversation Leaders Most Want to Have Is About Customers

When asked which conversation they would most like to have at the Aftermarket Business Platform — Power of 100, 60% of respondents selected what customers actually value — understanding service from the customer perspective. The second-ranked topic, breaking organizational silos, was selected by 37%. The 23-point difference is significant given how prominently AI and digitalization feature elsewhere in the research.

Service teams have access to a large amount of information about what customers actually experience. They see recurring failures, difficult maintenance tasks, repeated support requests, workarounds, and delays. They also see what customers ask for once an asset has been in operation for several years. Yet that knowledge does not always make its way into product development, sales, or commercial strategy in a form those functions can use. One respondent described it this way:

“Service often has the best view of what customers actually need next, but struggles to get that intelligence into product roadmaps or sales conversations in a structured way.”

– Survey Respondent, Service Strategy

That makes customer understanding relevant far beyond customer experience. It can influence what products are developed, what services are offered, what should be included in a contract, and where an organization should invest its service capacity. AI, automation, and connected assets can give service organizations more ways to interact with customers and more information about their equipment, but they do not determine what customers value.

The Less Visible Parts of Service Can Affect What Customers Receive

Some of the lowest-ranked topics in the survey are also some of the most operationally consequential.

Spare parts profitability ranked last among 16 topics respondents were asked to assess for future impact. Spare parts planning and inventory optimization, however, was selected by 25% as an area where they want to learn more, ranking fifth among 21 categories. Parts availability can determine whether a repair happens tomorrow or three weeks from now. A service organization can have excellent predictive capabilities and still disappoint the customer if the required component is sitting in the wrong warehouse.

Sustainability and circular service models ranked second lowest among the 16 topics. Yet extending asset life, retrofitting equipment, improving maintainability, and deciding when repair makes more sense than replacement can affect both lifecycle economics and the value customers get from their equipment.

Workforce planning, scheduling, and serviceability may attract less attention than AI, but they directly affect whether the right technician can reach the right customer with the right expertise and equipment. These decisions shape response times, technician utilization, and the cost of delivering service.

Data quality is similarly easy to overlook, but poor asset records, incomplete service histories, or inaccurate parts information can slow diagnosis, create repeat visits, and make the organization more dependent on a shrinking pool of experienced people who know how to work around the gaps.

Conversations for 2027

Our findings highlight several conversations that are becoming harder to separate from one another:

• The AI discussion is moving towards adoption, operational readiness, and measurable return. 
• Service is taking on more responsibility across the customer and asset lifecycle, which brings pricing, contracting, and risk allocation into the discussion.
• Workforce capacity is becoming a constraint even as relatively little attention is directed towards the processes that manage it.
• Data and predictive technologies are generating more intelligence, while organizations still need to connect that intelligence to the people and resources that act on it.

And underneath all of these topics is the customer. One respondent highlighted the longer-term issue this way:

“What capability that currently differentiates your service organization from competitors will no longer be a differentiator in five years? If you were building your service business from scratch today, what would you do completely differently?”

– Survey Respondent, Service Strategy

Many of the capabilities that currently appear distinctive will become more widespread. AI tools will become more widely available. Connected assets will generate more data. Digital service channels will become more common. However, it is harder to reproduce an organization’s ability to understand its customers, organize its resources around what is important to them, and consistently deliver on the promises it makes.

Explore the conversations ahead in Service at the Core — Copperberg 2027, which provides the broader picture behind the findings in this article, including the areas where service leaders are putting their attention, the capabilities they want to develop, and the tensions emerging between what the industry talks about and what service organizations need to make it work.