Key insights from the State of Service in Manufacturing 2025
Manufacturers today face mounting pressure from multiple directions. Customer expectations for faster, better service continue to rise while operations grow increasingly complex. Add emerging technologies like Industrial AI into the mix, and the manufacturing landscape looks dramatically different than it did just a few years ago.
The latest State of Service in Manufacturing report from IFS and Accenture surveyed 800 manufacturing leaders worldwide to understand the critical challenges reshaping service delivery.
One finding stands out above all others: service is no longer just a support function. It has evolved into a genuine profit powerhouse, helping manufacturers maintain resilience and competitiveness even during volatile times.
To unpack these findings and explore what they mean for manufacturers worldwide, IFS experts and Mike Gosling, Service Platforms Manager at Cubic Transportation systems recently sat down together for an in-depth discussion.
The Servitization Paradox: Why Only 25% of Manufacturers Have Fully Implemented It?
Servitization refers to the shift from selling products to delivering outcome-based services. While 94% of companies report that outcome-based service models have already impacted their operations, only 25% have fully implemented them.
This gap reveals a fundamental challenge for traditional manufacturers. Moving from a product-centric business model to delivering service-based outcomes requires a complete mindset transformation.
The transition also involves significant organizational restructuring. When you sell a product, revenue arrives at the point of sale. With servitization, revenue flows through subscriptions and service contracts over time. This creates a financial gap that some organizations view as too risky to navigate.
Beyond the revenue model, servitization demands new infrastructure to manage resources, contracts, time allocation, devices, and more. For many manufacturers, this represents a substantial operational shift.
The Rise of the Chief Service Officer
Recognition of service as a strategic priority is driving organizational change. According to the report, 61% of companies considering appointing a Chief Service Officer rank it as a top three priority.
This role signals more than a title change. It represents a commitment to partnership-based customer relationships, where service leaders engage directly with customers and guide strategic direction while navigating the sometimes difficult conversations that come with shared accountability.
When both provider and customer focus on the same outcome, the relationship fundamentally changes. Success becomes mutual, and the service leader orchestrates that alignment.
Rethinking Product Design
Moving to outcome-based service transforms how companies think about products themselves. Mike explains this transformation at Cubic perfectly:
“We are known for transportation systems, so people would transact with our gates and our ticket machines. But we don’t sell gates and ticket machines. We sell guaranteed hours of availability for retail and validation.”
This requires a mindset change, moving the focus beyond the product’s initial quality to its sustained performance everywhere, which in turn drives fundamental changes in product design. As Mike points out: “When you’re guaranteeing hours of availability, the last thing you want is someone on-site having to diagnose something for hours and hours. Modulizing your products is a great way of maintaining the outcome.”
Design for service differs fundamentally from design for product, and this realization is driving manufacturers to rethink engineering from the ground up.
The AI Adoption Gap: Why Most Manufacturers Struggle to Scale
AI dominates industry conversations. Yet while 96% of respondents claim to use AI in some form, only 28% have fully scaled it across service operations, presenting a significant implementation gap.
However, as the discussion revealed, the type of AI being deployed matters significantly. There’s a risk of “AI washing.” While consumer AI tools like ChatGPT or Copilot boost individual productivity, their value is limited in comparison to Industrial AI, purpose-built for the complexities of manufacturing service. Real value of AI comes from embedding specialized use cases into core business processes, such as:
- Intelligent scheduling that matches engineers to incidents based on multiple criteria such as location, skills, tools, fault type, and contractual impacts
- Predictive maintenance using IoT data to identify issues before they cause failures
- Spare parts forecasting that balances inventory costs against service requirements
- Knowledge management systems that capture expert techniques and make them accessible across the workforce
Successful AI implementations come with proper guardrails. Industrial AI affects the bottom line directly, so insights need careful consideration and control. Security concerns matter too, as business information must remain protected rather than exposed to open internet models.
Trust also plays a role, from confidence in data quality to the ability to act on insights and recommendations. Agentic AI, autonomous digital workers designed to plan and execute tasks independently, raises the stakes even more as they require organizations to trust in systems acting on their behalf.
How IoT Enables True Servitization
As Mike shared: “True servitization relies on monitoring and IoT because, otherwise, how can you complete the outcome if you’re not actually in control of what your devices are doing?” Without real-time visibility into equipment performance, delivering guaranteed outcomes becomes challenging.
Monitoring equipment continuously, often lets companies detect faults before customers notice them or they lead to failures, and at times, they can even be fixed remotely or bounced back to customers for simple fixes before real issues arise.
The data collected creates a rich pool of information such as messages, alerts, heartbeats or fault patterns across different locations, usage profiles etc. Combined with other data like parts consumption, it offers a strong foundation for machine learning models.
However, not all data proves equally valuable. Effective AI requires understanding the engineering context. For example, if a device is designed to flush alert messages after rebooting, naive analysis might conclude that reboots cause alerts. Teaching systems to understand design intentions prevents such hallucinations.
The real power emerges when this data flows back to product development. Instead of just fixing known issues repeatedly, manufacturers can identify root causes and correct them at the source. This creates a closed loop from design to service and back again, continuously improving both products and service delivery.
Sustainability as Competitive Advantage
Sustainability has moved from corporate responsibility checkbox to strategic imperative. An impressive 97% of survey respondents view sustainability as strategically important.
This shift reflects both regulatory pressure and genuine cultural change. Employees and customers increasingly demand sustainable practices.
Service is inherently sustainable as the focus shifts to running existing assets better, faster, and longer. Rather than replacing equipment prematurely, service-oriented companies maximize asset lifecycles through:
- Robust repair and refurbishment programs
- Lifecycle planning that optimizes when to replace versus maintain
- Early procurement of components as products near obsolescence
- Design choices that enable multiple repair cycles
Leading service organizations take pride in this circular economy approach. Technicians don’t just repair devices, they refurbish them to near-new condition before returning them to service. This mindset shift from transactional product sales to lifecycle management delivers both environmental and financial benefits.
The aerospace industry provides a compelling example. By analyzing flight data including thrust levels and operational parameters, manufacturers can extend maintenance intervals. Aircraft spend less time in hangars and more time flying. Everyone wins: the manufacturer, the airline, and passengers. The environmental impact of reduced maintenance activity adds another layer of benefit.
Supply Chain Disruption as the New Normal
Supply chain volatility has become a defining characteristic of modern manufacturing. A staggering 95% of companies experienced supply chain disruptions in the past 12 months alone.
Manufacturers thrive on consistency: knowing what’s coming, when it will arrive, where it’s sourced from, and at what cost. When these variables become unstable, for example due to recent tariff volatility, planning becomes significantly more complex.
The traditional just-in-time manufacturing model has increasingly given way to a just-in-case approach. Manufacturers now buffer inventory more heavily, accepting higher carrying costs to ensure availability.
Service organizations face an even greater challenge. Unlike production lines with predictable part requirements, service operations must forecast demand without knowing exactly when failures will occur. This requires sophisticated forecasting tools, often AI-powered, to balance inventory costs against service level commitments.
The Strategy Shift: Dual Sourcing and Nearshoring
To manage supply chain risk, manufacturers are increasingly adopting dual sourcing strategies. They maintain relationships with low-cost global suppliers while also developing alternative sources closer to home.
This nearshoring, or “friend-shoring,” approach reflects a growing recognition that the lowest-cost option is not always the most resilient. Having a reliable alternative close at hand provides critical flexibility when disruptions occur.
For service operations, spare parts management becomes a continuous optimization challenge. Holding too much inventory erodes profitability, while holding too little risks missed service commitments. Striking the right balance requires:
- Predictive analytics on expected failure rates
- An understanding of how major events affect usage patterns
- Strategic positioning of parts across service territories
- Procurement planning that accounts for product obsolescence and next-generation requirements
This balancing act never stops. Data-driven insights reveal patterns that enable tighter inventory management while maintaining service levels.
The Workforce Challenge
An overwhelming 98% of manufacturers report labor shortages or skills gaps.
The challenge has multiple dimensions. Many experienced field engineers are approaching retirement, taking decades of hard-earned knowledge with them. At the same time, manufacturing often struggles with perception issues among younger generations, frequently viewed as repetitive work involving long hours and rigid schedules.
Younger workers also grow up in digital-first environments, with expectations for modern tools and flexible ways of working. This shift is pushing manufacturers to rethink how they attract, enable, and retain talent.
Several strategies show promise.
Reframing the work from manufacturing to problem-solving can help reposition service roles. Younger generations often thrive in creative, analytical environments, and positioning service work as solving complex challenges rather than turning wrenches can broaden its appeal.
Modern mobile tools also play a critical role. AI-powered copilots can guide technicians through diagnostics step by step or automatically capture work notes. When devices connect directly to equipment for sensor data and real-time insights, work becomes more engaging and less administrative.
Knowledge management is equally important. When workers need guidance, they often turn to video. Creating searchable libraries of expert-led, video-based instructions meets workers where they already seek information.
Robust apprenticeship programs further strengthen workforce development. Programs that extend beyond basic training to include technical education, college partnerships, mentoring, and clear career progression help build long-term capability. In many cases, graduates become advocates and leaders, as seen in successful programs like Cubic’s.
Expanding the Talent Pool
Beyond traditional hiring, some manufacturers are exploring alternative labor models. The gig economy can provide access to students during holidays or individuals seeking flexible work aligned with family commitments.
Making this approach viable requires thoughtful preparation. Work must be broken into manageable tasks, knowledge systems must support less experienced workers, and products need sufficient modularity to avoid overwhelming complexity. Scheduling also needs to flex to accommodate these models.
When these elements align, manufacturers can access talent pools that were previously out of reach.
Leveraging Senior Expertise Differently
The aging workforce presents opportunities alongside challenges. Experienced technicians with deep knowledge can transition into remote expert roles.
Rather than visiting five or six customer sites daily, these experts can support many junior colleagues remotely. This model offers multiple benefits:
- Democratizes expertise across the organization
- Enables hybrid work arrangements with reduced travel
- Extends productive careers for workers who may struggle with physical field demands
- Accelerates learning for less experienced team members
This approach transforms a potential loss of knowledge into a multiplier effect.
The Predictive Maintenance Opportunity
Predictive maintenance represents a clear value proposition for industrial AI. The data exists. The algorithms work. The business case is proven. Yet adoption still lags.
Despite widespread discussion around IoT, AI, and outcome-based service, only 21% of manufacturers have truly adopted predictive maintenance.
Organizations that successfully implement it see meaningful gains, from reduced unplanned downtime and optimized resource deployment to lower inventory carrying costs and improved customer satisfaction. These advantages help them stay competitive in increasingly complex operating environments.
For the 79% still behind, predictive maintenance represents a powerful opportunity to move from reactive operations to proactive service.
Regional Variations in Adoption
While global insights show clear momentum in service transformation, regional differences reveal how priorities and maturity vary across markets.
The Nordics lead in AI integration and real-time emissions tracking, reflecting advanced digital maturity. Japan stands out for servitization maturity, with the highest proportion of manufacturers viewing service as strategic and prioritizing the Chief Service Officer role. The UK shows strong adoption of service technologies but lags in AI integration, while the Middle East leads in resilience and sustainability focus. DACH organizations face growing pressure to evolve, driven by high adoption of technologies that support distributed workforces and service scalability.
The Path Forward
Service transformation in manufacturing isn’t optional. It’s an imperative driven by customer expectations, competitive pressure, and economic reality.
The journey involves interconnected challenges:
- Shifting from product-centric to outcome-focused business models
- Transforming organizational structures and creating senior service leadership
- Redesigning products specifically for serviceability
- Implementing IoT infrastructure to enable monitoring and data collection
- Adopting AI with appropriate governance and guardrails
- Building sustainable practices into core operations
- Managing increasingly volatile supply chains
- Attracting, developing, and retaining skilled workforces
- Moving from preventative to predictive maintenance approaches
No manufacturer will solve all these challenges simultaneously. The key is starting the journey, learning from both successes and failures, and maintaining momentum.
The manufacturers that thrive in 2026 and beyond will be those that recognize service not as a cost center or necessary evil, but as their primary source of differentiation, customer loyalty, and profitable growth.
The transformation is underway. The question isn’t whether to participate, but how quickly and effectively your organization can adapt.
SOURCE: Mark Brewer (2026 January 22) From AI to Outcomes: How Manufacturers Are Transforming Service. IFS Blog. https://blog.ifs.com/from-ai-to-outcomes-how-manufacturers-are-transforming-service
