What Manufacturers Get Wrong About AI and Servitization

Success stories about AI-driven servitization in manufacturing rarely mention the integration challenges, unclear ROI, wasted resources, and pilots that never scale. Yet these obstacles define reality for most organizations more accurately than polished case studies do.

Statistics show that fewer than 30% of AI projects ever reach production, regardless of the industry. However, the problem isn’t in technology but in approach. The study exposes a pattern: companies rush to implement AI while ignoring the foundational work that actually makes transformation possible.

Most failures stem from a gap between what manufacturers envision and what proves achievable. Executives become drawn to the promise of digital service revenue and see AI as a locomotive that would immediately take them to an outcome-based model. But when it’s time to execute the initiative, they face real-world complications that weren’t visible upfront.

This is where things get tricky. Data from products, customer portals, CRM, ERP, and MES don’t align. Service teams can’t access the insights they need. Finance lacks a clear revenue model. AI teams build pilots that never integrate into the systems. And everything stalls.

The manufacturers who succeeded in turning AI and servitization into measurable ROI weren’t just lucky. They approached transformation differently, taking time to understand operational realities. The key is to know which traps to avoid. Let’s look at where most initiatives go wrong and what we can do differently.

Oversight #1: Treating AI and Servitization as Separate Initiatives

Many manufacturers implement AI apart from their servitization initiatives: one team runs a predictive maintenance pilot, another explores new service contracts.  When they work in isolation, the maintenance team can’t prove ROI without new service revenue, and the service team can’t price contracts without knowing failure patterns. As a result, businesses get two half-built solutions that create more complexity than value.

In contrast, the companies that figured this out early invest in creating a solid foundation and unified strategies for both initiatives. They build a Connected Product Enablement (CPE) platform — a single place where IoT telemetry, customer portals, and analytics work together. The platform allows AI to pull from real operation data, while service managers use the same data to shape uptime guarantees, dynamic pricing, or even new business models. This way, AI initiatives boost servitization outcomes while driving ROI and delivering sustainable value for the company.

Oversight #2: Ignoring Data Readiness

Today, manufacturers collect more data than ever, but without proper governance, it remains unclear how to use it effectively or extract actionable insights. Connected devices stream data in several pipelines: ERP manages finance, and service platforms track warranties, MES (Manufacturing Execution System) handles production metrics, each in a different structure. Even though it used to work well enough, it has also created data silos that now block AI initiatives.

AI needs to see the chain of cause and effect to deliver valuable insights. When maintenance logs, production data, and customer feedback live in isolation, algorithms can’t learn the patterns that truly drive downtime, quality loss, or service demand. As a result, predictive models become unreliable, insights remain partial, and executives lose confidence in AI value.

This is why data readiness is not a CTO’s whim, but a prerequisite for scaling successful AI pilots. Although it might take time and resources, the only way to succeed with AI is to have data governance practices in place. In a nutshell, that means:

• Establishing a single Master Data Management (MDM) system that connects assets, components, and service records.
• Defining data governance rules for ownership, quality, and update.
• Creating a scalable data infrastructure that will ensure interoperability between MES, ERP, IIoT, and CRM systems so none of all operational events, from sensor readings to warranty claims, remain unused.

The payoff is a reliable data-driven solution that delivers faster diagnostics, higher uptime, and rising customer satisfaction. This builds trust not only with clients but across your organizational teams, which can see the full potential of an AI project.

Oversight #3: No Clear Monetization Model

Even when data and AI are in place, many servitization programs may stall when pricing logic and value realization are undefined. Many businesses operate under the assumption that if they add digital functionality, customers will automatically pay for the “added value.” In practice, they won’t. At least without a clear value proposition and pricing structure.

Thus, any AI and servitization initiative will succeed when organizations have a clear strategy for how to work with their revenue logic. This requires choosing and building one of several proven pricing models:

However, pricing is not just about revenue capture, it’s a signal of your business model transformation. With a well-defined pricing model, a company demonstrates commitment to customer outcomes and differentiates itself from competitors who are still selling products alone.

Oversight #4: Failing to Align Internal Stakeholders

Adoption of AI and servitization initiatives is not about adding new technology but about changing how your entire organization works. This is why cross-functional alignment matters, requiring sales, finance, engineering, IT, and all other teams to work in sync.

The goal is to shift mindsets from product-centric to outcome-centric thinking, raising awareness about new initiatives in your organization. This spans recurring training, workshops, and championship programs around AI and data literacy to ensure everyone understands how their role contributes to business success.

However, awareness alone isn’t enough. It has to be paired with a well-established process to support cross-departmental collaboration. This way, teams can discuss insights from the field, share challenges, and join in their efforts to solve issues.

Solution: A Structured Approach from Design to Scaled ROI

The challenges outlined above are common, but they’re not inevitable. With a structured approach, organizations can navigate these pitfalls step-by-step. Success comes down to three fundamentals: knowing where you’re headed, being patient with the process, and learning continuously along the way.

Here’s a practical roadmap to guide your transformation journey and help you address challenges as they arise:

To Sum Up

AI is a powerful tool to boost your servitization efforts. Yet it goes beyond just technology implementation. Successful use cases show that when AI initiatives are treated as a part of a business transformation plan and broader strategy, they have a much higher chance of succeeding.

However, you don’t have to rush your transformation. It makes little sense to adopt innovation when your foundation is not ready. Begin with vision and alignment across people and processes, defining an approach to monetization, building data infrastructure, and only further move on with AI.

If these challenges sound familiar and you’re looking for a way forward, contact our team. Our experts will guide you through the process and assist you in overcoming challenges in AI and servitization adoption.

• #AI/ML
• #Manufacturing