Coen Jeukens is vice president of global customer transformation at ServiceMax. He works with customers and prospects to fully unlock the true value and potential of their service organizations. Prior to joining ServiceMax, Coen was the services contract director at Bosch where he implemented an outcome-based business model, with highly impressive results. Coen is also a regular keynote speaker at prominent field service conferences around the globe.
This is the second blog in a two-part series. For reference, read our previous blog: When Engineering and Service Don’t See the Same Asset
To pick up where we left off, let’s revisit our previous example. The company from the previous blog has four product lines and a $6 billion installed base. We know that engineering information makes it downstream to service, and product quality processes provide a connection back to engineering. Each function has the data it needs—but no shared view of the product over time.
Now, consider a rise in service costs for one product line. Service teams are reporting longer repair times and higher parts consumption. Engineering wants to understand if it was a design change contributing to the issue. Finance wants to know whether a certain configuration is eroding margin. Both lines of questioning could point to an answer.
Instead of tracing products back through multiple systems and handing questions to analysts, what if these business leaders had a dedicated system that unified product information across engineering, manufacturing, sales and service? Each physical asset in the field would be represented as an evolving digital record. These records would capture design intent from engineering, as-built configurations from manufacturing, sales and contract details, and service history from the field.
The idea of connected data across systems isn’t new, but a purpose-built system for monitoring the evolving lifecycle of products is. And it’s made possible with today’s technologies.
AI-assisted data mapping allows for assets and their components, subassemblies and configurations to be identified across systems when naming conventions differ, and part numbers change over time.
Version- and time-aware asset records track how each asset changes — building a chronological view from original design intent to as-built configuration and in-field modifications.
AI-enabled context via semantic layers for each team allows service, quality, engineering and other team members to ask questions in their own business language—such as which design revisions in ‘xyz’ product have more than 3 service visits associated to them in the first year of installation—without needing to know where the data lives or how it’s structured.
Vectorized asset representations convert each physical asset into a structured data profile. It allows asset records to be stored for quick comparisons—instead of saving a record of a pump as a static record with attributes; the system represents each pump as a living profile over time.
Figure 1: AI-first Asset Data solution with function-specific applications
Applying these technologies to close the gap between data and decision-making at scale is possible, and products are well within sight. While they will be in a category of their own, we believe their objective is clear—develop an AI-first Asset Data solution to power a foundation of continuous information that connects design and engineering to sales and service for a giant leap forward in bringing products to market.
From insight to action across the lifecycle
So, what if our example company had an AI-first Asset Data solution to work from? Their issue of rising service costs and the reasons behind them become clear, faster. Engineering, service, and finance are now working from the same evolving asset records, instead of debating which system holds the “right” data.
Engineering starts by isolating the design revision in question. Within minutes, they can see that revision across the installed base — which configurations it impacts, where those assets are installed, and their current health status in the field. Service data shows a clear pattern: assets built with the revised component experience longer repair times and higher parts consumption within the first year of operation. Finance overlays margin data and confirms that the same configuration is driving higher service cost and eroding lifecycle profitability.
What previously required weeks of analysis and reconciliation now happens in a single, shared view.
Armed with insight, engineering can make a confident decision whether it’s a design-related or service-related issue. For this example, they adjust the design to address the failure mode, update service guidance for assets already in the field, and work with manufacturing to phase the change into future builds. At the same time, service teams proactively identify at-risk assets, and product leaders understand the financial impact of both action and inaction across the installed base.
The real shift is not speed, but confidence and precision. Engineering teams are no longer reacting to issues long after they appear or debating decisions based on partial views of the truth. They can see—clearly and early—how design choices play out across the installed base, in real operating conditions, and at scale. That changes how products are improved, how risks are managed, and how innovation moves forward.
To bring our example to a close, our company is preparing to extend its fourth product line to the next big market. They no longer question whether a fast growing $6 billion installed base can be managed; they’re making decisions on how to compete with it. With an AI-first Asset Data foundation, scale no longer obscures insight. It amplifies it.