Author: Nick Frank

Making “Trusted Advisor” Real

For years, “Trusted Advisor” has been the go-to phrase whenever service leaders describe the relationship they want with customers. It’s used in sales kick-offs, service strategy decks, and job descriptions for everyone from field engineers to key account managers. The trouble is that the more a phrase gets used, the less it tends to mean. Ask ten people in your business what a trusted advisor actually does differently, day to day, and you’ll likely get ten different answers — usually some version of “being really good at customer service.”
That ambiguity is a problem, because for suppliers of industrial equipment, components and solutions, the trusted advisor relationship isn’t a nice-to-have. It’s the difference between being treated as a replaceable vendor and being the partner customers call before they’ve even finished defining the problem. So it’s worth being precise. We define a trusted advisor as:
A service professional whose relationship with a client goes beyond basic transactional expertise. They act as a long-term confidant, providing holistic guidance to help customers make critical, informed decisions — operational or strategic. The goal, through that relationship, is to add more value to both parties through collaboration.
Two words matter most in that definition: “both parties.” This isn’t altruism, and it isn’t really about being liked. Done properly, it’s a commercial discipline that benefits the customer’s operation and your order book at the same time. This article explores how that plays out in practice — and what it actually takes to build it into an organisation rather than just hope for it.

Leading with AI: A Blueprint for Service Innovation in Customer Support

At the recent Si2 Summit, ten service professionals came together to explore a pressing question: How do we lead with AI in the automation of customer support? The discussion was rooted in practical experience, facilitated by Si2 and enriched by expert contributions from Eva Kunczicky and Nicholas Bartschat. From this in-depth exchange emerged four guiding principles that any organization can use to meaningfully integrate AI into its service strategy:
• Mindset – Focus on software solutions, not AI buzzwords
• Value – Clearly define value and let the solutions follow
• Vision – Inspire with vivid, detailed future states
• Leadership – Listen deeply and enable innovation
By the end of this article, you will understand these practitioner-derived principles and be able to:
1. Develop a compelling vision for the use of AI in customer support—one that inspires both your team and your customers
2. Apply a simple five-step adoption process to identify high-value AI-based applications
3. Assess your organization against the key enablers for successfully implementing data- or knowledge-driven processes

Indeed this was a deep dive into the challenge of getting started, with industry experts Eva Kunczicky and Nicolas Bartschat acting as catalysts for the discussion.

Jumping to Success

We had great fun and learning at the Si2 Summit on the 20th where a small group of service professionals got to grips with how to “jump” to a vivid vision for using AI tools within their organisation. We went into depth about the process to understand and identify how AI tools can add value to the organisation, where to start and who is needed.

Indeed this was a deep dive into the challenge of getting started, with industry experts Eva Kunczicky and Nicolas Bartschat acting as catalysts for the discussion.

Educating the Service Leaders of tomorrow

This week, Nick Frank from Si2 spent 2 days with students from Cranfield University’s MSc in Sustainable Through-Life Engineering Services. These Operational, Logistics and Design professionals from the UK’s leading Engineering companies together with UK Ministry of Defence staff, want to upgrade their skills and capabilities for implementing Servitised business models in industries with complex, […]

The Road to High-Performing Service Teams

Every Service Director and Service VP wants a high-performing team—one that executes reliably, collaborates cross-functionally, and drives continuous improvement even under pressure. But in industrial markets, where service organisations sit at the intersection of engineering, commercial, and operational realities, high performance rarely emerges spontaneously.
Leaders love to claim they have created high-performing teams, but few truly understand the underlying mechanics that make sustained high performance possible.
Fortunately, decades of organisational research give us a clear blueprint on what leaders can do to create the environment for performance through processes, culture and metrics. When combined with modern insights into human performance and energy management, in other words what enables individuals to thrive rather than burnout, we can begin to offer service leaders a practical framework for creating predictable, resilient, high-performing teams.

Si2 at FSMTALKS Forum

Nick Frank will be speaking at the FSMTALKS  Forum in Kaunas, Lithuania in November on Customer Value. In particular how to use the Customer Value Iceberg Concept to discover new opportunities for servistisation and digitalisation. He will also run 2 roundtables where participants will have the opportunity to explore their iceberg.

Making your SERVICE Data Work For You

We’re hosting another SLN Summit on June 5th, where we’ll explore with Smith Detection their journey of turning unstructured data into structured intelligence and actionable insights. This won’t be an ordinary event; the audience will be small, perhaps 10-15 experienced service professionals, who will have the opportunity to delve deeply into the application over a full day.

Service in Industry

Deep Dive into the industrial service business.

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Service Innovation for value-driven opportunities:

Facilitated by Professor Mairi McIntyre from the University of Warwick, the workshop explored service innovation processes that help us understand what makes our customers successful.

In particular, the Customer Value Iceberg principle goes beyond the typical Total Cost of Ownership view of the equipment world and explores how that equipment impacts the success of the business. It forces us to consider not only direct costs associated with usage of the equipment such but also indirect costs such as working capital and risks.

As an example, we looked at how MAN Truck UK used this method to develop services that went beyond the prevailing repairs, parts and maintenance to methods (through telematics and clever analytics) to monitor and improve the performance and  fuel consumption of their trucks. This approach helped grow their business by an order of magnitude over a number of years.

Mining Service Management Data to improve performance

We then took a deep dive into how Endress + Hauser have developed applications that can mine Service Management data to improve service performance:  

Thomas Fricke (Service Manager) and Enrico De Stasio (Head of Corporate Quality & Lean) facilitated a 3 hour discussion on their journey from idea to a real working application integrated into their Service processes. These were the key learning points that emerged:

Leadership

In 2018 the Senior leadership concluded that to stay competitive they needed to do far more to consolidate their global service data into a “data lake’ that could be used to improve their own service processes and bring more value to customers. As a company they had already seen the value of organising data as over the past 20 years for every new system they already had a “digital twin” which held electronically all the data for that system in an organised fashion. Initially, it was basic Bill of Material data, but has since grown in sophistication. So a good start but they needed to go further, and the leadership team committed resources to do this.

  • The first try: The project initially focused on collecting and organising data from its global service operations into a data lake.  This first phase required the development of infrastructure, processes and applications that could analyse service report data and turn it into actionable intelligence. The initial goal was to make internal processes more efficient, and so improve the customer experience. E+H looked for patterns in the reports of service engineers that could:
    • Be used to improve the performance of Service through processes and individuals
    • Be used by other groups such as engineering to improve and enhance product quality.
  • Outcome: Eventhough progress was made in many areas, nevertheless, even using advanced statistical methods, they could not extract or deliver the value they had hoped   for from the data. They needed to look at something different.
  • Leveraging AI technologies: The Endress+Hauser team knew they needed to look for patterns in large data sets. They had the knowledge that self-learning technologies that are frequently termed as AI, could potentially help solve this problem. They teamed up with a local university and created a project to develop a ‘Proof of Concept’. This helped the project gain traction as the potential of the application they had created started to emerge. It was not an easy journey and required “courage to trust the outcomes, see them fail and then learn from the process”. However after about 18 months they were able to integrate the application into their normal working processes where every day they scan the service reports from around the world in different languages to identify common patterns in product problems, or anomalies in the local service team activities. This information is fed back to the appropriate service teams for action. The application also acts as a central hub where anyone in the organisation can access and interrogate service report data to improve performance and develop new value propositions.
  • Improvement:  The project does not stop there. It is now embedded in the service operations and used as a basic tool for continuous improvement. In effect, this has shifted the whole organization to be more aware of the value of their data.

Utilizing AI in B2B services

Regarding AI, our task was to uncover some of the myths and benefits for service businesses and the first task was to agree on what we really mean by AI among the participants. It took time, but we discovered that there are really two interpretations which makes the term rather confusing. The first is a generic term used by visionaries and AI professionals to describe a world of intelligent machines and applications. Important at a social & macroeconomic level, but perhaps not so useful for business operations -at least at a practical level. The second is an umbrella term for a group of technologies that are good at finding patterns in large data sets (machine learning, neural networks, big data, computer vision), that can interface with human beings (Natural Language Processing) and that mimic human intelligence through being based on self-learning algorithms. Understanding this second definition and how these technologies can be used to overcome real business challenges is where the immediate value of AI sits for today’s businesses. It was also clear that the implication of integrating these technologies into business processes will require leaders to look at the change management challenges for their teams and customers.

To understand options for moving ahead at a practical level we first looked briefly at Husky through an interview with CIO Jean-Christophe Wiltz to CIOnet where we learned that i) real business needs should tailored drive technology implementation, and ii) that before getting to AI technologies, there is a need to build the appropriate infrastructure in terms of database and data collection, and, most importantly, the need to be prepared to continually adapt this infrastructure as the business needs change.