Automated support powered by AI – First step in Si2’s journey

If you are reading this article, you are probably interested in how AI Consult Group have helped Si2 to automate our customer support.

Call Kaya on +49 611 76039705 to experience the power of AI powered support for yourself!

Tell her the language you want to speak and then ask her a question about Si2. It is as easy as that!

As consultants, we can easily talk about how to engage with new technologies—but let’s be honest: it is far easier to talk than to do. So, we decided to take a very different approach to our new partnership with Nicolas Bartschat, Managing Director of AI Consult Group.

We want to share our experiences through a series of blogs as we go along, so that the Si2 Service Leaders Community can learn with us. The theme I have used for more than 20 years when writing about change starts with a well-known Steve Jobs quote. If you want to test its validity, you can watch him explain it yourself. For me, this very simple piece of advice from a somewhat complex and idiosyncratic genius captures the essence of change for any technology innovation:

“You’ve got to start with the customer experience and work back to the technology—never the other way around.”

So, how did we get to this point?

We first crossed paths with Nicolas at a Si2 Summit—our small, highly focused workshops where professionals roll up their sleeves and go deep on one topic. That session tackled a question many service leaders are wrestling with: how to drive real process automation in service using advanced technologies, often bundled under the label “AI.”

Drawing on his hands-on experience at ASMPT, a global semiconductor equipment and solutions provider, Nicolas guided the group through a practical, no-nonsense checklist. At ASMPT, he had led the integration of AI into customer support, achieving impressive levels of automation. The chemistry with Si2 was immediate, and when Nicolas later founded AI Consult—dedicated to turning AI complexity into tangible business results—we naturally began exploring how our network could help bring these solutions to a wider audience.

The first time Nicolas showed us the technologies he works with, the reaction was instant excitement. Ideas started flying about who needed to see this—and it quickly became clear that talking about it wouldn’t be enough. We needed to show it in action.

And just like that, we committed the classic first mistake: thinking about the technology before fully understanding the value.

LESSON 1: Start with the Customer Experience

At Si2, were we going to make a lot of money from this arrangement? Was this really where the value lay for us? Or was the value in experiencing the technology ourselves and being able to talk about it in a way that adds more value for our customers?

When we really sat down and reflected, we realised the greatest value of this technology was actually in our own business.

Si2 is a small consulting firm with very lean overheads—no expensive offices or personal assistants. We have a website, a lot of knowledge, and deep experience. Most importantly, our sales process relies on helping clients get a real sense of who we are and how our insights and experience can deliver impact.

The more interactive we can make our website experience, the better. We also know that humans are social animals—we like to talk. So we asked ourselves: what if people could talk with our website in a way that adds value in real time, in an easy and engaging way?

With machine learning and natural language processing (NLP) technologies, there are now many applications that can analyse data and learn how to respond. Just try it on your own website with ChatGPT.

So, we decided to build a simple tool to see whether we could generate more enquiries by making it easier for visitors to explore their needs and engage with us to understand the options we can offer. In effect, we wanted to automate the first part of the classic pains-and-gains discussion.

That is exactly what this demonstrator is designed to do. We know it is far from perfect and has many imperfections—but it is a first step in the journey.

If you have read this far, we assume you have tried the demo. We would really appreciate it if you could send us your comments via our standard contact form. This will allow us to follow up and understand whether there is real value in this approach and whether we should proceed with fully integrating the solution into our lead-generation process.

Around March 2026, you can expect the next instalment of our story.

Contact us to know more

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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.