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:

  1. Mindset– Focus on software solutions, not AI buzzwords
  2. Value– Clearly define value and let the solutions follow
  3. Vision– Inspire with vivid, detailed future states
  4. Leadership– Listen deeply and enable innovation
 

By the end of this article, you will understand these practitioner-derived principles and be able to:

  • Develop a compelling vision for the use of AI in customer support—one that inspires both your team and your customers
  • Apply a simple five-step adoption process to identify high-value AI-based applications
  • Assess your organization against the key enablers for successfully implementing data- or knowledge-driven processes

1. The Mindset Shift: It’s Not About AI, It’s About Solving Problems

Too often, discussions around AI begin with the technology: algorithms, neural networks, data lakes, and dashboards. But the summit made one thing clear: AI should not be the starting point. Instead, we must frame our approach around software-enabled solutions—some of which may include advanced analytics or AI capabilities, and some of which may not. This was a key insight that Nicolas Bartschat brought from his personal experiences of introducing the idea of and then implementing AI applications to automate Customer Support process in ASMPT (a leading equipment manufacture of pick & place solutions for electronics manufacturing), as well as through numerous projects with AI Consult Group

Think in Terms of Software Solutions, Not AI Buzzwords

If we fixate too early on AI as the solution, we risk ignoring the problem we’re trying to solve. As service professionals, the aim is not to use AI for its own sake but to improve customer experiences, optimize support operations, and enable better decision-making. That might involve AI, or it might simply require a smarter integration of existing systems.

A better mindset is to approach AI as a possibility space—an area to explore potential solutions, not a foregone conclusion. By thinking of AI as part of a broader toolbox, we open up more paths to innovation and avoid technology-driven blind spots.

Curiosity Over Perfection: Embrace a Learning Culture

AI thrives in environments that are curious, experimental, and open to failure. At the summit, this was described as an attitude shift from perfection to exploration. Creating sandboxes—safe spaces where teams can experiment with AI applications—allows people to learn without the pressure of immediate results.

Mistakes are part of the process. Rather than penalizing early failures, organizations should embrace them as learning opportunities. This attitude encourages innovation and accelerates understanding of how AI can be applied in meaningful ways. From a Leadership perspective it is critical our organisation understand  when we are working in this Discovery space as opposed to a Delivery space to ensure realistic expectations are set for the outcomes envisioned. Other wise the credibility of the use of advanced analytics and most importantly the mindset that lies behind their success, is under mined.

Democratize AI: Make It a Team Sport

Another critical mindset shift is moving AI from a ‘watched sport’ to a ‘mass participation’ one. That means building basic data literacy across your workforce—not just among data scientists and IT teams. Everyone, from customer service reps to line managers, should understand how AI applications can support their roles and where it fits into the broader service strategy.

This democratization includes training, sensitization to data ethics, and clear communication about how data & technology will be used. Broad engagement across departments surfaces more use cases and avoids the common pitfall of AI efforts being siloed in innovation labs or tech departments.

2. The Value Imperative: Don’t Start With the Tech—Start With Value

Organizations often rush into Software (AI) projects with excitement, only to be disappointed when those projects fail to scale or deliver ROI. One of the summit’s strongest takeaways was this: value must be defined before solutions are selected. That means getting very clear on the outcomes you want to achieve.

From Discovery to Precision: Two Routes to Value

Through discussions with practitioners we settled on two paths to identifying value in AI initiatives:

  1. The Discovery Path – This is the “sandbox” or playground model where employees experiment with AI tools to discover potential value. It’s flexible and educational, helping teams see how AI could improve their work. This path is especially useful in the early stages of AI adoption, as it generates organic interest and surfaces real-world use cases.

  2. The Structured Path – Here, organizations follow a formal AI adoption process:
    1. Requirements Analysis: What problem are we solving? What are the constraints?
    2. Tool Landscape Review: What tools are out there?
    3. Evaluation & Selection: Which tools best meet our needs?
    4. Implementation: Who owns this and how will it be managed?
    5. Onboarding: How do we train people and integrate AI into workflows?

This structured approach helps ensure that software projects are grounded in business value and not just technological fascination.

Value as a Design Principle

The key is to embed value into every step of your AI journey. When you begin with value—in terms of cost reduction, improved service quality, customer satisfaction, or employee productivity—the right solutions tend to “shake out” naturally. You avoid the trap of implementing technology for its own sake and instead ensure that every initiative contributes to strategic goals.

3. The Power of Vision: Making the Future Vivid

Where mindset and value help orient teams in the present, vision inspires them for the future. But not just any vision—a vivid vision. At the summit, the importance of a vivid, shared picture of the future was emphasized as a central lever for change.

From Intangible to Tangible: Vision as a Catalyst

Data and AI can feel abstract or even threatening. To bridge that gap, leaders must create a concrete and emotionally resonant picture of the future—one that answers not just “What are we doing?” but “Why does this matter to me?”

A vivid vision helps overcome resistance to change by making the destination real and desirable. It helps people see the benefits, understand the journey, and emotionally invest in the outcome.

Crafting a Vivid Vision: A Practical Framework

Creating a vivid vision involves three steps:

  1. Jump Forward in Time – Imagine your service business three years from now. You’ve already succeeded. What do you see? What do you hear, feel, and observe? Who is collaborating? What’s different?

  2. Build a Story – Create a compelling narrative that your team can believe in. Describe how the business operates, how customers feel, and what role technology plays.

  3. Look Back to Today – Identify the biggest challenges you’ve overcome. How did you do it? This helps teams relate current actions to future outcomes.

The outcome of this process should be two fold:

  1. An Inspiring strapline: A short snappy sentence that is easily remember yet embodies the outcome of what our customers feel or desire. An example might be from my old company Husky, where for the best part of 25 years the vision was “Keeping Customers in the Lead” by producing solution that were faster, better and more consistent than the competition.

  2. Painting the picture: But to really engage people, this outcome has to be painted in vivid emotions and feeling for your people. Eva Kunczicky shared a wonderfully simple but effective which she has used as a service leader at companies such as Hilti, Kardex and now as a leadership coach: a simple Service Vision Mindmap with the key branches being the specific factors which our target audience can relate to an understand.

For example the phrase “keeping customers in the lead” might mean for the “customer’s perception of service” that they can ask questions and get answers 24/7. That they feel emotionally supported by us no matter how complex the problem or we will support them through the lifecycle of the product even if some parts are obsolete……

This is the detail that makes a vision ‘vivid’!

You can imagine that this technique does more than inspire—it shapes strategy. For the use of AI applications it turns mathematical algorithms and techniques from an abstract trend into a concrete enabler of a better future.

Why It Works

A vivid vision:

  • Provides strategic direction without micromanaging.
  • Aligns people around a shared purpose.
  • Builds an emotional connection between employees and the business.
  • Creates momentum by focusing energy on a compelling future state.


And most importantly, it invites collaboration. Vision is not a solo activity—it’s a social one. The more people are involved in shaping it, the more committed they are to realizing it.

4. Leadership: Enabling Innovation Through Listening and Structure

In the end, no AI project succeeds without leadership. But not leadership in the traditional, command-and-control sense. The summit participants described a new kind of leadership: one grounded in listening, enabling, and orchestrating systems for success.

The Four Enablers of Data-Driven Leadership

To truly lead with data, leaders must ensure four interdependent enablers are in place:

  1. Purpose – Why are we doing this? What strategic, operational, or customer outcomes are we trying to achieve? Purpose provides clarity, focus, and alignment. Projects without purpose often drift or fail.
  2. Data Architecture – What data do we need? Is it structured or unstructured? Can we collect and analyze it sustainably? Understanding the data landscape is essential before jumping into tool selection.
  3. Processes & Tools – How do data and AI integrate into daily workflows? Do our tools support decisions or just generate dashboards? Are they embedded in the service process or bolted on as afterthoughts?
  4. People – Are our employees engaged in this journey? Do they have the skills, mindset, and motivation to participate? Are we building a culture of sharing, experimentation, and continuous improvement?

Each of these components must be consciously designed. Overlooking even one can derail the entire effort.

Leadership as Enabler, Not Commander

The best leaders are listeners first. They tune into frontline insights, customer pain points, and team hesitations. They create psychological safety, where people can experiment, learn, and grow without fear of failure.

Then they enable execution by putting the right systems in place—governance, training, incentives, and support. In this model, the leader isn’t the hero; the organization is. The leader’s job is to make sure the conditions for success exist.

Conclusion: It’s Not About AI. It’s About Ambition, Clarity, and Connection.

If there’s one overarching lesson from the Si2 Summit, it’s this: Leading with AI isn’t about the tech. It’s about the thinking. The mindset we bring, the value we seek, the vision we share, and the leadership we provide are what determine success—not the cleverness of our algorithms or the flashiness of our tools.

So the next time someone says, “How are you using AI?”—don’t start by listing platforms or use cases. Start by saying:

  • We’re focused on outcomes, not tools.
  • We define value before we deploy.
  • We paint a vivid picture of the future and help people see themselves in it.
  • And we lead by listening, enabling, and building systems that empower everyone.

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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.