Successfully Monetizing IoT Services with doubleSlash

Today on the IoT Use Case Podcast we’re talking about three stages of a holistic IoT approach – from machine data all the way to making money: Connect, Make Smart, Monetize. Joining us from doubleSlash are Jonas Kaltenbach, IT Consultant and Product Owner IoT, and Marina Rast, IoT Sales and Partner Manager. And for anyone who has ever wondered how much digital products weigh – you’ll get the answer here. Enjoy the episode.
I’m your podcast co-host, Dr. Peter Schopf – feel free to call me Peter. And today we’re three people here in the studio. Before we introduce ourselves in more detail – Jonas, why is this episode worth listening to?

Jonas

We want to show what we have built throughout our history and how our three-part approach Connect, Make Smart, Monetize has evolved from that. We’ll give exciting insights into real projects and show how this approach has grown from Connect all the way to Monetize.

I’m definitely curious to hear more. Marina, from your perspective – what makes this episode exciting?

Marina

IoT is something almost everyone has heard of by now. It’s no longer just a future topic or a “nice to have” you deal with someday. We want to show that IoT can be a true revenue driver. Many companies have connected products today, but they’re not yet using them in a profitable way. That’s exactly where the magic lies for me. IT is no longer just an internal service provider – it can become the engine for new business models. With the right architecture, you create the foundation to build digital services and business models – and generate new revenue streams.

I believe that will be highly interesting and relevant for many listeners. Now of course the question is: Why are we talking about this? What is your role in all of this? Who is doubleSlash and what do you do?

Marina

With pleasure. Maybe first a few words about me: I’m Marina, Sales Consultant at doubleSlash. We are a software and IT service provider based in Friedrichshafen at Lake Constance. That’s also where we were founded in 1999 – so we’ve been around for more than 25 years. We also have offices in Stuttgart and Munich, and in total we are about 300 colleagues driving digitalization in companies. We provide IT design, development and also operations – so we see ourselves as a holistic partner.
We are particularly strong in the mobility sector – OEMs and suppliers – as well as mechanical engineering including medical technology, and we also do quite a bit in the public sector. We cover the entire IoT value chain, what we call our triad Connect, Make Smart, Monetize – and our goal is always to bring these three parts together in real projects.

And how long have you been with doubleSlash?

Marina

I’ve been working here full-time for more than four years, and before that I was already a working student at doubleSlash. So I’ve been working with doubleSlash for quite a while – and doubleSlash has been accompanying me for quite a while as well.

Great. Jonas, let’s talk about you for a moment. How long have you been with doubleSlash?

Jonas

I’ve actually been with doubleSlash a bit longer. After completing my dual studies in Business Informatics, I joined doubleSlash nine years ago as an IT Consultant. That means I’m usually the one in the projects who conceptualizes the topics, acts as the interface between business and development teams, and supports our customers — advising them, driving them forward, and shaping new software together. My focus — and that’s particularly relevant for today — is the IoT domain. When we talk about our triad, I’m the one dealing with Connect and Make Smart, while Marina joins later once things move toward Monetize.

I think it’s great that you take such a holistic approach — really from the shopfloor level with machines and sensors all the way to the point where the euros start flowing. And that’s not easy at all. There are a lot of challenges along the way. Let’s dive into Connect, Jonas. How do you approach this? Do customers come to you with a planned business model and you then determine which sensor data is needed? Or do you start from the machine and existing data, and eventually Marina steps in to drive monetization?

Jonas

First of all, when we talk about Connect, it’s not trivial at all. You might think you just take a device, machine or system and connect it — sounds simple at first, but it isn’t. You have to consider early on what data this device actually generates, and which of that data will later be useful — especially with monetization in mind. Which data creates real value? Which data helps, for example, field service engineers or customer support? These questions belong on the table right from the start.
You also need to check how a service can be deployed on the device so that all data is transmitted consistently and standardized to a platform. These heterogeneous data streams must be collected, structured — and above all — transferred securely to a central location so they can later generate business value. That’s why we always start working with our customers before the actual project kicks off: together with our technical experts we analyze what the true goal is and what they want to achieve. Then we deliberately begin with the Connect phase, discuss the right architecture and then implement the specific use case step by step.

Connect is definitely not easy. I can confirm that from my own project experience. The use case and story behind it are often clear — but the implementation turns out to be significantly more complex than expected. So it’s interesting to understand what drives things early in the process. Is it the concrete application and target vision? Or is it more about the data available and the opportunities arising from that? From your experience — what drives these conversations more strongly?

Jonas

I remember a project at ZEISS Microscopy. We started back in 2018 with the goal of establishing predictive maintenance. We connected the devices quite quickly — within one to two months. It was just one device type out of roughly ten different types in their portfolio. Fairly early on, we realized: Predictive maintenance is not as easy to implement as people often assume. You first need a sufficient history of data — and that data must be accurate, consistent, and standardized. Only then can data science teams develop meaningful machine learning models.
So you need to build the foundation first — set up stable processes, ensure the devices connect securely and reliably to the platform, and that the data is transferred correctly. Only after that can you move toward machine learning. In this project, the foundation was actually established quite early, but it still took roughly two years until predictive maintenance was truly achieved.

[09:11] Challenges, potentials and status quo – This is what the use case looks like in practice

That already takes us to the second step: Make Smart. And that’s not simple either — especially when we talk about AI in industrial environments. Right now, AI is highly hyped, particularly generative AI, which opens up completely new possibilities. But the “classical AI,” where you train a custom model on specific datasets for predictions, has been around for longer — and it has always been challenging to implement because you need the right data, including negative cases or failures, which happen rarely in reliable systems like those from ZEISS. What exactly did you address at ZEISS?

Jonas

At ZEISS, we were working in the microscopy division. Most people know ZEISS as a manufacturer of eyeglass lenses, but here we’re talking about high-tech microscopes — not the small ones from school science lessons. These devices are in the six-figure price range. Our goal was to support the ZEISS service organization so that they could optimize — and ideally predictively deploy — their service operations.
The use case was to analyze machine data using machine learning so that service technicians can act before a failure occurs in the field. They should proactively reach out to customers and say: “Something isn’t running as it should. I’ll come by in two weeks and fix it.” That’s classical predictive maintenance, based on algorithms trained on machine data.
At the same time, generative AI is becoming more and more relevant. ZEISS is making strong progress here. They feed maintenance sessions, instructions, and expert knowledge into generative AI. As a result, even less-experienced staff can ask a chatbot what to do: Which tools and materials are required? Which steps need to be performed? The chatbot can even assess critical faults and provide concrete recommendations. It’s a very efficient evolution of the Make Smart stage in this project.

A great example to clearly distinguish the two worlds: Classical AI — predictive models trained on machine data — and generative AI — enabling knowledge access and guidance. It’s great that you can support both approaches.
But Marina, let’s move over to Monetize. That’s the exciting part — the holistic journey from connectivity all the way to making money with digital offerings. What examples help illustrate this best?

Marina

It’s really fascinating. I often say: Connect and Make Smart are the basic prerequisites to monetize anything at all. You need connected devices — and you need data. Only then do many customers approach us, sometimes with a fully formed business model, sometimes just with a vision of what they would like to monetize. That could be a remote service for a machine, for example. But there are many other scenarios.
We recently had a project with EWE Go, which some may know as an e-mobility provider. The machines were already connected, digital offerings existed, data could be read out. But the IT architecture wasn’t scalable. For billing, several software components were missing to reliably handle the growing data volume. So we started with a vendor evaluation. We assessed which products exist in the market and can be integrated into the customer’s existing architecture. That’s highly individual — it differs from one customer to another.
And it is extremely important to bring the right stakeholders to the table and capture all requirements cleanly. We interact with very different roles: product management, sales, or IT when systems reach their limits. We speak with all departments so that we can recommend — and integrate — a system that truly fits. That’s a classic example within the Monetize phase.
Another example is digital services in vehicles. That’s something many of us already know from our private lives. With newer cars, you can book additional functions like driver assistance systems, entertainment, or comfort features. In one of our projects, we handle the entire IT behind subscription and billing for such services — all the way to third-level support. If an invoice doesn’t go through and we detect it’s critical, it lands directly with us. That is also an essential part of our work in Monetize.

You mentioned several exciting points — especially billing. People think it’s simple. I was involved in a larger project myself, in which we offered Software-as-a-Service for the first time, i.e., software that is paid for on a regular basis and therefore always remains up to date. instead of one-time license sales. Traditional ERP and accounting systems couldn’t handle this at all, and we ended up with strange requirements and creative workarounds to make it possible. How do you tackle that? That’s already relatively powerful.

Marina

There are indeed some fascinating stories. When companies come from traditional ERP systems — used to selling machinery — and suddenly start selling digital services, they sometimes create makeshift solutions. One customer told me they had to assign a physical weight of one kilogram to every digital product because weight was a mandatory field. We run into things like that all the time.
For us, the first step is always a full assessment. We need to understand what systems exist and what exactly the customer wants to sell. We have to know the strategy and vision behind it — and together with IT, check what is already implemented and how. Some customers are right at the beginning, others have already tried something and realized the system hits limitations. Others need consulting right from the start. That’s why we run workshops with customers to understand what exists and where they want to go. Only then can we design the right solution.

That makes total sense. And you mentioned vendor comparison as the second point, and gathering the requirements — not only capturing them, but also really turning them into a tender in the market. Usually you actively look in the partner ecosystem for something that has already been implemented. Do you fall back on your own ecosystem of partners and prefer that? Or do you go pretty openly into the market with tenders and use tendering platforms?

Marina

When I talk about the monetization, we have a partner network that we actively utilize. We keep it deliberately up to date because legal requirements change constantly and new trending topics keep emerging. We need to check whether our partners still fit, whether we need new ones, or whether some partners shift their focus over time. So we work very closely together and stay in constant exchange. Generally, we prefer to rely on our existing ecosystem. However, it can also happen that customers tender certain components separately right from the start and then look for someone to handle consulting and integration. That works well too. It is always exciting to see how such collaborations come about. The key is that we stay close to our partners and close to the customer to find the best possible solution.

[19:25] Solutions, offerings and services – A look at the technologies used

We now have Connect, Make Smart, and Monetize as three stages. You can look at them independently, but ideally they flow into each other. Jonas, how do you work together there? Do you at some point hand things over to Marina with the words, “now please monetize it”?

Jonas

It’s more of a continuous process. The foundation has to be in place: the data has to exist and be enriched — sometimes also with data from external systems. As soon as we see that business models can be derived from it, we start early on to exchange ideas with the customer and involve the experts from the Monetize area. Through workshops and trainings, we show the possible directions. And step by step, a project grows along our chain of Connect, Make Smart, Monetize and is handed over to the right expert teams.

I can imagine there are a number of challenges here, such as the make-or-buy decision. Especially larger companies with their own IT and their own data science teams often face the question: do we build it ourselves or get support? What are your insights on that?

Jonas

We’ve learned that many customers already have a broad mix of technologies in-house. In general, they are open to purchasing software. But standard software is often very generic. Adapting it to their specific requirements can take just as much effort as developing something tailored to their needs. Or it’s so rigid that it simply doesn’t work. Our approach at doubleSlash is therefore not to push a rigid standard product, but to first understand what the customer truly needs. Then we check which components from our own toolbox are a fit. We have prebuilt modules that we can combine and integrate depending on the use case. Of course, customizing is needed — but we don’t start from scratch every time. This gives us the flexibility to offer a tailored solution instead of forcing a one-size-fits-all approach.

You have gathered a lot of experience, especially because you look at the topic so holistically. Which pitfalls should be avoided when building digital services from scratch?

Marina

What we repeatedly experience in the Monetize area is this: everyone thinks we are perfectly on time, everything is going smoothly — and suddenly requirements show up that nobody knew about before. Often it’s simply because someone wasn’t involved or wasn’t informed. We’ve learned to ask very consistently at an early stage: Are really all relevant people at the table?
Another big factor involves tax regulations in different countries. Many of our customers operate internationally, and sufficient time must be planned, especially for IT conception. Brazil is a classic example: each federal state has different tax rules. It’s the same in the U.S., where regulations differ from state to state. Sometimes you spend six months just to cover everything correctly. There are products that already come with these tax codes integrated — but even that has to be checked and implemented properly. These issues can slow things down massively and become very frustrating if you discover them too late.

I understand that. Stakeholder management is a key topic. And having a shared target picture becomes incredibly important when projects get bigger — that’s something I’ve learned over time as well. You mentioned that many of your customers are large enterprises. That has advantages, of course. We also have an AI project at E.ON, a very large customer, where a lot is being invested in stakeholder management and training. But can you also support smaller customers in the midmarket who may not have those capacities? How do you help that target group?

Jonas

That is a very important point for us. We support medium-sized companies in implementing solutions with a more cost-sensitive approach. A good example is our Update Manager. It is an easily configurable tool that we bring with us. Whether the customer uses Microsoft Azure or PTC ThingWorx — our solution is adapter-friendly. We can quickly provide a solution without requiring a large budget. Especially with the Cyber Resilience Act coming into effect, these kinds of solutions are becoming very relevant.

[26:10] Transferability, scaling and next steps – Here’s how you can use this use case

If we look ahead and consider how your market is developing: the dynamics are high, and we talked earlier about AI. What is your vision for the future — especially from a Monetize perspective? Where do you see the most exciting developments?

Marina

One development we clearly see over the past years: business models evolve faster when there is a B2C approach behind them. An end user can decide and try things out independently. That’s why we see digital services in passenger cars much earlier than in commercial vehicles. In the B2B environment, however, an entire buying center is involved in decisions. That takes longer — but it is now moving strongly as well.
And wherever possible, AI will be used. For example, in an online shop context. If customers want to compare digital services, the data must be machine-readable — meaning AI-readable. That will become a big topic for us.
What we also clearly observe: many companies have largely completed the Connect phase. Now they really want to earn money with digital services and remote services. And there as well, AI plays a role. It can analyze customer data and help tailor offerings to be more attractive, more individual, and more profitable. I believe the market is clearly moving in this direction.

I find it very interesting that you say Connect is completed in many cases. Certainly not everywhere, but you’re right — companies have been working on it for a very long time. Sometimes decades. And now the foundation exists so that people can build on it. This doesn’t happen overnight — we’ve always said that. You have to start, collect data, and create the right structures. Your example from ZEISS shows that very well. Many have done their homework and can now move forward faster.
I would love to go much deeper with you on this topic. I imagine our listeners also have many questions. What’s the best way to reach you? And what would you like to leave them with today?

Marina

You can easily reach us on LinkedIn — just send us a message. And my wish is that IoT and the connectivity of devices are no longer seen as an end in themselves, but as a key to new digital business models. I hope companies become even bolder in the future and think more in the direction of Monetize. Maybe offer a service subscription — or allow customers to take over parts of the maintenance. I would like to be more creative and courageous.

Jonas

A continuous, end-to-end approach is crucial. Don’t just connect devices and collect data; ask yourself early on what business models are behind them and how recurring revenue can be generated from device data. With the holistic approach that we have refined over many years with our customers, companies can position themselves well for the future because they not only connect devices and collect data, but also develop new business models from this data.

Thank you both very much for the insights. I really enjoyed the conversation and I’m excited to hear what feedback we get from our community. Looking forward to next time!

Marina

Thank you, bye!

Jonas

Thank you, bye!