Illustration of a person standing at crossroads
Business Strategy
Innovation in Mittelstand

How we know when it’s time to break up…

Creating successful businesses is challenging - especially in B2B-sector where technology is complex and advanced.


If you are a Hidden Champion or Mittelstand company, there is a high likelihood that you are working on a new digital project with a startup or using agile, design thinking or SCRUM methodology — or you are planning to. If you want to know how to avoid wasting resources, continue reading.

Creating successful businesses is challenging. Especially in the B2B, manufacturing sector where the technology is complex and advanced. Invariably some form of industry 4.0 or Industrial IoT solution is being developed — meaning high prototyping costs. Furthermore, companies have to overcome many hurdles to ensure their products are customer-centric and their solution is problem-centric — rather than tech-focussed. Therefore, at wattx we follow a detailed, 5-step process for building new tech ventures. The steps involved in this process are:

  1. Discovery
  2. Definition
  3. Ideation
  4. Prototype
  5. Implementation

This standardized process ensures that we avoid common pitfalls and stop investing our resources early on if the project is not worth it. In this blog post, I’ll describe in detail when, why, and how we kill projects.

The selection process — from problem to venture: The journey of all our projects starts with discovering problematics in-depth. We analyse the problem and its consequences and define the stakeholders affected. We agree with Einstein’s obsession of defining the problem well before designing the solution. Most of our projects are rejected during this initial phase. However, the following illustration shows how many projects made it through our funnel and eventually became ventures, since our own founding in 2015:

Illustration of our Venture development process funnel
Our process ensures rigorous evaluation to promote only ideas with the most potential

Less than 5% of the projects whose problematics we were investigating turned into ventures. There are two reasons for this high rejection rate. First, our UX, Engineering, Data Science, and Venture Development experts jointly assess the shortcomings associated with the project concept and anticipate future problems that may arise at a later stage. We strongly believe that only a diverse team with different areas of expertise can achieve this early on. Second, we are radical in killing ideas. Before we move a project to the next stage of our venture building process, we hold so-called ‘Do or Die sessions’. During these sessions, we decide jointly whether we proceed with the project or not.

Four different do or die decisions
Do or Die session between stages of our venture building process

During each ‘Do or Die session’ our cross-functional teams discuss the progress of the project and check whether the specific milestones of the corresponding stage of the venture-building process have been reached. It is worth noting here that one size does not fit all. Each venture has its own context and shortcomings, even if they operate within the same market, and therefore the focus of each do or die session is different. However, we always answer the same questions.

Criterias for the do or die decisions like uniqueness, market size, market readiness, tech feasibility entry barriers

The projects are assessed based on the following key questions: Research shows that ventures usually start solving incorrectly identified problems, leading to the wrongful implementation of solutions, and thus, they waste time and valuable resources. Startups are already resource-deficient, so, misapplication of those limited resources is usually not something they can afford. This is why accurate problem identification is crucial from an early stage, allow time for the solution to be applied, and to recover from any mishaps that may occur. During the do or die session after the problem definition phase, we at wattx discuss the following questions in minute detail:

  1. What problem are we trying to solve?
  2. For whom is it a problem? Who are the users? Who are the decision-makers? What is their motivation?
  3. Is this the real problem, or is this just a symptom of a different root cause?
  4. How big is the problem?
  5. What do we already know about the problem? Can we calculate the damages?
  6. What are the consequences of the problem?
  7. How can the problem be solved?

By answering and documenting the questions above, we force ourselves to think of the problem and its consequences early on.

A project killed after the research stage: We had the idea of using a mobile image recognition-based tool for the automotive industry to identify specific parts, where there is a high number of very similar parts being produced. Soon enough, we realized that solely identifying parts is not sufficient. The automotive industry is also highly dependent on the traceability of parts in case of warranty, faulty productions to calculate risk, etc. Therefore, in the automotive industry, they are already using an identification system that focuses on specified serial numbers and not only part types. In this case, we concluded, our tool would only be useful for small car repair shops with low levels of digitalization.

In conclusion, we identified the problem, stakeholders involved and understood the process around it. We had proposed an advanced solution but realised that this solution failed to solve the problem for the initial intended stakeholder and they already had an alternative in place that solved their issue.

The secondary stakeholder that did have this problem was a far more fragmented market and was tougher to target, with a lower willingness to pay. Additionally, we felt that sooner or later, repair shops would adopt the digital process from the OEMs (Original Equipment Manufacturers) who supplied them. Thus, we decided to kill the project after the discovery phase.

By having this framework of questions to answer, we built a clear picture that highlighted the flaws of entering this market. Even though it sounds attractive to build a machine vision system that identifies parts and digitises this information for traceability and decision making support, it is, ultimately, a tech-driven solution that is looking for a problem to solve.

Other articles you might like
Four relevant problem spaces in carbon capture projects
The results of our carbon capturing market analysis indicate four relevant challenges, companies have to overcome
read the ARTICLE →
Stryza Interview
In this exclusive interview, we talk to Max Steinhoff, founder of Stryza, a promising venture of wattx.
read the ARTICLE →
Hacking Construction
wattx' first fully remote Hackathon. We divided the team into four different groups, each with experts from different wattx disciplines, to work on different challenges
read the ARTICLE →
How ethical is Artificial Intelligence?
How ethical is it to let AI decide on human survival? In nowadays world, Artificial Intelligence is faced with the same problem when dealing with autonomous driving for instance.
read the ARTICLE →
How to Give Constructive Feedback Correctly
Giving feedback can be a burden for both: the giver and the receiver. But with no feedback, there can be no improvement.
read the ARTICLE →
Future trends in the AR-based consumer goods industry
For AR to be fully adopted into the mainstream, it will require a breakthrough application, like on-site navigating in unfamiliar surroundings, e.g. big commercial centers or large train stations.
read the ARTICLE →