New technologies are easy to explain. But that doesn't mean they're effective.

Especially when it comes to Artificial Intelligence It turns out that many companies know AI is becoming important. Many have some initial ideas. Some are testing tools. However, there is often a lack of crucial step.

How does technological knowledge become a concrete application?
How does interest turn into a meaningful use case?
How does an idea turn into the next step?

These are exactly the questions that lead to genuine Transfer Competence lead.

How are new technologies, innovation methods, and future-oriented topics put into practice?

At TeleskopEffekt, we’ve been exploring this question for years—in banks and companies, in regional networks and on international study tours, and in workshops. At Werkbank32. Our experience has shown that technology transfer does not succeed through the mere transfer of knowledge. It succeeds when people receive guidance, experience technology firsthand, identify their own areas of application, and achieve concrete results.

Anyone who wants to introduce new technologies into a company should not start with the tool.
But rather with the application context.

🔹 What's the problem?
🔹 Who uses it?
🔹 Which processes are affected?
🔹 What data, skills, and decisions are needed?
🔹 Where does real value come from?
🔹 And what would be a small, realistic first step?

These questions are at the heart of effective research translation.
That is why we recommend a five-step approach.

Cognitive Maps as a Foundation for Technology Transfer

Technology transfer does not simply mean passing on knowledge. It is successful only when people can adapt a new technology to their own context. To do this, they need guidance. In research, cognitive maps understood as internal structural models. They help people recognize connections, compare options, and plan their actions.

 
When it comes to new technologies, these internal mental maps are often still incomplete: individual terms are familiar, examples seem exciting, and risks are discussed. But the connection to one’s own work remains unclear.
This is exactly where technology transfer comes into play.

When it comes to AI, the question „What can the technology do?“ must give way to the question: „What does this mean for our processes, data, roles, decisions, and customer relationships?“
A cognitive map helps establish these connections:

This is how abstract technological knowledge becomes a Guidance for taking action. How can this logic be put to use? By working with participants to contextualize AI and other new technologies, identify patterns of use, cluster use cases, pinpoint barriers, and determine next steps. The goal is not just for participants to learn more; it is for them to be able to apply the technology to their own organizations.

Theory of Identical Elements

Transfer is easier when Learning and Application Situations have as many common elements as possible. That is the basic idea behind the theory of identical elements. For our work, this means that anyone who is going to use AI cannot just talk about AI in abstract terms. Rather, the learning task should be closely aligned with the eventual application. Working on real-world challenges, one’s own processes, specific roles, and actual decision-making situations—this is how a bridge is built between learning and application. We use this approach, for example, in workshops with banks, in use case development, or in transfer formats where participants from the one's own daily work routine work out.

Traditional Technology Transfer

For a long time, the concept of technology transfer was understood purely as a process of passing on knowledge: knowledge is generated in one place and transferred to another. However, this understanding is no longer sufficient when it comes to new technologies. Today, what is needed instead is Interactive transfer processes. Companies, technology partners, the scientific community, public sector stakeholders, and users learn together. It’s not just about what a technology can do; it’s about how it can be effective in a specific context.

What can result from this 

 
Effective technology transfer provides guidance.
Effective technology transfer fosters connectivity.

This is how we create:
🔹 Initial use case concepts
🔹 Evaluated Areas of Application
🔹 Roadmaps
🔹 Transfer Guides
🔹 Basis for Decision-Making
🔹 Workshop Documentation
🔹 Materials for further internal work
🔹 Recommendations for follow-up formats

That's how a technological topic becomes the next step.

Our Understanding

For us, transfer competence means: making new technologies understandable; creating opportunities for their application; connecting stakeholders; ensuring results; and agreeing on concrete steps. When it comes to AI, this is precisely what matters. After all, it is not the technology alone that transforms companies, but rather the ability to apply it effectively.