Innovation ecosystems are becoming increasingly crucial for access to infrastructure, technology, knowledge, and talent. They allow for more effective and efficient networking and collaboration among companies, start-ups, research institutes, citizens, and politicians. This accelerates the adoption of innovative ideas while also minimizing investment risk, involving citizen stakeholders, and optimizing the decision-making and implementation process.

Every generation perceives the current dynamics of change as rapid and as an enormous challenge for the economy and the state. However, it is not only technology that advances so rapidly during times of significant industrial change. Often completely novel technologies and methodologies emerge from various other disciplines and bring about enormous change to our current business models.

Companies often respond well to technological advances in their own domains; they already have all the specialist departments and expertise they need. That said, they rarely have the appropriate structures in place, at least at the outset, to embrace new technological paradigms. For example, experts in gasoline engines initially have no expertise in electric motors and battery systems, injection molding specialists have no knowledge of 3D printing processes, and steel manufacturers have no expertise in hydrogen technology. In this same manner, automotive manufacturers must acknowledge their limitations as their product evolves into a “computer on wheels.”

The increased demand for highly skilled and specialized talent serves as an additional challenge to the creation of these novel technologies. These experts are extremely sought after and generally not available in sufficient numbers at the start.

In addition to the availability of talent, new technology often requires a new standard of infrastructure. Innovators either develop the new infrastructure alongside the technology itself, such is the case of quantum computing and 5G/6G, or they must launch the new technologies using existing infrastructure. This is the case for machine learning (ML), and artificial intelligence (AI), which use existing supercomputers, or huge data centers. This poses serious challenges for enterprises, as the infrastructure might be interchangeable, and new technology tends to adapt to the infrastructure that offers the best performance. AI processes, for example, were originally deployed on clusters of CPUs (central processing units) and later, high-performance, specialized GPUs (graphics processing units) or even TPUs (tensor processing units) for further development – only to then move on to quantum computers.

However, the implementation of this form of infrastructure is not without significant financial risk, and premature investment may be akin to “betting on the wrong horse”. That said, investing too late may result in a competitive disadvantage.

In summation, the quantity, speed, and plurality inherent in new technological advances pose major challenges for both organizations and society when it comes to adaptation as well as widespread adoption.

The network ecosystem approach

The key is to help existing enterprises and society at large adopt the latest in findings and technology from research institutes as well as the latest in commercial applications, as efficiently as possible.

In contrast to the traditional supply chains, we are accustomed to, modern network ecosystems enable companies to collaborate on equal footing, interacting bilaterally and multilaterally to share resources, energy, and knowledge for both individual and collective benefit. Every participant brings their own strengths to the table to advance the network. Based on a peer-to-peer approach, there is no “central node” or dominant party in this concept. Instead, so-called network orchestrators organize the ecosystem, hosting “hackathons,” challenges, regular events, publications, and other activities, whilst acting as a representative for the interests of the network and external members.

In contrast to existing industry interest groups, the focus in technology-oriented ecosystems is on bringing together the members, offering expertise, and pooling forces. Essential components of an innovative ecosystem include: a research institute with expertise in the subject area, the speed and agility of a start-up, and the power of a multinational corporation. Particularly in the software development space, network ecosystems offer an ideal forum for collaborative (open source) software initiatives. Partners with similar interests find a forum for rapid exchange and joint development projects in the network. As the central independent authority, network orchestrators also coordinate and develop common standards for all members.

Case study: the KI Park in Berlin

One example of a network ecosystem is the KI Park e.V. Given the speed of progress in AI – which promises to accelerate even further in the future with 5 or 6G and quantum computing – we need visionary minds to develop next-generation AI applications across all sectors of the economy and with an impact on society. Twelve renowned stakeholder organizations founded the nonprofit KI Park e.V. in October 2021, an ecosystem that facilitates powerful collaboration between enterprises, start-ups, research institutes and citizens. The founding members include Celonis, the University of Erlangen-Nuremberg, automotive OEM Volkswagen, automotive supplier Schaeffler, “Big Four” accounting firm Deloitte and the technical-scientific association Verband der Elektrotechnik (VDE). Since its founding, a wide range of other companies and start-ups have joined the network.

The Authors:
Prof. Dr. Sabina Jeschke, CEO of KI Park e.V., Founder & CTO at Quantagonia GmbH, member of the Vitesco Supervisory Board, former member of the Deutsche Bahn Management Board for Digitalization and Technology

 

 

 

Olly Salzmann, Chief Strategy & Growth Officer at KI Park e.V., Managing Director at Deloitte KI GmbH and Partner at Deloitte Consulting GmbH, Lead Technology Ecosystems