Foresight Report - Cycle I Author : Innovative Thuringia & Institute for Innovation and Technology (iit) in the VDI/VDE Innovation + Technology GmbH
Published: November 2025
1. Why Foresight?
Thuringia faces the challenge of ensuring its innovative capacity remains viable in an environment characterized by technological, social, and geopolitical upheaval, and of utilizing available resources and potential as effectively as possible. The question of how to actively shape the future is of central importance to decision-makers in politics, business, and science, as priorities must be set. In a time of profound technological and social transformations, strategic foresight serves to generate knowledge that guides understanding potential future developments. In 2024, Innovativ Thüringen launched a foresight process that systematically, data-driven, and participatory identifies key future topics for Thuringian innovation policy and anticipates their consequences. This process is intended to be repeated annually. Foresight (strategic forecasting) refers to a systematic, knowledge-based process for anticipating possible future developments in order to generate early guidance for strategic decisions and to identify viable courses of action. The aim of the process in Thuringia is to identify relevant trends in order to make long-term decisions based on a sound knowledge foundation and to open up future development paths for the Thuringian innovation landscape.
Within the framework of the Regional Innovation Strategy for Smart Specialization and Economic Transformation in Thuringia (RIS Thuringia), foresight forms a central element of governance and further development. Strategic foresight makes it possible to systematically identify future technological, economic, and societal trends and translate them into structured innovation pathways. By using foresight tools, areas for action are identified early on, allowing Thuringia to combine its existing strengths with emerging opportunities. Thus, within the RIS, foresight contributes to not only reactively addressing innovation potential but also proactively developing it and shaping resilient strategies for research, development, and value creation.
This report provides an overview of the foresight process and the results of the first foresight cycle, which was carried out by Innovativ Thüringen from June 2024 to June 2025 as a joint effort with the Institute for Innovation and Technology (iit) at VDI/VDE Innovation + Technik GmbH and is now being continued continuously.
2. Foresight process in Thuringia
A central element of foresight activities is ongoing trend analysis and monitoring, which is carried out by Foresight Team The foresight process is conducted by Innovativ Thüringen within the Thuringian State Development Corporation (LEG Thüringen). This recurring process combines quantitative analyses with qualitative assessments and involves the targeted participation of stakeholders from academia, industry, and public administration. The core of the process is the identification of trends that are not only technologically relevant but also open up strategic opportunities for Thuringia. The foresight cycle involves anticipating future developments, deriving business model patterns, identifying transfer potential, and developing project proposals in collaboration with relevant stakeholders.
Thuringia possesses excellent prerequisites for actively shaping future developments. Its innovation landscape is characterized by short distances, a high-performing cluster ecosystem, a high degree of specialization among companies, and a strong research infrastructure. Thuringia also already boasts a number of relevant networks and innovation formats that strengthen value creation. Existing expertise in areas such as sensor technology, image processing, energy, and medical technology enables the systematic occupation of specific niches and the development of new value creation pathways.
The foresight process for Thuringia was developed in close collaboration with the foresight experts at the Institute for Innovation and Technology (iit) within VDI/VDE Innovation + Technik GmbH. In a multi-stage process, trends relevant to Thuringia are identified, paths of possible future developments are anticipated, and opportunities for action within the Thuringian innovation landscape are recognized. Particular emphasis is placed on a data-driven and scientifically valid approach. The process follows a multi-stage procedure (see figure).
The scoping phase defines the search space and strategy, i.e., the systematic approach used to identify relevant technology trends for Thuringia as a hub of innovation. In the scanning phase, trend data is then analyzed according to the defined search strategy and condensed into trends of particularly high relevance for Thuringia through a combination of data analysis and expert evaluation. The identified trends are subsequently analyzed in greater depth in the anticipation phase using qualitative foresight methods to examine how these trends will develop in the coming years and what potential they might hold for Thuringia. The results of each cycle are communicated through reports, keynote speeches, and workshops, and disseminated to policymakers, businesses, and researchers in Thuringia. This process is repeated annually (foresight cycle). Before entering the next foresight cycle, the methodology is evaluated and the research design is adjusted.
3. Procedure and methods
3.1 Scoping
At the beginning of the scoping phase in the first cycle in 2024, the search area for trending topics was defined. This area was initially narrowed down thematically. The basis for this thematic narrowing is the five specialization fields already established in the Thuringian Information Systems Research (RIS Thüringen). These include: Industrial production and systems, Sustainable and intelligent mobility and logistics, Healthy living and the healthcare industry, Sustainable energy and resource use, ICT, innovative and production-related servicesIn close collaboration between the foresight experts of iit, the foresight team and the specialization field managers of Innovativ Thüringen, the data sources to be collected and the search strategies required for analyzing the data were determined for each specialization field.
The data selection was made according to the underlying research interest. Innovative Thuringia is pursuing this goal in connection with the RIS Thuringia a threefold objective: (1) strengthening the transfer of new scientific findings from research into marketable products and services, (2) strengthening the competitiveness of Thuringia as a business location, e.g. through cross-industry and cross-technology value creation networks, and (3) increasing the visibility of Thuringia as a business and innovation location and strengthening supra-regional networking.
This results in the following requirements for the selection of the database:
- Recognize the latest scientific research findings as early as possible.
- Identify interface issues across different industries (incorporating data on pre-competitive trends and trends already in use)
- Identify the driving forces behind trends for (i) differentiation potentials (Where are Thuringian actors in competition?) and (ii) cooperation and networking potentials (Where can actors from Thuringia cooperate with supra-regional actors in a specific field?)
For the analysis in the first cycle, scientific conference papers from the Scopus® literature database, funded projects at the European level in the CORDIS database, and RSS feeds from news blogs were used (see figure). A total of 151,433 conference papers, 5,909 funded sub-projects of European research consortia, and 97,882 news articles were evaluated.
3.2 Scanning
During the scanning phase, relevant data sources were systematically searched, processed, and thematically condensed to identify initial trends and key areas. The resulting thematic clusters were then progressively consolidated, qualitatively analyzed, and evaluated by experts according to defined relevance criteria. Based on this evaluation, key trends were derived, which were discussed in greater depth during the subsequent trend workshop and assessed in terms of their strategic importance for Thuringia.
Quantitative data analysis
- Using the developed search strings, the data sources described above were searched, and corresponding data sets (samples) were generated for the respective specialization fields. These samples of potentially relevant data sets were processed using text mining procedures to enable further analysis. A word embedding method (Word2Vec) from the field of Natural Language Processing (NLP) was used to analyze these relationships. The resulting word vectors form a high-dimensional semantic space in which contextually related terms are located close to one another. This allows for an aggregated view of the contained topic complexes.
- To explore the resulting vector space, a k-means clustering method was used. K-means clustering is an unsupervised pattern recognition technique in which data elements are grouped into clusters (k) based on their similarity. The goal is to achieve the highest possible similarity within the clusters and the greatest possible difference between the clusters. In this way, semantically similar terms could be grouped into clusters and visualized in the form of thematic maps. These maps allow for an aggregated representation of overarching thematic areas.
- Finally, the thematic clusters were transformed for spatial representation using Principal Component Analysis (PCA). PCA is a dimensionality reduction technique that maps high-dimensional data to a few principal components through linear transformation. This enabled a two- or three-dimensional mapping of the semantic space as well as a spatial representation of the clusters, thereby revealing content overlaps between the clusters and thematic relationships.
Qualitative analysis
- The thematic clusters identified in this way were subsequently analyzed in greater depth qualitatively by having the iit experts evaluate the content of the associated datasets. Using generative AI, the clusters were named, giving the trends they contained summarizing titles. In the next step, expert-based sense-making and the consolidation of the various source results enabled the content-related interpretation of the identified clusters, the classification of their significance, and the derivation of a concrete list of topics with relevant trends and developments. As a result, clearly defined and describable topics with future relevance were developed from the clusters. This resulting list of topics, comprising 45 individual themes from the five specialization areas, was then incorporated into the further process. It represented a broad spectrum of potentially relevant questions.
- The next step involved an online expert survey. In this online survey, members of the strategy advisory boards of the [organization name] addressed the 45 individual topics RIS Thuringia and other experts from Thuringia qualitatively evaluated the trends according to the following relevance criteria: (i) novelty, (ii) disruptive potential, (iii) potential opportunities for Thuringia, (iv) potential risks for Thuringia, and (v) relevance to action. Trends that received above-average ratings in one or more criteria were selected for further analysis. It was ensured that trends were selected from each area of specialization. The 20 most relevant trends formed the shortlist of topics that served as the basis for the trend workshop, in which more in-depth thematic classifications were undertaken. For these 20 trends, Trend profiles developed.
- The full-day trend workshop took place in person in Erfurt in November 2024. During the workshop, the 20 trends identified within the foresight process were prioritized and discussed in depth with key stakeholders. Participants included members of the core foresight team, specialization field managers from Innovativ Thüringen, and external experts in the respective subject areas. Following an introduction to the methodology and objectives, as well as an interactive round of introductions, the trends were prioritized in two small groups using the prioritization matrix described below (see figure). From these 20 trends, five topics were ultimately selected during the workshop for further foresight development.
Details about the trend workshop
The identified trends were evaluated along two dimensions: Firstly, the potential impact of a trend on innovation and value creation in Thuringia was assessed. Secondly, the degree of uncertainty regarding the future development of each trend was discussed.
It is important to note, from a methodological perspective, that all trends discussed in the trend workshop had already been identified beforehand as potentially relevant for Thuringia. A comparatively lower rating within the workshop therefore does not necessarily imply general irrelevance, but rather, from the perspective of the participating experts, a lower significance in direct comparison to other trends.
The experts assigned ten trends to the category of priority topics. In the next step, these priority topics were further refined in a professional discussion using the Future Canvas method, along the following categories:
- Opportunities: What positive opportunities does this trend offer in Thuringia? What innovation potential? What competitive advantages?
- Risks: What potentially negative impacts/uncertainties are associated with this trend? What dangers could arise for specific industries, etc.?
- Affected stakeholders: Which actors are directly or indirectly affected by this trend? What interests, needs, fears, etc., need to be taken into account?
- Approaches to action: How can stakeholders in Thuringia influence whether potential opportunities are seized and risks avoided through their actions? What strategic initiatives are necessary to respond to the trend of increasing trade fairs ?
The ten Future Canvases created in this way were documented on Metaplan boards and presented to the plenary. Finally, five priority topics were selected by scoring; these were then further analyzed in the anticipation workshops during the next phase of the foresight cycle.
3.3 Anticipation
The five focus trends identified in the trend workshop were further developed for in-depth analysis and discussion in two half-day workshops during the anticipation phase. The aim was to structure the potential medium- to long-term impacts of relevant changes and to derive initial implications for Thuringia as a location for innovation and value creation. Relevant experts from the Thuringian innovation system, as well as the specialization field managers of Innovativ Thüringen, were invited to ensure a broad range of expertise and practical knowledge.
The first anticipation workshop in January 2025 focused on analyzing future trends in health technologies and the healthcare industry. The work was carried out in two groups: (1) AI-supported, robotic, and immersive technologies for personalized health, with the potential to enable individualized and effective medical care through collaborative robotics and immersive applications; and (2) eHealth: AI for predicting, diagnosing, treating, and preventing diseases, with the goal of transforming healthcare through predictive analytics, precise diagnostics, and tailored prevention strategies.
The second anticipation workshop took place in February 2025 and focused on key technologies for industrial value creation. The participants worked in three groups on: (1) Context-sensitive sensors for adaptive systems in production, transportation, robotics, and energy supply; (2) Intelligent image processing for adaptive systems as a basis for autonomous, reactive, and learning systems; and (3) Quantum-based and neuromorphic technologies for computing and sensors with disruptive potential for diverse application areas.
Two complementary foresight methods were specifically selected for the workshops: (1) Futures Wheel and (2) the Visual Roadmap. The combination of both approaches links exploratory thinking under uncertainty with the structured derivation of concrete development paths.
Futures Wheel
The Futures Wheel is an established, by Glenn and Gordon The Futures Wheel is a developed tool for future analysis, particularly well-suited for complex and uncertain topics. It enables the systematic capture of the direct, indirect, and more distant impacts of a trend or event, without being constrained by linear thinking patterns. To support idea generation and encourage thinking outside the box, assumptions are placed at the heart of the Futures Wheel. In several rounds, conceivable consequences of the assumption are anticipated. First, immediate consequences (1st order) are identified, followed by secondary effects (2nd order), and finally, systemic reactions (3rd order) are considered. This approach provides a broad view of alternative chains of effects without pre-assessing the probability of individual scenarios. The method was chosen to reveal a multitude of development possibilities for each trending topic, thereby creating a robust foundation for further analysis.
Visual Roadmap
The Visual Roadmap Developed by the iit, the Futures Wheel is a strategic planning tool that presents future developments in a structured temporal and thematic way. Starting from a defined target scenario (e.g., a business model in 2035), it allows for a backward analysis of the applications, technologies, framework conditions, and measures required to achieve this goal. The method follows a clear four-level approach: (1) business models and desired impacts, (2) necessary products and services, (3) technological prerequisites, and (4) socio-economic framework conditions and concrete measures. This vertical and horizontal structure clearly visualizes critical milestones, dependencies, and areas for action. Its selection is based on the fact that it bridges the gap between visionary target scenarios and concrete action options, thus translating the opportunities identified in the Futures Wheel into an implementation-oriented logic.
The combination of these two methods proved particularly suitable for the Thuringian Foresight Process and the five focus trends selected here. On the one hand, the Futures Wheel allowed for the exploration of a broad range of possibilities under conditions of high technological dynamism and strategic uncertainty, revealing surprising or even opposing development paths. On the other hand, the Visual Roadmap ensured that these exploratively identified paths could be translated into a coherent future architecture – with clear time horizons, critical milestones, and defined areas of action. The methods thus complemented each other perfectly: the strengths of the Futures Wheel in creative, divergent idea generation and the identification of systemic interactions were combined with the strengths of the Visual Roadmap in convergent structuring, prioritization, and operationalization. This not only allowed the complexity of the trade fairs trends to be represented but also created a direct link to strategic decision-making and implementation processes in Thuringia.
In both workshops, the Futures Wheel was used in the morning to explore potential causal chains. In the afternoon, the Visual Roadmap was used, based on a desirable target vision and in the sense of a backcasting approach, to develop which technologies, products and services, as well as which political, social and infrastructural framework conditions, would need to be created by 2035 to realize these pathways.
4. Results: Focus Trends
The results of the first foresight cycle conducted by Innovativ Thüringen and the Institute for Innovation and Technology (iit) at VDI/VDE Innovation + Technik GmbH are listed and presented in detail below. Chapter 4 provides an overview of the focus topics that were addressed throughout the entire foresight process, including the anticipation workshop. Chapter 5 addresses further relevant future topics.
The following table provides an overview of all trend topics from Cycle I: 20 topics were addressed in the 2024 trend workshop. Of these, five were selected for the anticipation workshops.
4.1 Overview of trending topics
| Theme | Foresight process |
|---|---|
| AI-powered, robotic and immersive technologies for personalized health | Trend and Anticipation Workshop |
| AI-based eHealth systems | Trend and Anticipation Workshop |
| Context-sensitive sensors | Trend and Anticipation Workshop |
| Intelligent image processing for adaptive systems | Trend and Anticipation Workshop |
| Quantum-based and neuromorphic technologies | Trend and Anticipation Workshop |
| Commercialization and technologization of space research | Trend workshop |
| New approaches to tackling environmental and health challenges | Trend workshop |
| Holistic approaches to integrated energy management | Trend workshop |
| Advanced materials and technologies for energy and information systems | Trend workshop |
| Smart cities with advances in infrastructure, AI and cybersecurity | Trend workshop |
| Advanced autonomy and safety strategies for unmanned aerial systems | Trend workshop |
| Sustainable and energy-saving integrated heating/cooling systems | Trend workshop |
| AI, IoT and modeling technologies for agriculture, environmental monitoring and resource management | Trend workshop |
| Synergistic Digital Twins and IoT Systems | Trend workshop |
| Personalized Medicine: Diagnostics, Therapies & Health Strategies | Trend workshop |
| Multimodal AI applications and next-generation speech processing | Trend workshop |
| Innovative therapy platforms & biomimetics for personalized medicine | Trend workshop |
| Biotechnological innovations and genomics to improve quality of life | Trend workshop |
| Internet of Vehicles (IoV), intelligent transportation systems and cybersecurity | Trend workshop |
| Multimodal interactions and embodiment in hybrid environments | Trend workshop |
4.2 AI-powered, robotic and immersive technologies for personalized health – for medicine that becomes more individualized, accessible and effective
Personalized healthcare is on the cusp of a fundamental transformation. New technologies such as artificial intelligence (AI), robotics, and immersive technologies like virtual reality (VR) and augmented reality (AR) offer promising perspectives for making medical rehabilitation and care more individualized, efficient, and sustainable. At the heart of this trend lies the integration of data-driven decision-making with precision technologies and immersive therapies. Thuringia can play a pioneering role in this context by combining its technological and scientific strengths with a patient-centered culture of innovation.
The applications of these technologies are diverse: AI enables the automated analysis of health data, allowing for the creation of personalized therapy plans and nutritional and lifestyle advice. Robotics provides support, particularly in rehabilitation, for example, through robot-assisted physiotherapy or fine motor exercises for neurological conditions. Immersive technologies such as VR-based exergames not only increase patient motivation but also specifically improve users' balance, fitness, and pain management. Furthermore, telerehabilitation and telepresence robotics open up new possibilities for location-independent care and, at the same time, more intensive patient support.
Alongside these technological opportunities, however, it is also essential to address key challenges. The development and implementation of AI-supported, robotic, and immersive technologies requires a high degree of reliability, precision, and continuous calibration to build trust in the technology and, consequently, achieve a high level of acceptance among the target groups (doctors and patients). Comprehensive data protection and data security measures are a crucial prerequisite for this, as sensitive health data is processed in this field of application.
For the trend of AI-supported, robotic, and immersive technologies for personalized health, the qualitative analysis assumed that a private technology company would open a real-world laboratory for AI-based robotics in healthcare in Weimar in 2026, focusing on collaborative robots to support medical staff during surgeries and care tasks. A real-world laboratory is a testbed approach in which technological innovations are tested and further developed under real-world conditions together with practitioners.
Results of the Futures Wheel
The Futures Wheel project operated under the assumption of a real-world laboratory in Weimar (see above). The aim was to systematically explore the potential for future developments based on this assumption. Three key development pathways were identified, each revealing different potentials and challenges for Thuringia. The development pathways identified within the Futures Wheel project are:
Results of the Visual Roadmap
As part of the qualitative development of the insights gained from the "Futures Wheel" regarding the assumption of increased use of robotics and AI in Thuringia, experts identified three business models that are expected to generate economic success by 2035. The visual roadmap illustrates the relationship between (i) applications and business models, (ii) required technologies, and (iii) societal framework conditions necessary to achieve a positive target point for Thuringia as a location for innovation. The time horizon extends from 2025 to 2035.
Applications and business models
| Developed business models: | Products & Services: |
|---|---|
| Robotic systems for care and health A promising business model has been identified in the field of intelligent robotic systems for home care. This includes, among other things, ultra-lightweight, movement-assisted exoskeletons and new robotic systems that take over or supplement caregiving tasks. These technologies address demographic change and the increasing demand for individualized care in the home environment. |
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| Training and further education centers Another business model could emerge through specialized information and training centers focusing on robotics and AI applications in the healthcare and logistics sectors. Hospitals and other institutions could have their staff specifically trained in new technologies at these centers, thereby promoting not only technology acceptance but also its efficient integration into existing processes. |
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| Modular system for robotics & AI Finally, an open modular system that integrates various components from different robotics and artificial intelligence providers could represent a potentially scalable and adaptable business model. This would enable the precise configuration of technical systems depending on the area of application – be it in healthcare, logistics, or production – and open up new market potential for Thuringian companies. |
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Framework conditions
The successful development and market launch of these business models and the necessary technologies, products, and services require fundamental structural and systemic prerequisites. Political and strategic management and coordination: Targeted development demands clear political management and institutional coordination. State ministries, the LEG (the state's economic development agency), and industry-specific interest groups must jointly pursue a strategic vision. By setting coordinated priorities, resources can be pooled and targeted investments and funding measures established. Universities are key partners in research, education, and technology transfer. Education, skilled workers, and qualifications: Building a future-proof ecosystem for robotics and AI requires well-trained specialists. To this end, curricula at vocational schools, universities, and in continuing professional development programs must be modernized. Interdisciplinary expertise in robotics, software development, nursing, and medical technology is particularly important. Research institutions should be thematically and infrastructurally aligned with robotic and AI applications. Legal and regulatory frameworks: Reliable legal frameworks are essential for the marketability and safety of robotic systems. This includes harmonized liability rules across Europe, regulatory standards for the use of sensitive technologies, and standardized certification processes. These requirements build trust among users, developers, and investors – and enable cross-border scaling. Ecosystem and networking: A high-performing innovation ecosystem thrives on functioning networks. Existing centers of excellence, such as those in AI and medical technology, must be networked, synergies strategically promoted, and technological openness institutionally anchored. Cluster and network organizations, such as OptoNet eV or medways eV, play a central role here, acting as bridges between research, industry, and application.
Technologies
The following technological prerequisites are required for the successful development and market launch of the anticipated business models and related products and services:
| Technological requirement | challenges |
|---|---|
| Sensory systems and body signal processing The goal is the highly precise recording of physiological states of the human body in everyday life, in nursing care, or in the operating room. Various types of sensors (e.g., optical, bioelectrical, mechanical) are used to record body signals and vital parameters in real time. | Reliability under real-world conditions (e.g., humidity, movement), miniaturization, energy-efficient data transmission, high data rate with limited bandwidth. |
| Motor systems and actuators for medical robotics Mechanical precision, adaptive motion control, and biomechanical compatibility are at the heart of this key technology. It enables surgical assistance systems, exoskeletons, and robot-assisted care aids. | Dynamic adaptation to human movement patterns, material fatigue, certification of safety-critical drive systems. |
| Computing power & embedded hardware for edge AI AI-supported medical systems require high-performance, miniaturized hardware (e.g., specialized chips for image processing, sensor fusion, and machine learning algorithms). This high-performance hardware forms the backbone for the real-time capability of medical assistance systems. Specialized chips (e.g., ASICs, RISC-V) are crucial in this regard | Thermal efficiency, reliability in continuous operation, safety requirements for systems worn close to the body. |
| Methodology & Software Development for Medical Devices Medical software demands high standards in terms of reliability, traceability, and regulatory compliance. In addition to application development, generic methods are crucial, such as algorithmic validation, explainable AI, and semantic data annotation. The goal, therefore, is the development of highly reliable AI-based software. | Approval procedures according to MDR/IVDR, model robustness under bias conditions, black-box behavior in deep learning. |
| AI-based robotic systems for medical surgery Here, AI, sensors, motors, image processing, and control technology combine to create autonomous or semi-autonomous robotic systems. AI algorithms analyze image and sensor data in real time and support decision-making. | Safety certification, liability issues, the boundary between assistance and autonomy, approval issues for highly automated systems, and the ethical and legal distinction between assistance and autonomy. |
| AR/VR/XR for training and interaction XR technologies enhance interaction with medical systems. They are used in training clinical staff, patient education, and remote assistance, for example, through virtual training, interactive assistance, or remote consultation. A key focus is also the further development of immersive, intuitively usable interfaces. | Ergonomics, acceptance and usability in medical contexts, real-time capability with high data complexity, integration with clinical routines. |
| Licensing & Patenting in the MedTech Sector To ensure economic exploitation, intellectual property rights and legally sound licensing models are necessary. This is particularly important for AI methods, as these often lack a tangible component. | Speed and internationality of patent procedures, protection of intangible innovations (software, algorithms). |
Pathways to the future: AI-based healthcare robotics for more innovations
Based on this range of possibilities, Thuringia could generate economic value creation by 2035 either through a modular system for robotics and AI products or through an internationally oriented training center in the field of AI-supported robotics. To achieve this goal, the following is of central importance: learning from past mistakes and providing needs-based support to existing companies in line with an active industrial policy, creating data spaces and interface infrastructure, and providing the necessary framework conditions for research institutions and companies.
The visual roadmap for the future of AI-supported, robotic, and immersive technologies for personalized health outlines Thuringia's path toward becoming a highly networked healthcare and technology hub. Robotic systems for medical care, nursing, and logistics, as well as immersive technologies such as AR, VR, and XR, play a central role, opening up new possibilities for training, remote support, and decision assistance. The further development of sensor interfaces, software integration, navigation technologies, and active systems forms the basis for future-proof applications in real-time environments.
The potential applications range from robotic assistance in operating rooms and nursing care to simulation-based training formats and innovative healthcare services. AI-based services, modular systems for medical robotics, and new business models combining technology and personalized care are gaining increasing importance. This also opens up new roles for potential insurance providers who could cover AI-related risks. The roadmap makes it clear that suitable regulatory, technological, and organizational frameworks are necessary for the sustainable implementation of these technologies. Standards must be further developed, training systems adapted, and digital infrastructures expanded. It is therefore advisable to conduct a targeted survey within the Innovativ Thüringen network to determine which actors from science and industry are already involved in relevant standardization bodies at the national and European levels. Experience from other German states shows that companies and research institutions often lack the necessary resources to contribute their expertise to standardization processes for the benefit of the local innovation landscape. Therefore, it could be beneficial to identify relevant Thuringian stakeholders who receive funding from the state government to provide them with personnel resources. Furthermore, to achieve a high degree of coherence among the activities of Thuringian actors and to balance particular interests, it may be useful to establish an ongoing expert dialogue on the topic of "Standards Made in Thuringia." This dialogue would allow stakeholders from the Free State participating in standardization bodies to continuously exchange information with the state government and discuss common objectives. Cooperation between ministries, universities, economic development agencies such as LEG, and research platforms like OptoNet eV or medways eV is seen as a key lever for implementing shared goals. At the same time, transparent processes for citizen participation and public acceptance are essential.
Skilled workers are a critical success factor. New qualification profiles are needed, particularly at the interfaces between technology, nursing, software development, and medicine. Training centers, real-world laboratories, and platforms for education and professional development must therefore be systematically considered and institutionally anchored. The question of local robot training, digital support in everyday life, and the inclusion of diverse user groups is also becoming increasingly relevant. With regard to anticipating changes in job profiles, it could be beneficial to use forward-looking analysis of future competency requirements at the interface described above to specifically estimate which occupational profiles might emerge in the next five to ten years. This would make it easier for the state government and the continuing education landscape in Bavaria to drive forward necessary curricular, didactic, and capacity-related changes at an early stage.
4.3 AI-based eHealth systems – with the potential to fundamentally change medical care through predictive analysis, precise diagnostics and individualized prevention
The use of AI in eHealth systems marks a profound transformation in healthcare. Intelligent analysis of patient data enables earlier disease detection, supports diagnoses, and facilitates the development of individualized treatment plans. Preventive measures can be initiated more effectively, while access to medical care is improved and medical staff are relieved of routine tasks through automation. These technologies not only create efficiency gains but also open up new perspectives for personalized, more precise, and more comprehensive care. Concrete examples of AI-based eHealth systems range from AI-supported analysis of medical image data (e.g., X-rays or MRI scans) and digital health applications (DiGA) that support patients in managing chronic illnesses to the expansion of telemedicine consultations. Electronic patient records and health cards are also essential components of a digitally networked, AI-supported healthcare system.
These developments extend across the entire healthcare value chain. Both inpatient facilities and outpatient practices and home care services, as well as municipalities and their public health departments, could be directly affected as users of these technologies. Key players in the further development of these technologies include, on the one hand, companies in the medical technology, software development, and data analysis sectors, and on the other hand, research institutions, universities, and continuing education providers that ensure the necessary knowledge transfer and the training of future specialists.
Particularly in Thuringia, these technologies offer opportunities that extend beyond purely technological aspects. It is conceivable that regional disparities in healthcare provision could be mitigated, that medical care in rural areas could be better connected, and that new business models could be developed for companies. Companies and research institutions in the Free State already possess key expertise in medical informatics, AI development, and healthcare research.
For the qualitative analysis of the trend, the assumption was made that by the end of 2035, 60 percent of hospitals and medical practices in Thuringia will be using AI-based diagnostic systems. In January 2025, during an expert workshop, potential key development paths of the trend were identified using the Futures Wheel, and a future path was developed using the Visual Roadmap. This path describes how a positive target point for Thuringia as a location for innovation can be reached by 2035.
Results of the Futures Wheel
The Futures Wheel project was based on the assumption that "by the end of 2035, 60 percent of hospitals and medical practices in Thuringia will be using AI-based diagnostic systems." The goal was to systematically explore the potential of future developments based on this assumption. Three key development paths were identified, each revealing different potentials and challenges for Thuringia. The results showed three key development paths:
Results of the Visual Roadmap
As part of the qualitative development of the insights gained from the Futures Wheel, which assumes that 60 percent of hospitals and medical practices in Thuringia will be using AI-based diagnostic systems by the end of 2035, experts identified three business models that are expected to generate economic success in 2035. The visual roadmap illustrates the relationship between (i) applications and business models, (ii) required technologies, and (iii) societal framework conditions necessary to achieve a positive target point for Thuringia as a location for innovation. The time horizon extends from 2025 to 2035.
Applications and business models
| Developed business models: | Products & Services: |
|---|---|
| Data administrator/data trustee A promising business model is emerging in the area of data trusteeship in healthcare. Data trustees act as intermediaries between data-holding institutions (e.g., hospitals, laboratories), application-oriented stakeholders (such as startups or healthcare platforms), and regulatory bodies. They build trust through legally compliant data management, consent management, and data protection-compliant access structures. |
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| AI-based analysis of medical image data Another business model lies in the development and application of AI systems for medical image recognition. This involves not only diagnostic support but also pattern recognition, early warning systems, and structured documentation. The business opportunity lies in combining highly specialized image analysis methods (e.g., for radiological, dermatological, or histological data) with adaptive AI algorithms that can learn and adjust to new data. For Thuringian companies and research institutions, this presents potential opportunities in product development, service provision (e.g., as certified software providers), and clinical partnerships with imaging departments. |
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| Integrated healthcare models – medicine meets ICT In the long term, a scalable business model emerges through the integration of medical care with information and communication technology (ICT). This involves the intelligent networking of healthcare services, treatment recommendations, patient data, and care pathways. This model aims for continuous, personalized, and resource-efficient healthcare – in both inpatient and outpatient settings. Thuringian companies that combine technical infrastructure, medical expertise, and digital service logic can position themselves in this field as system integrators or platform providers. |
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| Therapy-oriented AI systems and decision support This business model is based on digital assistance systems for personalized therapy selection, e.g., in oncology or for rare diseases. Providers develop algorithmic suggestion systems that support physicians with up-to-date therapy recommendations. |
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| Regional supply partners and healthcare infrastructure providers A business model that aims for stable, comprehensive medical care with regional roots. The goal is to build network structures, care solutions, and innovation centers in rural areas – supported by digital systems. |
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Framework conditions
The introduction of AI-supported healthcare applications is taking place in several stages. Societal, regulatory, and institutional conditions are evolving in tandem. Along this timeline, key prerequisites can be identified that must be established step by step. Securing acceptance among key stakeholders: Even in the early stages, it is crucial that key players in the healthcare system recognize the potential of new technologies and actively participate in their development and testing. Acceptance by lead providers—large hospitals, research companies, or health insurers—acts as a catalyst for market entry, connectivity, and investment security. Early involvement also ensures the relevance and practical applicability of the emerging solutions. Enabling data collaboration: Access to high-quality clinical data remains one of the key bottlenecks for the development of adaptive systems. Therefore, the willingness to share data, particularly by hospitals, laboratories, and research partners, is an important foundation. This requires trust in governance structures: Data protection, purpose limitation, and security must be reliably regulated so that innovation and accountability go hand in hand. Ensuring the quality of dynamic systems: With the increasing use of AI in clinical processes, evaluation criteria must also be further developed. Traditional testing criteria fall short when systems are based on changing data or continuously learn. Accordingly, new quality requirements must be defined that integrate both technical robustness and ethical and clinical-practical aspects without stifling innovation. Shaping health insurance approval as a systemic bridge: Innovations only unfold their full potential when they are integrated into standard care. Health insurance approval is a key structural factor in this process: It determines availability, scalability, and reimbursement. Evaluation and reimbursement procedures must therefore be adapted to the characteristics of digital medicine – for example, with regard to algorithm dynamics, traceability, and validation. Methodologically anchoring diversity: Reliable AI in medicine must not be based on average data. Gender-sensitive sampling and the representation of children and adolescents are therefore not peripheral issues, but rather a methodological requirement for equal opportunities and clinical precision. Studies, training, and validation processes must systematically consider diversity to avoid biases and ensure appropriate diagnoses and treatment recommendations. Stabilizing societal acceptance of technology is crucial: Ultimately, the widespread adoption of AI-based systems depends on their high level of acceptance in everyday life. This acceptance arises not from technology alone, but from clear communication, explainable decision-making processes, and tangible improvements in care. A participatory development culture, transparent supply chains, and ethical reflection are essential factors for trust and openness to use.
Technologies
The following technological prerequisites are required for the successful development and market launch of the anticipated business models and related products and services:
| Technological requirement | challenges |
|---|---|
| Biomarker integration and screening technologies Early detection of individual health risks and more precise diagnostics require integrated, multimodal methods for identifying and combining biological markers. The integration of various biomarkers enables a more in-depth interpretation of molecular and imaging diagnostics, particularly in complex disease patterns. Screening, as a population-based early detection method, also benefits from automated analysis procedures and predictive models. Due to the need for data sharing and integration, the role of data trustees would become increasingly relevant. | Standardization of sampling and evaluation processes, validation of predictive markers, scalability for broad application. |
| Data technologies for secure and adaptive information processing The increasing interconnectedness of healthcare systems necessitates technological solutions for secure, privacy-compliant, and adaptable information sharing. Federated learning, as a technological solution for data sharing, enables the training of AI models without the need to centralize sensitive patient data. In addition, differential privacy is a key technology for safeguarding personal information, ensuring it remains statistically untraceable even after repeated use. | Computational effort of decentralized learning methods, securing distributed infrastructures, technical complexity when deploying in hospital systems. |
| Multimodal imaging and interpretable AI Multimodal imaging is crucial for precise and reliable medical diagnostics – that is, the combination of different imaging techniques (e.g., MRI, CT, ultrasound) and diagnostic data sources. Explainability solutions are essential for the clinical acceptance of such systems, making decision-making transparent – especially in AI-supported analyses. They form the basis for trust and physician reassurance. | Data alignment from different sources, real-time processing of large data sets, integration into clinical workflows. |
| Biotechnological therapies and testing systems With advances in regenerative medicine, stem cell-based therapies are moving into focus – both in personalized oncology and in tissue and organ regeneration. At the same time, the need for human-representative tests that better reflect physiological reality than classic animal models is increasing. Such systems utilize, for example, organ-on-a-chip technology or AI-supported cell culture analyses. | Standardization of biological materials, ethical approval, long-term outcomes. |
| Technology acceptance through user-centered design To sustainably integrate technological systems into healthcare, their usability and everyday practicality are crucial. Usability and UX design ensure that medical staff and patients alike can use new digital applications effectively and without barriers. They are therefore more than just a "surface" – they are an integral component of medical quality. | Interdisciplinary development processes, variability of medical usage scenarios, acceptance of heterogeneous user groups. |
Pathways to the future: Rethinking healthcare
Based on this potential, Thuringia could become a leading provider of personalized medicine by 2035. To achieve this goal, it is crucial to establish suitable framework conditions. These include reliable data backup systems, data infrastructure standardization, certifications, and public awareness and acceptance programs. The visual roadmap for eHealth in Thuringia outlines an ambitious vision of a digitally supported, personalized, and networked healthcare system. The intelligent use of AI technologies plays a central role, for example, in image analysis, therapy recommendation systems, and multimodal diagnostic solutions. Technological foundations such as data management systems, software development for clinical applications, testing platforms, and the integration of various biomarkers form the basis of increasingly data-driven healthcare.
A key objective is the development of new provider structures that combine genomics, AI, and medical technology, supported by trustworthy data infrastructures. This will also create new roles within the system, such as data trustees, curatorial institutions, and companies that analyze and validate clinical data. These actors will work closely with physicians, medical associations, hospitals, and patients. In the future, test development, certification, and application will be organized via platforms that combine both medical expertise and digital systems competence. Given the complexity and high ambition of the described potential future development, it should be examined whether the state government or Innovativ Thüringen should initiate a targeted cross-sectoral dialogue process that includes the stakeholders described above and in which necessary measures can be discussed to stimulate this development. This process should not only focus on what policymakers can do, but also on identifying the contributions each stakeholder can and will make to this overall vision. The dialogue process could be supported by a preliminary feasibility study for the development of a corresponding provider structure and trustworthy data infrastructure in the Free State.
The focus is on people, both as users of new systems and as part of an integrated care model. Aspects such as explainable AI, data protection, gender-sensitive sampling, and user-friendly design are increasingly being incorporated into development. At the same time, new demands are emerging for education and training. Future professionals must be able to combine both technological and medical expertise. Appropriate training formats and targeted continuing education programs in machine learning and health technologies are therefore essential.
Politically and structurally, a coordinated approach is needed, ranging from cross-departmental funding strategies and special translation programs to the early involvement of health insurance funds in approval and market entry issues. The development of digital healthcare infrastructure in rural areas, the creation of trustworthy data platforms, and the positioning of Thuringia as a model region for data-driven, personalized medicine form central pillars of this vision. It should be examined whether the establishment of a Trusted Data Center can be the crucial foundation for building a model region. Based on its function for the healthcare sector, the Trusted Data Center could also be conceived as a cross-sectoral and scalable solution. The center should not merely serve as infrastructure for providing computing power and storage capacity, but rather as an institutional platform that offers specialized expertise in the aggregation and use of sensitive health data. The implementation of advanced anonymization technologies is essential to ensure the secure and data protection-compliant handling of personal information. This center could not only serve as a central resource for secure data processing, but also act as a coordinating body for a federal AI architecture in healthcare. By promoting the interoperability of data systems and serving as a platform for the exchange and analysis of health data, it could play a key role in the development of data-driven, personalized medicine and simultaneously establish Thuringia as a pioneering region for trustworthy health data infrastructures.
eHealth is not a fringe issue, but a strategic field of action for medical quality, economic development, and social participation. Thuringia can play a pioneering role here if it succeeds in bringing together technology, trust, skills development, and financing.
4.4 Context-sensitive sensors – as a key to adaptive systems in industry, mobility and everyday life that intelligently capture and use environmental information
Context-sensitive sensors encompass sensor systems that capture and interpret relevant information from their environment. This involves processing various sensor data, such as optical or acoustic data, and amplifying it through the use of AI-based systems. In this way, context-dependent environmental data can be analyzed in real time, enabling adaptive responses. A combination of different sensors and their signals leads to a comprehensive environmental analysis with a high information content. Current applications range from autonomous navigation of drones or robot swarms to environmental monitoring and traffic control, as well as smart homes and mechanical and plant engineering. Context-sensitive applications still in the research and development phase exhibit significantly higher complexity. Challenges include the integration and real-time processing of large, heterogeneous datasets, the energy consumption of mobile applications, the performance of specified algorithms, and ensuring data protection and security. Furthermore, the lack of standardization currently hinders the interoperability of such systems.
A key application area that could gain importance in the future is the further development of context-sensitive smart city approaches. Here, intelligent sensor systems can contribute to making urban spaces more efficient, safer, and more sustainable – for example, through adaptive traffic management, intelligent energy distribution, or the early detection of environmental pollution. The trend is clearly moving towards the increasing networking and integration of sensor data, which, through AI-based analysis, enables new forms of urban planning, mobility, and public services. In the long term, context-sensitive sensor technology will not only open up new business models but can also make a significant contribution to the quality of life and resilience of urban and rural areas.
For the qualitative analysis of the trend, the assumption was made that by the end of 2035, Thuringia will be a leader in the development and production of context-sensitive sensors for manufacturing, transportation, robotics, and energy supply. In February 2025, during an expert workshop, potential key development paths of the trend were identified using the Futures Wheel. A future path was then developed using the Visual Roadmap, outlining how a positive target for Thuringia as an innovation hub can be achieved by 2035.
Results of the Futures Wheel
The Futures Wheel project was based on the assumption that "by 2035, Thuringia will be a leader in the development and production of context-sensitive sensors for manufacturing, transportation, robotics, and energy supply." The goal was to systematically explore the potential of future developments based on this assumption. Four key development pathways were identified, each revealing different potentials and challenges for Thuringia as a business location.
Results of the Visual Roadmap
As part of the qualitative analysis of the visual roadmap developed in the anticipation workshop, four business model approaches were identified that could unlock economic potential in the Free State of Bavaria based on technological developments and specific fields of application. The visual roadmap illustrates the relationship between (i) applications and business models, (ii) required technologies, and (iii) societal framework conditions necessary to achieve a positive outcome for the innovation hub. The time horizon extends from 2025 to 2035.
Applications and business models
| Developed business models: | Products & Services: |
|---|---|
| AVT as a product A viable business model emerges from the industrial scaling of assembly and interconnection technology (AIT) for sensor systems. Through targeted development with regard to cost-efficiency, real-time capability, and miniaturization, AIT can evolve into an independent product line applicable in a wide range of fields – such as environmental monitoring, production automation, or mobile sensor systems. This presents Thuringian suppliers with the opportunity to position themselves as system providers for modular AIT components. In particular, OEM-related applications and integration into existing machine infrastructures promise attractive sales markets. The link with regional software development for AIT-specific interfaces further strengthens vertical value creation. |
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| Sensor-as-a-Service A particularly dynamic business model is emerging in the area of usage-based sensor services. Modularly configurable multi-sensor systems make it possible to flexibly provide sensor functions as a service – for example, for temporary deployments in forestry, air quality measurement, or traffic monitoring. Customers gain access to sensors, data collection, and preprocessing without having to maintain their own proprietary hardware. For Thuringian companies, this model offers the potential to establish themselves as full-service providers of on-demand sensor technology – complemented by digital interfaces, GDPR-compliant data provision, and accompanying analytics services. |
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| Forecasting and assistance systems The intelligent analysis of multimodal sensor data enables scalable business models in the areas of predictive control and decision support. Sensor-based assistance systems allow, for example, early traffic jam forecasts, monitoring of critical infrastructure, and predictive maintenance. These systems combine sensor data fusion, real-time communication, and AI-supported interpretation models. This opens up application areas for Thuringia across the entire urban infrastructure, particularly in mobility, traffic management, and disaster relief. Local providers can act as integrators between sensor technology, software, and practical application. |
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| Sustainable sensor solutions A promising business model is emerging in the field of environmentally friendly sensor systems for temporary outdoor applications. Biodegradable sensors, such as those being developed at TITK (Thuringian Institute for Textile and Plastics Research), are opening up new application possibilities for environmental and agricultural sectors – for example, in soil moisture monitoring, forest fire prevention, and biodiversity monitoring. This model also addresses the increasing demands for sustainability and material recyclability. For Thuringian companies, this presents an opportunity to tap into new niche markets with innovative materials and sensor designs – both in public sector projects and in the rapidly growing environmental technology market. |
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Framework conditions
Establishing context-sensitive sensor technology as a future-oriented technology sector requires more than just technological innovation. Crucially, it demands a coordinated interplay of education, market knowledge, regulation, and ecosystem development. Along this development path, five key prerequisites can be identified that must be strategically developed and sustainably secured:
Systematically initiate the development of skilled professionals: Establishing a specialized degree program in Sensor Engineering marks a first structural step toward securing expertise in the long term. Furthermore, early-stage educational programs are needed to embed technological topics in schools and career guidance. Interdisciplinary qualification programs at universities and continuing education institutions are necessary to meet the increasing demand for skilled workers in the development, application, and maintenance of sensor-based systems. Ensure needs-based development through market proximity: For the economic scaling of sensor-based solutions, it is essential to closely link technological developments to real-world needs. Systematic market analyses and consumer insights provide the basis for market-driven innovations and prioritized development paths. Especially in domain-specific application areas, user needs must be identified early and fed back into technology development. Consider cybersecurity and regulatory requirements early on: With the networking of sensitive sensor systems, the demands on security and regulation also increase. The consistent implementation of existing regulations, particularly the European NIS-2 Directive on network and information security, is a key prerequisite for trust, market access, and investment. Sensor solutions must be designed with current security standards in mind from the development phase onward to minimize later adaptation costs and regulatory hurdles. Cybersecurity thus becomes an integral part of technological product development. Promoting interoperability as a fundamental requirement: Open, standardized interfaces are essential for sensor systems to be scalable and flexible. The targeted use of open-source technologies such as OPC UA creates the necessary technical foundation for cross-system communication, both in industrial and public infrastructures. Consistent interface acceptance prevents technological fragmentation. This results in seamless, modularly connectable systems that can also function in heterogeneous data environments. Strengthening innovation ecosystems spatially and structurally: The targeted development of regional ecosystems is necessary to sustainably anchor sensor-based value creation. This includes proactive strategies for attracting sensor and software companies, ideally in close proximity to universities, data centers, and technology transfer institutions.
Technologies
The following technological prerequisites are required for the successful development and market launch of the anticipated business models and related products and services:
| Technological requirement | challenges |
|---|---|
| Sensor diversity and multi-sensor systems A key element of technological innovation lies in the combination of diverse sensor principles: acoustic, optical, radar, and multiphysics sensors, together with intelligent data fusion software, form the basis for adaptive, context-sensitive sensor systems. These systems enable precise environmental perception in mobility, infrastructure monitoring, and Industry 4.0. | Different data frequencies and qualities, complex synchronization of sensors, robustness under varying environmental conditions. |
| Assembly and Interconnection Technology (AVT) AVT forms a technological core for the realization of highly integrated sensor systems. It enables the precise connection of electronic and non-electronic components – a crucial step towards the miniaturization and performance enhancement of sensor platforms. AVT is thus viewed not only as an enabler, but also as a potential product with its own market value. | High initial development costs, real-time capability, thermal and mechanical stability, quality assurance in series production. |
| System integration and interface capability The development of cross-application interfaces is crucial for the efficient integration of context-sensitive sensor solutions. The heterogeneity of sensor data sources necessitates technological solutions for harmonization and data transfer. Openness, modularity, and standardization are essential principles in this regard. | Cross-manufacturer interoperability, compatibility with legacy systems, real-time data availability. |
Pathways to the future: Context-sensitive sensor technology as a key technology
Based on this potential, Thuringia could generate revenue from Sensor-as-a-Service in various sectors by 2035. To achieve this goal, it is crucial to create data spaces and interface infrastructure and to provide the necessary framework for research institutions and companies. A key prerequisite for this is a strong commitment from international standardization bodies.
The visual roadmap for context-sensitive sensor technology describes how Thuringia can systematically expand its role as a center of excellence for adaptive, scalable sensor solutions by 2035 through targeted technology and infrastructure development. The starting point is the further development and combination of highly specialized sensors, such as acoustic, radar, or optical sensors, which are integrated into multisensor systems. These are complemented by AI-based software solutions that analyze complex environmental data, movement patterns, or system states in real time. Assembly and interconnection technology (AIT) plays a particularly important role. This involves the acquisition and evaluation of parameters such as speed, realism, and cost to validate the performance of intelligent sensor systems in practical applications. To support this development, the state government should examine the feasibility of establishing real-world laboratories for autonomous mobility in Thuringia. These laboratories would serve as test environments for context-sensitive sensor technology in transportation, logistics, and robotics. They should function not only as technological testing grounds but also as platforms for the development and implementation of standards and regulatory frameworks. Furthermore, Thuringia could be positioned as an international partner for autonomous mobility by pursuing a strategic partnership with global players in the field of mobility and technology.
These systems are realizing their potential in a wide range of applications. These include real-time forecasting in traffic and agriculture, the monitoring of critical infrastructure, and assistance systems that gradually lead to full automation. New solutions for scalable multi-sensor platforms that can be used across applications are also emerging in the field of automated driving technology (ADT). Sensor technology is thus becoming the central foundation for sustainable and adaptive systems, for example in traffic management, digital forestry, or environmental analyses. Ecologically oriented sensor solutions, such as biodegradable particle sensors or self-scaling monitoring systems, are also gaining importance.
Such an innovation system requires a high-performance data and cloud infrastructure. This infrastructure must be both data-sovereign and accessible. The roadmap refers to projects like the Thuringia Cloud, which aims to enable the secure, GDPR-compliant processing of large data volumes. This also necessitates open interface standards that allow for the flexible integration of various sensor technologies. In addition to the technical implementation, societal prerequisites must also be established, particularly regarding data sovereignty, transparency of automated decisions, and the establishment of trustworthy systems.
At the same time, a systematic development of specialist and training expertise is required. Degree programs such as Sensor Engineering, continuing professional development formats, and targeted talent promotion should contribute to securing both skilled workers and interdisciplinary project teams in the long term. Simultaneously, greater visibility of sensor-related career profiles is necessary, supplemented by modern communication formats, to promote societal acceptance of context-sensitive sensor technology.
In the long term, Thuringia aims for an integrated sensor ecosystem that harmonizes ecological sustainability, economic scalability, and social acceptance. AVT plays a key role in this, acting as a bridge between technological progress and real-world applications. The roadmap makes it clear that context-sensitive sensor technology extends far beyond a single technology. It forms the basis for new business models, data-driven services, and future-oriented regional development.
4.5 Intelligent image processing – as a basis for autonomous, reactive and learning systems in dynamic environments
Intelligent image processing utilizes advanced artificial intelligence (AI) and machine learning methods to analyze, interpret, and react to visual data in real time. This is no longer just about capturing and storing image information, but about developing adaptive systems that can learn from changing environments and continuously learn from new data. These systems combine high-resolution image sensors with powerful AI algorithms and are capable of handling complex tasks such as facial recognition, motion analysis, and object detection.
This technology is used in the automotive industry, where intelligent image processing systems enable autonomous navigation by precisely detecting obstacles, pedestrians, or traffic signs. In industrial manufacturing, intelligent image processing enhances quality assurance through automated defect detection on production lines. In medicine, intelligent image analysis supports the early detection of diseases, for example, through the automated evaluation of MRI or X-ray images. Furthermore, this technology is also gaining increasing importance in security and surveillance technology, for instance, for biometric access control or behavioral analysis.
Challenges lie primarily in the integration and processing of large datasets, ensuring the real-time capability of the systems, and adapting to varying lighting conditions and complex environments. Legal issues also arise, particularly regarding the use of sensitive personal data (e.g., facial recognition in public spaces). The trend toward intelligent image processing underscores that not only technical but also societal and legal considerations are crucial for the acceptance and sustainable application of this key technology.
For the qualitative analysis of the trend, it was assumed that Thuringia had become a pioneer in the field of intelligent image processing in the areas of AI-supported manufacturing, robotics, and autonomous mobility by the end of the 2020s. In February 2025, during an expert workshop, potential key development paths of the trend were identified using the Futures Wheel. A future path was then developed using the Visual Roadmap, outlining how a positive target point for Thuringia as an innovation hub can be achieved by 2035.
Results of the Futures Wheel
The Futures Wheel project was based on the assumption that "by the end of the 2020s, Thuringia will have become a pioneer in the field of intelligent image processing in the areas of AI-supported manufacturing, robotics, and autonomous mobility." The goal was to systematically explore the potential of future developments based on this assumption. Two key development paths were identified, each revealing different potentials and challenges for Thuringia as a business location.
Results of the Visual Roadmap
As part of the qualitative development of the insights gained from the Futures Wheel regarding Thuringia's assumption of a pioneering role in the field of intelligent image processing, experts identified five business models that are expected to generate economic success by 2035. The visual roadmap illustrates the relationship between (i) applications and business models, (ii) required technologies, and (iii) societal framework conditions necessary to achieve a positive target point for Thuringia as a location for innovation. The time horizon extends from 2025 to 2035.
Applications and business models
| Developed business models: | Products & Services: |
|---|---|
| Data Center & Computing Center A future-oriented business model lies in the development and operation of high-performance, regionally anchored data centers. These provide the necessary digital infrastructure for data-intensive applications, such as AI-supported map generation, the training of autonomous systems, and the processing of multimodal sensor data in mobility, healthcare, and manufacturing. Data centers enable sovereign, GDPR-compliant data processing while simultaneously ensuring local value creation. For Thuringia, this presents a strategic opportunity to strengthen digital sovereignty and establish its own data networks as the foundational infrastructure for future business models. |
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| Platform providers/cloud providers A scalable business model emerges from the role of providing cloud-based platform infrastructures for data-driven services. Platform providers coordinate the integration of sensor data, control systems, and AI analytics, thus creating the technical foundation for the cross-sectoral use of smart systems. The platforms act as an intermediary structure between data sources and users, offering standardized interfaces and enabling modular expansion through software services. This presents Thuringian companies with the opportunity to occupy key interfaces within the emerging data ecosystem. |
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| Data providers for AI-supported manufacturing processes A significant business opportunity lies in the specialized provision of industrially usable datasets for improving AI-supported manufacturing processes. Data providers collect, structure, and maintain high-quality, regulatory-compliant production-relevant data. Their offerings target companies that want to implement machine learning systems, digital twins, or adaptive production planning. These providers play a key role in data-driven manufacturing ecosystems and enable data-based value creation to be established even in small and medium-sized enterprises (SMEs). |
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| Complete solution provider in the field of autonomous mobility A comprehensive business model arises from the ability to provide complete system solutions in the field of autonomous mobility. Full-service providers combine sensors, software, vehicle technology, and cloud infrastructure to create deployable mobility solutions – for example, for rural areas, logistics services, or municipal traffic management. Through integration and standardization, they create marketable offerings that make complex technologies accessible to users. Thuringian providers could thus establish themselves as system integrators in a growing mobility segment. |
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| Thuringia as a full-service provider for adaptive manufacturing technologies A strategic business model emerges from positioning Thuringia as a location for adaptive manufacturing technologies with a high degree of modularity. At its core are scalable sensor systems, intelligent automated process control (APC) methods, and flexible software solutions that allow for dynamic adaptation to changing production conditions. The combination of hardware intelligence, interface standardization, and AI-supported control opens up access to innovative automation solutions, particularly for small and medium-sized enterprises (SMEs). Thuringia could thus become a prominent, comprehensive system provider along the digital manufacturing chain. |
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Framework conditions
For the successful development and market launch of the anticipated business models and the associated technologies, products and services, fundamental structural and strategic prerequisites are required:
Infrastructure Development and Visibility: Targeted investments in high-performance infrastructure are necessary to strengthen regional innovation capacity. The construction of a data center in Thuringia is identified as a critical prerequisite for data-intensive applications. Accompanying measures are needed to increase the visibility and public acceptance of such a data center. Access to computing power is thus established as a systemically important building block for data-driven business models. Technology Transfer and University Networking: Technology transfer from universities to economic application areas plays a central role in the market penetration of new sensor solutions. The Central State Distribution Center (ZLV) of the universities serves as an institutional interface for this. Transfer and start-up centers should be specifically expanded to promote spin-offs, collaborations with SMEs, and early product development. Innovation Culture and Regulatory Experimentation: Flexible testing environments are needed to test sensor-based technologies and their system integration. The establishment of real-world laboratories, facilitated by appropriate experimental clauses, creates space for technical testing, participatory development processes, and regulatory alignment. Thuringia can play a pioneering role here through exemplary pilot projects and simultaneously contribute to the development of standards. Cluster-oriented value creation: The formation and further development of value-creating clusters is understood as key to sustainable regional development. The targeted development of regional networks between companies, research institutions, and start-up actors creates robust innovation ecosystems that ensure long-term competitiveness and help open up new markets.
Technologies
The following technological prerequisites are required for the successful development and market launch of the anticipated business models and related products and services:
| Technological requirement | challenges |
|---|---|
| Multispectral sensing and multimodal imaging A key technological area lies in the development of multispectral sensors (VIS–IR) and multimodal imaging systems that combine data from different sources – optical, thermal, or radar-based. Combined with intelligent lighting and multimodal data analysis, this results in systems with high precision and a broad range of applications. | Calibration of heterogeneous sensor sources, real-time processing of large data sets, synchronization of visual and non-visual information channels. |
| Edge computing and decentralized computing architectures For data-intensive applications – such as autonomous systems or real-time monitoring – high-performance chips are required at the network periphery. They enable local data processing with low latency. | Energy efficiency under full load, thermal stability, integration into miniature hardware. |
| AI-supported control and navigation The combination of AI tools with precise navigation (sensor data fusion) forms the backbone of adaptive systems in mobility, production, and logistics. Expert systems that access sensor data make decisions in complex, dynamic environments. | Interpretability of AI decisions, data quality from heterogeneous sources, latency times in decision-making. |
| Communication technologies and connectivity A robust connectivity infrastructure is required for the secure and low-latency transmission of sensor and machine data. Scalable communication solutions are particularly crucial for mobility applications or distributed sensor systems. | Network stability under high load, protection against third-party access, interoperability between system components. |
| Data centers and digital infrastructure for computing power Computationally intensive sensor systems require locally anchored data centers with high storage capacity and high-performance infrastructure. These serve not only for data processing but also as a security anchor for sensitive information. | Sustainable energy use, high availability, data protection compliance. |
Pathways to the future: From innovation hub to full-service provider of adaptive technologies
Based on this potential, Thuringia could become the land of digital hidden champions 2.0 by 2035, positioning itself as a full-service provider of adaptive manufacturing technology, for example, with adaptive robots for diverse applications. To achieve this goal, it is crucial to provide research facilities and support new innovation cycles. The starting point is the consistent advancement of image processing technologies: from multispectral sensor systems and highly integrated edge chips to AI-based imaging, navigation, and control systems, new core technologies are emerging that are being transferred into a wide range of applications. Particularly noteworthy are sensor-based real-time analysis in mobility, logistics, and maintenance contexts, as well as the development of machine learning systems for map generation and condition monitoring.
These technologies translate into concrete products and services: for example, sensor/software kits for mobility applications, training systems for AI use, or comprehensive Mobility-as-a-Service solutions. Applications range from autonomous vehicles and agricultural and medical robotics to digital twins for urban infrastructure. Data centers and data-intensive platforms form the backbone of these innovations, both in research and operational scaling. To realize this potential, the previously recommended Trusted Data Center should not only serve as central infrastructure in the healthcare sector but also as a critical component for implementing Thuringia's vision as a "Land of Digital Hidden Champions 2.0." This center could play a key role in the development of adaptive manufacturing technologies, particularly through its expertise in the secure handling of sensitive and highly complex data. The platform should process data from various industries, such as the mobility and logistics sectors, and make it available for the further development of AI-supported image processing, navigation, and control systems. The data center could serve as an interdisciplinary hub, enabling the secure exchange and use of data from various sectors – from autonomous vehicle technology and digital agriculture to robot-assisted maintenance and repair processes. By employing advanced anonymization technologies and data protection-compliant procedures, the center would not only contribute to the development of new technologies but also strengthen trust in data-intensive, AI-driven innovations. Combined with sophisticated computing capabilities, this could allow Thuringia to grow not only as a provider of adaptive manufacturing technology but also as a pioneer in the secure and effective use of data in future-oriented industries.
Realizing this potential requires targeted structural measures. The establishment of testbeds, real-world laboratories, cloud platforms, and platform providers is essential. Relevant clusters, universities, and technology carriers must be connected and strengthened through coordinated funding instruments. The development of high-performance data centers, tailored to data-intensive future technologies, is a key component.
In the long term, Thuringia can thus secure its position as an integrative player in the global innovation landscape of autonomous systems. The combination of research excellence, forward-looking infrastructure, and strategic location policy will enable it to act as a full-service provider for adaptive manufacturing and autonomous mobility. In particular, the ability to integrate products, data services, and applications will differentiate Thuringia's role in the market.
This roadmap makes it clear: Intelligent image processing is far more than a technological field – it is a strategic area of action for the development of new value chains, societal applications, and industrial policy positioning. However, this requires a consciously orchestrated structural transformation along the lines of technology, transfer, infrastructure, and governance.
4.6 Quantum-based and neuromorphic technologies – as radically new approaches to computing power and sensor design, with disruptive potential for a wide range of application areas
Quantum-based and neuromorphic technologies have the potential to produce highly precise yet energy-efficient sensors, as well as novel hardware for future computer systems. They combine cutting-edge principles of quantum mechanics and spintronics, or neural networks. The goal is to unlock new dimensions in computing power, sensor design, and data analysis. These technologies utilize quantum mechanical effects or—in the case of neuromorphic chips—neural structures to enable measurements on the nanometer scale, thereby unlocking the potential for radical performance improvements. In Thuringia, these developments are finding fertile ground for these pioneering approaches, thanks to a strong ecosystem of optics and photonics companies, internationally renowned research institutions, and an innovation-friendly network structure.
Current research already reveals promising examples. For instance, diamond quantum sensors for highly sensitive NMR spectroscopy enable the precise analysis of metabolic processes at the molecular level, while spintronic sensors can detect magnetic fields in the femtotesla range. Superconducting Josephson neurons serve as the basis for energy-efficient neuromorphic data processing, and 2D materials such as graphene enhance the performance of electronic components. These technologies form the foundation for disruptive innovation leaps in a wide variety of application areas.
Looking at the opportunities, both quantum-based and neuromorphic technologies offer great potential for the creation of new value chains and the development of entirely new products and services. By integrating these technologies, Thuringian companies could tap into new markets and position themselves as high-tech providers in international competition. The close collaboration between cutting-edge research and industry also opens up opportunities for innovative startups, particularly in the SME and start-up sectors, while simultaneously expanding scientific excellence makes the region visible and attractive to skilled professionals worldwide. Last but not least, quantum-based sensor systems also promise new applications in environmental and resource protection, for example, for more precise environmental monitoring solutions.
For the qualitative analysis of the trend, the assumption was made that Thuringia, with its expertise in superconductivity and QPiC, will contribute to the development of the next generation of computers and establish itself as a center for quantum sensing in the 2030s. In February 2025, during an expert workshop, potential key development paths of the trend were identified using the Futures Wheel, and a future path was developed using the Visual Roadmap, describing how a positive target point for Thuringia as an innovation location can be achieved in 2035.
Results of the Futures Wheel
The Futures Wheel project was based on the assumption that "Thuringia, with its expertise in superconductivity and QPiC, will contribute to the development of the next generation of computers and establish itself as a center for quantum sensing in the 2030s." The goal was to systematically explore the potential of future developments based on this assumption. Four key development pathways were identified, each revealing different potentials and challenges for Thuringia. These four pathways demonstrate that quantum technology offers Thuringia not only technological but also economic and socio-political transformation potential, provided the necessary structural conditions are strategically developed now.
Results of the Visual Roadmap
As part of the qualitative development of the insights gained from the Futures Wheel regarding Thuringia's potential significant contribution to the advancement of quantum technology, experts identified five business models that are expected to generate economic success by 2035. The visual roadmap illustrates the relationship between (i) applications and business models, (ii) required technologies, and (iii) societal framework conditions necessary to achieve a positive target point for Thuringia as a location for innovation. The time horizon extends from 2025 to 2035.
Applications and business models
| Developed business models: | Products & Services: |
|---|---|
| Quantum computer data center-as-a-service A future-oriented business model emerges from the development and operation of a specialized quantum computing center, operating as an "as-a-service" model. This enables research institutions, startups, and established companies to gain low-threshold access to quantum-based computing power without having to maintain their own hardware. Standardized interfaces allow the computing capacity to be flexibly used for a wide range of applications, such as materials simulation, optimization tasks, and AI-supported data analytics. This presents Thuringia with the opportunity to position itself as a location for digital sovereignty and highly specialized computing infrastructure. |
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| Materials analysis as a service A scalable business model lies in providing high-precision materials analysis as a service. By using quantum-sensitive sensor systems, the chemical and physical properties of materials can be analyzed with the highest accuracy. This opens up new possibilities in quality control, environmental analysis, and medical diagnostics. SMEs, in particular, benefit from access to highly specialized analytical capacities without having to build their own laboratories. Thuringian providers can establish themselves as reliable partners for production-related analytics and innovation projects. |
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| Quantum-based sensor solutions for specialized markets A high-growth business model arises from the development and marketing of quantum-based sensor solutions for specific industrial and societal applications. These include, for example, sensor systems for medical diagnostics, environmental monitoring, materials analysis, and the maintenance of complex systems. By combining measurement precision with miniaturization, such solutions are particularly well-suited to markets with stringent regulatory requirements or extreme environmental conditions. |
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Framework conditions
For the successful development and market launch of anticipated quantum technology business models, as well as the underlying applications and technologies, fundamental structural and strategic prerequisites are necessary: Cooperation with large companies and the establishment of joint innovation structures: A future-oriented innovation system in the field of quantum technologies relies on robust partnerships with larger companies. Joint labs with leading industrial players can significantly accelerate the transfer from research to application. Large corporate participation is essential not only for scaling but also for compatible standardization and industrial implementation. Innovation funding must be specifically targeted at such alliances to be effective. Education, skills development, and awareness-raising: A qualified workforce is crucial for long-term innovation capacity. This requires education and training initiatives that specifically involve schools and universities and are tailored to the specific requirements of quantum technologies. Early orientation, basic technological education, and practical training formats are necessary to secure the next generation of specialists. Research infrastructure and technology transfer centers: Sustainable research and development activities require specialized infrastructure. This includes measuring equipment, cleanrooms, and high-precision laboratory environments. To utilize these resources efficiently, central R&D infrastructures should be established and complemented by an innovation start-up center. This center would provide guidance, knowledge transfer, and targeted support for technology-driven startups. Use of existing infrastructure by start-ups and SMEs: Start-ups in the field of quantum and sensor technologies need access to highly specialized infrastructure without having to provide it themselves. Renting existing cleanrooms and production facilities to young companies creates an economically viable bridge between research and production. At the same time, this creates early application spaces for technological maturity levels. Systematic networking of users and stakeholders: The transfer of quantum innovations into industrial value chains requires a strong, interdisciplinary network. Targeted networking of users, technology carriers, and research institutions enables continuous exchange on needs, feasibility, and scalability. In addition, a dedicated science and technology forum for quantum technologies should be established to serve as a dialogue platform for businesses, science, society and politics.
Technologies
The following technological prerequisites are required for the successful development and market launch of the anticipated business models and related products and services:
| Technological requirement | challenges |
|---|---|
| Material analysis and miniaturization of system components Highly specialized applications in quantum and high-precision technology require powerful methods for materials analysis. These enable the detailed characterization of new materials and the verification of reproducible material properties during the manufacturing process. In parallel, the miniaturization of system and subsystem components allows for integration into compact, energy-efficient platforms. | Capturing material behavior at the nanoscale, analysis under extreme environmental conditions, compatibility with other manufacturing processes. |
| Semiconductor technology and microfabrication The further development of semiconductor technology represents a key foundation for scaling up new quantum systems. In combination with advanced microfabrication, precise components for integrated quantum platforms can be realized. | Cleanroom production with high quality standards, thermal stability, compatibility with photonics and electronics. |
| New photonic materials and substrate fabrication The development of novel photonic materials enables the targeted fabrication of chip systems with enhanced functionality, particularly for light control, modulation, and detection. The associated substrate fabrication is essential to ensure controlled growth processes, mechanical stability, and integrability. | Reproducibility of material properties, high optical quality, thermal conductivity. |
| 2D materials and superconducting materials Innovative 2D materials such as graphene and superconducting materials are opening up new fields of application for lossless signal transmission, sensors, and quantum memory. These materials are particularly relevant for energy-efficient components and new switching principles. | Process stability in manufacturing, integration into existing architectures, long-term material availability. |
| Lithium niobate technology for autonomous driving applications Lithium niobate technology for autonomous applications: Lithium niobate is one of the key materials for modular quantum devices and optical signal processing. Due to its high modulation efficiency, the material is suitable for applications in autonomous systems. | controlled doping, integration with photonic platforms, industrial scalability. |
| Integrated diffractive chip systems Diffractive elements enable complex light manipulations to be achieved in a compact space. Integrated chip systems with such functionalities are essential for quantum components in communication and sensor technology. | Precision manufacturing, thermal stability, alignment of complex optical paths. |
| Neuromorphic materials and technologies Neuromorphic materials are of central importance for future AI-based and sensor-dense systems. They allow the simulation of neural information processing at the material level and open up new avenues in hardware-oriented AI development. | Material reliability under continuous stress, interoperability with classic electronics, limited production processes. |
Pathways to the future: Quantum sensors as an industrial lever
The roadmap outlines how Thuringia can expand its role as a center for quantum sensing technology and contribute to the development of the next generation of computers well into the 2030s. At its core are two complementary innovation strands: firstly, the development of high-performance hardware based on QPiC, neuromorphic chips, and superconducting technologies; and secondly, the development of corresponding quantum software, including algorithmic control, system architectures, and ethical frameworks. Combining these two strands opens up new potential in human-technology interaction, medical diagnostics, and industrial automation. Against this backdrop, the development of a targeted export promotion strategy for quantum technologies is advisable. This strategy would not only support companies in their internationalization efforts but also strengthen their networking with global markets and partners. This could be achieved through the establishment of international partnerships, participation in global quantum initiatives, and the provision of specialized export centers for quantum products. In parallel, the expansion of infrastructure, particularly in the areas of skilled worker development and the digitization of administrative processes, should be promoted in order to support the rapid scaling of quantum technologies and to ensure long-term sustainable growth.
Technological value creation begins with fundamental key technologies such as photonic materials, semiconductor manufacturing, 2D materials, superconducting materials, and integrated chip systems. Building upon this foundation, new application systems are emerging – particularly in the field of context-sensitive quantum sensors for environmental monitoring, maintenance, security, and medicine. Platform-based system integration and the development of a robust supply chain form the basis for industrial scaling. New business models are emerging, including those centered around analytics-as-a-service, modular system solutions, and OEM-related manufacturing.
To realize this potential, significant infrastructural prerequisites must be established. These include additional cleanrooms, specialized measurement infrastructure, application centers, and technology testbeds. Simultaneously, systematic transfer pathways between science and industry must be strengthened. Innovation centers, start-up platforms, or deep-tech incubators are conceivable options. Investors, cross-cluster networking, and science-practice collaborations will become strategic levers for scaling and achieving market viability for quantum technology solutions. To this end, the state government could examine the feasibility of establishing a Quantum Health Lab in Thuringia, serving as an innovation center for the development and validation of quantum-based diagnostic methods. This center could function as a test environment for new technologies, thereby accelerating the approval processes for new medical devices. Creating an interdisciplinary network of experts from medicine, quantum physics, and regulatory fields would pool the necessary expertise to establish Thuringia as a global leader in quantum medicine.
This requires accompanying economic policy measures that reduce bureaucracy, facilitate investment, and enable targeted location policies. This is particularly relevant with regard to land availability, the demand for skilled workers, and a welcoming culture for international talent. Only through holistic management—from basic research to export products—can we prevent resource bottlenecks, fragmented funding structures, or rigid regulations from hindering growth potential. Targeted collaboration between established companies, startups, and research institutions is essential in this regard. In this context, the state government should consider initiating a Quantum Accelerator Program specifically designed to support startups and companies in the field of quantum and neuromorphic systems. Such a program could provide founders with the necessary support through targeted financial assistance, tax incentives, and the reduction of bureaucratic hurdles. Additionally, a network of innovation centers and tech incubators should be established to act as catalysts for technological development and market launch. This would not only encourage the establishment of quantum tech companies, but also pave the way for the creation of new value chains.
In the long term, Thuringia can position itself as an internationally visible technology hub through this development, both in specialized markets such as quantum medicine and environmental sensing, and in cross-cutting fields such as mechanical engineering, robotics, and autonomous mobility. The visual roadmap makes it clear: Thuringia is on the cusp of growing from isolated areas of expertise to a strategically networked quantum ecosystem, provided that infrastructure, talent, capital, and coordination can be effectively aligned.
5. Results: Further topics for the future
In addition to the five focus trends identified as particularly relevant for Thuringia's future during the foresight process and further developed using anticipation methods, other future topics were discussed at the trend workshop. These trends were evaluated in the prioritization matrix with regard to their anticipated impact on Thuringia and the uncertainty of their future development. This enabled a systematic distinction between immediate areas of action, trends for further consideration in the foresight cycle, strategic areas for observation, and topics not currently relevant for further processing.
5.1 Further Trends for the Foresight Process
The priority search areas, characterized by high influence and simultaneously high uncertainty, include, in addition to the five focus trends, the commercialization and technologization of space exploration, new approaches to addressing environmental and health challenges, holistic approaches to integrated energy management, advanced materials and technologies for energy and information systems, smart cities with advances in infrastructure, AI, and cybersecurity, and advanced autonomy and security strategies for unmanned aerial systems. These topics remain in the priority trend repository and will be re-analyzed in the scanning process step of the next cycle.
Commercialization and technologization of space research
Space exploration is becoming a priority topic in the foresight process, as its high potential impact, coupled with significant uncertainty, makes it a key strategic area of observation. New private sector actors and advancing technology are fundamentally transforming this previously state-dominated field. Key developments include the reusability of launch vehicles, autonomous exploration robotics, innovative spacesuits, VR-based training environments, and space-based communication infrastructures. At the same time, strategic issues such as interplanetary sustainability, the utilization of extraterrestrial resources, and international cooperation are gaining increasing importance.
Alongside technological innovations, new questions arise, such as those concerning regulation and security, the equitable distribution of resources, and the role of state space agencies in conjunction with private actors. Furthermore, strategic competition between geopolitical actors, particularly the USA, China, and India, continues to intensify, potentially leading to shifts in power. At the same time, this trend offers significant impetus for related technological fields such as sensor technology, materials development, artificial intelligence, and quantum communication.
Implications for Thuringia
For Thuringia, this trend could offer far-reaching opportunities in the long term, particularly where existing technological expertise can be combined with the new requirements of the aerospace industry. Relevant players such as Jenoptik AG, Jena-Optronik GmbH, SpaceOptix GmbH, the Fraunhofer Institute for Applied Optics and Precision Engineering IOF, the Friedrich Schiller University Jena, the Thuringian State Observatory, and aerospace initiatives like LRT e. V. already possess the capability to connect with key future fields of space technology in the areas of optics, sensors, communication, and system integration.
Because the development of this trend is characterized by a high degree of uncertainty, it could be beneficial not only to observe this area but also to analyze it in depth using targeted foresight methods. This would involve examining: What future scenarios are associated with the technological advancement and commercialization of space travel? What strategic options could arise for Thuringia as a result? How can existing strengths in the state be leveraged? One possibility would be to expand existing networks beyond Thuringia, for example, through closer ties to ESA activities or supra-regional collaborations with other German states such as Saxony. Strategically anticipating specific applications and their consequences, for example, in the areas of quantum communication or Earth observation, could also help to make the potential tangible.
Overall, it seems worthwhile to classify this trend not only technologically but also strategically, especially since the inclusion of space travel in the title of the Federal Ministry of Education and Research, along with its position in the high-tech agenda, underscores its growing importance. This could open up a space of opportunity for new value chains, international partnerships, and forward-looking narratives that could provide impetus for Thuringia's innovation landscape, extending beyond the field of space travel.
New approaches to tackling environmental and health challenges
Due to their high potential impact coupled with significant uncertainty, the data-driven collection and analysis of environmental and health data is considered a priority in the foresight process. Technological advances increasingly enable the collection and systematic evaluation of environmental and health data at high spatial and temporal resolution. The use of AI, machine learning, IoT sensors, and satellite technologies allows for a better understanding of complex environmental systems, the early detection of risks, and the more targeted development of preventive measures. Applications range from real-time air quality monitoring and the detection of illegal environmental activities to large-scale models, such as those used in the EU project Destination Earth, which maps the interactions between natural processes and human activity.
These data-driven technologies open up new possibilities in civil protection, resource management (e.g., water, waste, land use), forest and species conservation, and resilient infrastructure. At the same time, the cross-sectoral use of such systems, for example in urban planning, agriculture, and healthcare, is coming into focus.
Implications for Thuringia
This trend presents various opportunities for Thuringia. Data-driven environmental technologies could help to predict risks such as floods or droughts more accurately and to use resources (e.g., water or agricultural land) more efficiently. Applications also exist in the context of air pollution control, forest monitoring, and the tracking of microplastics, which could bring both ecological and economic benefits. It would be sensible to build upon existing initiatives for digital environmental monitoring and to expand them specifically with AI-based predictive models. Pilot projects in flood-prone areas or urban heat islands could provide a concrete basis for application and scaling. Likewise, consortia of government, science, and industry could be formed to test new data-driven business models in relevant fields such as agriculture, the circular economy, or healthcare.
Overall, such technologies could help increase adaptability to climate change, reduce environmental impacts, and simultaneously promote innovation in environmental technology. Therefore, this topic should continue to be closely monitored.
Holistic approaches to integrated energy management
The trend toward integrated energy management systems is assessed in the foresight process as a priority topic with high potential impact and simultaneously high uncertainty. It is based on the increasing integration of decentralized generation, storage, and consumption units. The focus is on increasing energy efficiency, reducing CO₂ emissions, and achieving greater independence from centralized supply structures. This is achieved, among other things, through the combination of advanced storage technologies (e.g., compressed air and battery systems), the use of renewable energy sources, and the flexible conversion of energy forms. Digital tools, such as those for analyzing and visualizing energy flows and emissions in real time, also play a key role. These enable informed decision-making, both for municipal planning processes and for private sector applications. The realization of climate-neutral neighborhoods or the use of waste heat recovery in local heating networks are examples of the increasing convergence of technological innovation and sustainable planning.
Implications for Thuringia
For Thuringia, it could be beneficial to treat the development of such integrated energy systems as a strategic future issue. Above all, this should be done in conjunction with the goals of municipal heat planning, the expansion of renewable energies, and regional value creation.
Pilot projects in small and medium-sized towns or industrial clusters could help test practical applications and achieve tangible success. Utilizing existing infrastructure, such as for wastewater heat recovery or integrating solar thermal energy and heat pumps into local networks, also appears promising. To increase technical and social acceptance, information campaigns, participatory formats, and transparent cost-benefit analyses could be helpful. Furthermore, collaboration with innovation stakeholders such as the Fraunhofer Institute for Optronics, System Technologies and Image Processing's (AST) Institute for Applied Systems Technology, the EDIH European Digital Innovation Hub Thuringia, or specialized companies like revincus GmbH could be expanded to jointly develop scalable solutions.
Advanced materials and technologies for energy and information systems
The development and application of advanced materials is considered a priority topic in the foresight process, exhibiting both high potential impact and high uncertainty. It opens up new possibilities in energy conversion, energy storage, and information processing. Technologies such as perovskite solar cells, thermal batteries, and photonic systems promise significantly higher energy efficiency and offer new opportunities for miniaturization, increased flexibility, and resource conservation in a wide range of fields.
A particular added value of the topic lies in the combination of technologies with intelligent materials research: Self-healing materials, biocompatible systems and new forms of data storage can enable future-oriented applications, e.g. in medical technology, sustainable electronics or for the resilience of critical infrastructures.
Implications for Thuringia
For Thuringia, it could be very promising to strategically network the existing excellent research in the field of materials science (Friedrich Schiller University Jena, Ilmenau University of Technology, Fraunhofer Institute for Ceramic Technologies and Systems IKTS) with industrial partners in order to advance application projects for specific challenges in the energy and digitalization sectors. Cooperation with IT networks such as ITnet Thuringia or with players in the digital economy could also help to unlock transfer potential. Likewise, it would be beneficial to integrate existing expertise in battery and storage research, e.g., at CEEC Jena or IBU-tec advanced materials AG, with topics related to the circular economy, for example, through battery recycling consortia.
Given the complexity of these topics, it would be advisable to also use new methodological formats such as foresight workshops or real-world laboratories to explore possible future paths and promote technology-neutral development perspectives.
Smart cities with advances in infrastructure, AI and cybersecurity
Smart cities are considered a priority topic in the foresight process because they have a high potential impact on key future areas while simultaneously being associated with considerable uncertainty regarding technological, social, and regulatory frameworks. They represent the targeted integration of digital technologies into urban infrastructure and aim to increase efficiency, safety, quality of life, and sustainability. Artificial intelligence, sensors, IoT, 5G, as well as AR and VR, create new opportunities in traffic management, emergency response, infrastructure monitoring, and energy supply. Data protection and cybersecurity become integral components of resilient urban development, particularly through data-minimizing technologies such as privacy-preserving technologies and the use of edge computing.
Implications for Thuringia
For Thuringia, it could be beneficial to view smart city initiatives not only as infrastructure projects, but more as innovation and transformation projects for the cities themselves. The targeted combination of R&D funding, participatory approaches, and new technological applications can contribute to a more intelligent networking of urban and rural areas and to the targeted exploitation of innovation potential.
Exchanging information with other regions could help to use resources efficiently and leverage synergies. It would be worthwhile to share experiences from pilot projects and to promote the wider application of drone-based solutions or digital twins for urban cultural assets. This is particularly relevant in the context of collaborations between public transport operators, research institutions, and regional tech companies, for example, within the framework of consortia or testbeds for adaptive traffic management, urban emergency management, or intelligent logistics solutions.
Advanced autonomy and safety strategies for unmanned aerial systems
Unmanned aerial systems (UAS) are categorized as a watchlist topic in the foresight process because they are currently associated with low impact and low uncertainty. Through the use of artificial intelligence and machine learning, they are increasingly evolving into highly autonomous systems capable of handling complex navigation and control tasks in real time. This is particularly relevant for new drone types such as electrically powered, vertical take-off and landing (eVTOL) systems, which could play a role in urban air mobility in the future. At the same time, UAS open up innovative application possibilities in demanding fields such as infrastructure inspection, disaster relief, and precision agriculture. The increasing improvements in fault tolerance, energy efficiency, and system robustness are making UAS increasingly feasible in sensitive operational environments. Simultaneously, these systems place high demands on data security, regulatory compliance, and reliable communication technology.
Implications for Thuringia
Even though there is currently no widespread industrial use or strong research focus in Thuringia, opportunities for the Thuringian innovation landscape could arise in the long term in connection with specialized sensor technology, integration into critical infrastructures or in the area of automated logistics solutions in rural areas.
Therefore, it would be sensible to initially place this trend on a watchlist. It is conceivable to regularly examine whether relevant developments emerge in adjacent technology fields, such as photonics, optics, microsystems technology, or traffic-related digital applications, which could offer future connections to the topic of UAS and eVTOL. Monitoring regulatory and technological developments, for example, in the context of approval procedures or airspace structuring, could also provide insights into whether and how Thuringia can benefit from developments in this field and play a shaping role.
In a future foresight cycle, it would therefore be conceivable to specifically search for potential niche applications for Thuringia. This would include the development of test environments, the trialing of safety-critical systems, and the development of accompanying technologies such as communication standards, energy solutions, and emergency management. Overall, it seems sensible to continue monitoring this topic closely and to identify technological breakthroughs and changing market conditions that could create new impetus for regional application.
5.2 Impulses for immediate action
The category of immediately actionable trends, characterized by high influence and low uncertainty, includes the development and application of synergistic digital twins and IoT systems, the use of AI, IoT and modeling technologies in agriculture, environmental monitoring and resource management, sustainable and energy-saving integrated heating/cooling systems (Chapter 4.8) and personalized medicine: diagnostics, therapies and health strategies.
These impulses flow directly into the operational work of Innovativ Thüringen. They serve to align the priorities of current R&D projects, to initiate R&D consortia in the short term, and to provide impetus for the design of workshops and technology events.
Synergistic Digital Twins and IoT Systems
Digital twins are among the topics with high impact and comparatively low uncertainty in the foresight process and are therefore considered key drivers for immediate action. They are virtual representations of physical objects or systems that receive, analyze, and reflect back data from the real world in real time. In combination with the Internet of Things (IoT), this creates a comprehensively networked system that enables continuous monitoring, analysis, and optimization of technical processes. This synergistic connection allows, among other things, more precise condition monitoring, predictive maintenance, improved resource utilization, and data-driven decision-making processes in real time.
Application areas are diverse: from smart factories and energy management in buildings to the inspection of technical infrastructures. The trend is characterized by increasing maturity, but still faces challenges such as a lack of interoperability between systems, integration into existing infrastructures, high data security requirements, and the absence of standardized interfaces.
Implications for Thuringia
Given Thuringia's industrial structure, which has a strong focus on mechanical engineering, automation, and energy efficiency, the trend toward digital twins and IoT systems could have particular strategic relevance. Many companies in the Free State operate in sectors that would benefit from closer integration of physical and digital systems.
It would be beneficial for the Innovativ Thüringen initiative and other stakeholders in the innovation landscape to examine the extent to which relevant activities already exist in Thuringia and whether there are any strategic gaps or particular areas of strength. A systematic mapping of relevant industry and research partners in the state would be conceivable, for example, to highlight existing approaches and promote targeted collaborations. Low-threshold formats to support SMEs, such as demonstration environments, networking opportunities, or workshops for identifying specific use cases, could facilitate further development in Thuringia. Furthermore, closer collaboration with scientific institutions, for example in Ilmenau or Jena, could make a significant contribution to technology transfer and further development.
AI, IoT and modeling technologies for agriculture, environmental monitoring and resource management
AI, IoT, and modeling technologies are considered topics of high impact and comparatively low uncertainty in the foresight process – thus providing concrete impetus for immediate action. They open up diverse new possibilities for a data-driven, resource-efficient, and resilient design of agriculture, environmental monitoring, and resource management. By combining real-time data from sensors, machine learning, and predictive modeling, environmental changes can be better anticipated, natural hazards detected earlier, and natural resources used more effectively. This could lead to significantly more efficient and sustainable agricultural processes, water management, and environmental monitoring.
Implications for Thuringia
For Thuringia, it seems sensible to further expand existing strengths in the field of environmental and agricultural technologies through targeted support of digital applications. Pilot projects for the integration of AI and IoT in agriculture could help to realize both ecological and economic benefits. This could be achieved, for example, through intelligent irrigation control, automated drone use, or early warning systems for extreme weather events.
Furthermore, it would be conceivable to invest more heavily in digital infrastructure in rural areas to enable wider application of such technologies. Developing practical training programs in cooperation with universities, technology transfer institutions, and the agricultural sector could help to sustainably embed knowledge of these technologies.
Exchanging experiences with other regions or initiatives could also be helpful in identifying concrete use cases and proven solutions. Model projects that combine, for example, sensor technology, remote sensing, and predictive analytics could serve as a blueprint for scaling such approaches regionally. Finally, it would be beneficial to examine links to environmental and climate policy at the state level; key points of connection would be climate change adaptation, the biodiversity strategy, and the Water Framework Directive. Here, the use of data-driven technologies could also contribute to achieving overarching sustainability goals.
Sustainable and energy-saving integrated heating/cooling systems
Integrated heating/cooling systems are considered key drivers for immediate action in the foresight process, as they are associated with high impact and comparatively low uncertainty. The increasing demands for energy efficiency, decarbonization, and climate adaptation are bringing integrated heating/cooling systems into sharp focus for technological and political strategies. These systems are designed to capture and control heating and cooling demands across sectors and sustainably meet them through the use of renewable energies and intelligent storage technologies. Particularly when combined with heat pumps, solar thermal energy, waste heat recovery, and digital control solutions, they offer new opportunities for optimizing energy flows and reducing dependence on fossil fuels. Through adaptive control, load management, and cross-sectoral coupling, security of supply, economic efficiency, and climate protection can be combined.
Implications for Thuringia
For Thuringia, this trend offers numerous opportunities to strategically integrate technological development, energy efficiency, and location attractiveness. Existing industrial infrastructure, municipal heating networks, and energy-efficient renovation programs could be significantly optimized through the targeted integration of innovative combined heat and cooling systems. Promoting intelligent system solutions in commercial areas, industrial clusters, or neighborhoods undergoing energy-efficient transformation appears particularly promising. Pilot projects for cross-sectoral coupling, such as combining industrial waste heat utilization with residential heat generation or integrating seasonal storage, could have both ecological and economic benefits. Closer collaboration with regional energy agencies, municipal utilities, and research institutions would further contribute to testing innovative technologies in practical settings and gradually introducing them to the market.
Furthermore, it seems sensible to specifically align existing funding instruments and planning frameworks with the support of such integrated systems. The development of demonstration plants and accompanying training and further education programs in the areas of system integration, digitalization, and energy management could significantly contribute to permanently anchoring the necessary expertise in the region. Strategic points of contact also exist at the state policy level. Programs such as the Thuringian Climate Act, the Heat Transition Roadmap, or the state's sustainability strategy could be specifically linked to technology-based solutions for heat/cooling integration. In this way, the trend can make a substantial contribution to the implementation of cross-sectoral climate goals while simultaneously strengthening regional innovation in the field of sustainable energy systems.
6. From trend analysis to implementation: Continuing the foresight results
With the completion of the first foresight cycle of Innovativ Thüringen, prioritized and qualitatively condensed technology trends relevant to the Free State's innovation policy are now available. These results form the basis for further steps in knowledge transfer, networking, and development, as well as for in-depth analyses and continuous monitoring in subsequent foresight cycles. The Innovativ Thüringen foresight team plays a coordinating role and drives the tracking of knowledge transfer activities.
The following measures are examples of practical follow-up. These include, among other things, topic-specific transfer workshops and networking events, in-depth analyses of individual topic areas, and also the continued monitoring of the watchlist during the scanning process.
Based on the topics identified in the first foresight cycle, specific areas can be further explored in more in-depth analyses. These deep dives allow for a detailed examination of strategically relevant aspects and the evaluation of concrete implementation options.
To design deep dives that are both substantively sound and impactful, clear thematic boundaries, methodological expertise, and the active participation of relevant stakeholders from research, industry, and administration are essential. Furthermore, robust data foundations, suitable analytical formats, and, last but not least, a structured process architecture for securing and operationalizing results are required. The selection of topics for in-depth study should be based on transparent criteria, such as the expected innovation potential for Thuringia, existing development pathways, or regional competence profiles. The goal is to refine promising future topics and, based on the results of the deep dives, to develop them into targeted innovation projects.
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