AI Challenge results
Smart and sustainable solutions
This is about making AI methods usable for specific applications in companies and public authorities, as well as establishing regional networks with ongoing activity in order to promote the topic in Baden-Württemberg in the long term.
Smart Sustainable Solutions
Our event partners
The workshop was organized in cooperation with Fraunhofer IOSB, the City of Freiburg (DIGIT), Freiburg Wirtschaft Touristik und Messe (FWTM), Freiburger Verkehrs AG (VAG), and the Spielplan4 agency.
The AI Challenge theme of our region
Sustainability is traditionally an important issue in the Freiburg region and shapes many economic, social, and municipal activities and initiatives. It is therefore not surprising that the search for topics for an AI challenge quickly focused on various aspects of sustainability, but always in connection with the question of how sustainable solutions can also improve the quality of services and what economic benefits this can bring.
The workshop focused on the search for software solutions that can meet this triad of requirements through the use of artificial intelligence (AI) processes. This gave rise to the leitmotif "Smart Sustainable Solutions" with the following target groups:
- Local governments, public institutions, and authorities looking for AI solutions for sustainability.
- Companies and start-ups that already offer innovative AI solutions for reducing resource consumption and improving quality.
- Data providers: Organizations that can offer relevant data sets on tourism, traffic and material flows, recycling, etc.
- Technical managers and AI experts
- Innovators
Our topics
The workshop is divided into four thematic strands: tourist visitor management, charging management, circular economy, and virtual marketplace.
Discover the project exposés from the individual thematic strands and be inspired by the diverse approaches:
Topic #1 - Tourist visitor management
The region and the city of Freiburg are known as attractive destinations for visitors from Germany and abroad. However, in order to maintain the quality of visits and tourist stays in the long term, control measures are increasingly necessary. The question arises as to where many visitors are currently staying and where many will be in the future. This topic therefore deals on the one hand with monitoring and forecasting tools, but on the other hand also with the question of how and what incentives can be created to promote resource-saving and environmentally conscious behavior.
Topic leader: Dr.-Ing. Christian Kühnert (Fraunhofer IOSB)
AI Guide
DEVELOPMENT
Project objective
- Development of a personalized AI guide for Freiburg
- Enhanced visitor experiences through personalized recommendations
- Better management of visitor flows
DEVELOPMENT
AI system and data
- Intelligent chatbot tailored to the city of Freiburg
- Deep contextual understanding for events
- Access to a wide variety of data platforms with events in Freiburg
Value proposition
- Smaller events are becoming more prominent
- Relieving users of the burden of planning and searching for events
- Targeted information for specific user groups (e.g., barrier-free access)
OPERATION
Business model
- Public funding (EU, federal government, state) through the promotion of digitization and inclusion
- Partnerships with event organizers (e.g., affiliate links, better ranking)
OPERATION
Resources and partners
- Cooperation partners: City of Freiburg; Freiburg Wissenschaft Touristik Messe GmbH
- Data: Events from different organizers and platforms
- Technology: Platform for chatbot, APIs for external data sources
Freiburg offers visitors and residents a wide range of opportunities. In recent years, information on events and tourist attractions has been bundled together in a data platform. However, users still have to check the individual planning of events themselves, depending on factors such as accessibility, family suitability, or crowds. A personalized AI guide that knows this kind of info and can respond to individual user needs would significantly enhance the visitor experience in Freiburg.
The challenges for the AI Guide lie in collecting the necessary information about events, consolidating it into an AI data platform, integrating weather data and current visitor numbers, and continuously updating the data to ensure high quality.
AI processes can support the AI Guide as follows:
- AI-supported chatbot that has access to the AI data platform (e.g., via the Freiburg Urban Data Room) and provides recommendations.
- AI agent with highly detailed, personalized, and up-to-date answers that go beyond other internet offerings
Topic #2 – Charging management for e-mobility
This topic thread discussed how charging management for e-mobility can be supported and optimized in the Freiburg public transport system. The following aspects were discussed: capacity utilization forecasts for VAG vehicles, traffic flow management, interfaces with energy suppliers, charging management for electric vehicles, and vehicle energy consumption.
Topic lead: Dr. Andreas Wunsch (Fraunhofer IOSB)
Next Stop: Charging
DEVELOPMENT
Project objective
The goal is to develop an intelligent charging management system for an electric bus fleet. This should reduce electricity costs, enable optimal market-price-oriented charging, and allow for demand-oriented charging/discharging of buses and storage units. The system should contribute significantly to increasing energy efficiency and be designed to be scalable (growing fleet, new storage units, power sources, and charging points).
DEVELOPMENT
AI system and data
- Various subsystems: charging optimization (buses + storage), forecasting system for bus power requirements, forecasting system for power availability (own production), etc.
- Diverse data sources: deployment plans, control center data, location data, traffic data, weather, etc.
Value proposition
cost reduction
Convenience in workflows and user-friendliness thanks to end-to-end process optimization with linked data sources within the company
- Contribution to achieving the goal of CO2 neutrality
- Basis for fleet scaling
OPERATION
Business model
Value creation and cost reduction within your own company.
- Transferability and distribution of the solution to other transport companies.
OPERATION
Resources and partners
Transport companies (various points of contact: service planning, workshop, IT, control center, electrical systems, etc.)
- Developer/provider of software systems
- If necessary, service providers for the installation of new technical components (if this cannot be done internally)
NextStop:Charging addresses the challenges of charging management for growing electric bus fleets operated by public transport companies. Currently, charging is often rigid and inefficient, without consideration for electricity price fluctuations, vehicle requirements, or the availability of renewable energies. The solution links internal and external data sources and uses AI-based forecasts and optimization methods to intelligently control charging times, energy quantities, and power sources. This reduces electricity costs, increases energy efficiency, maximizes the use of self-generated electricity (e.g., PV, wind), and integrates the nationwide availability of renewable energies in the electricity mix into the charging strategy. The system is designed to be scalable and meet specific requirements for critical infrastructure. It represents an important building block on the road to CO₂ neutrality and enables the sustainable, economical operation of electric bus fleets.
NextStop:Charging can potentially be transferred to other transport companies and other electric vehicle fleets. The continuous integration of additional data sources and optimization processes increases the potential for cost and CO₂ reduction. The system can make a significant contribution to optimizing and accelerating the expansion of sustainable mobility in the region and beyond.
Topic #3 - Circular economy
The goal of the circular economy is to use resources as efficiently as possible and avoid waste by keeping products, materials, and raw materials in the economic cycle for as long as possible. This is summarized by the so-called R strategies (e.g., reduce, reuse, recycle, recover), which cover the entire life of a raw material or product, starting with resource extraction, through the product life, to the end of its life. How should AI systems be designed to promote the circular economy? What issues can be addressed with them?
Topic leader: Dr.-Ing. Thomas Usländer (Fraunhofer IOSB)
Circle.Wood
DEVELOPMENT
Project objective
IT system for high-quality wood recognition/classification using AI processes, including
- Value recognition of wood waste/wood residues
- Automatic generation of online ads
- Logistical support for material delivery (e.g., travel and storage information, packaging and transport instructions)
DEVELOPMENT
AI system and data
sensor module
- sensor data management
- ML system
- Material ontology for wood
- wood pulp database
- Semantic matching module
- user guidance
- logistics module
Value proposition
automated wood species identification/classification
- Recognize the value of (wood waste) materials and propose an R strategy
- Create ad in resource marketplaces
- High-quality AI-based (interactive) matching despite semantic differences (information asymmetry)
- logistical support
OPERATION
Business model
Cost-covering, non-profit task
VS.
intention to make a profit
OPERATION
Resources and partners
€200,000 (demonstrator) – €2 million (full expansion)
- Partners:
- Users from the wood industry
- R&D partner for sensor technology
- SW and AI engineers
- IT service provider
Circle.Wood is an AI-based solution for the timber industry that automatically recognizes the value of wood waste and wood residues, suggests suitable R strategies, generates corresponding advertisements in resource marketplaces, and also provides support for the logistics of loading and transporting wood waste and wood residues. Circle.Wood is to be developed in close cooperation between players in the timber industry and IT/AI service providers and engineers.
Topic #4 - Virtual Marketplace
In this topic thread, an AI-supported matching tool is being designed that connects solution seekers and providers from small and medium-sized businesses, facilitates exchange, and promotes regional value creation.
Topic leader: Philipp Hertweck (Fraunhofer IOSB)
Intelligent Business Meeting (IBM)
DEVELOPMENT
Project objective
AI-supported matching for efficient networking at the Freiburg SME Congress
- Automated creation of interest profiles for participants
- Optimized group allocation for structured networking sessions
- Increased participant satisfaction through time-efficient contact establishment
- Increasing the value of the conference through higher-quality networking
Objective: Higher networking quality, time savings in finding contacts, increased participant satisfaction
DEVELOPMENT
AI system and data
AI technologies:
- Clustering algorithms for grouping participants based on goals and interests
- Natural Language Processing (NLP) for extracting relevant information, e.g., from registration data and company websites
- Optimization algorithms for optimal participant matching and group assignment
Data basis:
- Registration information: Company, position, industry, etc.
- Questionnaire: Business objectives/interests, types of cooperation sought, industry preferences, etc.
Value proposition
For conference participants:
- Time-efficient networking through targeted contact recommendations
- Overcoming time constraints when identifying relevant business contacts
- Personalized matching based on individual goals
- User-friendly solution without additional apps (paper output during check-in)
For conference organizers:
- Increase in participant satisfaction
- Differentiation from other business events through innovative technology
- Structured networking sessions instead of unorganized contact searches
- Potential for scaling to other events
OPERATION
Business model
Customer group: Organizers of business conferences and congresses
Financing model:
- Phase 1: Congress organizer bears all development and operating costs
- Phase 2: Sale/licensing of the system to other event organizers
- Revenue streams: Project financing, license fees, service fees for adjustments
Market potential: Regional to supraregional B2B events with a focus on networking quality
OPERATION
Resources and partners
Key partners:
- IT service provider: Software development with AI engineering expertise
- Congress organizer: operation, data access, and operational integration
Critical resources: high-quality participant information
Operational implementation: Integration into existing check-in processes, on-site matching, paper output without additional IT infrastructure at the event
The AI application "Intelligent Business Meeting" is designed to enable the matching of individuals and groups of people for value-adding, personal meetings within the framework of events. The following challenges must be overcome: Participants have only a limited amount of time to find their peers or the desired conversation partners. The extra effort involved should be as minimal as possible and no new communication channel should be necessary, i.e., integration into existing processes is essential. It would be desirable to have an automated creation of an interest profile. AI support therefore aims to cluster participants, create interest profiles, and optimize group allocation for specific moderation formats (one-on-one, World Café, etc.).
Become part of our network!
The community management of the KI-Allianz Baden-Württemberg specifically connects business, science and politics in the regions in order to promote the exchange of knowledge and the application of AI technologies. The Community Management of the AI Alliance is also represented in the Karlsruhe region.
If you are interested in a result or an entire topic and would like to find out more or get involved, please contact our Community Management for the Karlsruhe region at
:
Faissal Esmati
faissal.esmati@ki-allianz.de
Impressions and comments from "Smart Sustainable Solutions"
We are all dependent on each other.
The great thing about AI (artificial intelligence) is that I can bring many aspects together.
How can AI help us ask the right questions?
What is special about this workshop format is that the providers do not develop solutions that can subsequently be offered to users, but that users themselves are directly involved in the design.
Our plan worked. The participants were inspired by the kick-off event and there was a lack of time, not a lack of ideas.