Challenges in 2024

AI-driven audio-based anomaly detection in machine operation

Develop an AI-driven solution to monitor and analyse audio signals from machines operating continuously on a shift schedule, aiming to detect anomalies and pinpoint fault occurrences. This approach seeks to reduce the time required for fault detection and intervention by learning typical machine noises and identifying deviations.

Owned by Packwell GmbH & Co. KG

AI-powered wildfire detection and response system

Climate change has significantly increased the length of wildfire seasons, the frequency of wildfires and the total area burned. This year, wildfires across Europe have devastated approximately 260,000 hectares of land, damaging infrastructure, prompting evacuations and displacing thousands. Extreme heat and low rainfall in the Mediterranean Basin have made countries like Italy, Spain, Greece, Croatia, Tunisia and Algeria particularly vulnerable to wildfires. For instance, over 120,000 hectares have burned in Greece, far exceeding the yearly average.

Owned by EIT Digital

AI-driven decision support for value chain optimisation

Implementing an AI solution to support decision-making across the entire value chain and complex machinery presents significant challenges. Accurate analytics require the integration and quality assurance of data from various sources. Decision-making processes involve complex variables that necessitate sophisticated AI algorithms and deep domain knowledge. Achieving real-time decision-making demands high-performance computing to minimise latency. Interoperability and adherence to standards are critical for seamless data flow and AI deployment, while robust security measures are essential to protect sensitive data from cyber threats.

Owned by EIT Manufacturing

AI-driven solutions for sustainable farming practices

Implementing sustainable farming practices faces significant challenges in optimising resource use, minimising environmental impact and ensuring long-term sustainability. These challenges lead to inefficiencies, higher costs and environmental degradation, hindering efforts toward sustainable farming. Enhancing farming practices through the application of AI technology to achieve more efficient operations, reduce environmental impact and improve overall sustainability.

Owned by EIT Food

Leveraging AI for tailored healthcare solutions

Delivering personalised medicine faces significant challenges despite advancements in medical science. Tailoring treatments to an individual patient's unique genetic makeup, lifestyle, and medical history is complex, resulting in suboptimal treatment outcomes, increased healthcare costs and patient dissatisfaction. Enhancing the ability to provide personalised medicine through the application of AI technology to ensure more effective treatments, reduce adverse effects, and improve overall patient outcomes.

Owned by EIT Health

AI-driven solutions for sustainable land use and forestry management

Sustainable land use and forestry management face significant challenges, including difficulties in optimising land use, preserving biodiversity and mitigating the impacts of climate change. These issues result in inefficient land management, loss of biodiversity and increased carbon emissions. Enhancing land use and forestry practices through the application of AI technology to ensure more efficient operations, reduce environmental impact and promote sustainability.

Owned by EIT Climate-KIC

AI-driven solutions for requirement management and idea generation

Adopting Generative AI for requirement management and idea generation presents challenges, such as ensuring the AI understands complex contexts and produces high-quality, relevant outputs. Integrating AI into established workflows requires careful planning to avoid disruption and addressing bias and ethical considerations is crucial to prevent skewed suggestions. Utilising Generative AI to streamline requirement management and idea generation, ensuring high-quality outputs, seamless integration into workflows and ethical, unbiased AI behaviour.

Owned by EIT Manufacturing

AI-driven demand forecasting for agrifood industry

Accurate demand forecasting within food production and distribution processes remains a significant challenge, despite the use of traditional methods. Inaccurate predictions lead to overproduction, stockouts, increased operational costs, and significant food waste. Enhancing demand forecasting capabilities through the application of AI technology to ensure more efficient operations, reduce waste, and improve overall customer satisfaction.

Owned by EIT Food

AI-enhanced metro public transportation management

Efficiently managing metro public transportation remains a critical challenge, characterised by peak hour congestion, operational inefficiencies and user dissatisfaction due to prolonged wait times and inadequate real-time information. These issues escalate operational costs and compromise system sustainability. Implementing AI-driven solutions to optimise metro transportation management, enhancing service quality, operational efficiency and sustainability while reducing environmental impact.

Owned by TMB (Metro network of Barcelona) 

AI-driven energy system optimisation

Despite advancements in renewable energy technologies, our energy company faces challenges in optimising energy production, integrating diverse energy sources and efficiently managing energy consumption. These issues contribute to higher operational costs, suboptimal energy utilisation and continued reliance on fossil fuels. Utilising AI technology to transform our energy systems, enhancing operational efficiency, reducing carbon emissions and promoting sustainable energy practices.

Owned by EIT Climate-KIC

AI-driven quality assurance enhancement

Despite rigorous standards and controls, many manufacturing processes face challenges in maintaining consistent and reliable product quality. These issues often lead to increased costs due to rework, waste and customer dissatisfaction. The primary challenge lies in enhancing quality assurance (QA) processes to ensure higher product quality, reduce defects and improve overall efficiency. The goal is to leverage AI technology to revolutionise QA processes, addressing issues such as inconsistent quality detection, delayed feedback, data overload and resource intensiveness.

Owned by EIT Manufacturing

AI-powered image processing of quay walls and fenders

The Port of Bilbao, with over 25km of berthing quays built between 1960 and 2024, faces significant challenges due to harsh weather and chemical conditions, as well as potential damage from docking ships. These challenges include identifying concrete pathologies and evaluating the state of port equipment such as fenders, bollards, and ladders. Currently, the port police department conducts mandatory, time-consuming, and risky manual visual surveys, often in adverse conditions. The aim is to develop an automated, intelligent system using AI-powered image processing on floating or flying platforms to capture images of the quay walls autonomously, enhancing safety and efficiency

Owned by Port of Bilbao

Do you have an AI-driven solution for any of these challenges?

Hand in your solution ideas via the questionnaire and get a chance to participate in the AI Challenge 2024.