AI-DAPT is an EU-funded innovative research project focused on addressing critical challenges in AI deployment, particularly related to data utilization, model reliability, and adaptability. It aims at reinstating the pure data-related work in its rightful place in AI and at reinforcing the generalizability, reliability, trustworthiness and fairness of Al solutions.

• AI-DAPT is working towards reinstating the importance of data in AI, enabling adaptable AI pipelines while keeping the Human in the loop. 

• The project introduces smart and trustworthy automation across the Data/AI Ops lifecycle from design to deployment, driving end to end pipeline orchestration 

• The project harnesses sophisticated Explainable AI (XAI)-driven methods to trigger data operations, ensuring transparency and accountability throughout the process. 

• AI-DAPT is driving AI innovation blending hybrid science-guided and AI-based models, enhancing predictive accuracy and adaptability to diverse datasets, when scientific knowledge is present. 

• The newly delivered AI-DAPT Reference Architecture provides a modular, policy-aware blueprint guiding integration, scalability, and regulatory alignment. 

• Demonstrating tangible innovation, AI-DAPT validates its results in real-world applications across industries like Health, Robotics, Energy, and Manufacturing, while integrating its advancements into existing AI solutions for market impact. 

Today, Artificial Intelligence (AI) has paved a long way since its inception and has started experiencing exponential growth across various industries and shaping our world in ways that were once thought impossible. As AI transitions from research to deployment, leveraging the appropriate data to develop and evaluate AI models has evolved into one of its greatest challenges. Data are in fact the raw material and the most indispensable asset fuelling much of today’s progress in AI, generating previously unattainable insights, assisting more evidence-based decision-making, and bringing tangible business/economic benefits and innovation to all involved stakeholders. However, despite their instrumental role in determining performance, fairness, and robustness of AI systems, data are paradoxically characterised as the most under-valued and de-glamorised aspect of AI while a data-centric focus is typically lacking in the current AI research.

Main challenges are the following:

• Challenge 1: Poor data preparation and planning delays AI development 

• Challenge 2: Messy data in terms of heterogeneous/ contradicting/ redundant values, missing entries, and inconsistent structure 

• Challenge 3: Data is not self-explanatory, well described and suitable for use beyond its original concept 

• Challenge 4: Low re-usability of data, features and models 

• Challenge 5: Representative data that are appropriate for AI may be hard to access or not be even available 

• Challenge 6: Synthetic data needs to become more accurate 

• Challenge 7: Detection of bias and mitigation remains complex 

• Challenge 8: AI models developed “in vitro” are having a hard time in the real world 

• Challenge 9: Insights gained through data/AI observability are not yet actionable enough to trigger appropriate remedy actions 

• Challenge 10: XAI methods are interpretable only by data scientists 

• Challenge 11: Balanced use of Physics-based Models and AI is necessary to some problems

AI-DAPT aims to deliver an innovative and impactful research agenda that will provide tangible benefits to a variety of stakeholders that struggle with making AI services. Seeking to reinstate the pure data-related work in its rightful place, and reinforcing the generalizability, reliability, trustworthiness, and fairness of Al solutions, AI-DAPT vision relies on the implementation of an AIOps framework to support and automate AI pipelines that continuously learn and adapt based on their context. It enables proper purposing, collection, documentation, (bias) valuation, annotation, curation and synthetic generation of data, while keeping humans-in-the-loop across five axis: (i) Data Design for AI, (ii) Data Nurturing for AI, (iii) Data Generation for AI, (iv) Model Delivery for AI, (v) Data-Model Optimization for AI.  

AI-DAPT brings forward a two-fold data-centric mentality in AI:  

• Data: AI-driven automation for data pipelines based on Explainable AI (XAI) techniques as well as synthetic data generation and observability.  

• Model: Automation on AI model building and hybrid science-AI solutions, bringing together data-driven AI models and science-based (first-principles) models that build on high-quality data. 

Bridging the gap between data-centric and model-centric AI, AI-DAPT will turn over a new leaf in trustworthy AI and will nurture an ecosystem involving all AI and data value-chain stakeholders. The aim is to enhance their prosperous collaboration in order to deliver and apply innovative AI-driven methods that rely on smart and dynamic end-to-end automation of data, AI training/inference pipelines in the cloud-edge computing continuum. 

To demonstrate the actual innovation and added value that can be derived through the AI-DAPT scientific advancements, the AI-DAPT results will be validated in two ways:  

• By applying them to tackle real-world challenges in four key industries: (4) Health, Robotics, Energy, and Manufacturing. 

• By integrating them into various AI solutions, whether open source or commercial, already present in the market.

Health: 

• Non-invasive, personalised diabetes monitoring. AI-DAPT’s Health Pilot integrates wearable PPG sensors with advanced AI models to enable accurate, continuous, and non-invasive blood glucose estimation—improving daily diabetes management without discomfort. 

• AI for early risk detection and personalised care. By combining clinical, contextual, and real-time bio signal data, AI-DAPT identifies at-risk individuals and tailors model outputs, supporting both preventive interventions and personalised treatment strategies.

Robotics: 

• Human-Centric Automation with AI-DAPT. By combining biometric and environmental data with adaptive AI pipelines, the Robotics Pilot enhances cognitive ergonomics, reducing operator stress and improving productivity and workplace safety in industrial environments. 

• Smarter AI for Safer Industry. AI-DAPT enables predictive monitoring of worker mental and physical states, helping manufacturers prevent overload and accidents while integrating explainable and trustworthy AI into real-world industrial operations. 

Energy: 

• AI-DAPT enables smarter, greener homes. The Energy Pilot uses AI-driven heating control and predictive analytics to reduce household energy use, lower costs, and empower consumers to participate in a more flexible and sustainable energy grid. 

•  AI for a resilient energy market. By combining real-time household data with advanced market forecasts, AI-DAPT supports suppliers in improving demand prediction and grid management, paving the way for scalable, trustworthy AI solutions in the energy sector. 

Manufacturing: 

• AI-DAPT brings predictive intelligence to aviation maintenance. By integrating AI-driven analytics into hoisting equipment management, the Manufacturing Pilot reduces downtime, improves scheduling, and enhances safety in aviation manufacturing environments. 

• From reactive repairs to proactive efficiency. Through the use of real-time sensor data, legacy ERP systems, and workforce optimisation, AI-DAPT enables smarter planning of inspections and maintenance, ensuring readiness, compliance, and cost-effectiveness.

AI-DAPT pioneers a data-centric approach in AI, seamlessly integrated with a model-centric, science-driven methodology throughout the AI-Ops lifecycle. This innovative framework introduces end-to-end automation and AI-driven systematic techniques to facilitate the design, execution, observability, and lifecycle management of resilient, intelligent, and scalable data-AI pipelines. Therefore, it is expected that the project will deliver a wide range of services. A preliminary list of results includes: 

• AI-DAPT Data Lifecycle Management Methods & Services 

• AI-DAPT AI Lifecycle Management Methods & Services 

• AI-DAPT Data and AI Execution Methods & Services 

• AI-DAPT Data-AI Insights Methods & Services 

• AI-DAPT Data-AI Pipeline Monitoring Methods & Services 

• AI-DAPT Platform Management Methods & Services 

• Hybrid AI Models 

• AI-DAPT Framework