General details
EDIHs involved
Challenges
Moisture problems affect more than 15 million European households—around 17%—and approximately 400,000 properties in Denmark alone. The issue is growing due to climate change, increased rainfall, and aging sewer systems. Traditionally, moisture diagnosis relies on manual measurements and subjective assessments, which often lead to misdiagnoses and costly errors. The Institute for Moisture Technology aimed to develop a method that is more accurate, faster, and usable by multiple professional groups by combining sensor data with AI-driven interpretation.
Solutions
The Alexandra Institute collaborated with the Institute for Moisture Technology to create an AI-based diagnostic system. Key components included:
- Thermal Data Analysis: Leveraging wall measurements to detect moisture patterns.
- Machine Learning Algorithms: Classifying four distinct moisture types using clustering and predictive models.
- 3D/AR Platform: Providing a digital building model for visualizing moisture distribution, enabling easier understanding for less experienced professionals.
This integrated approach combined sensor-based measurements, clustering analysis, and AI interpretation to deliver precise diagnoses and automated reporting.
Results and Benefits
Accuracy Improvement: The AI-driven method increased diagnostic accuracy by over 80% compared to manual interpretation.
Efficiency Gains: Automated reporting reduces time and complexity for professionals, minimizing human error.
Scalability: The solution is designed to evolve into a full IT system for contractors, architects, and consultants, with plans for partner certification to enable broad adoption.
User Empowerment: The 3D/AR platform enhances understanding and decision-making, especially for non-specialists.
Perceived social/economic impact
The solution addresses a widespread and costly problem, reducing the risk of structural damage and health hazards caused by moisture. By improving diagnostic accuracy and efficiency, it lowers maintenance costs and prevents unnecessary repairs. On a broader scale, the technology supports sustainable building practices and resilience against climate-related challenges. Economically, it positions SMEs to offer advanced services, creating new business opportunities and strengthening competitiveness in the construction and property sectors.
Measurable data
- Accuracy Improvement: Diagnostic accuracy increased by over 80% compared to manual interpretation.
- Number of Tests: The method was validated through 600 building inspections.
- Moisture Types Identified: AI model can classify 4 distinct moisture types.
- Time Savings: Automated reporting significantly reduces time spent on manual interpretation (exact percentage or hours can be added if available).
- Market Scope: Addresses 15 million European households affected by moisture issues (≈17% of homes), including 400,000 properties in Denmark.
- Scalability Potential: Designed for use by contractors, architects, and consultants, with plans for partner certification.
DMA score and results - Stage 0
T0 DMA - 46%
- Digital Business Strategy: Reasonably Advanced
- Digital Readiness: Medium
- Human-Centred Digitalisation: Reasonably Advanced
- Data Management: Reasonably Advanced
- Automation and Intelligence: Basic
- Green Digitalisation: Medium
Strengths
- Digital Business Strategy:
The company has a clear plan, resources, and leadership support for digitalisation. Investments have been made and more are planned. There is readiness for organizational change and openness to digital-driven business models. - Human-Centred Digitalisation:
Training programs for reskilling/upskilling exist, and employees have adequate digital skills for current tasks. There is some involvement in shaping digital processes and career development opportunities for digitally skilled staff. - Data Management:
Many processes are already digitised, and data is stored in structured formats. Cybersecurity measures and backup procedures are in place, and employees are aware of cyber risks.
Weaknesses
- Digital Readiness:
Limited adoption of mainstream technologies beyond basic tools. Advanced solutions like ERP, CRM, e-commerce, and integrated platforms are not fully implemented. Potential for significant improvement in connectivity and customer-facing digital tools. - Automation and Intelligence:
Almost no automation or AI integration in business processes. This is a major gap compared to more digitally mature competitors. - Green Digitalisation:
Environmental considerations are minimal in digitalisation decisions. Digital technologies are not actively leveraged to reduce emissions, waste, or improve sustainability.
Overall Assessment Before Service
The organisation was at an average digital maturity level. While strategic intent and some foundational capabilities were in place, operational execution lagged behind—especially in automation, advanced technology adoption, and sustainability. Focused investments in AI, integrated systems, and green digitalisation were needed to unlock competitiveness and efficiency.
DMA score and results – Stage 1
T1 DMA Scores - 60%
- Digital Business Strategy: Reasonably Advanced (unchanged, but more concrete plans and leadership engagement)
- Digital Readiness: Reasonably Advanced (improved from Medium)
- Human-Centred Digitalisation: Advanced (improved from Reasonably Advanced)
- Data Management: Advanced (slightly improved from Advanced, now fully integrated and secure)
- Automation and Intelligence: Advanced (significant improvement from Reasonably Advanced)
- Green Digitalisation: Advanced (improved from Medium)
Improvements Over Time
- Digital Business Strategy:
The company maintained a strong strategic foundation but now has clearer execution plans and leadership actively driving digital initiatives. Investments are more targeted toward advanced technologies like AI and IoT. - Digital Readiness:
Major progress was achieved by implementing ERP/CRM systems and expanding e-commerce and digital marketing capabilities. Connectivity and collaboration tools are now fully integrated, enabling remote work and virtual learning. - Human-Centred Digitalisation:
Training programs were scaled up and tailored to advanced technologies. Employees now actively participate in innovation and decision-making, supported by digital work environments and career development paths. - Data Management:
Data governance matured with real-time access, interoperability across systems, and advanced analytics for business intelligence. Cybersecurity measures were strengthened, and continuity plans are fully operational. - Automation and Intelligence:
This area saw the most significant leap. AI-driven automation was introduced in administrative, financial, and operational processes, improving productivity and reducing costs. Predictive analytics and smart workflows are now in place. - Green Digitalisation:
Sustainability became a core focus. Digital solutions now actively reduce environmental impact through energy-efficient processes, paperless administration, and optimized resource use. The company sources energy from sustainable providers and tracks material usage digitally.
Overall Impact
The organisation moved from average maturity to highly advanced maturity, positioning itself as a frontrunner in its sector. The EDIH service accelerated adoption of advanced technologies, improved resilience, and strengthened competitiveness globally.
Lessons learned
Do’s:
- Combine domain expertise with AI capabilities for innovative solutions.
- Use real-world data and iterative testing to refine algorithms.
- Provide intuitive visualization tools to support diverse user groups.
Don’ts:
- Rely solely on manual interpretation—subjectivity leads to errors.
- Underestimate the importance of cross-disciplinary collaboration.
- Ignore scalability and usability when designing technical solutions.
Need support?
Consult our catalogue to locate the Eupopean Digital Innovation Hub nearest to you and accelerate your company's digital transformation.