¶ UC3 - Servitization and service delivery enabled by Zero administration
This use case supports the transition from traditional product ownership to service-based business models by using smart contracts, edge AI, streaming analytics, and automated data exchange. The goal is to connect service providers, customers, partners, devices, and financial systems through a transparent and legally binding digital infrastructure. Real-time data streams from connected machines and service activities are analyzed using machine learning, neural networks, statistical models, and edge deployment mechanisms. The expected result is a scalable “zero administration” service-delivery platform that reduces administrative cost, enables automated transactions and audits, supports consumption-based services such as robotic lawn mowing, and contributes to circular and more sustainable business models.
| # |
Technology Name |
Description |
| 1 |
Automatic event and data sharing to orchestrate service delivery |
Automatic event and data sharing is developed to considerably reduce the administrative overhead in servitization. The technology supports service delivery orchestration by enabling AI-supported sharing of relevant events and data between the involved actors and systems. |
| 2 |
AI models development |
Methods and platform support are developed to reduce the time needed to develop AI models required for service delivery. The focus is on shortening the end-to-end AI model development process from data ingestion and preparation to deployment and integration. |
| KPI # |
Related Technology |
KPI Target |
| 1 |
Automatic event and data sharing to orchestrate service delivery |
Reduce the cost of administration in service delivery by at least 80%. |
| 2 |
AI models development |
Reduce the time needed to develop AI models for service delivery by 80–90%. |
| KPI # |
Baseline |
| 1 |
1,760 hours of work during one year for 50 contracts. |
| 2 |
40 working days. |
| KPI # |
Validation Method |
| 1 |
Measure how many contracts a service coordinator can handle during the same period of time. This is used to measure time saving per contract and to demonstrate scalability. |
| 2 |
Measure the time needed to develop AI models from the start of the work until model deployment. The measured stages include data ingestion and preparation, model development and training, model optimization and validation, and deployment and integration. |
- 70% reduction of time for developing and integrating a ML model in the digitalized industrial domain.
- Improved performance and faster time-to-market.
- Reduced manufacturing costs in the long term.
- Providing documented methods for dynamic application of tools to improve sustainability.
The use case directly contributes to the reduction of time required for developing and integrating ML models by targeting an 80–90% reduction in AI model development time for service delivery. It also supports improved performance and faster service deployment by reducing the end-to-end AI model development cycle from the baseline of 40 working days. The automatic event and data sharing technology contributes to cost reduction by decreasing the administrative effort required for service delivery by at least 80%. In addition, the servitization concept supports more sustainable and circular business models by enabling consumption-based services, automated coordination, and reduced administrative overhead.
| AI Toolbox Tool |
Use within the Use Case |
| LLM Tool |
Used to support AI-assisted service delivery workflows, including the interpretation of service-related information, generation of service-support outputs, and automation of administrative tasks in the zero-administration service model. |
| Prompt Context Injection Tool |
Used to provide relevant service, contract, device, and operational context to LLM-based components, enabling more grounded and use-case-specific AI support for service orchestration. |
| Embedding Tool |
Used to represent service events, contract information, operational records, and related textual data in a searchable semantic form, supporting similarity-based retrieval and knowledge reuse across service-delivery processes. |
| Tabular Prediction Tool |
Used to support machine-learning-based analysis of structured service and operational data, helping to develop AI models required for more efficient and automated service delivery. |
| Time-Series Anomaly Detection Tool |
Used to analyse real-time data streams from connected machines and service activities, supporting the detection of unusual operational patterns that may trigger service actions or automated coordination. |