ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications

Editor's comments and enhancements are shown in green. [ Reviewed]

The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • [1] Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • [2] Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • [3] Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • [4] Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • [5] Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
  • [6] Functional aspects, trustworthiness, and societal concerns
  • [7] AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.

No. 48 ID: Use Case Name: Value-based Service
Hybrid deployment: Cloud and on-premise deployment in the production field
ScopeProcess and status data from production and product use sources are the raw materials for future business models and services.
Objective(s)The objective of this use case is the provision of remote services for product and production based on (generic) service platforms. This use case can be seen as a fundament for the deployment of arbitrary AI remote services.
(up to
150 words)
Service platforms collects data from product use – for example machines or plants – and analyses and processes this data to provide tailor-made individualized services, e.g. optimized maintenance at the proper time, or the timely provision of the correct process parameters for a production task currently being requested. Companies offering these services (service providers) occupy the interface between the product provider and the user.
Complete Description Use Case description taken from [1,2,3]. In the consumer area, the increased interconnectivity of users which has made it possible to collect user data has made a whole new range of services possible. For example, navigation systems in our cars not only determine the shortest route, but also the quickest, as the traffic situation is assessed in real time based on movement data from other users. Entertainment media is no longer purchased rather made available as needed using streaming services. The services offered extend beyond simply making the products available. The individual customer receives optimized offers, based on user data: the quickest route during rush hour, or music tailored to that customer’s taste.
Similar developments are occurring in an increasingly interconnected industrial environment. Services that go significantly beyond simply providing a production unit – a contemporary example is leasing – are gaining in importance and are changing the classic value-added processes and business models.

Key aspects
At the heart of this application scenario are IT platforms that collect data from product use – for example machines or plants for production purposes – and analyze and process this data to provide tailor-made individualized services. This could include for example optimized maintenance at the proper time, or the timely provision of the correct process parameters for a production task currently being requested. The collected data could be product parameters, for example the machines and plants required for manufacture, the product status information, or data from the production process or the upstream supply process. Even the characteristics of the processed raw materials or the parts of the product could be included. The goal is to use this data as a raw material for optimizing products and production processes and for new services. This can help to not only improve existing value chains but also perhaps create new value-added elements.

Effect on value chains
The industrial environment today is influenced in principle by two actors – the product provider (i.e. manufacturers of production facilities and service providers) and the customer (product users, i.e. production facility operators), who work together with varying degrees of intensity.

With the introduction of Value-Based Services an additional actor enters the scene, operating IT platforms that it uses to provide new services to both classic partners. This platform operator could be a new element of the value chain, that is, an autonomous company. However, this role could be taken on by product providers by increasing their value added compared with the current situation.

Product providers make their product data and parameters available. On the basis of all of this user data, new services can now be developed, such as individual optimized maintenance or specific operating and process parameters that optimize or even expand production capabilities of the existing infrastructure. The companies offering these services (service providers) occupy the interface between the product provider and the user. The result is that the share in the value chain spanning from the product provider to the user can be shifted significantly, compared with the situation today. The user can then distinguish between the products by considering the accompanying services or the possibility of expanding those services even after purchasing the product, and no longer primarily by the (physical) specifications mandated by the product provider. This makes it very attractive for the product provider to use such platforms and to offer new services on them.

Value added for participants
In this application scenario the value added for the product provider stems from the availability of a multitude of process data from various application scenarios, which the user can apply to further development of its product port-folio. As an operator of related IT platforms, the product provider can offer new services. In this way, it strengthens customer loyalty and increases its portion of value added.

StakeholdersCustomer (product user), platform provider, service provider, product provide
Assets, Values
Threats &
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives

AI Features Task(s)Reasoning and autonomous problem solving in the platform, services based on the platform use AI features, e.g. for predictive maintenance, data semantics (cf. [5,6] for an overview)
Terms &
Concepts Used
Standardization needs for setting up this use case is currently under further investigation. Some initial intentions on standardization needs are the following: For this use case, standardization can be seen as enabler because an agreement on a (small set of) communication protocols would facilitate to connect to the platform and use this protocol also for device2device communication. Since services running on a platform are not aware of an implicit sematic of data sources (machines, sensors, actuators, …), an explicit semantic or a common vocabulary is need describing data and enable reasoning about machine states on premise (on the machine/edge) as well as on the cloud. For cloud2cloud communication and cloud federation, further interoperability standards are required on communication level as well as on data semantics level.
& Issues
Societal Concerns Description
SDGs to
be achieved
Industry, Innovation, and Infrastructure
Data Characteristics
Volume (size)
(rate of change)
Scenario Conditions
No. Scenario
Triggering Event Pre-condition Post-Condition

Training Scenario Name:
Step No. Event Name of
Description of

Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Description of

Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Description of

Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Description of

Specification of retraining data
No. Type Reference Status Impact of
use case
1 Working Group on Research and Innovation of the Plattform Industrie 4.0. Aspects of the Research Roadmap in Application Scenarios, Working Paper, German Federal Ministry for Economic Affairs and Energy, 2016. Link
2 Working Group on Research and Innovation of the Plattfom Industrie 4.0 and Alliance Industrie du Futur: Plattform Industrie 4.0 & Alliance Industrie du Futur : Common List of Scenarios. 2018. Link
3 Communication Promoters Group of the Industry-Science Research Alliance and German National Academy of Science and Engineering. Recommendations for implementing the strategic initiative INDUSTRIE 4.0, Final report of the Industrie 4.0 Working Group, April 2013. Link
4 Bo-hu LI, Bao-cun HOU, Wen-tao YU, Xiao-bing LU, Chun-wei YANG. Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering. 2017
5 Lee, Jay, Hung-An Kao, and Shanhu Yang. "Service innovation and smart analytics for industry 4.0 and big data environment." Procedia Cirp 16 (2014): 3-8.

  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • High quality company whitepapers and presentations
  • Publicly accessible sources with sufficient detail

    This list is not exhaustive. Other credible sources may be acceptable as well.

    Examples of credible sources:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: 2018.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
    URL: 2013