AI Suitability Assessment

Summary: The development team should assess the suitability of using AI in the software system they plan to build.

Type of pattern: Process pattern

Type of objective: Trustworthiness

Target users: Business analysists, architects

Impacted stakeholders: Development teams

Lifecycle stages: Requirements

Relevant AI ethics principles: Human, societal and environmental wellbeing, human-centered values, fairness, privacy protection and security, reliability and safety, transparency and explainability, contestability, accountability

Mapping to AI regulations/standards: ISO/IEC 42001:2023 Standard.

Context: AI technology is not always the best fit for every problem. Using AI in a software system can introduce additional complexity and risk. Thus, it is important to make sure that the benefits outweigh the potential drawbacks.

Problem: How can we determine whether to adopt AI in the design or not?

Solution: Before the development team starts to build a software system with AI, it is crucial for the team to carefully consider the suitability of using AI to solve the specific problem and to address the corresponding user needs. The team should assess whether the software system would benefit from the incorporation of AI or whether it may be negatively impacted. It is essential to ensure that the use of AI adds value to the overall system. Sometimes a heuristic-based approach may be more appropriate, because it may be easier and cheaper to develop and can provide better predictability and transparency compared to an AI-based system. To determine the suitability of using AI, developers should take into account the purpose and context of the system, whether sufficient data is available
to train an AI model, how well the model is performing, the potential outcomes of the AI systems, and so on.


  • Value of AI: AI suitability assessment helps the development team to make sure the use of AI will add value to the design and will not degrade the software system.
  • Readiness: AI suitability assessment helps the team identify if they have the appropriate data and infrastructure to train the AI model and support the system.


  • Time and cost: Conducting AI suitability assessment can be time-consuming and costly.
  • Lack of expertise: The team may not have the necessary expertise to perform a proper assessment.

Related patterns:

  • AI mode switcher: The result of AI suitability assessment may affect the design of AI mode switcher.

Known uses: