RAI Maturity Model

Summary: An RAI maturity model is used to assess an organization’s RAI capabilities and its readiness to utilize AI responsibly.

Type of pattern: Governance pattern

Type of objective: Trustworthiness

Target users: RAI governors

Impacted stakeholders: AI technology producers and procurers, AI solution producers and procurers, RAI tool producers and procurers

Lifecycle stages: All stages

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: EU AI Act, ISO/IEC 42001:2023 Standard.

Context: AI is a transformative technology that is expected to have a significant impact on society and industry. Many organizations view AI as a top strategic priority. According to the 2022 Gartner CIO and Technology Executive Survey, almost half of organizations have either already deployed AI technologies or plan to do so within the next year. However, as organizations seek to adopt AI, they may encounter challenges that could impact their business if they are not aware of their own RAI maturity.

Problem: How can the RAI maturity of organizations be effectively assessed?

Solution: An RAI maturity model is used to assess an organization’s RAI capabilities and readiness to implement [1, 2]. The model includes dimensions such as impact, governance, development, and people, which represent the capabilities that contribute to an organization’s RAI maturity. To assess an organization’s RAI maturity, all of the dimensions should be evaluated to determine the overall level of RAI maturity.


  • Accelerated AI adoption: By using the RAI maturity model, organizations can assess their AI dimensions that contribute to the adoption of AI technology.
  • Increased AI capability: The RAI maturity model helps organizations assess their current capabilities and readiness in relation to responsible AI. By using the model as a guide, organizations can take steps to address deficiencies and increase their overall RAI capabilities.


  • Complexity: The RAI maturity model can be complex with many dimensions and criteria that need to be evaluated. This can make it challenging for organizations to understand and use the model effectively.
  • Limited quality: The effectiveness of the RAI maturity model relies on the quality of the model. The maturity model should clearly define the assessment dimensions and provide a method for rating the maturity for each dimension.

Related patterns:

  • RAI certification: The assessment results of the RAI maturity model could be used to certify an organization’s RAI capability and support the issuance of an ethical certificate.
  • Building code: RAI certification could use building codes as the inspection standard.

Known uses:

  • Gartner’s AI Maturity Model defines five levels of maturity regarding the use of AI in an organization: awareness, active, operational, systemic, and transformational.
  • Microsoft’s AI Maturity Model describes four maturity levels: foundational, approaching, aspirational, and mature.
  • IBM’s AI Maturity Framework identifies seven dimensions: impact on your business, value to the end client, technology sophistication, trustworthiness, ease of use, AI operation model, and data. For each of the dimensions, there are three levels: silver, gold, and platinum.
  • Element AI’s AI Maturity Model highlights five dimensions: strategy, data, technology, people, and governance. The maturity is defined across five levels: exploring, experimenting, formalizing, optimizing, and transforming.
  • Boston Consulting Group defines four stages of RAI maturity: lagging, developing, advanced, and leading.
  • Salesforce identifies four stages in their maturity model for building a responsible AI practice: ad hoc, organized and repeatable, managed and sustainable, and optimized and innovative.


[1]   Alsheibani, S.A., Y.P. Cheung, and C.H. Messom. Towards An Artificial Intelligence Maturity Model: From Science Fiction To Business Facts. in PACIS. 2019.

[2]   Fukas, P., et al. Developing an Artificial Intelligence Maturity Model for Auditing. in ECIS. 2021.