"The South African AI MAF serves as a foundational tool for guiding the country’s progress towards AI maturity and is tailored to the country’s unique socioeconomic, cultural, and technological realities. It furthermore aligns with broader continental ambitions (i.e. African Union’s 2024 Continental AI Strategy), which reflects a strong commitment to leveraging AI for sustainable development. This implies the ethical and inclusive development and implementation of AI in ways that address local challenges. In South Africa, GIZ, through the FAIR Forward project, has collaborated closely with the Council for Scientific and Industrial Research (CSIR), the South African Institute of Chartered Accountants (SAICA) and researchers from the University of the Western Cape (UWC) to develop the AI Maturity Assessment Framework (AI MAF) for South Africa. A design-science methodology was applied to develop the AI MAF for South Africa. The approach involved a structured literature review process, co-design workshops with cross-sectoral stakeholders, expert interviews, validation workshops and external expert review. This first version of the AI MAF consists of eight domains:
Domain 1: International and Continental Profile as AI Leader
Domain 2: An Enabling Regulatory Environment
Domain 3: Citizen Engagement With and Empowerment Through AI
Domain 4: Education and Workforce Enablement
Domain 5: Innovation and Research
Domain 6: Growth of the AI Ecosystem
Domain 7: Data and Digital Infrastructure
Domain 8: Security and Privacy
Each domain includes sub-domains with specific indicators. A total of 68 indicators are included in this 2025 instrument, aimed at reflecting the country’s unique socioeconomic, cultural, and technological realities. An assessment instrument (rubric) to measure the level of maturity of these eight domains was adapted from the maturity measurement scale set out in ISO 37004 (Governance Maturity Model – Guidance). It reflects the structured progression of the ISO standards, employing a maturity scale that ranges from Level 0 (No evidence) to Level 5 (Continual optimisation or improvement). It is grounded in the principle of evidence-based assessment, evaluating maturity through the lens of activities and inputs (Levels 1–2) that lead to measurable outcomes and impact (Levels 3–5). The application of this instrument in the first iteration of assessing AI maturity in South Africa indicated that the country is in the early stages of AI maturity, with seven of the eight domains assessed at Levels 1–2 on the maturity scale, and only one domain reaching Level 3. This suggests that all domains demonstrate some level of foundational ‘input activities’, signalling a positive intent and growing awareness of the importance and relevance of AI, with some initial movement towards demonstrating positive outcomes. Overall, the generally low maturity scores indicate that progress across the domains is fragmented. This could be attributed to still limited strategic ecosystem coordination, targeted investment and capability building. The evidence-based approach enables a reflection of the South African landscape and supports strategic alignment across government, industry, and academia. The framework and interactive dashboard – developed through this project to enable accessible visualisation of the data – serve as foundational reference points for supporting South Africa’s evolving AI ecosystem. An AI agent developed as part of the initiative facilitates regular updates, helping ensure that the AI MAF remains a relevant strategic tool." (Executive summary, pages 8-10)