A practical, credible and scalable pathway for recognising capability in responsible AI adoption — focused on the effective use, governance and implementation of AI, not technical model development alone.
AIAA's Accreditation and Certification Framework is designed for executives, professionals, advisers, educators, project leaders and operational teams. AIAA credentials recognise practical AI literacy, responsible-use practices, data and privacy awareness, implementation judgement, governance and risk management, and the ability to translate AI capability into measurable organisational value.
Built for executives, professionals, advisers, educators, project leaders and operational teams — not model developers alone.
Independent of any platform, product or supplier. Capability is assessed, not tooling preference.
Recognition rests on demonstrated education, experience and applied judgement — never on attendance.
Grounded in responsible-use practice, data and privacy awareness, governance and risk management.
Designed to work for organisations of every size and sector, from SMEs to national institutions.
Structured to support ongoing professional currency as AI capability and regulation evolve.
Recognition of foundational knowledge and commitment to responsible AI adoption.
Recognition of relevant education, experience and demonstrated capability in applying AI within an organisational setting.
Professional certification based on mapped education, experience evidence, referee validation, an online examination and an Applied AI Adoption Statement.
Recognition of distinguished leadership, experience and contribution to responsible AI adoption and the profession.
Associate (AAIA) and Member (MAIA) are the entry pathways into the profession. Certified (CAIA) and Fellow (FAIA) are conferred through assessed advancement and cannot be purchased.
The integrity of the Framework is safeguarded by the AIAA Accreditation and Certification Committee. Operating under authority delegated by the AIAA Board, the Committee brings together globally recognised AI leaders and highly experienced business and technology executives. This combination of academic authority and practical executive judgement ensures that AIAA's credentials remain rigorous, relevant and responsive to the realities of organisational AI adoption.
Distinguished University Professor | Nanyang Technological University, Singapore
An internationally recognised authority in artificial intelligence and data science. A Fellow of the Australian Academy of Science, ACM and IEEE, his research spans machine learning, computer vision and foundation models.
Committee contribution: Global research leadership, insight into emerging AI capability and risk, and scientific rigour in AIAA's professional standards.
Associate Professor, School of Computer Science | University of Technology Sydney
Leads the Data Visualisation Research Lab within the UTS Visualisation Institute. Her work in data visualisation and visual analytics has applications across finance, biomedical research, food safety, smart cities and social media.
Committee contribution: Data and visual literacy, standards development, education quality, applied assessment and the communication of complex evidence for business decision-making.
Distinguished Professor of Computer Science | University of California, Davis
Director of the UC Davis Center for Visualization and Head of VIDI Labs. An ACM and IEEE Fellow, his research spans data visualisation, visual analytics, artificial intelligence, machine learning, human-computer interaction and high-performance computing.
Committee contribution: Data interpretation, explainability and the responsible use of complex information in high-stakes decisions.
AI Strategy and Technology Executive | Co-Founder, CybernetX AI / FortifyEdge
More than three decades of experience across space, telecommunications and advanced technology. His work encompasses cognitive and edge AI, human-machine teaming and deployment in safety-critical and operational environments.
Committee contribution: Commercial strategy, use-case selection, human factors, implementation discipline, scalable adoption and practical technology-risk management.
Chief Executive, Catalyst | Co-Founder and Director, AIAA
A senior business transformation, governance and operational executive. A former DuPont executive, he has delivered strategic and performance outcomes for major organisations across resources, energy, infrastructure and industrial sectors.
Committee contribution: Board and employer perspectives, governance, organisational capability, change leadership and a focus on measurable business outcomes.
Academic leaders bring deep knowledge of AI capability, data, visualisation, emerging research, limitations and risk. Business leaders bring practical experience in strategy, governance, organisational change, implementation, workforce impact and measurable outcomes. Together, they help ensure that AIAA credentials remain current, rigorous and relevant to real organisations.
Through the Committee's stewardship, AIAA's Accreditation and Certification Framework will continue to evolve alongside AI technology, responsible-use practices, regulation and organisational needs. The objective is clear: professional recognition that employers, members and stakeholders can understand and trust — grounded in responsible practice, credible evidence and demonstrated AI-adoption capability.