An AI capability assessment is a structured evaluation of an institution's ability to adopt, govern and develop artificial intelligence effectively. It helps organisations understand their current level of AI maturity, identify capability gaps and prioritise future investment.

The QS AI Capability Assessments are designed specifically for higher education and assess AI capability across teaching, research, governance and operations.

An AI capability framework defines what good AI capability looks like, while an AI capability assessment measures how an institution performs against that framework.

The QS AI Capability Framework provides the methodology. The QS AI Capability Assessments use the framework to benchmark institutional AI maturity, identify strengths and gaps, and provide actionable insights for improvement.

The Self-Assessment enables institutions to benchmark their own AI maturity using a structured framework, scoring guidance and reporting tools.

The Analyst-Led Assessment includes independent review and validation by QS specialists, providing externally validated maturity levels, strategic recommendations and greater credibility for stakeholders, funders and partners.

Yes. The assessments help institutions understand current AI maturity, strengthen governance and identify priorities for future development.

By providing a clear view of capability across the institution, the assessments support AI strategy development, investment planning, responsible AI implementation and leadership decision-making.

The QS AI Capability Self-Assessment and QS AI Capability Analyst-Led Assessment measure institutional AI maturity across governance, operations, teaching and research.

Using the QS AI Capability Framework, the assessments evaluate how effectively AI is being adopted, managed and supported across the institution, helping leadership teams identify capability gaps, strengths and strategic priorities.

Universities assess AI capability to understand current AI maturity, identify capability gaps and establish priorities for future development.

A structured assessment helps leadership teams benchmark capability across teaching, research, governance and operations, providing evidence-based insights that support AI strategy, investment decisions and responsible AI adoption.

The QS AI Capability Assessments provide a higher education-specific approach to evaluating institutional AI maturity and identifying opportunities for improvement.

The Assessments are designed for higher education institutions seeking to understand their current AI capability, identify capability gaps, and align stakeholders around a shared roadmap for digital transformation.  There is no one size fits all approach. The Assessments are for senior executives, faculty and programme leaders and those who want a structured approach to AI adoption with clear roadmap steps.

Choose the Self-Assessment when the primary objective is internal benchmarking, identifying capability gaps, understanding and adopting best practices, and planning priorities. Choose the Analyst-Led Assessment when your institution also needs independent evidence validation, externally credible maturity levels, detailed recommendations, certificates and badges, or support for external stakeholder confidence.

Yes. Institutions can begin with the AI Capability Self-Assessment to benchmark their AI maturity, identify capability gaps and build internal understanding. If independent validation and externally credible maturity levels are later required, your institution can upgrade to the Analyst-Led Assessment through the available upgrade pathway.

Institutions will gain a clear view of their current AI capability, including key strengths, gaps and priority areas for action.

• Prioritise effort, investment and resources
• Improve alignment of AI initiatives across the leadership, faculty and professional services
• Support responsible governance and oversight with clear evidence
• Build a structured foundation for AI strategy and future planning
• Strengthen confidence in decision-making and AI investment priorities

The AI Capability Framework is a higher education-focused model that combines evidence, stakeholder insight, and a structured taxonomy to support AI adoption at scale. It is built on four pillars, 14 indicators, over 33 sub-indicators and five levels of maturity, enabling institutions to assess, benchmark, and enhance their AI maturity.