Are your AI measurement systems telling you what you need to know?
Artificial intelligence is increasingly embedded in government strategies, development programmes and organisational transformation efforts. However, many existing measurement systems show where AI exists without providing sufficient evidence about adoption, effectiveness or outcomes.
This executive training equips policymakers, researchers and practitioners to assess, design and strengthen AI measurement systems so that evidence supports better decisions, investments and governance.
POWERED BY THE THREE-LAYER AI VISIBILITY FRAMEWORK (3L-AiVF)
Measurement systems need to be designed according to the questions decision-makers need answered.
The central question: how do we know what we know about AI?
Are our current AI indicators and frameworks fit for purpose?
Participants learn to review existing AI indices, digital transformation frameworks and monitoring systems to understand what they can and cannot demonstrate.
How should we measure AI adoption from the beginning?
Participants learn how to design measurement architectures aligned with specific policy, investment and programme questions.
Does the available evidence support the claims being made?
Participants learn to assess whether evidence is sufficient to support statements about AI-enabled improvements.
How can current AI measurement tools become more analytically useful?
Participants learn to identify gaps, limitations and opportunities for strengthening measurement approaches.
How do organisations ensure they are commissioning credible AI evidence?
Participants learn how to develop requirements, assess methodologies and interpret AI measurement outputs.
Participants will be able to:
No matter the sector, organisations rarely fail because they lack data. They struggle because evidence is fragmented across disciplines, institutions and decision-making processes. Quadripoint Nexus Consult brings together four complementary perspectives to help organisations understand problems more completely and design solutions that are practical, evidence-informed and sustainable.
Understanding what should be measured, how it should be measured and whether available evidence is fit for purpose.
Understanding incentives, trade-offs, efficiency and the broader drivers of development outcomes.
Connecting evidence to policy choices, implementation priorities and long-term organisational objectives.
Recognising that institutions, leadership, capacity and human behaviour ultimately determine whether change occurs.
Measurement distinguishes presence, adoption and outcomes. Economics evaluates value creation. Strategy aligns AI with organisational goals. People remain central to responsible adoption and governance.