Focusing on Observable Outcomes While internal processes may remain partially opaque, we can measure understand enough about potential failure modes to implement effective controls We can observe and measure complex systems we rely on daily – from weather forecasting to traffic management – operate with a measure The measure of success isn't perfect predictability but rather consistent reliability in achieving beneficial

What we mean by Value Most organizations measure value as margin: revenue, cost, productivity, output Stakeholder Value is, in essence, the measure of promises kept. The difficulty is that only one of these is measured well. Margin appears on every dashboard. The fallacy is this: define a role by its measurable tasks, automate the tasks, and unintentionally eliminate

These are often described in qualitative terms but may also have defined measures of effectiveness to These may consist of measures of performance, conformance, risk, or other attributes that are used to A point worth mentioning is that measures of effectiveness are usually associated with outcomes and often measured as progress towards these outcomes. Assessments are usually conducted more frequently to measure the ability to achieve outcomes as opposed

obligation Define what constitutes evidence of compliance Define how progress against outcomes will be measured With this in mind the following are important measures to collect: Measures of Effectiveness (MoE) Measures of Compliance (MoC) – critical to compliance, where failure maybe cause for reassessment of the program Measures of Performance (MoP) – measures that relate to the operations of the compliance

4 types of obligations 4 compliance functions 4 purposes 4 measures

Which threats and opportunities should we contend with and with what measures? to help you identify risk but there are few that help you identify where and how to implement risk measures Are Your Risk Measures Valid?

AI assurance measures can include testing and validation procedures, performance monitoring, and quality AI safety measures can include risk assessments, safety testing, and ethical considerations related to These measures ensure that AI systems are designed and implemented in a way that meets safety, ethical Measures of AI Governance, Assurance, and Safety The following are steps that organizations can take This means that personal data must be protected, and cybersecurity measures must be implemented to prevent

quantification of such things as carbon footprint is becoming the new calculus by which processes are measured How do I measure the environmental impacts of a process?

When it comes to compliance many organizations never get past "shall" statements let alone achieve any measure been implemented ends up taking on a life of its own diverting resources to maintenance and corrective measures functions, behaviours and interactions exist and are working together at levels sufficient to produce a measure

objectives, principles, and practices that have worked on the production floor where improvement is measured by cycle time reductions has not been as effective when applied to areas where improvement is measured remove essential activities and outcomes such as: evidentiary artifacts needed to verify compliance risk measures

It is the standard the advisor is measured against, so it has to be built deliberately. A comparator is simply a check that measures one thing against another. and the accountability are already yours; the work is making them explicit enough for the model to be measured

Compliance in Practice The Lean Startup approach for AI compliance focuses on three key principles: Build-Measure-Learn Instead, they must implement practical compliance measures and learn from their application in real-world Compliance Accounting : Establish clear metrics for measuring the success of your compliance program,