Establish clear governance, standards, and accountability to ensure AI is used safely, ethically, and consistently across the organisation. The diagnostic gives leadership teams a common structure for reviewing responsible AI capability across governance, ethics, risk, transparency, data, organisational readiness and continuous improvement.
Organisations can adopt AI faster than their governance, risk controls, decision rights and workforce awareness develop. This creates inconsistent oversight, fragmented accountability and limited visibility across the AI lifecycle, making it difficult for leadership teams to understand where responsible AI capability is strong, where material gaps remain and which improvements require coordinated attention.
This diagnostic is designed for senior leaders and cross-functional decision-makers responsible for AI, corporate governance, risk, compliance, data, technology, operations, people and organisational change across organisations developing, deploying or overseeing AI-enabled systems.
The assessment covers seven connected capability groups and 28 named capabilities. Together, they provide a structured view of how the organisation directs AI decisions, defines responsible principles, manages risk and compliance, supports transparency, governs data, prepares its people and improves oversight over time.
Establishes the formal structures, roles, and decision-making processes that guide how AI is developed, deployed, and monitored across the organisation. This ensures that AI initiatives are aligned with corporate governance, risk management, and business objectives.
Defines the values and standards that shape how AI is designed and used, ensuring it supports fairness, accountability, and respect for human rights. These principles provide a clear ethical foundation for every stage of the AI lifecycle.
Focuses on identifying and managing the risks associated with AI, ensuring compliance with laws and regulations, and assigning clear ownership for AI decisions and outcomes. This capability builds organisational confidence in responsible AI operations.
Ensures that AI systems are understandable, traceable, and open to review. This includes documenting decision logic, communicating AI purpose and performance clearly, and enabling both internal and external stakeholders to trust AI outcomes.
Covers the governance of data used in AI systems, ensuring it is accurate, secure, unbiased, and managed responsibly throughout its lifecycle. Strong data stewardship underpins reliable, ethical, and high-quality AI performance.
Focuses on building the knowledge, values, and behaviours that support responsible and effective AI use across the organisation. It ensures that staff understand AI risks and opportunities, act ethically, and are equipped to support AI adoption in a way that aligns with organisational principles and societal expectations.
Focuses on keeping AI systems reliable, safe, and relevant by monitoring their performance, identifying issues, and improving them over time. This ensures that AI continues to operate as intended, adapts to changing business or regulatory conditions, and delivers outcomes that remain fair, accurate, and effective.
You receive a structured view across seven responsible AI governance capability groups, with a clear basis for comparing perspectives, identifying strengths and gaps, prioritising leadership attention and establishing a baseline for future reassessment.
The diagnostic is intended to improve the quality of responsible AI governance discussions. It can help leadership teams establish a clearer shared view of capability, expose where accountability or decision-making is unclear, identify where data, transparency, skills or monitoring practices need attention, and sequence improvement initiatives more deliberately. It does not guarantee compliance or business performance; it provides a structured basis for governance review and prioritisation.
The following examples illustrate typical situations where organisations use this playbook. They are intended to show when the assessment is most valuable and how it can help leadership teams identify capability gaps, build consensus, and prioritise improvement initiatives.
Business challenge: Different business units are advancing AI initiatives with inconsistent oversight, unclear decision rights and uneven links to corporate risk, compliance and data governance. Leadership lacks a common view of whether accountability, incident management and regulatory review are sufficiently coordinated.
How SuccessOf.ai and the playbook are used: The leader brings together a cross-functional group from risk, compliance, data, technology and operations to assess Governance and Oversight Structure; Risk, Compliance, and Accountability; and Data Stewardship and Integrity. The shared structure helps participants compare perspectives, identify strengths and gaps, and understand where capability weaknesses are constraining responsible adoption.
Beneficial result: The group gains a clearer shared view of governance gaps, stronger focus on decision ownership and a more deliberate sequence for improving oversight, risk management, data controls and future reassessment.
Business challenge: AI use is expanding across teams, but explainability expectations, human oversight, ethical principles, staff awareness and ongoing monitoring are not consistently defined. Fragmented practices make it difficult to judge readiness before scaling higher-impact AI use cases.
How SuccessOf.ai and the playbook are used: The leader uses the playbook with colleagues from technology, data, people, legal, operations and business leadership to review Ethical and Responsible AI Principles; Transparency and Explainability; Skills, Culture, and Awareness; and Continuous Monitoring and Improvement. The assessment creates a common structure for comparing perspectives, establishing a shared view and prioritising areas for leadership attention.
Beneficial result: The leadership group develops better alignment on responsible AI expectations, clearer priorities for human oversight, explainability, skills and monitoring, and a baseline for reassessing preparedness as AI use develops.
It assesses seven connected capability groups: Governance and Oversight Structure; Ethical and Responsible AI Principles; Risk, Compliance, and Accountability; Transparency and Explainability; Data Stewardship and Integrity; Skills, Culture, and Awareness; and Continuous Monitoring and Improvement.
A cross-functional leadership group should participate, including relevant decision-makers from AI, governance, risk, compliance, data, technology, operations, people and change functions. Comparing these perspectives helps reveal areas of alignment and uncertainty.
Leadership teams can use the structured results to establish a shared view of current capability, identify governance strengths and gaps, clarify where ownership or controls need attention and sequence practical improvement priorities.
Yes. The capability framework provides a consistent baseline that an organisation can revisit to discuss progress, changing risks, governance maturity and emerging priorities as its AI use develops.