RA Diagnostic

We are at ease when multiple conflicting priorities exist—we don’t rush to resolve tension prematurely. *

When unexpected disruptions occur, we treat them as learning opportunities, not just risks to be controlled. *

People here are encouraged to challenge the way things are done, even when systems are performing well. *

Decisions are often made without full information, yet we still act decisively and review intelligently. *

We listen carefully to outlier voices, dissent, and weak signals—even when they are inconvenient. *

We are good at noticing when the context changes—and adjusting accordingly, even mid-flight. *

Our long-term success depends on the health of the wider systems (environmental, social, economic) we operate within—and we act like it. *

Environmental and social impacts are considered at the start of strategic decisions, not added as an afterthought. *

Our core business model helps to solve problems in the wider world, not just avoid creating them. *

We routinely consider the second- and third-order effects of our actions, not just direct impacts. *

Teams are encouraged to design solutions that serve both immediate needs and wider system health. *

Our performance measures include indicators of ecosystem health or social wellbeing—not only financial results. *

Collaborations often result in outcomes that could not have been predicted or planned in advance. *

We have spaces (formal or informal) where people from unrelated functions come together to make sense of complex problems. *

Language barriers (jargon, disciplinary assumptions) are actively worked with, not ignored or papered over. *

We deliberately bring in perspectives from outside our sector or field when facing novel challenges. *

Accountability here isn’t just structural—it’s personal and cultural. *

People feel safe raising ethical concerns, even when those concerns challenge powerful interests. *

We actively question whether our governance processes reinforce or undermine trust. *

When using AI, we start with the question: what kind of intelligence is needed here—human, artificial, or both? *

Our teams are trained to understand both the power and the limits of AI—not just how to use it. *

Decisions shaped by AI are routinely reviewed and questioned by people, especially where values or consequences are involved. *

We have mechanisms to identify when human judgement should override or constrain automated outcomes. *

We have scenarios and strategies for how AI could reshape our business, our workforce, or our impact. *

People are encouraged to reflect in action, not only after the fact. *

We make time and space for learning—even when it competes with delivery pressures. *

The organisation adapts its structures and strategies in response to what individuals and teams are learning. *

Learning loops span the organisation—insight in one area regularly informs others. *