What are the nine factors of AI fitness?
AI fitness is six foundational capabilities and three measures of AI use.
- AI foundations: strategy, investment, workforce, data and technology, governance, and innovation
- AI use: breadth and depth, sophistication, and capturing value from industry convergence
Explore the graphic below to discover more and benchmark your organisation’s fitness against sector peers and the AI leaders.
Want to test yourself? Our quiz will give you a sense of your organisation’s baseline score, and strengths and weaknesses.
Explore the fitness factors
Tap on the graphic below to learn about each factor—and how well leaders are applying them.
Breadth and depth
This factor captures how much AI is used across your organisation’s value chain and how deeply AI is deployed into workflows within each function.
The AI leaders’ score for breadth and depth is roughly twice as high as the rest.
Sophistication
This factor is a measure of a company's most advanced AI applications. Think of this variable as a spectrum—from using AI simply to summarise long texts all the way through to building autonomous, self-optimising agents. The AI leaders are twice as likely to use AI that operates autonomously.
Capturing value from industry convergence
This factor assesses the extent to which AI enables cross-sector competition or collaboration. That could be sensing emerging value pools between sectors, responding to shifts in customer needs, or collaborating across sectors to unlock new value from ecosystem partnerships.
AI leaders are more likely to use AI to derive growth from industry convergence, the strongest AI fitness factor influencing AI-driven performance.
Innovation
This factor captures how innovation-friendly—yet rigorous—a company is. Does your business have dedicated innovation infrastructure, like sandbox environments? Embedded ownership of innovation within business units? And a cadence of portfolio reviews to test, prioritise, scale and stop AI initiatives?
AI leaders are more likely to provide dedicated innovation infrastructure and conduct frequent reviews of innovation portfolios to scale up AI initiatives.
Governance and risk
The security, access controls, regulatory compliance processes, ethical frameworks, and oversight bodies needed to manage risk from AI design to deployment.
AI leaders are 1.6x as likely to have a Responsible AI framework that guides AI strategy—including use case selection, design, deployment, and ongoing monitoring.
Data and technology
This factor is the degree to which a business has modern, scalable platforms and trusted, varied data sources accessible to everyone. Also critical: reusable AI components and replicable, redesigned workflows in priority applications.
Compared to the chasing pack, AI leaders are more than twice as likely to have eliminated outdated and costly IT applications, systems, and infrastructure.
Strategy
The strength of connection between corporate strategy and AI deployment. Does the organisation have a prioritised AI road map? Is every use case linked to a clear business objective? Is business impact tracked? And is someone accountable for every critical AI outcome?
Investment
This factor measures the funding and resourcing for AI. Are investment levels sufficient? Can resources be reallocated as priorities shift while still supporting longer-horizon innovation?
Leading companies are more likely to invest sufficiently, reallocate funds with agility, and invest for long-term results.
Workforce
This factor is a measure of whether leaders and employees have the skills, incentives, collaboration models, and levels of trust needed to build AI and use it effectively in day-to-day decisions.
AI leaders are 1.7 times as likely as other firms to say their employees participate in ongoing, role-based AI-learning sessions. And those employees are twice as likely to trust the insights generated by AI.
