Ask most senior leaders whether they feel on top of the AI transformation agenda and the honest answer is likely to be no. The scale of what is being asked is unlike anything in their experience. It is not one capability gap, but several converging at once. Each urgent, none clearly prioritised.
That is the difficulty with how AI transformation is often framed. The conversation tends to produce a list: AI fluency, governance, workforce redesign, commercial translation, systems thinking, speed, ethics. The implicit message is that all of it matters and all of it is needed now. For many executives, that feels less like clarity and more like overload.
The more useful question is not just what matters, but what matters most, and in what order.
Across leadership teams, a pattern is emerging. The organisations struggling to convert AI ambition into results are not those lacking investment or intent, but those unable to prioritise the tensions that sit at the heart of transformation. Two in particular stand out, because they consistently expose the gap between confidence and readiness.
Boards asked what they want from their leadership in an AI-augmented organisation are highly likely to prioritise speed, telling leadership to move faster; decide with less information; deploy ahead of competitors. In a market where AI capability is evolving faster than strategy cycles, the instinct to prioritise pace is understandable.
Investment patterns reflect this urgency. Deloitte’s 2026 State of AI in the Enterprise report, drawing on over 3,000 senior leaders across 24 countries, found that 84% of organisations increased their AI budgets last year, with the dominant talent strategy being the acceleration of AI fluency across the workforce.
What the same data also shows is that the investment is not converting. Only one in four organisations have moved 40% or more of their AI pilots into production. Just 20% report high preparedness on talent. Revenue growth from AI remains an aspiration for 74% of organisations against a reality for just 20%. Fewer than half are making significant adjustments to their talent strategies, and more than a third are using AI at surface level with little or no change to existing processes.
It means money is going in, transformation is not coming out.
Moving quickly is not the same as moving effectively. The gap between the two is where executive reputations are currently being made or damaged.
This is where governance re-enters the conversation, however, often too late and misunderstood. The term itself still carries unhelpful connotations: compliance, overheads, constraint. As a result, it is frequently deprioritised in favour of visible momentum.
The evidence, however, points in the opposite direction. Organisations where senior leadership actively shapes AI governance consistently realise greater value than those that delegate it. Governance is not a brake on speed; it is the condition under which speed becomes safe, scalable, and defensible.
The regulatory environment has made this explicit. Frameworks such as the EU AI Act, alongside existing regimes like the UK’s Senior Managers and Certification Regime, are formalising accountability for AI outcomes. This is no longer abstract. If systems fail, whether through bias, data exposure, or flawed decision-making, the organisation is liable, and leadership is accountable. “The model did it” is not a defence that regulators or courts will accept.
Recent cases have reinforced this reality.
In February 2024, Air Canada was found liable after its AI chatbot gave a grieving customer incorrect information about bereavement fares. The airline argued the chatbot was a separate legal entity responsible for its own actions. The tribunal rejected this entirely. The case has since been cited across multiple jurisdictions as the moment the accountability gap in AI deployment became legally indefensible.
Contrast this with Robinhood’s approach to its AI-powered financial crimes investigation system, which built validation agents checking every output, full audit logs for regulatory explainability, and human oversight at every decision point. The result was a 20% efficiency gain in investigative workflows and a system that regulators can audit and leadership can defend.
The widely cited ruling by the airline chatbot providing incorrect customer information made clear that organisations cannot distance themselves from the actions of their AI systems. By contrast, organisations embedding oversight, auditability and human validation into AI decision-making are demonstrating that governance and performance are not in conflict, they are mutually reinforcing.
The leadership challenge, then, is not choosing between speed and governance. It is recognising that without governance, speed is fragile and often undermining.
If the speed-versus-governance dynamic is the most visible leadership tension in AI transformation, the workforce question is another that demands urgent and considered attention. However, it is sometimes overlooked in the rush to drive efficiency savings through automation.
The economic logic for using AI to redesign operating models is clear. Automation, consolidation, and more AI-enabled roles can materially improve efficiency. On paper, the case is straightforward. In practice, this is where financially rational decisions become leadership risks.
Organisations too often focus on those whose roles are removed or redefined, neglecting to mitigate the impact on those who remain. Organisations that restructure without a credible people narrative do not simply lose the people who leave, they can lose the confidence of those who remain. With that, they may lose discretionary effort, institutional knowledge and the informal networks that transformation depends on.
The efficiency gain may be delivered, but the capability to build on it is often diminished.
This is where many transformation programmes quietly underperform. The structural change is achieved, but the conditions required for sustained performance are weakened in the process.
The capability required here is not empathy as a soft skill, it’s the ability to make difficult structural decisions with clarity and pace while maintaining the conditions under which high-performing people choose to stay and contribute. That combination is rarer than boards generally acknowledge and its absence is one of the less visible but more consequential reasons AI transformation programmes underdeliver.
There is a further dimension that receives less attention at board-level. The executives being asked to lead workforce redesign are themselves operating in an environment of considerable personal uncertainty. The roles being automated, consolidated or redefined are not exclusively below them in the hierarchy. For some, the capabilities that built their careers are among those the market is beginning to discount. This is a dynamic Rialto sees consistently in its work with senior leaders in transition – the difficulty of driving change with conviction when the ground beneath your own position is also shifting. Navigating it requires a degree of psychological clarity that technical upskilling alone does not provide.
This is not a reason to slow the pace of change. It is a reason to be deliberate about which leaders are positioned to drive it and what support the organisation is providing to those who are not yet there.
The tension between speed and governance is often framed as a trade-off: move fast or govern well; compete or comply. Similarly, workforce transformation is framed as a structural exercise: redesign the model and execute.
The organisations that are translating AI investment into sustained value are not those choosing one side of these tensions. They are those whose leadership teams are resolving them, treating governance as an enabler of speed and workforce decisions as both structural and human challenges that must be addressed simultaneously.
PwC’s 2025 Responsible AI research found that 60% of executives said governance boosts ROI and efficiency while 55% reported improved customer experience and innovation as a direct result of responsible AI practices. Yet nearly half acknowledged that turning those principles into operational reality remained a challenge. The value of governance is appreciated, but many organisations are falling short when it comes to embedding it across functions and departments.
The organisations building resilience, innovation and enduring growth into their business models through AI transformation are those that understand which elements are load-bearing right now and need direct attention.
For most, that includes governance, workforce credibility and accountability for how restructuring decisions are made and experienced.
This is also where a more grounded view of executive readiness is needed. In ongoing work with senior leaders, and through current research into executive AI relevance, a consistent picture is emerging: confidence in certain areas, genuine gaps in others and a broader recognition that the demands are arriving faster than preparation.
The leadership task is to distinguish between what is urgent, what is foundational and where the risks of inaction are compounding in ways that are not yet visible on the surface.
For executives navigating this evolving landscape, the immediate question is whether they are prioritising the right tensions and addressing them in the right order.
The organisations that will look back on this period as a point of competitive advantage are unlikely to be those that moved fastest in isolation. They will be those where leadership teams made structural decisions at pace, embedded governance early and managed workforce transition without eroding the human foundations of performance.
One of the consistent challenges at executive level is the absence of an external reference point: a clear view of how peers are interpreting the same pressures, where they are placing emphasis, and where confidence diverges from actual readiness.
This is precisely the focus of current Rialto research into executive AI relevance. Through ongoing work with senior leaders, and a structured survey designed to capture how leadership teams are prioritising capability, risk, and investment, we are seeing an increasingly clear picture of where organisations are actually placing weight, and where the most material gaps sit.
The survey will provide a dataset which is missing in the current market. Findings will be shared in aggregated form with contributors, offering a more grounded view of how peers are navigating these same tensions, how they perceive and manage priorities. It will enable leaders to gain a clearer picture of how they fit into the broader landscape, both in terms of their own professional development and their organisational readiness.
For most, AI transformation is not constrained by awareness or ambition. It is constrained by effective prioritisation in the face of the overwhelming pace of change and competing challenges.
At the centre of it all, the difference between progress and underperformance increasingly comes down to a single capability: the ability to decide what matters most and act on it first.
The survey remains open for a limited time and takes just five minutes. More details can be found here: Executive Relevance in the Age of AI.
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