The Rise of the AI Strategist

2 minute read

AI is now deployed in at least one function in 88% of organisations. Yet 56% of CEOs report capturing neither revenue nor cost savings from it.   The gap between AI deployment and business value is rarely caused by the technology itself. It is more often the result of a missing strategic framework that links AI investment to business priorities, measurable outcomes and executive accountability.

Crucially, this is not a middle-management execution issue. It sits at the top of organisations, where strategic direction, prioritisation and ownership of outcomes are set. In many cases, AI is being deployed without the level of executive clarity required to convert activity into value.

This challenge is now showing up at the leadership level. In an exclusive Rialto survey of professional clients, supported by feedback from our strategy consultants and executive career coaches, the single biggest capability gap threatening executive relevance was said to be the inability to connect AI to commercial strategy. Forty-four per cent of respondents named it as their primary concern.  Nothing else came close.

What AI strategy actually means

Strategy, in this context, is not a slide deck or a digital transformation roadmap. It is the answer to four specific questions that every board should be asking at a minimum and every C-suite executive should be able to answer clearly:

1. What commercial problem is AI solving, and for whom?
2. Which measurable outcomes define success, and over what timeframe?
3. Who is accountable for delivery, and how is that accountability embedded in leadership reviews?
4. What governance structure ensures that AI decisions are made with appropriate oversight?

Through its work with senior leadership teams, Rialto has consistently observed that organisations unable to answer these four questions often struggle to convert well-intentioned AI experimentation into a defined route to measurable business value.

Once these foundations are established, organisations are better positioned to address the practical challenges of implementation, including managing governance, accelerating decision-making and cross-functional collaboration.

The question for any executive reading this is whether their ability to align AI with commercial objectives, set measurable outcomes and govern for results is visible to the people who make decisions about leadership, succession and future organisational capability. In a market increasingly shifting from experience-based to skills-based evaluation, boards are asking not only what leaders have achieved, but whether they possess the capabilities required for the next phase of growth and transformation. Demonstrable AI leadership capability is rapidly becoming one of those differentiators.

Download the full insight, including FAQs below.

Once an executive transition is underway, the question about when and how to use AI shifts from strategic to operational. How much should you lean on it? Where does it genuinely save time and sharpen your approach? And where might it quietly undermine the credibility you have spent a career building?

Used with clear intent, AI can add genuine value in the early stages of a transition that previously cost executives considerable time and effort. The gap between executives who use it effectively and those who do not is less about the tools themselves and more about the clarity they bring to the task. The executives getting the most from AI are specific about what they are asking it to do. They use it to pressure-test positioning, compress research, prepare for interviews and sharpen the consistency of their personal brand narrative. They treat it as a thinking partner, not a ghostwriter. They rarely ask it to produce anything they actually intend to send.

Nor do they entirely trust it – and with good reason. They know it is a useful preparation and sense checking tool, but it can never (at least in its current capabilities) offer the nuance, industry and sector knowledge or emotional intelligence required in senior-level decision-making.

There is also a quieter but important consideration: data exposure. Feeding full career histories, board-level experience, compensation details or strategic thinking into public AI tools carries risk. For executives operating under NDAs, fiduciary duties or sensitive market conditions, this is not a marginal concern and should be understood before the first prompt is used.

Rialto consultants support professionals seeking or considering an executive transition to understand where AI genuinely adds value and where caution is required. We help clients identify and address capability gaps, strengthen executive positioning and build a robust, defensible narrative that meets the expectations of senior hiring processes.  AI can and should be used during an executive transition, but understanding where it strengthens the process and where human judgement, experience and relationships remain irreplaceable. That judgement cannot be reliably outsourced to Claude, ChatGPT or Gemini.

Does GenAI open new opportunities – or limit them?

Multiple sources consistently point to 70-80% of senior executive roles never being publicly advertised. They are filled before they reach the open market through networks, trusted relationships and retained search.

AI tools are built for visible markets. They can help you compete in the 20-30% of roles that are publicly posted. They have no meaningful access to the rest.

Executives who spend a transition optimising their profile for job boards while neglecting relationship-building and strategic visibility are severely limiting their options and reach.

Non-executive and portfolio career conversations rarely begin with a CV; they start with an introduction, often years before a board seat becomes available. Internal moves, whether a promotion, a lateral step across a portfolio or a repositioning after restructure, are decided by sponsorship, political capital and the visibility you have already built. Neither responds to keyword optimisation.

Where AI becomes a liability in Executive Transition

Recruiters and boards are increasingly alert to AI’s levelling effect, where almost anyone can enhance language, polish positioning and inflate perceived capability. As a result, candidates can no longer assume that an immaculately polished application will secure an interview.

UK research by CV Genius found that 80% of hiring managers dislike AI-generated CVs and cover letters, 74% can spot an AI-written job application and 57% are less likely to hire applicants who appear to have used AI tools. At senior level, where search professionals are specifically assessing authenticity, cultural fit and the distinctiveness of a leadership narrative, generic AI buffing will often see even brilliant candidates rejected at the first review.

Importantly, recruiters do not have the time to deconstruct a narrative to separate substance from polish. Where AI has inflated positioning beyond lived experience, candidates risk being exposed at interview when depth, specificity and judgement are tested under pressure.

The texture of real leadership experience is difficult to fabricate. When asked to describe a transformation initiative, a credible executive can move beyond outcomes to the reality of execution: the stakeholder resistance encountered along the way, the trade-offs made under pressure, the moment board confidence nearly shifted, or the individual whose support proved harder to secure than anticipated. These details are not embellishment but the structure of credible leadership narrative.

AI-generated accounts, by contrast, tend to be smooth. They are logically coherent but lack resistance. They describe what was achieved, but not what was navigated. Experienced panels listen precisely for that difference – the friction, the constraint and the judgement calls made under ambiguity.

This is also where the gap between narrative and lived experience becomes most visible. Human coaches and advisors play an important role in helping executives surface and articulate this underlying complexity – ensuring that achievements are grounded in context, not just presented as outcomes.

The risk of getting this wrong also extends well beyond a single interview process. The executive search ecosystem is small, interconnected and highly conversational. A candidate who has over-claimed, or who under-delivers under scrutiny, can remain visible within a network where reputational memory is longer than most executives assume.

Where AI is relevant in an active transition

AI is a legitimate and increasingly powerful tool in executive transition. Used well, it can help senior leaders reduce time spent on preparation, structure thinking and improve efficiency in parts of the executive transition process. However, its value lies in complementing, not replacing, the judgement, challenge and contextual insight provided by experienced executive transition advisors and coaches.

At Rialto, many of our consultants have themselves operated in senior leadership positions. That experience matters. Executive transition is rarely just about producing stronger documents or preparing for interviews; it is about navigating complex career decisions, market realities, leadership positioning and personal transition with clarity and credibility. AI can support elements of that process, but it cannot replicate lived executive experience, market intuition or the depth of challenge that comes from an experienced advisor who understands both leadership and transition first-hand.

CV and LinkedIn optimisation. AI can be effective in helping executives test the clarity of their narrative and improve readability so that it lands with an audience that spends seconds, not minutes, reading it. It can flag inconsistencies in language, improve compatibility with applicant tracking systems and help you crystallise a complex career history into coherent positioning.

At senior level, however, effective positioning requires more than polished language. Executive coaches and transition advisors help ensure that a profile reflects genuine leadership substance, market relevance and strategic differentiation, rather than simply producing a more refined version of generic executive language.

Research and market intelligence. AI can compress the research phase of a job search considerably. It can map board and leadership team structures, analyse sector trends, summarise publicly available company information and support hypothesis-building around target organisations; tasks that previously took days now take hours.

For an executive building a credible, sector-specific case for their next move, this is time well spent. However, as above, it should never replace investment in human relationships. Experienced executive advisors bring contextual understanding that AI cannot access: insight into leadership dynamics, board priorities, organisational culture, succession considerations and the informal market signals that often shape senior hiring decisions before roles become visible externally.

Interview preparation AI can also act as a useful initial partner when preparing for interviews. It can help structure responses and test articulation of strategic thinking.

However, executive interviews are rarely assessments of technical answers alone. Senior hiring processes evaluate credibility, judgement, self-awareness, resilience and leadership presence under pressure. Experienced coaches help executives prepare for these dimensions through challenge, feedback and informed perspective grounded in real leadership experience, something AI cannot authentically replicate.

Personal brand development. AI can help executives build the consistency and strong identity that makes them discoverable to the right recruiters and influencers across LinkedIn, thought leadership content and board-facing narrative. For leaders with strong underlying credentials who have never invested time in communicating them effectively, this is a meaningful accelerant, but it is only part of the story.

Strong executive brands are not built through polished content alone. They are built through credibility, clarity of leadership identity, track record and differentiated perspective. AI can help refine articulation, but it cannot create the underlying substance that ultimately distinguishes senior leaders in competitive markets.

Across all of the above four uses, the executives getting the most from AI treat it as a thinking partner rather than an authority. They use it to sharpen thinking, test positioning and accelerate preparation, while relying on experienced human counsel to challenge assumptions, interpret context and support the deeper strategic decisions that shape long-term career trajectory.

Executive Transition Support with Rialto

Ask an AI tool how to land your next executive role and you will get a credible-sounding answer in seconds. Ask a Rialto consultant the same question and the first response will usually be a different question: what are you actually trying to build, and what are you willing to leave behind or invest in to achieve it?

Rialto works with leaders navigating executive transition, AI driven transformation and organisational change. We help clients understand where AI adds value, and where it introduces unnecessary risk or distortion.

Our consultants bring contextual market knowledge, network access and the kind of long-term professional relationship required at senior level: the ability to challenge narrative, interpret market signals and support decision-making beyond the next role.

If you are in an active transition and want support navigating the tools and the process, we would welcome a conversation.

You may also be interested in reading our insight, Should Executives use AI to Plan their Careers?.

Frequently asked questions

Can recruiters tell if you have used AI to write your CV? Often, yes. UK research suggests that around three quarters of hiring managers can identify AI-generated job applications, and over half are less likely to progress candidates who appear to have leaned heavily on AI tools. At executive level the risk is higher because search consultants are specifically looking for distinctive voice, authenticity and lived experience that AI struggles to fabricate convincingly.

How do I find executive jobs that are not advertised? Around 70-80% of senior roles are filled through networks, trusted referrals and retained search before reaching public job boards. The most reliable route is to invest, well before a transition, in relationships with search professionals in your sector, peer networks and board contacts. Visibility through considered thought leadership, board memberships and a credible LinkedIn presence also helps you appear on shortlists you never see advertised.

Can AI help me prepare for an executive interview? Yes, for structuring thinking and practising articulation. It can help you anticipate questions, practise articulating your strategic thinking and pressure-test your answers. However, it cannot replicate live human evaluation. Preparation should therefore always be tested through real conversation with experienced professionals who understand the constantly changing expectations of the audience you will face.

Artificial intelligence is now more routinely being used by executives to support career exploration, positioning and executive transition planning. From CV refinement to market research and narrative development, its use is no longer experimental. However, its usefulness in senior-level decision-making remains far less clear.

Among Rialto clients navigating executive transitions, two concerns are raised consistently: whether recruiters can detect AI-assisted applications, and whether AI should be trusted to design an executive career strategy.

The short answers are: they can so use it appropriately; let it provide insight, but never rely on it unthinkingly.

Both questions reveal something important about where executives currently are with these tools: curious, cautious and not entirely sure where the line is. That uncertainty is understandable. AI tools have become genuinely sophisticated, but the marketing around them has consistently outpaced the honest conversation about their limitations. Getting this wrong at senior level carries real consequences, particularly in the face of structural downward pressure in parts of the job market.

How AI should be positioned in executive career planning

A few things are worth holding in mind:

1. AI is genuinely useful for initial testing of your positioning, accelerating research and refining your personal brand narrative as an executive. The moment you let it generate your story or your decisions, you lose the clarity and authenticity that define genuine leadership.
2. Executive transitions rarely follow on-paper logic. They involve identity, emotion and personal circumstances as much as logistics. Up to half of executive transitions are later viewed as failures or disappointments, rarely due to technical capability, but more often because of mismatch, which can be exacerbated by the use of AI .
3. At senior level, AI should be seen as an input into thinking, not a substitute for human thinking.

Using AI to support executive career planning

1. Clarify what you actually want from the next stage

Authenticity and honesty in career planning are essential to avoiding destabilising wrong steps. This starts with working through difficult questions: what you really want and need from a role, what you are willing to offer, where your limitations may be at this stage of your career and where you see yourself in five to ten years.

Is this the right time for a leap upwards? Will the role offer the right level of challenge? Are you moving into a declining sector out of urgency, when you might be better to pause, reskill or pivot into a growth area?

These are precisely the conversations Rialto consultants are having weekly with senior leaders across sectors. Do get in touch if we can support you in this way.

AI can help structure these questions, but it cannot interrogate your assumptions with the depth or challenge required at this level. (Read previous insights on High Performer to Executive Leader and High Stakes Executive Career Pivots.)

2. Interpret market reality and timing

Understanding market conditions is critical. The UK senior job market has tightened sharply. ONS data shows vacancies at their lowest level since early 2021, with 2.5 unemployed people per vacancy. What sustains executive relevance in this environment is AI-ready leadership capability and nuanced emotional intelligence, which boards are now actively assessing, not generic and indistinct AI-generated responses.

In this environment, timing and positioning matter as much as capability.

AI is genuinely useful for stress-testing your positioning, accelerating research and refining your personal brand narrative as an executive. It can support rapid research and scenario testing, helping you map sectors, roles and emerging trends.

However, interpretation – what is relevant to your specific profile and trajectory – remains a human judgement, not an AI one.

3. Assess your transferable authority

The executives who build resilient careers in the AI economy share certain characteristics that have nothing to do with their CV software or responses to Gen AI prompts.

They understand their transferable authority: what they have achieved, but also the specific credibility, network and strategic perspectives that are genuinely transferable across contexts.

They invest in their visibility within the markets where the next opportunity is most likely to emerge. They have relationships with search professionals, peers and board members that exist before any transition begins.

And they have worked through the harder questions about the kind of role they want to do next, the conditions in which they perform best and the sectors and organisations where their capabilities will be genuinely valued.

While AI can help refine how this is articulated, it cannot build the underlying capital.

4. Validate decisions through trusted advisors or executive career coaching

Executive transitions are rarely technical exercises. They are high-stakes decisions involving identity, confidence, timing and risk.  This is where trusted advisers, mentors or coaches play an essential role: challenging assumptions, identifying blind spots and grounding decisions in lived market experience and emotional intelligence.

At senior level, career progression is not purely linear – and nor should it be. The strongest executive transitions often emerge from a combination of deliberate planning and opportunistic recognition – the ability to identify moments where a role, challenge or organisation presents a unique intersection of timing, capability and unmet need.

A move will rarely fit neatly into a pre-defined trajectory, but a well-timed and considered one should enhance an individual’s distinctive position in the market over time.

The role of trusted external counsel is to test these decisions with objectivity: to distinguish between momentum and opportunity, between reactive change and strategic advantage, and between short-term appeal and longer-term positioning strength.

A large language model does not have the context, the professional relationship or the emotional range to navigate any of that alongside you.

What AI Cannot Replace in Executive Leadership and Career Planning

Emotional intelligence and context cannot be automated. Leaders of high-performing teams consistently identify emotional and social intelligence among the most important success factors and as human capabilities that technology cannot replicate.

Boards and search committees know that organisational growth and security depend on hiring genuine AI talent: executives and senior leaders who can navigate AI transformation, not just those who can show they are familiar with AI tools.

There is a meaningful difference between a leader who has used ChatGPT to polish their profile and one who can articulate a credible, considered position on workforce transformation and organisational redesign.

Executive Career Planning with Rialto

At Rialto, we help clients identify exactly where AI tools add value and where to step back.

Our consultants bring contextual market knowledge, network access and the kind of long-term professional relationship that career strategy at senior level actually requires.

If you are thinking seriously about your next move, or about building the kind of executive career that will remain relevant as the AI economy matures, we would welcome a conversation.

(See our companion insight, Using AI in an Active Executive Transition – and Where It Can Trip You Up.)

Frequently asked questions

Should I use tools such as ChatGPT, Gemini or Claude to help plan my career?

For research, stress-testing your positioning and understanding the markets where your capabilities are most valued, yes. For generating your strategy, your narrative or your decisions, no. The executives who get the most out of AI treat it as a thinking partner that sharpens their own thinking, rather than a content generator that does the thinking for them.

Will AI replace executive search?

No. Executive search at the most senior level is built on relationships, judgement and the ability to assess cultural and strategic fit. AI tools support search consultants with research, scheduling and shortlisting, but the core work of senior search remains human and relational. If anything, the rise of AI is increasing the value of trusted human advisers, not reducing it.

What is AI-ready leadership capability and why do boards care about it?

AI-ready leadership capability is the ability to lead an organisation through AI transformation. It includes making sound judgements about where AI should and should not be embedded into decision-making, redesigning workforce structures and roles, and bringing leadership teams and boards through the change. Recent UK research from the CIPD shows that boards are now actively assessing for this in senior hires. Familiarity with AI tools alone is no longer enough.

The Leadership Tensions at the Heart of AI Transformation

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.

Speed vs Governance:

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.

Workforce restructuring vs responsibility.

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.

What This Means for Executive Leadership

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.

A More Focused Question

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.

“Harnessing machine learning can be transformational, but for it to be successful, enterprises need leadership from the top. This means understanding that when AI changes one part of the business, other parts must also change.” Erik Brynjolfsson, Stanford Institute for Human-Centered AI

Brynjolfsson is one of the world’s most cited economists on technology and productivity, a Stanford professor who has spent three decades studying what separates the few organisations that extract real value from transformative technology – which we will call the 6% club – from those that do not. He finds it an organisational issue: failure to consider the structural, governance and cultural changes needed to lead through AI transformation inevitably leads to under-achievement and disillusion.

Eighty-eight per cent of organisations globally now use AI in at least one business function, yet only around 6% qualify as genuine AI high performers – businesses attributing more than 5% of EBIT directly to AI and reporting significant value across the enterprise. The remaining 94% are somewhere between enthusiastic experimenter and quietly disillusioned pilot operator. Most have the tools. Very few have the results.

What the 6% are actually doing

These high performers do not have access to better technology. What distinguishes them is organisational. McKinsey found that high performers are 3.6 times more likely to be pursuing transformational, enterprise-level change through AI and nearly three times more likely to have fundamentally redesigned their workflows in the process. Bolting AI onto existing processes is a false economy that leads to wasted resources, lost opportunities and competitive drag. The 6% rebuild those processes around what AI can actually do.

They are also three times more likely to have senior leaders who actively own and champion AI, genuinely modelling its use and driving its integration into strategic decision-making. This is the strongest single predictor of enterprise-level AI impact in the data. When senior leadership treats AI as a technology upgrade, the organisation stalls. When they treat it as a strategic shift that requires them personally to change how they work, the organisation moves.

The high performers apply the same capital discipline to AI investment as they would to a major acquisition: clear strategy aligned with organisational objectives, defined milestones and criteria for adjusting or closing underperforming initiatives. They manage AI investment across three horizons: foundational infrastructure (two to four year payback), near-term productivity (six to twelve months) and longer-term transformation (ongoing). They do not allow short-term return pressure to collapse everything into the second horizon at the expense of the first and third.

The Kyndryl Readiness Report, drawing on 3,700 senior leaders, found that 61% of CEOs now face intensified pressure to demonstrate AI returns compared with the prior year, while 53% of investors expect positive returns within six months or less. Responding to that pressure by sacrificing infrastructure and transformation investment to feed short-term results is one of the primary reasons organisations get trapped in pilot purgatory. Honest, clear communication from the outset – managing expectations, helping stakeholders understand realistic timescales and reimagining how success is measured – is itself a leadership responsibility. Equally, so is recognising when to kill a pilot that is not working, and to explain why.

The governance gap

Two-thirds of organisations remain in experimentation or piloting phase, lacking the operating model maturity to convert deployment into value. The most common single failure is the absence of clearly named executive ownership for AI outcomes across product, legal, risk and compliance. When nobody is explicitly accountable for what AI is doing across the organisation – which McKinsey found to be the norm – innovation slows, risk accumulates and resources are wasted.

Most organisations view governance as a constraint. The 6% experience it as a competitive advantage: the mechanism that builds stakeholder trust, enables faster decision-making within defined boundaries and provides the audit trail that allows boards to demonstrate responsible operation to regulators, investors and customers.

Regional AI regulatory frameworks add further complexity. The EU AI Act is now in phased application, with penalties reaching 7% of global annual turnover for high-risk non-compliance. The UK places the burden of interpretation directly on boards, making personal executive accountability the operative principle. In the US, enforcement is arriving through litigation rather than legislation, making documentation, testing and explainability the primary risk mitigation tools. Working across different regions demands flexible compliance models, but across all three regimes AI governance is a board-level responsibility and the expectation that it can be delegated to IT or legal functions is no longer sustainable.

What boards and leadership teams must actually do

Moving from the 94% to the 6% requires coordinated evolution across five interconnected dimensions. Here are five questions your board should be able to answer:

Who in your organisation is accountable if your AI produces a wrong outcome? In most organisations, nobody can answer that. Executive accountability means designating named individuals responsible for AI outcomes across every relevant function – product, legal, risk, compliance and people – with those owners demonstrating AI literacy in capital allocation decisions.

Are you asking how AI could transform how this work is done, or just how to make existing processes faster? Workflow redesign is the single most powerful lever in the McKinsey data. High performers decompose roles into task sets, identify which activities are best automated, which augmented and which require human judgement, and rebuild performance metrics around value delivered rather than activity completed. (See our previous insight, Redefining Work in an Human/Machine Era.)

Is your AI training a one-off event or embedded into how people work every day? McKinsey’s data shows that high performers embed at least 81 hours of annual AI training per employee into operations. Sixty-three per cent of employers globally identify capability gaps as their primary barrier to AI scaling, yet most continue to look externally for capabilities that reskilling could develop internally at lower cost and with less disruption.

Have you defined what failure looks like before you start? Capital discipline with kill-switch criteria means defining in advance, at the point of approving any AI initiative, when a pilot gets shut down rather than scaled. The organisations accumulating the most expensive AI failures are those that never established what insufficient progress looked like.

Can you explain to every stakeholder – employees, customers, regulators, investors – exactly how AI is influencing decisions that affect them? Stakeholder trust architecture is an operational requirement, not a PR exercise. In an environment where 51% of organisations report AI-related incidents, eroded trust is difficult to rebuild. High performers are more than twice as likely to have defined human-in-the-loop validation processes – 65% versus 23%.

Measuring returns beyond the financial

McKinsey found that function-level returns in software engineering, manufacturing and IT regularly reach 10-20% cost reductions, with marketing and product development seeing revenue uplift above 10% in leading deployments. But the ROI conversation in most boardrooms is still too narrow. Organisations measuring only financial return are missing both the value and the risk.

Two thirds of organisations in McKinsey’s survey report AI-driven improvements in innovation capacity, while 45% report improved customer satisfaction and 36% see strengthened competitive differentiation. These are leading indicators of future financial performance. Organisations tracking only EBIT impact miss the earlier signals that tell them whether their AI investment is building the capabilities that will compound into revenue.

Stakeholder trust is measurable and its erosion is one of the most expensive and least discussed AI risks. Customer trust in AI-mediated decisions, employee confidence in the organisation’s approach to workforce impact and investor trust in governance quality all affect the cost of capital, talent retention and customer lifetime value in ways that do not appear in short-term financial metrics. Regulatory standing carries an implicit financial value that almost no organisation currently quantifies, and boards that require AI investment proposals to include a regulatory exposure assessment alongside the financial case are making a sound capital allocation decision, not an over-cautious one.

Leadership seeking to help their organisations break into the top 6% can learn much from the earlier pioneers — both what to do, and what not to do.

JPMorgan Chase: lessons learned in an $18 billion experiment

JPMorgan Chase is the most thoroughly documented example of an organisation in the 6%. Its AI programme has more than 450 live use cases delivering between $1.5 billion and $2 billion in annual value. More than 200,000 employees use its proprietary LLM Suite platform daily and AI-attributed benefits have grown 30-40% year-on-year. AI coding assistants have lifted developer productivity by 10-20% across a technology workforce of 63,000, its Coach AI advisory tool contributed to a 20% increase in gross sales in asset and wealth management between 2023 and 2024, while fraud prevention and operational efficiencies saved a further $1.5 billion.

What explains it? Not the technology. JPMorgan uses many of the same foundation models available to every competitor. What distinguishes the bank is its governance architecture: a firmwide Chief Data Officer mandate aligning data platforms with model risk management, legal and security functions across every business line; rigorous ROI measurement at the individual initiative level; and a board-level treatment of AI as a core operating function. As JPMorgan’s own Chief Analytics Officer put it: “There is a value gap between what the technology is capable of and the ability to fully capture that in an enterprise.” Their answer to that gap has been structural and the returns reflect it.

The bank also acknowledges the risks candidly: recouping the $18 billion investment will take time, and the technology comes at human cost, with a projected 10% reduction in operations headcount. Organisations carry an ethical and societal responsibility to mitigate those potentially significant losses.

MD Anderson Cancer Center: a $62 million structural failure

In 2012, MD Anderson partnered with IBM to build an AI clinical decision support tool for oncologists. The goal was to democratise world-class cancer care, giving any oncologist anywhere access to the diagnostic intelligence of one of the world’s leading cancer institutions. Five years and $62 million later, the contract expired before the system had been used on a single real patient. Inquests found the failure organisational rather than technological: the system was incompatible with existing platforms, scope had ballooned, the original six-month delivery timeline had been extended twelve times and no one with clear authority had been accountable for keeping the project within workable boundaries. It failed where JPMorgan succeeded – in governance, data foundation, accountability and the integration of human and technical design.

The window is narrowing

The gap between the 6% and the 94% continues to widen because AI advantage compounds. The organisations that have redesigned their workflows, built their people’s capabilities and embedded governance into their operating models are iterating faster and learning more with every cycle. Their data gets richer, their models improve and the distance between them and the organisations still running disconnected pilots increases.

The structural work needed – governance architecture, operating model redesign, talent investment, cross-functional accountability – is neither glamorous nor fast. The 6% understood this earlier than most. They made different choices, at the leadership level, about what kind of organisation they were building. That, ultimately, is the only gap that matters.

This insight is edited from a section of the first Rialto AI Business Leaders Circle Strategic Briefing of 2026, a biannual benefit of membership, which also includes the opportunity to help shape the future of AI in UK business with a seat at the table of the All-Party Parliamentary Group for AI (APPG AI) alongside MPs and other leading figures across government, academia and investment.

You can find out more about joining here

The human-machine era marks a shift in how organisations think about work, productivity and capability.

AI is no longer confined to isolated tools or functions; it is becoming embedded across workflows, influencing decisions, coordination and execution at scale.

Today’s leaders face two immediate challenges. First, they must understand how their own role is changing and what they must do to remain relevant and impactful. Second, they must collaborate with boards, partners and executive teams to redesign organisations where humans and machines complement rather than compete.

Here, we examine why organisations must pause and reflect on the structural, governance and workflow redesigns needed to truly harness the power of AI without draining innovation, talent and goodwill.

The insight distils some of the key lessons from just one chapter in the latest in-depth executive briefing offered as part of membership to the AI Business Leaders Circle.

Market Signals and Emerging Concern

The underlying dynamics are more nuanced than alarming headline narratives about mass layoffs suggest.

In the United Kingdom, sustained AI deployment is beginning to translate into measurable organisational restructuring. A 2026 analysis by Morgan Stanley found that UK firms operating AI systems for at least 12 months reported an average 8% net reduction in roles attributable to automation, one of the highest rates observed among developed economies, including the United States, Germany, Japan and Australia.

This suggests that once AI moves beyond experimentation into embedded operational use, structural workforce effects can materialise relatively quickly.

However, the picture in the United States, which leads the world in AI adoption and innovation, indicates a more complex pattern. Broader analysis shows that only around 4% of US layoffs last year were directly connected to AI implementation. In many cases, reductions were anticipatory with organisations “getting lean” ahead of projected efficiency gains rather than responding to proven displacement.

Some companies have also been accused of “AI-washing”: using automation narratives to obscure weaker performance, cost pressures or post-pandemic over-expansion.

At the same time, forward-looking warnings are intensifying. Dario Amodei, CEO of Anthropic, has argued that AI could eliminate up to half of all entry-level white-collar roles within five years. Supporting this concern, data suggests that graduate roles, apprenticeships and junior positions without degree requirements have declined significantly since late 2022.

Entry-level roles are capability incubators. They serve as the training ground where professionals develop judgement, institutional understanding and domain expertise required for future leadership.

If AI disproportionately compresses these early-career pathways, organisations may inadvertently hollow out their own talent pipelines. The result would not be immediate productivity loss but a delayed capability crisis emerging within five to seven years.

AI Job Displacement to Value Creation

According to the World Economic Forum (WEF), by 2030 an estimated 170 million new roles could be created globally (14% of current employment), while 92 million existing roles (8%) may be displaced, resulting in net growth of 78 million roles. However, this headline figure masks a deeper structural tension. Over the same period, global population growth of an estimated 338 million will place additional pressure on employment systems, productivity and social infrastructure.

For senior leaders, the defining issue is not whether AI creates or destroys more jobs in aggregate. It is whether organisations can manage the pace and sequencing of transition.  Organisations that actively redesign work, invest in skills and support effective human-machine collaboration will be the ones better positioned to absorb disruption and realise productivity gains.

The WEF also indicates that while machine-led tasks are growing, the majority of work still requires human-led judgement or structured human-machine collaboration. Rather than whole roles disappearing, jobs are being reconfigured into portfolios of tasks, where routine activities are automated and human effort concentrates on judgement, creativity, emotional intelligence and strategic contribution.

Organisations must examine whether they are redesigning work intentionally, or allowing automation to reshape roles by default?

Evidence suggests that AI generates substantial economic value, but that value is unevenly distributed.

PwC’s 2025 Global AI Jobs Barometer found that AI-skilled workers earned a 56% wage premium in 2024, the most AI-exposed industries achieved 27% growth in revenue per employee (three times that of less exposed sectors), and productivity growth has almost quadrupled in industries most exposed to AI since generative AI’s advent in 2022, rising from 7% to 27%. These figures suggest that AI creates substantial value, but concentrates that value among workers who can effectively leverage the technology.  AI does not automatically create productivity. It rewards preparedness.

Two Strategic Paths: Augmentation vs Displacement

The contrast between BMW and Klarna illustrates how strategic choices determine whether AI augments or erodes organisational capability.

BMW’s Augmentation Approach

In late 2024, BMW launched AIconic, a multi-agent AI system serving its purchasing and supplier network. The system integrates 10 specialised AI agents that streamline tender analysis, supplier data management and quality checks. With over 1,800 active users performing 10,000 searches monthly, the solution demonstrated immediate value.

What differentiates BMW is not the technology itself, but the organisational design accompanying it. Critically, BMW provides digital training and special AI innovation spaces for employees at all levels, enabling them to acquire digital literacy and share new skills throughout the organisation.

The financial results prove substantial: BMW’s AI stud correction laser alone saved over $1 million annually while enabling workforce optimisation and redeployment to higher-value activities. Rather than eliminating roles, BMW redesigned workflows around human-machine collaboration, with AI handling data-intensive tasks while humans focused on strategic supplier relationships and complex negotiations. The company now has hundreds of AI use cases in series production and plans to make every process AI-supported in the foreseeable future. Employees transitioned from routine data processing to relationship management and strategic decision-making, creating genuine career progression rather than displacement.

Klarna’s Displacement Trajectory

Swedish fintech Klarna pursued a dramatically different path. Between 2022 and 2024, the company eliminated approximately 700 positions (40% of its workforce), replacing most of them with AI-powered customer service systems developed with OpenAI. CEO Sebastian Siemiatkowski initially celebrated the transition, proudly announcing the workforce reduction and positioning Klarna as AI’s most aggressive adopter in fintech.

The consequences materialised rapidly. By early 2025, customer service ratings collapsed as users reported generic, repetitive responses inadequate for complex issues. The company’s Glassdoor rating plummeted from 3.8 in 2022 to 3.0, signalling severe damage to employee morale and employer brand. Siemiatkowski was forced to publicly admit: “Cost unfortunately seems to have been a too predominant evaluation factor. We went too far.”

By mid-2025, Klarna began rehiring human customer service agents, implementing what it termed an “Uber-style” flexible workforce model. The CEO acknowledged that AI systems lacked the empathy and nuanced problem-solving essential for customer support. The episode, dubbed “The Klarna Effect” by industry observers, represents a cautionary tale of AI deployment prioritising short-term cost reduction over sustainable capability development.

The differential outcomes between BMW and Klarna stemmed from strategic intent and execution discipline, not technology capability.

Impact on Executives

In the AI era, executives are increasingly responsible for leading human-machine systems rather than purely human ones. This requires fluency in AI and data capability, understanding of workflow architecture, governance literacy and organisational redesign competence.  The leadership role shifts from command and control towards capability curation: setting direction, defining guardrails and ensuring alignment between strategy, systems and people.

When speaking to Rialto consultants, many leaders report limited confidence in their understanding of AI and uncertainty about where best to develop. Many report higher stress levels and say they are reassessing career sustainability in the face of accelerating technological change. This matters because leadership confidence and coherence strongly shape how change is experienced across an organisation.

AI investment that is matched by leadership capability consistently delivers stronger ROI. Where leadership understanding lags technology deployment, organisations risk destabilising workflows, eroding trust and undermining the very productivity gains AI promises. (See previous insights on AI Learning for Executives: Building Competence for Transformation and Transition and AI is Changing Everything – How can Executives Stay Ahead?)

Board-Level Governance: The Strategic Imperative

Effective AI workforce transformation requires board-level governance that recognises AI adoption as strategic transformation, not merely operational implementation. Yet governance maturity remains uneven. A 2025 global survey by Deloitte of 700 board directors and executives across 56 countries found that 31% report AI is not on the board agenda, while 66% say their boards lack sufficient knowledge or experience in the domain.

This governance gap carries material consequences. According to MIT research, organisations with digitally and AI-savvy boards outperform peers by almost 11% in return on equity, while those without lag 3.8% below industry average. Meanwhile, analysis by McKinsey reveals only 15% of boards currently receive AI-related performance metrics, despite workforce transformation representing one of the highest-risk and highest-impact areas of AI deployment.

Strategic alignment therefore requires formal oversight mechanisms. Boards should mandate regular AI impact assessments covering ROI by business unit, the proportion of AI-enabled processes, workforce reskilling progress and regulatory alignment. Yet Deloitte reports that only 5% of organisations have fully incorporated AI into their core business plans, highlighting a material disconnect between ambition and integration.

Workforce capability oversight must also move beyond informal reporting. Human capital committees must track talent pipeline development, ensuring skills necessary for AI transformation are being built systematically.  This includes monitoring reskilling participation rates, AI fluency at leadership levels and retention of AI-capable talent. Capital allocation frameworks must rigorously assess AI investment proposals, balancing short-term efficiency gains against long-term capability development and resisting the “Klarna temptation” to prioritise headcount reduction over institutional resilience.

Risk oversight requires structured approaches to monitoring algorithmic bias, data privacy breaches, compliance failures and workforce displacement risks. The AI Incident Database tracked a 26% increase in AI incidents from 2022 to 2023, with a further 32% increase in 2024.

Finally, boards must recognise cultural stewardship as a governance responsibility. AI strategy affects organisational reputation, employee trust and psychological safety, all of which materially influence adoption success. In the human–AI era, culture is strategic infrastructure.

Redesigning Workflows: Beyond Automation

Redesigning work is now a strategic leadership decision that determines whether AI amplifies human capability or erodes trust and engagement. The BMW example illustrates this principle: rather than automating entire procurement processes, BMW decomposed workflows into component tasks, assigned appropriate tasks to AI agents while elevating human roles to focus on strategic supplier relationship management, negotiation strategy and risk assessment requiring contextual judgement.

Process orchestration becomes a distinct capability requiring new roles and skills. Someone must design workflows determining when tasks move from human to machine and back, establish quality control mechanisms and identify failure modes.

Quality assurance mechanisms must evolve substantially, as AI systems produce outputs that appear authoritative but may contain subtle errors or contextually inappropriate recommendations.

Organisations that succeed treat human-machine redesign as core strategy, rather than a side-effect of technology adoption. They invest deliberately in workforce capability, embed AI into workflows with intent and prioritise organisational resilience over narrow cost reduction.

Managing Structural Role Reduction Responsibly

Not all roles can be redesigned or augmented indefinitely. Evidence suggests that up to 40% of current roles could be affected by AI, making some degree of workforce restructuring unavoidable. Responsible leadership requires early modelling of which functions are likely to consolidate within two years. Transparent communication and structured transition planning mitigate long-term cultural damage.

Where exit is inevitable, early honest communication and genuine transition support including career coaching and skills assessment, often serves employees better than extended uncertainty. The organisations managing this transition most effectively also provide reskilling for viable internal alternatives, clear timelines and meaningful severance and outplacement support that enable affected workers to plan their next moves while still employed.

Creating a Resilient Culture

As AI reshapes work and skills simultaneously, AI transformation depends on cultural readiness. Organisations that treat culture as a soft issue or delegate it entirely to HR typically struggle to scale AI beyond pilots.

CIOs and CDOs are increasingly required to work in close partnership with CHROs, CFOs and CPOs to align technology adoption with workforce design and capability development.

Leaders must ask, does the organisation reward learning, judgement and responsible experimentation, or does it default to risk aversion, silence and short-term cost control? The answer increasingly determines whether AI investment translates into sustainable growth. Klarna’s Glassdoor ratings fall demonstrates how aggressive AI deployment without cultural preparation can destroy the trust and psychological safety required for sustainable transformation.

The Path Forward

The WEF projections suggest net job growth, but the maths reveal the deeper challenge: 78 million net new roles against 338 million population growth means transition management becomes the defining leadership competence of the next decade. Technology deployment is the simple part. Workforce transformation is the challenge that will differentiate successful organisations.

The executives who navigate this transition successfully will treat workforce capability as strategically foundational to successful technology deployment. They will invest in learning infrastructure as deliberately as they invest in computing infrastructure. They will redesign workflows around human-machine collaboration rather than automating legacy processes. They will communicate honestly about displacement risks while providing genuine transition pathways. They will choose augmentation over a displacement trajectory that hollows out.

The alternative is the worst-case scenario where short-term efficiency gains hollow out organisational capability, workforce displacement outpaces transition support and the benefits of AI accrue narrowly while the costs distribute broadly. This outcome is not inevitable, but as Klarna demonstrates, it is entirely possible when AI is treated primarily as a cost-reduction tool rather than as a strategic transformation requiring deliberate workforce design.

About Rialto

The human-machine era will not be defined by the speed of automation, but by the quality of organisational judgement guiding it. AI will reward those who design deliberately and penalise those who optimise prematurely. The question is no longer whether work will change but whether leaders will change fast enough to shape it.

Rialto partners with executives to navigate strategic workforce transitions in the AI era. We work alongside leadership teams to assess organisational capability, design human-machine workflows, and develop transition strategies that balance productivity gains with capability development. With deep expertise in executive capability development, transition and organisational transformation, Rialto provides trusted strategic counsel during periods of structural change and transition.

Contact Rialto on +44 (0) 20 3746 2960 to discuss your workforce transformation strategy or find out more about the  AI Business Leaders Circle.

A Seasonal Leadership Reflection for 2026

Hands up who’s exhausted and ready for a pause. For many leaders, this year has demanded sustained resilience. The supercharged evolution of AI has been enough to test even the most technologically confident among us, while regulatory pressure and a persistently slow hiring market have made this something of an annus difficilis for those carrying organisational responsibility, to misquote our late Queen.

As we look ahead to 2026, leadership is increasingly defined not just by decision-making, but by how leaders hold uncertainty, distribute accountability and sustain performance through ongoing disruption.

With that in mind, we invite you to ease into the festive wind-down with our Christmas-themed leadership quiz. It is intentionally light-hearted!

Answer instinctively and tally which letter you choose most often. You may gain a useful insight into how you lead, only with less trauma than the spectral visitations and personal upheaval that accompanied Scrooge’s famous leadership transformation.

Take the Christmas Leadership Quiz

1. Which Christmas film best reflects how you lead?
   A) It’s a Wonderful Life – (focused on purpose, values, legacy)
   B) Home Alone – (like its lead character, quick-witted, decisive, self-reliant)
   C) The Holiday – (It’s all about managing other people’s needs and expectations)
   D) Die Hard – (Dealing with multiple threats and taking charge to avoid disaster)
2. You’re hosting Christmas dinner. What’s your style?
   A) Planned, tested, calm
   B) You take charge and improvise
   C) Everyone brings something
   D) Big vision, lots happening
3. Which Christmas retailer do you most admire?
   A) John Lewis – trust and emotional connection
   B) Amazon – speed and execution
   C) M&S – consistency, quality and care
   D) A small independent – creativity and agility
4. A key decision you made this year didn’t land. You:
   A) Reflected openly and adjusted course
   B) Fixed it quietly and move on
   C) Talked it through with the team
   D) Reframed it as “part of the plan”
5. Your reaction to Last Christmas on the radio:
   A) Traditions matter
   B) Enough already
   C) It connects people
   D) Incredible durability but could do with remastering for the current age
6. It’s 20 December and a problem appears. You:
   A) Check it aligns with core principles
   B) Solve it yourself
   C) Pull the right people together
   D) Absorb it along with everything else
7. Your team’s energy in mid-December is best described as:
   A) Tired but committed
   B) Running on adrenaline
   C) Supporting one another
   D) Stretched thin
8. Someone offers to help with a complex task. You:
   A) Welcome the support
   B) Decline – it’s quicker if you do it
   C) Accept and share ownership
   D) Thank them, but keep control
9. Which festive phrase sounds most like you?
   A) “Let’s do this properly”
   B) “I’ll just sort it”
   C) “Let’s work it out together”
   D) “We’ll make it work somehow”
10. If your leadership were a Christmas item, it would be:
    A) A star – guiding and consistent
    B) A lone reindeer – strong but overworked
    C) A bustling table groaning with food collaboratively prepared
    D) Fairy lights – bright, but easily tangled

Your Leadership Style Explained

Mostly As – The Purpose-Led Anchor

You provide stability, direction and a clear sense of what matters. In uncertain conditions, people look to you for reassurance and moral clarity. The risk is that consistency hardens into rigidity. As 2026 brings further volatility, regulation and AI-driven change, your opportunity is to hold purpose steady while allowing strategy, structure and ways of working to evolve around it.

Mostly Bs – The Lone Solver

You are decisive, capable and reliable under pressure. When things are urgent or ambiguous, you step in and get things moving. The risk is isolation. Struggling to ask for help or admit when something hasn’t worked quietly limits learning, increases personal strain and teaches teams to defer rather than contribute. In 2026, your leadership impact will grow fastest if you practise sharing uncertainty earlier and modelling that asking for help is a strength, not a failure.

Mostly Cs – The People-First Leader

You lead through trust, collaboration and shared ownership. Teams feel safe, engaged and supported, which builds resilience over time. The risk is drift. In fast-moving environments, a strong desire for inclusion can slow decisions or blur accountability. As the pace of change accelerates in 2026, your challenge will be to pair empathy with clarity, making timely calls while keeping people with you.

Mostly Ds – The Complexity Carrier

You are comfortable holding ambiguity, competing priorities and constant change. You keep things moving when others feel overwhelmed. The risk is overload. Absorbing too much can normalise pressure, mask structural problems and quietly erode performance. In 2026, the step-change will come from simplifying boldly, naming trade-offs clearly and designing systems that reduce dependence on your personal capacity.

Leading Forward: Reflection, Renewal and Readiness for 2026

Christmas has a habit of revealing truths. The leaders who will progress fastest into the New Year will be those who notice their patterns and habits, take time to reflect honestly and consider what might need to change, whether within themselves or the organisational culture and systems they lead.

This moment of pause matters. Rest and reflect are not indulgences; they are strategic enablers.  Also, eat drink and be merry. Fun, connection and recovery act as biological and psychological reset mechanisms for the bran and body, restoring the capacity for focus, learning and resilience.  Warmth and belonging provide emotional renewal, something no strategy deck can replace.

Or, as Dr Seuss phrased it so beautifully in How the Grinch Stole Christmas:

“Maybe Christmas”, he thought, “doesn’t come from a store”.

“Maybe Christmas… perhaps… means a little bit more.”

With very best wishes for the season from all at Rialto.

In the first two parts of our AI skills special, we explored why and how executives should build continuous AI learning into leadership development programmes.

This third and final part turns to an equally – if not more – critical issue that will define which organisations truly thrive in this fast-moving era: preparing the workforce through upskilling, rather than simply seeking to reduce headcount.

When used responsibly, under secure and ethical supervision, and embedded across all levels of the organisation, AI capability and confidence can combine to act as rocket fuel for performance and innovation.

AI has the potential to serve as a highly responsive, interconnected nervous system that touches every part of the business. It can bring data-driven insight to the very core of strategy – from how the company goes to market, to how it manages talent and responds to competitive pressures.

While it’s essential that implementation is led by an AI-literate CEO and CFO, supported by functional leaders, any blockages caused by ineffective or unsafe use across the wider organisation will limit progress, ROI, and stakeholder confidence.

According to McKinsey, C-suite leaders are 2.4 times more likely to cite employee readiness as a greater barrier to AI adoption than their own skills. Yet employees are already using GenAI tools three times more than their leaders realise.

For executives and HR leaders facing this disconnect, and the broader disruption required to realise AI’s full potential, the first step is to address a structural challenge: most employees lack the cognitive tools to thrive in transformed workflows, while those leading workforce strategy often lack the diagnostic tools to measure capability gaps accurately.

Research from McKinsey and the World Economic Forum continues to highlight skills shortages as the single biggest obstacle to organisational transformation. Sixty-three percent of employers see capability gaps as a major barrier through to 2030. Despite this, many still look externally for talent that could be developed internally, often at lower cost and with less disruption, while laying off staff displaced by automation.

This pattern reflects an absence of understanding and systematic workforce assessment that risks destabilising businesses, society, and even the wider economy.

A more constructive approach is to audit workforce skills against current and future objectives – uncovering untapped potential, latent strengths, and opportunities to enhance capabilities from within.

Establishing a credible baseline: The audit framework

Assessing workforce readiness for technological change requires moving beyond traditional talent assessment methods. Standard competency frameworks, based on current job roles, simply don’t provide the data organisations need in a constantly evolving technological environment.

Instead, a multidimensional evaluation is needed, one that captures three critical dimensions: technical proficiency in emerging tools, cognitive flexibility across domains, and the ability to adapt behaviour under uncertainty (in other words, resilience, agility, and adaptability).

An effective audit should map current capability against anticipated requirements around 18 months ahead, not just today’s job descriptions. This requires cross-functional collaboration and open data sharing.

Organisations should conduct this assessment through structured interviews with functional leaders rather than relying exclusively on self-reported surveys These discussions reveal not only competence but also psychological readiness and appetite for change. The distinction matters: a moderately skilled employee with high motivation can outperforms technically proficient colleagues resistant to new ways of working.

The audit should also reflect the organisation’s unique context. For instance, manufacturers may need capability in computer vision or predictive maintenance; customer service teams in natural language processing and data-driven platforms; finance teams in modelling and causal inference; and content creators in understanding the limits and verification needs of generative models. This level of specificity helps avoid the all-too-common pitfall of theoretical training disconnected from practical reality.

Distinguishing trainable from structural capability gaps

Not every capability gap can be bridged through training alone. Some deficits stem from deeper factors, such as cognitive orientation or the nature of experience built up over years of professional practice.

For example, sometimes individuals who have constructed careers through hierarchical advancement within narrowly defined specialisations can find it difficult to sustain the continuous reorientation that technological change demands. Addressing these cases requires sensitivity and support, not blame. Senior executives may benefit from targeted leadership development and coaching to strengthen the soft skills that underpin digital and AI-driven transformation.

Recognising the difference between trainable and structural capability gaps allows for more informed decisions about retention, redeployment, and recruitment. The World Economic Forum highlights analytical thinking, resilience, and cognitive flexibility as the most in-demand competencies for 2025, qualities that require cultural reinforcement across the organisation, not just classroom instruction therefore a task which can be more complex and challenging than hard skills training.

Organisations that take this nuanced view can avoid costly mistakes such as unnecessary restructuring or over-automation, which can lead to anxiety and disengagement.

Audits should therefore include behavioural indicators of adaptability beyond anything that standard competency assessment can provide such as how individuals have handled previous operational change, their curiosity about unfamiliar domains, and their willingness to self-learn. These behavioural markers often predict success in technological transitions better than traditional performance measures.

Identifying roles requiring structural transition

Up to 40% of current roles could be displaced by AI, meaning some restructuring will be unavoidable. Certain jobs face genuine obsolescence, not just transformation requiring skillset adjustments. Research from Adzuna demonstrates that graduate positions, apprenticeships, internships and junior roles without degree requirements have fallen by approximately 32% since November 2022, now comprising 25% of all UK job listings down from 28%. These shifts call for honest reflection rather than optimistic retraining narratives.

The strategic question organisations must confront is whether investing resources in retaining individuals in functionally declining positions serves institutional or individual interests. Often neither party benefits from extended employment in roles that gradually diminish in scope and compensation. Acknowledgment of this reality, coupled with genuine transition support including financial security, career coaching and skills assessment for alternative employment, can serve departing employees better than struggling on in positions of diminishing significance.

Roles requiring such structural transition should be identified through financial modelling rather than hope. Evaluate which functions will consolidate through automation or shift to fundamentally different competencies within two years. The results will support workforce transition planning with greater honesty than aspirational but unevidenced upskilling narratives.

Building continuous learning architecture aligned with strategic objectives

Organisations that navigate technological change successfully tend to share one structural feature: learning is embedded into day-to-day operations, not treated as a separate HR function.  This approach transforms learning into a process of structured problem-solving within real work contexts, supported by data and feedback loops.  Agentic AI platforms can support and augment this process.

This requires establishing a dynamic skills architecture that maps current organisational competencies against anticipated future requirements at the level of specific work functions rather than abstract capabilities. This might involve identifying precisely which analytical techniques the finance team will require, which communication protocols the sales force needs, which quality assessment procedures the manufacturing operation demands. This specificity transforms learning from generic skill acquisition into targeted capability development demonstrably connected to organisational performance.

Implementation involves designating accountability for this architecture at the executive level, not within training departments. The Chief Financial Officer bears responsibility for ensuring the analytical and technological capabilities necessary for projected operational models. The Chief Operating Officer owns capability alignment in production operations. This assignment of accountability could prove more important than the quality of any particular course offering.

Organisations should expect that roughly 70% of capability development will occur through structured problem-solving within actual work contexts rather than formal instruction. The remaining 30% can benefit from targeted coursework, typically micro-credentialed programs of four to eight weeks rather than extended academic sequences. Timing matters. For example, technical instruction proves most effective when delivered immediately before operational application rather than months in advance. Lessons that can be applied quickly and practically help contextualise and reinforce learning.

Sustaining Organisational Adaptability Beyond Current Change Cycles

The capability requirements focused upon in 2025 may be less relevant by 2027 while specific technical competencies in demand will shift and soft skills that differentiate performance will evolve. Organisations that construct learning systems flexible enough to accommodate successive technological transitions outperform those that optimise for current requirements.

This flexibility requires close collaboration between HR leadership and executive coaching. Coaching relationships with senior leaders catalyse the self-awareness and cognitive flexibility that enable them to lead organisational evolution, minimising any resistance grounded in lack of confidence or fear of displacement.

Individuals who engage authentically with executive coaching demonstrate markedly greater capacity navigating structural change, maintaining team engagement during transition and modelling the adaptability organisations require of their broader workforces.

The investment in executive coaching during periods of material technological change generates returns that extend well beyond individual leader development. It establishes organisational culture where development is seen as built in rather than remedial intervention, where explicit acknowledgment of capability gaps reflects analytical maturity rather than professional vulnerability and where learning partnerships with external experts enhance rather than threaten internal capability building.

Organisations that embed executive coaching alongside workforce auditing and continuous learning architecture can significantly outpace competitors approaching these elements separately. The senior leader who has examined their own constraints and potential through coaching partnership will appear more credible when advocating difficult organisational transitions. A leadership team aligned through shared development experience makes more coherent strategic decisions regarding workforce capability realignment. Organisational cultures that show senior leadership engaging continuously in external refection and development normalise the adaptability the organisation requires throughout its workforce.

Measuring what matters: linking development to performance

One of the most common pitfalls in workforce development is failing to connect learning initiatives to measurable business outcomes. Upskilling only delivers real value when employees can apply new capabilities directly to their roles and when the impact is visible to leadership, stakeholders, and the board.

Measurement systems should therefore track how specific skill investments translate into performance. For example, if customer service functions deploy natural language processing tools, measurement systems should track what different interactions and tools are designed for  and what quality improvements were achieved. If finance teams develop advanced modelling capabilities, systems should quantify how these capabilities improved forecast accuracy or decision quality.

This level of specificity requires that HR leaders and finance leaders collaborate to build measurement frameworks rather than each maintaining separate administrative systems. The collaboration may reveal misalignments between capability investments and actual strategic priorities and enable careful and ongoing recalibration.

Ultimately, auditing workforce readiness for AI isn’t just about tracking current skills against job descriptions. It’s about honest evaluation, identifying which roles can evolve, which require transition, and how learning can be embedded into operations and linked directly to performance outcomes.

Organisations that approach this challenge with rigour, empathy, and transparency will build the resilience and agility needed to thrive through successive waves of technological change.

If you would like to discuss strategic planning of upskilling and reskilling needs for individuals or teams, Rialto has 85 consultants specialising in every aspect of organisational transformation and executive leadership development. Please do get in touch to arrange an initial consultation.

In this second part of our three-part series on upskilling for the AI era, we explore the distinct AI skills needed by today’s executives and how they fit into any ongoing programme of professional development.

Whether making a personal executive transition, receiving executive outplacement or driving organisational transformation, AI literacy is now an essential skill that should be considered as part of any development or change initiative. Executives who integrate AI mastery into a continuous learning agenda, spanning both personal and organisational transformation, will remain competitive and relevant in a rapidly evolving landscape.

As highlighted in our previous insight on how executives can stay ahead of the AI curve, of the $30 billion spent on AI globally, only 5% is seeing a return on investment. T his may be partly due to metrics and measurements not catching up with what success looks like, but progress is too often also impeded by executives’ glacial response as the technology accelerates exponentially in real time.

As former Cisco CEO John Chambers observed, half of executives “won’t have the skills to adjust to this new innovation economy driven by AI because they were trained to move at the speed of a five-year cycle as opposed to a 12-month cycle.”

Senior leaders therefore need to continuously reinvent themselves to stay aligned with the pace of technological evolution.

Building the right AI competencies

Below, we look at specific AI skills sets for executives who face distinct requirements when building AI competency. This guide provides an overview of core AI skills executives should consider acquiring and examines how training can be incorporated into broader leadership development strategies.

Skill 1: AI Strategy, Appraisal and Value Framing

Why it matters: Executives must identify where AI creates measurable return, build business cases and sequence pilots into scaled capability, recalibrating and updating according to technological advances which may otherwise outrun specific projects and lead to shareholder value erosion through misaligned investments or missed opportunities. Leaders who map use cases to financial outcomes gain competitive advantage.
Related competencies: Strategic foresight, scenario planning, critical and creative thinking.

Skill 2: AI Governance, Risk and Compliance

Why it matters: Boards and C-suites are prioritising governance, auditability and regulatory readiness amid a fragmented regulatory landscape, where inadequate oversight can expose organisations to severe fines or reputational damage from incidents such as bias scandals. Governance is a rising board agenda item, helping attract top talent through ethical practices and building resilience by managing the inherent complexities of scaling AI, while fostering ESG alignment and stakeholder trust.
Related competencies: Stakeholder collaboration, ethical decision-making, resilience.

Skill 3: Data Literacy and Decision Science

Why it matters: Executives who interpret model outputs, ask the right questions of data teams and set measurable KPIs are more effective sponsors of AI projects. This skill facilitates literacy in relation to decision frameworks, enabling navigation of volatile markets and bridging analytical gaps for informed sponsorship, particularly when aligning with UK initiatives around data protection and digital information that demand robust, privacy-conscious handling.
Related competencies: Data governance, analytical and critical thinking, cultural sensitivity.

Skill 4: Generative AI Literacy and Prompt Design

Why it matters: Executives need practical fluency with generative tools so they can assess vendor claims, pilot real workflows and set safe guardrails, unlocking productivity gains while mitigating risks such as hallucinations leading to flawed decisions or unintended outputs. Amid the rise of multimodal trends, this becomes essential for integrating tools like enterprise Copilots and scaling pilots without misuse, in line with UK recommendations for safe adoption that emphasise responsible experimentation and organisational safeguards.
Related competencies: Strategic foresight, ethical decision-making, change management.

Skill 5: People Leadership for Augmented Work

(Part three of this series will examine workforce upskilling.)
Why it matters: Adoption failures arise when leaders treat AI as a technology or tooling problem rather than one of people and process change, overlooking the human elements of redeployment and upskilling that can enhance team creativity and improve retention in blended workforces. This fosters resilience in hybrid AI-human environments, addressing the transformative shifts in job roles and skills needs, and ties into broader workforce strategies. Leadership skills supporting redeployment and upskilling are flagged in employer surveys as essential.
Related competencies: Strategic workforce foresight, stakeholder collaboration and influence.

Skill 6: Responsible AI and Ethics

Why it matters: Bias mitigation, explainability and responsible deployment are areas where executives must make trade-offs between speed and trust. Courses increasingly include practical governance frameworks to support these decisions.
Related competencies: Ethical judgement and integrity, strategic foresight and systems thinking.

From learning to leadership practice

Developing the above competencies requires structured and intentional learning. The next step is therefore understanding how executives can build and apply them effectively. While AI learning opportunities are widely available, their effectiveness depends on context and application. As with learning a new language, the greatest value comes not from theory alone but from practical use and cultural understanding.

A range of flexible programmes now support executives in building these capabilities. Some offer on-demand, video-based content with downloadable certification (e.g. LinkedIn Learning, Microsoft, DeepLearning.AI). Others blend live instruction with self-guided modules or in-person engagement.

However, without strategic framing, such courses may lack the nuance required to translate learning into leadership impact. Incorporating executive coaching or providing structured professional development can help align AI learning with transition goals, business transformation objectives, and broader leadership capabilities such as ethics and human-first implementation.

Learning formats: matching goals and learning style

A wide spectrum of AI learning options is available to meet different executive needs, schedules, and learning preferences. To optimise the benefits of AI education, Rialto consultants recommend beginning with compact, high-quality micro-courses for immediate familiarity, followed by targeted intensive programmes aligned to sector or functional priorities. Ongoing micro-learning and peer discussion groups can then sustain progress.

Bite-size and micro-learning courses provide rapid, low-cost access to foundational AI literacy, typically requiring a commitment of four to twenty hours. They are particularly effective for boards and senior teams seeking immediate fluency, offering practical exposure to areas such as prompt engineering and vendor assessment. These short, modular courses, available from providers such as DeepLearning.AI and LinkedIn Learning, make learning highly accessible and inclusive. However, they generally offer limited depth in areas like governance, data architecture, and strategic trade-offs, and they tend to provide fewer networking opportunities or weaker credentials. As a result, they are best suited for establishing baseline literacy, developing tool-specific competence, or supplementing more intensive development initiatives.

For leaders seeking deeper engagement, intensive executive AI programmes offer a more comprehensive approach, often spanning three to eight weeks. These programmes address advanced themes such as AI governance, data architecture, vendor strategy, and organisational change management, while also enabling participants to build peer networks with other senior leaders. Providers such as MIT Sloan, Harvard Business School, Oxford, and Wharton offer faculty-led experiences with access to implementation playbooks and sector-specific case studies. Although these programmes require a higher time and financial investment, they provide the strategic depth and board-level perspective essential for developing AI maturity across organisations and for positioning executives for future leadership transitions.

Sustaining relevance through responsible AI Leadership

As AI continues to redefine the leadership landscape, executives who commit to continuous, structured learning will be best placed to lead responsibly, transform their organisations, and remain relevant through disruption. AI fluency is not an isolated technical skill; it is now a cornerstone of strategic foresight, ethical leadership, and cultural adaptability. Embedding AI capability within broader professional and organisational development enables leaders to make informed, values-driven decisions that build resilience and trust in a rapidly evolving economy.

Rialto supports this journey through its programme of complimentary invitation-only events  exploring AI and leadership topics. With 85 consultants operating globally, Rialto helps executives strengthen leadership capability, navigate transition, and align AI learning with strategic transformation goals.

Executives can also contact our research department for examples of leading AI learning programmes and providers—including Harvard Business School, LinkedIn, Deloitte, and others—that Rialto clients have successfully undertaken. To learn more, email research@rialtoconsultancy.com.

Despite £30 billion global investment in AI, just 5% is seeing ROI. The potential is there – how can organisations convert it into real returns? In the first of our three-part AI skills special, we look at how the landscape is changing and what leadership must do to stay ahead, stay relevant and seize the initiative through executive transitions and organisational transformation.

The Risks of Outdated Leadership in the AI Era

It has become unequivocally clear that the executive and economic landscapes are undergoing structural change, irreversibly and at unprecedented speed, as AI capabilities and reach expand exponentially. What were previously long-term trends have become short cycles and the skills required to remain competitive are now evolving in real time.

Meanwhile, the market is becoming increasingly challenging (see our latest executive outlook), meaning it has never been so crucial for senior leaders to be able to differentiate themselves and stay a clear length ahead of technological and cultural trends.

Thus, all executives are facing a stark reality: traditional leadership qualities remain essential, but without demonstrable, up-to-the-minute digital and AI capabilities, they risk being seen as out of touch with the markets they serve.

Leaders who allow their skills to become dated or even obsolete can also become an organisational risk if they are trying to operate in the same ways they have done traditionally.

Agility and adaptability are key to leaders and their workforces.

Ryan Roslansky, CEO of LinkedIn, said his top piece of advice in this febrile business culture is to “remain a lifelong learner…seek out opportunities to learn new technologies, because the ability to adapt and learn how to learn is going to set you apart.”

In the first of our three-part AI skills special, we will look at why executives need to commit to continuous learning – how the market is changing now and how it is shaping up for the rest of the decade. What do C-suite and other senior leaders need to understand and why are AI skills for executives replacing traditional skills and qualifications in executive and board level job specifications?

Part two will define and explain the most in demand AI-related technical and soft skills which are relevant to different C-suite roles and how executives can access effective learning to adapt their management style, culture and skillsets to stay relevant, including the highest rated education tools.

And the third part will examine how to audit and upskill workforces, identifying any shortages on the market of in demand expertise and soft skills and ways of future-proofing human-first organisations.

Why every executive must commit to continuous AI learning

In brief, guesswork based on fragmented or limited understanding is dangerous.

Too much AI adoption has been ill thought through or driven by hype, leading to failing pilots, disappointing ROI or financial losses, misdirected resources, stakeholder and staff scepticism and investor hesitance.

A recent MIT report found that despite up to £30 billion worth of global investment in AI, an astonishing 95% is not yet seeing any return.

Here is the difficulty: go too quickly, and leadership risks reputational and organisational damage; too slow and the landscape will have already evolved, allowing the more agile, AI-literate and aligned competition to streak ahead, gaining the innovative edge and grabbing new markets.

Choose the wrong projects and a business’s trajectory could be thrown way off target. Yet blanket adoption – expecting every knowledge-based employee to use Microsoft Copilot or Google Gemini without training, oversight, ethical and security precautions or impact assessment – carries its own extremely high risks.

It’s a precarious balancing act, and only the most AI literate who are willing to commit to constant learning can keep that tightrope taut.

AI Fluency: The Core Executive Skill of the Future

The MIT study concluded: “The core barrier to scaling is not infrastructure, regulation, or talent. It is learning. Most GenAI systems do not retain feedback, adapt to context, or improve over time.”

While it was referring to the failure of systems to learn, it is up to leadership to define the goals, mechanisms and understand the capabilities and limitations of any AI end use under their management.

Executives need to understand the tech and what they want it to do, to set metrics and measurements. They must start with the problem, look for AI solutions and constantly analyse the data/output and recalibrate the mechanism, input and goals accordingly.

They need to work with data analysts, department leadership and teams to identify which pilots are showing the best potential for scaling up and what organisational transformation and resource allocation is needed to optimise the technology.

The Changing Leadership Job Market and AI’s Impact

AI fluency will, then, be the most important core executive skill to lead the best prepared organisations as we move into the next phase of the AI hype cycle: past the peak of the hype – possibly where we are now – and through the trough of disillusion, into the scope of enlightenment and on to the plateau of productivity.

This shift is reflected in recruitment data and in the way that executives are presenting themselves online.

According to LinkedIn, global C-suite executives listing AI literacy on their profiles have tripled in two years and 88% of senior leaders said accelerating AI adoption was a top business priority for 2025.

Labour market analyst Lightcast reports that postings mentioning generative AI skills specifically are up 800% for jobs outside IT and computer science since the launch of ChatGPT in 2022, This is not marginal demand. It represents a core realignment of what employers are seeking and organisations need.

It also found that postings mentioning at least one AI skill came with a 28% salary bump, including in business management and operations and human resources.

According to Indeed, management consulting roles saw the biggest increase in Gen AI job titles.

From MBAs to Continuous AI Learning: The New Executive Education

A generation ago, it was enough to invest in an MBA, professional qualifications or sector-specific training early in a career and then rely on experience and reputation during executive transition.

Today, the pace of change is so rapid that Gartner predicts 30% of current executive skills will be obsolete by 2030. The World Economic Forum’s Future of Jobs Report 2025 says 44% of workers’ core skills will change in the next five years, with leadership roles no exception.

Every few months, emerging technologies are developing beyond recognition – in just the two years since ChatGPT4 brought generative AI to the masses (see previous insight on how it impacts leadership) it is now being used in one form or another by 65% of the global knowledge-based workforce. Just as most of us were getting to grips with it, along came agentic AI, which can reason and execute complex workflows, and now we must anticipate the seismic impact that emerging Artificial General Intelligence will have.

Continuous upskilling is becoming as integral to senior leadership and executive transition as financial acumen or strategic foresight.

So, the challenge for executives is to demonstrate ongoing mastery of core leadership and governance skills while integrating technological literacy into their professional identity.

The online learning market reflects this urgency. Coursera, which offers 10,000 courses from universities and businesses, says enrolments on its 700 GenAI modules surged 195% in a year, with 8 million people signing up while platforms such as edX, LinkedIn Learning, and Udemy report that courses tagged “AI for executives” or “AI governance” are among the fastest growing.

Executives who commit to learning report tangible benefits. A 2024 PwC study found that leaders who invested at least 10 hours per month in structured learning were twice as likely to achieve promotion into board-level roles compared with peers who did not.

It also found that 88% of directors believed a single action could improve board effectiveness and 45% of them said seeking education or training on key topics was likely to have the biggest positive impact.

AI-literate leaders also report higher confidence in navigating disruptive change and greater retention of top-performing teams, as employees responded to leaders seen as forward-looking and capable of guiding organisations through uncertainty.

The nature of continuous learning is also changing. Where once it was dominated by in-person training and formal qualifications, the growth of online platforms has lowered barriers to entry. Executives can now engage in micro-learning, modular courses and personalised coaching, often alongside their daily responsibilities.

This flexibility is vital. Evidence is increasingly showing that “bite-sized” online courses that fit into a working week yield greater returns than traditional longer qualifications. This aligns with the rise of “stackable” credentials that allow leaders to build recognised expertise in areas such as AI ethics or advanced analytics without taking time away from work.

Looking ahead, the next two to five years will further intensify these demands. In 2023, McKinsey forecast that generative AI could deliver $4.4 trillion annually to the global economy by 2030 through corporate use cases, while simultaneously displacing or reshaping millions of jobs.

It now identifies the greatest challenge being how to unlock that long term potential while steering organisations through the shorter-term disruption with fewer measurable rewards. Executives will need to navigate this difficult period by combining governance and foresight with hands-on understanding of new technologies.

The New Baseline: AI Competence as a Leadership Expectation

The implications for leadership are profound. Those who embrace continuous learning will be positioned to seize opportunities in growth sectors, from healthcare technology and green energy to advanced manufacturing and financial innovation. Those who resist risk being left behind in roles that no longer exist. Evidence already suggests that traditional functions are declining. In the UK, entry-level corporate support roles fell by over a third in the past year and routine managerial oversight positions are shrinking in number and remuneration. In contrast, leadership roles requiring digital transformation skills are growing faster than average, even in a broadly flat labour market.

Executives should be using AI in almost every task – from preparing meetings and briefings to auditing and enhancing their own skills and those of the workforce; digesting the latest and most relevant market news and information; identifying risks and opportunities, driving innovation and efficiencies and getting closer to the market; improving customer and employee experience and anticipating trends and opening markets and preparing their organisation’s response; allocating resources and analysing data. The possibilities are, literally, endless and evolving every day.

Skills once considered optional are now a baseline expectation. Demonstrating competence in AI strategy, data governance and digital transformation is becoming as fundamental to boardroom credibility as financial discipline or regulatory compliance. The path to relevance lies in proactive learning, visible adaptation and the ability to tell a convincing story about how personal skills align with emerging business needs.

Staying Relevant in Executive Transition with Rialto

As the pace of change accelerates, every executive must think seriously about how to stay relevant, maintain credibility, and secure a strong executive trajectory in today’s market. This requires not only mastering the fundamentals of leadership but also committing to continuous AI learning and digital fluency.

If you are considering how best to position yourself for the next stage of your career or an upcoming executive transition, Rialto can support your journey. In addition, you can join the Rialto AI Business Leaders Circle — a forum enabling members to gain insider access to the conversations shaping the future of AI, including private briefings in the House of Lords, strategic insights from global AI experts, and the chance to influence national policy through the All-Party Parliamentary Group on AI.

Designed specifically for Executives, C-suite leaders, and senior decision-makers, membership offers a unique vantage point on real-world AI adoption and sector-specific ROI.

This is your opportunity to shape the dialogue, build organisational AI fluency, and secure your place at the forefront of business model transformation.

To find out more, book an appointment to speak to one of our team today: