Joining the 6% Club: What It Actually Takes to Make AI Work

“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:

The world is evolving from Generative AI – passive and prompt-based – to Agentic AI: active, autonomous systems. As these agents integrate into core business functions, they are reshaping the world of work. Nvidia CEO Jensen Huang envisions having a workforce one day made of 50,000 people and 100 million agents.

This shift demands a fundamental rethink of leadership. Unlike traditional automation, Agentic AI performs multi-step tasks, applies judgement, learns and acts on goals with far less oversight. It’s more than process efficiency – it’s a redefinition of roles, workflows and governance. In our last insight, we looked at the ways in which leaders can futureproof their own careers, adapting their offering to exploit new opportunities in the face of AI-driven structural change in the executive job market. Here, we look at how they can lead hybrid human/agentic workforces effectively, harmoniously and ethically, a skill that will become increasingly critical to organisational success – and sought after – in the months and years to come.

The Strategic Leadership Imperative

As machines move further into the realms of white-collar and knowledge-based tasks and functions, organisations might opt to prioritise efficiency savings or growth.

While a third of CEOs are looking to reduce headcounts through AI, the more strategic approach is to augment human productivity and build blended workforces that work in harmony to drive growth, innovation and employee engagement.

The presence of autonomous software agents introduces a novel dimension to organisational leadership. Leaders will be expected to supervise hybrid teams composed of human personnel and algorithmic agents. This requires the development of new competencies. Familiarity with the operational logic of digital agents will be essential. Leaders must be equipped to evaluate outputs critically, interpret process transparency and respond proportionately when systems operate beyond their intended parameters.

Crucially, leaders will also need to communicate clearly with their human teams about the purpose and limitations of automated counterparts. Trust, which has traditionally been fostered through interpersonal credibility and relationships, must now be extended to the technological instruments of the organisation, and language will be important: framing AI agents as co-pilots or support systems, not the competition. This can only occur through transparency, continuous education and robust governance frameworks that ensure human agency remains at the centre of decision-making.

Every executive function has a part to play:

• CEOs must set a vision rooted in human dignity and long-term employability.
• CFOs should fund re-skilling, mobility and adaptive workforce planning.
• COOs need to redesign workflows to preserve meaning and clarity.
• CTOs must deliver tailored training based on user needs and experience.
• Diversity Officers must prevent inequality by ensuring inclusive access to tools and training.
• HR should coordinate all of this, monitor sentiment and protect trust during change.

Redefining Roles and Responsibilities

The rise of agentic systems requires a thorough reassessment of job structures across multiple domains. As well as the more obvious deployments, such as next-gen natural language chat bots offering hyper-personalised customer services and sales, these systems are increasingly capable of performing complex tasks such as project coordination, data interpretation, predictive planning such as maintenance and supply chains, talent acquisition and even elements of strategic planning.

Within the next five years, digital agents could hold operational responsibility for roles traditionally reserved for junior to mid-level professionals including research analysts, procurement coordinators, account managers and elements of compliance monitoring.

Autonomous agents offer multiple potential advantages including operational consistency, accelerated task execution, round-the-clock availability and increased productivity.

There is also a clear opportunity for diversification of career trajectories. As digital agents assume routine functions, leaders should seek opportunities to release and upskill human workers to pursue more complex, collaborative or experimental work, increasing employee engagement, satisfaction and productivity. They will also need to consider hiring externally or redeploying from within to integrate the new human-AI coordination roles that will become core to successful hybrid workforces, including ethical oversight officers, systems auditors, data managers and verifiers and augmentation strategists. Every task and product created and serviced by AI will need human oversight.

Managing Risks and Governance Challenges

Human-AI integration raises tough questions. Displacement is real, especially in task-heavy sectors. Training helps, but change is accelerating and jobs are being lost in vast numbers before new ones emerge to replace them in this most disruptive stage of adoption. Salesforce’s Agentforce AI now handles 85% of customer queries – half of one 9,000-person department was redeployed. Just 500 of those reassigned to tech roles could save $50 million – but 4,500 risk unemployment.

Agentic systems can also embed bias or make decisions with no clear accountability. If left unchecked, or allowed to run without boundaries, they could undermine fairness, culture and trust. Those boundaries must be reviewed and redesigned constantly, employing human nuance, creativity and empathy.

Over-reliance is another danger. Agents are tools, not substitutes for judgement or ethics. Leaders must draw clear lines between what machines can do and what only humans should do. Strong governance is essential: audit systems, ensure traceability and keep decision-making transparent.

Preparing for the Future of Work

In the short term, expect AI agents in scheduling, reporting and knowledge management, embedded in existing tools and acting on contextual triggers. In five years, probably less, we are likely to see them support decisions in client services, forecasting and procurement, or even initiate and execute actions independently, within set boundaries.

Agents trained on internal data will align more closely with company goals. But success hinges on more than rollout – it’s about education. Workers need to understand why AI is here, how it works, and what it means for them.

Organisations must invest in critical thinking, digital literacy and cross-disciplinary collaboration. Structures should support integrated teams – humans and AI agents working together, not in parallel.

Inclusion is vital. Transformation must account for geographic, demographic and educational diversity. Tailored support, designed by humans, for humans, is essential to ensure everyone can take part.

Leadership Responsibility and Societal Impact

The shift to human-AI teams affects more than companies – it touches labour markets, income, regional economies and cohesion. How many jobs it will displace is a matter of debate. A Goldman Sachs report estimated 300 million jobs could be lost by 2030, the World Economic Forum (WEF) places it at a far less dramatic nine million, while Ford CEO Jim Farley said half of white-collar jobs could go.

However, the WEF also says 11 million jobs could be created by AI.

Executives have their part to play in what happens next. They must not only manage internal change but engage with policy, education and social impact. That means supporting displaced workers, reporting automation’s effects and acting transparently.

Ethical leadership balances innovation with inclusion. The opportunity is real – but so is the obligation. AI agents won’t define success. Leadership will. Aligning people and technology around shared goals is the ultimate goal for humanity.

Rialto support executives and leadership teams to protect core business operations while integrating emerging technologies to develop disruptive strategies and models. Our expert teams help leaders to reflect and gain perspective on their leadership approach, organisational processes and strategies to breakthrough stagnation and drive sustainable progress.

Whether you’re seeking to accelerate innovation, redesign operation, or strengthen your teams, Rialto executive career and business change coaches are ready to support you. Contact us today to explore how our strategic leadership and collaboration solutions can propel your organisation forward.

Three-fifths of C-suite executives in the US currently leveraging Generative AI are actively seeking roles in organisations that demonstrate more advanced AI adoption, according to a late 2024 survey.

This trend underscores the transformative impact of Generative AI on leadership expectations, where forward-thinking leaders perceive advanced AI integration as a catalyst for innovation and strategic advantage. Those ahead of the curve recognise that the gap between AI adopters and laggards is widening and with it, the risk of Executive profile irrelevance.

GenAI is transforming how organisations operate, including automating routine tasks, driving strategic decisions and innovation, sharpening customer insights, lowering costs and enabling highly personalised services.

According to McKinsey’s 2024 Global Survey, nearly 70% of businesses now use at least one GenAI tool, with 40% planning significant investment increases. In the UK, the House of Lords has urged targeted AI upskilling for leaders. Meanwhile, US boards are already demanding AI literacy as a core competency, while countries like Singapore, China, and South Korea are outpacing much of the West in AI infrastructure investment and policy development.

Despite the momentum, an EY survey found that only 27% of UK executives feel confident navigating AI transformation. Many admit they’re uncertain how AI will impact their roles, teams, or business models. This, coupled with the rapid pace of technological advancements and concerns about workforce displacement, can lead to heightened anxiety amongst some, hesitancy, and even active resistance.

At the same time, global contrasts are becoming more pronounced. While some regions and sectors, particularly in Asia, demonstrate a greater appetite for innovation and calculated risk, others are proceeding more cautiously. China and South Korea, for instance, are making significant investments in AI infrastructure and policy frameworks, aiming to secure leadership positions in the next wave of technological progress.

In contrast, the UK and EU are working to strike a balance between regulating AI responsibly and pushing forward to maintain competitiveness. This dual focus on ethics and innovation reflects a broader strategic challenge: advancing quickly enough to realise AI’s full potential while building the necessary trust, capability, and governance mechanisms.

For executives, this is not simply a precarious balancing act but a pivotal leadership moment: an inflection point that calls for clarity, agility, and collaboration across disciplines and borders.

Drawing from the Rialto team’s experience with executives across global regions, several capabilities consistently emerge as critical for leading in this dynamic age of GenAI. Embracing these capabilities can empower executives to harness Generative AI’s full potential, transforming challenges into opportunities for growth and innovation.

Leadership Capabilities for a GenAI era

Technological Fluency: Executives need not be technologists nor need to code, but they must possess a clear understanding of AI’s capabilities and limitations, to be able to ask smart questions, distinguish hype from substance and align solutions to strategic goals. Equally important is the ability to manage expectations as AI initiatives can require extended timeframes for ROI and organisational integration. Continuous learning is essential.

To stay ahead, many leaders are joining executive groups like the Rialto AI Business Circle to share insights and stay current on emerging trends

Ethical Foresight and Governance: With increasing regulatory scrutiny and stakeholder concerns, leaders need a visible, principled stance on AI’s responsible use. This includes addressing algorithmic bias, safeguarding data privacy, protecting intellectual property and mitigating environmental impact. In fact, 76% of business leaders now anticipate significant cultural and ethical shifts driven by AI that will require proactive management.

One route for influencing and gaining insight in this area is through membership in the UK Government’s All-Party Parliamentary Group on Artificial Intelligence, which allows business leaders to help shape policy and safeguard standards.

Human-AI Collaboration Design: Effective leadership involves integrating AI in ways that complement, rather than replace, human expertise. Leaders must understand where human judgment remains indispensable and craft workflows that enhance rather than diminish it.

Internal resistance remains a challenge. Two thirds of C-suite leaders admit cultural tension risks harming AI rollouts while 42% said they were “tearing their organisations apart”.  Concerns about job security and societal impact remain prevalent. Companies must invest in resilience and cybersecurity, while leaders have a critical role in addressing employee concerns through open dialogue and collaborative planning.

Strategic AI Investment: With finite resources, executive teams must prioritise AI investments that align with core business objectives, balancing immediate efficiencies with long-term capability building. This demands a level of digital and GenAI fluency across all senior leaders. A well-calibrated AI investment strategy may allocate 60% to enhancing current operations, 30% to adjacent innovations, and 10% to exploratory or disruptive initiatives. Avoiding “tech for tech’s sake” is imperative.

Change Management Mastery: GenAI isn’t a plug-and-play fix, it represents a fundamental cultural shift. Effective transformation requires compelling communication, room for experimentation and the empowerment of internal champions. Celebrating early successes builds momentum and trust. Equally, leaders must create psychologically safe environments that support learning, innovation, and adaptive thinking in the face of change.

Cross-Functional Collaboration: With 71% of executives acknowledging that AI remains siloed in many organisations, integration is a clear priority. Leaders must break down traditional barriers between technology, operations and strategy by fostering AI-focused cross-functional teams, aligning KPIs and enabling secure but open access to shared data. In this era, AI transformation must be a seen as a team sport, a collaborative endeavour driven by shared purpose and organisational coherence.

What’s Coming: GenAI Leadership by 2030

Over the next five years, Generative AI will continue to mature and with it, the demands and expectations placed on executive leadership will evolve significantly.

Rialto predictions and expectations include:

Regulation Will Get More Serious: The UK may diverge from EU regulation post-Brexit, seeking innovation-friendly policies while maintaining ethical standards. Meanwhile, the US and leading Asian economies are advancing their regulatory approaches more quickly. Executives will need to remain informed, agile, and engaged, shaping policy through industry bodies, public discourse and cross-sector collaboration.

Democratised AI Development: No-code and low-code platforms will enable non-technical teams to build their own AI solutions independently. Leadership will shift away from acting as a gatekeeper and towards becoming a governance steward, ensuring that innovation thrives within strategic, ethical, and security parameters.

Decision Support Systems Will Become the Norm: Executives will increasingly rely on AI-generated insights to model scenarios, assess risks and guide decisions. But human judgment will remain crucial, particularly in areas requiring ethical nuance, stakeholder empathy or complex interpersonal dynamics.

Leadership Styles Will Change: Traditional hierarchical models are giving way to systems thinking and collaborative leadership. The GenAI-ready executive must be a learning leader, comfortable with ambiguity and skilled in facilitating diverse perspectives.

AI as a Team Member: Executives will lead hybrid teams in which AI tools aren’t just assistants but creative collaborators. This will alter how teams are formed, how success is measured and how value is co-created.

Coaching and Feedback Wil Become Increasingly Vital: Expect AI-powered leadership coaching, real-time behavioural analysis and personalised learning paths. Leaders who cultivate self-awareness and value space for reflection, not just technical knowledge, will rise above and stand out.

Authenticity Will Matter More Than Ever: In a world of synthetic content and automated interactions, human presence and integrity will become premium leadership qualities. Both customers and employees will increasingly seek transparency, integrity, and empathy from those at the top.

Board AI Literacy Will Become a Requirement: By 2030, AI fluency is likely to be mandated for directors in regulated sectors and widely expected across others. Progressive leaders are already preparing for this shift, embedding AI knowledge at board level today.

GenAI is more than a technology trend, it represents a strategic and cultural reset. The most successful executives will approach it with vision, humility and a willingness to reinvent. They will view AI not as a threat to manage but as a partner in rethinking how value is created and sustained.

By developing fluency, leading ethically, designing for collaboration and continuously adapting and upskilling, leaders can future-proof not only their careers but also the organisations they serve in a world being redefined by intelligence, both artificial and human.

Get Involved

A limited number of spaces are now open for senior leaders to join the Rialto AI Business Leaders Circle. This cross-sector initiative connects business, policy and technology leaders to shape the UK’s AI future.

To explore membership options, schedule a call with Rialto director Richard Chiumento, an APPG AI Permanent Advisory Board Member here.

Regardless of your age, experience and seniority, a personal digital brand can be a huge differentiator in securing a new role, premium consultancy projects, or advisory or non-executive director (NED) positions. This is vital in a job market where opportunities are limited, sought-after skillsets and experience requirements change regularly, and competition now includes not only local but also global peers. Every candidate wants to stand out from the crowd and get noticed, and today’s shifting marketplace is making it increasingly difficult to do so.

Just like a strong brand helps a business cut through the noise and establish itself in the marketplace, a strong personal digital brand will help an executive to make a lasting impression, particularly with recruitment practices shifting online and an increasing amount of talent being sourced digitally. Even if a senior professional isn’t looking for a job change, their personal digital brand can open the door to other professional opportunities.

Here are some initial steps you could take to begin carving out your own personal digital brand.

What is a ‘Personal Digital Brand’?

A ‘personal brand’ is a recognisable, uniform, and consistent impression an individual makes on others. It is often based on the person’s experiences, expertise, actions or achievements within their given industry or role. Simply put, it is the professional image you present to your peers, and the way that they perceive you.

A more effective form of this is a ‘personal digital brand’, which is what we at Rialto help our clients develop. As the name suggests, a digital personal brand is the image that a professional presents digitally, whether it be on social media, email correspondence, or thought leadership pieces such as blogs. The personal digital brand is not an amendment or an alternative to the traditional personal brand. Rather, the personal digital brand is simply the more forward thinking, future-ready version. Much of today’s networking, thought leadership, recruitment, and business-building activities happen with a digital element involved. Think about how you present yourself in prospecting emails, LinkedIn communications, and even on Zoom. These activities have become an integral part of business life, and must be considered in the holistic big picture of your brand.

Your personal digital brand ensures that your online presence matches what people experience when they meet you in person, and vice versa. If you are a strong voice in your industry at conferences or in meetings, you should continue this online. Alternatively, if you are very vocal online, you should not act timidly in person. For your brand to be a success, it needs to be consistent at all times.

Determining Your ‘Why’

When beginning to establish your brand, you must first answer a few crucial questions. What do you believe in, or stand for? When you leave an interview, conference, or client meeting, what would you like the others in the room to remember about you? What skills, accomplishments, or traits would you like to be known for? And most importantly, what makes you different? What do you offer that others may not? What makes you you?

This process of self-reflection is similar to Simon Sinek’s ‘Golden Circle’ model. Sinek’s simple yet powerful model discusses how the world’s most influential leaders inspire action by starting with ‘why’ they do what they do. Answering these questions should give you an idea of not only who you are, but also what you’re interested in, talented at, or passionate about. Knowing this will help you to seek out opportunities that will cater to your skills, and help you to find a role that you will actually enjoy. Having a clear idea of what your ‘why’ is helps pinpoint your unique selling proposition in the job market and what you want to be known for amongst your peers.

Thinking Long-Term

If you are undergoing the process of developing a personal digital brand, it is highly likely that you have a reason for it. A personal digital brand helps you become recognisable or even memorable in a highly saturated job market, and can help you secure or even attract a coveted position. Even when you are not actively searching for a role, your personal digital brand continues to work for you as a basis for thought leadership, and is a great way to re-establish oneself in a sector or as part of an evolving tech transformation. Down the road, this may lead to advantageous introductions to new contacts, keynote opportunities, or good publicity.

Whatever your end goal may be, it is crucial to have clarity on the big picture when you are in the process of developing your brand. The self-reflection you have completed in the initial steps of brand development should help to paint the picture.

Once you know what you’re working towards, you can begin to think about what actions will help to carve that path forward. You should be able to determine which of your skills, attributes, or accomplishments are best to champion in order to reach your endgame goals. Your brand will become much more refined and polished as a result.

Determining Your Start Point

Once you have an idea of what you would like your brand to be and where you would like it to take you, a wise next step is to conduct a ‘personal brand audit’ to determine where you currently stand. Ask your colleagues, friends, and family to choose some adjectives to describe you. Are these in line with the ideas you have about yourself, the impressions you would like to leave others with, or the professional image you would like to project?

If this feedback is not consistent with what you had in mind, do not be discouraged. People grow and change all the time, and your brand is completely within your control. Perhaps this peer feedback identified traits or skills that you overlooked previously, or downplayed. Maybe your peers helped provide a few areas where you could improve. At least now you have a better idea of where you need to focus your energy in order to create a consistent, authentic, and strong personal digital brand.

Champion Your Greatness

Once you feel confident in your brand, it is time to put it into words. The best way to do is to develop a short elevator pitch that expresses the main points concisely, yet accurately. This statement should provide a good overview of who you are and what you are about, but leave enough out so that others are interested in learning more.

This pitch will become a critical tool in your professional arsenal. You can deploy it in one-to-one introductions with key contacts, or in an interview when asked the dreaded “tell us about yourself” question. You can use it in the biography section of your social media profiles to leave any visitors to your page with a strong first impression.

It is easy to say what you stand for, but it can be harder to prove it. However, for your brand to be authentic, you need to be able to back up your words. Begin to identify examples from your life or career that best tell the story of who you are. Choose examples that highlight the key skills you identified in the previous steps, or solid examples of times you lived out your ‘why’.  These are great to have on hand in interviews, and help to solidify your brand in the minds of others. Online, you can become a champion for your personal digital brand via the topics you comment on and the content you share. We will go into further detail about this in later articles, but for now just remember that consistency is the key to authenticity. Your personal digital brand will only work for you if it is truly reflective of who you are and what you have to offer professionally.

We see businesses reinvent themselves and overhaul their brands with time, and you should do the same. As you grow, learn, and develop, your brand should change with you. The hardest part is developing one to begin with, but do not be afraid to adapt it over time. The best brands are those that remain true and authentic throughout the test of time.

As businesses continue to face economic volatility, rapid technological advancements, and shifting workforce expectations, the role of C-suite leaders is evolving at pace.   Some of the most pressing challenges discussed at the start of 2025 are beginning to snap into focus: Trump’s trade wars are destabilising economies, crashing markets and holding up global supply chains; increased defence spending across Europe is eating into GDP and, in the UK, new tax and wage burdens on employers are about to bite.

Meanwhile, Generative AI is becoming increasingly omnipresent and sophisticated, reshaping competitive dynamics across every industry and AI agents are offering opportunities for increased efficiency and insight to improve customer experiences thus enabling even further data driven decision making. In all cases, the disruptive influences must be harnessed for positive transformation, rather than allowed to run riot or be neglected.

For those considering executive career transitions, further leadership development, or future-proofing their personal brand, keeping abreast of these emerging challenges and strategically positioning themselves accordingly will be essential.

Here we look at the market dynamics and landscape for four of the critical functions, chief executive officers, chief operating officers, chief finance officers and chief human resources officers.

Chief Executive Officer: Balancing Growth, Disruption, and Stakeholder Expectations

CEOs are no strangers to rapid change, but 2025 presents an entirely new level of complexity at the intersection of technology, geopolitics, customer expectations and social change.   While financial performance remains critical, today’s CEO’s must weigh up short term results against long-term innovation and resilience, navigating economic shifts, sustainability commitments, and regulatory pressures.  They will need to rely on instinct developed through experience but also be able to leverage the latest data analytics.

CEOs in 2025 are expected to serve as both visionaries and pragmatists, charting ambitious growth while preparing for potential economic headwinds. The modern CEO must balance shareholder demands against broader stakeholder responsibilities.

A key priority for CEOs continues to be AI adoption, with CEOs needing sufficient technical acumen to steer decisions about AI implementation, data governance, and cybersecurity without getting lost in technical details. The risk of investing in technology without a clear business need and ROI is high. CEOs must take a human-first approach, ensuring AI augments rather than disrupts their workforce, product, or service. Many are establishing direct reporting relationships with newly created technology leadership roles alongside traditional C-suite positions. Rialto market mapping data shows chief sustainability officer, chief compliance officer and chief technology officers profiles growing rapidly as CEOs bring in expertise to ensure strength in these increasingly crucial areas.

CEOs are increasingly expected to drive workforce ambition and loyalty, stepping up to the plate to communicate a compelling brand narrative incorporating purpose, direction and ensuring tangible societal contribution. Younger generations are increasingly seeking purpose-driven leadership, looking for companies that align with their values.  Managing change, particularly around automation and restructuring, requires transparency and empathy—staff need to feel valued, not like they are expendable assets in exercises to cut overheads.

For those looking for CEO roles, the number of peer profiles continues to grow, while vacancies fall and competition for roles intensifies. CEO salaries rose by around 2% last year, compared to 7% for all workers, reflecting the shifting demands on leadership. Traditional hierarchies are flattening, meaning CEOs must take a more collaborative approach to leadership, ensuring they are adaptable and ready to reposition their skills for an evolving market.

Chief Operating Officer: From Efficiency Expert to Innovation Enabler

The COO role has undergone perhaps the most dramatic evolution among C-suite positions. Traditionally focused on operational efficiency and process optimisation, today’s COOs are now challenged to build operational architectures that deliver more consistent performance while adapting quickly to supply chain disruptions, regulatory changes and shifting consumer expectations

Technology has become central to the COO agenda, with predictive analytics, process automation, GenAI and digital twins offering a way to model future market conditions and prepare for sudden changes. Beyond managing existing operations, COOs increasingly serve as innovation enablers—creating the organisational conditions for experimentation while maintaining performance standards. Many now oversee both core operations and innovation initiatives, bridging the gap between current capabilities and future requirements.

The pandemic-era emphasis on supply chain resilience continues to shape the COO agenda, with increased focus on nearshoring, supplier diversification, and inventory optimisation. Sustainability goals add another dimension, requiring COOs to reduce environmental impact while maintaining cost efficiency. They are increasingly expected to adopt circular economy principles and responsible sourcing practices with the ability to report on sustainability efforts and demonstrate their link to cost savings and operational improvements now a critical skill.

For those seeking COO positions, Rialto market mapping data shows global and UK profiles up by a sixth in the last 12 months. This may be due to more individuals recognising the need to be public facing to draw on broader networks of reliable business contacts and equally those that are building their presence in anticipation of restructuring needs.  Either way, UK vacancies in this area are up by a third after a big dip in September 2024 therefore executives will need to clearly demonstrate their AI literacy, digital expertise, and strategic adaptability if they want to compete and remain ahead.

Chief Financial Officer: The Strategic Guardian behind Business Resilience

The role of CFOs has undergone a profound transformation. While financial stewardship remains fundamental, CFOs now spend significantly more time supporting strategic initiatives and driving transformation.  As 2025 progresses, CFOs face the ongoing complex challenges of balancing growth investments, ESG reporting, and financial resilience in an increasingly unstable global economy.

At the time of writing, global trade tensions and unpredictable tariffs are making financial forecasting more challenging than ever. CFOs must be prepared to reallocate resources quickly, responding to economic shifts with agility. The days of long-term, rigid financial planning are gone—scenario planning, risk modelling, and real-time decision-making are now essential tools in the CFO’s arsenal.

Modern CFOs require expertise in digital finance technologies that enable real-time decision support rather than retrospective reporting. Advanced data analytics capabilities have become essential for scenario planning, risk assessment, and identifying growth opportunities hidden in financial data. CFOs can also add the tools of predictive analytics and digital twins to their utility belts to be able to prepare for worst and best case scenarios.

Perhaps most significantly, CFOs now serve as translators between financial outcomes and business strategy—helping operational leaders understand financial implications of their decisions while communicating complex financial performance in business terms to diverse stakeholders. This expanded role requires stronger communication skills and broader business acumen than traditionally expected from finance leaders.

While global CFO profiles continue to grow steadily, UK vacancies spiked in mid-2024 before settling at a higher level than the previous year.  Executives looking for a CFO role need to demonstrate technological literacy, strategic foresight, and strong communication skills to translate complex financial data into clear business strategies.  Financial acumen is a given, but skills may not be viewed as transferable as they once were therefore a deep dive into a target organisation’s near and long term goals and how it fits into the global economic and technological landscape will enable applicants to maintain a competitive edge.

Chief Human Resources Officer: Workforce Transformation and Leadership in an AI Enabled Era

With talent now recognised as a core competitive advantage, the CHRO role has never been more crucial.  Companies are facing multigenerational workforce challenges, AI-driven job transformations, hybrid workforce management and evolving employee expectations—and HR leaders are at the centre of it all.

In 2025, HR leaders must balance organisational agility with workforce stability, ensuring their companies can adapt to rapid AI-driven transformation and economic volatility while maintaining a strong employer brand.

AI-powered tools are revolutionising recruitment, performance management, and workforce analytics, but HR leaders must ensure technology enhances, rather than undermines, human decision-making. AI can optimise hiring processes and skills matching, but without careful oversight, it can also reinforce bias or create over-reliance on data-driven insights without considering the human element. CHROs must take a proactive role in AI ethics, ensuring that AI-driven decision-making in talent acquisition, promotions, and workforce planning remains transparent, fair, and aligned with company values.

The employee experience is now a critical differentiator in attracting and retaining top talent. CHROs must reimagine workplace environments—both physical and digital—to ensure they support collaboration, flexibility, and productivity. AI-driven analytics can provide real-time insights into workforce engagement and wellbeing, helping HR teams anticipate attrition risks, burnout, and evolving employee expectations. However, HR leaders must avoid an impersonal, data-driven approach, ensuring that engagement strategies maintain a strong human connection and company culture.

The ongoing mental health crisis and shifting generational priorities mean that wellbeing strategies must go beyond traditional benefits and DEI initiatives create truly inclusive cultures . Employees expect tailored support, career development opportunities, and inclusive workplace cultures where they feel valued. The CHRO will need to lead conversations on AI’s role in career development, steering the way for automation to augment and enhance jobs and providing reskilling opportunities where necessary.

Rialto market mapping data shows HR Director and CHRO roles rose rapidly at the end of 2024, possibly reflecting the increasing importance of strategic workforce leadership. Candidates looking to secure these roles must demonstrate AI literacy, change management expertise, and a deep understanding of how HR is evolving into a business-critical function. Those who position themselves as both talent strategists and ethical AI stewards will be best placed to lead organisations into the future of work.

The Evolving C-Suite: Leadership Beyond Traditional Boundaries

Against this complex backdrop, C-suite roles are evolving significantly, requiring executives to develop new capabilities, embrace digital transformation, and adopt an agile leadership approach. The boundaries between traditional functional responsibilities are blurring as interconnected challenges require stronger collaboration.

Today’s executives must combine deep functional expertise with broader business acumen and the ability to work across organisational boundaries. For those seeking new opportunities, understanding how their roles are evolving and refining their skillsets accordingly will be key to remaining competitive in a rapidly shifting market.

If you want to more successfully navigate today’s increasingly complex executive employment market or wish to develop new capabilities, or improve your positioning for your next role, expert executive transition and development coaching can provide the insights and strategies you need. Get in touch today to explore opportunities and future-proof your career.