AI-Powered Workforces – Adding Value Through Strategic Upskilling

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.

Generative AI and the associated apps, tools and platforms, are heralded by many as the most efficient way for busy executives to crunch data, draw insights, elevate their brand and support pivotal career moments such as preparing for interview or creating a new profile

But what is real, what is hype, and what are the risks around trusting generative models to match you with the right opportunity – and to analyse and represent your unique set of talents, experience, attributes and requirements to the wider world, especially potential employers?

There is no doubt that GenAI can and should be used to support any aspect of professional development and career transitions. It is an invaluable resource, able to digest vast amounts of data and present it in a succinct, clear, comprehensible format in seconds. It can look for patterns, analyse current trends, forecast future ones and create highly personalised content.

However the technologies do come with limitations and challenges, so Rialto consultants use them alongside traditional coaching techniques and optimisation of networks, knowledge and experience to support clients looking for new leadership roles.

The Balanced Approach: Integrating AI and Human Coaching

The most successful executive transitions leverage both AI tools and human coaching in complementary ways. This integrated approach ensures executives benefit from technological efficiency while maintaining the authentic human connection essential for navigating complex executive career transitions. The future belongs to leaders who can skilfully combine these resources, using AI to enhance, rather than replace, the human judgment that ultimately drives successful executive journeys.

The time factor involved in mastering new technologies at such crucial and stressful junctures in life can also be an obstacle to their effective use. Hopefully, executives will only be seeking to transition once every five to nine years. Who wants to spend weeks training on specific AI tools for each stage of the journey, when this may be the only time they use those apps in their life? With the current pace of change, the tools will be obsolete or at least unrecognisable by the time the same individuals are back out on the market, all going well.

Luckily, there are experts on hand who know exactly which tools to use and how to use them. No need to reinvent the wheel, far better to just sit at it and drive, with an experienced human co-pilot who can navigate and smooth the way.

Where AI needs the human touch:

While jobseeking apps such as Indeed are useful for entry level jobs, and tools like ResumAI, Rezi, and Teal employ algorithms to analyse executive CVs against industry standards and specific job descriptions, the increasingly exacting, multi-layered processes and stages of executive hiring in this ultra-competitive market demand laser-sharp, sophisticated strategies which must be refined and adapted constantly.

This takes a human touch. emotional intelligence, instinct and delicate nuance that AI simply doesn’t have, at least not yet.

Contextual Understanding AI tools frequently struggle with nuanced industry contexts and organisational cultures. They may fail to recognise that what works in one sector could be inappropriate in another or vice versa. For example, they cannot differentiate tone and language a candidate might use if moving from a creative industry to a financial institution or pick up on the requirements of a generic position in a niche organisation. They may also struggle with cultural sensitivities. Emotional intelligence, instinct and human experience are essential in decision-making.

Authenticity Gaps This is a crucial one. The standardised language generated by AI tools will always undermine the authentic voice that distinguishes truly compelling executive profiles. Technology might be able to magic up what it perceives to be the perfect candidate for a position and help an applicant get through the first sift, especially if the organisation is using AI tools itself to create a longlist from the hundreds of initial inquiries. However recruiters increasingly report detecting AI-generated content that lacks personal perspective and genuine insight.  An AI generator cannot delve into a person’s memory and recall the unique experiences and successes relevant to the requirements of the role that make any individual stand out from the pack. Human coaches excel at building trust, drawing out this buried treasure, understanding individual needs and ensuring that the candidate on paper aligns with the candidate in person. They might use AI with a client to help structure this process, but it takes emotional intelligence and insight to interact in a productive and meaningful way to assess and truly represent an individual’s value proposition.

Strategic Limitations While AI excels at tactical optimisation, it falls short in strategic career guidance. These tools cannot effectively evaluate whether a particular role aligns with an executive’s holistic long-term aspirations, values and life priorities. AI cannot read a person’s character or recognise when they are genuinely lit up over a subject. A mentor or coach works hard to get to ‘really’ know their client and build trust to be able to hold up the mirror and guide them towards a career transition that will ultimately provide fulfilment and ensure continuous professional development. They can think several moves ahead like a chess pro, compared to the limited, chance-based algorithms of AI. AI may suggest openings in the market  and the generic skills being sought but good coaches can narrow down opportunities in the executive market that align with their client’s complex matrix of talents and requirements and support them to understand any gaps in their personal skillsets or experience. Experienced coaches bring extensive networks and relationship capital that open doors to opportunities never posted publicly. These connections frequently lead to the most fitting and rewarding executive placements.

Relationship Dynamics Executive hiring remains fundamentally relationship-driven, with cultural fit and leadership chemistry playing decisive roles. AI tools cannot replicate the human intuition that recognises when a leader will thrive in a particular organisation’s culture. Candidates should seek a more rounded view of the company’s culture, style and hierarchy. Nothing can replace the unedited information passed from human to human with nuance and personal opinions. Executives seeking new opportunities will feel more confident and comforted being able to access insider knowledge and trust their instincts.

The Personalisation Paradox While AI promises personalisation, its reliance on historical data means recommendations often gravitate toward conventional career paths rather than innovative trajectories. Executives seeking transformative roles may find AI guidance constraining rather than liberating. There is no “thinking outside of the box” or ability to assess challenges against opportunities and the individual’s appetite for risk vs need for stability depending on the stage of their career or family responsibilities, for example.

Algorithm Bias AI systems reflect the data they’re trained on, potentially perpetuating existing biases in executive selection. Women and minority executives should be particularly attentive to how AI tools might unintentionally minimise leadership qualities that don’t conform to traditional models. A human coach or mentor can navigate these sensitive issues and help identify unique attributes and how to optimise them.

The Technology Learning Curve Despite their promise, many executive-focused AI tools require significant time investment to use effectively. The learning curve can be steep, particularly for leaders less comfortable with emerging technologies. Executives may find it difficult to navigate the ever-evolving myriad of available options and integrate them into their workflows effectively.  Where time is an individual’s most valuable resource, AI can certainly be a frustrating adversary rather than an invested and trusted advisor.

Adaptive Strategy and Reflective Thinking Executive coaches continuously refine their approach based on subtle cues, market shifts, and emerging opportunities. This adaptability enables the development of dynamic career strategies that AI’s more rigid frameworks cannot replicate. As candidates progress, they gain deeper self-awareness, often adjusting their ambitions or realising they may find greater success and fulfilment on an entirely different path. Having a human mentor to listen, respond, and reflect helps clarify thinking, challenge assumptions, and inspire innovative career approaches. These conversations often lead to breakthrough insights that algorithmic interactions simply cannot generate. The best coaches provide candid feedback about executive blind spots, communication patterns, and leadership presence that AI tools cannot detect. This honest perspective is essential for authentic development and successful transitions.

Perhaps the most crucial element missing from AI, however, is compassion. Try telling Microsoft CoPilot that you didn’t get the job you desperately wanted after six gruelling rounds of interviews. It will generate a handful of practical suggestions in plain English—but what you truly need is a human being who knows you, acknowledges your effort, empathises with the disappointment, and guides you forward with encouragement and insight.

GenAI vs Executive Career Coaching

AI tools and platforms are a valuable addition to the multi-dimensional, structured approach executives should take when navigating executive career transitions. They offer efficiency, data-driven insights, and practical support—but they remain just one piece of the puzzle.

While AI continues to evolve, it still lacks the instinct, emotional intelligence, strategic foresight, and human connection that define truly effective executive career transition support. A great executive coach does more than provide insights, they challenge, mentor, and inspire. They recognise the nuances of each individual’s journey, helping leaders uncover opportunities they might never have considered and navigate setbacks with resilience.

A time may come when AI can fully replicate these qualities, seamlessly integrating empathy, strategic thinking, and creativity into its responses. But for now, the ability to truly understand, adapt, and empower remains uniquely human.

Executives are increasingly using AI platforms to support their professional development and pivotal career moves. These technologies can assist in a multitude of ways, including condensing vast volumes of text into checklists and relevant insights, helping to provide oversight of target companies, key contacts and job descriptions and optimising online networking and personal branding opportunities.

As discussed in our previous blog, Why AI can’t replace Career Coaches, these tools should be used as an aide, however, not a replacement for hard work or where needed collaboration with an experienced coach or mentor. When used in isolation, they can be more of a hindrance than a help, with algorithms narrowing perspectives rather than broadening them. This can lead to a loss of nuance and missed opportunities for meaningful reflection and personal growth. Additionally, applications processed through the same standardised large language models can sound repetitive and robotic, failing to convey the unique attributes and character of the individual.

With these caveats in mind, here is a guide to some of the most popular AI applications executives are exploring—and how to use them effectively.

LinkedIn’s suite of AI tools are increasingly being tested by executives to create content, research potential candidates, and manage professional profiles.  The platform’s AI-powered job recommendations analyse your profile, skills, and network to suggest relevant senior roles. Their Premium AI writing assistant helps craft compelling headlines, “about” sections, and accomplishments that resonate with recruiters. User feedback indicates these tools have dramatically improved profile visibility, with executives reporting up to 40% more views after implementing AI-recommended optimisations. An invaluable start, but relying on LinkedIn alone misses opportunities to access the hidden job market and lacks the emotional intelligence that a human coach can provide and the work that goes into really uncovering aspirations, values and ambitions.

Research: Open source Generative AI platforms such as ChatGPT and Claude will analyse vast amounts of information and, with the right prompts, edit them into succinct checklists and manageable briefings. Executives can reduce the amount of time spent researching target companies, markets, sectors and target contacts. However, they have a tendency to hallucinate, so all facts should be checked before being repeated in job applications or interviews. Poor prompting will provide poor results, possibly leading to crucial information being missed. GenAI cannot take the place of more sophisticated and personal insights from people who work in a company or close to it. What it should be used for is blank-page thinking: pulling together ideas and background for further exploration.

Networking Alternatives or complementary assistants include Nudge and Connectifier which allow executives to leverage their professional networks more effectively during transitions. These platforms identify warm connections within target organisations, suggest relationship-nurturing actions, and highlight networking opportunities.  This capability is particularly valuable for executives looking to pivot into new industries or roles.

However, these tools do not always provide the same invaluable focus that a coach or wider network connections can offer. A coach or trusted contact can suggest networking activities based on direct knowledge of job openings, facilitate warm introductions, and unlock hidden opportunities. Additionally, human coaches bring practical insights, share personal experiences, and provide recommendations on approached based on their experiences and firsthand understanding of different work environments.

Resume and CV AI Optimiser Tools like ResumAI, Rezi, and Teal evaluate content, structure, keyword optimisation, and impact statements. Executives have mentioned that these tools can support with thinking through transferable skills when transitioning between industries or functional areas.  However, these tools should be used only as a source of inspiration rather than a final solution. While they offer general advice based on patterns, they lack the ability to understand the nuances of personal circumstances, including individual experiences, cultural background, and life situation.

Interview Preparation Platforms: InterviewGPT and Yoodli have gained popularity for their ability to simulate interview scenarios and provide personalised feedback. They analyse responses for content strength, delivery clarity, and non-verbal cues. Users praise the convenience of 24/7 practice opportunities and the objective feedback. However, many note that these tools struggle with the nuanced, relationship-driven nature of high-level interviews, where cultural fit and leadership presence are paramount.

Additional platforms such as Comprehensive.io and Aiola can also provide value, offering AI-driven compensation analysis, helping executives understand their market value and negotiate more effectively. These tools assess factors such as industry, location, company size, and specialised expertise to establish realistic compensation targets.

Executive Positioning and Personal Branding Jasper AI, DALL-E and Copy.ai have become go-to resources for executives crafting their personal narratives. These tools assist in generating executive bios, thought leadership article outlines, and social media content and visuals that position leaders in their industry.

While users appreciate the efficiency these platforms provide, they often find them clunky and lacking in nuance. Significant human refinement is required to ensure authenticity and strategic alignment. Additionally, without advanced user expertise, these tools can be time-consuming and frustrating to work with.

Using AI for Executive Career Transition

For those who enjoy the challenge of mastering new technologies, AI-driven platforms can be highly effective in streamlining executive job searches. They help narrow down target positions, provide broad insights into markets, sectors, and organisations, benchmark an individual’s compatibility within those parameters, and identify emerging executive trend

However, these tools can also be time-consuming, frustrating, and inherently flawed. Over-reliance on them may hinder personal growth and limit the achievement of long-term career aspirations. At Rialto, our consultants help clients navigate the complexities of these ever-evolving technologies, integrating them effectively into a holistic, future-focused career strategy as part of our comprehensive executive transition services.  Our team also provide the emotional intelligence, contextual understanding, strategic guidance, networking connections, and long-term relationship-building that are crucial for a truly successful

Over the past decade the Rialto team have successfully assisted more than 7,500 senior executives accelerate their career trajectories. If you are seeking structured, expert support for your current or planned executive transition, ongoing professional development, career strategy, or organisational change initiatives, including Gen AI adoption, contact us for a free, confidential consultation.

As we move into year three of Generative AI, its potential for enhancing operations, driving innovation and building a competitive edge is becoming ever clearer, as are the challenges and risks.

The world’s most innovative companies have moved, or are moving, beyond experimentation to integrate AI-first models, adjusting spending and recalibrating business strategies to maximise ROI and stay ahead of the curve. PwC found almost half of the US’s Fortune 1000 companies now have AI fully embedded in their workflows, with a third using it in their products and services.

This year, priorities should include solidifying foundational structures, measuring outcomes and adjusting programmes to make Gen AI work effectively and safely and secure that advantage. Those late to the party or failing to understand the critical need to constantly evolve and manage Gen AI may struggle to ever catch up.

In a recent survey by Ernst & Young (EY), 97% of senior business leaders reported positive returns on their AI investments with a third planning to spend £8 million or more on AI initiatives this year while UK software buyers expect to increase spending by an average 5-15%. Organisations that commit 5% or more of their total budget toward AI are seeing more positive returns than more cautious investors with the biggest in operational efficiencies, (84%) and employee productivity (83%).

It is essential for c-suite executives to have a full and proper understanding of the AI landscape, both within their sector or industry and beyond. Trying to experiment or get to grips with Generative AI in a bubble or silo is like constantly trying to reinvent the wheel when budgets would be better spent targeting funding to improve its performance. Progressive organisations will research thoroughly the tools, programmes and platforms used by competitors and sector leaders to learn what has and has not worked for them and how they are prioritising their AI budgets in 2025 and beyond.

Chaotic implementation has led to lost ROI and confidence in some early iterations of Gen AI-powered programmes as over-eager organisations put the cart before the horse, buying the latest hyped-up tools or platforms through FOMO (Fear of Missing Out) without really understanding their value, testing them or building sound foundational infrastructure. Only 12% reported using sandboxes in one survey, for example, leaving too much to chance and increasing risks of damaging failures. Getting it right demands a disciplined approach with co-operation and collaboration from every department and at every level.

UK senior decision makers told Capterra’s 2025 Tech Trends that successful technological implementation was the greatest challenge they now faced as they moved onto the next phase of adoption, followed by training and upskilling employees, economic and geopolitical pressures, assessing value and risk of AI and identifying the right technologies to invest in.

The most innovative companies will be patient, appreciating that real returns on investment may take years to materialise in terms of profit, but that agile, future-focused and strategically aligned Gen AI-led programmes will ensure long term competitive growth.

(See our previous insight on the five stages of AI maturity)

Here we look at trends within the three main focuses for the AI spending priorities c-suite executives should be considering over the next 12 months: Tech, data and upskilling the workforce.

Spending on Tech:

Globally, spending on hardware and devices, including computers and smartphones, is likely to grow by £10 billion to £118.5 billion, with Covid lockdown working-from-home technology nearing the end of its useful life and new AI-powered devices offering far more possibilities.
Spending on software is expected to see an even greater increase, accelerating by 13.2% in 2025 to £230.5 billion.

Most software buyers in the UK expect to spend between 5-15% more on digital systems this year as they seek to increase ROI on their AI investments, according to the Capterra research. Six in 10 will dedicate one to four months choosing the right product and 38% see implementation as a key challenge.

The survey found security will be the highest priority, followed by AI, IT management, IT architecture and business intelligence and data analytics.

Automation: Justina Nixon-Saintil, vice president and chief impact officer at IBM, believes AI automation will be the story of 2025. “Any tasks or jobs in the company that could be automated by AI will happen within the next year,” she said.

Alicia Pittman, global people team chair at the Boston Consulting Group said a priority should be custom GPTs and mini-automations to build bottom-up power, enabling entire knowledge-based workforces to boost productivity and quality. She said: “It’s super quick, and it doesn’t require big investments or processes.”

CRMs: This year, more companies are expected to move away from in-house Gen AI solutions towards buying partner solutions. Customer Relationship Management (CRM) platforms such as HubSpot, Salesforce and Amazon AWS are constantly improving their AI-powered offerings with broad options for customised integrated systems that can enhance almost all business objectives, from identifying new product or market opportunities and analysing big data to hyper-personalised marketing and sales which vastly improve customer experience, boost sales and build loyalty.

Fortune Business Insights predicts that the CRM market will more than double from the £50 billion spent in 2022 to £120 billion by 2029. Most platforms offer free, simplified versions of subscription models which can keep costs down, such as Microsoft Pilot and Salesforce Einstein, enabling smaller businesses and start-ups to capitalise on these fast-evolving Gen-AI powered technologies.

Spending on Data:

As the AI landscape matures, decision-makers at innovative organisations will look to upscale, standardise and refine AI use with connected, clean data across all functions and lines of their organisations to ensure it remains relevant, agile and that risks are understood and managed.

Two in five UK companies identified data quality as the greatest challenge to successful AI adoption in a survey by Hitachi Vantara. Nearly half reported significant challenges with data storage and 56% admitted to using less than half their data. Meanwhile 83% of senior business leaders say stronger data infrastructure would enable faster AI adoption.

Gen AI is only as good as the data on which it is trained and building scalable and flexible data architecture that can manage speed, variety and volume of data is critical to enable any organisation to scale up programmes and ensure maximum ROI, potentially accelerating adoption by 30%. The IBM Institute for Business Value found that poor data quality costs the US economy around $3.1 trillion a year.

Companies like Netflix and Tesco have shown the value of substantial investment in data and data infrastructure, able to process huge datasets to hyper-personalise their services, innovate, and get closer to their markets. Innovative enterprises are investing in tools including ETL (Extract, Transform, Load) processes, data lakes, or iPaaS (Integration Platform as a Service) solutions to optimise the value of their data.

Cloud storage: More than half of IT spending in key market segments is projected to shift to the cloud by the end of 2025, with global spending on cloud computing services expected to reach £1 trillion. Organisations are moving towards multicloud, open data storage to avoid vendor lock-in.

The UK government has welcomed news of £25 billion investment in data centres which will provide more computing power and data storage building infrastructure to boost AI development and innovation.

Businesses will need to manage 150% more data by 2026 and Gartner predicts that spending on data centres will climb by 15.5% in 2025 on top of a 35% rise in 2024.

Security: With this increasing reliance on data and cloud storage, security becomes ever more essential, especially in sensitive sectors such as finance, defence and healthcare. IBM reported the average cost of a data breach at more than £3.5 million in 2021. Gartner expects cybersecurity spending to increase 15% in 2025.

ESG: Organisations also need to think about the energy costs and impact on Environmental, Social and Governance (ESG) credentials of increased use of Gen AI and other technologies, investing in renewable sources wherever possible. Two thirds of senior leaders fear the negative impact of increased AI use on their sustainability targets and energy supply.

Steve Wanner, EY head of Americas Industrials & Energy said: “Leaders are waking up to the energy challenges inherent in scaling AI. To create innovative solutions that enable energy efficient and sustainable AI growth, companies must collaborate across the value chain, connecting the dots from energy providers to the end-use AI customer.”

Technology could also be part of the answer. Deloitte found three quarters of public companies planned to invest in AI-powered reporting tools to help them evaluate, analyse and share ESG data to comply with tightening regulations worldwide.

However, the biggest rewards are likely to be found in the joining up and safe (anonymised) data-sharing of and between AI systems, which demands greater collaboration within and between organisations, sectors and industries.

Spending on upskilling:

This year, CEOs and other c-suite decision-makers will be more hands on and, hopefully, AI-literate, and therefore committed to restructuring operations so that departments have access to data scientists and AI leads as well as focusing on educating and upskilling all knowledge-based workers and ensuring investment is more disciplined, methodical and targeted.

The speed of Gen AI evolution has taken even tech experts by surprise since ChatGPT opened it up to the masses in November 2022, so it’s hardly surprising that most of the workforce, from CEOs to customer agents and even IT managers, often feel overwhelmed and even intimidated by it.

Almost half of companies admit to lacking the know-how to integrate AI while 90% of executives say they do not know their workforce’s AI skill and proficiency. Four in five IT professionals say they are confident they can adapt but just 12% have significant experience working with AI. Organisations should consider the users of the technology before they buy it and the current skills landscape to avoid workforce burnout and unsafe or under-use of the tools and platforms.

This skills gap threatens to seriously destabilise and restrict the opportunities offered by Gen AI while increasing risk. Babies born in 2025 will be the first of Generation Beta and will grow up with AI all around them. Until they mature, businesses need to retrain their own workforces and bring in data science and Gen-AI planning expertise where it is lacking.

Tech companies are ahead of the curve on this. Amazon developed a Machine Learning University, investing heavily in training and development programs to build its internal capabilities.

IBM has made a commitment to scale up two million of its workers in AI by 2026. Nixon-Saintil said. “There’s a sense of urgency in making sure we are not leaving people behind.”

The growing sophistication of Natural Language Processing (NLP) will continue to enable employees at all levels to leverage AI, so the workforce needs to undergo continuous learning to keep up with new and evolving tools, platforms and emerging risks. Staff who will be using Gen AI models such as Chat GPT, Microsoft CoPilot and Google’s Gemini need to learn to craft clear prompts, interrogate the responses and use them to augment their own productivity and quality of work while understanding the inherent risks and having a clear chain of supervision.

EY says 59% of organisation are planning to increase training for workers on the responsible use of AI in 2025, up from 49% six months ago.

Investment in AI is only expected to absorb around a fifth of IT spending next year. Much more, then, will go into infrastructure and the people required to make it work. Both programmes need to be organisation-wide to enable AI-first business models.

Senior leadership also need to prioritise investing in their own AI literacy to make rational, evidence-based decisions before spending on AI programmes. In the EY survey, 54% of respondents said they felt they were failing as a leader as they struggled to keep up with AI’s rapid growth.

The pressure to act decisively is intensifying. Yet many leaders find themselves navigating incremental changes, unsure of how to transform their business models or confidently prove GenAI’s ROI.

Responding to feedback from our c-suite and senior leadership clients, Rialto are facilitating a virtual strategic collaboration programme between leaders from across the globe, to share experiences, perspectives, and best practices on GenAI adoption. It is designed to support leaders with the critical insights, tools, and actionable strategies needed to broaden their understanding of the complexities & opportunities of GenAI.

All participants in the programme will receive a personalised and group alignment report, to support them to more confidently lead their organisation in the GenAI era

To find more details and register onto the Adoption of GenAI Global Virtual Dialogue click here.