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.
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.
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.
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.
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.
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.
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.
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