Bailey McGrady explores how financial services organisations are rethinking talent development as skills shortages, AI and shifting employee expectations challenge traditional role-based models. She examines why skills-led, AI-enabled strategies are becoming critical to workforce agility and performance, and why many firms struggle to turn ambition into execution. Drawing on Catalyst’s market insight, the article highlights where competitive advantage is being created and lost, in the gap between strategy and delivery.
Rapid technological change, shifting client expectations and ongoing skills shortages are forcing organisations to rethink how they build and sustain capability. Traditional role-based models, built for stability and predictability, are struggling to keep pace with a market defined by constant change. At the same time, expectations of work itself are shifting, with employees demanding clearer development pathways, greater mobility and more personalised career experiences.
Against this backdrop, two powerful forces – skills-based workforce strategies and artificial intelligence (AI) – are reshaping how organisations attract, develop and retain talent. Insights from KPMG¹ and Deloitte² reinforce this direction of travel, highlighting a future of talent management built on agility, data-driven decision-making and increasingly personalised employee experiences.
From a Catalyst perspective, this conversation matters now because while many financial services organisations understand the theory behind skills-led and AI-enabled talent models, far fewer have been able to translate that ambition into action. We often see firms investing heavily in transformation initiatives, yet still relying on outdated role structures, fragmented skills data and manual workforce decisions. This disconnect creates friction, slows decision-making and limits return on investment. It is in this gap between intention and execution that competitive advantage is increasingly being won – or lost.
KPMG’s research highlights a fundamental shift: skills are becoming the currency of dynamic and flexible organisations. As roles evolve and technology disrupts traditional job patterns, organisations should pivot from role-based structures to skills-led models. This approach allows for better workforce planning, internal mobility and employee engagement.
In practice, this shift is particularly critical in financial services, where regulatory change, digital transformation and evolving client expectations are reshaping roles faster than traditional job architectures can keep pace. From our conversations with candidates and HR leaders, one recurring challenge emerges: capability often exists within the organisation, but it is difficult to see, access or redeploy. Skills-based models surface this hidden capability, enabling firms to redeploy talent rather than defaulting to external hiring.
However, these advantages only materialise when skills frameworks are tightly linked to business priorities. We often advise clients to start by identifying the small number of critical capabilities that will differentiate their organisation over the next three to five years – whether that’s digital risk, regulatory change, data science, or client advisory skills – rather than attempting to catalogue every skill across the enterprise from day one. When skills strategies become too broad, they risk becoming academic rather than operational.
What we see working best is a phased approach. Talent marketplaces, for example, are most effective when piloted within specific business lines or transformation programmes, allowing organisations to test adoption and impact before scaling. Similarly, apprenticeship and early careers programmes become far more powerful when they are explicitly aligned to future skills gaps, rather than legacy role definitions.
Whilst the skills-based model sets the foundation, AI is the driving force for smarter, more personalised talent experiences. Deloitte’s research reveals that although AI adoption in financial services talent functions is still nascent, only 18% of firms are using generative AI in HR – the opportunity ahead is significant.
What we find particularly interesting is that many firms are experimenting with AI in recruitment or learning, but stopping short of embedding it into core talent decision-making. This often stems from understandable concerns around risk, governance, and data quality. However, the organisations gaining the most traction are those that position AI as an augmentation tool – enhancing human judgment rather than attempting to replace it.
Employee retention: Predictive analytics identify flight risks and burnout indicators, enabling proactive interventions.
Performance management: AI uncovers high-performance traits and mitigates bias in evaluations.
Used responsibly, these applications allow HR leaders to move from reactive to predictive talent management. In candidate conversations, we increasingly hear frustration from high performers who feel performance processes lag behind the sophistication of the work they do. AI-enabled insights, when paired with strong leadership capability, can make performance conversations more timely, objective and development-focused.
AI also enables a shift from pull to push analytics, allowing HR leaders to receive real-time insights without manual data extraction. This creates space for more strategic collaboration between HR and the business, enhancing both performance and engagement.
Despite the clear upside on offer from adopting AI for Talent Management, there remain some challenges HR leaders and their teams must address:
In our experience, these challenges are less about technology and more about organisational readiness. AI cannot compensate for fragmented data ownership, unclear accountability, or a lack of trust between HR, IT, and the business. Financial services firms that succeed here treat data governance, ethical AI principles, and change management as foundational, not secondary, to deployment.
To succeed, organisations must:
This means starting with a clear view of where critical people data sits today, who owns it, and how it flows across systems. We often recommend establishing joint HR–IT governance forums to prioritise use cases and manage risk collectively. Equally important is building capability within HR teams themselves – data literacy and confidence in interpreting insights are now core leadership skills, not specialist add-ons.
As financial services firms navigate the complexities of digital transformation, a skills-led, AI-enabled talent strategy is essential. By placing skills at the heart of workforce planning and leveraging AI to personalise and optimise the employee experience, organisations can build resilient, future-ready teams.
The organisations that stand out are those that move beyond experimentation and commit to reshaping how talent decisions are made at every level. This requires courage from HR leaders willing to challenge legacy models and from business leaders prepared to trust data-informed insights.
The journey may be complex, but the destination is clear: a more agile, inclusive, and intelligent approach to talent development. This will allow for enhanced success in: recruiting, performance management, learning and development, succession planning and increased retention.
From a Catalyst standpoint, our role is to help organisations bridge ambition and execution – connecting strategy, systems and talent so these models deliver measurable impact, not just conceptual change. To discuss how a skills-led, AI-enabled talent strategy could work in practice within your organisation, please feel free to reach out directly. I’m always happy to share what we’re seeing across the market and explore how these approaches can be applied in a way that delivers real, measurable impact.
Sources:
¹ KPMG | Talent Development in the context of a dynamic skills-led organisation
² Deloitte | AI usage in Financial Services Talent Management Function
