How people will define the next era of AI in financial services

An intelligent enterprise imagines a financial services industry where informed customers access personalised products and services, delivered by a seamlessly integrated human and digital workforce that leverages data and AI at scale. Research shows that over half of global organisations, including financial services firms, are now experimenting with AI use cases powered by frontier large language models, edging closer to this vision.

Yet the path from a handful of pilots to a truly AI‑enabled organisation remains uncertain. Multigenerational teams, uneven AI adoption, legacy technology, layered compliance controls, and fragmented data all introduce friction. As a result, many firms are discovering that technological progress alone does not translate into enterprise value.

Realising value from AI is therefore not just a technology challenge; it is a workforce challenge. While technology is the enabler, it is the re‑imagining of work through workflow, role, and skill redesign that determines success. By adopting a human‑first mindset, firms have a powerful opportunity to rethink how work is structured and how human expertise and AI capabilities can be combined most effectively.

To capture the competitive advantage of becoming an intelligent enterprise, financial services firms must fundamentally redesign their operating models, and People teams have a critical role to play in this transformation. Below, we set out five essential areas for firms to consider.

1. Redefine workflows, not just roles

Many early AI use cases focus on incremental automation within existing roles. These roles are typically defined by workflow ownership, with clear accountability for delivery, escalation, and control. However, our experience across financial services shows that AI does not diminish roles evenly. Its impact is inconsistent.

More importantly, AI’s real power lies not simply in augmenting roles, but in automating and optimising end‑to‑end workflows that cut across multiple functions. To unlock meaningful value, firms must therefore move beyond role‑based thinking and decompose work at the workflow level. This enables new levels of effectiveness, efficiency, and resilience.

In practice, this shift drives the formation of more cross‑functional teams with broader skill sets. Redesign should start with identifying high‑value customers and business journeys, mapping the underlying workflows, and modelling how AI reshapes demand across roles and teams. Workforce heatmaps can make these changes visible, highlighting where capacity, oversight, and control requirements will shift and where workflows can be simplified or re‑engineered.

Emerging roles such as ‘journey oversight lead’ or ‘control and escalation specialist’ illustrate how firms are formalising accountability for managing AI‑enabled processes. As this redesign takes hold, organisations are likely to evolve away from traditional pyramid or diamond shapes towards a flatter kite‑shaped structure with a greater proportion of experienced, high‑judgement roles concentrated at the centre.

2. Develop skills of judgement, oversight, and collaboration

Across sectors, AI automation is expected to take on more tasks than are currently performed by humans alone or through human–machine collaboration. In effect, the automation potential within organisations now exceeds the scope of existing work. This signals a step‑change in how work is structured and where human expertise adds the most value.

While the debate about the net impact of AI on workforce size continues, evidence from financial services points to a clear shift in the nature of human contribution. Value increasingly lies in strategy setting, judgement, ecosystem management, and the handling of complex, ambiguous, or fast‑moving situations.

This shift places greater emphasis on collaboration, influencing, relationship management, and systems thinking, with the ability to understand how changes ripple across the organisational ecosystem. What distinguishes AI‑enabled organisations is not just the importance of these skills, but the level at which they are required. Skills that were once developed gradually through decades of experience are now needed much earlier, as human roles at all levels take on responsibility for overseeing, validating, and guiding AI‑driven work.

3. Evolve leaders to become orchestrators of hybrid human‑agent teams

As AI redistributes work across humans and agents, leadership must evolve beyond traditional people‑management models. Performance management, for example, takes on new meaning in hybrid teams: leaders are no longer evaluating individual effort alone, but are accountable for how effectively humans and machines are orchestrated to deliver reliable, safe, and measurable outcomes.

Leadership roles will therefore evolve into orchestrators of hybrid teams of people and AI agents. Coordinating AI capabilities, managing risk, and governing outcomes become core leadership responsibilities and critical differentiators.

To enable this shift, firms should invest in developing orchestration‑related leadership skills, including human-AI decision‑rights design, AI‑informed performance and outcome management, and ethics and accountability governance in hybrid teams. Management spans and layers will also need recalibration, guided by indicators such as decision‑cycle time, workload, and team health rather than traditional human‑only ratios.

Crucially, the value leaders are expected to add in terms of AI capability building, decision quality, and engagement must be clearly defined, consistently measured, and explicitly rewarded.

4. Shift from curious cultures to relentless reskilling

As AI reshapes work, traditional role definitions rooted in hierarchy or functional silos will give way to a more skills‑based view of the workforce. Talent will need to move fluidly across workflows as AI alters capacity and market demands shift.

Supporting this transition requires moving beyond episodic learning towards structured, deliberate, and continuous reskilling. Firms should start by defining a future‑fit skills framework across priority roles, spanning data and AI literacy, domain expertise, and oversight capability. Mapping current skills gaps provides a foundation for targeted intervention.

From there, skill‑based learning pathways can be aligned to clear certification and assessment standards, while hiring and internal mobility criteria are updated to prioritise AI‑oriented capabilities such as judgement, oversight, and decision‑making. When embedded across development, recruitment, and career progression, this approach strengthens both organisational performance and the employee experience, improving productivity, satisfaction, attraction, and retention in parallel.

5. Strengthen the AI talent pipeline through fluidity

As organisations become flatter, more senior in composition, and more reliant on experience‑based judgement, the viability of traditional graduate pipelines is increasingly questioned. Yet the heightened need for learning, mentorship, and the development of judgement and influence means early‑career roles will not disappear – they’ll evolve.

Firms should strengthen their talent pipelines by introducing structured apprenticeship and early‑career models that build AI fluency and oversight capability from the outset. As roles broaden to encompass blended responsibilities, HR architecture, including job families, levelling frameworks, and reward structures, must evolve to reflect this expanded scope.

Career pathways and workforce composition will also need to become more fluid, enabling talent to be mobilised as capacity is freed or constrained and as AI adoption progresses unevenly across the organisation. As routine execution declines, traditional skills taxonomies and linear progression models will quickly become outdated. In their place, broad AI fluency spanning output supervision, problem framing, and strategic interpretation will become a baseline requirement.

Redesigning work, not just deploying AI

The coming years will demonstrate that becoming an AI‑enabled financial services organisation is not determined by technological sophistication alone. Real advantage will accrue to firms willing to challenge the structural norms that have governed the industry for decades and to fundamentally rethink how work is designed and delivered.

It requires reconsidering processes, ways of working, and the backbone elements of operating models, and creating the space to think differently. While daunting, the opportunity is clear. Redesigning leadership, organisation structures, roles, and skills in concert is essential.

Firms that take this cohesive, enterprise‑wide approach and commit to mastering workforce planning in an AI‑enabled world will move beyond experimentation and towards building truly intelligent organisations.