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Your AI investment is probably working. That's the problem. Small wins are masking a bigger failure: the inability to scale them.
PwC research shows that while the majority of executives expect AI to materially reshape value creation, relatively few are seeing enterprise-level gains in productivity or profitability. Even the firms we see leading the way—those already capturing 20% to 40% productivity improvements in targeted roles and workflows—face a real risk that those gains will evaporate.
The constraint isn’t the technology. It's execution and imagination.
The financial services firms building for the future are planning to use AI to fundamentally reimagine their workflows, free capacity at scale, and reinvest those savings into a firmer foundation for managing growth, not just cut costs.
Every executive and functional leader—whether you sit in operations, risk, technology, compliance, or the front office—should own the vision for how work in your domain will be reimagined. You may partner with centralized AI teams and HR, but no one understands your workflows, your people, and your value chain better than you do. And for financial services firms, the shift comes with an added requirement: AI-enabled work should remain aligned with evolving regulatory expectations.
Are you ramping up your use of entry-level talent, helping your junior employees use AI to become your new generalists? Are you “agentifying” mundane tasks across your organization in all-new workflows so you can unlock new capacity? As roles evolve, and AI takes on more work, how are oversight and compliance being built into your new ways of working? Are you starting to rethink how you measure and reward your employees?
The good news? You’re not alone if you haven’t yet begun this effort. According to PwC’s April 2026 America in motion survey, rebalancing the workforce model between human roles and AI tops the list for where financial services executives are now focusing their company’s direction over the next 12 months.
The leaders getting this right aren't waiting for a perfect playbook. They're moving now to build the workforce that AI makes possible before their competitors do. Here’s what you need to know.
Spotlight: An insurer builds AI around its people
An insurance company is exploring how AI can support its growth while maintaining a strong focus on its people and customers. The company is dealing with real capacity challenges—many employees are stretched thin and working long hours to keep up. Leadership sees AI as part of the solution, not just for efficiency, but for improving how work gets done day to day.
Instead of relying on entry-level roles focused on repetitive tasks, the organization is starting to rethink those positions to emphasize learning, problem-solving, and more value-added work earlier on. AI is taking on administrative and routine work, giving employees more space to focus on higher-impact activities. Over time, leaders believe this shift will make roles like underwriting more analytical and engaging, helping to modernize career paths while still supporting development from the ground up.
How AI is remaking the financial services workforce
Transformative benefits come when you rethink workflows for what people and AI can do together. In financial services, we’re seeing this hybrid human-AI workforce come to life in four ways.
Traditional roles: When it comes to risk, regulatory accountability, fiduciary responsibility, and high-value decisions, people are irreplaceable. Executive decision-makers, senior relationship managers, financial advisors, and compliance specialists will remain essential, though their numbers may decline.
AI is poised to change these jobs. Rapidly improving copilots and agents already offer people data, analysis, forecasts, and simulations to guide better, faster decisions. But the importance of these roles for accountability, governance, and trust will persist or even grow. You’ll have to understand how you value those specialist roles.
AI-enabled workers: When financial professionals work with AI in new agentic workflows, where agents execute more transactional and repetitive work, cycle times can shorten, do-overs can decline, and quality can improve. Roles may have the same name, but their nature could change.
- Credit analysts transition to exception handling, risk oversight, and portfolio-level decision-making as AI agents automate data gathering and initial risk assessments.
- Insurance underwriters shift to reviewing outputs, managing exceptions, and applying judgment in complex cases as agents perform risk assessments by extracting insights from unstructured data such as claims history and policyholder behavior, and generate first drafts of policy language and pricing recommendations.
- Recruiters concentrate on candidate engagement and decision-making as agents coordinate interviews, summarize profiles, and generate interview insights.
- Operations teams focus on exceptions, oversight, and control integrity as agents execute tasks such as payments processing, trade settlement, claims adjudication, and customer onboarding—handling data intake, workflow routing, and issue resolution while escalating only complex or high-risk cases.
- Software engineers focus on agent orchestration, system design, and quality assurance as agents generate, test, and troubleshoot code.
With these and other AI tools, many financial service professionals can quickly grow productivity, turning their roles into hybrid ones that are neither traditional nor fully agentic, but AI enabled.
Digital AI agents across operations: In the coming years, AI agents could eventually take on more than half of today’s operational activities and workflows, particularly as multi-agent systems take on end-to-end, cross-functional processes.
- Loan processing agents can collect documentation, validate data, identify exceptions, and initiate approvals.
- Claims handling agents can assess coverage, integrate third-party data, and draft settlement recommendations.
- Finance and operations agents can reconcile accounts, monitor anomalies, and prepare management reporting.
AI agents require suitable enterprise architecture, oversight, and governance. But tested approaches are emerging and agent adoption is multiplying across financial services operations.
New technical and orchestration roles: As designing, managing, and overseeing AI systems become central to financial services, we’re seeing new roles emerge.
- Traditional execution roles evolving into AI supervisors
- The rise of digital-native roles focused on building and managing agents
- Business analysts taking on orchestration roles, helping design and implement agent-enabled processes
Where AI can reshape your workforce structure
As AI-enabled workforce models spread, your workforce structure could become obsolete due to key changes taking shape today.
- Early-career employees can add value more quickly. As AI agents take on the routine tasks that once trained junior employees, organizations should have new ways to build foundational capabilities. Many of today’s leading organizations are turning to structured learning, hands-on experience with AI-enabled workflows, and earlier exposure to higher-value work. As AI provides access to specialized skills, these workers can take on higher-value tasks such as synthesizing insights for client discussions, investigating exceptions, supporting live deal or underwriting decisions, and contributing to risk assessments or scenario analysis.
- You may need fewer specialists. AI can augment access to expertise, enabling many of those same early-career employees to deliver greater value and end-to-end outcomes as “AI-enabled generalists.” Deep expertise—complex derivatives structuring, regulatory interpretation, model validation, or specialized underwriting (such as cyber or catastrophe risk)—may become concentrated in fewer roles, while the broader workforce becomes more flexible, outcome-oriented, and cross-functional.
- Your workforce structure may change. As AI does more entry-level work, you will see junior employees supported by more diverse training and development (such as mentorship programs, scenario-based learning, or AI coaching). It’s likely that some junior employees may advance more quickly. But as AI takes on coordination, reporting, and process management, the number of mid-level layers may be squeezed. At the same time, the midlevel of generalists may grow as they flex in different roles and oversee agents, rapidly onboarding and using agents to execute even specialized, high-value tasks.
These changes can move you from structures defined by span of control to ones defined by capacity and outcomes, giving you faster decision-making and tighter alignment between work and outcomes. And with so much automation, this new workforce could reduce cycle times, grow consistency, and let you scale as needed without shifting your headcount. But you should rethink talent strategies, elevate change management as a leading discipline, and upgrade risk, governance, and accountability to help address both AI’s new capabilities and new expectations from regulators.
Spotlight: A global player puts agents to work in hiring
A global financial institution is redesigning its recruiting process using an agent-based model to improve hiring speed and outcomes. By mapping the process end-to-end, the organization identified where AI can handle tasks like sourcing candidates and coordinating interviews, while people handle key decisions and oversight.
As this model scales, the organization is already seeing productivity gains, reducing time spent on manual coordination and administrative tasks, accelerating time-to-hire, and enabling recruiters to manage a higher volume of roles without increasing headcount. While reductions of 50% were possible in some functions, the company opted for smaller cuts, shifting its workforce mix toward higher-value work such as advising hiring managers and improving the candidate experience. Formal upskilling programs offer in-house talent the opportunity to transition into these new roles and, ultimately, to be part of the broader transformation.
Boards, risk teams, and the new rules of AI accountability
As AI handles sensitive data and transactions, governance is often critical, but traditional, after-the-fact controls and oversight won’t be able to keep up with real-time AI execution. The solution is to embed controls, accountability, and triggers for human intervention directly into agentic workflows. In claims processing, for example, you’ll want to define and encode when an AI agent can recommend or approve settlements, when humans must make the call, when and where oversight takes place, and who is accountable.
At the same time, AI is significantly expanding the capabilities of boards and senior decision-makers. With access to real-time insights, simulations, and AI-generated recommendations, executives can make faster, more informed decisions across their areas of responsibility. This shift has the potential to blur traditional boundaries between management and oversight, challenging the role of boards that have historically relied on periodic reporting and retrospective review.
Governance models should strive to evolve. Boards should upskill and engage more dynamically with AI-driven insights, while executive teams define clearer structures for how information flows, how decisions are validated, and where accountability sits. This may require rethinking how the board exercises its role, including how to assess information, challenge decisions, and evaluate management in a more dynamic environment.
5 moves to turn AI gains into enterprise value
AI is too important for you to waste time on point solutions that people don’t adopt, that don’t scale if they succeed, and that don’t show up in the numbers that count. Instead, create a change program to enable true workforce transformation.
In financial services, this transformation should also be designed with regulation in mind. As AI becomes embedded in core workflows, decisions should remain transparent, explainable, and auditable, requiring organizations to build risk and governance into the design from the start.
Here’s what you can do right now.
- Set a clear AI narrative and get the right people on board.
Start by bringing together strategy, HR, technology, and risk specialists and getting clear about what AI means for your organization and your workforce. Some may find this new world of work uncertain, but when organizations offer clear direction, adoption and engagement can surge. Be transparent about how roles may change, how employees will be supported, how success will be measured, and what opportunities this creates. Make sure top leadership is engaged and changing the way they work by visibly adopting AI themselves. - Identify where speed, scale, and impact intersect.
Many organizations are starting with areas like software development, but too often they stop at automating individual tasks. Greater value comes from stepping back to assess entire workflows and to identify where AI can fundamentally reshape how work gets done. This includes understanding the “art of the possible,” even if realization takes time. - Use a structured approach to redesign work.
It’s not just about deploying AI tools. Industry-leading organizations are developing structured, iterative approaches to map workflows, test new ways of working, and build in the right data, controls, and governance from the start. This helps you in designing changes that are practical, scalable, and aligned with regulatory expectations. - Redesign how work and roles are structured.
As workflows change, the way work is organized needs to change too. This includes redefining roles, rethinking team structures, and deciding how people and AI can work together day to day. Decisions about how to develop entry-level talent, how managers oversee both people and agents, and where specialist expertise is required. Organizations are also rethinking how they reward work, connecting compensation more directly to business value, rather than primarily to tenure or experience, and aligning pay to newly defined roles and contributions. - Plan for the workforce impact—and act on it.
Redesigning work can help create capacity. The question is what you do with it. Organizations should actively plan how to redeploy talent, reskill employees, hire for new capabilities, and determine where to make reductions or invest. Those that treat this as an ongoing discipline can be better positioned to capture more value.
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