Financial services AI workforce gap: PwC

Key takeaways

PwC's 2026 Financial Services Workforce AI Survey shows firms moving aggressively on AI—but many are still unprepared for the workforce transformation it requires.
AI is reshaping talent strategies in financial services, from higher pay for AI skills to new approaches to hiring, upskilling, and leadership development.
Strong AI governance is becoming a business imperative as financial services firms confront shadow AI, regulatory risk, and increasingly autonomous AI agents.

Financial services firms may say they’re planning for an AI-enabled workforce, but most really aren’t. They’re often planning for a smaller workforce—and hoping AI fills the gap.

There’s a difference between modeling how many people you can cut and designing the workforce you’ll actually need. Among financial services leaders, 42% say they’ve done high-level modeling to understand the changes in labor capacity from AI across their entire company, and nearly eight in 10 expect their workforce to shrink by at least 20% over the next five years.

But far fewer have taken the harder next steps of defining what new roles look like (and what skills will likely be required), redesigning talent pipelines for a world with fewer entry-level workers, and building career paths that can develop AI-fluent leaders from the ground up. Of those with some workforce modeling in place, only 50% have looked at the impact of redesigning processes or workflows to account for AI.

How is AI changing workforce planning in financial services? We recently surveyed more than 1,000 leaders at US financial services firms about their use of AI, and the results give a clear picture of how their expectations for employees are changing. They want people to use AI, they’ll provide them training and upskilling, and—notably—they’ll pay them more.

Here are the key findings from our survey.

Firms feel the need for speed

Nearly three-fourths of the executives we surveyed say their organization is moving faster to remain competitive, yet an even greater share say they’re not moving fast enough to keep pace with AI innovation.

90%

of financial services executives say firms need to get more comfortable moving quickly in the age of AI.

70%

say their organization is moving faster to remain competitive, but...

77%

say their organization is not moving fast enough to keep pace with AI innovation.

Regulatory and compliance requirements are part of the reason, creating disincentives for firms to radically reshape their businesses. But leaders have more direct control over another factor slowing organizations down: employee hesitancy due to concerns about job losses, continued change, and other factors. Forty-four percent say that employees are concerned about job security or role changes from AI, 43% say employees use AI only when required rather than proactively, and 40% say employees feel overwhelmed by the pace of AI-driven change.

In a related finding, 34% of leaders point to change fatigue as a key barrier preventing their firms from scaling AI across the workforce.

Firms are paying more for AI skills

The value that companies see in AI is filtering down to employee paychecks. Ninety-one percent say they’re increasing compensation for employees with AI skills. More than half (58%) say they will tie compensation directly to AI-enabled productivity. Perhaps most surprising, 86% agree that AI skills training is more valuable than an MBA for many new hires.

PwC’s recent AI Jobs Barometer shows similar findings. AI professionalizes some jobs, by reshaping them to require even more human expertise, in areas like critical thinking, team-building, and creative problem-solving. Compared with “democratized” jobs (which AI can make it easier for non-experts to perform), professionalized jobs are thriving, growing twice as fast in terms of volume and showing 42% higher wage growth.

To source these skills, our survey shows that FS firms are taking several steps.

62% plan to hire new employees with AI-specific skills in the coming year.
61% plan to upskill or reskill existing employees.
57% plan to partner with external vendors or service providers.

What this means for FS executives:

Differentiate through compensation. Make increased wages for AI skills an explicit part of your employee value proposition. It should be clearly communicated—both to current employees and to applicant pools—and built into performance management schemes.
Make a buy-or-build decision on talent. To gain some skills and capabilities, your firm’s faster and more cost-efficient approach may be to acquire them either from vendors or by buying an AI-native company.
Continue investing in AI training and upskilling. Given how rapidly AI is evolving, firms can move beyond formal, structured training and make it a continuous part of daily work. Industry-leading programs are peer-to-peer and on-the-job that give employees the opportunity to learn through direct experience in a meaningful context. You can also focus on role-specific training, rather than broad AI training for the entire enterprise.
Make training more efficient. Shift HR’s approach away from developing training materials internally and toward curating materials from other places (including vendors). To the extent your firm does develop training and upskilling content internally, you can use GenAI to expedite that process. Many firms increasingly have AI itself train employees, with the right governance and guardrails.

A growing consensus that organizations will likely shrink

To be clear, some employee concerns about job security are valid. Financial executives say they are modeling the impact of AI on workforce capacity. Most have at least started this process, and 42% say they’ve done enterprise-wide modeling—a number that will likely go up over time as capabilities improve and as the implications of AI on workforces start to become clearer.

One aspect potentially holding them back is data. When asked about the biggest barriers to scaling AI across the workforce, 41% of FS leaders cited fragmented or low-quality data, more than any other issue, meaning they won’t be able to model the impact of AI, assess the taxonomy of roles and skills, and determine what can be agentified without better data.

Even at the current level of modeling, however, most executives have concluded that they’ll need fewer people. Nearly eight in 10 say that their workforce will shrink by at least 20% over the next five years. Among layers of the organization, 30% point to entry-level roles as most vulnerable to disruption from AI, followed by middle management (26%). A recent analysis from PwC says workforce reshaping is likely unavoidable, but reduction should follow strategy, not substitute for it.

What this means for FS executives:

Reassure your people. Clarify expectations, give employees a clear message of what your organization’s AI-enabled future looks like, and make sure people have the skills they require to succeed in that future. Avoid knee-jerk workforce reductions based on models that may not pan out. In recent months, as leaders learn to account for and contemplate the cost of AI (number of tokens users across the organization are consuming), some organizations are thinking differently about the economics of AI—and perhaps projecting more modest financial gains than they originally anticipated.
Reallocate top talent and build the skills—and leadership pipeline—for new roles. AI can do more than just create efficiencies. Implemented well, it helps unlock growth opportunities and create new roles. Identify your high-potential talent and, if their position will likely be impacted by AI in one part of the organization, help them transition into growing areas of the business. Perhaps some people in tasks that get automated can be upskilled to fill the growing demand for agent management and oversight. At the same time, rethink your talent pipeline. A smaller number of entry-level workers today means a smaller pool from which to develop the next generation of managers and leaders, so making deliberate investment in career pathways and leadership development is more important than ever.
Redesign and restructure the organization. Consider the implications of AI for your organizational design and operating model—not just your headcount. Determine which horizontal workflows should be established, who will be accountable for them, and where those roles should be located. Make deliberate decisions about how to organize the remaining leadership structure, including which senior leaders should remain close to customers and business operations, to support faster decision-making and AI-enabled ways of working.

Productivity gains from AI, but an uncertain payoff

Over the past year, many firms have focused their AI workforce efforts on improving productivity (cited by 49% of executives), reducing time on routine work (48%), and integrating AI into day-to-day workflows (46%). Among functions, leaders see the biggest productivity gains in technology and software engineering, risk management, and operations.¹

But some firms still struggle to see the financial impact from their large-scale AI investments. Seventy-seven percent say that most AI investments are not delivering measurable ROI. How should firms measure the workforce value of AI? Look at areas like productivity and adoption ahead of financial metrics like cost savings or revenue growth.

Delivering on the promise of AI requires baseline measures that companies have long applied to other technology initiatives, things like benchmarking performance, setting targets, and building a clear business case before funding. The current enthusiasm for AI may have pushed some organizations to short-circuit this and take a me-too approach to implementation, without clear business metrics in place. Instead, sector leaders will likely benefit from a willingness to move fast and adopt an innovation mindset to learn through experience.

What this means for FS executives:

Translate efficiency into financial metrics. If you can't quantify the gains from an AI initiative in financial terms, something may be wrong. Many firms have found that real value comes from redesigning processes and workstreams to deliver a better customer experience and tap into new revenue opportunities.
Have a clear strategy to reallocate capacity freed up by AI. Many low-value, repetitive tasks can be handed off to agents, and firms should consider how employees can better use that additional capacity. Training? Higher-value work? Stronger customer relationships? Without a deliberate strategy, time (and the value of that time) may simply leak out. At the same time, give your employees the unstructured time and space to practice and play with AI tools. As models become more differentiated and mature, it will likely become increasingly important for people—especially high-potential talent—to learn through hands-on experience.
Have a clear business case for any AI initiative. Include a baseline of current performance, a financial target for improvement (rather than simply tracking activity), and a timeline to achieve that goal. Assess performance with the same kind of financial rigor you would use to determine whether any other investment has paid off.

A growing need for governance

On governance, the survey results show a mixed picture. The good news? Nearly nine in 10 executives say their firm has clear ownership and accountability for AI agent decisions.

Digging one level deeper into the numbers, there’s no consensus on which role owns the risk of material harm from AI agents.

27% say it falls with the CEO and board.
16% say it falls to a technology leader.
15% point to a risk and compliance leader.
12% point to a business unit leader.
8% point to the human who acted on the AI output.

That wide disparity reflects the nascent state of AI governance, and how firms are still sorting through the risk implications. More troubling, employees are resorting to “shadow AI” at work—using their own tools and solutions rather than enterprise-approved options. More than half of respondents say this happens to a moderate extent, 35% say it happens to a significant extent, and 90% say that employees using AI products outside of centrally governed tools created regulatory risk.

What this means for FS executives:

Assign accountability. Firms should determine the right place for risk to sit within an organization. As the technology matures and regulatory rules catch up, this may get folded into the firm’s overall enterprise risk management framework, where the mechanisms and capabilities for addressing and mitigating risks already reside.
Take a structured approach to implementation through change management. The regular, ongoing release of new AI models has resulted in an ad hoc approach to implementation for many firms. A formal change management program can lead to better results, particularly regarding structured governance and guardrails over who has access to AI-enabled solutions, what those tools should and should not be used for, and what kind of autonomous actions AI agents are permitted to take.
Put technological controls in place. Particularly at larger firms, the solution often comes down to controlling access to solutions. Industry-leading firms have often established libraries of AI agents, broken down by role and function, with governance already in place over the decisions and actions that the agent can take. Smaller firms may need less elegant solutions, such as limiting access by seniority or job title.
Create consequences. Establish (or update) consequence models for managers and leaders who have oversight over agents that create material harm. Crack down on shadow AI. Set clear policies about which models are allowed on company systems and devices and communicate why those are important for regulatory compliance. Block non-enterprise-approved AI solutions on corporate networks and devices.

About the survey

PwC surveyed 1,004 executives at financial services firms with at least $500 million in revenue, all director-level or above, May 12–22, 2026. The respondents were equally split between asset and wealth management, banking and capital markets, insurance, and private equity.