Artificial intelligence is fundamentally transforming workforce strategies and compensation structures across the United States financial services industry, according to a new PwC survey of more than 1,000 director-level and executive-level professionals at American financial firms. The survey points to a broader recalibration underway: firms are not only adopting AI tools, but also rethinking which skills they reward, how they organize work, and where they expect future productivity gains to come from.

The findings show a clear shift away from traditional credentials in favor of AI fluency. Eighty-six percent of respondents said AI skills training is more valuable than an MBA for many new hires, while 91% reported offering increased compensation to employees with AI capabilities. Furthermore, 58% plan to link pay directly to AI-enabled productivity. In practice, that means AI is becoming part of hiring, pay, and performance decisions rather than remaining a separate technology initiative.

To build these capabilities, firms are pursuing multiple channels: 62% intend to hire new employees with AI-specific skills, 61% plan to upskill their existing workforce, and 57% expect to engage external vendors as partners. That mix suggests financial firms are trying to move faster than internal training alone would allow, while still building internal capability around roles that are increasingly expected to work with AI day to day.

Workforce Restructuring on the Horizon

The growing emphasis on AI proficiency is unfolding alongside significant workforce restructuring. Nearly eight in ten executives anticipate their organizations will shrink by at least 20% over the next five years. Entry-level and middle-management positions are expected to be the most vulnerable, underscoring how AI is already shaping planning around staffing levels and organizational layers rather than just individual tasks.

While 42% of respondents report having conducted enterprise-wide modeling to evaluate AI's impact on labor capacity, far fewer have progressed to redesigning workflows or creating career pathways to develop an AI-fluent leadership pipeline. That gap indicates many firms are still in the assessment stage, even as they prepare for a smaller and more technology-enabled workforce.

Employee Sentiment and Adoption Challenges

Employee attitudes toward AI present a concurrent challenge for leadership. Forty-four percent of executives report that staff are concerned about job security. Forty-three percent observe that employees use AI only when required rather than adopting it proactively, and 40% say workers feel overwhelmed by the pace of AI-driven transformation. Additionally, 34% identified change fatigue as a significant barrier to scaling AI initiatives across the workforce.

Those responses help explain why adoption efforts can be uneven even in firms that are investing heavily in AI capability. Building technical tools is only part of the transition; getting employees to trust, use, and integrate them into routine work remains a separate hurdle.

Productivity Gains, Uncertain ROI

Despite the strategic prioritization of AI, measurable financial returns remain elusive for most firms. Seventy-seven percent of executives say the majority of AI investments are not yet delivering quantifiable return on investment. Nevertheless, companies report productivity improvements in technology, risk management, and operations. Nearly half have directed their AI workforce initiatives toward improving productivity, reducing routine tasks, and embedding AI into daily workflows.

For now, that leaves many firms balancing near-term efficiency gains against longer-term uncertainty about how to measure success. The survey suggests that, in this sector, AI is being treated as a workforce and operating model issue as much as a technology one.

Governance and Data Challenges

Governance remains a critical unresolved issue. Nearly 90% of respondents say their firms have clear ownership for AI agent decisions, yet there is no consensus on where accountability for material risk ultimately resides. Responsibility is divided across chief executives, technology leaders, risk officers, and business unit heads.

The use of shadow AI—unauthorized tools operating outside centrally governed systems—is reportedly moderate at more than half of firms, and 90% acknowledge that such practices introduce regulatory risk.

Fragmented or low-quality data was identified by 41% of respondents as the single largest obstacle to scaling AI across the workforce, highlighting the operational barriers firms face as they attempt to convert AI enthusiasm into sustainable, governed outcomes.