Are Agents the New Interns or New Executives?

Executive Summary

The proliferation of AI agents within financial services organizations raises a pivotal question: are these systems simply automating repetitive tasks once assigned to junior staff, or are they taking on strategic functions that touch the realm of executive responsibilities? This paper explores how AI agents are reshaping workforce structures and workflows in the sector. Drawing on leading industry and academic sources, we examine how financial organizations can balance efficiency gains with trust, talent evolution and change management requirements.

Introduction

AI agents now perform a range of functions, from handling structured data entry to participating in complex analytical work. As these systems become more prevalent, organizations must address not just operational impacts but also the cultural and strategic shifts involved in adopting an AI-driven operating model.

Rethinking Roles: Are Agents Filling Gaps or Redefining Leadership?

The “Intern” Analogy: Automation at Scale

The introduction of AI agents has enabled banks and insurers to accelerate and streamline repetitive processes such as regulatory reporting and onboarding. According to PwC, as many as two-thirds of leading banks recently automated a significant portion of their compliance burden, resulting in substantial reductions in manual error rates and processing times (PwC 2024). For instance, in insurance operations, automated review by AI has demonstrated tangible operational cost reductions. Such outcomes mirror the classical use of interns and junior staff, with AI providing scalable task execution and freeing human workers for higher-value work.

The “Executive” Vision: Agents in Strategic Functions

More advanced applications see agents completing tasks such as risk analytics, market modeling or co-developing audit documentation. Industry reports, including those from McKinsey and Deloitte, have documented how investment and asset management organizations use AI-powered systems as “second opinions” in scenario analyses, helping managers spot risks or surface investment opportunities earlier than manual methods alone (McKinsey 2024). These are not simple support roles; instead, AI agents act as collaborators in leadership decision-making, suggesting that their function is beginning to overlap with that of high-skilled and executive employees.

Hybrid Workflows: Towards Agent-Augmented Teams

Rather than replacing either tier outright, a hybrid paradigm is emerging. For example, operational tasks are often initiated by agents and completed or overseen by human specialists, integrating AI efficiency with human judgment. This approach, noted by consultancies and validated by academic studies, has led to major improvements in throughput metrics such as turnaround times for loan approvals, while maintaining oversight in high-stakes decision areas. Firms that blend agent and human input report better cost control and improved morale—highlighting the synergy of “augmented” rather than automated or replaced workforces.

Organizational Impact: Skills, Structures, and Change

Talent Models: Adapting to the AI Era

Quantitative research reveals clear shifts in talent demand. As organizations automate more routine operations, the need for process-heavy junior roles is declining, while there is a marked uptick in hiring for AI oversight, governance, and integration specialists (PwC Jobs Barometer 2024 ; EY 2025). Upskilling programs have become central to HR strategies in larger organizations, with more than half of top-tier financial firms rolling out mandatory digital training for managers. Human roles are becoming more supervisory, analytical and cross-disciplinary.

Trust and Risk: Governance for Agentic Outputs

With greater autonomy comes heightened risk and scrutiny. Leading supervisory authorities, including the Bank of England, have publicly highlighted the imperative for clear accountability frameworks when algorithmic agents participate in decision loops (Bank of England, 2025). Meanwhile, the World Economic Forum has reported on the variable degree of trust organizations place in agentic outputs, often depending on the transparency of models and the clarity of escalation protocols. A key insight is that dual-control mechanisms—where agent recommendations are reviewed and actioned by humans—reduce both error rates and compliance concerns (WEF 2025).

Change Management: Lessons on Adoption and Resilience

Managing Resistance and Fostering Buy-In

Resistance to AI transformation is not uncommon, upwards of four in ten employees in some large institutions express concern about career risk and organizational transparency during early adoption phases (WEF 2025). Yet, documented best practices, such as inclusive planning, clear communication and payback guarantees can halve resistance rates and double realized productivity improvements (ResearchGate 2024). Case studies indicate that stepwise adoption and high employee participation correlate with the smoothest transitions.

Building Adaptability and Institutional Learning

The most successful financial institutions treat AI integration as a journey, not a discrete event. Leaders prioritize feedback processes, rapid iteration and the cultivation of a “test and learn” culture. This approach diminishes staff turnover and accelerates the realization of value from AI investments (Nature 2025), with case evidence showing that iterative, adaptive organizations are able to amplify benefits while dynamically managing emerging risks.

Conclusion

AI agents in financial services should not be boxed into “intern” or “executive” categories. Their unique value comes from their flexibility: automating mundane tasks while expanding the capacity for strategic analysis and oversight when paired with human expertise. Organizations that blend the technical strengths of agents with the judgment and adaptability of people will not only unlock efficiencies but will also power new forms of innovation and resilience.

References

• PwC, “AI Agents for Finance: Automating Routine Tasks” (2024)
• PwC, “Jobs Barometer 2024”
• McKinsey, “Superagency in the Workplace” (2024)
• EY, “How Artificial Intelligence is Reshaping Financial Services” (2025)
• Deloitte, “Agentic AI in Banking” (2025)
• Fujitsu, “AI Agents in Financial Services” (2025)
• Bank of England, “Financial Stability in Focus” (2025)
• World Economic Forum, “Artificial Intelligence in Financial Services” (2025)
• Nature, “Organizational Change Management and AI” (2025)
• ResearchGate, “The Role of AI in Transforming Organizational Change Management” (2024)