Building AI resilience in financial institutions: PwC

Kevin Foutch

Principal, Workday Alliance, PwC US

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Building resilience in financial institutions through responsible AI and automation

Artificial intelligence (AI) and intelligent automation are no longer peripheral technologies in financial services — they’re actively reshaping the industry’s foundation. As leaders consider their transformation roadmaps, the question is no longer if these tools will matter, but how responsibly and effectively they can be deployed.

At this year’s Workday Rising in San Francisco, PwC hosted a conversation with financial services executives on redefining resilience in the digital age. While the session included one institution’s journey, the lessons apply broadly to every bank and financial institution seeking to build strength, agility and trust in an AI-driven future.

From hype to practical adoption

The past two years have seen AI dominate headlines — particularly with the rise of generative AI. For many banking leaders, the initial response has been a mix of curiosity and caution. The excitement is clear: automation and AI promise to reduce manual work, enhance reporting and accelerate insights. In fact, according to PwC’s latest Pulse survey, 58% of CFOs report they’re investing in AI and advanced analytics.

But hesitation lingers, often rooted in concerns about governance, regulatory expectations and data protection.

Institutions are asking:

Where should AI be deployed first?
How do we help confirm our models are explainable and auditable?
What’s the right pace — fast enough to stay competitive, but careful enough to stay compliant?

Early adopters are finding success by starting with well-defined, low-risk use cases — such as drafting internal policies, generating routine reports or automating administrative tasks. Many are also exploring how to embed these capabilities within platforms like Workday, where AI can streamline processes, employees already use every day. These 'crawl, walk, run' strategies allow organizations to gain confidence while building the structures that enable scale. The key lesson: AI is moving from hype to practical adoption. Leaders who balance experimentation with strong governance are beginning to see measurable benefits — without overextending their risk appetite.

Responsible AI: a resilience imperative

In banking, resilience has historically meant regulatory capital, stress testing and risk management discipline. Today, resilience also depends on digital maturity: the ability to deploy advanced technologies responsibly while protecting data, customers, and reputation.

This shift requires:

Governance frameworks that define appropriate uses for AI: For example, using AI to draft board presentations may be encouraged, but relying on it for credit underwriting decisions should remain out of scope.
Training programs that help employees understand what AI can and cannot do: Workers need guidance on when AI should be treated as a supportive tool versus when human judgment must remain primary.
Oversight processes that require business cases and approvals: Conduct necessary due diligence before AI tools are deployed — helping confirm every use of automation is intentional, documented and monitored.

Responsible AI isn’t just about compliance. It’s rooted in building systems that enhance trust and prepare institutions to withstand regulatory scrutiny, cyber risk and reputational challenges. In an industry where confidence is currency, resilience now means being both innovative and careful.

The power of collaboration

Financial institutions are inherently complex organizations with interdependent functions, so AI adoption can’t succeed if pursued in silos. The most resilient institutions are those where finance, HR, IT and risk leaders closely collaborate to shape governance models and align strategies.

Why does this matter? Because decisions in one area inevitably impact another. An AI-enabled HR system that automates workforce analytics, for example, must be aligned with finance and compliance leaders to avoid misaligned reporting or data-sharing risks.

Collaboration can also allow institutions to prioritize investments. With dozens of potential AI agents, copilots and dashboards available, leadership teams should agree on the “highest and best use” cases —whether that’s compliance automation, faster financial close, improved customer onboarding or smarter fraud detection.

The lesson for banks: build cross-functional collaboration early. AI transformation is not an IT project. It’s an enterprise-wide shift that requires shared ownership, shared guardrails and goals.

Building trust in the age of AI

Perhaps the greatest challenge for financial services is not technical — it’s cultural. Surveys consistently show that while executives are enthusiastic about AI’s potential, only a minority currently trust automated decision-making to operate independently.

This trust gap should be closed if institutions are to scale responsibly. Practical steps include:

Transparency: Explain how AI models are trained, what data they access and how outputs are validated.
Bias monitoring: Regularly review models to check for unintended bias or errors that could lead to compliance or reputational risks.
Auditability: Document every step of model deployment so regulators and internal stakeholders can trace decisions back to their sources.

Regulators are already signaling their expectations: clear documentation, robust governance and the ability to demonstrate responsible use. Banks that treat these not as “check the box” requirements, but as core trust-building practices, will be better positioned to reassure regulators, customers, and investors alike.

Moving from experimentation to scale

For many financial institutions, the challenge today isn’t whether to use AI, but where to focus first. The sheer number of available AI agents and copilots can feel overwhelming.

The most successful organizations approach this methodically:

Pilot with purpose: Select use cases with clear ROI, such as variance analysis, contract review or policy drafting.
Measure outcomes: Track efficiency gains, cost savings and employee adoption.
Scale responsibly: Once proven, expand use cases while maintaining governance guardrails.

Leaders shouldn’t let the abundance of options lead to paralysis. Early movers emphasize the importance of progress over perfection. AI adoption is a journey; each step builds confidence, capability and resilience.

A call to action for financial leaders

Resilience in financial services is being redefined. It’s no longer just about preparing for the next downturn or regulatory exam. It’s about equipping organizations with the digital agility to thrive amid constant disruption.

AI and intelligent automation, when deployed responsibly, enable institutions to:

Empower employees to focus on higher-value, strategic work
Strengthen governance and compliance
Accelerate insights and decision-making
Enhance customer, regulator, and market trust

The industry is in motion. The leaders who embrace AI with both boldness and responsibility will not only withstand disruption — they’ll shape the future of resilient, intelligent financial services.