Beyond the Tech: Why the Next Era of Banking AI Focuses on People and Strategy

The phase of skepticism surrounding artificial intelligence in the financial sector is officially over. Today, financial institutions are no longer debating whether AI works; instead, they are shifting their focus toward maximizing profitability and scaling their technology investments.

According to Keri Smith, banking and capital markets AI and data lead at Accenture, the industry is moving rapidly past the experimental phase. The goal now is to align AI tools with overarching strategic goals and realize a tangible impact on the bottom line.

Surprisingly, late-adopting banks might have a unique advantage. While falling behind in traditional tech adoption is usually fatal, the democratization of AI tools, abundance of blueprints, and shared industry lessons make it highly feasible for latecomers to leapfrog early adopters.

The Evolving Rules of AI Ownership and Governance

As AI applications mature, banking leaders are rethinking how these systems are managed and governed within their organizations. Key shifts include:

• Decentralized Decision-Making: Early AI initiatives were heavily centralized to ensure strict guardrails. Today, decision-making is shifting toward individual business units, though central oversight remains crucial for security.
• Breaking Down Silos: Cross-bank communication is critical, especially with the rise of agentic AI. Operating these advanced models in isolation can lead to fragmented strategies and operational risks.
• Regulatory Advantages: Existing financial regulations around model risk management have actually prepared banks for AI governance, giving them a structural advantage over non-regulated industries.

1. Reshaping the Banking Workforce Through Continuous Learning

Fears of mass automation often dominate headlines, but the real friction in AI adoption stems from a lack of transparency and communication from management. Many employees are not resistant to AI; in fact, there is a growing grassroots demand among staff members who do not want to be left behind.

To address this, financial institutions must prioritize what Smith describes as “evergreen upskilling”—an ongoing, continuous training process tailored to individual employee needs. As AI automates routine tasks, banks must proactively plan how to redeploy freed-up intellectual capacity into higher-value roles.

2. The New Mandate for Bank Leaders and Boards

Successfully scaling AI requires significant capital allocation and strategic direction, putting the responsibility squarely on board directors and executive teams. Unlike past technological shifts like cloud computing, which leaders could manage with a high-level understanding, AI demands deeper technical literacy.

To avoid getting stuck in the “proof-of-concept” phase, executives must ensure that AI projects are aligned with the bank’s broader business goals from day one. Strong, active sponsorship from senior leadership is essential to transition pilot projects into enterprise-scale tools.

3. Redesigning the Organizational Chart

The acceleration of AI will inevitably collapse traditional bank hierarchies. Functions that once operated in isolation—such as compliance, risk management, and product development—must now collaborate closely to build and deploy AI models safely.

Managing these newly integrated teams requires leaders who possess a rare combination of technical competency and deep empathy. Leaders must be able to translate complex technical concepts across different departments to guide their teams through this historic structural transformation.

Source: thefinancialbrand.com