Capturing Value Through Strategic Implementation of AI
Executive leadership is critical for AI success. For retail banking executives, the most crucial insight from McKinsey’s latest AI research is that CEO involvement directly correlates with AI success. The survey findings conclusively show that a CEO’s oversight of AI governance is the element most strongly associated with higher bottom-line impact from an organization’s generative AI use, particularly at larger institutions where it demonstrates the greatest effect on EBIT.
This contradicts a common instinct among financial institutions to delegate AI implementation to IT or digital departments. As McKinsey senior partner Alexander Sukharevsky notes, "Many companies’ instinct is to delegate implementation to the IT or digital department, but over and over again, this turns out to be a recipe for failure." Successful AI deployment requires transformation, not just technology adoption, and demands executive-level resource allocation and change management leadership.
For retail banks specifically, this means AI initiatives must be framed as wholesale business transformations rather than technology upgrades. The top team must actively participate in establishing governance structures, aligning with the finding that on average, organizations have two senior leaders jointly overseeing AI governance.
Workflow redesign drives value capture. The research identifies workflow redesign as having the single biggest impact on an organization’s ability to realize EBIT benefits from generative AI. However, only 21% of respondents say their organizations have fundamentally redesigned workflows as they deploy generative AI.
This represents a significant opportunity for retail banks. Financial processes often involve repetitive workflows across customer service, lending operations, compliance, and risk management. By fundamentally rethinking these processes around AI capabilities rather than simply adding AI to existing workflows, banks can achieve significantly greater productivity improvements.
For example, rather than using AI merely to accelerate loan document review, forward-thinking banks are redesigning entire lending workflows — from application to underwriting to servicing — with AI capabilities integrated at every stage. This comprehensive approach yields greater value than piecemeal application of AI tools within legacy processes.
Financial services leads in workforce impact expectations. The survey reveals a telling pattern specific to financial services. While across all industries a plurality of respondents (38%) predict generative AI will have little effect on workforce size in the next three years, financial services respondents stand out as the only sector significantly more likely to expect workforce reductions than no change.
Looking at functional areas, respondents most often predict decreasing headcount in service operations (48% expecting some reduction) and supply chain management (47% expecting reduction). For retail banks, this suggests customer service operations will likely see the most substantial workforce changes, requiring proactive talent management strategies.
At the same time, 50% of respondents whose organizations use AI say they will need more data scientists in the coming year. This indicates retail banks should prioritize both reskilling existing talent and recruiting specialized AI expertise, with emphasis on roles that can design, deploy, and monitor AI systems.
Dig deeper into AI in banking coverage:
- Is AI Overtaking the Role of Bank Relationship Managers?
- Is AI’s Promise to Deliver Competitive Advantage a Dangerous Mirage?
- You Won’t Find GenAI Under The Hood of BofA’s Erica… Yet
The Scale Advantage: Why Larger Organizations Lead in AI
The survey findings consistently show that larger organizations ($500 million and more in annual revenue) are changing more quickly and implementing more comprehensive AI strategies than their smaller counterparts. They are twice as likely to have established dedicated teams to drive AI adoption and to have created clearly defined roadmaps for implementation.
This scale advantage manifests in several ways relevant to retail banking executives:
1. Risk management focus: Larger organizations are much more likely to be addressing cybersecurity and privacy risks, critical considerations for financial institutions handling sensitive customer data.
2. Deliberate talent strategy: Larger organizations report hiring more AI-specific roles including data scientists, machine learning engineers, and compliance specialists. For retail banks, this talent acquisition strategy is essential for developing proprietary AI capabilities.
3. Flexible implementation structures: Larger organizations employ a hybrid approach to AI deployment, centralizing risk and compliance while distributing technical talent and adoption resources across business units. This balanced model appears particularly effective in complex, regulated environments like banking.
For mid-sized retail banks, this suggests the importance of strategic partnerships and potentially shared services models to achieve the scale advantages of larger competitors.
