How Organizational Rewiring Can Capture Value from Your AI Strategy
AI initiatives are rapidly proliferating and penetrating more and more functional areas and teams. A new study from McKinsey looks at AI programs globally and across multiple industries and pinpoints best practices in leadership, governance, and risk management, as well as skills and staffing.
By David Evans, Chief Content Officer at The Financial Brand
The report: The State of AI
Source: McKinsey & Co.
Why we picked this report: Identifying best practices in AI adoption and implementation is an urgent priority for leaders balancing strategy, innovation and budget allocations.
Executive Summary
Recent McKinsey research reveals a significant shift in how organizations are implementing AI to deliver measurable value. While still in early stages, companies are establishing operational structures and processes crucial for realizing AI’s potential, particularly with generative AI.
The findings indicate that successful AI implementation demands organizational "rewiring" — redesigning workflows, strengthening governance, and mitigating emerging risks. Financial services organizations are particularly positioned to benefit, with survey respondents in this sector more likely than others to expect workforce reductions from AI implementation. For retail banking executives, this signals an imperative to adopt a CEO-led, enterprise-wide transformation approach rather than delegating AI initiatives to IT departments or pursuing incremental deployment.
Key Takeaways
- CEO oversight of AI governance correlates most strongly with higher bottom-line impact, particularly in larger organizations where it shows the greatest effect on EBIT attributable to generative AI.
- Workflow redesign has the biggest impact on an organization’s ability to capture EBIT from generative AI use, yet only 21% of organizations have fundamentally redesigned workflows.
- Financial services respondents are the only sector much more likely to expect workforce reductions than no change from generative AI implementation.
- Organizations that track well-defined KPIs for generative AI solutions see the greatest bottom-line impact, but fewer than 1 in 5 companies currently do this.
- Larger organizations ($500 million and more in revenue) are implementing more comprehensive AI strategies, including centralized risk management, dedicated adoption teams, and strategic roadmaps.
What we liked about this report: Its comprehensiveness, both in terms of its global scope, multi-industry perspective, and organizational detail.
What we didn’t: The report is a little coy on one big question: Will AI drive or enable significant headcount reductions?
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:
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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.
AI Adoption Best Practices for Retail Banking
The research identifies several adoption practices strongly correlated with successful AI implementation. For retail banking executives, five practices stand out as particularly relevant:
1. Track well-defined KPIs: The practice most strongly associated with bottom-line impact is establishing and monitoring specific key performance indicators for AI solutions. For retail banks, this means linking AI initiatives directly to metrics like cost-to-income ratio, customer acquisition cost, fraud reduction, or loan processing time.
2. Establish a clearly defined roadmap: Particularly effective in larger organizations, creating a phased implementation plan enables coordination across teams and business units. Banking executives should develop multi-year AI roadmaps aligned with broader digital transformation strategies.
3. Create comprehensive trust approaches: Given banking’s dependence on customer trust, establishing mechanisms to foster confidence in AI use is essential. This includes transparency around data usage, compliance protocols, and governance frameworks.
4. Enable senior leader role modeling: The survey shows that when executives actively use and advocate for AI tools, adoption accelerates throughout the organization. Retail banking leaders should personally incorporate generative AI tools in their work and visibly champion their use.
5. Establish capability training by role: Creating structured learning programs tailored to different roles ensures all employees can effectively contribute to AI initiatives. For retail banks, this means developing specific AI training tracks for customer-facing staff, risk managers, product developers, and executives.
The Future: From Experimentation to Transformation
McKinsey’s research indicates that while AI use is accelerating dramatically (78% of organizations now use AI in at least one function, up from 55% a year ago), most organizations are still in early implementation stages. Only 1% of company executives describe their generative AI rollouts as "mature."
For retail banking leaders, this reality check suggests both opportunity and urgency. The potential for competitive advantage remains substantial for early transformation leaders, but the window for gaining this advantage is narrowing as adoption accelerates.
As McKinsey senior partner Alex Singla observes: "The organizations that are building a genuine and lasting competitive advantage from their AI efforts are the ones that are thinking in terms of wholesale transformative change that stands to alter their business models, cost structures, and revenue streams — rather than proceeding incrementally."
For retail banking executives, this means embracing AI as a strategic imperative that requires rethinking fundamental business models, not merely implementing new technology tools. The most successful banking institutions will be those that undertake comprehensive organizational rewiring, driven by active C-suite leadership, clear strategic roadmaps, and a willingness to fundamentally redesign how they operate.
Editor’s note: This article was prepared with AI language software and edited for clarity and accuracy by The Financial Brand editorial team.
