How To Use The AI Leadership Option Matrix
Dr. Brian Massey
Helping FS Leaders Solve The Organisational Challenges Of AI | Managing Partner at Anordea | Strategy Advisor, Professor & Keynote Speaker
Hello and welcome to the latest edition of Banking On AI Strategy. In this edition we’ll review some recent industry developments in AI, and look at which CXO level roles are best positioned to lead an AI transformation and why.
AI Industry Updates:
Competition: Big 4 Race to Implement Agentic AI
Deloitte, EY, PwC, and KPMG, are aggressively adopting agentic AI to automate routine and increasingly complex tasks. This new wave of AI, known as agentic AI, goes beyond generative AI by autonomously completing goal-oriented tasks with minimal human input. Deloitte and EY, in particular, are investing billions in AI, leveraging partnerships with Nvidia and others to accelerate adoption in areas such as tax processing and financial analysis. PwC and KPMG are also rapidly deploying AI agents to support software development, audit processes, and client services. While these technologies are being pitched as augmentative rather than fully substitutive, they are expected to reduce future hiring demands and reshape the workforce, with significant implications for productivity, operational efficiency, and service delivery models across the industry.
For financial services and banking, these developments signal an impending shift in how advisory, audit, compliance, and even core operational functions are performed. As the Big Four deploy AI agents internally and externally, financial institutions, many of which rely on these firms for strategic, regulatory, and digital transformation guidance, will face growing pressure to adopt similar technologies. In particular, agentic AI could transform risk assessment, regulatory compliance, internal audit, customer onboarding, and financial planning functions. However, the promise of increased efficiency and scalability must be balanced with new governance, oversight, and ethical concerns, especially given the regulatory intensity of the sector. Banks and financial institutions that fail to move quickly risk falling behind on productivity and cost-efficiency, while those that embrace agentic AI with robust controls may unlock new levels of agility and strategic advantage.
Innovation: NatWest Developing OpenAI Collaboration
NatWest has become the first UK-headquartered bank to partner with OpenAI, aiming to integrate generative AI across its customer support and internal operations as part of a broader digital transformation strategy. The collaboration grants NatWest early access to OpenAI’s products and consultancy, supporting over 275 AI initiatives, including enhancements to its customer chatbot, Cora, and its internal assistant, AskArchie. These tools are already demonstrating impact. Cora’s generative AI capabilities have boosted customer satisfaction by 150% and reduced reliance on human agents. NatWest is particularly focused on using AI to improve fraud detection and reporting, with the goal of reversing the trend of customers preferring phone-based fraud alerts, which currently slows down the bank’s response times.
For financial services and banking more broadly, this partnership sets a precedent in how AI can be deployed at scale to enhance operational efficiency, regulatory responsiveness, and customer experience. By embedding generative AI into fraud prevention and digital banking experiences, NatWest illustrates how banks can reduce cost-to-serve ratios while improving risk management and personalisation. This also signals a strategic shift: banks that establish direct access to AI innovation pipelines, like NatWest now has with OpenAI, may outpace competitors in agility and service innovation. However, as AI becomes more embedded in frontline services, institutions must balance efficiency gains with transparency, trust, and robust governance to manage risks, especially in fraud detection, customer data handling, and AI bias.
Innovation: Assessing the JP Morgan AI Rollout
In a candid interview, Teresa Heitsenrether, head of AI at JPMorgan, shared insights into how the world’s largest bank is rolling out generative AI internally to enhance productivity, streamline operations, and set the stage for long-term automation. Currently in its early stages, the initiative is already reaching two-thirds of employees, who use AI tools for tasks like internal research and customer service assistance. JPMorgan’s phased strategy begins with boosting employee efficiency and gradually moves toward integrating AI with the bank’s proprietary processes, with the ultimate aim of enabling AI models to reason, act autonomously, and replicate the decision-making of seasoned financial professionals. Despite its ambitious vision, the bank remains cautious about significant risks, including data governance, hallucinations, AI ethics, customer trust, and reputational backlash, all of which could complicate its journey toward AI maturity.
For financial services and banking, JPMorgan’s approach serves as a blueprint, and a cautionary tale, of how AI can be scaled responsibly across a highly regulated, risk-sensitive industry. The bank’s emphasis on internal enablement before full automation shows a measured path that others in the sector may follow, particularly in areas like research, client service, and investment analysis. However, the long-term implications are profound: as AI begins to emulate complex human reasoning and decision workflows, it could redefine operational models, workforce structures, and competitive dynamics. For smaller banks and financial institutions, this is a reminder that AI leadership is being forged not in labs, but in real-world, high-stakes environments. While they may lack the resources of JPMorgan, they will eventually benefit from the tools, standards, and innovations shaped by these early adopters, once the dust of risk, regulation, and ROI begins to settle.
AI Blueprint
Today’s newsletter is sponsored by Anordea’s AI Blueprint. At Anordea, we advise CXOs and senior leaders across Financial Services, helping them tackle their toughest problems. With the rapid pace of development and change in the AI space, many leaders are faced with a paradox of how to build on and govern the technology that is as complex as it is powerful.
Our AI Blueprint solves this paradox quickly and effectively by giving leaders immediate insight into the state of AI across their organisation and industry, highlighting the opportunities and challenges which need to be addressed to maximise their AI opportunity in a safe and well-governed manner. Based on our leading expertise and ongoing research in the AI strategy, transformation and governance field, the AI Blueprint offers a fast and high impact solution to one of the biggest challenges facing leaders today.
Working with a dedicated member of our senior leadership team, the benefits of this process include ROIs in the strategy, governance, operational, financial and personal arenas. For those interested in exploring what our AI Blueprint involves, and how it can help your organisation gain a leading advantage over competitors, reach out to one of our senior partners here or contact us at info@anordea.com.
Who Should Lead Your AI Transformation?
As AI becomes a central driver of competitive advantage, every organisation embarking on AI transformation must answer a crucial early question: Who should lead it? While co-leadership models or steering committees can support progress, large-scale transformations typically require a single, empowered internal leader. The choice of this lead implementer, someone with strategic influence, operational reach, and sufficient bandwidth, will be one of the most important decisions a CEO makes in this process.
Who Are the Real Contenders?
In practice, the ideal leader often emerges from one of four CXO-level roles: the Chief Transformation Officer, Chief Innovation Officer, Chief Strategy Officer, or Chief Technology Officer. However, each option comes with trade-offs in scope, resources, and organisational fit. While it’s tempting to consider outsiders or consultants for the role, AI transformations must be driven from within. External experts can guide and advise, but true, sustainable transformation requires internal ownership and embedded authority.
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