AI roles are the most secure and promising career path in banking
Financial institutions are undergoing a fundamental shift. The pace isn’t slowing down, it’s accelerating, and it’s centered around artificial intelligence. Banks are cutting staff in some areas, but AI-related hiring is not only holding steady, it’s increasing. That tells you where the future of banking is headed. And it’s not vague or speculative. This is active, deliberate transformation. According to Evident, a financial AI benchmarking firm, the top 10 banks increased their AI staffing by 13% in just six months. These banks now hire one AI specialist for every 50 new roles. The growth is real and sustained.
Let’s break that down. AI developers, data engineers, and infrastructure experts, these people are now essential. Banks aren’t buying AI hype. They’re investing where they see long-term impact: cost-reduction, faster decision-making, better risk management, scalability. While banking headcount is declining, roughly 3% down across the industry over the last two years, AI roles are up double digits. That divergence signals one thing clearly: if your job builds, manages, or scales AI, you’re in demand.
For senior decision-makers, the message is straightforward. Don’t waste time. Move resources where they generate momentum. Redirect investments toward people and systems that build leverage, and AI isn’t optional anymore, it’s foundational.
Alexandra Mousavizadeh, CEO of Evident, put it bluntly: “AI roles may be the only safe jobs in banking right now.” That’s just where the sector is heading.
Leading banks are rapidly expanding their AI recruitment and delivery capabilities
The AI leaders aren’t waiting. JPMorgan Chase, Wells Fargo, and Citigroup are deploying capital and hiring forcefully in AI. Same goes for HSBC and Barclays in the UK, and BNP Paribas and BBVA in Europe. This isn’t a side project. These are full-scale investments with expected returns. Right now, the top 10 institutions by AI headcount aren’t just hiring more, they’re seeing more use cases come to life. More importantly, they’re seeing measurable returns.
Evident’s recent findings show this clearly: top AI-enabled banks use AI in twice as many parts of their business compared to slower movers. They’re also 1.5 times more likely to report actual return on investment from those deployments. That’s critical. Because in AI, execution, not intention, is what drives economic impact.
The competitive gap in banking is no longer just about capital access or geographic scale. It’s about how well you operationalize AI. That means hiring data talent and having the architecture, teams, and leadership alignment to roll it out at scale across the enterprise. Hiring is just one dimension. You also need the ability to move that talent into systems with real delivery pipelines. Otherwise, you’re just cash burning behind competitors who’ve got AI already running in production environments.
Executives should respond to this not with hesitation, but with urgency. Banks that are slow to commit won’t stay competitive. The leaders are evolving fast, and that advantage compounds.
Strategic investments in AI leadership
When a bank trains 200 of its most senior leaders in artificial intelligence, it’s not just preparing for disruptive change, it’s taking active ownership of it. Lloyds Banking Group is doing exactly that. This is top-down transformation with strategic intent.
The bank partnered with Cambridge Spark, an AI skills training provider, to embed AI capability directly into its leadership ranks. That’s smart. You don’t get lasting traction by isolating AI initiatives in technical teams. Senior executives need to understand what these systems can do, and can’t do, to make fast, informed decisions. It de-risks the roadmap and reduces friction between strategy and execution.
At the same time, Lloyds is scaling its infrastructure by deploying Google Cloud’s Vertex AI, a development platform for machine learning and generative AI. Over 300 data scientists at Lloyds are now using it. That means the tooling exists, the technical teams are engaged, and the leadership is equipped to clear organizational blockers. When all three align, you get velocity.
This level of integration is what defines serious players in the AI space. The contrast between shallow pilots and scalable platforms is huge. Executives who don’t prioritize both skills and systems will be chasing efficiency while competitors are building it into the structure.
Banks at the early stages of AI adoption are under increasing pressure
The gap between the banks moving fast on AI and those just getting started isn’t getting smaller. It’s getting wider. That’s a problem for late adopters. The time to experiment quietly has passed. AI is moving into production, and the results are showing up in ROI, operational gains, and customer experience.
Evident’s CEO, Alexandra Mousavizadeh, made it clear: banks still trying to define their strategy or struggling to hire technical talent are falling behind. Meanwhile, institutions already executing well are doubling down. They aren’t scaling cautiously, they’re scaling with precision backed by data.
The pressure is real. Enterprises that delay full integration of AI simply won’t be competitive operationally or financially. Leaders need to realize they’re not being compared to where they were twelve months ago, they’re being compared to what the best banks are deploying now. And that standard keeps rising.
AI and machine learning are becoming mainstream in financial services
Artificial intelligence is not new to UK finance anymore. It’s already inside most firms, shaping operations, building efficiencies, and supporting compliance. According to a joint survey by the Bank of England and the Financial Conduct Authority, 75% of the 120 financial services firms surveyed are already using AI in some form. That’s not early adoption. That’s normalization.
And it’s not limited to large banks. The applications stretch across sectors, credit risk models, fraud detection, customer service automation, and investment analytics. These are critical workflows. AI doesn’t replace people in these areas; it makes them faster, more consistent, and scalable.
From a regulatory perspective, this level of uptake signals something important. Supervisors see AI as a permanent fixture in financial processes. Oversight frameworks will evolve, and firms that don’t have a clear AI governance model in place will find themselves exposed to reputational and operational risk.
Nuance to Consider:
For executives, the core concern is moving AI from isolated pilots to fully governed infrastructure. That means knowing where AI is used, how it’s trained, and what fail-safes exist. As AI becomes more embedded, the boardroom should be asking not just what AI can do, but how accountable, traceable, and auditable those decisions are.
Focused GenAI initiatives illustrate targeted innovation and governance
Generative AI is catching more attention, and not just from technologists. Decision-makers are actively exploring where it makes business sense. ING is a clear example. The bank’s CTO, Daniele Tonella, describes their GenAI approach as “conservatively aggressive.” That’s not about buzz, it’s about aligning experimentation with real business outcomes.
ING narrowed GenAI exploration to five areas: customer onboarding, contact centers, enhanced due diligence in wholesale banking, hyper-personalization in retail, and internal engineering tools. These are high-impact domains that touch revenue, compliance, and user experience. Importantly, every deployment is subject to strict governance, overseen by the Chief Operating Officer. That removes random experimentation and channels focus.
Challenger banks are moving quickly too. Zopa Bank, under CEO Jaidev Janardana, is applying GenAI to speed up software development and improve operational response times by better understanding customer sentiment. This kind of execution elevates GenAI beyond novelty and makes it an accelerant for real productivity.
Key takeaways for decision-makers
- AI roles are recession-resistant in banking: Banks are cutting overall headcount but increasing AI hiring by double digits, signaling where real strategic value lies. Leaders should prioritize AI-focused workforce planning now to future-proof talent pipelines.
- Top banks are scaling AI with measurable ROI: Institutions leading in AI hiring are realizing 2x more use cases and 1.5x greater ROI. Executives should accelerate AI investment and implementation to stay competitive with top-performing peers.
- Leadership training and tooling unlock delivery at scale: Lloyds is equipping 200 senior leaders with AI skills and deploying Google’s Vertex AI across data teams. Leaders should align technical platforms and executive capability to drive faster, integrated adoption.
- Lagging on AI execution increases strategic risk: Banks slow to define and staff their AI strategies are falling further behind. Boards should treat delayed AI adoption as a business risk and move aggressively to close execution gaps.
- AI is already mainstream in UK financial services: With 75% of firms using AI, oversight is increasing, and expectations around governance are rising. Executives should ensure all AI use is documented, governed, and audit-ready to meet evolving regulatory standards.
- GenAI is being applied with targeted governance: ING and Zopa show how GenAI delivers value when paired with clear focus and senior oversight. Leaders should apply GenAI in defined business areas under strong controls to ensure efficiency without introducing risk.
