For the first time in six years, AI has been mentioned more times than ESG by the top executives of European banks when going through their 2024 annual results.i In fact, AI mentions have more than doubled since last year. This is a clear indicator of the mindshare AI enjoys today among the leadership of European banks.
AI adoption is well on its way within European banks across a wide variety of areas (see Figure 1). Well, this is just the nascent stage, with most use cases focusing on efficiency benefits. The use cases around AI will in due course become more complex and span multiple areas across the bank. This will open up a range of opportunities but will also present a new set of challenges to the leadership of banks across Europe. Leaders will need to reimagine their roles from that of decision makers to navigators of ambiguity, guides on ethics, and enablers of human-AI coordination.
Strategic alignment: AI with a tangible, measurable purpose
AI initiatives need to be aligned closely to business goals. The initiatives identified should be those that bring material value in terms of meeting business objectives; it is crucial that this value is assessed by bankers and not technologists. Leaders should not treat AI as a project for the next IT program in the bank but should see it as an instrument to reimagine customer experience, ensure regulatory compliance, and drive competitive positioning. Without a strategic view, even advanced and complex AI projects could end up as siloed experiments. In practice, this would require having AI embedded in the strategic planning process of every business unit and focusing on the high-impact, measurable use cases tied to core objectives, rather than dabbling with disconnected pilots.
Cultural transformation: Fostering a pro-AI mindset
Uncertainty on employment, job displacement, and degradation have come out as key concerns in a research on the impact of AI on banking employment in Europe.iii There is a real fear that AI will replace people and leaders will have to assuage these concerns. They need to impress upon everyone that the more likely reality is that the future of banking lies in augmented intelligence, where humans and AI systems work together. Implementing AI at scale requires a profound cultural shift within traditional banking organizations. It comes as no surprise that 64% of the CEOs believe that the success of AI will depend more on the people than on the technology itself.iv Top executives must therefore function as agents of change, promoting a culture that embraces AI rather than fears it. This involves educating employees at all levels about the benefits of AI, addressing the anxiety about job displacement, and demonstrating strong commitment to ethical use of AI.
Workforce management: Upskilling, reskilling, and retaining
AI is redrawing the skills required within a bank. It cannot only automate a host of repetitive tasks, but can also perform fairly sophisticated activities. This will have a direct impact on how employees do their daily tasks with AI, possibly completely changing the nature of their work. Leaders will need to focus on the optimum ways to upskill and sometimes completely reskill the employees. This will require banks to manage multiple tracks, with one track for most employees gaining a good understanding of broad AI fluency in the context of their jobs and other tracks for advanced technical and domain capabilities. AI skills are in high demand and will continue to be in the near future; banks will have to actively engage, challenge, nurture, and incentivize skilled employees.
Ethics and trust: Ensuring responsible AI
Any AI-related conversation in Europe will have the themes of trust and ethics come up continuously. It is no surprise then that in a survey of 30,000 Europeans, when it comes to building AI trust, data security and confidentiality ranks as a top priority (66%) and ranks higher than concerns like track record of accuracy (59%) or transparency of AI decision-making process (57%). It is imperative to have a ‘trustworthy AI’ that is aligned to societal values, transparent, explainable, auditable, accurate, and with required human oversight. Leaders should involve the banks’ compliance teams early on, implement strong AI governance frameworks, layout clear ethical guidelines, ensure bias testing, and put in place review processes for AI models. Banks should also consider establishing an ethics review board having cross-functional oversight.
Breaking silos: Collaboration between technology and business
AI spans across all departments and functions and needs close collaboration among all stakeholders. In a pan-industry survey, 65% of the CEOs were of the view that an organization’s success in AI adoption is related to the quality of the collaboration between business and technology. Traditionally, banks have been somewhat rigid, with each department working in silos. Leaders should establish cross-functional teams for AI projects, bringing together stakeholders from compliance, risk, operations, and front-line business areas alongside data engineers. This ‘one team’ approach will ensure that AI solutions address genuine business pain points and can be smoothly integrated into daily operations. The management should encourage and ensure that senior executives share common goals that incentivize them and their teams to collaborate and enable agility with demonstrable value realization across AI use cases.
Regulatory complexity: Navigating the GDPR and the EU AI Act
Europe has one of the most stringent regulatory environment in the space of data and artificial intelligence. Managing regulations even on a normal day is not easy and AI brings its own set of complexities. The main relevant regulations are the General Data Protection Regulation (GDPR) and the EU AI Act. GDPR imposes strict restrictions on the use of personal data and grants individuals’ rights regarding automated decision-making and hence implicitly restricts full machine-driven decision making for use cases that may be sensitive in nature. The EU AI Act adds another layer of complexity by classifying AI systems into risk tiers having corresponding obligations. For example, a high-risk system will have strict obligations around robustness, transparency, audits, and oversight. Banks can turn these regulations into advantage by ensuring strict compliance and building an environment of trust among all stakeholders, most of all end users and partners.
Steering through the AI revolution
Leaders have a critical role to play in helping their banks navigate as AI reshapes Europe’s banking landscape. If the journey of adopting AI at scale is overseen well, it will unlock the immense potential this technology has to offer. The map for the journey is clear: invest in people and culture; set up the guardrails; enable collaboration; uphold the vision. Successfully negotiating these exciting times can help a bank be well placed for the fast-emerging AI-powered era.
