Artificial intelligence (AI) adoption has increased significantly across organizations and industries over the past years. However, the breadth of adoption remains limited with many organizations still in the experimental phase, with implementation confined to specific functions.
According to the latest McKinsey Global Survey on AI conducted in Q1 2024, half of the 1,300+ organizations surveyed have adopted AI in just one business function. Moreover, 28% of organizations have yet to implement AI in any capacity, indicating that the journey to comprehensive AI integration is far from complete.
The study also reveals that organizations using AI, especially generative AI (genAI), typically utilize the technology in marketing and sales, product and service development, or IT.
In the banking sector, McKinsey emphasizes the need for institutions to move beyond experimentation and transform core business functions with advanced AI systems. This need is especially urgent as the global banking sector faces challenges including uneven labor productivity results, slowing revenue and loan growth, as well as increased competition from businesses beyond banking, such as private credit firms, fintech startups, neobanks, payment solutions businesses, and non-bank providers.
AI offers a solution to these challenges and puts banks on more solid footing in the years to come, particularly in boosting labor productivity as employees continue to delegate a growing number of routine tasks to increasingly sophisticated and capable AI systems.
The consulting firm outlines a comprehensive blueprint for banks to maximize AI value, emphasizing enterprise-wide integration and robust infrastructure development.
First, banks must view AI as a transformational tool for strategic priorities such as boosting revenue, differentiating the bank from competitors, and driving higher satisfaction for customers and employees. A bold, enterprise-wide vision is essential to realize the full value that AI can deliver.
Secondly, banks must focus on transforming entire domains, processes, and journeys rather than just deploying narrow and siloed use cases. While small-scale tools like chatbots or document summarizers offer incremental value, they fall short of delivering substantial business value.
To scale AI across the enterprise, banks need a robust AI stack powered by multi-agent systems. These systems should be capable of handling complex workflows, such as evaluating loan applications, that require processing structured and unstructured data.
Finally, banks must set up critical enablers to support their AI transformation. These enablers should include cross-functional business, technology, and AI teams along with a central AI control unit which coordinates enterprise decisions across functions, drives governance and adoption of standardized risk guardrails, and promotes the reusability of AI capabilities.
AI adoption surges
Usage of AI has increased significantly over the past years, with an especially strong acceleration in 2024. Between 2017 and 2023, global AI adoption remained relatively steady, hovering around 50%, according to the McKinsey Global Survey. However, in 2024, this figure jumped to 72%, with more than two-thirds of respondents in nearly every region reporting active AI usage.
In Switzerland’s banking sector, adoption remains moderate but reflects the global trend of accelerated growth in 2024. The EY Banking Barometer 2025 reveals that while 18% of Swiss banks reported having no plans to use AI in 2023, this figure declined to just 8% in 2024. At the same time, the share of banks that had implemented their first AI-based applications doubled, rising from 7% in 2023 to 14% in 2024.
The study, conducted in November 2024 among 100 banks in Switzerland, also found that 18% were conducting pilot projects last year, considerably more than in 2023 (12%). Almost all other banks were investigating applying AI in specific cases (21%, compared with 20% in 2023) or at least in general (38%, compared with 43% in 2023). Only 9% still said AI is not an issue, down from 18% in 2023.
Swiss banks identified several areas poised for active AI deployment moving forward, citing process automation (67%), regulation and compliance (57%), as well as IT development and data management (29%) as the most impactful applications of AI in the banking business.
Featured image credit: edited from freepik
