Despite growing enthusiasm for artificial intelligence (AI), its practically adoption within Switzerland and Liechtenstein’s banking sector remains notably limited.

According to a new study by Synpulse, which polled 26 banks and financial services providers in the two countries, there is a clear gap between interest in AI technologies and their actual deployments, with an interest-to-adoption ratio of 15:1 at the use-case level.

In particular, the study found that while 78% of respondents identified AI as relevant, only 5% have actually adopted AI. Even more telling, just 27% have at least one live AI solution in operation, while 35% have yet to launch any AI initiatives.

AI hotspots in banking operations

Nevertheless, the study highlights several operational areas as prime opportunities for AI integration, or what Synpulse deems “AI hotspots”. These are the functions and processes where AI demonstrates high relevance and strong expected benefits.

In retail banking, it names credit origination and documentation as the top AI hotspot. Currently, origination remains largely manual and document-intensive, resulting in slow turnaround times and inefficient allocation of skilled staff. In this context, AI offers significant opportunity through automation across document extraction, credit-file completeness validation, the generation of first-draft credit papers and term sheets, and covenant summarization.

In private banking, corporate action processing is identified as the top AI hotspot. These processes involve a lot of documents, tight deadlines, significant error costs, and voluntary events requiring precise interpretation.

According to the Synpulse report, the use of generative AI in this area could deliver approximately 50% full-time employee (FTE) savings within virtual corporate action (VCA) teams, alongside accuracy rates exceeding 95% in data extraction. Concrete AI implementations here include SWIFT message parsing, the creation of golden copy of announcements, auto-drafted client notifications, and position or entitlements reconciliation.

Beyond these primary sectors, other operations stand out as promising candidates for AI enhancement. In reconciliation, large break volumes continue to be cleared manually, delaying close and adding risk. AI-driven solutions could provide intelligent matching, break classification, and autonomous root-cause investigation.

Similarly, payment processing involves high volumes that generate constant exceptions demanding speed and accuracy. Relevant AI use cases in this area include real-time screening, fraud detection, and exception prioritization.

Finally, asset transfer processing presents its own set of difficulties, including unstructured instructions and multi-party steps that make transfers susceptible to delays and errors. Complex cases frequently require multiple interactions with several intermediaries. Additionally, emails and PDFs are processed manually, consuming valuable specialist time and exposing institutions to reputational damage and regulatory risks.

According to Synpulse, agentic AI could achieve 30% FTE savings in asset transfer team, while enabling over 98% in accuracy in data extraction.

AI adoption in finance surges

AI adoption in the Swiss banking sector stands in stark contrast to worldwide trends. According to a 2026 study by the Cambridge Centre for Alternative Finance (CCAF), which surveyed more than 600 financial institutions, AI vendors, and regulatory authorities, 81% of industry players have adopted AI at some level, with 40% reporting advanced AI adoption, including “Scaling” or “Transforming”.

Globally, AI applications in finance concentrate primarily on operational and back-office functions, with the most mature and widely adopted use cases being process automation, data visualization, and software development, with adoption rates of 79%, 75%, and 75%, respectively.

AI deployment is already demonstrating measurable improvements. Technology, data, and product departments reported the strongest gains, with 79% of respondents citing positive outcomes. Back office and operations followed closely at 75%, reinforcing the pattern of AI delivering tangible benefits where processes are well-defined and data is readily available.

Despite growing AI adoption, the CCAF study also highlights persistent challenges, especially around data quality, fragmented systems, technology and infrastructure challenges, and limited institutional capabilities.

AI visibility and shifting search behavior

Beyond improving operational efficiency, AI has emerged as a critical factor in how customers discover and evaluate financial services as soaring consumer adoption of conversational AI platforms like ChatGPT transformed these chatbots into key sources of information and, effectively, new channels for marketing outreach.

According to new research by Swiss software company hypt, usage of large language models (LLMs) for search surged dramatically from just 6% in 2025 to 45% 2026. At the same time, classical search engines for local recommendations declined from 83% in 2025 to 71% in 2026.

The shift from search engines to AI chatbots is expected to continue moving forward, with Gartner predicting a 25% decline in search engine volume by 2026 due to AI chatbots and other virtual agents.

Featured image: Edited by Fintech News Switzerland, based on image by thanyakij-12 via Magnific