Consumers are increasingly utilizing AI-powered chatbots to address everyday inquiries and facilitate decision-making processes. This trend requires banks and financial institutions to enhance their visibility on prominent AI chatbots, including ChatGPT and Gemini.

A recent research study conducted by experts at the Lucerne University of Applied Sciences and Arts (HSLU) examines the complexities of large language model optimization (LLMO) and AI visibility. The study employs the case of Bernerland Bank to illustrate how regional banks, smaller institutions, or even fintech startups can adapt to this evolving landscape.

AI chatbots are gaining prominence and experiencing increased adoption for search-related tasks. A 2025 Claneo study on search engine optimization (SEO) surveyed 2,000 individuals in Germany and the US and found that while traditional search engines still dominate search with a 67% market share, AI chatbots follow behind at 20%.

The study also reveals that for simple information, Google leads at 50.5% while for complex topics, AI chatbots, at 38.6%, are almost on par with Google at 40.3%. This highlights that AI chatbots are highly relevant for complex information needs and are emerging as a strong alternative to traditional search engines.

This trend underscores the growing significance of LLMs for banks, with LLMO emerging as a critical component of modern digital marketing strategies.

Bernerland Bank’s AI search strategy

For smaller institutions like Bernerland Bank, blog posts present a unique opportunity to deliver high-quality, well-structured, and optimized content on a bank’s websites, thereby achieving high engagement and delivering value. In particular, posts that simplify complex topics, address practical questions, or maintain a clear regional or thematic focus tend to perform well on AI systems.

Bernerland Bank is a regional bank with locations in Emmental, Oberaargau, and Seeland, providing services encompassing payments, savings, investments, financing, and retirement planning. Recognizing its strong local presence, Bernerland Bank is strategically enhancing its visibility on AI chatbots and AI search extensions, focusing specifically on its core regions rather than a wider audience.

A pertinent example of this strategy is its blog post titled “Erbschafts- und Schenkungssteuer in Bern und Solothurn – ein Wegweise.” This comprehensive guide explains the intricacies of inheritance and gift taxes within the Swiss cantons of Bern and Solothurn, regions where Bernerland Bank operates.

In the post, the bank demonstrates its regional expertise in the Bernese Oberland area and positions itself as a competent partner for local financial matters. Concurrently, it addresses prevalent customer inquiries and concerns, simplifying a complex tax topic in an easily comprehensible manner. This establishes trust, expertise, and most importantly, a starting point for future consultations on topics such as wealth planning, estate planning, and inheritance investment.

The post has garnered significant attention, and has been directly cited as a source in key AI systems, with its content frequently quoted when questions are asked about gift tax in the Canton Bern.

Results from Peec AI, an AI search analytics platform for marketing teams and brands, confirm this visibility. When prompted with “gift tax Canton of Bern”, Bernerland Bank’s website is recognized as a source and cited by AI systems to answer relevant questions.

The post performs well on traditional search engines as well. In the last year, the article appeared over 120,000 times in Google search results and was clicked approximately 4,500. It was found particularly frequently in searches such as “gift tax Canton of Bern,” “inheritance tax Solothurn,” or “calculate inheritance tax Canton of Bern”, demonstrating that it is reaching the bank’s targeted audience.

Best practices

Bernerland Bank and its Bern-based inbound marketing agency Nordfabrik have developed a set of best practices for LLMO. This strategy involves:

• Identifying topic potential and areas where the bank currently lacks visibility on Google and in AI systems;
• Defining a content strategy and determining the necessary content to build visibility, such as blog posts, FAQ pages, product pages, and explanatory guides;
• Integrating SEO and LLMO into the overall marketing strategy, training employees to create AI-friendly content, and actively sharing it on platforms like LinkedIn;
• External reinforcement with targeted public relation activities, through, for example, articles in regional media; and
• Measuring success with key performance indicators (KPIs) such as changes in visibility, mentions in AI responses, or the development of reach.

The HSLU academics notes that while new AI search systems may present challenges, especially for smaller providers, they also offer new opportunities to offer deliver clear, credible, and relevant content. This allows these players to position themselves as leaders and experts in specific niches and domains.

For large institutions, LLMO marks a shift in thinking, moving away from broad messaging and towards clear profiles and recognizable strengths. This approach helps establish them as the “best bank” across various topics, including retirement planning, mortgages, and investments.

The future of search

Consumers are embracing LLMs at a fast pace, a shift that is causing a decline in clicks and website traffic from search engines.

Intuit Mailchimp, an email software platform, has seen a steady drop in web traffic since AI-assisted search started allowing people to gather information about the company and its products without visiting its sites, according to Ellen Mamedov, global director of SEO at Intuit Mailchimp.

For Back Market, a marketplace for refurbished electronics, LLM searches only account for 0.2% of traffic. However, this traffic was 470 times higher in mid-2025 than it was a year prior, Joy Howard, CMO at Back Market, told the Wall Street Journal last year.

Back Market is now changing its SEO practices accordingly. It is focusing more intently on updating individual product pages, using a more conversational tone which LLMs like ChatGPT prefer.

In addition to content, technical search elements, such as page loading speed and snippets of code used to track user activity, are more crucial for bots of AI-driven systems than for traditional search engines, according to Mailchimp’s research. These bots are designed to assimilate and process information as swiftly as possible, which is why they favor websites that load faster and which are optimized for machines rather than human readers.

The shift 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.

This comes amid the rise of so-called “zero-click search”. A survey conducted by Bain and Dynata polled 1,000 people in December 2024 and found that 80% of consumers now resolve 40% of their online search queries without clicking any links. This underscores a structural shift in how information is consumed online, reflecting how AI-powered interfaces are reshaping search.

Featured image by MH Stock on Freepik