In a recent survey by Catalant consultant Ted Perkins, 60% of 500 consumers reported using AI in financial product decisions. When someone asks an AI assistant for a credit card recommendation or HELOC rate, it returns an answer instantly. Your firm is either in that answer or it isn’t.

This shift is already changing how financial services demand is formed. AI is no longer a research aid; it is increasingly acting as a gatekeeper, determining which products are even considered. This will meaningfully affect how financial services firms market themselves in a world where AI gatekeepers are already picking winners and losers.

Recently, Jeff Barden, Managing Director of Financial Services at Catalant, interviewed Ted, a consultant and former banker who helps organizations commercialize advanced technologies, including agentic commerce tools, to explore the implications of this shift and how firms can succeed in this changing landscape.

AI is the new ‘first filter’

For decades, financial institutions optimized for human behavior. Brand investment, SEO, and distribution strategy were built around how people search, compare, and decide.

That model is ending.

Consumers are already using tools like ChatGPT and Gemini to compare credit cards, evaluate banking products, and assess insurance options. AI systems return ranked responses based on how well available data matches the query. If a product does not surface, it effectively does not exist.

These systems evaluate offerings using training data and retrieved content. The process is opaque, varies across models, and is largely disconnected from traditional marketing signals. Brand strength and visibility in legacy channels do not guarantee inclusion.

The core problem for financial institutions is straightforward: most have no idea how they appear in AI-generated recommendations or whether they appear at all.

A new paradigm for digital visibility

Traditional digital visibility was designed for humans. AI changes that.

AI agents do not respond to brand campaigns, design, or reputation in the way people do. They perform a clinical evaluation, analyzing terms, structure, constraints, and product fit. This decouples brand marketing from product discovery.

Competing in this environment requires new practices:

• Generative engine optimization (GEO): Structuring product information and content so large language models can accurately interpret and recommend offerings. GEO is distinct from SEO, which targets human-facing search behavior.
• Headless legibility: Ensuring product data is accessible and parsable by AI agents, even when there is no human-facing interface involved. If information is not structured for machines to read, it is effectively invisible.

Listen: Ted Perkins evaluates the difference between human search and AI evaluation and how that leads to a focus on GEO.

AI recommendations are already diverging from legacy channels

The consequences of failing to adapt to GEO are not theoretical. As a real-world example demonstrates, AI models are already making editorial choices with profound financial implications.

When multiple leading AI models were asked the same question, “What are the best travel rewards credit cards?” the results varied significantly. Brands such as United, Hyatt, and Citi appeared inconsistently across models. Marriott Bonvoy, a top pick on traditional review sites like NerdWallet, did not appear in any of the responses.

This matters for revenue. Ted’s research found that 30% of consumers already use AI assistants to identify or compare credit card options, a figure that is likely to grow.

The takeaway is clear: visibility in traditional channels does not transfer to AI. High search rankings and prominent placement on review sites mean little if an AI model does not surface the product. The logic behind these recommendations is hidden, with no clear way to understand why one product appears and another does not.

Credit cards are just one example.

Beyond retail banking

This is not a consumer-only issue. The first-filter effect applies wherever decision-makers turn to AI:

• Wealth management: A prospective client asks an AI for a financial advisor in their area. The model returns a shortlist. Advisors not included are never evaluated.
• Commercial banking: An AI assistant evaluates treasury services based on available data, not relationships or brand recognition.
• Insurance: An independent agent queries an AI for a complex policy. The carriers surfaced depend on how clearly product details are structured for machine interpretation.

The implications extend across financial services. The distribution model itself is shifting.

Three no-regret moves

The AI landscape will continue to evolve, but waiting for certainty is not a strategy. There are practical steps financial institutions can take now that remain valuable regardless of how the market develops:

1. Assess current visibility: Understand how your firm, brand, and products appear across major AI models today.
2. Benchmark competitors: Identify where AI systems favor alternative offerings and why. These gaps are often structural, not substantive.
3. Build the capability to adapt: A one-time audit is not enough. Firms need ongoing intelligence into how AI agents evaluate them and how that evaluation is changing.

Listen: Ted Perkins outlines the uncertainties in the AI landscape.

Is your business ready?

Generative AI is already shaping how financial decisions are made. The shift is subtle, but the consequences are material.

The question is straightforward: do you know how your firm appears to AI systems today and whether you appear at all?

Firms that understand and optimize for this new reality will stay relevant. Those that wait risk being filtered out entirely.