By Yaacov Martin, CEO of Jifiti.
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As global online traffic shifts to AI, and AI agent-driven online sales hit new records, banks and lenders must adapt to a new reality.
We are witnessing the impending death of the traditional banking funnel. AI and AI-agents have been linked to billions of dollars in online sales. But this isn’t just a shopping trend; it's poised to become a fundamental shift in how credit is discovered. Large and medium banks and lenders who are not adapting to AI will be completely left out of this new game. The only way forward for lenders is to solve this discoverability gap by moving from consumer-facing channels to agent-facing technologies.
While most bank executives are focused on using AI to streamline operations and cut costs, the classic efficiency play, they are missing the more disruptive shift. AI is driving an efficiency revolution, but Agentic AI is driving a revolution of access. It is transforming the very 'front door' of lending. For banks, the risk isn't just about how you operate; it’s about whether you are even visible when an agent decides with which entity a customer engages and interacts.
AI Agentic Commerce is Already Here and Thriving
Salesforce’s 2025 holiday shopping report found that AI and AI agents influenced sales for a total of $262 billion in the U.S. during the 2025 holiday season. The season saw a record-breaking figure for online sales of $1.29 trillion globally, and $294 billion in the United States.
AI-driven embedded lending, and especially Buy now, pay later (BNPL) have become increasingly common options driving retail payment terminals and ecommerce sites. AI-embedded lending is changing not only how consumers spend their money but how banks and lenders offer credit. These options are partially responsible for holiday season numbers.
The Salesforce report found that year-over-year (YOY) sales growth rose to 4% in the U.S., and AI and agents played a role in a massive percentage of holiday shopping, accounting for 20% of all retail sales.
But what does this mean for banks and lenders who have not updated their digital infrastructure and still operate with legacy systems and traditional lending funnels? Invisibility.
When an AI agent picks the financing option for a shopper in milliseconds, the bank’s brand, reputation and credit terms become irrelevant, if their credit options are not discoverable. If an agent can’t ‘read’ your credit product, you don’t exist in that context.
The Invisible Funnel: Visibility and Credit Selection Pulled Away from Bank-Owned Channels
What online shoppers see today is heavily AI-narrow. Increasingly, they start their product search on AI tools like ChatGPT, Claude, and Gemini, before moving onto online retailers' websites. Ultimately, in the age of Agentic Commerce, the average shopper won’t even leave the AI agent’s environment to make their purchases. And lending is heading in the same direction.
In simple words, if you are not optimized for AI platforms and are not integrated with AI Agents via MCPs, the chances that tomorrow’s consumers will even see your financing offerings are significantly reduced. The good news is that getting inside this new AI funnel is becoming more and more accessible and cost-efficient through third-party platforms.
AI is being deployed at a dual-layer, and banks need both. Progressively, more consumers are no longer reaching banks via their websites, but through AI search channels like ChatGPT or Perplexity. Internally, banks are also using core lending AI tech to streamline fraud detection, underwriting and scoring.
AI Agent Capabilities in the Checkout
AI agents are not just integrated into webpages to provide users with recommendations. They are also deeply embedded in shopping cart and payment options.
In late January 2026, IBM reported that AI agents are already acting on behalf of consumers and businesses. They research, negotiate, and complete purchases on the users’ behalf, with often no humans in the loop. Companies like Visa with “Intelligent Commerce” and Mastercard with “Agent Pay” are moving forward to seamlessly integrate payments into AI agents’ shopping and purchase journeys.
Embedding payment and financing options within agentic AI journeys are designed to create frictionless customer experiences, drive revenue, and ensure relevancy in the agentic AI age. Banks and lenders that want to remain visible, relevant and competitive need to support agent credentialing, have the necessary data frameworks, and understand what is required from a compliance and regulatory standpoint.
Given the complexities, the high costs of building and managing AI systems in-house, and evolving regulation demands built around user data and AI technologies, many banks are outsourcing their AI agents and AI-ready systems to third-party fintech providers.
How to Get on ChatGPT, Gemini, and Genspark’s Radar?
At the end of the day, despite the inspiring advances and technological jumps that AI has taken, bank leaders only need to focus on two priorities. The first is how to get their data, products, and services read, recognized and recommended by AI platforms.
Retailers and banks cannot integrate their offerings directly on sites like ChatGPT or Genspark as if they were loading a product on Amazon or embedding their financing at a point of sale. However, they can make changes to their data structures and workflows to ensure AI agents are able to scan and digest their offerings. This means making all data machine-readable and all workflows digital.
READ MORE: AI Agents Cannot Open Bank Accounts. Three Moves Suggest They Will Not Need To.
To an AI agent, a PDF is a black box. What an AI agent will read more efficiently is data stored in APIs and structured metadata. Banks must translate their complex credit policies into consumable logic. This is the democratization of credit: making even the smaller banks’ loan terms as 'crawlable' and 'agent-ready' as global titans and fintechs.
Banks are used to consumers coming to their 'digital front door.' In the Agentic era, there will be no door. There will only be an agent acting as a proxy. If a bank only builds a great app, it is building a destination that ultimately no one will visit.
Using formats like Schema.org, for example, a bank’s webpage can clearly label data like product name, interest rate, fees, eligibility, and terms, in a way AI bots can read. Pages need to be crawlable and clean, not hidden behind logins or paywalls, and must load without content blocks.
One Integration Can Connect Banks with Hundreds of Thousands of Retailers
The second priority for banks is to make their products available inside third-party agentic commerce shopping and checkout systems. This may sound tricky, but it's not. Think of this as how airlines moved from phone booking to online platforms like Expedia or how hotels moved to Booking using APIs. You can think of APIs as software that connects two systems together, in this case, the API is the MCP (Modern Context Protocol) that connects AI agents to the bank’s system.
Banks shouldn't aim to build a thousand individual endpoints to participate in the age of agentic commerce. Instead, by leveraging partnerships with third-party providers that support both orchestration as well as AI agentic lending, lenders gain access to a 'universal translator.' This allows banks of all sizes, from smaller community banks to tier-1 financial institutions, to scale digital distribution instantly, providing the liquidity while the platform provides the connectivity to agentic commerce environments.
Lending orchestration platforms connect financial institutions with merchant networks. allowing banks to integrate with hundreds or thousands of retailers with just one integration, rather than building time-consuming and costly individual partnerships.
Together, this ecosystem allows banks to scale digital distribution to the place where today’s consumer is increasingly making their purchases, without managing dozens or hundreds of separate integrations.
Third-party technology partners that are built for compliance from the ground up typically manage the implementation of and adherence to key industry standards, helping institutions keep pace with regulatory expectations and security best practices.
Final Thoughts on Pivoting from ‘Consumer-Facing’ to the ‘Agent-Facing’ Reality
Online shopping channels and lending funnels have changed. While the capabilities, the technology, and AI processes are still being shaped, any bank or lender can develop a simple but effective AI digital transformation framework without compromising on critical compliance and regulatory oversight. This framework allows banks to pivot from consumer-facing systems to agent-facing technologies by making their loan programs and data sets AI-readable, and partnering up with the right third-party integration provider to reach new and existing customers.
