Recap
AI shatters a decades-old channel model. The banal truism “meet the customer where they are” is code for “spend more on mobile.” Vendors have met the demand with a flexible device format, or more likely, tacking on a separate platform. The strategy is wrong; the solution is wrong.
Each app is a channel (a delivery mechanism and interface). An AI tool is a channel and a platform (an infinitely scalable software tool). The average consumer has a new experience; the proficient consumer gains immense power to structure and manipulate information.
Banks are not built for a channel-as-platform. They are limited by by the technology and business siloes created for reaching customers over decades.
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US consumers most often use mobile to manage their bank accounts and it's most common with millennials and Gen Z. The Gen Alpha data isn’t real; see the thought experiment below. (Source: American Bankers Association-Morning Consult, Fintech Notebook)
“Mobile is the forever future” is absurd. A thought experiment: What will Gen Alpha’s channel use look like in 2030? 100% mobile?
That would be like saying in 2006 that “online banking will always be the future.” “Mobile-first” and “mobile-native” have been industry shorthand for years. Shorthand today is suddenly “AI-native” and “AI-first.”
Banks are stuck on the mobile era. The 2026 channel model doesn’t exist.
This is the second article in a series that explores how AI will influence consumer expectations and behavior in banking.
“It feels like the world is accelerating, but not to benefit society as a whole…
Society breaks when disruption touches the social fabric, and breaks faster when society has no time to adapt.”
Deep dive
A channel is a delivery mechanism for something. In the digital realm, it’s an app. In banking, it’s an app for financial information and tasks. Market sizing is easy: Age group links directly to device ownership and the adoption of new technologies. Demand changes with demographics. The business case is clear.
With AI, the frame is cracking. Trust in AI varies widely, and the link to adoption is loose at best. Strategies for customer-facing AI aren’t as clear as saying, “our customer strategy is our channel strategy is our mobile strategy,” and link it to Gen Y and Gen Z consumers. And even that gets murky: Gen Alpha hasn’t played out.
Channel-as-platform
Product scope and user control are clear for the average app (a channel; a delivery mechanism and interface). Features are built or assembled by a developer, and the output is purposely constrained.
Chime, for example, is an “average app.” For an AI app like ChatGPT, product scope and user control are nearly limitless.1 Prompt-and-response pairs are constrained only by the model. Users can customize a system by writing in English.
ChatGPT is also a platform (an easily customized tool that hosts other apps) and a channel-as-platform. Its context vastly exceeds data that’s retrievable from a traditional database, and it it can compete with traditional apps directly. That should terrify companies whose apps just furnish a laundry list of features.2
…channel-as-platform…should terrify companies whose apps just furnish a laundry list of features.
Channel (mobile) strategy cannot be “build or buy a new app that has more features, or jump on the bandwagon for something flashy and new. The question to address is, “what is a channel in 2026, and what’s its value?
The commercial value of customer-facing AI
The commercial value of an AI tool is hard to measure; it’s unproven as a customer acquisition and engagement tool. Usage can be measured broadly (active users, the frequency and outcome of customer behaviors). But with AI, it’s hard to say what exactly consumers are doing, what it might be worth for a platform participant, like a bank, and the cost of participation in the first place. Without a prompt history and results, it’s a bet. Nearly nobody knows the odds.
Share of consumer ChatGPT messages broken down by high level conversation topic. Values are smoothed over the trailing 28 days (source: OpenAI/NBER). See “Resources” at the end of the article.
The AI labs do a lot of research on AI use and periodically publish findings. The data her can’t be split into financial services use cases but it’s a hint of what consumers are using these tools for.
From the notebook
Traditional consumer surveys measure legacy channels pretty effectively, since they are convenient, discrete frames for the delivery of certain products and services; They fit bank technology and operational siloes.
They don’t flexibly adapt to how consumer behavior when for a new channel, or a channel defined broadly (a delivery mechanism and interface).
“Which method do you depend on most to manage our bank account?” / Online, mobile, branch, ATM, etc. reinforces the channel model that this article argues against.
Where is this going?
Nobody knows how front-end AI will play out in financial services. It will not just be to drop bank statements into ChatGTP.3 Nor will it be a magical PFM tool, or a chat-only interface.4 Some fintechs and enterprise software companies have thought through how AI might work as a channel, or part of a channel (a delivery mechanism and interface). AI as a platform is not so clear. Part 3 will focus on what’s being done now, what’s working, and address questions that are still open.
Resources
Thanks for reading Fintech Notebook: A weekly GTM intelligence briefing read by fintech operators, strategists, and investors. Connect on LinkedIn or visit my website @ tylerbrown.co.
AI disclosure: The author used AI to assist chart creation.
1 Sometimes that’s a problem: “The user’s path of least resistance is to ask short, easy questions, hope for decent answers, and move on. If the answers aren't clear, or if they are formatted in a way that’s hard to digest, the product fails.” (July 9)
2 Guilty: token turnkey apps that say “we have mobile banking but don’t care about it that much.” That is channel capitulation, not strategy.
3 Or AI of choice - Gemini, Perplexity, Claude. ChatGPT is a convenient example, not a pattern or endorsement.
4 I’ve criticized both. See footnote no. 1.