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Before chasing AI, Bank of America wants banks to fix their data first

A lot of the conversations around artificial intelligence tend to circle back to one question: which institution has built the smartest, most capable model?

Bank of America argues the model quality isn’t the point; an AI feature is only as reliable as the data behind it. That reliability determines whether AI can be trusted to support decisions or should be kept out of high-stakes ones entirely. 

Bank of America’s thinking on AI comes into focus through three perspectives: Matthew Davies, Head of Global Payments Solutions at Bank of America’s warning about fragmented data, EricaAssist’s role as an employee copilot grounded in years of client history, and CEO Brian Moynihan’s caution toward frontier models. All point to the same philosophy: AI should be treated as a supportive step in the process, not the one making high-stakes decisions. 

Capital misallocation is the real risk

Bank of America has built its AI strategy to avoid the trap of treating AI as a shortcut to efficiency, an approach that often results in costly, misguided AI investments. Davies notes that the pressure every company now feels to adopt AI fast or risk falling behind is itself the real danger. “The biggest risk and challenge is misinvestment rather than underinvestment,” he says. 

Bank of America has traced that misinvestment risk back to fragmented data scattered across ERP systems, treasury platforms, bank portals, and acquired businesses. Because these systems speak different languages, even the most advanced AI layered on top struggles to reconcile the underlying information. Davies emphasizes that without high-quality, standardized data, there’s no real foundation for automation, forecasting, or any AI solution built on top of it. Tearsheet’s recent reporting on Intuit Credit Karma echoes the same idea. Rather than building standalone AI assistants, the company built them on a shared view of a customer’s financial life, recognizing that better AI starts with better data and context.

For Bank of America, improving data quality delivers value long before any AI enters the picture. Standardized data alone reduces the manual reconciliation, duplicate entries, and reporting errors that consume hours of finance teams’ time every week. Davies outlined the bank’s order of operations: standardize the data first, automate repetitive tasks next, and only then pursue bigger AI initiatives.

EricaAssist: Inside Bank of America’s own data-first playbook

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Why banks can’t policy their way out of shadow AI

Employees at financial institutions are using AI tools their employers never approved for tasks that touch sensitive customer data.  This unsanctioned use of AI, often called shadow AI, is not a toothless problem. In May 2026, an employee at Pennsylvania-based CB Financial Services, parent company of Community Bank, uploaded a file containing customer names, Social Security numbers, and dates of birth into an unauthorized AI application while preparing a presentation. The employee bypassed the bank’s approved AI tool for a personal account on a personal device.  The bank caught the exposure quickly enough to get the data deleted before it could be used to train the vendor’s model. Notably, the bank already offered a sanctioned AI tool. The employee simply chose not to use it.  The instinct inside most banks and credit unions is to treat this as a policy failure and respond with tighter restrictions, more monitoring, and firmer language in the acceptable-use policy. But that response gets the diagnosis wrong, according to Corey Gross, VP and Head of Data & AI at Q2 Holdings. “When employees bypass a sanctioned tool, they’re telling leadership teams that the approved option isn’t getting the job done.” In his view, the root cause sits upstream of governance entirely: institutions are buying AI tools without first understanding the workflows those tools are meant to support. Instead of writing stricter acceptable-use policies, Gross suggests the more useful question is why employees felt they needed to go around the tool they were given in the first place. “It’s rarely a governance or compliance issue,” he said.

The distinction sounds subtle, but it points to a different set of priorities for any bank trying to scale AI responsibly. It suggests that starting with the design of the work itself is more critical than the framing of the rules that surround it.

Workflow redesign has to come before the AI rollout

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Intuit Credit Karma wants to answer the question every PFM app avoids

Intuit Credit Karma is building AI-driven optimization systems to take the guesswork out of recurring financial decisions by helping users determine their best next move.

With the launch of Credit Karma Intelligence, Debt Assistant, Refund Assistant, and Paycheck Assistant – all sitting on Intuit’s Consumer Platform – the company is creating a suite of AI-powered tools that tackle different financial decisions while drawing on a shared, evolving understanding of a customer’s financial life.

Gurpreet Singh, head of product at Intuit Credit Karma

“Financial decisions don’t happen in isolation,” says Gurpreet Singh, head of product at Intuit Credit Karma. “A recommendation for paying down debt, allocating a tax refund, or using an upcoming paycheck only makes sense when it’s informed by someone’s broader financial situation, including their income, cash flow, existing debt, spending patterns, connected accounts, and the goals they’ve told us they care about.”

Integrated AI-powered financial platform: Singh emphasizes that the firm didn’t build Debt Assistant, Refund Assistant, and Paycheck Assistant as standalone AI tools. Instead, they were built to support the financial decisions people make throughout the year as they manage debt, get paid, receive a tax refund, and make trade-offs between competing priorities. By grounding each experience in the same understanding of a member’s financial situation, goals, and history, the platform can deliver guidance that’s consistent across those moments instead of treating each one like a new, standalone problem.

“We’re not building a collection of individual AI tools, but an AI-powered financial platform that understands where someone stands, adapts as their situation changes, and delivers guidance that’s grounded in their financial data,” says Singh. “That’s what an integrated ecosystem makes possible.” 

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AI, bank CEOs, and the emerging jobpocalypse debate

16,000 jobs are being lost per month due to AI, according to Goldman Sachs data.

Bearing the brunt of this augmentation-automation mindset are Gen Z, who are caught in an untenable “low hire, low fire environment”, according to Federal Reserve Chair Jerome Powell. 

But looking at communication coming out of Wall Street, it seems like AI is only making bankers faster, better, and cooler at their jobs. 

In today’s story, we look at murmurs and public addresses coming out of Wall Street to see exactly how the C-suite is planning to tackle AI-driven mass unemployment, also known as jobpocalypse. 

Standard Chartered CEO says what all bank CEOs are thinking

In an ill-timed slip-up that became a PR nightmare, Standard Chartered CEO Bill Winters,  a veteran of 11 years at the bank, described its efforts to streamline operations in a way that drew criticism: “It’s not cost-cutting. It’s replacing in some cases lower-value human capital ​with the financial capital and the investment capital we’re putting in.”

The clinical term hit a public nerve. Winters has since issued an internal memo as well as a public-facing apology for his choice words, while also stating that most outlets reporting on the story have taken his statement out of context. Here is the statement reproduced in full:

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Micro Case Study: How American Express is underwriting AI agent error to unlock trust in $trillion-scale agentic commerce

The big question

If AI agents can execute payments, what guarantees that they execute the right payments?

The move

American Express has launched Agentic Commerce Experiences (ACE) Developer Kit, which formalizes and verifies user intent before any transaction takes place. The firm has also introduced what it calls an “industry-first” protection against AI agent error, agreeing to cover eligible transactions when an agent executes an authorized but unintended purchase.

For example, a user asks an AI agent to book a “quiet hotel room under $250”; the agent finds a deal and completes the booking, but if it’s next to a busy street, the transaction is valid yet misaligned with intent.

How it works

The new ACE Kit shifts payments from simple authorization to intent-driven execution:

  • User intent is captured as a structured, verifiable, and enforceable input.
  • That intent is authenticated and tied to tokenized credentials before any transaction is initiated.
  • Agents can transact on behalf of card members only within clearly defined, authenticated intent and control layers.
  • Amex extends purchase protection into agent-executed transactions.

Instead of resolving disputes after the transaction, the system aims to reduce ambiguity before execution.

“The model includes card member enrollment and authentication and gives card members the ability to manage controls directly in the Amex app – using structured intent, spend limits, merchant preferences, and tokenized credentials so the agent can only act within clearly defined boundaries set by the card member,” noted Luke Gebb, EVP and Head of Global Innovation at American Express. 

“The core of our approach is that intent is not treated as a loose instruction – it’s treated as a structured, verifiable representation of Card Member intent that the system can evaluate and enforce,” he added.

How is this different?

Traditional payment systems answer one question: Was this transaction authorized?

Agentic commerce introduces a harder one: Did this transaction reflect what the user actually meant?

That gap between execution and intent is emerging as one of the weakest links in AI-driven commerce.

McKinsey estimates that agentic and AI-driven commerce could generate trillions of dollars in economic impact by the end of the decade, but only if trust in automated execution scales alongside it.

Amex’s move directly targets that trust layer. Its closed-loop network provides end-to-end visibility across users, agents, credentials, and transactions, allowing it to link intent, execution, and liability within a single system.

Why it matters

By underwriting agent error, Amex is enabling AI-driven payments, but also pricing and absorbing a new category of risk.

That changes the equation for adoption. Agentic commerce won’t scale simply because agents can transact; it can scale when users trust that those transactions are executed correctly.

In the Chart: Amex’s take on securing the agentic commerce stack

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Lili CTO Liran Zelkha on building AI that disappears

The fintech industry has spent the better part of the last two years racing to add AI to its products. Chatbots have been bolted onto banking dashboards. Summaries have been appended to transaction histories. Assistants have materialized inside apps that users open, at best, once a week. Liran Zelkha, co-founder and Chief Technology Officer of Lili, a financial platform built for small business owners, thinks most of this activity is pointed in the wrong direction. Zelkha has spent years thinking carefully about the relationship between technology and small business owners: customers who are skilled at their craft, pressed for time, and rarely interested in learning a new interface. This customer profile has shaped the architecture of Lili from the beginning, and it informs Zelkha’s view of where AI in finance could genuinely move the needle: The goal is not a better in-app AI experience. The goal is to make a company’s financial capabilities available through whatever AI the customer has already chosen to trust. “The business owner shouldn’t have to learn our app to get value from us,” Zelkha says. “They should be able to ask their AI companion about their cash flow and get a real answer, backed by Lili.” …

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How agentic commerce is making execution, intent, and credit actionable inside payments

Agentic commerce is minimizing the distance between execution, intent, and credit within payments. Functions that once operated in separate layers of the stack are becoming more connected and responsive to one another.

And as that happens, the rest of the stack can’t stay passive. Authorization needs to interpret, not just validate. Credit has to be adjusted per transaction, and infrastructure has to carry context, beyond just credentials.

Across the stack, different players are enabling this shift in different ways, using agentic AI to reduce the friction between how decisions are formed and how they are carried out.

Here’s how it’s playing out.

Stripe and the push to make agents transactable

Stripe is addressing the execution layer of this shift, enabling AI systems to complete transactions once a decision is made.

Its product direction, including work around agentic commerce in addition to stablecoin infrastructure, is aimed at making AI systems economically native. The Agentic Commerce Protocol (ACP), developed with OpenAI, is an attempt to define a shared language between merchants and AI agents so transactions can happen without bespoke integrations for every system.

The firm is trying to normalize the idea that agents will increasingly initiate commerce flows, and the system has to treat that as a normal input.

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How American Express is fixing the weak link in agentic commerce

Agentic commerce, where AI agents anticipate a user’s needs and act on their behalf, is starting to move from conceptual presentations into real-world pilots. These systems are expected to transform the entire transaction journey, from discovery and comparison to checkout and even post-purchase management. The economic upside of agentic commerce could be significant, with estimates from McKinsey & Company pointing to trillions of dollars in potential impact by the end of the decade.

But that future remains constrained by execution. That gap between decision and transaction is already shaping how these systems are designed.

It’s precisely this fault line that American Express (Amex) is targeting. With the launch of its Agentic Commerce Experiences (ACE) Developer Kit, the company is introducing a framework that enables AI agents to execute transactions on a user’s behalf but only within clearly defined, authenticated intent and control layers.

This balance between automation and constraint may become a key design principle of early agentic commerce.

Luke Gebb, EVP and Head of Global Innovation at American Express

“With the ACE Kit, the goal is to make purchases seamless without losing control or the trust, security, and service card members and Merchants expect from American Express,” says Luke Gebb, EVP and Head of Global Innovation at American Express.

“The model includes card member enrollment and authentication and gives card members the ability to manage controls directly in the Amex app – using structured intent, spend limits, merchant preferences, and tokenized credentials so the agent can only act within clearly defined boundaries set by the card member.”

At the same time, Amex is addressing the AI trust deficit more directly. Alongside ACE, it is rolling out Amex Agent Purchase Protection, extending safeguards to cover transactions carried out by AI agents, as an acknowledgment that before autonomy scales, accountability must be built in.

So, ACE is designed to do three things:

  1. Let AI agents transact using Amex cards

  2. Ensure those transactions reflect verified user intent

  3. Extend Amex’s protections into this new, agent-mediated layer

The real problem isn’t payment execution – it’s intent

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Citizens’ CIO on the ethos that led the bank into the cloud and beyond

Not all banks are stuck in the mainframe era.

Today, we look at Citizens, which has been on an impressive modernization and innovation journey, speaking with the bank’s Chief Information Officer and Head of Technology Services, Michael Ruttledge, to understand how one of America’s oldest institutions shed the weight of legacy technology, moved entirely to the cloud, and built the organizational ethos to carry its progress forward.

The start of the road

Citizens was once owned by the Royal Bank of Scotland, which divested its stake in 2015. Ruttledge, who joined the firm in 2019, was coming into an organization already in the process of modernizing. “When I joined it was a good time because people started to move more applications to the cloud and more were moving into agile development,” he said.

Since his joining, the firm has been focused on its “Next Gen Technology” initiative that focuses on 5 main pillars and serves as the spine for its modernization efforts:

  1. Empowering the development cycle: Move into an agile environment through developing DevSecOps tools and test automation.

  2. Enhancing communication within the infrastructure: Leverage APIs to modernize the technology stack.

  3. Improving internal capabilities and talent: Upskilling the current workforce because the bank had previously skewed towards outsourcing and developed considerable technical debt within the team.

  4. Transitioning away from mainframes: Moving the infrastructure to the cloud and remote servers.

  5. Fortifying the core: Protecting its core banking software by enhancing stability and security against cyber threats.

For a firm that was established in 1828, and (in Ruttledge’s words) the “last company on the planet to be using IBM Big Insights,” the Next Gen Technology initiative has been able to realize big results: “We’re the only super regional bank that is completely in the cloud. All of our business apps are either in the Azure or AWS public clouds and we are now in the process of decommitting our data centers in North Carolina,” he shared.

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The ‘discovery’ problem in embedded finance – and how OMB Bank found the right fintech partner

For Missouri-based community bank OMB Bank, finding the right fintech partner used to be a slow, manual process. Executive Vice President and Chief of Staff Jessica Sims recalls working from static PDFs of the bank’s preferences, followed by endless back-and-forth emails whenever a fintech expressed interest. The process worked, but painfully slowly, and promising opportunities often slipped through the cracks.

That changed when OMB discovered Backpack, a university payments fintech on Treasury Prime’s AI Marketplace, whose priorities perfectly aligned with the bank’s focus areas. OMB moved quickly, creating a smooth collaboration that benefited both parties.

The ‘discovery’ problem in embedded finance

Embedded finance is a three-way intersection: banks provide the regulated foundation, fintechs handle the tech and integrations, and non-financial brands deliver the end service to users.

Despite all the innovation and growth in the space, one part of the process has seen little advancement: how banks and fintechs find each other in the first place. Behind the scenes, most teams still spend weeks sorting inbound interest, chasing warm introductions, and manually assessing early-stage fit, with many discussions that go nowhere – long before compliance or economics even enter the conversation.

Finding fintech partners that truly align with a bank’s strategy, risk appetite, and operating model remains slow, manual, and opaque – the very problem OMB faced before meeting Backpack.

Treasury Prime believes that discovery, not diligence, is the real bottleneck in embedded finance equations – and that AI is now mature enough to fix it.

In December 2025, Treasury Prime launched its new AI Marketplace to change that starting point. The platform automates partner discovery and accelerates collaboration between banks and fintechs.

“Discovery is where banks feel the most friction,” says Chris Dean, co-founder and CEO of Treasury Prime. “The biggest change is that banks are no longer starting from a blank slate with every new fintech conversation.” 

Chris Dean, co-founder and CEO of Treasury Prime

 

 

 

 

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