Bank of America Says Data Quality Fuels AI Success

Watch more: Summer School With Bank of America’s Matthew Davies

For finance chiefs, the technology challenge is no longer a lack of options. It is an excess of them.

Chief financial officers are being presented with an expanding menu of artificial intelligence tools, real-time payments capabilities, forecasting platforms, treasury systems and automation products, each promising faster decisions, greater efficiency or more control. The pressure to act is intensifying as competitors announce pilots and boards ask how the company is using AI. But the most significant danger facing finance leaders is that they will invest in technology without first defining what the investment is supposed to accomplish.

“If you start by defining the outcomes that you want to achieve, whether that’s better liquidity visibility, stronger controls, greater efficiency, faster decision-making, then assess the technology against those goals, that will really help drive you in the right direction,” Matthew Davies, head of Global Payments Solutions, EMEA, and global co-head of corporate sales, GPS at Bank of America, told PYMNTS for the 2026 PYMNTS original series “Summer School.”

“The biggest risk and challenge is misinvestment rather than underinvestment,” Davies said. “You need to strip it back and focus on solving specific business challenges, not simply just introducing the latest shiny technology.”

That distinction is especially important as payments, data and AI become more interconnected. Modern payment infrastructure produces the real-time information AI systems need. Better data improves forecasting and controls. Automation creates capacity for higher-value work. But none of those benefits materialize without integration, governance and adoption.

The discipline sounds obvious. In practice, it is frequently lost.

The CFO’s 2026 Tech Test Focuses on Value, Not Hype

As CFOs take on broader responsibility for liquidity, operational resilience, data governance and technology returns, the finance technology playbook is less about keeping pace with every new capability than establishing conditions under which innovation can produce measurable value.

“You want to prioritize those solutions that have proven real-world use cases and measurable business impact,” Davies said. “If you haven’t set the right measures up front, how can you judge the outcomes of your decisions?”

Successful modernization requires collaboration across treasury, finance, technology, cybersecurity, data and risk. It also requires change management, implementation support and a phased approach that expands capabilities only as value is demonstrated.

“The real test for CFOs in 2026 is not keeping pace with innovation,” Davies said. “It’s about identifying investments that strengthen visibility, liquidity and decision-making while delivering measurable business value.”

The shift reflects a more volatile macro environment. Companies are managing liquidity across multiple markets, currencies, banks and legal entities while responding to geopolitical disruption, changing interest rates and sophisticated fraud threats.

“Payments are increasingly viewed as a strategic enabler of liquidity management, risk control and, frankly, enterprise-wide efficiency rather than just, historically, a back-office utility type process,” Davies said.

Near-real-time visibility into cash positions allows treasury teams to make faster funding and investment decisions. It can also help companies move liquidity where it is needed without waiting for fragmented reports or end-of-day reconciliation. But the larger opportunity lies in the data surrounding the payment.

“Treasury teams [are] increasingly relying on payments and the data that sits around payments to help them with their cash flow forecasting, capital allocation and strategic planning decisions,” Davies said.

Automate What’s Repetitive, Not What’s Strategic

As financial data feeds enterprise AI systems, payment infrastructure is becoming part of the enterprise’s financial intelligence system. But fragmented data across ERP systems, treasury platforms, bank portals and acquired businesses limits the effectiveness of any advanced technology layered on top.

“If you don’t have high-quality standardized data, then you don’t have the foundation that you need for effective automation, forecasting, financial decision-making and ultimately, any AI solution that you want to put on top of it,” Davies said.

“Many organizations discover that improving data quality delivers value just by itself, even before you start planning the technology infrastructure you’re going to place on top of that,” he added.

A finance leader cannot make a high-stakes liquidity or capital allocation decision with conviction if cash positions are incomplete, definitions differ across systems or information must be manually reconciled before it can be trusted. For CFOs, that changes the sequencing of modernization. The highest-return AI initiative may begin with data standardization, systems integration and control design rather than a high-profile pilot.

“The most immediate opportunity is to automate those repetitive manual tasks [and] improve operational efficiency across the finance processes,” Davies said.

When finance teams spend less time assembling reports, matching transactions and resolving routine exceptions, they can devote more attention to cash forecasting, scenario planning, risk assessment and strategic decision support.

“The goal is not AI for AI’s sake,” Davies said. “It’s really looking at AI and applying it where it solves real business challenges and delivers measurable value.”

Watch the full PYMNTS TV “Summer School” interview with Matthew Davies to hear more about:

Why the biggest technology risk for CFOs is misinvestment, not underinvestment. Davies explained why finance leaders should begin with clearly defined business outcomes, such as stronger liquidity visibility, better controls and faster decisions, rather than chasing the latest technology.

How modern payments and better data are becoming strategic finance infrastructure. The discussion explored how real-time payments visibility can improve cash forecasting, capital allocation and risk management, while standardized data creates the foundation for automation, AI and more confident decision-making.

Where the potential exists. Davies said the clearest near-term opportunity lies in automating repetitive finance processes, but warns that disconnected pilots, weak governance and underutilized tools can leave companies with more complexity instead of measurable returns.