The landscape of financial technology has transformed from a quest for options to a challenge of overwhelming abundance for Chief Financial Officers (CFOs). As the year 2026 approaches, finance leaders are navigating an ever-expanding array of artificial intelligence (AI) tools, real-time payment capabilities, sophisticated forecasting platforms, advanced treasury systems, and a plethora of automation products, each promising enhanced decision-making, increased efficiency, and greater control. The pressure to adopt these innovations is mounting, fueled by competitor advancements and board-level inquiries into the company’s engagement with AI. However, the most significant pitfall identified by industry experts is the risk of investing in technology without a clear, predetermined definition of the desired outcomes.
Matthew Davies, Head of Global Payments Solutions, EMEA, and Global Co-Head of Corporate Sales, GTS at Bank of America, emphasized this critical distinction during a recent interview as part of the PYMNTS original series, "Summer School." He articulated, "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." Davies further elaborated on the inherent risks, stating, "The biggest risk and challenge is misinvestment rather than underinvestment. You need to strip it back and focus on solving specific business challenges, not simply just introducing the latest shiny technology."
This nuanced approach is particularly vital as payments, data, and AI become increasingly intertwined. Modern payment infrastructures are generating the real-time data essential for AI systems. Enhanced data quality, in turn, bolsters forecasting accuracy and strengthens internal controls. Automation frees up valuable human capital for higher-value strategic tasks. Yet, these transformative benefits are contingent upon seamless integration, robust governance, and widespread adoption across an organization. While the principle of outcome-driven technology investment appears self-evident, its practical implementation often proves elusive.
The CFO’s 2026 Tech Imperative: Prioritizing Measurable Value Over Innovation Frenzy
As CFOs assume expanded responsibilities encompassing liquidity management, operational resilience, data governance, and the demonstrable return on technology investments, the strategic imperative for finance technology is shifting. The focus is moving away from merely keeping pace with every emerging capability towards establishing the foundational conditions that enable innovation to yield quantifiable business value.
Davies underscored the importance of this strategic recalibration. "You want to prioritize those solutions that have proven real-world use cases and measurable business impact," he advised. "If you haven’t set the right measures up front, how can you judge the outcomes of your decisions?" Achieving successful modernization necessitates a collaborative effort involving treasury, finance, technology, cybersecurity, data, and risk management departments. Furthermore, it requires a strategic approach to change management, dedicated implementation support, and a phased rollout that gradually expands capabilities only as tangible value is demonstrated.
"The real test for CFOs in 2026 is not keeping pace with innovation," Davies concluded. "It’s about identifying investments that strengthen visibility, liquidity, and decision-making while delivering measurable business value." This paradigm shift is a direct response to an increasingly volatile global economic environment. Companies are now tasked with managing liquidity across diverse markets, multiple currencies, various banking relationships, and numerous legal entities, all while navigating geopolitical disruptions, fluctuating interest rates, and sophisticated fraud threats.
Payments Evolve: From Back-Office Utility to Strategic Enabler
The traditional view of payments as a mere back-office operational function is rapidly evolving. Davies observed, "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." This redefinition positions payment systems as critical components of an organization’s financial intelligence infrastructure.
The advent of near-real-time visibility into cash positions empowers treasury teams to make more agile funding and investment decisions. It allows companies to reallocate liquidity where it is most needed, bypassing the delays associated with fragmented reporting or end-of-day reconciliations. However, the true strategic advantage lies not just in the payment itself, but in the rich data that accompanies it.
"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 explained. This wealth of payment-related data, when effectively harnessed, can provide invaluable insights for critical financial planning and decision-making processes.
Data Foundation: The Bedrock of Effective AI and Automation
As financial data becomes a critical input for enterprise-wide AI systems, the underlying payment infrastructure is transforming into an integral part of the organization’s financial intelligence ecosystem. However, the effectiveness of any advanced technology deployed on top of this infrastructure can be severely hampered by fragmented data residing across disparate Enterprise Resource Planning (ERP) systems, treasury platforms, bank portals, and acquired business entities.
"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 asserted. He further highlighted a common organizational discovery: "Many organizations discover that improving data quality delivers value just by itself, even before you start planning the technology infrastructure that you’re going to place on top of that."
A finance leader cannot confidently make high-stakes decisions regarding liquidity or capital allocation if cash positions are incomplete, definitions of key financial metrics vary across systems, or if information requires manual reconciliation before it can be deemed trustworthy. This reality necessitates a strategic resequencing of modernization efforts. The most impactful AI initiative might, in fact, commence with the fundamental tasks of data standardization, systems integration, and robust control design, rather than a high-profile, yet potentially superficial, pilot project.
Automating the Mundane, Elevating the Strategic
The immediate and most accessible opportunity within finance departments lies in automating repetitive, manual tasks to enhance operational efficiency. Davies articulated this priority: "The most immediate opportunity is to automate those repetitive manual tasks and improve operational efficiency across the finance processes."
When finance teams dedicate less time to the laborious processes of report assembly, transaction matching, and routine exception resolution, they are liberated to concentrate on more strategic activities. These include refining cash flow forecasting, conducting scenario planning, performing in-depth risk assessments, and providing critical decision support to executive leadership.
"The goal is not AI for AI’s sake," Davies emphasized. "It’s really looking at AI and applying it where it solves real business challenges and delivers measurable value." This philosophy underpins a move towards leveraging technology as a tool to solve tangible business problems, rather than as an end in itself. The strategic application of AI, therefore, is rooted in its ability to drive demonstrable improvements in financial performance and operational effectiveness.
The Broader Implications: Navigating a Complex Financial Ecosystem
The insights from Matthew Davies highlight a critical juncture for CFOs and their finance departments. The overwhelming array of technological solutions, while offering immense potential, also presents significant risks if not approached with a clear strategic vision. The emphasis on defining desired outcomes before selecting technologies, prioritizing proven use cases with measurable business impact, and building a robust data foundation are not merely best practices; they are becoming essential prerequisites for successful digital transformation.
The evolving role of payments as a strategic enabler underscores a broader trend of finance functions becoming more integrated into the core business operations and strategic decision-making processes. By leveraging the data embedded within payment flows, organizations can gain deeper insights into their financial health, optimize liquidity, mitigate risks, and inform strategic planning with greater accuracy and agility.
The challenge for CFOs in 2026, therefore, is not simply to adopt new technologies, but to strategically deploy them in a manner that drives tangible business value. This requires a disciplined approach, a commitment to data quality, cross-functional collaboration, and a clear understanding of how technology can solve specific business problems. By focusing on these core principles, finance leaders can navigate the complexities of the modern technological landscape and unlock the full potential of digital innovation for their organizations.
The PYMNTS "Summer School" series, featuring in-depth discussions with industry leaders like Matthew Davies, aims to provide CFOs and finance professionals with actionable insights and strategic guidance to navigate these evolving challenges and opportunities. The series underscores the importance of a strategic, outcome-oriented approach to technology adoption in the current dynamic economic climate.
