CFOs Capture B2B Payments Digitization Value by Targeting Year-Two Gap

The biggest risk in B2B payments today isn’t failing to modernize. It’s assuming that modernization alone delivers value.

The real test arrives in what many finance leaders privately call the “year-two problem,” or the period after regulatory migration deadlines pass and new systems go live. That’s when firms discover that upgraded rails do not automatically produce cleaner data, faster decisions or better working capital outcomes. Instead, they confront the deeper challenge of re-engineering internal data quality and execution processes to extract measurable performance gains.

In this new phase of B2B innovation, execution velocity, the speed and accuracy with which an organization turns transactional data into action, is emerging as the KPI that separates genuinely transformed enterprises from those that merely upgraded their software.

The deeper lesson of the year-two problem is that modernization is infrastructural, but transformation is architectural. Upgrading to a new payments platform is a project. But building an organization that can consistently execute on real-time, standardized, artificial intelligence-enabled data flows is an operating model.

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From System Upgrades to Performance Architecture

In an environment where AI and automation are expected rather than exceptional, the differentiator is not access to tools. It is the speed and reliability with which those tools are embedded into daily workflows.

Execution velocity captures the organization’s ability to move from insight to action without friction. It measures how quickly an invoice discrepancy is resolved, how rapidly a new supplier can be onboarded with validated data, how seamlessly a real-time payment can be reconciled against an order, and how efficiently cash positions can be updated across subsidiaries.

This is the year-two problem. It surfaces when the adrenaline of migration fades and the strategic question becomes unavoidable. How fast, and how effectively, can we execute on the data we now possess?

The answer increasingly depends on how organizations reconceive their buyer-supplier relationships. Historically, transactional data was treated as an internal artifact. Today, however, it is becoming a shared asset.

“You’re starting to see a lot more sophistication when it comes to that dialogue between buyers and suppliers,” Daniel Artin, head of strategic partnerships at Boost Payment Solutions, told PYMNTS in January, adding that faster payment terms, better data and fairer economics are aligning interests in ways that were rare even a few years ago.

When invoice line items are consistently coded, when payment terms are clearly structured, and when remittance information flows in machine-readable formats, both sides can automate reconciliation and unlock working capital efficiencies. But this only works if execution velocity is high.

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AI and Automation as Table Stakes

The rapid normalization of AI in enterprise finance underscores this shift. A year ago, predictive analytics in accounts payable or anomaly detection in procurement were considered advanced capabilities. Today, they are fast becoming table stakes.

Findings in the January edition of The CAIO Report from PYMNTS Intelligence revealed that, across industries, companies are converging on the same handful of high-impact uses for agentic AI. Rather than fragmenting into niche or industry-specific uses, the report found agentic AI adoption is clustering around a common set of high-leverage functions, including customer insight, product lifecycle management and strategic analytics.

Yet AI magnifies the year-two problem. Machine learning models are unforgiving of inconsistent data. If vendor names are not standardized, if invoice categories vary by business unit, and if historical payment behavior is incomplete, the outputs degrade quickly.

“Gone are the days where you can have a great product and a great service, and your invoices aren’t any good,” North Vice President of Product Management Greg Gorman told PYMNTS this month.

Execution velocity provides a lens for measuring that responsiveness. How quickly can a company shift payment terms to support critical suppliers during a disruption? How rapidly can it reroute payments across networks if a corridor is compromised? How fast can it generate accurate cash forecasts when market conditions change?

These questions cut across functions and can require coordination between treasury, procurement, compliance and technology teams. Execution velocity, then, is not a narrow operational metric. It is a composite measure of how well the enterprise’s performance architecture converts digital capability into business results.

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