Smarter Spend: AI-Powered AP for Data-Based Decision-Making

Despite years of digital progress, many accounts payable (AP) teams still rely on manual, error-prone processes that slow payments, obscure cash flow, hinder spend management and heighten fraud exposure. Automation with artificial intelligence (AI) technology is emerging as a solution—not only accelerating invoice workflows but also enabling data-driven financial decisions at scale. While adoption is growing, with nearly three-quarters of organizations using AI in AP, automation maturity remains modest, leaving many manual steps in place. Trust is essential to achieving full automation maturity. Teams that succeed on this point stand to transform AP into a strategic advantage, unlocking real-time visibility, stronger control and smarter working-capital decisions.

From Manual to Intelligent AP

Turning AP Data Into Decisions

Trusted AI, Human Control

Building the Foundation for Data-Driven AP

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From Manual to Intelligent AP

Manual invoice processing still dominates AP, hiding spend patterns while draining time and resources. Intelligent automation gives finance teams the data clarity to see where money moves, but full automation remains limited.

Manual workflows hide spending patterns.

Today, manual tasks continue to prevail over large portions of invoice processing. Apart from consuming staff time, extending cycle times, and increasing errors and fraud risks, one of the most significant consequences of manual workflows for AP is weakened spend control. When invoice data is captured inconsistently or approvals remain fragmented, finance teams struggle to assemble a unified picture of spend, accruals and supplier activity. These gaps reduce forecasting accuracy and make it difficult to identify anomalies early. At worst, according to Edenred Pay, manual processing of supplier payments can leave AP departments “flying blind,” with poor visibility and data silos hamstringing cash forecasting, spend management and decision-making.

66%

of AP teams reported an increase in manual workload over the past year.

Few solutions to this problem have garnered as much attention as AI-powered automation. AI in AP can eliminate these blind spots by improving data quality, standardizing critical fields and highlighting trends. However, even many organizations already adopting AI have not begun to tap its capabilities on this front.

Limited automation leaves visibility gaps.

Industry data highlights just how entrenched manual dependency remains. In a recent survey of more than 2,300 finance professionals, only 7% of organizations had fully automated AP, while 42% still relied on predominantly manual workflows. Moreover, two-thirds (66%) reported an increase in manual workload over the past year.

Another recent study specifically examined AP departments’ usage of AI-driven automation systems. It revealed that although 72% of companies reported adopting AI for AP in the past two years, only 55% are optimizing or scaling these deployments. Meanwhile, 45% remain in early pilot phases focused on workflow assistance rather than advanced automation that includes spend analysis. Just 22% report full, at-scale usage. This means that many AP teams—even those using AI—may still lack the real-time visibility needed to anticipate cash demands or evaluate supplier behavior proactively. As organizations move beyond simple workflow acceleration and toward intelligence-driven spend management, AP has the power to become a critical enabler of financial strategy.

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Turning AP Data Into Decisions

AI makes AP data actionable. With unified visibility across invoices, approvals and payments, finance leaders can forecast cash flow and manage spend with confidence and control.

AI unlocks spend intelligence.

79%

of organizations using AI in AP report measurable performance gains.

As automation deepens, AI’s real advantage lies in the intelligence it unlocks. AI addresses manual frictions directly by automating invoice capture, matching and routing, reducing cycle times and human error. The result is more velocity, fewer exceptions and AP talent redeployed to strategic work. However, beyond automation, AI converts invoice and payment data into actionable insights, enabling finance leaders to forecast cash, optimize payment timing and strengthen spend management and supplier strategies in real time. As noted by Edenred Pay, AI automation in finance “makes the invisible visible,” revealing hidden spending patterns and opportunities for cost recovery that manual systems often miss.

One of the strongest benefits is spend optimization. With near-real-time visibility into obligations, leaders can evaluate the financial impact of early-payment strategies, pinpoint categories where spending is drifting off plan, and identify suppliers whose behaviors signal risk. AI can support scenario modeling—helping finance teams understand the downstream effects of different payment schedules or vendor terms—while also surfacing exceptions that warrant a closer look. These insights support faster, better-aligned financial decisions without removing human oversight. Powered by AI, AP becomes a command center for smarter spend decisions.

AI is serving up wins for AP, but its potential remains untapped.

Research offers promising evidence of AI’s impact on AP. Nearly eight in 10 organizations (79%) using the technology in this context report measurable performance gains, including faster invoice processing (50%), accelerated approvals (46%) and greater employee satisfaction (44%). Financial gains are emerging as well: 42% report improved discount capture, 36% reduced processing costs and 34% enhanced supplier relationships. These advantages stem from standardized, machine-readable data that enables AP to evaluate spend patterns, identify outliers and support more accurate cash-flow planning.

However, findings also confirm that much of AI’s aptitude in this regard has yet to be harnessed. While one-third of CEOs believe cash forecasting and spend analysis are the areas that would benefit the most from AI, only 26% are currently prioritizing these use cases. Encouragingly, 82% plan to invest in AI for AP in the next 12 months, with 34% intending to prioritize its cash-flow forecasting ability. With AI-enhanced visibility, AP teams can optimize payment timing, improve liquidity forecasting and strengthen enterprise spend governance. So why aren’t companies tapping the technology’s full potential for data-based AP decision-making? According to survey results, it’s a matter of trust.

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Trusted AI, Human Control

Automation is most effective when balanced with oversight. Transparent, human-guided AI builds trust across suppliers and teams, ensuring that data-driven efficiency never comes at the expense of governance.

AI is emerging as a co-pilot on AP teams—not a human replacement.

As AI investment rises, trust continues to be central to expansion, with data privacy, security and compliance cited most frequently as remaining hurdles. To advance, AP teams will need to develop a strong implementation framework to build trust in AI-assisted decisions. Edenred Pay, for example, sees AI evolving as a steadfast partner—offering intelligent recommendations, validating data, surfacing risks and supporting real-time decisions while keeping finance teams firmly in control. As these capabilities mature, AP leaders will shift from tactical automation gains to sustained strategic value—securing efficiency, insight and resilience across the invoice-to-pay life cycle.

64%

of organizations view AI as a productivity enhancer, not a substitute for human judgment.

Substantiating this view, 64% of organizations see AI as a productivity enhancer, not a substitute for human judgment. However, concerns remain: 47% cite data privacy or security risks, 40% worry about accuracy, and 38% cite maintenance and implementation costs. Nearly all organizations want a human-in-the-loop model: 46% want human review of every AI decision, while 45% support reviewing only exceptions. This underscores the importance of transparent models and decision-explanation capabilities.

Governance and transparency will shape AI’s next phase.

Even with all its potential, AI’s effectiveness depends entirely on the governance structure surrounding it. Building confidence in AI-assisted AP requires more than technical capability—it requires an operational framework that clarifies how the technology makes decisions and how those outputs are validated. Clear approval logic, well-defined exception pathways and transparent data standards help finance teams understand when to rely on AI outputs and when human review is required. As organizations strengthen these governance practices, comfort with AI grows, enabling teams to expand automation safely while preserving the control and auditability required in financial operations. Trust, not speed, remains the determining factor in scaling intelligent AP.

The data bears this out. Among non-adopters, barriers align closely with governance maturity: 37% cite regulatory concerns, 29% worry about AI decision quality, and 30% require clear security and compliance assurances before moving forward. Decision transparency is also a prerequisite: 35% want demonstrated accuracy benchmarks, 32% want proof of trustworthy outputs, and peer validation ranks among the top influences on adoption decisions. Strengthening governance will be key to expanding AI usage responsibly.

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Building the Foundation for Data-Driven AP

AP teams are under pressure to deliver faster, more accurate and more strategic insights. AI is emerging as an immensely powerful tool to help organizations modernize invoice-to-pay workflows and improve spend visibility, but laying the groundwork for trust is vital.

PYMNTS Intelligence offers the following actionable roadmap for companies looking to move toward AI-supported AP automation maturity:

Build governance early. Define approval logic, escalation triggers and decision-explanation requirements.

Prioritize strategic use cases. Start with spend analysis, cash forecasting and payment-timing optimization to unlock financial impact quickly.

Adopt human-in-the-loop controls. Maintain human oversight while allowing AI to do the heavy lifting of reducing manual burdens and surfacing insights faster.

Advance in phases. Move from workflow acceleration to intelligence-driven planning as trust, accuracy and governance solidify.

AP modernization is ultimately a journey toward better visibility, stronger control and more confident financial decisions. By pairing thoughtful governance with smarter data foundations, finance leaders can use AI to elevate—not replace—human judgment. Organizations that take these steps now will be positioned to turn AP into a source of real-time insight, enabling more agile spend strategies and greater resilience in the years ahead.

AI is redefining what’s possible in accounts payable. When organizations pair intelligent automation with strong governance, AP stops being a back-office function and becomes a real-time engine for financial insight. Edenred Pay believes AI should make finance teams stronger, not replace them. By elevating data quality, enhancing visibility and giving leaders the confidence to act, we’re helping companies turn AP into a strategic lever for smarter spend, stronger control and better business decisions.”

Alex Hoffmann
General Manager and CEO, Edenred Pay, North America