AI in Accounts Payable Accelerating

Explore topics and key findings

AI momentum is building, and finance leaders are embracing the change.

AI adoption is now mainstream, though many are still scaling

Nearly three-quarters (72%) of organizations report using AI in AP or finance. Top technologies include workflow automation (58%), generative AI (53%), and data extraction tools (44%). Adoption maturity is mixed: 55% are optimizing or at scale, while 45% remain in pilot or early scaling.
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What this means: AI is moving beyond experimentation and into daily workflows, but many organizations are still in transition.

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AI is seen as a partner, not a replacement

64% of respondents view AI as a productivity enhancer or digital assistant rather than a role replacement. Leadership support is strong, with more than 80% reporting leadership backing. But trust boundaries remain: 91% expect some form of human review of AI outputs.
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What this means: Adoption is framed more as augmenting teams than eliminating them. Human oversight remains essential, while leadership support suggests the debate is no longer “if” but “how fast.”

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Investment momentum is strong

Half of respondents say their organizations are “very likely” to invest in both AP automation and AI-powered tools in the next 12 months. Priorities include data extraction (44%), invoice approvals (42%), and fraud/compliance monitoring (41%). Confidence in adoption is tied to proof of accuracy and performance, security assurances, and peer validation.
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What this means: Budgets are opening up for AI in AP, but proof points and trust remain prerequisites. Investments will cluster around data-heavy, error-prone processes where the impact is most tangible.

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Addressing security and trust is key

Among the 28% of organizations not yet using AI in AP or finance, security and compliance concerns remain the biggest barrier, with 37% citing regulatory issues as their primary reason for holding back. Trust and data-related concerns also weigh heavily, with 29% worried about AI decision quality and 27% pointing to challenges with data accuracy or availability.
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What this means: While non-users remain cautious, their concerns highlight the importance of building trust through accuracy, transparency, and compliance — signaling once these barriers are addressed, adoption could accelerate quickly.

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AI adoption and outlook differ by market segment

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Mid-sized firms lead in AI readiness

Mid-sized organizations stand out as confident adopters of AI in AP. A strong majority (83%) say their teams understand AI’s potential. Data confidence is equally strong, with 83% of mid-sized companies expressing AI readiness.

And, 87% of mid-sized organizations are more likely to invest in AI-powered tools in the next 12 months.

What this means: Mid-sized companies represent the “sweet spot” for AI in AP, balancing readiness, leadership support, and measurable confidence.

Enterprises adopt AI faster, but face hurdles with scaling

Larger firms with $500+M in revenue and companies processing more than 10,000 invoices a month are more likely to use AI in AP (80% and 71%, respectively).

While more than half of companies with 5,000 or more employees (53%) are already using autonomous systems, internal resistance can be a barrier, and is more pronounced in high-volume environments (32%). Still, scale brings results: 62% of billion-dollar firms using AI are reporting faster invoice processing.

What this means: Large enterprises with high invoice volumes are more likely to use AI, but system integration and stakeholder resistance remain hurdles.

A future vision of intelligent, agile and empowered finance teams

Change management is a critical component of AI

Hybrid tech stacks broaden AI adoption

Organizations mixing unified and separate systems in AP show broader adoption and deeper KPI tracking than those with single system setups. These companies are more likely to apply AI across all AP areas, with data extraction topping the list when supported by a defined AI strategy (50% vs. 27% without).

ERP integration is also critical: 86% of fully integrated organizations report strong cash flow and spend visibility, compared with only 33% of non-integrated peers.

What this means: Hybrid stacks create breadth in AI use and openness to new technology, but measurable outcomes hinge on ERP integration and a clearly defined AI strategy.

Leaders and staff split on AI outlook

Executives and mid-level managers consistently report stronger confidence, higher adoption, and broader expectations for AI in AP compared to staff-level respondents. Leaders are more likely to describe AI as a system that can independently manage financial processes (31% executives vs. 18% staff).

By contrast, staff members are more likely to see AI as a digital helper for repetitive tasks (47% vs. 26% of executives) and are more inclined to associate it with potential role replacement.

What this means: Leaders view AI as a strategic driver, while staff see it mainly as task automation — or even a risk. Closing this perception gap is essential for broad adoption.

The big picture

AI is no longer confined to experiments or edge cases

Access the full report for exclusive adoption trends, practical use cases, and the gaps, priorities, and investments shaping the next era of finance.

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Plan to invest in AI tools in the next 12 months

see AI as a productivity enhancer or digital helper

Already use some form of AI in AP or finance

Report having a defined AI strategy

Cite data extraction as the most common AI entry point

Benchmarking the growth of AI in AP

AI momentum is building, and finance leaders are embracing the change.

AI adoption and outlook differ by market segment

Change management is a critical component of AI

AI is no longer confined to experiments or edge cases

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