Finance teams have largely settled the question of whether AI belongs in their operations. What they have not settled is where it pays off.
Nearly two-thirds of mid-market finance professionals (63%) plan to increase investment in AI tools and capabilities in 2026, according to AvidXchange's 2026 Trends Survey of more than 500 finance professionals conducted in September 2025. AI and machine learning now rank as the top technology category where these teams are directing new investment. The stated reasons are practical rather than aspirational: 41% cite operational efficiency and productivity, and 27% point to better data analysis and decision making.
Confidence is high among teams already using the technology. Of those currently using AI tools, 79% say they are confident their organization can achieve maximum ROI from its AI investments. When asked where returns have actually materialized, respondents named increased efficiencies (42%), improved accuracy (39%), better decision making through data insights (35%), and reduced operational costs (35%).
Taken together, the numbers describe a market that has moved past pilots. The harder question is what happens to the efficiency once it exists.
The compounding question
One finding points toward an answer. Among finance teams that have already seen a return from AI, 44% are reinvesting those cost savings into additional technology or automation. Another 44% are directing them toward strengthening data security and compliance, and 35% into new products or service lines.
That reinvestment pattern matters because it suggests the first efficiency gain is not the end of the story. When time and cost savings flow back into connected technology, improvements begin to accumulate across processes, data, and decisions rather than stopping at a single workflow.
Accounts payable is a useful place to watch this play out, because AP sits at the intersection of data quality, internal controls, and cash movement. Value in AP begins with better invoice data. Cleaner, more reliable data supports faster approvals, stronger controls, more informed decisions, and more efficient payment execution. Each stage depends on the one before it.
Where AI creates value across the AP lifecycle
The lifecycle offers several distinct points of leverage.
Invoice capture. AI can extract key invoice fields, match them against ERP records, and reduce manual entry. Cleaner data at intake limits the errors and inconsistencies that otherwise surface later in approvals, reporting, and payments.
General ledger coding. Coding each invoice remains one of the most manual and error-prone steps in AP. Systems that suggest coding based on an organization's historical decisions reduce effort, improve consistency, and spare teams the repeated work of researching how similar invoices were handled before.
Approvals. Historical invoice and approval activity can accelerate purchase order matching and surface guidance inside the approval process itself, where the decision is actually made.
Insights. Natural language analytics make AP data easier to interrogate, which moves the team from reporting on what happened to acting on it.
Payments. This is the stage most often left out. When payment execution stays manual or disconnected from everything upstream, the return on AI can be diminished. Carrying invoice and workflow intelligence through to payments helps streamline operations, reduce fraud risk, and strengthen the supplier relationship.
What to evaluate
For finance leaders assessing AI-powered AP tools, a few questions separate durable value from a near-term efficiency bump.
Does the AI improve the quality of invoice data, or only the speed of extraction?
Does intelligence appear inside the workflows the team already uses, or does it require people to work somewhere else to get the benefit?
Does accurate invoice data and approval history carry through to payment execution, or does the process break at the last step?
Does the solution monitor behavior and transaction patterns continuously, adapting its guardrails as conditions change?
Adopting point solutions, or tools with isolated AI features, may create real near-term gains. But technology that embeds AI throughout the AP lifecycle can create compounding benefits, helping teams get more from their AI investments and move beyond automation toward more intelligent finance operations.
Finance teams have moved past asking whether AI can create value to asking whether their processes are set up to capture that value across the entire AP lifecycle.
To learn more about where finance teams are seeing AI ROI, how AI creates value across the AP lifecycle, and what to look for in an AI-powered AP solution, download the full guide.
