AI in Accounts Payable Accelerating

Key findings

Finance leaders are embracing AI and process evolution is underway

86%

of respondents say their AP operations are centralized

Automation is widespread, but maturity is limited

86% of respondents say their AP operations are centralized, and two-thirds process more than 1,000 invoices per month. ERP integration is near-universal (91%), but partial or incomplete syncs leave manual steps in place. Pain points remain consistent: manual data entry (37%), high processing costs (36%), and slow approvals (34%).

‍

What this means: Most organizations have adopted automation basics, but significant manual work still exists. Incomplete ERP integrations are a bottleneck, and advanced automation is not yet the norm.

54%

track data processing accuracy, signaling metric gaps remain for optimal efficiency

Performance tracking focuses on basics, not automation maturity

Many organizations track data accuracy (54%), invoice processing time (52%), and approval time (46%), but only 18% measure no-touch rates. Payment delays are common: 65% report at least occasional late payments, creating forecasting and vendor relationship challenges.

‍

‍What this means: Finance leaders are focused on efficiency, but without measuring automation maturity, they may be overstating progress. Persistent delays show gaps remain between adoption and outcomes.

72%

of organizations report using AI in AP or finance

AI adoption is now mainstream, though many are still scaling

Nearly three-quarters (72%) of organizations report using some form of 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.

‍

‍What this means: AI is moving beyond experimentation and into daily workflows, but many organizations are still in transition. Adoption spans both practical and emerging tools, showing a sector that’s maturing unevenly.

64%

view AI as a productivity enhancer or digital assistant

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. Trust boundaries remain: 91% expect some form of human review of AI outputs, with accuracy, security, and cost cited as top concerns.

‍

‍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.”

Role shifts and workforce changes

Respondents share that AI is reshaping staffing, responsibilities, and team structures — in some cases leading to restructuring, and in others driving upskilling, role expansion, and the creation of new positions like Automation Specialists and AI Analysts.

51%

prefer best-of-breed tools, especially among ERP-integrated, AI-ready organizations

Decision-making is collaborative, but discovery is fragmented

IT and Finance Systems leaders (60%) most often lead tool evaluations, with CFOs, senior finance leaders, and AP managers also heavily involved. Discovery is diverse — vendor outreach, online research, industry publications, events, and peer recommendations all play a role. Best-of-breed tools are preferred by 51%, especially among ERP-integrated organizations with defined AI strategies.
‍

‍What this means: Buying decisions are distributed across multiple stakeholders, requiring a broad engagement strategy. Preference for best-of-breed tools shows organizations will trade simplicity for capability.

44%

of respondents cite manual data entry as the top issue they want AI to solve

Pain points and opportunities overlap

Manual inefficiencies still weigh heavily on AP, with manual data entry (37%) and high processing costs (36%) topping the list, followed by invoice coding errors (28%). However, these persistent challenges map directly to AI’s strengths: 44% of respondents name manual data entry as the top issue they want AI to solve, making it the clearest candidate for automation. High costs, slow approvals, and compliance gaps also rank high, highlighting opportunities for AI to reduce workload, boost accuracy, and streamline processes.
‍

‍What this means: Addressing these pain points with AI offers clear, immediate wins. Leaders can use this alignment to prioritize adoption strategies where ROI will be most visible.

82%

of respondents say their organizations are likely to invest in AI for finance or AP in the next 12 months

Investment momentum is strong

Eighty-two percent of respondents say their organizations are likely to invest in AI for finance or AP 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. Training for both AP teams and leadership is seen as essential.

‍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.

Concerns around ROI and trust

While enthusiasm for AI in AP is strong, some respondents voice concern about unclear ROI, cost-effectiveness, and affordability. Others raise issues of trust and accuracy, citing risks around data security, transparency, and the need for human oversight.

Survey snapshot: Current AP operations and tech stack

Most organizations surveyed have centralized AP operations, with 86% of respondents reporting a centralized structure, underscoring the push for unified control. However, many high-volume processors still maintain decentralized or offshore operations, highlighting uneven progress toward full automation.

Invoice volumes are substantial, with two-thirds processing over 1,000 invoices monthly, amplifying the need for automation. Despite widespread tech adoption, manual bottlenecks persist — from data entry and high processing costs to approval delays and compliance risks.

Tool adoption shows a fragmented landscape. Workflow-based and invoice management systems are common, yet manual and automated methods coexist. While AI and RPA adoption hover around 45%, reliance on spreadsheets and manual input remains high, signaling a transitional phase rather than full digital maturity.

‍Many organizations (69%) have attempted to build automation internally, but face recurring barriers — integration complexity, security concerns, limited resources, and inconsistent results. This underlines the growing preference for proven, pre-built automation solutions.

Perception of AI in finance

AI is seen as a productivity tool, and for some, an autonomous partner

AI is largely seen as a productivity partner, with strong leadership backing and high readiness, but trust boundaries keep humans in the loop

• AI is largely seen as a partner rather than a replacement, with 64% of respondents viewing it as a productivity enhancer or digital helper and far fewer seeing it as autonomous or a substitute for human roles. Appetite for giving AI full decision-making control remains limited, underscoring the importance of human oversight. Leadership support, strong team understanding, and confidence in financial data create favorable conditions for adoption.

• Nearly all respondents see value for AI in finance and AP, with trend, pattern, and anomaly detection the most widely recognized function. Still, concerns around security, accuracy, and cost persist, even among AI-ready organizations, reinforcing the need for human review. A small minority with low confidence in financial data remain hesitant, showing how trust and readiness continue to shape adoption.

Perspectives differ by role, stage, and adoption level

• Executives and mid-level managers are more likely than staff to view AI as a productivity tool and report strong leadership support.

‍• Organizations already using AI or operating with ERP integration consistently score higher in readiness and openness to advanced capabilities, while non-users and those without integration are more likely to cite accuracy, security, and integration challenges.

‍• Experience plays a critical role as more organizations use AI and integrate their systems, the more confident they become in expanding its role, while those not yet using AI remain cautious, framing adoption as both a technical and cultural hurdle.

64%

of respondents view AI as a productivity enhancer or digital helper, and only a few (5%) see it as a substitute for human roles

Detailed findings

AI is viewed as a productivity enhancer, adding value across a wide range of tasks.

AI is overwhelmingly seen as a tool that supports people rather than replacing them, with a combined 64% describing it as either a productivity enhancer or a digital assistant for repetitive tasks. While 28% show openness to autonomous AI that independently manages financial processes, most believe that humans must work collaboratively along with AI. Only 30% believe decisions should be made without human involvement. Job displacement concerns are low — just 5% view AI as a replacement for roles, and only 3% associate it with content generation or chatbots.

Perceptions of AI’s value span a wide range of finance and AP tasks. Identifying trends and anomalies leads at 52%, pointing to analytics as one of the clearest early wins for adoption.

‍What this means: Organizations see AI as a trusted partner that enhances productivity and provides analytical insight, but clear boundaries around autonomy remain, with human oversight still central to adoption.

Which of the following best describes how you currently think about AI in finance / AP?

A productivity tool used by people to streamline their work

32%

A digital "helper" that supports repetitive or rules-based tasks

32%

A system that independently handles financial processes

28%

A replacement for certain roles or teams

A conversational or content-creation chat bot

0% 5% 10% 15% 20% 25% 30% 35%

Which of the following functions do you believe AI can help with in finance or AP?

Please select all that apply.

Identifying trends, patterns, or anomalies in financial data

52%

Extracting data from documents (e.g., invoices, receipts)

50%

Executing tasks typically done by humans (e.g., coding invoices, matching POs)

49%

Automating multi-step finance or AP workflows

46%

Learning from data to improve over time

45%

Processing invoices from receipt to payment

45%

Generating written content or summaries (e.g., email replies, memos)

45%

Making decisions without human involvement

30%

None of the above

0% 10% 20% 30% 40% 50% 60%

Leadership shows strong support for AI adoption, and most organizations are confident in their financial data to move forward.

Leadership backing is strong, with 48% saying their leaders are very supportive and another 33% somewhat supportive — over 80% in favor overall. Understanding of AI in AP is also high, as 42% report their teams understand it very well and 35% somewhat well. Confidence in financial data readiness is widespread, with 79% expressing trust in their data (45% very confident, 34% somewhat confident). Only a small minority (6%) lack confidence, and for over half of them, weak data readiness is already slowing AI exploration.

‍What this means: Strong leadership support, team understanding, and confidence in financial data create favorable conditions for AI adoption, but for the few without data trust, progress is likely to stall.

How would you describe the level of support for AI adoption for finance or AP workflows among your organization's leadership?

0% 10% 20% 30% 40% 50%

48%

33%

13%

Very supportive Somewhat supportive Neutral Somewhat resistant Actively resistant

Very supportive

48%

Somewhat supportive

33%

Neutral

13%

Somewhat resistant

Actively resistant

To what extent does your confidence in your organization's financial data impact your willingness to explore AI tools for finance or AP workflows?

Responses are from among those not very or not at all confident in their organization financial data.

It's a major barrier – we're not exploring AI because of concerns about our data

11%

It's a moderate barrier – we're exploring AI, but data concerns are slowing us down

57%

It's a minor barrier – we're aware of data issues, but still moving forward

25%

It's not a barrier – our data confidence isn't affecting AI exploration

0% 10% 20% 30% 40% 50% 60%

How confident are you that your organization's financial data is clean, accessible, and ready to support AI tools for finance or AP workflows?

45% Very confident

34% Somewhat confident

15% Neutral

5% Not very confident

1% Not at all confident

AI awareness is strong across organizations

Most respondents report a high level of understanding of potential AI application in AP – 42% say their organization / team understands very well, and another 35% say somewhat well.

Adoption readiness is rising, but security, accuracy, and workforce concerns still hold weight

Many organizations appear ready to adopt AI, but hesitation remains. Data privacy tops the list of concerns, cited by 47%, reflecting persistent unease about handing sensitive financial data to AI systems. Risks of errors or incorrect outputs (40%) and high implementation or maintenance costs (38%) also weigh heavily, while 29% worry about job displacement or workforce reduction. Nearly all respondents expect human oversight: 46% want every AI decision reviewed, and another 45% favor reviewing exceptions or flagged items.

‍What this means: Appetite for AI adoption is growing, but organizations will only move forward confidently if security, accuracy, cost, and human oversight are addressed head-on.

What concerns, if any, do you or your organization have about using AI in finance / AP?

Select all that apply.

Data privacy or security concerns

47%

Risk of errors or incorrect outputs

40%

High implementation or maintenance costs

38%

Regulatory or compliance risks

35%

Difficulty integrating with existing systems

34%

Job displacement or workforce reduction

29%

Resistance from internal stakeholders

24%

Lack of transparency or explainability

23%

We have no concerns at this time

10%

0% 10% 20% 30% 40% 50%

Compliance concerns

While sentiment is generally positive, some respondents raise concerns about maintaining compliance and oversight, citing reliance on AI, occasional errors, and challenges in exception handling.

Where AI is already at work

Most organizations report using AI, though tool depth and scale of use vary

72%

of organizations already use some form of AI in finance or AP, signaling adoption is the norm

AI adoption in AP is now mainstream, with most organizations using it to speed workflows, improve accuracy, and shift staff toward higher-value work

Survey results show that 72% of organizations already use some form of AI, making adoption the norm. Usage is most common among mid-sized companies, high-volume invoice processors, and those with full ERP integration. Maturity levels vary, with just over half already optimizing or operating at scale, while others remain in pilot or scaling phases. Top technologies include workflow automation, generative AI, chat-based tools, and computer vision, reflecting both established use cases and active experimentation. Applications now span the AP lifecycle — from early-stage processing and fraud detection to approvals, payments, and reporting — underscoring AI’s expanding role.

AI strategies and KPI tracking are also becoming standard, with 82% of users reporting defined plans led by IT and data teams in partnership with finance leaders. Processing time, accuracy, and cost per invoice dominate measurement, and most users report benefits such as faster turnaround, improved approvals, and greater employee satisfaction. Non-users see the most potential in automating high-volume, error-prone tasks, but remain cautious due to concerns around security, compliance, trust, and data readiness. Overall, AI is firmly embedded in daily AP operations, with momentum building as strategies mature and adoption expands.

82%

of users have a defined strategy for using AI in AP or finance workflows

Detailed findings

Maturity levels vary, with many still scaling

Adoption of AI in AP is broad but still young, with 72% adopting in the past two years — 37% in the 1–2 year range and 35% in just the past 6–12 months. Progress is evident, as 55% report they are beyond pilot phases: 33% are optimizing fully implemented systems, while 22% are already operating at scale.

‍What this means: AI in AP has clearly moved past experimentation, but many organizations are still early in their journey, leaving significant room for growth and maturity ahead.

How long has your organization been using AI in its AP or finance workflows?

9% Less than 6 months

35% 6–12 months

37% 1–2 years

18% More than 2 years

1% I'm not sure

Adoption is growing, but depth of use varies

While over half (55%) of respondents say they’ve either fully implemented AI, a sizable 45% of respondents are still in pilot or scaling phases with AI use, signaling that many organizations are in transition rather than fully mature.

Which best describes your organization's current stage of AI adoption in AP?

Piloting – limited use by a small team or for a single workflow

14%

Scaling – expanding use across teams or workflows, but not yet fully deployed

31%

Implemented – fully deployed across intended teams / workflows, but still being optimized

33%

Operational – fully implemented, optimized, and actively used at scale

22%

0% 5% 10% 15% 20% 25% 30% 35%

AI use spans the AP lifecycle, with strong traction in workflow automation and
data-heavy processes

AI adoption is strongest in workflow automation (58%) and generative AI (53%), with organizations prioritizing tools that reduce manual workloads. Other technologies such as computer vision and data extraction (44%), autonomous systems (42%), and machine learning (40%) show broad experimentation, though no single approach dominates this middle tier.

On the application side, AI is being used across the AP lifecycle — from early invoice capture to approvals and reporting. Data extraction (46%) is the most common entry point, enabling automation further downstream. Core processes including invoice ingestion, fraud detection, approvals, payments, and reporting (36–40%) also show solid traction, underscoring AI’s growing role in speeding and safeguarding AP operations.

‍What this means: AI is no longer confined to niche use cases; it is being applied across the AP workflow, with early adoption in data-heavy tasks paving the way for broader process automation and risk reduction.

What types of AI technologies is your organization using for AP or finance workflows?

Select all that apply.

AI-enhanced workflow automation (e.g., routing or triggering actions based on predicted needs)

58%

Generative AI (e.g., summarizing documents or creating draft communications)

53%

Chat-based AI (e.g., chatbot or virtual assistant for vendor or internal inquiries)

45%

Computer vision and data extraction (e.g., reading scanned documents or handwritten invoices and extracting data)

44%

Autonomous decision-making systems (e.g., approving, matching, or flagging without human input)

42%

Machine learning for pattern detection or forecasting (e.g., spend trends, cash flow)

40%

Natural language processing (e.g., extracting information from documents or emails)

36%

0% 10% 20% 30% 40% 50% 60%

Where is AI currently being used in your organization's AP or finance workflows?

Select all that apply.

Data extraction from invoices or receipts

46%

Invoice capture or ingestion (e.g., reading PDFs, emails, paper scans)

40%

Fraud detection or compliance monitoring

40%

Invoice approval workflows

39%

Payment scheduling or execution

39%

Reporting, dashboards, or performance analytics

38%

Cash flow forecasting or spend analysis

36%

Exception handling or flagging anomalies

31%

Vendor or supplier communication / management

30%

Supplier risk or compliance management

30%

PO / invoice matching

30%

Line-item coding or classification

27%

0% 10% 20% 30% 40% 50%

KPIs and outcomes show AI delivers measurable gains in speed, accuracy, and efficiency

Processing time, accuracy, and cost per invoice dominate as the leading KPIs for AP performance, each cited by about a third of respondents. This reflects a strong focus on speed, precision, and cost-efficiency in AP workflows. Nearly all organizations using AI are tracking impact in some form, with only 3% reporting that they do not monitor KPIs at all.

Most respondents (79%) report measurable benefits from AI adoption, led by faster invoice processing (50%) and quicker approvals (46%). Many also cite improved employee satisfaction (44%), stronger payment timing or discount capture (42%), and enhanced reporting (41%), showing both operational and experiential improvements. Financial and external-facing benefits are emerging as well, with lower processing costs (36%) and better vendor or supplier relationships (34%) noted, though these remain less dominant compared to speed and accuracy gains.

‍What this means: Organizations are tracking AI’s impact with increasing rigor, and the benefits are clear — AI consistently delivers faster, more accurate, and more efficient AP operations, while beginning to unlock broader financial and relational value.

Has your organization seen any measurable outcomes so far from using AI in AP or finance?

79% Yes

13% No

8% I'm not sure

Automation of routine tasks

Survey respondents note AI is automating routine tasks such as data entry, invoice processing, and approvals — accelerating workflows, minimizing bottlenecks, and completing tasks much faster than before.

If you answered 'yes', in which areas have you seen measurable outcomes using AI?

Select all that apply.

Faster invoice processing

50%

Faster approval cycles

46%

Higher employee productivity or satisfaction

44%

Improved payment timing or discount capture

42%

Improved reporting or real-time visibility

41%

Improved compliance or fraud detection

40%

Fewer errors or improved accuracy

39%

Improved decision-making or forecasting insights

37%

Lower processing costs

36%

Improved vendor or supplier communication / management

34%

0% 10% 20% 30% 40% 50%

Which KPIs does your organization use to measure the effectiveness of AI in AP or finance workflows?

Select all that apply.

Invoice processing time (from receipt to approval or payment)

37%

Invoice accuracy

37%

Invoice processing cost (per invoice)

34%

Fraud detection or compliance metrics

32%

Invoice exception rate (% of invoices needing manual review)

31%

Invoice approval cycle time

30%

Return on investment (ROI)

30%

On-time payment rate

30%

Number of invoices processed per FTE

29%

Time-to-post (invoice entry into ERP)

28%

Straight-through processing (STP) rate

28%

Days payable outstanding (DPO)

28%

User satisfaction or ease-of-use feedback

27%

Early payment discounts captured vs. missed

25%

Touchless / no-touch invoice rate

22%

We do not track KPIs to measure AI effectiveness

0% 10% 20% 30% 40%

Insights from those not using AI in finance or AP

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. Many are still in exploration mode, as 36% report they are evaluating options but have not committed. 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.

‍What this means: While non-users remain cautious, their concerns highlight the importance of building trust through accuracy, transparency, and compliance assurances — signaling that once these barriers are addressed, adoption could accelerate quickly.

Why is your organization not currently using AI tools in AP or finance workflows?

Select all that apply.

Security, compliance, or regulatory concerns

37%

We're currently evaluating options

36%

Lack of trust in AI or concern about decision quality

29%

Concerns about data quality or data availability

27%

Lack of leadership support or buy-in

24%

Budget constraints or limited resources

24%

Too many higher-priority initiatives

23%

Lack of internal expertise or resources

21%

Unclear value or ROI

20%

Uncertainty about how to get started or what to prioritize

17%

Too complex or difficult to implement

14%

We're restricted to using centralized / approved tools only

13%

AI capabilities not needed for our current scale

13%

We previously piloted AI tools but did not continue

0% 10% 20% 30% 40%

Data extraction, ingestion, and reporting lead as top opportunities for non-users

Among organizations not yet using AI, the strongest opportunities lie in foundational, high-volume tasks. Data extraction leads at 45%, followed by invoice ingestion, reporting, and PO matching (each at 38%), reflecting demand for support in early-stage processing. Fraud monitoring, invoice approvals, and payment execution (35–37%) also rank highly, showing interest in risk reduction and streamlined decision points later in the workflow.

When asked about pain points they most want AI to solve, manual data entry stands out at 44%, underscoring its persistent burden and priority for automation. Frequent errors (33%) and manual payment processing (30%) also remain key frustrations, highlighting accuracy and execution as ongoing challenges.

‍What this means: Non-users see AI’s greatest potential in tackling repetitive, error-prone tasks across the AP lifecycle. Their focus on early-stage processing and persistent pain points suggests clear entry points for adoption, with efficiency and accuracy gains serving as the strongest motivators.

Which areas of your organization's AP or finance workflows do you believe could benefit most from AI?

Select all that apply.

Data extraction from invoices or receipts

45%

Invoice capture or ingestion

38%

Reporting, dashboards, or forecasting

38%

PO / invoice matching

38%

Fraud detection or compliance monitoring

37%

Invoice approval workflows

35%

Payment scheduling or execution

35%

Cash flow forecasting or spend analysis

33%

Exception handling or flagging issues

28%

Line-item coding or classification

27%

Vendor or supplier communication / management

21%

Supplier risk or compliance management

18%

None of the above

0% 10% 20% 30% 40% 50%

What are the top challenges or pain points you would like AI to help solve in your AP or finance processes?

Select up to three.

Manual data entry or processing

44%

Frequent errors or exceptions

33%

Manual payment processing

30%

Slow invoice approval cycles

25%

Late payments or missed discounts

23%

PO matching or document matching

22%

Invoice processing efficiency per FTE

22%

High cost per invoice

21%

Disconnected systems or tools

17%

Difficulty scaling for invoice volume

11%

None of the above

0% 10% 20% 30% 40% 50%

Motivators driving AI investment center on reliability and efficiency

When considering AI for AP and finance, organizations are most motivated by outcomes that improve reliability and efficiency. Fewer errors (43%) and faster invoice processing (39%) top the list, followed by reducing manual effort (28%) and lowering costs (25%). Broader goals such as employee satisfaction, fraud detection, scalability, and forecasting (14–19%) are also noted, but remain secondary to speed and accuracy gains. If piloting AI, most organizations would begin with invoice capture (39%), PO matching (37%), fraud monitoring (34%), or reporting (34%) — areas seen as low-hanging fruit with high potential impact.

‍What this means: Organizations are ready to invest in AI where it can immediately boost accuracy, speed, and efficiency, with early adoption likely to focus on repetitive, high-volume processes that deliver quick, measurable wins.

Which outcomes would be most important to your organization when deciding whether to invest in AI for AP or finance workflows?

Select up to three.

Fewer errors or improved accuracy

43%

Faster invoice processing

39%

Reduced reliance on manual work

28%

Lower processing costs

25%

Clear return on investment (ROI)

22%

Improved reporting or real-time visibility

22%

Higher employee productivity or satisfaction

19%

Improved compliance or fraud detection

17%

Greater scalability as volume increases

15%

Improved decision-making or forecasting insights

15%

Improved vendor or supplier communication / management

14%

Reallocation of roles / FTEs

11%

None of the above

0% 10% 20% 30% 40% 50%

If your organization were to pilot AI, where would you most likely apply it first?

Select all that apply.

Invoice capture or ingestion (e.g., reading PDFs, emails, paper scans)

39%

PO / invoice matching

37%

Fraud detection or compliance monitoring

34%

Reporting, dashboards, or analytics

34%

Invoice approval workflows

30%

Cash flow forecasting or spend analysis

26%

Exception handling or flagging anomalies

25%

Payment processing

25%

Vendor onboarding or risk scoring

13%

N/A – We will not pilot AI

0% 10% 20% 30% 40%

Benchmarking the growth of AI in AP

The current state and future trajectory of AI in AP

Humans and machines: A collaborative future

Finance leaders are embracing AI and process evolution is underway

AI is seen as a productivity tool, and for some, an autonomous partner

Detailed findings

Most organizations report using AI, though tool depth and scale of use vary

Detailed findings

Rising investment plans position AI as a core driver of AP transformation

Detailed findings

AI is established in AP, and adoption
is poised to accelerate

About Vic.ai

Ready to see Vic.ai in action?

Benchmarking the growth of AI in AP

The current state and future trajectory of AI in AP

Humans and machines: A collaborative future

Finance leaders are embracing AI and process evolution is underway

AI is seen as a productivity tool, and for some, an autonomous partner

Detailed findings

Most organizations report using AI, though tool depth and scale of use vary

Detailed findings

Rising investment plans position AI as a core driver of AP transformation

Detailed findings

AI is established in AP, and adoption is poised to accelerate

About Vic.ai

Ready to see Vic.ai in action?

AI is established in AP, and adoption
is poised to accelerate