Artificial intelligence is no longer confined to dashboards, analytics reports, or predictive insights. In modern finance operations, a new wave of intelligent systems—known as agentic AI—is transforming how work gets done. Rather than simply analysing data, these autonomous agents execute tasks, make rule-bound decisions, and complete workflows with minimal human intervention.
Finance leaders are now leveraging agentic AI to generate measurable return on investment (ROI), particularly within accounts payable (AP) automation. By converting manual, repetitive processes into self-operating digital workflows, organisations are unlocking efficiency gains, cost savings, and operational scalability.
This article explores how agentic AI is reshaping finance departments, why accounts payable has become the primary proving ground, and how governance, procurement strategy, and workforce transformation influence ROI outcomes.
The Rise of Agentic AI in Enterprise Finance
Traditional AI deployments in finance largely focused on analytics:
- Forecasting cash flow
- Predicting payment risks
- Generating financial summaries
- Detecting anomalies for review
While valuable, these systems required human interpretation and action. Insights did not automatically translate into operational execution.
Agentic AI closes this gap.
These systems operate as autonomous digital workers capable of:
- Processing transactions
- Validating compliance rules
- Triggering approvals
- Executing payments within thresholds
Unlike generative AI, which produces content or insights, agentic AI performs governed actions inside enterprise systems.
ROI Performance: Agentic AI vs Traditional AI
Recent enterprise performance benchmarks highlight a significant ROI gap.
- General AI initiatives delivered average ROI of 67%
- Agentic AI deployments achieved 80% ROI
This performance differential stems from execution capability. Autonomous agents remove labour costs, reduce processing errors, and accelerate financial cycles—directly impacting bottom-line performance.
For CIOs and CFOs, this shift is influencing automation budget allocation. Investment is moving away from experimental AI pilots toward operationally embedded agent systems.
Boardroom Pressure Accelerates Adoption
Executive leadership is increasingly demanding measurable outcomes from AI investments.
Research conducted by Basware in partnership with FT Longitude reveals mounting boardroom expectations.
Key findings include:
- Nearly 50% of CFOs face leadership pressure to implement AI
- 61% of finance teams deployed AI agents primarily as experiments
- Many early pilots lacked defined business outcomes
These exploratory deployments often struggled to produce ROI because they focused on testing capabilities rather than solving operational bottlenecks.
From Experimentation to Execution
Early AI models generated insights but stopped short of workflow action.
For example:
- A system might flag duplicate invoices
- But a human had to investigate and resolve them
Agentic AI eliminates this friction by embedding decisions directly into process pipelines.
As Jason Kurtz, CEO of Basware, explains, leadership tolerance for open-ended AI experimentation is declining. Boards now expect AI to deliver measurable operational value rather than conceptual innovation.
This marks a strategic pivot—from “AI for exploration” to “AI for execution.”
Why Accounts Payable Is the Ideal Use Case
Finance departments are prioritizing high-volume, rule-based functions for agentic deployment. Accounts payable stands out as the most suitable environment.
Research indicates 72% of finance leaders view AP as the primary starting point.
Why AP Works Well for Agentic AI
- Structured Data Inputs
Invoices follow standard formats and contain predictable fields. - Rules-Driven Workflows
Compliance checks, approvals, and tax validations operate under defined logic. - High Transaction Volume
Automation delivers exponential time savings at scale. - Audit Accountability
Financial traceability ensures governance alignment.
These characteristics create a controlled environment where autonomous agents can operate safely and effectively.
Core Agentic AI Applications in Accounts Payable
Organisations are deploying agents across multiple AP functions, including:
1. Invoice Capture and Data Entry
AI extracts invoice data from PDFs, emails, and scanned documents—eliminating manual entry.
2. Duplicate Invoice Detection
Agents identify repeat submissions using pattern recognition and vendor matching.
3. Fraud Identification
Suspicious billing behaviours are flagged or blocked automatically.
4. Overpayment Prevention
Systems cross-verify contract terms, quantities, and pricing.
5. Compliance Validation
Tax codes, regulatory requirements, and internal policies are checked autonomously.
Approximately 20% of finance leaders already use agents daily for invoice ingestion and data extraction.
Data Quality: The Foundation of Agentic Performance
Autonomous decision-making depends on high-quality training data.
Basware’s AI infrastructure is trained on a dataset exceeding two billion processed invoices. This scale enables context-aware decision modelling.
With sufficient historical exposure, agents can distinguish between:
- Legitimate anomalies
- Vendor billing patterns
- True processing errors
This reduces false positives and minimizes human escalation requirements.
As Kevin Kamau, Director of Product Management for Data and AI at Basware, describes it, accounts payable serves as a “proving ground” where scale, governance, and operational control converge.
Build vs Buy: Procurement Strategy for Agentic AI
Technology leaders must determine how to source agentic capabilities.
However, the term “AI agent” spans a wide spectrum—from simple automation scripts to fully autonomous cognitive systems—complicating procurement decisions.
Deployment Preferences by Function
Accounts Payable
- 32% prefer embedded vendor solutions
- 20% build in-house agents
Financial Planning & Analysis (FP&A)
- 35% prefer in-house builds
- 29% choose embedded solutions
Strategic Procurement Framework
A pragmatic decision rule is emerging:
Buy When the Process Is Standardised
Functions like AP are common across industries. Vendor platforms offer pre-trained models and faster deployment.
Build When the Process Is Differentiated
Strategic finance functions—like forecasting models unique to a business—offer competitive advantage when developed internally.
This hybrid approach balances speed, customization, and ROI.
Governance: The Key to Scalable Autonomy
Despite ROI potential, trust remains a barrier.
Research shows 46% of finance leaders will not deploy agentic AI without robust governance frameworks.
This caution reflects legitimate concerns:
- Regulatory compliance risks
- Financial misstatements
- Autonomous approval errors
However, leading organisations treat governance not as a barrier—but as an enabler.
Governance as a Deployment Accelerator
Enterprises with mature governance frameworks deploy agents more aggressively and successfully.
For example:
- 50% of governance-confident leaders use agents for compliance validation
- Only 6% of low-confidence leaders do the same
Governance mechanisms include:
- Approval thresholds
- Audit trails
- Escalation triggers
- Decision logging
These controls allow agents to act autonomously within safe operational boundaries.
Treating AI Agents Like Junior Employees
Anssi Ruokonen, Head of Data and AI at Basware, recommends a workforce analogy.
Organisations should treat AI agents like junior hires:
- Train them with structured tasks
- Monitor performance
- Gradually increase responsibility
Human oversight remains critical during early deployment phases, ensuring accountability and trust calibration.
Workforce Impact: Displacement or Transformation?
The rise of digital workers raises concerns about job displacement.
Research indicates one-third of finance leaders believe workforce disruption is already underway.
However, many organisations report role transformation rather than elimination.
Tasks Most Commonly Automated
- Invoice data extraction
- Document classification
- Payment matching
- Compliance checks
These functions are repetitive and low-strategic in nature.
Shifting Finance Talent to Higher-Value Work
Automation frees finance professionals to focus on:
- Liquidity strategy
- Vendor negotiations
- Risk management
- Financial planning
The shift moves teams from task execution to strategic oversight—enhancing organisational operating leverage.
Operating Leverage and Financial Close Acceleration
Agentic AI also accelerates financial close cycles.
Benefits include:
- Faster invoice approvals
- Real-time ledger updates
- Reduced reconciliation delays
This enables finance leaders to make faster capital allocation and liquidity decisions without increasing headcount.
Usage Intensity Correlates With ROI
Adoption maturity strongly influences returns.
Organisations using agentic AI daily report significantly higher ROI than those limiting deployment to pilot programs.
Why Frequency Matters
- Continuous learning improves model accuracy
- Workflow integration deepens automation impact
- Staff trust increases through exposure
Controlled scaling builds institutional confidence.
The Risk of Unguided AI Experimentation
Not all deployments succeed.
Data shows:
- 71% of low-ROI teams implemented AI under leadership pressure
- Only 13% of high-ROI teams did the same
This indicates that rushed, unguided experimentation undermines value realization.
Successful organisations deploy AI with:
- Defined objectives
- Workflow embedding
- Governance controls
- ROI measurement frameworks
Embedding AI Into Financial Workflows
The most effective agentic systems operate invisibly within enterprise platforms.
Rather than functioning as standalone tools, they integrate into:
- ERP systems
- Procurement platforms
- Payment gateways
- Compliance engines
This deep embedding ensures AI actions occur in real time, not as post-process analysis.
Compliance and Regulatory Alignment
Finance functions operate within strict regulatory environments.
Agentic AI supports compliance through:
- Automated tax validation
- Regulatory reporting checks
- Audit trail generation
- Policy enforcement
Autonomous compliance reduces legal risk while improving reporting accuracy.
Security and Fraud Prevention Benefits
AI agents enhance financial security by:
- Monitoring unusual invoice behaviour
- Identifying vendor spoofing attempts
- Detecting payment anomalies
Because agents operate continuously, they provide real-time risk surveillance beyond human capacity.
Future Outlook: Autonomous Finance Operations
Agentic AI adoption is expected to expand beyond accounts payable into:
- Accounts receivable
- Treasury management
- Expense auditing
- Procurement optimization
Future finance departments may operate with hybrid workforces—human strategists supported by autonomous execution agents.
Strategic Imperatives for Finance Leaders
To maximise ROI from agentic AI, executives should:
- Start with structured, high-volume workflows
- Ensure data quality and training scale
- Implement governance guardrails early
- Embed AI into operational systems
- Scale autonomy gradually
This disciplined approach mirrors how organisations onboard and develop human employees.
Conclusion
Agentic AI represents a decisive shift from insight generation to operational execution in enterprise finance.
By automating complex, rules-driven workflows, particularly in accounts payable, autonomous agents deliver measurable ROI, cost efficiency, and scalability.
Research from Basware and FT Longitude underscores the urgency for organisations to move beyond AI experimentation toward governed, outcome-driven deployment.
With strong data foundations, procurement alignment, and governance frameworks, agentic AI can transform finance departments into high-efficiency, strategically focused operations.
As boardroom expectations rise, one reality is clear: AI that acts—not just analyses—will define the next era of financial performance.