Artificial intelligence is steadily reshaping enterprise finance. What began as automation for data capture and invoice matching is now evolving into something more ambitious: autonomous digital agents capable of acting on behalf of finance teams.
Basware has taken a significant step in this direction by introducing AI agents into its invoice lifecycle management platform. Building on its existing InvoiceAI capabilities, the company is positioning these new tools as foundational elements of what it calls “Agentic Finance” — a model where AI systems execute financial tasks under strict governance controls.
The vision is bold: near-perfect, touchless invoice processing in the short term, and ultimately a future where AI agents transact on behalf of enterprises to deliver faster decisions, stronger compliance, and measurable business outcomes.
But how realistic is the goal of “100% automated, 100% compliant, and 100% protected” invoice processing? And what does this shift mean for accounts payable (AP) teams worldwide?
From Invoice Automation to Agentic Finance
Invoice automation is not new. For years, finance platforms have focused on:
- Optical character recognition (OCR)
- Automated invoice matching
- Workflow routing
- Exception handling
However, these tools typically require significant human oversight. Finance teams still manage approvals, resolve disputes, answer supplier queries, and generate reports.
Basware’s latest AI agents aim to go further. Rather than simply assisting with data extraction or validation, they are designed to:
- Provide contextual guidance
- Answer operational questions
- Interpret transaction data
- Execute actions within predefined policy boundaries
Jason Kurtz, CEO of Basware, describes this evolution as a transition from transaction processing to strategic enablement. In his view, AI agents can handle repetitive finance queries, freeing teams to focus on higher-value decision-making.
What Is Agentic Finance?
Agentic Finance refers to a structured environment in which AI entities operate autonomously — but within controlled boundaries.
Unlike basic automation scripts, AI agents:
- Understand context
- Learn from patterns
- Interact through natural language
- Execute predefined actions
- Escalate exceptions when required
Crucially, they operate under governance frameworks.
This distinction matters. Finance functions are risk-sensitive environments where compliance, auditability, and accountability are paramount. Delegating tasks to AI without controls would introduce unacceptable risk.
Basware’s model embeds autonomy within guardrails.
AI Agents in Accounts Payable: Immediate Impact
The most immediate operational impact of Basware’s AI rollout is in accounts payable (AP) — an area historically burdened by manual processing and high volumes of repetitive tasks.
1. AP Business Agent
The AP Business Agent provides contextual guidance to users handling invoices. It analyzes transaction status and recommends next steps.
For example, it can:
- Flag invoices stuck in approval workflows
- Suggest corrective actions for mismatched data
- Identify compliance risks before escalation
This shifts the AP function from reactive troubleshooting to proactive management.
2. AP Data Agent
The AP Data Agent allows users to query invoice data using natural language.
Instead of generating reports manually, finance staff can ask:
- Which invoices are awaiting approval in Germany?
- Which suppliers offered early payment discounts last quarter?
- What is the average approval cycle time for a specific entity?
By removing the need for complex reporting tools, AI makes financial intelligence accessible instantly.
The result: fewer manual queries, reduced email back-and-forth, and faster insights.
The Road to 100% Automated Invoice Processing
Basware’s ambition is clear: complete automation across invoice processing.
In practice, achieving “100% automated” means:
- Zero manual data entry
- Fully automated matching
- Autonomous exception handling within policy limits
- Predictive fraud detection
- Automated compliance verification
However, perfection in finance is notoriously difficult. Edge cases, regulatory differences, and supplier variability complicate automation efforts.
The key lies in layered intelligence:
- Structured Data Foundation
- Machine Learning Enhancements
- Generative AI Interfaces
- Policy-Based Governance Controls
By integrating AI agents into its platform rather than offering them as standalone tools, Basware aims to make automation systemic rather than superficial.
Governance: The Core of AI in Finance
The most significant challenge in agentic finance is governance.
AI systems can only be trusted if they are:
- Explainable
- Auditable
- Policy-aligned
- Transparent
Basware routes AI agent actions through what it calls a central policy engine. This engine applies:
- Business rules
- Compliance requirements
- Risk thresholds
- Authorization protocols
These controls function as “autonomy gates,” ensuring AI does not exceed its delegated authority.
In practical terms, this means:
- AI can recommend approvals but cannot override controls.
- High-risk transactions require human validation.
- Every action generates an audit trail.
This approach addresses a common fear among finance leaders: loss of control.
Adoption Trends: Experimentation vs. Operationalization
Despite widespread AI hype, adoption in finance remains uneven.
A survey conducted on behalf of Basware found:
- 61% of organizations have experimented with AI agents.
- 25% do not fully understand what an AI agent looks like in practice.
The survey gathered insights from 200 finance leaders across:
- United States
- United Kingdom
- France
- Germany
These findings suggest that many enterprises remain in exploratory phases rather than full-scale deployment.
The gap between experimentation and operational transformation often comes down to three factors:
- Data readiness
- Governance clarity
- Cultural acceptance
Basware’s strategy appears focused on moving customers from pilot programs to fully embedded AI workflows.
Changing the Role of Accounts Payable Teams
One of the most compelling implications of AI agents is workforce transformation.
Traditionally, AP teams spend significant time on:
- Manual invoice verification
- Repetitive supplier queries
- Status tracking
- Data reconciliation
If AI agents absorb these tasks, the AP role shifts toward:
- Exception analysis
- Supplier relationship management
- Cash flow optimization
- Strategic cost control
In theory, this transition elevates AP from a back-office processing unit to a strategic contributor.
However, this transformation depends on how organizations manage change. Automation without training or role redesign can create friction rather than efficiency.
Upcoming AI Agents in Development
Basware is not stopping with AP Business and Data Agents.
Planned developments include:
Supplier Agent
Designed to manage invoice disputes and payment queries, this agent could:
- Communicate directly with suppliers
- Summarize conversations
- Propose resolution pathways
- Reduce response times
AP Pro Agent
A generative AI assistant intended to help staff resolve complex processing questions via conversational interfaces.
These additions move the platform closer to comprehensive agentic workflows.
Early User Experience: Operational Gains
One early adopter cited by Basware is Billerud, a paper manufacturer.
According to Jesper Persson from Billerud, benefits included:
- Improved invoice quality
- Increased processing efficiency
- Tangible cost savings
AI performance tends to improve over time as systems learn from additional data. Continuous refinement is essential to sustain value.
Data Quality: The Hidden Determinant of Success
AI agents are only as effective as the data they rely on.
For finance departments, this means:
- Clean supplier master data
- Standardized invoice formats
- Clear business rules
- Digitized historical records
Poor data hygiene undermines AI performance.
Organizations seeking to adopt agentic finance must first assess:
- Invoice exception rates
- Data consistency levels
- Workflow maturity
- ERP integration strength
Without strong foundations, AI risks amplifying inefficiencies rather than resolving them.
Trust and Delegation: The Psychological Barrier
Beyond technical readiness lies a deeper issue: trust.
Finance leaders must determine:
- Which decisions can be delegated?
- At what risk thresholds?
- Under what compliance structures?
Delegation is not binary. It is incremental.
Enterprises may begin by allowing AI agents to:
- Provide recommendations only
- Execute low-risk actions
- Manage routine supplier queries
Over time, as confidence grows, autonomy may expand.
The Strategic Value of Agentic Finance
When implemented correctly, AI agents in finance can deliver:
- Faster invoice cycle times
- Reduced processing costs
- Lower error rates
- Improved compliance consistency
- Better working capital optimization
More importantly, they can transform finance from reactive processing to proactive strategy.
For example:
- AI can identify early payment discount opportunities.
- It can flag anomalous supplier behavior.
- It can detect compliance risks before audits.
These insights drive measurable business outcomes.
AI in Finance Is Not an Add-On
Basware emphasizes that AI is embedded within its platform architecture — not a bolt-on feature.
This distinction matters because:
- Add-ons often create integration gaps.
- Standalone AI tools may lack governance integration.
- Disconnected systems weaken audit trails.
Embedding AI ensures consistency across workflows.
Keys to Agentic Success in Finance Departments
For organizations considering AI agents in accounts payable, several critical success factors stand out:
1. AI Model Quality
Accuracy, adaptability, and explainability determine effectiveness.
2. Data Integrity
Clean, structured invoice data is non-negotiable.
3. Governance Framework
Business rules must be translated into machine-enforceable logic.
4. Organizational Willingness
Leadership must define clear delegation boundaries.
5. Change Management
Employee training and role redesign are essential.
Without alignment across these areas, agentic initiatives may stall.
The Future of AI-Driven Accounts Payable
The trajectory of AI in finance suggests increasing autonomy — but not unchecked autonomy.
The likely future includes:
- Real-time compliance monitoring
- Autonomous dispute resolution
- Predictive cash flow forecasting
- Continuous fraud detection
- Self-optimizing payment timing
By 2026, Basware plans to release additional AI tools aimed at expanding delegation capabilities.
The challenge will not be technical capability alone — it will be balancing automation with accountability.
Conclusion: Automation With Accountability
Basware’s AI agents represent a significant step toward intelligent finance automation. The move from invoice digitization to agentic execution signals a broader industry transformation.
Yet, the promise of “100% automated, 100% compliant, and 100% protected” processing depends on more than technology.
Success requires:
- High-quality data
- Strong governance
- Cultural readiness
- Incremental delegation
Agentic Finance is not about replacing human oversight. It is about amplifying it — using AI to eliminate routine burdens while preserving strategic control.
If executed carefully, AI agents could redefine accounts payable as a strategic powerhouse rather than a transactional necessity.
The next few years will determine whether enterprises fully embrace this shift — or remain in experimentation mode.