Recent Gartner’s survey reported that CFO’s AI adoption is slowing down. In our conversations with finance leaders and operations teams, one theme comes up crisp and clear: the challenge is not AI’s capabilities but a lack of clear and practical use-cases.
Informed by these discussions, our solutions team has systematically identified the AI use cases that leading enterprises are deploying across finance operations today. This blog synthesizes those insights to offer a strategic framework for organizations looking to adopt AI in Finance with clarity and intent.
How and where AI is creating value in finance
Accounts Payable automation and Invoice Processing
A global pharmaceutical company with an extensive vendor network faced a growing volume of invoice-related inquiries, placing a significant burden on its finance operations team. Much of the team’s time was spent responding to routine status checks and follow-ups, limiting capacity for higher-value work.
In accounts payable, the critical constraint sits between matching and resolution. Invoice processing waits on receipts, confirmations, price clarifications, or approvals that sit outside AP’s direct control. So, tracking an invoice status often requires manual follow-ups.
Accounts Payable AI agents that sit inside enterprise ecosystem can surface invoice status, identify the reason an invoice is blocked, and provide relevant insights. It surfaces whether an invoice is blocked by a missing receipt, an unresolved variance, or an approval delay, while the context is still current.
Approval hierarchies, tolerance rules, and posting logic remain unchanged. It enhances the reconciliation process and invoice processing.
Accounts Receivable and Cash Application
In accounts receivable, payments often arrive with incomplete or unclear references. Resolving them requires checking open invoices, prior payments, deductions, and customer history. This information sits across bank statements, billing systems, and customer records.
AI finance automation supports here by bringing relevant context together at the point of review. Finance teams see likely matches and exception drivers without searching across systems. Control remains with finance. The benefit is reduced investigation efforts.
Financial Close and Reconciliation
Accounts close at different speeds. Some reconcile cleanly. Others depend on upstream activity, intercompany alignment, or late adjustments. Teams often discover this variation too late in the cycle.
AI supports close by tracking reconciliation progress and dependencies throughout the period. It highlights which accounts are trending toward delay and why, allowing teams to act before close pressure builds.
The close structure does not change. The sequencing improves.
Forecasting and Performance Analysis
Forecasting challenges rarely stem from model design. They stem from interpreting movement quickly enough to respond. Internal decisions and external factors move the actuals. Variances appear before scheduled review cycles. Finance teams need to decide what warrants attention and what does not. Understanding these changes requires pulling data from multiple sources and comparing it with plans and assumptions.
AI with predictive analytics in finance supports this judgment by flagging material deviation and surfacing contributing drivers earlier. It presents contributing drivers alongside the variance. Forecast ownership and decision-making remain with finance teams.
Forecasting and Performance Analysis
Why are leaders moving towards Agentic AI in finance?
Leading organization revealed that finance AI projects embedding AI into existing workflows as role-aware agents, that operate alongside core systems, are more successful than deploying AI as standalone implementation or isolated features.
Unlike rule-based automation, Agent-based systems remain active throughout the workflow. They track context and be role relevant, as transactions move across stages, systems, and owners.
An accounts payable agent, for example, monitors invoice flow across intake, matching, approvals, and posting. When dependencies shift such as a pending receipt or delayed approval, the agent surfaces that context, so teams can respond at the earliest. Approvals and postings continue to run through the ERP.
During financial close, agents track readiness across accounts and entities, identifying unresolved reconciliations and blocking dependencies as they emerge. Finance teams retain full authority over decisions and sequencing, but gain a clearer, real-time view of what requires attention.
In forecasting and reporting, agents monitor changes in actuals, flag material deviation, and support analysis without altering planning models or approval structures.
This is what agentic AI means in a finance context today. Not autonomous decision-making, but continuous support within a human-in-the-loop model, where finance teams retain full control. Saxon’s Fin AIssist is designed to support the exact challenge. It is a role-aware, agentic AI assistant for enterprise finance that operates within existing ERP and procurement environments, including SAP, Oracle, NetSuite, and Workday. Rather than replacing systems, it works alongside them, using context from across the ecosystem.
Key considerations for implementing AI in finance operations
Judgment and accountability remain human
Activities that rely on interpretation of accounting standards, policy decisions, or external negotiation do not transfer cleanly to AI systems. Finance teams continue to own these decisions.
Data architecture shapes outcomes
AI reflects the structure and quality of underlying data. Fragmented data models, inconsistent master data, or delayed integrations limit what AI can surface, regardless of model capability. AI cannot correct architectural gaps on its own. This is where data services partners like Saxon can help you with modernizing your data architecture.
Explainability is not optional
Finance teams need to understand how an output was produced, especially for material transactions. AI outputs that cannot be traced back to source data and logic create review and audit challenges.
Governance defines safe boundaries
AI must operate within existing control frameworks. Approval flows, segregation of duties, access controls, and audit trails remain enforced. AI does not bypass these requirements. This human-in-the-loop approach is central to sustainable adoption
Ethical use depends on supervision
Bias, data misuse, and unintended behavior are risks when systems operate without oversight. Finance environments require clear supervision models, defined escalation paths, and periodic review of AI behavior.
These limitations do not reduce the relevance of AI in finance. They define the conditions under which it can be applied responsibly and sustained over time.
Where to start adopting AI in finance?
Accounts Payable & Receivable (AP/AR): Automate the “mundane” by using AI for invoice data capture, automated GL coding, and three-way matching to eliminate manual data entry.
Fraud Detection & Risk Monitoring: Deploy real-time anomaly detection to flag suspicious transactions and potential security breaches faster than manual reviews.
Financial Planning & Analysis (FP&A): Enhance forecasting with predictive modeling that analyzes historical trends and market data to improve budget accuracy.
Customer Service & Operations: Utilize conversational AI chatbots to handle routine balance inquiries or document requests 24/7, freeing your team for complex advisory roles.
Compliance & Audit: Streamline regulatory reporting and audit prep by using AI to scan legal texts and identify relevant rule changes.
What to expect next
You can expect:
Human Potential:
Automates repetitive tasks so your team can focus on high-level strategy.
Self-Learning:
Learns from repetitive patterns and exceptions to handle scenarios coming next.
Near-Perfect Accuracy:
Eliminates costly manual data entry errors and catches inconsistencies instantly.
Faster Decisions:
Provides real-time insights that allow you to react to market changes as they happen.
Ironclad Security:
Monitors every transaction 24/7 to flag and stop fraud before it causes damage.
Better Forecasting:
Uses historical data patterns to predict future cash flows with much higher precision.
Continuous Operations:
Keeps essential processes like bank reconciliations running 24/7 without human intervention.
Scalability:
Handles massive growth in transaction volumes without the need to hire additional staff.
Cost Control:
Identifies hidden spending leaks and duplicate payments that are easy to miss manually.
Explore our SAP AI assistants can help you bring AI wherever you work and transform your ERP and drive measurable outcomes within weeks.
Closing perspective
AI in finance is redefining the scale and structure of how finance teams can handle large volumes. Adding agentic automation into the enterprise workflows is going to maximize throughput while retaining accuracy and human judgement.
When applied with discipline, AI strengthens visibility, coordination, and timing, while leaving judgment, approvals, and ownership exactly where they belong.
For finance leaders, who want to explore how these ideas are being applied in enterprise finance environments, you can review our Agentic AI assistant for enterprises – AIssist to understand how Saxon approaches AI-enabled support for finance workflows within existing ERP ecosystems.