Agentic AI for AP: A new era for finance leaders

Traditional automation has reached its limits. Finance leaders are under increasing pressure to modernize accounts payable operations, reducing costs, ensuring compliance, and delivering insights that drive efficiency. The next evolution is agentic AI: autonomous systems that make decisions, adapt in real time, and continuously learn from outcomes.

This evolution will see accounts payable professionals shift from admin-heavy, repetitive tasks to becoming managers of AI agents, requiring finance leaders to address employees' training and development needs. With a focus on invoicing and expense auditing, this white paper explores how agentic AI transforms AP workflows, covering autonomy levels, hybrid workforces, and transformation strategies.

The urgency for agentic AI

According to the Microsoft Work Trend Index 2025, 82% of business leaders say this is a pivotal year to rethink key aspects of strategy and operations. Furthermore, 81% expect agents to be moderately or extensively integrated into their company's AI strategy in the next 12-18 months.

For CFOs and finance leaders, the question is no longer if AI agents will arrive in AP—it's how quickly they can be implemented responsibly.

The evolution of autonomy in accounts payable

Accounts payable is perfectly matched to agentic AI adoption. Its workflows and standard operating procedures (SOPs)—high-volume, rule-based, and data-driven—are ideal for intelligent automation.

Agentic AI evolves through distinct levels of autonomy, each delivering progressively greater independence and strategic value.

How agentic AI operates in accounts payable

AI agents can automate many SOP-driven accounts payable functions. In invoice processing and expense auditing, where workflows are high-volume, repetitive, and rule-based, AI agents can quickly learn, replicate, and optimize.

For example, an AI agent in invoice processing can capture invoice data through an AI-powered AP inbox—extracting details using OCR and language models. It cross-references purchase orders, vendor data, and contract terms. Once completed, the agent either approves the invoice or escalates it for human review if it detects potential flags. As it does so, it learns and applies that learning, so similar future scenarios can be resolved without escalation.

An AI agent in expense auditing extracts and categorizes receipts, checks compliance with policy, and detects duplicates or anomalies. It continually learns and improves through the feedback it receives. For the business, this leads to faster cycle times, fewer errors, and stronger compliance.

The hybrid workforce: Humans and digital coworkers

Agentic AI ushers in a new, hybrid workforce model where humans and intelligent agents—often referred to as digital coworkers—work together and collaborate.

Digital coworkers execute well-defined, repeatable tasks with speed and accuracy, while human professionals focus on strategy, governance, and innovation. The balance between the two will define the next generation of finance teams.

In a hybrid finance team, human managers collaborate with AI agents across departments like T&E and AP. Each agent specializes in specific tasks—from data extraction to compliance checks—while humans provide oversight, handle exceptions, and ensure accountability.

Redefining finance roles in the age of agentic AI

As digital coworkers take on repetitive work, finance professionals will move into higher-value roles. Companies using AI agents will require their human professionals to oversee and train them, manage escalations, and ensure compliance. Effectively, they will be the managers of AI agents.

Finance leaders need a plan to address upskilling in data interpretation and validation, prompt and policy design, workflow optimization, supplier relationships, and AI oversight and ethics. These will be the most sought-after skills of the future.

Building trust through human oversight

Trust and accountability are essential to any AI-driven finance operation. Best-in-class agentic AI systems integrate human-in-the-loop mechanisms, escalating uncertain or high-risk cases to a human manager for review.

Why agentic AI outperforms traditional automation

Traditional automation, such as RPA, macros, or static machine learning, focuses on execution. Agentic AI adds adaptation: it perceives context, makes real-time decisions, and learns from outcomes.

Instead of following preset rules, agentic AI understands and adapts to changing data, policies, and SOPs. It learns from feedback rather than requiring constant human updates. It doesn't just automate tasks; it manages entire workflows and decision-making processes.

Your agentic AI adoption roadmap

Agentic AI adoption is a journey. Finance leaders should set their own pace based on readiness and goals. Having a methodical approach maximizes team value while minimizing disruption.

Build vs. buy: Why best-of-breed vendors lead

As finance organizations evaluate how to implement agentic AI, one strategic question dominates: Should we build our own agents in-house, or adopt best-of-breed vendor platforms built for finance?

Developing proprietary agents can seem attractive—promising customization and direct control. However, in practice, in-house builds often encounter critical barriers: advanced expertise requirements, continuous retraining needs, and integration management.

Another serious concern is AI hallucination, when a model generates inaccurate or fabricated outputs. In finance, a hallucination could lead to misclassifying an invoice or making an incorrect payment. Vendor platforms mitigate this risk through rigorous model testing, curated datasets, and human-in-the-loop controls.

The strategic impact of agentic AI

The transformation of finance operations through AI is happening now. Organizations that postpone implementation risk falling behind competitors already reaping the benefits.

CFOs must decide between continuing with operations becoming more inefficient and costly, or adopting AI-powered solutions that enhance speed, accuracy, and strategic value.

AppZen brings these practices together with enterprise-grade AI built specifically for finance teams, delivering automated audit, fraud detection, and security at scale while freeing CFOs and their teams to focus on strategic initiatives.

The future of accounts payable

Agentic AI is not a passing trend—it's a structural shift in how finance operations will function. Early adopters will capture both operational and strategic advantage, while late adopters risk inefficiency and losing top talent.

Future finance organizations will be defined by hybrid intelligence: humans providing judgment, AI providing scale, and both together driving the next era of financial excellence.