Smarter software is not enough.
We’re entering a new phase in the AI journey, one where productivity and return on investment (ROI) is more about agency than basic automation. According to Gartner:
“By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously.”
Tom Coshow, Sr. Director Analyst, Gartner
AI is moving from being an assistant to being an actor. It’s beginning to handle multi-step tasks, make decisions, and adapt in real time like a junior team member. This is a major value add for teams trying to scale and the shift is redefining how finance teams will be expected to operate. Instead of relying on dashboards or alerts, they’ll lean on AI agents to proactively resolve exceptions, route approvals, or ensure compliance, while people stay focused on strategy. The most forward-thinking finance teams are already leading their respective industries into that future. Research and advisory firms like Gartner are taking notice.
Understanding the AI agency gap
Let’s zoom in on a crucial concept shaping the look of this enterprise AI future—what Gartner calls the AI Agency Gap.
On the left, we have low-agency systems—static, reactive, and built for simple tasks in predictable environments. Like traditional, deterministic chatbots or rule-based automation, these tools can execute commands, but they don’t think, learn, or adapt.
Contrast that with the high-agency systems on the right. These are adaptive, capable of proactive planning, operating in complex environments, and even making autonomous decisions. There remains a wide gap between the independent abilities of most LLM-based assistants and humans, though Gartner suggests that gap is closing. From our perspective, it’s closing quickly.
Gartner’s chart highlights the messy in-between space most enterprises are still stuck in, relying on tools that lack the intelligence to operate beyond scripted tasks. LLM-based assistants, indicated by the gray dots on the chart, have pushed us part of the way there, with contextual understanding and conversational abilities. True business value is derived when AI reaches its full potential, when it can independently drive outcomes, not just assist humans.
Human skills are still unmatched in a wide variety of areas. But, for the first time, AI agents are entering the domain of human-level activity, rather than cognition alone. If we were to add data points for AppZen’s AI Agents, for example, they would sit immediately to the left of the dark blue points on this graph. They’re managing complexity, making decisions, and executing workflows without constant oversight. Closing this agency gap is expected to define the next generation of productivity in finance operations.
What makes agentic AI different?
To understand why AI agents represent such a transformative leap, let’s clarify what sets them apart from the AI tools we use today.
Think of an AI agent as a conductor in an orchestra. Just as a conductor coordinates multiple musicians, adapts to the performance in real-time, and ensures the entire piece comes together harmoniously, AI agents orchestrate multiple tools and systems to achieve complex business outcomes.
Unlike traditional AI that simply responds to your prompts, agents are:
Goal-oriented, working autonomously toward specific business objectives
Tool-using, accessing and coordinating multiple systems, from ERPs to email
Context-aware, understanding business rules, supplier relationships, and compliance requirements
Adaptive, adjusting their approach based on new information and changing situations
In practice, this means an AI agent handling accounts payable tasks checks your AP inbox for you, extracts invoice data, validates it against purchase orders, checks compliance requirements, communicates with suppliers, resolves discrepancies, and routes invoices for approval, all while learning from each interaction.
AI agent outcomes for finance teams
Why should finance teams care about AI agents?
Although AI agents are only recently entering the finance space, our data shows a major step decrease in manual processing efforts over existing AI tools. Early deployments of AI agents indicate faster turnaround times, leading to less friction with vendors and happier employees. One of the largest areas of improvement is in error reduction, which directly boosts compliance, reduces audit risk, and increases trust in the system, overall.
Where finance teams struggle to deliver scalable, responsive support to system users, AI agents enable self-service for both employees submitting expenses and vendors tracking invoices, freeing up finance to focus on higher-value work. AI agents also give teams real-time visibility into spend trends and anomalies. Instead of waiting for end-of-month reports, finance leaders can act in the moment.
When you combine the outcomes of more efficiency, fewer errors, lower cost, better experience, and smarter decisions, it’s clear that AI agents are a strategic shift.
Five fundamental shifts in finance operations
This reinvention of automated financial operations is optimizing for speed, intelligence, and scale. Let’s look at the transformations in how work gets done with a shift to agentic AI.
Process execution. Agentic AI flips the script on human-driven processes supported by basic automation. Agents orchestrate entire multi-step activities like invoice processing autonomously, meaning end-to-end without handoffs, and people provide the agents support, instead.
Exception handling. Manual bottlenecks disappear. Agents intelligently resolve or escalate tricky exceptions to the team based on context, dramatically reducing delays and workload.
Decision-making. Instead of waiting for a dashboard or report, agents proactively make context-aware decisions in real time, driving faster, smarter financial actions.
Workforce composition. We’re moving from teams of people supported by static robotic process automation (RPA) to hybrid teams where humans and AI agents collaborate. People focus on the strategic exceptions and oversight, and AI agents handle the rest.
Financial control. Rather than periodic audits and manual checks, agentic AI enables continuous compliance and anomaly detection, enforcing policies in real time instead of after the fact.
Is now the right time to adopt agentic finance?
Leaders embracing AI adoption are already learning how to manage hybrid human-AI teams. These individuals, along with the “frontier firms” rapidly adopting AI, are defining the competitive landscape of tomorrow. One day, your team may be augmented by AI that independently executes processes. Gartner’s research makes that trajectory clear. The question is whether your organization will lead this transformation or follow it.
The competitive advantage won’t go to companies with the most advanced technology, but to those who best integrate AI agents into their operations, starting small with task automation, building toward autonomous exception handling, and ultimately creating intelligent, collaborative finance operations. This seamless, human-AI collaboration will deliver unprecedented efficiency, accuracy, and strategic insight.
How important is it to begin today, preparing teams and processes for a world where AI agents are collaborators? If the research firms studying industry trends and marketplace activities are indeed correct, organizations that answer, “Very,” and act accordingly will be the ones seeing the real ROI in the technology as they write the success stories of the next decade.
Contact us today to see a live demonstration of AppZen’s AI Agents and learn more about how they can help your business gain a competitive advantage today.
