Finance leaders are operating under constant pressure. Economic uncertainty, regulatory change, talent constraints, and the demand for faster reporting are increasing workload across finance operations. At the same time, AI is advancing quickly, and the volume of new terminology often creates confusion instead of clarity.
In the session From Brains to Bots: What Agentic AI Means for Finance Leaders, Laura Elliston, Senior Manager, Product Marketing at Quadient, explored how agentic AI differs from traditional automation, and how finance teams can adopt it in a practical, responsible way.
Why are finance teams feeling AI fatigue?
Many organisations already use automation and analytics, but key workflows still rely on manual intervention and disconnected systems. Rules based automation is useful for consistent repeatable steps, while predictive AI helps teams identify patterns and risks. However, in many cases, people still need to interpret outputs, decide what to do next, and then execute actions across multiple tools. That gap slows execution, increases inconsistency, and creates operational friction.
Laura described this as AI fatigue and confusion. Finance teams are trying to keep up with fast moving innovation while still delivering accuracy, compliance, and reliability every day.
What is agentic AI, and why it is different?
Agentic AI refers to systems that can take action toward a goal, not simply provide information or recommendations. Instead of stopping at an answer, an agent can plan steps, execute tasks across systems, monitor outcomes, and adjust when conditions change. Importantly, it operates within guardrails defined by the organisation, including role-based access, approval checkpoints, and escalation rules.
This represents a shift from task automation to goal driven execution. For finance teams, it can mean fewer handoffs, faster cycle times, and more consistent processes, while keeping human oversight where it matters.
Where can agentic AI support finance workflows?
Laura highlighted several use cases where agentic AI can streamline day to day operations.
In accounts payable, it can support invoice capture, validation, matching, and anomaly detection, while escalating exceptions for review.
In accounts receivable, it can help with smart invoicing, collections actions, and credit risk monitoring based on payment behaviour and account signals.
In payments, it can assist with cash flow decisions, payment timing, and vendor self-service interactions, such as invoice status queries and dispute handling.
Across these areas, the value is consistent. Agentic AI can reduce manual effort, improve speed and consistency, and free teams to focus on analysis, stakeholder communication, and strategic decisions.
Governance and guardrails for responsible adoption
Laura emphasised that governance is essential for safe deployment. Leaders should define what actions agents can perform, when approvals are required, how exceptions are handled, and how actions are logged for auditability. Privacy and security also matter. Systems should only access the data needed for the task, and they must respect role-based permissions.
A practical path to adoption
Finance teams can start with contained workflows that are repeatable and low risk. From there, they can map processes end to end, improve data quality, and scale gradually with clear measurement and governance. With the right structure and oversight, agentic AI can help finance organisations move from insight to action and transform workflows one process at a time.