As agentic AI moves from assisting to acting, clearing reconciliations, freeing up cash, and running controls with limited human touch, the defining question for finance leaders is no longer can we automate, but can we stand behind it. Autonomous only earns its place when it's also accountable and audit-ready.

In our June session, only 2% of finance leaders called their operations truly autonomous, and roughly a quarter flagged governance and controls as the sticking point. This follow-up goes straight at that governance layer.

Join Genpact and industry peers for a candid, execution-focused discussion on how CFOs, financial controllers, and audit executives are embedding agentic AI across core finance workflows — reconciliations, treasury, and controls automation — with the transparency, human oversight, and evidence trails that regulators, auditors, and boards now expect. Expect honest lessons on what breaks, and a practical playbook for staying audit-ready as agentic finance scales.

What the session will cover:

1. From autonomous to accountable — closing the trust gap: As agentic AI shifts from assisting to acting — clearing reconciliations, freeing cash, running controls — the question moves from “can we automate?” to “can we stand behind it?” With a majority of finance executives and risk leaders saying governance can’t keep pace, AI readiness is now a standing audit-committee item, not an innovation topic.
2. Governing agentic finance in real workflows such as reconciliations, treasury, financial controls: Production-grade examples, not theory, across the workflows where agents already act — and where risk spans bias, drift, autonomy, and third-party exposure. Standing behind those decisions demands traceable data lineage and two-level explainability: how each decision is made, and how multi-agent systems reach their outcomes.
3. Trust by design through human oversight, evidence trails, and converged controls: Autonomy without accountability is a risk, not a strategy. Because AI risk sits at the intersection of governance gaps, weak controls, and lax third-party oversight. The panel makes the case for robust human-in-the-loop accountability and a converged model spanning AI governance, cyber, and vendor oversight. 
4. Staying audit-ready as agentic finance scales: Practical sequencing for embedding transparency and evidence trails ahead of year-end and PBC-list season. As point-in-time audits struggle to keep pace with evolving AI, the shift is toward continuous assurance and responsible AI as an audit imperative — covering model risk, data lineage, bias testing, and explainability at scale.