As artificial intelligence evolves from assisting employees to independently executing business processes, finance leaders face a critical challenge: How can organizations increase automation without sacrificing accuracy, accountability, or control?
This question was central to “Governing Autonomous Agents & Actions,” a panel discussion at SOLIXEmpower 2026 in San Diego this October.
Moderated by James Massa, Senior Executive Director of Software Engineering and Architecture at JPMorganChase, Emagia Founder and CEO was joined on the panel by Valquir Correa, Vice President of Corporate Finance at Baha Mar and Govil Kumar, Senior Finance Executive and Segment Finance Leader at TIC Solutions.
Drawing on Emagia’s experience with global enterprises, Emagia’s Founder and CEO outlined how organizations can deploy autonomous AI agents responsibly through stronger data foundations, defined operating boundaries, and progressive automation.
Two Opportunities for AI Transformation in Finance
Gundavelli distinguished between two categories of finance work where AI is creating significant opportunities.
Activity-based
Activity-based operations
Repetitive processes such as accounts receivable, credit management, collections, cash application, and accounts payable. AI agents can increasingly perform tasks involving document interpretation, data processing, and established workflows.
Knowledge-based
Knowledge-based activities
Forecasting, financial modeling, scenario analysis, and profitability optimization. Here, AI helps finance professionals interpret information and make better-informed decisions.
For CFOs and Global Business Services (GBS) leaders, the opportunity extends beyond reducing operational costs. AI can improve efficiency, working capital, profitability, and strategic decision-making.
However, Gundavelli emphasized that successful deployment begins with the fundamentals.
Data Readiness: The Foundation of Autonomous Finance
One of Gundavelli’s strongest messages concerned data quality.
Real-world example
She described an enterprise environment involving approximately 200,000 customers where invoice delivery problems sometimes originated from inaccurate contact information.
An AI agent cannot reliably manage customer communications if email addresses, customer records, or other essential information are incorrect.
Organizations must therefore prioritize data validation, cleansing, and standardization before expanding autonomous execution.
The Semantic Layer
Gundavelli also highlighted the importance of the semantic layer, which connects technical data structures with business meaning.
Terms such as claims, deductions, chargebacks, and payments can have different interpretations across industries and organizations. AI systems need this context to interpret enterprise information accurately.
For companies operating across multiple ERP systems, business units, and geographic regions, data readiness and semantic consistency are essential to reliable AI performance.
Establishing Guardrails for AI Agents
Finance departments have long relied on standard operating procedures to define employee responsibilities, authorization limits, and escalation requirements.
Establishing Guardrails for AI Agents
Finance departments have long relied on standard operating procedures to define employee responsibilities, authorization limits, and escalation requirements.
Gundavelli explained that AI agents require comparable controls.
Organizations must specify what information agents can access, which actions they can execute, and when human approval is necessary.
For example, an AI agent evaluating a credit application should not invent missing financial information or approve credit beyond established thresholds.
These guardrails must be built into agent configuration and governance processes.
The larger challenge is defining which responsibilities belong to AI agents and which should remain with finance professionals.
Three Modes of AI Adoption: Assisted, Supervised, and Autonomous
A particularly practical contribution from Gundavelli was her explanation of Emagia’s three operating modes for AI agents.
Rather than immediately granting full autonomy, organizations can expand an agent’s responsibilities as reliability and confidence increase.
In assisted mode, AI prepares information or recommends actions, but employees review and approve the results.
Gundavelli described an enterprise receiving thousands of customer emails across multiple countries and languages.
An AI agent can translate an incoming Portuguese-language message into English, prepare a response, and translate it back into Portuguese.
Initially, employees review these communications before they are sent, ensuring accuracy while building confidence in the technology.
Supervised mode allows greater automation for predictable activities while retaining oversight for exceptions.
A straightforward request for an account statement may be handled routinely. However, a message requesting a statement while expressing dissatisfaction with an invoice may indicate a dispute requiring human attention.
The agent can prepare the appropriate response while an employee reviews more complex circumstances.
Once agents demonstrate reliable performance within specific scenarios, organizations can authorize them to execute selected tasks independently.
Importantly, autonomy applies to defined activities rather than every possible situation.
Protecting Customer Relationships Through Governance
Gundavelli emphasized that not every customer interaction should be delegated to an autonomous agent.
Suitable for automation
Routine payment reminders and straightforward collections activities.
Needs human involvement
Strategic customers, sensitive relationships, and complex disputes often require direct human involvement.
Finance leaders must establish clear policies determining which customers AI agents may contact independently and when employees should retain control.
This reinforces an important principle: AI governance must protect customer relationships as well as financial accuracy.
Finance Team Readiness Remains a Major Challenge
When asked about the biggest unresolved obstacle to autonomous agent deployment, Gundavelli identified finance team readiness.
Finance professionals are navigating rapidly evolving technologies while adapting established processes and responsibilities.
Gundavelli compared the challenge to How to Train Your Dragon: what initially seems intimidating becomes manageable as people understand the technology and learn to work with it.
For finance leaders, successful adoption requires education, experimentation, change management, and clearly defined expectations.
Technology alone cannot deliver transformation without employees prepared to use it effectively.
Why Order-to-Cash Is a Priority
Asked which finance workflow she would prioritize over the next 12 to 18 months, Gundavelli selected Order-to-Cash (O2C).
The process directly influences working capital and how efficiently enterprises convert sales into collected revenue.
AI agents can support credit management, invoicing, collections, payment reconciliation, and related workflows by reducing manual effort and improving execution.
For CFOs seeking measurable returns from AI investments, O2C provides opportunities to connect operational efficiency with financial performance.
How Emagia Supports Governed Autonomous Finance
Emagia’s AI-Native, End-to-End Autonomous Finance Platform enables enterprises to introduce intelligent AI agents across Order-to-Cash operations.
Through configurable workflows, defined agent responsibilities, and assisted, supervised, and autonomous operating modes, Emagia supports progressive automation with appropriate human oversight. This approach helps organizations modernize financial operations while maintaining governance, accountability, and operational control.
The Path Forward: Autonomy With Accountability
The SOLIXEmpower discussion reinforced that responsible AI transformation requires more than sophisticated technology.
Gundavelli’s insights highlighted four essential priorities:
For CFOs, Controllers, Treasury executives, and GBS leaders, the objective is not simply replacing manual tasks with automation.
It is creating an operating model where AI agents and finance professionals work together to improve efficiency, strengthen decision-making, and deliver sustainable business value.
Frequently Asked Questions (FAQ)
What is autonomous agent governance in finance?
It involves defining the policies, permissions, approval limits, and oversight mechanisms that control how AI agents execute financial activities.
What are the three modes of AI agent deployment?
Assisted mode requires human approval, supervised mode provides oversight for exceptions, and autonomous mode permits independent execution within established boundaries.
Why is data readiness essential for Agentic AI?
AI agents depend on accurate information and business context. Incomplete records, inconsistent definitions, or poor data quality can undermine automated decisions and actions.
Why is Order-to-Cash a priority for autonomous finance?
O2C combines high-volume processes with direct impacts on receivables, working capital, and customer relationships, making it a valuable area for AI-enabled operational improvement.
