The overarching theme at the Gartner UK Finance Symposium & Xpo 2026 in London in early June (and the Gartner North America Symposium just days before it in National Harbor, MD) was the rise of autonomous finance solutions. But the real breakout conversations and analysis focused on execution, scale, and avoiding the “pilot trap.”
One recurring theme throughout the proceedings was clear: organizations are already moving beyond traditional automation toward AI-Native autonomous finance, where intelligent AI Agents continuously perceive, reason, act, and learn across core finance operations.
This article highlights five of the biggest themes emerging from the conference as well as the top insights from Emagia CEO/Founder Veena Gundavelli’s Day 2 speaking session on best practices and use cases for AI in finance—and what all of these mean for finance leaders preparing for the future of back-office finance.
1. Companies that use AI are setup for “breakaway” advantage for shifting their business model to exponential advantage compared to their peers and competitors who do not use AI
Transition to AI-native autonomous finance shifts organizations from linear efficiency to exponential business advantage. Breakaway companies deploy self-learning AI agents that continuously perceive, reason, and act outside legacy ERP systems. As transaction volumes grow, this compounding operational intelligence automatically sharpens predictive accuracy and risk management without increasing overhead. By replacing rigid, rules-based silos with dynamic AI platforms, leaders compress financial cycles to optimize working capital in near real-time.
2. Organizations need to stop building finance AI like “accidental factories”
Approximately 71% of finance teams report low impact from AI because they are chasing disconnected, isolated pilots.
Breakaway companies do not do piecemeal transformations—they systematically design scalable, end-to-end agentic AI infrastructure from day one, just the way factories are built by systematic planning—plan for end-to-end process transformations.
3. Shift from cost-cutting to enterprise upside with AI
Traditional ROI planning focuses on incremental speed improvements or headcount reduction. The real winners use AI to radically alter operational models and business outcomes—turning finance from a cost center into a strategic growth engine.
4. The Order-to-Cash (O2C) mandate: O2C is the ultimate value generation use case for this AI shift
True autonomy means replacing rigid, rules-based silos with dynamic, interconnected AI platforms so finance leaders can move into pure strategic business partner role.
5. Clarity on AI solutions as they relate to ERP systems
A common and important consideration at present is whether finance leaders should wait to use AI agents within ERP systems. Many CFOs are confused and concerned about AI in ERP.
Gundavelli argues that, because ERPs are systems of record and systems of truth, they need to be fortified. AI agents exist for operations and for insight. They should operate at a different layer.
Autonomous Finance platforms are this new emerging layer with agentic AI orchestration—driving O2C, P2P, R2R and other financial processes.
AI agents:
- Access data and documents from ERPs, CRMs, and internal/external systems: They act as an operational nucleus, securely consolidating data, and documents across your entire enterprise ecosystem to eliminate traditional software silos.
- Perceive the information collected: They move beyond passive data ingestion to actively interpret, contextualize, and understand the real-time implications of the financial information they receive.
- Reasoning: They apply advanced algorithmic logic to evaluate complex scenarios, weigh operational variables, and determine the optimal path forward.
- Take action: They execute end-to-end workflows and handle transactional steps directly within the system without requiring human intervention.
- Learn continuously to reach goals: They constantly analyze outcomes and refine their own processes, ensuring that operational efficiency and predictive accuracy compound over time.
In essence, AI agents operate outside the ERPs to enhance efficiency and have maximum business impact.
AI Agents Are Rapidly Transforming Order-to-Cash For Early Adopters
During her presentation, “AI Agents for Autonomous O2C: Use Cases and Best Practices,” Gundavelli outlined how organizations can deploy purpose-built AI agents across the entire Order-to-Cash lifecycle—from order management and credit through billing, collections, cash application, deductions, and customer engagement.
Rather than viewing these functions as isolated workflows, finance leaders can (and should) view them as part of a unified, AI-native operating model that continuously improves cash flow, productivity, and customer experience.
Governance Will Separate Leaders from Laggards
Another critical message echoed during the Gartner Symposiums and in Gundavelli’s London presentation is that successful AI adoption for finance operations often depends as much on governance as technology.
Organizations must establish:
- Financial approval thresholds
- Data quality standards
- AI behavior controls
- Compliance and audit frameworks
The session outlined a four-dimensional governance framework spanning financial controls, data integrity, AI behavior, and regulatory compliance—ensuring organizations can confidently scale AI while maintaining transparency and oversight.
AI Adoption Should Follow a Phased Roadmap
A key takeaway was that autonomous finance is not achieved through a single implementation event.
Instead, organizations need to plan a thorough and realistic roadmap, one that builds capabilities in stages.
Gundavelli outlined a three-wave deployment approach to implement and deploy AI agents for O2C operations:
Wave 1 – Foundation
- Cash Application
- Order Management
- Billing & Customer Payments
Wave 2 – Intelligence
- Collections
- Credit Risk
Wave 3 – Optimization
- Deductions
- Fully orchestrated Order-to-Cash
Each phase builds on the data and intelligence generated by the previous one, creating a scalable path toward autonomous finance.
The CFO’s Role Is Changing
Perhaps the biggest takeaway from Gartner UK Finance Symposium was that AI is changing the role of finance leaders themselves.
Rather than managing increasingly transactional processes, finance teams are expected to:
- Improve working capital
- Predict cash flow more accurately
- Manage customer risk proactively
- Deliver strategic business insights
- Govern AI-enabled operations
As AI agents assume more routine operational work, finance professionals can focus on higher-value decision-making and business partnerships.
What Finance Leaders Should Do Next
The organizations making the greatest progress with AI are not attempting to automate everything at once.
Instead, they are focusing on:
- Building strong data foundations
- Starting with high-impact use cases
- Establishing governance early
- Measuring business outcomes—not just technology adoption
- Scaling AI through a phased, enterprise-wide strategy
As demonstrated throughout Gartner UK Finance Symposiums, the conversation has evolved well beyond automation. Finance leaders are now exploring how AI-native operating models can create more resilient, intelligent, and autonomous finance organizations.
Conclusion
The Gartner UK Finance Symposium & Xpo reinforced that finance transformation is entering a new chapter.
Agentic AI, autonomous finance, and intelligent Order-to-Cash operations are no longer emerging concepts—they are becoming practical strategies that organizations are actively implementing today.
For finance leaders, the challenge is no longer deciding whether AI belongs in finance. It is determining how to deploy it responsibly, govern it effectively, and align it with measurable business outcomes.
FAQs
Why is governance important for AI in finance?
Governance establishes the financial controls, data standards, compliance policies, and oversight mechanisms that allow organizations to deploy AI responsibly. Strong governance helps ensure AI decisions remain transparent, auditable, and aligned with business objectives.
What is Agentic AI?
Agentic AI refers to AI systems that can perceive information, reason through decisions, take actions, and continuously learn from outcomes. Unlike traditional automation, AI agents can adapt to changing business conditions while operating within defined governance frameworks.
