CFO Roundtable Discussing How AI will Transform the Finance Function

Our speakers, Matt Prebble, Paul Prendergast and Mateus Begossi along with Russell Reynolds Associates consultant Ben Jones, hosted an exclusive roundtable with CFOs to discuss how AI is transforming the finance function.

	Matt Prebble Data & AI EMEA Lead Accenture
	Paul Prendergast CFO & Enterprise Value EMEA Lead Accenture

They key trends brought up surrounding the future of finance include; finance as the architect of value, changes to talent and experience, data to power insight, automation and AI are everywhere and here to stay and finally drive efficiency, impact and profitability.

In light of this, changes are inevitable for the future skillsets of CFOs and their team which will include:

1. Agile Practices
2. Business & Commercial Partnership
3. Data Analytics
4. Storytelling
5. Technology Literacy

1. AI-Driven Automation & Agentic Capabilities

From Automation to Autonomy

The webinar consistently differentiated between traditional automation (e.g., RPA) and what they termed “agentic AI.” Unlike simple, rule-based automation, agentic AI refers to systems that can interpret intent, coordinate multiple tasks, and even handle exceptions independently. This evolution is seen as a significant enabler for transforming end‐to‐end processes in finance.

Real-World Use Cases

Several demos highlighted AI’s ability to streamline complex tasks. Examples include:

• Record-to-Report (R2R): AI agents automating journal entries, reducing manual intervention, and speeding up month-end close.
  
  
• Source-to-Pay: Automation in invoice processing, handling multiple languages and complex tax scenarios, which can potentially achieve up to 90% digitization effectiveness.
  
  
• Enhanced Forecasting: AI is also used to significantly compress time in forecasting exercises, which traditionally would take weeks.

2. Digital Transformation & Integration

Strong Digital Core is Essential

A recurring point was that most successful AI initiatives build upon a robust data foundation and a clean digital core. The speakers emphasized that without well-integrated ERP systems and quality data, even the most advanced AI applications won’t deliver meaningful value.

System Integration & Vendor Ecosystem

There was discussion on how major vendors like SAP, Oracle, Microsoft, and Salesforce are evolving their platforms to support AI capabilities. The integration of these vendor solutions into existing IT landscapes and cloud infrastructures is seen as critical to the overall success of AI deployments in finance.

3. Transformation of the Finance Function & Role Evolution

Shift in Finance Roles

With AI taking over repetitive, low-value tasks, the role of finance professionals is evolving. CFOs and finance teams are increasingly expected to act as strategic partners—focusing more on analytics, forecasting, strategic planning, and storytelling rather than merely processing transactions.

New Skill Requirements

The conversation highlighted that organizations need to rethink talent acquisition and training. Future finance roles will require a blend of traditional financial skills and expertise in data analytics, process management, and even familiarity with AI technologies.

4. Strategic Impact Beyond Operational Efficiency 

Value Creation and Strategic Decision-Making

While much of the discussion centered on operational efficiency (cost reduction, speed, and accuracy), there was also significant focus on AI’s strategic potential.

For example:

• Pricing & Promotions: AI can help refine pricing strategies and promotions through better segmentation and predictive analytics.
  
  
• Enterprise Digital Twin: The concept of a digital twin was mentioned as a way for organizations to simulate and optimize the entire value chain, thereby influencing strategic planning and resource allocation.
  
  
• Working Capital Optimization: Enhanced data insights can lead to better management of receivables, payables, and inventory, directly impacting a company’s liquidity and profitability.

5. Roadmap for AI Adoption & Future Considerations

• Continuous Reinvention: The deployment of AI in finance is not viewed as a one-off project. Instead, it’s a continuous journey that requires ongoing tuning, monitoring, and integration with evolving business needs. This iterative process is critical to realizing long-term benefits.
  
  
• Challenges & Critical Success Factors: Despite the promise of AI, several challenges were acknowledged:
  • Data Quality & Integration: Without a clean, well-integrated digital foundation, AI efforts may falter.
    
    
  • Governance & Human Oversight: Even with advanced AI agents, human oversight remains necessary to ensure traceability and to intervene when needed.
    
    
  • Security Concerns: With increased digital interconnectivity, ensuring robust security measures and gaining stakeholder trust (such as C-suite and IT security approvals) is paramount.

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