The Future of the Finance Workforce
Learn how AI is reframing the finance functions across the enterprise and get a glimpse into what the future holds with this new evolution in business operations and value generation.
With artificial intelligence (AI) becoming a greater presence in the workforce, finance leaders are compelled to adopt to gain value from its expanding benefits and efficiencies. To do so requires rethinking workplace structures that are a hybrid of humans and digital helpers.
The webcast delves into the profound impact of AI on the finance workforce and the evolution of the next generation of finance. The core objective is to equip business leaders with actionable insights on how to navigate the transformative changes AI will bring over the coming years, particularly focusing on agentic AI.
Panelists discussed the following topics:
- How will the rise of agentic AI reshape today’s finance functions?
- What are the emerging roles, responsibilities, and operating structures enabled by AI?
- Where should finance leaders focus their technology and data investments on to maximize value delivery?
- What are best practices to help reimagine the finance organization using AI?
The AI Journey: A Multi-Stage Evolution
AI adoption is a multi-stage, long-term journey, not a singular event, which typically follows three distinct phases:
This initial stage involves adopting generative AI tools and building foundational AI capabilities to capture early productivity gains. Many companies are currently at this stage.
Moving beyond discrete pilots, this stage focuses on integrating AI into workflows, products, services, and value streams to achieve greater enterprise-wide value.
The most advanced stage involves redefining business models and entire ecosystems with AI at the center, fundamentally reshaping the function and the company.
While many organizations are in the Enablement phase or transitioning to the Embedding phase, the pace and application of AI can vary significantly even within different functions of the same organization (e.g., tax might be embedding, while treasury is still enabling).
Three Approaches to AI Enablement in Finance
There are three primary ways organizations are approaching AI enablement:
Deployment of Discrete Use Cases: This is currently the dominant approach, characterized by low risk and the ability to "fail fast." However, its disadvantages include fragmented adoption, difficulty in scaling, and unclear ROI. A striking statistic from the MIT "State of AI in Business 2025" report reveals that 95% of AI pilots fail due to adoption issues, underscoring the critical need for effective change management.
End-to-End Process or Role Reengineering (Agentic AI): This approach is gaining traction due to the unique capabilities of agentic AI. Unlike Robotic Process Automation (RPA), which often requires significant maintenance and automates only portions of a process, agentic AI can orchestrate entire end-to-end processes. This allows for a complete redefinition of functional process areas, roles, and even technology strategies, potentially replacing niche, function-specific SaaS applications. However, this remains in its early stages but is rapidly maturing.
AI-First Mindset: This is considered the most transformative approach, where an organization starts from scratch, asking "what can AI not do?" This mindset fundamentally rethinks how finance operates, with AI at its core, rather than as an add-on.
Visions for the Future of Finance
AI's impact on finance operations suggests that, in the future, up to 80% of finance activities will be automated and AI-enabled. While the timeline will vary by organization, this significant automation will free up human capacity.
In this developing scenario, traditional corporate functions are expected to merge. With automation, roles like FP&A, accounting, sales finance, and procurement will converge, leading to a more integrated "back office." AI will become the main interface for interaction, potentially superseding traditional ERP systems by integrating data across various platforms.
As AI takes on more responsibility, managing enterprise risk, ensuring data accuracy, and automating controls will become even more crucial to prevent issues like AI "hallucinations" or "drift." The profile and career path for finance professionals will transform, which necessitates a re-evaluation of required skills and career development.
Evolving Roles and Required Skill Sets
AI will reshape specific finance roles in the future in the following areas:
- Finance Operations: Roles like Accounts Receivable (AR) analysts will shift from execution-focused tasks to becoming "commercial advisors," handling exceptions and providing strategic insights.
- FP&A: These teams will move from repetitive data gathering and cleansing to acting as "intelligent advisors," leveraging predictive modeling for "what-if" scenario analysis and strategic decision-making, rather than spending weeks on forecasts.
- New Roles: The emergence of an "AI resources team" is anticipated, focused on evaluating agent performance, enhancing models, and managing AI as a component of the workforce.
The "checklist for a modern finance function" emphasizes foundational skills like financial acumen and business/industry acumen as "table stakes." However, new, critical skills include:
- Intellectual Curiosity: Deemed fundamental, enabling professionals to question AI outputs, explore new applications, and adapt to rapid technological change.
- Data Science: The ability to work with data scientists and engineers within finance.
- AI and Digital Literacy: Understanding the benefits, drawbacks, and appropriate use of various AI tools (e.g., knowing when to use generative AI versus agentic AI).
The Evolving Role of the CFO is predicted to evolve into a dual function:
Responsible for ensuring the integrity, compliance, and controls of financial data and processes
Focused on driving shareholder value, engaging with investors and analysts, and identifying growth opportunities.
This split acknowledges the increasing complexity and demands on finance leadership, allowing for specialized focus.
Reimagining Processes and Addressing Challenges
The presentation outlines a practical approach to reimagining roles: decomposing processes, identifying opportunities for machine learning, generative AI, and agentic AI, and then blueprinting new ways of working. An example blueprint for an AR analyst transforming into a commercial advisor demonstrates how multiple AI agents can execute tasks, freeing up human capacity for higher-value activities.
Change Management and Talent: Clear vision, transparent communication, and active stakeholder engagement are all important in fostering AI adoption. So-called "citizen development" – allowing employees to experiment with AI tools – to build comfort and generate new ideas. Talent sourcing will also adapt, with internships playing a key role in identifying individuals with the necessary skills and curiosity. The focus is on augmenting and redistributing work, not outright elimination of roles.
Architectural Implications: Agentic platforms have the potential to replace function-specific SaaS solutions. The future involves an AI-enabled experience where users can query data across disparate systems without physically moving it, leading to better controls and faster analysis. Data quality, while a concern, is expected to improve as AI learns and maps data.
The Criticality of Controls: Robust controls are critical to help mitigate risks associated with AI which could include:
- Ethical Risks: Addressing biases and "drift" in AI models.
- Governance and Oversight: Establishing "trusted AI" frameworks to ensure accuracy and accountability.
- Cybersecurity and Privacy: Protecting sensitive company data when using AI tools.
- Regulatory and Compliance: Leveraging AI to understand and adapt to new regulations, while ensuring adherence.
- Workforce and Cultural Risks: Maintaining positive employee experiences and culture amidst significant technological change.
Meet our team
