AI-Leadership in Finance: Framing, Structuring, and Evaluating for Strategic Transformation

Written By Jay Baghal

Introduction: Leadership in the Age of AI-Enabled Finance

AI and ML are rapidly transforming Financial Planning & Analysis (FP&A) functions across organizations. A 2024 Gartner survey found that 58% of finance departments already use AI—up 21 points from 2023. Yet, success is not just about the tech stack; it’s about how leadership frames, structures, and evaluates these changes so FP&A teams deliver more accurate insights, faster decisions, and strategic value.

Framing: Making AI Relevant for Finance

Successful transformation starts by framing AI’s role in terms that reinforce enterprise values, the FP&A team’s identity, and a shared vision for the future.

Aligning AI with Finance Values

  • Accuracy & Trust: AI moves FP&A from manual spreadsheets to real-time analysis, driving higher accuracy and reducing human error.

  • Agility & Transparency: Leaders should communicate how AI enables forward-looking, scenario-driven forecasts—where FP&A teams can model risks and opportunities with speed and depth, improving transparency.

  • Customer & Business Partnering: For FP&A groups that value business collaboration, highlight how AI-powered insights strengthen stakeholder conversations.

Framing AI for Professional Identity

  • Empowerment, Not Replacement: AI frees analysts from repetitive data work—now 76% of finance teams have automated reporting—letting professionals deliver more strategic analysis. On the opposite end, AI integration within finance occurs within the context of ‘savings’ within the finance function

  • Focus on Analytics & Insights: With AI, the FP&A role evolves towards business partnering and proactive scenario planning, moving beyond backward-looking reports.

Framing Technology for Finance Teams

  • AI as a Strategic Co-Pilot: Modern FP&A teams adopt tools like ChatGPT (for executive messaging), Power Query, ai excell, and bespoke finance copilots to augment workflows.

  • Human + Machine Collaboration: Leaders must clarify that success depends on people and algorithms working in tandem to deliver actionable insights.

Structuring: Building Future-Ready Finance Functions

Leadership structuring determines whether FP&A’s transformation makes lasting impact.

Centralized vs. Embedded Data Teams

  • Embedded AI/FP&A Teams: Most organizations start by placing FP&A analytics within finance at various levels (country, regional, corporate, and COE), enabling rapid, function-specific improvements—like automated forecasting, fraud detection, and capital allocation.

  • Strategic AI Leadership: Progressive companies elevate FP&A with direct reporting lines to the CFO and for multnationals this could be the regional, Divsion, and functional CFOs, or structured collaboration with IT and data teams. This allows broad scenario modeling across departments and functions.

There’s no inherently right or wrong way to structure a data team—it’s a strategic choice. Some organizations benefit from a service-oriented team, while others thrive with a C-suite-led data function. Leaders must recognize that each structure drives the development of different types of algorithms output and the organizational requirements in role design, collaboration, and agility.

Evaluating: Measuring Finance’s AI-Driven Success

Robust evaluation is essential for building trust, learning, and demonstrating ROI—with unique considerations for finance. Building algorithmic capabilities in finance is a transformative change, one that impact all the finance function and requites input from other stakeholders throughtout the organization.

Strategy & Performance Metrics

Finance leaders need to upfront articulate a strategy and the related performance measurement as the finance function would naturally do for any strategy. These are some of the performance measurements used within finance:

  • Forecast Accuracy: Some companies report significantly better prediction accuracy and dramatic cycle time reductions—e.g., annual forecasts cut by tens of thousands of FTE hours using ML.

  • Capital Allocation & Efficiency: AI has helped companies improve operational efficiencies, cashflow generation, and improve margins. This demonstrate positive returns on ROI.

  • Continuous Audit/Risk Management: Generative AI tools pilot fraud detection, increasing accuracy and audit speed.

Communication & Stakeholder Buy-In

  • Narrate Trade-Offs in Plain Language: Leaders should demystify AI decision models using clear, jargon-free summaries. Communicating how trade-offs to AI generated output measured vs speeds and accuracy is important for increase adoption.

  • Drive Stakeholder Engagement: Engage business owners early, encourage bottom-up innovation, and give teams permission to challenge traditional workflows.

  • Decide on Relevant ROIs and Communicate it: Leaders can demonstrate and evaluate the success of their initiatives through return on investment (ROI) calculations. In the context of machine learning (ML) implementation in forecasting, cashflow, margin expasnion, ROIs can help leaders demonstrate the model’s effectiveness to relevant stakeholders within the organization.

Conclusion: Framing, Structuring, Evaluating AI for Finance Success

As organizations build algorithmic capabilities, leaders play a critical role in shaping the vision, designing the structure, and assessing progress. In future blogs, we’ll examine other key success factors for enabling finance transformation and integrating AI algorithems and flows

References (Alphabetical)

Jay Baghal
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