How FP&A Teams Use AI: Forecasting, Reporting, and Analysis

Artificial intelligence has moved from experimentation to execution in financial planning and analysis. During FP&A Con, an expert panel led by CFO and AI author Glenn Hopper explored where AI already delivers value in FP&A, where expectations run ahead of reality, and how finance teams should approach adoption.

The discussion featured Nicolas Boucher (AI for Finance thought leader), Christian Martinez (Finance Analytics Manager, Kraft Heinz), and Gabriela Gutierrez (FP&A leader and founder of Tabs). Their combined perspectives provide a clear picture of how AI actually shows up inside finance teams today.

How Widely FP&A Teams Use AI Today

A live audience poll during the session showed 47% of FP&A professionals already use AI in their work. Usage drops sharply when measured by daily or weekly reliance, which highlights an important pattern: experimentation runs ahead of operational integration.

Most finance teams interact with AI at a surface level, while a smaller group embeds it deeply into forecasting, analysis, and automation workflows.

Where FP&A Teams Apply AI in Practice

Panelists agreed that AI adoption in FP&A follows a predictable progression.

Early-stage use cases

→ Drafting emails and internal communications

→ Writing policies, including AI usage policies

→ Automating repetitive documentation tasks

These uses help teams build comfort with AI while saving incremental time.

Operational use cases delivering real value

→ Forecasting and scenario modeling using machine learning via Python

→ Driver analysis across large datasets

→ Variance analysis at scale

→ File consolidation and data transformation

→ Predictive analytics for revenue and demand

Teams using AI through code-driven workflows consistently report higher accuracy and stronger insight than teams relying on text-only prompts.

Why ChatGPT-4o Changed the Conversation

Several panelists highlighted ChatGPT-4o as a practical inflection point for FP&A.

With the right prompts, FP&A teams can now:

→ Run advanced forecasting models

→ Perform Monte Carlo simulations

→ Apply seasonal and volatility analysis

→ Generate Python-based models inline

This reduces the gap between finance expertise and data science execution, while keeping humans accountable for validation and judgment.

Common misconceptions About AI in FP&A

The panel addressed several misconceptions holding finance teams back.

AI as a one-shot answer engine

AI works best through iterative prompting and refinement. Precision improves as context improves.

AI as a math engine

Language models excel at reasoning and structure. Reliable calculations come from code execution, embedded analytics tools, or Python-generated models.

AI as a replacement for finance judgment

AI accelerates analysis and pattern recognition. Decision ownership stays with finance professionals.

AI maturity before data maturity

Clean, structured, accessible data determines AI success more than model choice.

The Biggest Barriers to AI Adoption in FP&A

Another live poll surfaced the most common blockers:

→ Lack of understanding and awareness

→ Data privacy and security concerns

→ Data quality issues

→ Skill gaps inside finance teams

→ Budget constraints

Training, governance, and secure enterprise environments emerged as the fastest ways to move past these barriers.

Which FP&A Processes Benefit Most from AI

When asked where AI adds the most value, participants ranked:

→ Financial analysis

→ Reporting and consolidation

→ Budgeting and planning

Reporting remains the biggest time drain, while analysis delivers the highest strategic upside. AI enables teams to move effort from manual preparation toward insight generation.

Why Python Matters for FP&A

A recurring theme throughout the session: Python has become the unlock for finance AI.

Python enables FP&A teams to:

→ Automate data ingestion and consolidation

→ Run forecasting models securely

→ Perform cohort and heat-map analysis

→ Scale without exposing sensitive data

Generative AI lowers the learning curve by generating usable Python code aligned with finance use cases.

How FP&A Teams Should Get Started

The panel outlined a maturity-based approach.

Small teams

→ Train teams on general-purpose AI tools

→ Use AI features already embedded in Excel, Power BI, and Copilot

→ Start experimenting with Python through guided prompts

Mid-sized teams

→ Adopt AI-enabled FP&A and accounting tools

→ Automate reporting and cross-department workflows

→ Introduce forecasting and driver models

Large enterprises

→ Build internal AI assistants and chatbots

→ Fine-tune models using proprietary data

→ Integrate AI into core finance platforms with auditability

Across all stages, data readiness and governance determine success.

Key Takeaways from FP&A Con

→ AI adoption in FP&A continues to accelerate, led by practical use cases

→ Forecasting, analysis, and reporting deliver the strongest ROI

→ Python bridges the gap between finance and advanced analytics

→ Human oversight remains central to trustworthy AI outcomes

→ Data maturity sets the ceiling for AI maturity