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L1 · AI use cases across finance8 min read

FP&A and treasury: from faster analysis to better decisions

FP&A and treasury are where AI can change decisions, not just speed up work. That is also where most finance AI investment is not going yet.

FP&A uses

  • Forecasting: machine-learning forecasts alongside driver-based models, with the drivers and assumptions visible.
  • Scenario modeling: generate and compare scenarios (price, volume, FX, cost inflation) quickly, with the model in a planning tool rather than in a chat window.
  • Variance commentary: draft the "why" behind movements from actuals, budget and operational notes.
  • Self-service analysis: answer business-partner questions from governed data.

Treasury uses

  • Cash forecasting: short-term forecasts from bank, AR and AP data; explain forecast misses.
  • Liquidity scenarios: stress tests across facilities and covenants.
  • Bank-fee and payment analytics: spot fee leakage and unusual payments.

Productivity versus decision quality

Gartner's March 2026 survey found 45% of CFOs say their AI investments lean toward productivity, while only 20% say they lean toward decision quality. Gartner advises shifting more investment toward use cases that improve decision-making, enable scenario analysis, find growth opportunities and build reusable assets, and measuring them against enterprise objectives rather than hours saved.

Keep forecasts honest

  • Measure forecast accuracy (for example, MAPE or bias) for AI and human forecasts side by side.
  • Keep a human owner for every forecast that drives guidance, covenants or capital decisions.
  • Record assumptions, so a forecast can be explained to the board and auditors.

What this means for the CFO

FP&A is where you, personally, can show what AI-first finance looks like. Ask for one decision-quality use case this year — scenario modeling or AI-assisted cash forecasting — measured on forecast accuracy and decision speed, not hours.

Keep the human owner: any forecast that feeds guidance, covenants or capital allocation must be explainable by a person who signs it.

In treasury, start where the data is cleanest: short-term cash forecasting from bank, AR and AP data. Track forecast accuracy weekly against the current method for a full quarter before letting the AI forecast feed liquidity decisions, covenant tests or guidance.

Real example

Gartner's survey of 204 finance leaders (March 2026, published July 20, 2026) found 45% of CFOs say their AI investments lean toward productivity, while 20% say they lean toward decision quality. Gartner advised CFOs to shift more investment toward AI use cases that improve decision-making, enable scenario analysis, identify growth opportunities and build reusable assets. Source: Gartner — 45% of CFOs say their AI investments lean towards productivity, 20% towards decision quality (Jul 20, 2026) ↗

Use it

Quick check · 3 questions

  1. 1.According to Gartner's 2026 survey, what share of CFOs say their AI investment leans toward decision quality?

  2. 2.How should an AI forecast be judged?

  3. 3.Where should scenario math run?

0 of 3 answered

Sources

Last reviewed 2026-10-01.