79% of FP&A teams use AI - mostly for operational wins

79% of FP&A teams are using AI - mostly for quick ops wins

FP&A adoption is high, impact is mixed. A new Drivetrain survey of 258 FP&A professionals found 79% are using AI, primarily to speed up tasks like Excel automation and report polishing.

Strategic use is lagging. As the report puts it, "AI is helping teams move faster but not necessarily smarter."

How teams are using AI today

• 93% use ChatGPT or similar LLMs
• 22% use Microsoft 365 Copilot
• 20% use AI features inside BI tools
• 17% use AI-native finance tools (e.g., Drivetrain)

The focus is on automation and formatting, not on scenario modeling, planning cycles, or guiding cross-functional decisions.

Strategy gap: moving fast, not thinking better

Few FP&A teams are applying AI to driver-based scenarios, dynamic reforecasting, or decision support. That leaves planning cycles stuck in manual loops and limits finance's seat at the table during operating reviews.

The opportunity: use AI to expand scenario coverage, accelerate insights, and tie finance models to operating levers that business partners care about.

Hiring is shifting toward technical talent

Finance leaders expect new roles like AI analyst and AI systems expert to become staples over the next 2-3 years. As Drivetrain's CEO Alok Goel noted, "the most critical new finance hires won't have finance backgrounds - they'll have technical skills that barely existed when today's CFOs started their careers."

Skills that matter most

• 87% cite data storytelling and communication as critical for the next three years
• 65% cite modeling and analytics

Translation: pairing clear narratives with AI-assisted analysis will outperform raw modeling in impact and adoption.

The learning gap is real

• 68% spent five or fewer hours last month upskilling on AI
• 15% spent zero hours
• 4% spent 10+ hours

Enthusiasm is high, but time investment is low. That slows experimentation, policy creation, and value capture.

Policy is behind adoption

• 28% have formal AI guidance
• 39% have informal guidance
• 26% have no policies at all

Without guardrails, risk, quality, and vendor sprawl increase - and momentum stalls when legal steps in.

What to implement this quarter

• Publish a lightweight AI policy: data handling, approved tools, human-in-the-loop review, and vendor evaluation criteria.
• Run structured upskilling: internal workshops, monthly AI challenge days, team prompt contests, and peer-led use case shares.
• Pick three strategic use cases: driver-based scenario modeling, variance explanation with LLMs, and cross-functional forecast reviews with AI summaries.
• Measure ROI: cycle time reduction, forecast accuracy delta, number of scenarios run per planning cycle, adoption rates, and hours upskilled per FTE.
• Clarify data foundations: system-of-records, PII handling, access controls, and model documentation.
• Define roles: AI analyst (prototyping), model steward (governance), and a prompt library owner (quality and reuse).

A practical tooling mix

• LLMs (ChatGPT-class): draft commentary, variance analysis, meeting summaries, and email prep.
• Microsoft 365 Copilot: automate Excel and PowerPoint tasks, create baseline reports, check formulas.
• BI AI features: ask-data queries, anomaly detection, and quick visual explanations.
• AI-native finance tools: scenario modeling, driver trees, plan/forecast workflows, and cross-functional planning.

Use the right tool for the job and connect outputs into your planning cadence, not just ad-hoc experiments.

Execution checklist for FP&A and Operations

• Target a 30-50% cut in monthly forecasting cycle time.
• Increase scenarios per cycle from 2-3 to 8-10 with automated drivers.
• Track forecast accuracy and narrative quality with standardized rubrics.
• Set a minimum 6 hours/month AI learning target per team member.
• Review AI policy and model performance in every quarterly business review.

As Nvidia's Jensen Huang put it, "You're not going to lose your job to an AI, but you're going to lose your job to someone who uses AI." The teams that move from formatting help to decision support will set the pace for the function.

Upskill your team

If your time investment is trending below the bar, start now. Curated options to accelerate hands-on learning:

• AI courses by job role for finance and operations teams
• AI tools for finance to evaluate and pilot

AI is already in your stack. The advantage goes to teams that turn it into better scenarios, faster cycles, and clearer decisions.