Dear colleagues,

As AI moves deeper into Finance, one challenge is becoming increasingly clear: much of the knowledge that makes FP&A work still sits in people’s heads rather than in systems. The featured report below notes that only around 30% of AI success comes from models and technology, while 70% depends on organisation, workforce and skills. Our 2026 FP&A Trends Survey reinforces this challenge: while 81% use data in most decisions, only 19% report advanced data quality, and just 11% have real-time or near-real-time access.

The CFO’s AI Agenda: From Automation to Advantage and the articles below examine what it takes to turn tacit finance knowledge into something reusable: codified business logic, clearer definitions, stronger governance and a deliberate way to preserve human judgement.

As AI starts to interpret the business directly, the undocumented knowledge Finance has relied on for years becomes one of the first things leadership has to surface.

Warm regards,

Larysa Melnychuk

CEO and Founder at FP&A Trends Group

Explore how CFOs can go beyond automation by strengthening the data, processes, governance, and AI fluency finance teams need to realize measurable value from AI.

In this report, learn how to:

• Build trusted data and a semantic foundation for AI.
• Standardize finance processes and lead with value.
• Prepare teams and governance for agentic AI.

Read the report →

When critical finance logic lives inside individual spreadsheets, the real risk is not the file. It is that assumptions, decision rules and recurring analysis disappear with the person who built them. Abhi Chandna shows how Finance can turn that knowledge into governed, reusable infrastructure that both the organisation and AI can trust.

Read how Finance makes business logic reusable →

If business leaders can question finance data directly through AI, FP&A may no longer control the path between question and answer. Katya Tolochkova argues that Finance therefore has to move upstream, shaping definitions, data architecture and guardrails before the AI layer is built.

Read what changes when Finance is no longer the intermediary →

Automation can remove routine work, but it can also remove the experiences through which Finance learns judgement. Lavan Verma asks a harder talent question: how do you automate the task without weakening the ability to challenge assumptions, recognise errors and supervise AI well?

Read the talent implication of automating the basics →

Our webinars are free and educational. Join live when you can, or register and we will send you the recording to watch in your own time.

The upcoming sessions take this issue into two parts of the finance operating model: how planning, data and technology can shorten the path from signal to decision, and how FP&A can preserve judgement, challenge and influence as AI takes on more analytical work.

Decision intelligence depends on more than faster analysis. The session focuses on how Finance connects planning, data and technology so teams can interpret change faster and respond with greater consistency.

Register now →

As AI expands Finance’s analytical capacity, the harder question is what FP&A does with that capacity. This session looks at how Business Partners can enter decisions earlier, challenge assumptions, frame options and bring the judgement and influence that technology cannot replace.

Register now →