San Francisco · 28 May 2026 · 15 seats

Business Breakfast · By invitation only15 seats · 28 May 2026 · San Francisco

How to make AI
work on your
finance data.

Findings from 26 finance leaders we sat with last month — brought to a 15-person invite-only breakfast in San Francisco.

Pavel Doležal, Giorgio Pontillo, Michal Hruška

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Date

Thursday, 28 May 2026

Time

8:30 – 11:00 PDT

Venue

San Francisco

Location TBA

Format

15 seats

Invitation & confirmation only

Five things to take away

If you read nothing else.

The question changed

70% of CFOs ranked AI for financial workflows a top-3 priority for the next 6 months. The question is no longer if — it’s how.

The accuracy gap

A CFO showed us an AI year-end cash forecast that was 30% off — because the data sat in OLAP cubes built for humans, not LLMs. The most common failure mode we heard.

Excel isn't the enemy

"Still mostly Excel-driven" remains the #1 reality. The pain isn't Excel — it's that numbers don't match across ERP, BI, and the board pack.

CFOs are quietly building

Roughly a third are personally using Claude, Claude Code, or Cursor — bypassing IT entirely. “I used to connect Excel to BI. Now Claude builds it itself.”

Working AI > AI

The breakfast theme landed because most attendees had already tried AI on finance data and watched it not work. They came to find out why.

Read the full Prague recap →

The “data quality” number is lower than it should be — because most teams frame it as a symptom, not a root cause. Almost every conversation eventually returned to: “...but the data isn't trustworthy enough yet.”
— Pattern across 26 CFOs, Keboola Finance Breakfast Prague, April 2026

Real stories from the room

What kept coming up — across almost every conversation.

The accuracy gap: when AI confidently lies to your CFO

"I asked Claude what the cash position would be at year end. The data is in cubes. The answer was about 30% off. I'd love to know how to connect the data better — or how to restructure it so AI can actually use it."

— CFO, multi-entity media group

That story landed because almost everyone in the room had a version of it. OLAP cubes, BI dashboards, and consolidated packs are built for humans — they assume the reader brings context the data doesn't carry. LLMs don't bring that context. If your dimensions are inconsistent across granularities, the model will confidently combine them into a wrong answer.

The lone CFO problem

"I'm the only one on the finance team playing with this."

— CFO, multi-entity media group

A pattern more widespread than we expected: the CFO is the only person on the finance team experimenting with AI. Not the CTO. Not a “data team.” The CFO themselves, in evenings and weekends, with a personal Claude or Cursor licence. Two failure modes follow: experiments don’t compound, and the moment that person changes role, the practice dies.

Build vs. buy: the benchmark has changed

"I used to connect Excel to BI. Now I know Claude can build it itself. So why would I pay six figures for a tool?"

— CFO, PE-backed tech holding

The bar a finance platform must clear is now much higher. If a platform doesn’t deliver governed, AI-ready data with traceability within ~8 weeks, a CFO with a Claude licence will simply build the first useful version themselves — and stay in that local maximum.

Pitfalls

What's failing — and what to do instead.

Six anti-patterns we heard repeatedly. Each pairs with a corrective practice you can apply immediately.

No. 01

Don't

Ask AI questions your data model can't answer.

If your model splits cash flow across legal entities, currencies, and time grains, an LLM silently combines them wrong. The 30% accuracy story is canonical.

✓

Do instead

Build a semantic layer first.

Definitions, grain, joins, lineage — written once, used by every AI tool you connect.

No. 02

Don't

Buy a tool to replace a person you haven't understood.

AI doesn't replace controllers. It removes the admin part of a controller's job.

✓

Do instead

Replace admin with AI; redirect humans to judgment.

If your seniors spend 80% on admin, the question isn't "can AI replace them" — it's "why are seniors doing admin?"

No. 03

Don't

Trust an ERP-native add-on for cross-entity reporting.

"Fine for accounting, unusable for running the business." The ERP is for transactions, not AI workloads.

✓

Do instead

Add a thin governed layer next to the ERP.

Live next to the ERP — not inside it. The layer survives the next migration.

No. 04

Don't

Try a shiny tool, abandon it, never ask why.

Tool bolted onto dashboard data, answers looked right but couldn't be audited, trust evaporated. The pattern repeats.

✓

Do instead

Diagnose the data foundation, not the tool.

The right question was never the tool. It was what the tool was pointing at.

No. 05

Don't

Run a one-person AI practice.

If exactly one person in finance knows how the AI workflows work, you have a single point of failure — not an AI practice.

✓

Do instead

Pair the CFO with a controller, not with IT.

The second person to learn the tool should be the one who knows what a “trusted number” looks like.

No. 06

Don't

Report through PowerPoint.

ERP → Excel converters → PowerPoint → email to board. Four manual handoffs. Four chances for numbers to drift.

✓

Do instead

Move boards onto governed live dashboards.

That AI can both read from and annotate. Kill the export-paste loop.

Program

The morning, start to finish.

Five blocks across two and a half hours. Tight, structured, conversational.