Your CFO team understands cash, and yet the board is not sure.

The reason is simple.

The data is scattered across your bank export, your AR aging, your AP items, payroll, sales orders, and a tax schedule that lives in someone’s inbox.

The timing is uncertain, because revenue recognized is not cash in the bank, and an invoice issued is not an invoice paid. The assumptions never get written down, so nobody can audit them later.

Scenarios are clumsy, built by hand in a copy of the copy of the model. And the weekly refresh has no reliable trail, so when a number moves, you can’t say why.

PwC says cash forecasting is about when cash actually hits the bank, not when revenue is recognized or an invoice is issued. Every sale has to land in the week the customer is expected to pay, not the week you booked it. That single distinction is where most forecasts quietly go wrong.

And it’s exactly where AI helps, if you use it right.

I built a full 13-week cash flow forecast with Claude.

Claude kept the Excel model as the source of truth, then built an interactive dashboard that lets you stress-test cash, not just look at it.

What impressed me most is what the dashboard didn’t hide.

The dashboard surfaced the exact review items, the threshold breaches, and the scenario trade-offs a CFO needs to see before making a call. It showed the weak spots instead of papering over them. That’s the difference between a dashboard that decorates a number and one you can actually decide from.

Let me show you how I built it using the cash flow model builder skill (.md) and the full prompts I used.

Let’s dive in.

How to build a full 13-week cash flow forecast with Claude

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It’s better than GPT-5.6 and its brother, Fable 5 at half the cost.

Finance would love Opus 5 for only one thing:

Claude Opus 5 is stronger at verifying its work and iterating carefully until it succeeds.

To get the best out of Claude, you must set it up like a CFO.

Claude’s configuration is five separate layers that stack.

Each one does something the layer below it can’t.

1. Privacy & Memory carries who you are.

2. Instructions teach Claude how to act like a CFO.

3. Projects hold your company context.

4. Skills build identical dashboards.

5. Connectors like NetSuite remove manual boring work.

Configure one and you get a marginal improvement.

Configure all five in order and the thing stops feeling like a chatbot and starts behaving like a real CFO. Not in a scary way.

Claude Opus 5 is best for every CFO

It’s more powerful than other models.

It’s the new default model on Claude Max, and the strongest model on Claude Pro.

Here is the order every CFO must follow.

Let’s dive in.

How to set up Claude Opus 5 in finance

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It’s better than GPT-5.6 and its brother, Fable 5 at half the cost.

Finance would love Opus 5 for only one thing:

Claude Opus 5 is stronger at verifying its work and iterating carefully until it succeeds.

To get the best out of Claude, you must set it up like a CFO.

Claude’s configuration is five separate layers that stack.

Each one does something the layer below it can’t.

1. Privacy & Memory carries who you are.

2. Instructions teach Claude how to act like a CFO.

3. Projects hold your company context.

4. Skills build identical dashboards.

5. Connectors like NetSuite remove manual boring work.

Configure one and you get a marginal improvement.

Configure all five in order and the thing stops feeling like a chatbot and starts behaving like a real CFO. Not in a scary way.

Here is the order every AI-native CFO must follow.

Let’s dive in.

Opus 5 is best for every CFO

It’s more powerful than other models.

It’s the new default model on Claude Max, and the strongest model on Claude Pro.

5 Claude settings every finance person must use

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When Deloitte asked what CFOs actually use AI for inside the finance function, the top answer, at 51 percent, was operational productivity.

But before diving into that, every CFO must understand there are two kinds of finance people.

The ones who follow and the ones who think.

The process-followers

They move a number from one place to another.

The work is necessary today, but it’s entirely mechanical. There’s no judgment in it. You’re a pair of hands moving data across a gap that software is about to close.

Just to be clear, the process itself isn’t bad. It’s that the human simply following the process is the redundant part. The thinker isn’t a maverick; they’re someone who knows when the process is wrong.

Didi looks at that work and can’t find a reason it survives.

I don’t see why we need someone who takes care of payments to vendors. I literally can’t see why we need it.

The thinkers

They look at a number and ask why.

By contrast, thinkers see COGS jump while revenue falls, and they know something’s wrong before anyone tells them. They decide what the number means and what to do about it.

That work doesn’t shrink when AI arrives.

It explodes because, for the first time, one thinker can cover the ground that used to need five process-followers just to assemble the data.

Most finance people think their job security comes from knowing the tools. Knowing the ERP, being fast in Excel, and owning the close process.

That’s backwards.

The better you are at the mechanical work, the more replaceable you become, because that work is the first thing to go.

The people who feel safest are the most exposed.

The US Bureau of Labor Statistics projects that bookkeeping, accounting, and auditing clerks will lose 94,300 jobs between now and 2034, a decline of 6%.

But the interesting thing is over the same period, accountants and auditors grow five percent, and financial analysts grow six percent.

Read on.

What AI is doing to CFOs

When Deloitte asked what CFOs actually use AI for inside the finance function, the top answer, at 51 percent, was operational productivity.

• Meeting transcripts. Email drafts. Administrative work.

• Financial planning and budgeting came second at 44 percent.

• Analyzing financial data came third at 41 percent.

In the office of the CFO, in the middle of 2026, the most common application of the most significant technology of our lifetime is writing emails faster.

That is the split.

Not between finance people and machines. Between two kinds of finance people, both of whom now have AI, using it for entirely different things.

The best study we have on this is not a survey or a prediction.

Researchers from MIT and Stanford analyzed more than 200,000 transaction-level records across 79 accounting firms and surveyed 277 accountants, published in the Journal of Accounting Research. They measured what changed when accountants actually started using AI. MIT Sloan’s summary is here.

The finding is not that people were replaced.

It is that their time moved.

This is what people miss when they argue about whether AI takes finance jobs. It does not take the job. It takes the part of the job that was never the valuable part and hands the time back to whoever knows what to do with it.

If you have judgment, you now have leverage on it.

If you do not, the thing you were doing just got cheaper.

Finance salaries are growing

If the split were theoretical, compensation would not have moved. It has.

Robert Half’s 2026 research, based on more than 400 US finance and accounting leaders, found that 87 percent now pay higher salaries for candidates with specialized skills in the same role. The specific skills they pay for are financial reporting, data analytics, and financial modelling.

Not speed at transaction processing but Interpretation.

Deloitte’s quarterly survey of 200 CFOs at companies above a billion in revenue makes the intent explicit. Half named digital transformation of finance as their top priority for 2026. And the leading talent priority, cited by 49 percent of them, was automating processes to free employees for higher-value work.

There is one more force worth naming, because it explains why the change is happening faster in some places than others. The profession has a pipeline problem.

There are fewer CPA candidates.

Firms are not automating routine work purely to cut cost.

Many are automating because they can’t hire, and every hour of judgment they can free up is an hour they could not buy on the market at any price.

The shortage and the automation are not opposing forces. They are the same story. Mechanical capacity is getting cheap while judgment stays scarce, and the gap between the two is widening.

The Bottom Line

Everyone has the AI now.

What remains is the question of what you point it at.

If you use it to produce the same work faster, you bought efficiency, and efficiency is a fine thing to buy. It is also the thing your competitor bought, from the same vendors, in the same quarter, and it shows up in nobody’s strategy.

If you use it to change what your finance function spends its time deciding, you bought something else entirely, and the data suggests hardly any people have.

The split is never between finance people and machines.

It is between the people who use a general-purpose thinking tool to type faster and the people who use it to think about more things.

And that’s all for today!

See you on Sunday.

Whenever you’re ready, there are 2 ways I can help you:

1. Advertise with the AI CFO Office.

2. If you’re building an AI-powered CFO tech startup, I’d love to hear more and explore if it’s a fit for our investment portfolio.

I’m Wouter Born. A CFOTech investor, advisor, and founder of finstory.ai

Find me on LinkedIn

This is the Born2Cycle monthly finance snapshot.

Our analysts rebuild it every month, by hand.

But if you connect Claude to your ERP and ask for it.

You’ll get a snapshot. The numbers will be right, straight from the source system, every figure traceable. But it won’t be this dashboard. And next month you’ll get a third version… different metrics, different layout, different comparisons, and different charts.

Nothing wrong, exactly.

Just never the same, and now you have months you can’t put side by side.

The problem is Claude can read your data. It can’t read your judgment.

And that’s exactly why every CFO must build skills.

You can build a skill that can understand your company.

The skill is not a better prompt. It’s a container for your judgment, applied the same way every month, without you in the room.

Let me show how to build your own CFO skills in Claude.

Read on.

Build your own CFO skill in Claude

Step 1: Connect your systems

Most of you don’t have a NetSuite MCP server ready, so I’ve set up a demo one you can use for free. It’s our Finstory server, serving real NetSuite data from Born2Cycle.

Everything below runs today, on real multi-entity data, without touching your systems.

In Claude, go to Customize, then Connectors, and add this one:

Finstory Demo Data Analysis
https://demo.finstory.ai/mcp-data-analysis

Click Connect.

Sign in with any Google or Microsoft account, or use the one-time sign-in link.

Click Allow access, and Claude connects to the Born2Cycle demo server and dataset.

Copy/Paste Prompt

Read more

Insider Members are a small, practical group of top CFOs who are willing to go ahead and participate in the LIVE events. The live events are open only to Insider Members to keep it practical and interactive.

But regular paid monthly/annual members get full access to the editorials recap of live events, like here:

In the live event I showed CFOs

How to build a skill from scratch

• What a skill actually is, in plain terms, and why it beats a prompt.

• Building all three live, from start to finish, in my own Claude environment.

• How a skill forces Claude through the same steps every time: inventory the inputs, flag the exceptions, check its own math, and write the summary.

• Why you should build your own instead of downloading mine.

The skills every finance function must own

• A month-over-month dashboard that is identical every time. Same accounts, same comparisons, same visuals.

• A budget variance analysis that drills into what matters, hunts the drivers, asks you questions, and writes commentary sharp enough to send.

• A board deck that follows the same layout and pattern every month.

Insider members can watch the full recording below

Read more

This is the Born2Cycle monthly finance snapshot.

Our analysts rebuild it every month, by hand.

But if you connect Claude to your ERP and ask for it.

You’ll get a snapshot. The numbers will be right, straight from the source system, every figure traceable. But it won’t be this dashboard. And next month you’ll get a third version… different metrics, different layout, different comparisons, and different charts.

Nothing wrong, exactly.

Just never the same, and now you have months you can’t put side by side.

The problem is Claude can read your data. It can’t read your judgment.

And that’s exactly why every CFO must build skills.

You can build a skill that can understand your company.

The skill is not a better prompt. It’s a container for your judgment, applied the same way every month, without you in the room.

Let me show how to build your own CFO skills in Claude.

Read on.

How to build your own CFO skill in Claude

Read more

Thursday, July 16, at 11:00 AM ET.

This is not a demo.

This is a 60-minute, hands-on walkthrough of how to build the one thing that separates CFOs getting real value from Claude and the ones still typing prompts into a chat box. Skills.

Live. Ask questions.

Challenge what works and what does not.

Claude Masterclass for CFOs (LIVE)

The CFOs getting the biggest results are not writing better prompts. They are building skills. A skill is a workflow you teach Claude once that then runs the same disciplined way every single time.

Anthropic’s own finance team runs this way. Their CFO has said a report that used to take hours now takes 30 minutes, and the heaviest Claude user on the team is not the youngest analyst; it is the head of tax.

CFO reading this knows the moment arrived. The question is no longer whether to learn Claude. It is how fast.

What I will cover

The AI foundation most CFOs skip

• Why live data from your ERP is only half the problem.

• Why every CFO must use a Project for any serious finance work.

• How to set up a Project so Claude holds your company’s numbers and rules every time.

• The one instruction that makes Claude’s output safe for a board.

How to build a skill from scratch

• What a skill actually is, in plain terms, and why it beats a prompt.

• Building all three live, from start to finish, in my own Claude environment.

• How a skill forces Claude through the same steps every time: inventory the inputs, flag the exceptions, check its own math, and write the summary.

• Why you should build your own instead of downloading mine.

The skills every finance function must own

• A month-over-month dashboard that is identical every time. Same accounts, same comparisons, same visuals.

• A budget variance analysis that drills into what matters, hunts the drivers, asks you questions, and writes commentary sharp enough to send.

• A board deck that follows the same layout and pattern every month.

The strategic layer

• What to build this quarter and what to wait on.

• Where skills save real time and where they do not.

• How to make your finance function AI-native without losing the audit trail.

Who is this for

CFOs, VPs of Finance, FP&A leaders, and controllers. Anyone running a finance function that needs to be ready for what comes next.

What to expect

A 60-minute live masterclass. I will build skills in my actual Claude environment, from a blank Project to a working skill library, and you will leave knowing how to build your own. Open Q&A throughout. Honest discussion of what works and what breaks.

Live masterclasses are exclusive to Insider Members. That’s the highest tier.

Regular paid members will get an editorial recap after the live event.

The full written walkthrough of all three skills. How they’re built, the prompts, what broke, and what I’d do differently. And we’re giving you our demo MCP server, so you can connect it and run the whole thing yourself tomorrow. Not read about it. Run it.

That’s the part I’m most interested in. Some of you will build something better than what I show today to Insider Members only, on your own data, with knowledge I don’t have about your business.

If you want the live experience and recordings, upgrade to Insider Member. If you are already an Insider member, the registration link is below.

Read more

This is not a demo. This is a 60-minute, hands-on walkthrough of how to build the one thing that separates CFOs getting real value from Claude and the ones still typing prompts into a chat box. Skills.

Live. Ask questions. Challenge what works and what does not.

Claude Masterclass for CFOs (LIVE)

Here is what most CFOs still do not understand about Claude.

The CFOs getting the biggest results are not writing better prompts. They are building skills. A skill is a workflow you teach Claude once that then runs the same disciplined way every single time.

Anthropic’s own finance team runs this way. Their CFO has said a report that used to take hours now takes 30 minutes, and the heaviest Claude user on the team is not the youngest analyst; it is the head of tax.

And KPMG put Claude in the hands of 276,000 advisors.

PwC trained 30,000 of their employees on Office of the CFO workflows.

Jamie Dimon publicly built a dashboard with Claude in 20 minutes.

Every CFO reading this knows the moment arrived. The question is no longer whether to learn Claude. It is how fast.

What I will cover

The AI foundation most CFOs skip

• Why every CFO must use Project for any serious finance work.

• The one instruction that makes Claude’s output safe for a board.

• How to set up a Project so Claude holds your company’s numbers and rules every time.

How to build a skill from scratch

• Building a monthly financial review skill live, start to finish.

• What a skill actually is, in plain terms, and why it beats a prompt.

• How to turn one working skill into a library your whole team can run.

• How a skill forces Claude through the same steps every time: inventory the inputs, flag the exceptions, check its own math, and write the summary.

The skills every finance function must own

• Monthly close and variance commentary.

• Board pack assembly and narrative.

• Cash flow forecasting and scenario work.

The strategic layer

• What to build this quarter and what to wait on.

• Where skills save real time and where they do not.

• How to make your finance function AI-native without losing the audit trail.

Who this is for

CFOs, VPs of Finance, FP&A leaders, and controllers. Anyone running a finance function that needs to be ready for what comes next.

What to expect

A 60-minute live masterclass. I will build skills in my actual Claude environment, from a blank Project to a working skill library, and you will leave knowing how to build your own. Open Q&A throughout. Honest discussion of what works and what breaks.

Live masterclasses are exclusive to Insider Members. That’s the highest tier.

Regular paid members will get an editorial recap after the live event.

If you want the live experience and recordings, upgrade to Insider Member. If you are already an Insider member, the registration link is below.

Read more

So I spoke with Didi Gurfinkel, CEO of Datarails.

Didi sits in a rare seat.

He listens to hundreds of finance teams every month, and he sees exactly where they’re all heading. So when he told me half the work in his finance department won’t need a person soon, I was surprised.

I asked Didi, Three years from now, who’s still on your finance team?

He didn’t soften it.

I look at my finance department, and I can’t see why we need someone just processing a payment to a vendor. Or chasing a receivable. I literally can’t see the reason anymore.

Didi runs Datarails.

He works with hundreds of real CFOs and controllers telling him what they’re trying to do with AI and exactly where it breaks. Almost nobody on earth has that view. And he’s telling you… The people who move numbers from one box to another are done.

He splits finance into two kinds of people.

The ones who think, and the ones who simply follow processes.

The process-followers move the numbers. Execute the invoice, chase the bill, and key the entry. The thinkers decide what the number means.

The process-followers are going. All of them.

If that shocked you, good.

The conversation got heated.

Here’s everything Didi covered:

• The last finance jobs

• Why Didi told his customers to not use his own software

• Why AI fails in finance

• The role of Anthropic and OpenAI

• The future of AI in finance Didi is betting on

If you’re a CFO, CEO, VP of Finance, or Controller/FP&A, this is your AI blueprint; you must read it to move forward.

Let’s dive in.

The last finance jobs

Didi splits every finance person into two kinds.

The ones who follow and the ones who think.

The process-followers move a number from one place to another

The work is necessary today, but it’s entirely mechanical. There’s no judgment in it. You’re a pair of hands moving data across a gap that software is about to close.

Just to be clear, the process itself isn’t bad. It’s that the human simply following the process is the redundant part. The thinker isn’t a maverick; they’re someone who knows when the process is wrong.

Didi looks at that work and can’t find a reason it survives.

I don’t see why we need someone who takes care of payments to vendors. I literally can’t see why we need it.

The thinkers look at a number and ask why

By contrast, thinkers see COGS jump while revenue falls, and they know something’s wrong before anyone tells them. They decide what the number means and what to do about it.

That work doesn’t shrink when AI arrives. It explodes because, for the first time, one thinker can cover the ground that used to need five process-followers just to assemble the data.

Most finance people think their job security comes from knowing the tools. Knowing the ERP, being fast in Excel, and owning the close process. That’s backwards. The better you are at the mechanical work, the more replaceable you become, because that work is the first thing to go. The people who feel safest are the most exposed.

Why Didi told his customers to not use his own software

A few weeks ago Didi ran a customer advisory board in New York. Dozens of his own paying customers in one room, the kind of event where you expect customers to ask for more of what they already love.

He walked from table to table asking how Datarails could be better.

Every single request was about the screen.

• Make the dashboard print better.

• Add this metric to that view.

• Move this column.

Customers who pay him real money, asking him to polish the window they look through. And he told them to think differently.

Take Claude and build any dashboard you want in minutes. It stays connected to the same data, the same permissions, the same source of truth. Why use our interface at all? No reason.

How unexpected. A software CEO, standing in front of the people who pay for his software, telling them the part they can see is the part that no longer matters.

He understands something that the customers in that room didn’t.

The dashboard was never the product. It was just the window.

The actual product, the thing worth paying for, was always the data foundation underneath: the consolidated, trusted, defined source of truth that the dashboard happened to display. For 30 years you needed the vendor’s window because you had no other way to look at the data.

Now you have Claude. You can ask the foundation anything, in plain English, and get a dashboard, a memo, a board deck, whatever you want, in next to no time. The window is anywhere and everywhere. Only the foundation is the hard part.

So Didi did something almost no software company has had the nerve to do. He restructured his entire company around making his own interface obsolete. He built new teams whose whole job is moving customers off his screens and onto Claude, with Datarails feeding the data underneath.

He calls it the finance operating system, or FinanceOS

He’s cannibalizing the visible part of his product on purpose, because he’s certain the hidden data layer is where all the value was hiding the whole time.

Look at every finance tool you pay for and ask which part is the window and which part is the foundation. You’re probably paying a fortune for window-dressing.

Why AI fails in finance

AI and finance are built on opposite values.

For a moment, walk through what finance actually demands:

• Consistency: the same number every time.

• Trust: you can rely on it.

• Repeatability: run it multiple times and get the same result.

• Auditability: trace every figure to its source.

Those four principles are the entire foundation of the profession. A number that changes when you ask twice isn’t a number; it’s a guess.

Now look at what generative AI is.

It’s probabilistic.

It doesn’t calculate the answer. It predicts the most likely answer and hands it to you, dressed up with the confidence of a veteran CFO. Ask it the same question twice and you can get two different answers, because generating a fresh response every time is literally how it works.

As Didi put it.

Everything finance admires, consistency, trust, repeatability, and auditability, is exactly what AI is not.

Didi has watched a lot of technology cycles, and he says he’s never seen anything like it.

There’s always a gap between how people talk about new technology and the reality. But here it’s two completely separate worlds.

The labs: AI research companies such as Anthropic, OpenAI, Google, and DeepMind are convinced that finance is the function AI changes most. The finance teams trying it are the most stuck. Those two facts are landing on the same desk, and it’s making CFOs feel frazzled.

If a chatbot drafts a contract and drops a clause, a lawyer reads it and catches the mistake. Finance has no such safety net.

If an AI runs through a hundred million transaction rows and hallucinates a one percent error, you will never find it. It’s buried. And one percent of a large company’s numbers is millions of dollars; walking into your board deck wearing a suit, looking exactly like every correct number around it.

So your pilot didn’t fail because AI is weak.

It failed because you aimed the most powerful tool in a generation at the one function that can’t survive a confident wrong answer, and you gave it nothing underneath to keep it honest.

It handed you something that looked brilliant, and you couldn’t stake your name on a single line of it. That’s not a failure of the tool.

That’s the predictable result of skipping the foundation.

The role of Anthropic and OpenAI

Anthropic shared which jobs AI will replace first.

Every CFO must see this chart. Blue is what AI can do to your function. Red is what it’s doing right now. The gap in finance is massive.

The biggest promise for AI and the worst progress in execution are seen within the CFO’s Office.

For a couple of years, the assumption was that you win AI by owning the computation. You run the models, you burn the tokens, you charge a premium on top. Datarails’ own early pricing worked like that: buy a block of tokens and pay more when you need more. That whole concept is collapsing.

As I told Didi on the call, all the tokens are moving to Claude. You don’t want to own the token business anymore. Let Anthropic and OpenAI burn the compute and fight over the models.

It’s a brutal, expensive, winner-take-most race, and it’s not your race.

So if the model isn’t where the value is for finance, where does it land?

In the layer that makes the model trustworthy for your business. The labs win the raw intelligence. Whoever builds the foundation that intelligence stands on wins finance. That’s the whole bet, and it’s why Didi stopped trying to out-feature everyone else and went all-in underneath the model instead of on top of it.

For you as a CFO, the takeaway is direct.

Don’t get into a bidding war over which model is smartest this month. They’ll all be brilliant and they’ll all be roughly equal. Spend your energy and your budget on the layer underneath, because that’s the part the labs will never build for you, and it’s the part that’s actually yours to own.

The future of AI in finance Didi is betting on

Now Didi zooms all the way out, and this is the part no CFO has heard yet.

He isn’t building a product. He’s building a category that doesn’t exist yet.

Call it the Snowflake for finance. A financial operating system. It’s a new category.

And he said something I haven’t stopped repeating.

Building a new category is like running a marathon, and if you look behind you and nobody is there, it means you’re running the wrong way. For months he was alone with this message. Now he’s hearing his competitors start to use his exact words:

• Foundation.

• Consistency.

• The layer underneath.

To most founders that’s a threat. To him, it’s the first proof he picked the right race because finally there are runners behind him.

Today, only companies with genuinely complex reporting ever bought an FP&A system. Everyone else, the vast majority, limped along on spreadsheets and called it good enough. Didi thinks that era is over. Every finance team on earth will need this foundation, because you simply cannot run AI on top of nothing.

Everyone will have a financial operating system. You can’t run Claude on one MCP for Salesforce, one for your ERP, and one for your budget spreadsheet, and expect consistency. There is no question about it. That turns a few-hundred-million-dollar niche into a market ten times the size.

Didi has a rule at Datarails for measuring customer health.

The single biggest jump in value happens at one specific moment: when a customer stops feeding the system only finance data and starts feeding it operational data too. Inventory. Shopify orders. Sales pipeline. Headcount.

Why does that one move matter so much?

Because the second your foundation holds operational data next to financial data, FP&A stops being a finance function and becomes a company function. Now the FP&A person isn’t explaining last month’s variance to the CFO.

FP&A teams are catching a shift in inventory or a crack in the sales pipeline and walking it straight to the COO before anyone asks. Their reach jumps from the finance department to the whole business, all the way up to the CEO. In fact, Didi baked flexibility into Datarails’ architecture: FinanceOS uses a “relational database,” giving AI tools, such as Claude or ChatGPT, an unlimited environment with endless dimensions, columns, and rows.

Then I asked Didi the question that matters most to you.

What happens to the companies that try to get there without building this foundation?

Most finance teams will try to build without a foundation, but they won’t see the full cost of this decision until it’s too late. He laid out two paths. Look closely, because you’re about to pick one.

The first is the big-company path

You take your finance data and pour it into the corporate data lake, the Snowflake or Databricks or BigQuery your IT team already runs, right next to all the other company data. It works, sort of.

But here’s the trap nobody mentions.

Finance data is alive in a way other data isn’t.

The definitions shift, a new budget line appears, the mapping changes, and the model gets revised constantly. So you end up hiring a dedicated finance-IT person whose entire job is keeping the finance definitions aligned with the data lake.

In fact, hundreds of Datarails customers choose Datarails and Snowflake at the same time. Didi points out that FinanceOS isn’t competing with Snowflake or Databricks.

• The FinanceOS category exists because finance teams have to update their model, adding a department, restructuring a P&L, changing an allocation, or locking a forecast version… without filing a JIRA ticket and waiting for the next sprint. In addition, FinanceOS is built around a long-running finance reality: teams work in Excel. FinanceOS fills a significant gap of things Excel is genuinely bad at, such as acting as a database and store of data.

• FinanceOS keeps Excel connected to a governed system, while Snowflake forces finance to export data and rebuild models every month.

Didi says a data warehouse alone is the wrong place for finance models, because finance can’t own a model that lives in Snowflake.

You typically wait on IT to change a definition that used to be yours to control. Their structure often forces finance to export data and rebuild models. For a function whose entire reason to exist is owning the numbers, you just quietly handed away your independence. Most CFOs walk straight into this and only feel the loss years later.

The second is the duct-tape path

You wire Claude to a few MCP connectors yourself and make do.

You’ll pull a trial balance out of the ERP, sure. You’ll automate a little. It won’t be zero. But you’ll get a sliver of the potential, running the same manual consolidation you run today with a chatbot stapled to the side.

And here’s the specific thing that breaks: pulling a trial balance is easy, but a proper cash flow statement or a real invested-capital view… those take deep knowledge of how to build them correctly.

That knowledge has to be embedded once, into the foundation, so it runs the same way every month. Improvise it each time across a tangle of connectors, and you get your old chaos back, just with better autocomplete.

The future he’s building takes neither path.

The foundation is built once. The definitions are set once and held. And the AI on top finally behaves the way finance needs it to.

The Bottom Line

For two years the vendors sold you the tip of the iceberg.

The pithy insights, the pretty dashboards, the auto-generated board narrative. They skipped the foundation because building it is slow and unglamorous work that doesn’t close a sale in a thirty-minute demo.

But finance doesn’t run on impressive.

It runs on trust. And trust comes from exactly three things. One source of truth. A layer that holds your definitions. And a human who owns the result. Skip any of them and no model on earth will save you, because you’ll get an answer that’s right this week and wrong the next, and you’ll stake your name on it without knowing which week you’re in.

Everyone is about to have the same AI.

The same Claude, the same models, the same raw horsepower.

So the model cannot be your edge. The finance teams that win over the next three years won’t have a smarter AI than you. They’ll have done the boring, brutal work underneath it that you were tempted to skip.

The process-followers are going.

The thinkers are about to inherit the most valuable seat in the building, the one that sees the whole company, is right next to the CEO, and finally allows them to spend their time on judgment instead of data entry.

Make absolutely sure that’s the seat you’re aiming for.

The foundation takes time, and the clock started without you.

The hard part was never the numbers.

It was always the foundation.

And that’s all for today.

See you on Thursday!

Whenever you’re ready, there are 2 ways I can help you:

1. Advertise with the AI CFO Office.

2. If you’re building an AI-powered CFO tech startup, I’d love to hear more and explore if it’s a fit for our investment portfolio.

I’m Wouter Born. A CFOTech investor, advisor, and founder of finstory.ai

Find me on LinkedIn

They stopped writing prompts almost entirely. They build skills and workflows. You teach Claude once, and then they run in the same disciplined way every single time.

Anthropic went from $9 billion to over $30 billion in revenue in just 4 months.

Krishna Rao is the CFO who runs the finance function behind that number. On the Invest Like the Best podcast, he talked about how his team uses Claude Skills to do their own monthly close.

The numbers he gave are remarkable.

Statutory financial statements for all of Anthropic’s legal entities are now produced using Claude. A human checks the output. Claude does the work.

The team has built a library of finance-specific skills, more than 70 of them at last count, sitting in a shared repository that everyone can access.

On top of that library, they built one skill called MFR. The monthly financial review skill. Krishna’s quote on it.

It can produce our monthly financial review. It’s 90 to 95% ready and then all of our discussion becomes about what do we do, what are the implications, not what exactly happened. Because Claude is not just reporting the weather. It’s also helping to think about drivers and why the number changed the way it did.

A weekly revenue or compute utilization report that used to take hours now takes 30 minutes. The biggest internal Claude user inside the finance team is not the 22-year-old who joined with a coding background. It is the head of tax.

That is what the closed-loop MFR looks like when it works.

Every CFO can build a version of it.

One MFR skill installed inside Claude. It reads the actuals. It surfaces the signals. It builds the visual reasoning. Then it refuses to produce the deck until the CFO answers four guided questions, each grounded in the evidence the skill built first.

Krishna’s MFR skill is built on Anthropic’s books.

You can build on your books.

When you just chat, you start from nothing every time

Always start with Claude projects, then skills.

Go to claude.ai.

Click Projects on the left, then click New Project.

Projects

A Project holds your context for good. Your company facts, your data dictionary, how you define net revenue, what counts as returns, your fiscal calendar. Claude reads all of it with every prompt, so you never re-teach it what your company is. This is also where you park the rule. Cite every number, or flag it.

Skills

Open the skill manager in Claude and Upload your skills.

A Skill runs a task the same way every time. Your monthly variance commentary. Your reforecast. Your board brief. You build it once, and Claude pulls it on its own when the work calls for it.

For example, the variance skill reads the 24-month P&L, reconciles the revenue, dissects the trade promotion, recomputes every figure in code, and hands back board-ready commentary with each number tied to its source. It does in one pass what used to take you an afternoon of copy-paste and chain-of-thought prompting.

Using projects and skills, CFOs would get accurate results.

Claude Masterclass for CFOs Who Want to Build CFO Skill Stack (LIVE)

We are hosting a private Claude Masterclass for CFOs on Thursday, July 16, at 11:00 AM ET.

This is not a demo. This is a 60-minute, hands-on walkthrough of how to build the one thing that separates CFOs getting real value from Claude and the ones still typing prompts into a chat box. Skills.

Live. Ask questions. Challenge what works and what does not.

Here is what most CFOs still do not understand about Claude.

The CFOs getting the biggest results are not writing better prompts. They are building skills. A skill is a workflow you teach Claude once that then runs the same disciplined way every single time.

Every CFO reading this knows the moment arrived. The question is no longer whether to learn Claude. It is how fast.

What I will cover

The AI foundation most CFOs skip

• Why every CFO must use Project for any serious finance work.

• The one instruction that makes Claude’s output safe for a board.

• How to set up a Project so Claude holds your company’s numbers and rules every time.

How to build a skill from scratch

• Building a monthly financial review skill live, start to finish.

• What a skill actually is, in plain terms, and why it beats a prompt.

• How to turn one working skill into a library your whole team can run.

• How a skill forces Claude through the same steps every time: inventory the inputs, flag the exceptions, check its own math, and write the summary.

The skills every finance function must own

• Monthly close and variance commentary.

• Board pack assembly and narrative.

• Cash flow forecasting and scenario work.

The strategic layer

• What to build this quarter and what to wait on.

• Where skills save real time and where they do not.

• How to make your finance function AI-native without losing the audit trail.

Who this is for

CFOs, VPs of Finance, FP&A leaders, and controllers. Anyone running a finance function that needs to be ready for what comes next.

What to expect

A 60-minute live masterclass. I will build skills in my actual Claude environment, from a blank Project to a working skill library, and you will leave knowing how to build your own. Open Q&A throughout. Honest discussion of what works and what breaks.

As a paid member, you get full access to all written content.

Live masterclasses are exclusive to Insider members.

If you want the live experience, upgrade to Insider Member. If you are already an Insider member, the registration link is inside the announcement letter.

Whenever you’re ready, there are 2 ways I can help you:

1. Advertise with the AI CFO Office.

2. If you’re building an AI-powered CFO tech startup, I’d love to hear more and explore if it’s a fit for our investment portfolio.

I’m Wouter Born. A CFOTech investor, advisor, and founder of finstory.ai

Find me on LinkedIn

The reconciliation ties out. The variance is tiny. You move on, because chasing a $240 difference across thousands of transactions is not where you spend a Thursday.

That is exactly how a clean number hides a dirty one.

I just ran a weekly cash reconciliation for a demo company, Born2Cycle, through Claude. Bank file, ERP cash ledger, AR aging, and AP aging. The headline variance came back at $240. On a normal week, you sign that and move on.

But Claude refused to.

It found the $240 was not a small difference.

It was two large errors that nearly canceled each other out. An $18,000 rent payment that never made it into the ERP and a duplicated $18,240 Shopify payout sitting in the bank file twice. Netted together, they looked like a harmless $240.

Pulled apart, they were a missing expense and a double-counted deposit, either one big enough to matter to your board.

It did not ask me to take its word for it.

It handed me an Excel workbook with real formulas I could trace and check myself.

Claude worked like a real CFO analyst, caught what the clean number was hiding, and gave me the receipts to verify it.

In today’s letter, I will give you two CFO skills, the sample files, and the exact prompts, so you can run this and then start shaping it to your own numbers.

Let’s dive in.

Automate Cash Reconciliation and Reporting with Claude as a CFO

You need two skills.

1. cash-reconciliation-reporting.skill

2. cfo-cash-dashboard.skill

The first builds the controlled reconciliation layer.

The second turns it into the dashboard.

Both are below with the sample files and the prompts.

Download the skills below and copy/paste the prompts

Read more

This is not a demo. This is a 60-minute, hands-on walkthrough of how to build the one thing that separates CFOs getting real value from Claude and the ones still typing prompts into a chat box. Skills.

Live. Ask questions. Challenge what works and what does not.

Claude Masterclass for CFOs (LIVE)

Here is what most CFOs still do not understand about Claude.

The CFOs getting the biggest results are not writing better prompts. They are building skills. A skill is a workflow you teach Claude once that then runs the same disciplined way every single time.

Anthropic’s own finance team runs this way. Their CFO has said a report that used to take hours now takes 30 minutes, and the heaviest Claude user on the team is not the youngest analyst; it is the head of tax.

And KPMG put Claude in the hands of 276,000 advisors.

PwC trained 30,000 of theirs on Office of the CFO workflows.

Jamie Dimon publicly built a dashboard with Claude in 20 minutes.

Every CFO reading this knows the moment arrived. The question is no longer whether to learn Claude. It is how fast.

What I will cover

The AI foundation most CFOs skip

• Why every CFO must use Project for any serious finance work.

• The one instruction that makes Claude’s output safe for a board.

• How to set up a Project so Claude holds your company’s numbers and rules every time.

How to build a skill from scratch

• Building a monthly financial review skill live, start to finish.

• What a skill actually is, in plain terms, and why it beats a prompt.

• How to turn one working skill into a library your whole team can run.

• How a skill forces Claude through the same steps every time: inventory the inputs, flag the exceptions, check its own math, and write the summary.

The skills every finance function must own

• Monthly close and variance commentary.

• Board pack assembly and narrative.

• Cash flow forecasting and scenario work.

The strategic layer

• What to build this quarter and what to wait on.

• Where skills save real time and where they do not.

• How to make your finance function AI-native without losing the audit trail.

Who this is for

CFOs, VPs of Finance, FP&A leaders, and controllers. Anyone running a finance function that needs to be ready for what comes next.

What to expect

A 60-minute live masterclass. I will build skills in my actual Claude environment, from a blank Project to a working skill library, and you will leave knowing how to build your own. Open Q&A throughout. Honest discussion of what works and what breaks.

As a paid member, you get full access to all written content.

Live masterclasses are exclusive to Insider members.

If you want the live experience, upgrade to Insider. If you are already an Insider member, the registration link is below.

Read more

Half of white-collar work will be gone.

Entry-level finance roles will be cut first. You have heard it so often it started to feel true. You believed it a little. Everyone did. It is hard not to believe it when the founders building the technology and the outlets covering it all say the same thing at the same volume.

Here is what none of them told you.

The largest real study of AI and jobs just came out, based on actual company spending and actual hiring across 21,559 firms. It found the opposite of the headline. The companies going hardest on AI are not cutting their teams.

They are growing teams and hiring juniors fastest of all.

So either everyone reporting on this got it wrong by accident, or they had a reason to keep you afraid. It is the second one, and once you see the reason, you can’t unsee it.

This is the career advice nobody selling you something can afford to give.

What the data actually says, why the fear was manufactured, and what to do with your next three years while everyone else is still panicking.

Let’s dive in.

Why the people warning you can’t stop warning you

Follow the money and the fear explains itself.

The AI founders raised the biggest version of the story.

When the CEO of a major AI lab said this technology would push unemployment toward twenty percent, that was not a forecast. It was a pitch to investors and a warning to every company to buy fast or fall behind.

The number has quietly softened since. The fear has not, because the fear is what moves the product. The media runs the same play.

A reporter who stakes a story on a prediction cannot walk it back without admitting they were wrong, and being wrong in public is the fastest way to lose their authority.

So once “AI is coming for your job” became the story, it stopped being a question anyone got rewarded for answering honestly. They keep saying it because they can’t afford to stop.

I have nothing to sell you and no headline to defend.

So here is the part they leave out.

What the largest real study actually found

Ramp and Revelio Labs published a working paper this month.

It’s called: A New Look at AI’s Impact on Jobs.

They threw out the surveys and used real spending records, the actual money companies paid for AI, matched against real hiring data across 21,559 US firms.

The companies that adopted AI most aggressively grew headcount 10.2% over the next two years. Entry-level roles, the ones everyone swore would go first, grew fastest at 12%. Managers grew 6.7%. The companies that barely touched AI saw no growth at all.

And the heaviest hiring landed in information, finance, and insurance. Your industry.

That is the reverse of what you were sold.

The firms leaning hardest into AI are hiring the most and hiring juniors fastest.

Now I will do the thing the hype merchants never do and give you the other half. The authors say plainly this does not prove AI creates jobs. It is a correlation, and the companies already growing were the ones that could afford to adopt.

And Goldman Sachs estimates AI has cut roughly 16,000 net jobs a month over the past year, hitting younger workers hardest.

Ignoring that number would make me exactly like the people I just described.

So here is the truth that sits between the two lies.

• AI is not taking finance jobs. It is taking finance tasks. The processing, the manual rebuild, the moving of a number from one system to the next. That work is leaving.

• But the people who do judgment get more done, and the companies that adopt seriously grow, which is why their headcount climbs instead of falls. The apocalypse was oversold. The job was never the target. The task was.

What to do with your CFO career now

Stop trying to get good at the tool.

This is the instinct almost every CFO has, and it is the wrong one. You watch AI arrive, so you go learn the current model the way you once learned the ERP. But the model changes every few months, and mastery of today’s version is the least durable skill you can build.

Learn what to ask of it and how to judge what it gives back.

That skill compounds. It never expires.

Then sort your team through one lens.

Who does judgment and who does processing?

The processing is what AI absorbs. The judgment is what it makes more valuable. Move your people toward the judgment now, while you have the runway to do it on purpose instead of scrambling after a bad quarter forces your hand.

And decide which CFO you intend to be.

AI can build the analysis. It can’t stand in front of your board and put its name on the number. It cannot own the result. That signature, and the judgment behind it, is the part of your job that grows more valuable as everything around it gets automated.

The CFOs who see this stop racing the machine at processing and start building the one thing it will never have.

The Bottom Line

The people telling you AI will erase your career are not evil. They are stuck. The founders need you afraid so you buy faster. The headlines need the story scary so they keep their authority.

Neither can afford the quiet, unglamorous truth, so they keep shouting the loud one long after the data stopped backing it.

The quiet truth is this.

The companies winning with AI are hiring, not firing. The 7% pulling ahead did not buy a smarter model than you. They built better architecture around the same tools everyone has, pointed AI at the processing, and moved their people up into the judgment.

The other 93% are standing still, waiting to see whether the headlines come true.

They will not. The task is what AI takes. The judgment is what it pays for. Build your career on that second one, and in three years you will look up to find that the thing everyone called the end was the most valuable seat in the building.

And that’s all for today.

See you on Thursday.

Whenever you’re ready, there are 2 ways I can help you:

1. Advertise with the AI CFO Office.

2. If you’re building an AI-powered CFO tech startup, I’d love to hear more and explore if it’s a fit for our investment portfolio.

I’m Wouter Born. A CFOTech investor, advisor, and founder of finstory.ai

Find me on LinkedIn

Your CFO team understands cash, and yet the board is not sure.

The reason is simple.

The data is scattered across your bank export, your AR aging, your AP items, payroll, sales orders, and a tax schedule that lives in someone’s inbox.

The timing is uncertain, because revenue recognized is not cash in the bank, and an invoice issued is not an invoice paid. The assumptions never get written down, so nobody can audit them later.

Scenarios are clumsy, built by hand in a copy of the copy of the model. And the weekly refresh has no reliable trail, so when a number moves, you can’t say why.

PwC says cash forecasting is about when cash actually hits the bank, not when revenue is recognized or an invoice is issued. Every sale has to land in the week the customer is expected to pay, not the week you booked it. That single distinction is where most forecasts quietly go wrong.

And it’s exactly where AI helps, if you use it right.

I built a full 13-week cash flow forecast with Claude.

Claude kept the Excel model as the source of truth, then built an interactive dashboard that lets you stress-test cash, not just look at it.

What impressed me most is what the dashboard didn’t hide.

The dashboard surfaced the exact review items, the threshold breaches, and the scenario trade-offs a CFO needs to see before making a call. It showed the weak spots instead of papering over them. That’s the difference between a dashboard that decorates a number and one you can actually decide from.

Let me show you how I built it using the cash flow model builder skill (.md) and the full prompts I used.

Let’s dive in.

How to build a cash flow forecast with Claude

Step 1: Install the cash flow model builder skill

You’ll get 2 skills and test files.

1. Cash flow model builder skill

2. Cash flow scenario dashboard skill

Download the skills below, then copy/paste the prompt to build a cash flow model.

Read more

Today’s rate is the lowest it will ever be.

In 2 hours, the prices go up

Sunday, June 28th at 5 PM ET

• Monthly $45/month → $65/month

• Annual $199/year → $249/year

• Insider Member (live AI masterclasses + meetings): $600/year → $799/year

Your current rate stays locked in for as long as you’re …

Read more

There are 8 finance-specific skills.

You will get the skills to prepare board meetings, run monthly financial reviews, reforecast, build McKinsey-style dashboards, run an audit, and build a closed-loop finance function.

A weekly revenue or compute utilization report that used to take hours now takes 30 minutes with Claude skills - Krishna Rao, CFO of Anthropic

Claude has read more financial statements than you could in a hundred lifetimes. Every public filing, every amortization table, every annual report ever posted.

But it has not seen your company.

It doesn’t know how you define net revenue, what your trade accruals look like, or which four customers carry your quarter. That proprietary reality is the one thing all that training can’t give.

That’s why every CFO must use skills.

Each skill can be used however you want.

Below, I’ll show you one in action, the Monthly Financial Review, run end to end on a real set of month-end files.

Download the full CFO SKILL Stack below

Read more

This price change doesn’t touch you for as long as you stay subscribed. Consider this a heads-up and a thank you for being in early.

If you’re reading on the free list, the price change is the part that matters to you. Prices go up Sunday, and today’s rate is the lowest it will ev…

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Most CFOs are using Claude wrong.

They are unsure what the first prompt should be and how to get more accurate answers from Claude as a CFO. They don’t know how to use Projects and Skills.

With Claude, CFOs can run month-end close and do serious financial planning and analysis. Run variance commentary and build a board deck. But everything has to be accurate and it’s possible.

At our recent Claude Masterclass, Glenn Hopper showed our Insider members the version that actually works. Glenn is a real AI CFO who builds AI into finance teams for a living. He took one real month-end close and ran it end to end, live.

You must build a Project to hold the company’s context, run a Variance Commentary skill to produce the analysis, and then stack a second skill on top to turn that analysis into a board-ready dashboard. KPI tiles, a gross-to-net bridge, and a waterfall.

The difference is the architecture underneath Claude.

In today’s letter, you’ll learn how modern CFOs build a month-end variance dashboard in Claude using Skills and Projects.

You’ll get two ready-to-use skills.

• Variance commentary skill (.md)

• Mckinsey dashboard skill (.md)

And by the end of this newsletter, you’ll understand how to use multiple Skills, create projects and instructions. Upload the files, test projects, add skills and build a board deck to impress your CEO and the board.

Let’s dive in.

Claude Masterclass for CFOs

How to build a month-end variance dashboard with Claude

Read more

As a regular paid member, you will get the editorial walkthrough. But if you want to be part of the live meetings, ask questions and interact with the room. Then become an Insider Member.

This is the best investment in your AI CFO journey.

Prices go up Sunday, June 28th at 1 PM ET

• Insider Member (live AI masterclasses + meetings): $600/year → $799/year

Your current rate stays locked in for as long as you’re subscribed.

Lock in your rate before prices go up

The price goes up because the room keeps getting more valuable.

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