Welcome to AI in Excel in 2026.
How to build financial models in under an hour.

I use this in my business, for my clients, and for my own personal finances.

I project net worth/equity 5 years from now.
It keeps me focused on what actually matters.

Here is the exact setup:

𝗧𝗮𝗯 𝟭: 𝗔𝗰𝘁𝘂𝗮𝗹𝘀

Pull revenue, cost of goods, payroll, and overhead from your books.

Enter it by month.

This is your starting point.

𝗧𝗮𝗯 𝟮: 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁

Roll your actuals forward.

Adjust for known changes: new hires, price increases, slower months.

Now you can see the next 12 months before they happen.

𝗧𝗮𝗯 𝟯: 𝟱-𝗬𝗲𝗮𝗿 𝗣𝗹𝗮𝗻

Use the same structure.
Project where the business goes if you stay on track.
Change one assumption and watch the whole thing shift.

Here is the workflow:

1️⃣ Open Excel.
2️⃣ Ask AI to build a 3-statement financial model in 3-tabs: Actuals, Forecast, and 5-Year Plan.
3️⃣ Enter your real numbers in the Actuals tab.
4️⃣ Roll them forward in the Forecast tab.
5️⃣ Use the 5-Year tab to see the trajectory of the business.

Every client I work with has this built in the first 30 days.

Once it exists you stop making decisions off your bank balance.
You start making them off the numbers.

Want the full prompt to send Excel AI to build this out?

Note: This builds the structure. You will need to refine it for your business.

Follow or connect with me and comment "Model."

I spent 1 hour writing a detailed prompt.
Claude built a financial model in return.
I saved at least 15 hours of manual work.

That's not a shortcut. That's a different way of working.

What used to take half a day now takes minutes.

Not because AI is better at finance.
But because it removes the mechanical part of the work.

Here's how I actually use it:
– exploring margin scenarios across pricing and delivery
– testing headcount changes before making a hiring decision
– building first versions of financial models in one session
– structuring investment decisions before capital is committed

The role doesn't change.

The speed does.

One honest observation:

Maybe 1 in 10 from my immediate network of CFOs and senior finance managers uses AI at this level.

The rest – a free plan, simple tasks, nothing more.

Reality is far from what you see in your feed.

AI doesn't replace financial expertise.

It amplifies the person who already knows what to look for.

I'm planning a live session where I'll show exactly how I use this in real finance work.

No slides. No theory. Just live.

If you'd like to join – reply below or send me a message directly.

I use AI almost every day in my finance work—building models, pressure-testing assumptions, drafting executive communications, working through complex financial structures.

And honestly? I have no idea if that makes me early, average, or behind.

I’ve been heads-down the past 12 months and recently realized I have very little visibility into how others are actually using AI day-to-day.

Not the headlines or vendor demos—the real workflows.

For example, I’ve found the Claude add-in for Execl especially useful working with existing models to create additional what-if scenarios to evaluate different options from a financial perspective. (I’ve also found a human in the loop and detailed eye is necessary, as Claude has changed some existing formulas erroneously.)

So I’m genuinely asking: if you’re in finance, ops, or a leadership role—how are you actually using it?

→ What’s changed in your workflow (if anything)?
→ What’s been surprisingly useful?
→ What’s been a dead end?

No polished takes needed—just honest ones. Especially interested in hearing from operators in it day-to-day.

You spend days building financial models and slide decks.
What if it took 10 minutes?
👉Join my free masterclass to see how: https://lnkd.in/dqQm8FjH

Here's what just happened:

One P&L. Three scenarios. A full 5-year model — in 5 minutes.
Then a live slide deck — in 5 more.

No copy-pasting between sheets.
No rebuilding slides from scratch.
No waiting a week to show updated numbers.

Here's how it works 👇

Step 1: Give AI your P&L and define your scenarios

- Base: sales +3%/yr, margin steady, costs +2%
- US expansion: more aggressive top-line with explicit levers
- Pessimistic: sales −2%, margin −1pp, costs −1%

AI links everything end-to-end.

Each scenario pulls from the same assumptions sheet — so there's no copy-paste drift.

You tweak one driver → the entire model updates instantly → the math stays consistent.

Step 2: Ask for a dynamic slide deck

The deck reads from the same model.

You can present revenue, gross margin, and EBIT across all three scenarios — and change assumptions live in the meeting.

"What if US grows faster in 2030?"
→ One number changes
→ The charts update dynamically
→ You show the result right there — no need to wait another week

Think about that for a second:

You go from a raw P&L to a working model + interactive deck in 30 minutes.

The same work that used to take your team days.

⚠️ But don't skip governance

Treat AI like a sharp junior analyst on your team.

- Review the logic
- Stress-test edge cases
- Check formulas the same way you would with a human
- And use a corporate/enterprise environment — don't upload confidential data without a secure license

AI builds fast. Your job is to verify smart.

The finance professionals pulling ahead right now aren't working harder.
They're building in minutes what used to take days — then spending their time on the decisions that actually matter.

👉 Join my free AI CFO Masterclass:
https://lnkd.in/dqQm8FjH

💬 What financial model would you build first with this workflow?

🔁 Repost if your team still spends days on models and decks.

The scale-up finance teams I'm working with who are pushing the edge of what AI can do are all switching to Claude Cowork. The last months acceleration in this space can't be ignored.

The same AI that writes and runs code now works directly inside your workbooks and across your local files. It reads your models, debugs formulas, cleans messy data, simplifies overcomplex logic, and iterates on its own work.
Inspired by Ruben Hassid's post on building investment bank-grade financial models with Claude in Excel (https://lnkd.in/dptByTBu), I ditched the ChatGPT -> copy-paste -> Excel loop. Now I lean fully into Claude Cowork.

I'm not the only one. Secret CFO described it well - they built a unit economic model feeding an investment case by, in their words, "just streaming semi-coherent thoughts into the prompt box". No formatting. No structuring. Just context in, model out (https://lnkd.in/d2guUdcu). The CFO Office ran a full month-end workflow: vendor contract ingestion, spend-vs-contract cross-referencing, inconsistency flagging, and dashboard creation. Three markdown files, two data exports, zero code. Fifteen minutes to set up.

Here's what I tested:

I used (the ring fenced enterprise version of it that keeps your data private) Cowork on our Actuals, our context, our terminology. After 10 minutes of prompting and some Q&A it asked for, it built me a reforecast and valuation model in 20 minutes.
One that actually made sense.

That same work used to take me (optimistically) roughly a week in calendar time once you factor in meeting blocks, context switching, and iteration cycles, and 2-3 FTEs.
This is the first time I've seen AI efficiently integrate into the native workflow for FP&A and Corporate Finance. Not a chatbot you paste into for inspiration. Not a copilot that understands your spreadsheet maths but not your business logic. An agent that works across your files, understands your context, and produces real outputs.

On the Controllership/Accounting side, we've been fortunate to work with the team at Stacks for deployed AI. Now, impactful solutions are finally reaching the rest of the finance stack as well, Anthropic just shipped five open-source finance plugins (financial analysis, IB, equity research, PE, wealth management) with connectors to FactSet, S&P Capital IQ, and LSEG built in.

If you're still copy-pasting between a chatbot and Excel, or running this all yourself as we did for the last decade, try this. Anything you do monthly in Excel, and in operational finance that's a lot of shuffling, can now be automated in your native workspace. That unlocks time for analysis, controlling, and the value-adding work that actually matters.
Looking forward to hooking up and giving the Netsuite connector to Claude a spin this week!

The workflow shift is material.

I build financial models, the complex kind with circular references and logic spread across 10 sheets where one wrong cell ruins everything.

Started using Claude in Excel last week just to see what it could do. Honestly did not expect much.

This thing actually understands the files. Like really understands them, not just surface level. It follows circular references, tracks dependencies, keeps up with formulas referencing other formulas. And it finds mistakes I would have missed completely, small stuff buried deep in the logic.

What normally takes me a week of back and forth I’m now doing in a few hours. Built a full model in one day that would usually take me five.

I’m not someone who gets excited about tools easily but this one actually saved me real time. If you do anything serious in Excel just try it

Microsoft released Agent Mode in Excel.

Think Copilot, but instead of just suggesting, it acts inside your workbook.

It builds, edits, and reasons step by step.

I put together a full guide for CFOs, Finance, and FP&A teams on how to use this new AI capability for finance:

https://lnkd.in/eaFuD7vH

And a video guide: https://lnkd.in/eWjVxGrG

It also includes a step by step on how to "download" it.

This way you can see what has been tested and know if it can help you to automate reporting, forecasting, and even build valuation models!

If you want me to send the Excel file with the results just comment "AI Agent Mode" and I can send!

Here’s what Agent Mode can do live in your workbook:

✅ Automate budget roll-forwards across tabs
✅ Merge messy data into clean Pivot-ready tables
✅ Generate full variance analysis with commentary
✅ Build multi-tab models like DCFs & 3-statement forecasts
✅ Iterate until the output matches CFO-level standards

Also remember before submitting a prompt, you can select which model you prefer to use, Agent Mode in Excel supports the latest Anthropic and Open AI models available to you.

To switch between models, use the model picker dropdown.

And the best part? Everything stays editable.

Some sample prompts you can try right now:

"Build a 5-year DCF model with revenue, OPEX, EBITDA, FCF, and terminal value. Format outputs in CFO-ready style."

"Consolidate actuals from multiple sheets into one clean variance-to-budget report with conditional formatting."

"Generate a revenue forecast with 3 growth scenarios (Base, Optimistic, Downside) and plot a chart of outcomes."

"Draft a month-end financial summary with key drivers, risks, and opportunities based on this dataset."

"Reshape this dataset into a management-ready P&L with standard financial formatting and subtotals."

Hope this guide helps!

Some notes from Microsoft's announcement:

How to try it

1. Use https://excel.new to quickly create new Excel workbooks.

2. Select Home > Copilot and open Copilot chat.

3. Select the Tools menu and choose Agent Mode.

4. Start with an outcome-based simple prompt, like “Build a loan calculator that computes monthly payments based on user inputs for loan amount, annual interest rate, and term in years. Generate a schedule showing month, payment, principal, interest, and remaining balance. Present the results in a clear, formatted table.”

Tell me in the comments if you have questions or what was your result! :)

Notes from Microsoft's website on Availability

Agent Mode in Excel requires either a Microsoft 365 Personal or Family subscription (with an AI credits plan), a Microsoft 365 Premium subscription, a commercial Microsoft 365 Copilot subscription, or a Copilot Chat-eligible Microsoft 365 or Office 365 business or enterprise subscription.

Question I get often: How can I use AI in my business? Here's how.

I just built a full 5-year DCF valuation model inside Excel using Claude.
Revenue projections across 4 scenarios. Free cash flow waterfall. Terminal value. Sensitivity analysis.

This is not so much about the DCF. This is about what's now possible with AI and how it can help achieve business outcomes.

This is part of the series on:
How You Can Use AI in Your Business. Follow along if you want practical examples.

Investment Banking: Ranking the Best AI Tools for Financial Modeling (2026) 🤯

We extensively tested OpenAI's ChatGPT, Anthropic's Claude, Microsoft Copilot Agent Mode, and Shortcut on a real three-statement Excel model using investment banking standards.

Here’s what actually works, and what doesn’t.

Quick Answer: The Rankings 👇

Best Overall: Shortcut

Close Second: Claude (Opus 4.6)

Third Place: Microsoft Copilot (GPT-5)

Distant Fourth: ChatGPT (GPT-5.2)

The Testing:

Criteria 1: How It Feels Working With The Tool

Understanding the Assignment:

Winners: Claude and Shortcut

Claude and Shortcut asked thoughtful clarifying questions after receiving the prompt, about:

• Forecast preferences
• Revenue segmentation
• Share repurchases
• Layout decisions
• Schedule structure

That behavior closely resembles what you’d want from a good junior analyst.

Copilot and ChatGPT asked none.

Speed:

Winner: Shortcut

Shortcut and Claude completed the setup in ~15 minutes vs ~25 minutes for Copilot.

ChatGPT took close to an hour.

A decent analyst would have taken 1-2 hours to complete the assignment.

So all were faster than a human analyst at initial setup.

Criteria 2: Data Extraction, Formatting and Best Practices

Formatting:

Winner: Shortcut

Shortcut and Claude produced the most “investment-bank-like” outputs.

Shortcut was more consistent with input coloring and structure.

Claude missed several formatting conventions.

Copilot ignored IB formatting entirely.

Accuracy:

Winner: Copilot

Copilot won, but this was disappointing across the board.

Shortcut and Claude hallucinated significant portions of historical data.

In both cases, the errors were subtle enough to be dangerous, with slightly incorrect line items all adding up to correct subtotals.

Shortcut’s second attempt returned almost no mistakes.

Claude continued to generate bad data.

Fixing this would require careful cell-by-cell auditing that takes longer than just inputting the numbers yourself.

As a rule, analysts should not rely on these agents to find data and should instead upload PDFs and spreadsheets for the agents to work with.

Copilot and ChatGPT were more accurate.

ChatGPT’s presentation was the least polished, but its historical balance sheet was easiest to audit.

Had Shortcut and Claude used correct data, they would have won as they were attempting a more analytically rigorous presentation.

Shortcut was also going into the footnotes to break out certain items when appropriate.

Sourcing and Commenting:

Winner: Claude

Claude provided the best explanations of where data came from and why certain modeling decisions were made.

Copilot added no comments.

ChatGPT added too many.

Shortcut did some, but less consistently.

Claude was also the only tool to backsolve EBITDA correctly.

The Bottom Line:

Shortcut and Claude significantly outperform Copilot and ChatGPT.

But right now, even the best tool still underperforms a Junior Analyst.