I have been using Excel for 30 years. I thought I knew what it could do and what I couldn’t.
Then I described a problem out loud to an AI and it solved it in about 40 seconds.
Not a simple problem. A complex Budget model problem that had to work across multiple company locations, handle inconsistent GL account exports, flag unrecognized accounts automatically, and use lookup logic that did not break when the data came in a slightly different order than last month. Not to mention the need to update the model each year for the new year and any change that developed over the past year. It was nothing less than a headache of dropping and chasing formulas and date changes.
I have been building and improving this model for the past 6 years, getting better every year, but not perfect.
Then came Claude. I added the Claude extension to excel and my life changed.
I opened the model, clicked the Claude extension and instructed Claude to review the model and give me its take on the model, its layout, purpose and to look for problems. Done
That was the moment I stopped thinking of AI as a research tool and started thinking of it as a finance partner.
I am a CFO, not a developer. I am never going to learn to write INDEX/MATCH formulas from memory. I do not need to. What I need to do is describe the outcome I want and let Claude charge in. The prompt doesn’t have to be perfect. It just needs to get you started and from there you orchestrate the process. Don’t let uncertainty hold you back. The more you use it, the more you use it.
This is the first post in a series about what using Claude with Excel actually looks like in practice, not the demo version, the daily version.
#AIinFinance #Excel #CFO #Productivity

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

I watched a $3.5M acquisition almost fall apart during a board review.

Not because the strategy was wrong.
Not because the market changed.

Because of a single circular reference error in the debt schedule.

One hashtag#REF! error appeared. Then another.

Suddenly, the NPV didn't make sense. The IRR was jumping. The Board lost confidence in 10 minutes.

I spent 14 hours that weekend rebuilding the entire model from scratch. At that point, I had been in finance for 15 years.

And there I was, tracing cell dependencies like an intern at 2 AM.

That’s when it hit me: the manual way is a massive risk.

We accept "messy data" and "manual linking" as normal. It isn't. It’s a liability.

Today, that same error takes me 60 seconds to fix.

I use AI to debug formulas, clean messy imports, and review models.

I don't need to be a Excel advanced. I just need to know how to direct the AI agent inside my sheet.

What took 14 hours now takes 14 minutes.

You can either keep grinding manually, or you can learn the new way to work. Ready to level up? Join our Corporate Finance Hub® and master AI in finance. Stop fixing formulas. Start making decisions.

Join here:

https://t.co/SU0kSRB1i5

Also, do not forget to join my Webinar : AI in Action: 10 Real Use Cases for FP&A:

luma.com/xjj56tuc

I was a panelist on a panel about AI with Naturally NorCal yesterday. Here is what I learned while teaching.

I have had a thought bouncing around my head for a long while. Yesterday, it finally crystallized. A 2x2 came together in my head to help map out how we should actually be using these tools.

On one vector is my level of expertise. In some areas, like CPG financial operations, I am an expert. In others, I am less than a novice.

On the other vector is the criticality of the output. I use AI for strategic, foundational projects at Cultivar, but I also use it to make cartoons with my kids.

Here is how those quadrants break down:

1. Expert + Critical Output: When I know the subject matter deeply and the work matters, AI helps me 10x my productivity. It handles the heavy lifting so I can focus on the strategic edge.

2. Novice + Critical Output: This is where AI is dangerous. If you do not have the expertise to spot a hallucination or a systemic error in a complex P&L, AI alone is not sufficient. You still need to call in outside experts.

3. Novice + Non-Critical Output: When the stakes are low, AI is a wonderful way to play. For me, this is helping my kids unleash their imaginations by creating books and cartoons.

4. Expert + Non-Critical Output: When I am an expert but the output is not my primary focus, I find that using other experts' tools is the most productive use of time. For example, the team at Glimpse is helping us scale our trade spend management service line with their AI-powered deduction dispute tools.

We are all trying to find that balance between precision and speed. How are you using AI in your daily ops?
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Hi, I’m Pedro, CEO of Cultivar. We help CPG Founders feel confident about their finances by managing accounting, inventory, trade spend, and bev alc compliance so you can focus on growing the brand.

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.