"I made this is in Excel" used to make you feel proud..

But, in 18-months, Excel reporting won’t impress anyone, it will date you.

There're 2,693 finance pros in my community. And a big problem I see is numbers that look super boring (that people also don't trust).

So, to help you with this, I'm running this live masterclass with Ramp. On April 30th I'll build 3 AI-powered dashboards from scratch in 60 minutes - You'll see:

• How to construct AI-powered dashboards for 1) departmental costs, 2) revenue pipeline, and 3) inventory from scratch
• The validation techniques that keep your AI-generated dashboards accurate and trustworthy (well before you share them)
• The trick to turning AI experiments into usable workflows you can use every day to stay ahead.

You'll get the exact prompts, process, and validation checks I use to create powerful dashboards. Use them to impress your team and create a bigger impact straight away.

​Save your free seat here before it's gone.

Before AI, I waited 6 months for a dashboard. It never came.

It took 3-4 days of work in Excel for my team to create cost centre reports for each department.

Then a BI team arrived and we were so happy we thought we'd have everything automated, and on a dashboard by the end of the month.

Six months later? Nothing.

Now AI can build the same thing in 10 minutes.

But thinking about this now, I don't think the BI team failed because they were slow.

I think they failed because nobody ever agreed on what the dashboard was for.

And here's what's crazy. Procter & Gamble had solved this exact problem 14 years ago. Before AI even existed. They built dashboards around decisions (not data) and killed more than 80% of their BI reports doing it. I just didn't know yet.

Dashboards you build too quickly become AI slop that nobody looks at. So today, I'm going to show you the method P&G proved works over a decade ago — and how to apply it to your AI dashboards this afternoon.

Avoid becoming a 'Data Decorator'

The moment you want to show an AI-built dashboard lands in front of the CEO, the board, or your team, the rules change.

You need to have put real time into the design thinking first. Things like the decision you want to make, the KPIs that support it, and the layout.

If you skip this, you are just creating AI slop that shows your data in a different way. And your credibility goes with it.

You become a ‘data decorator’ where your CEO looks, nods, and forgets about it.

In my AI Finance Accelerator we make all participants create a dashboard using AI in week 2. But, one that they can then use to create an impact in their business. Here are some examples of what they produce:

The 'Decision Cockpit'

Back to P&G.

In 2012, their Business Services Group President Filippo Passerini asked a different question.

Instead of "what dashboards should we build?" he asked, "what decisions do our leaders need to make?"

Then he built dashboards around those decisions. He called them Decision Cockpits. And they replaced more than 80% of P&G's standardized reports, which were then deployed to 50,000+ employees across 50+ offices.

Source | Practical Analytics​

Passerini's own description: his dashboards were "focused on forward-looking projections rather than historical reporting."

That difference (building around the decision, not the data) was the focus in 2012 when P&G did it without AI.

It's still the focus in 2026, when you can build the same thing with AI in 10 minutes.

Your edge is your brief.

You know which decisions matter. You know which KPIs drive them. You know what is important to the board.

AI can build in 10 minutes what a BI team could not build in 6 months.

But only you can decide what the dashboard is for.

5 steps to your first decision-first dashboard

Step 1. Name the one decision (on a Post-It, before AI)

Write one sentence: "This dashboard helps [role] decide [action] each [cadence]."

Example: "This dashboard helps the CEO decide whether to approve Q2 hiring requests, monthly."

If you cannot finish the sentence, do not build the dashboard.

Step 2. Name the owner and the cadence

Who acts on it, and how often? If the answer is "everyone, whenever" - stop.

A dashboard with no named owner has no owner. Pick one name and one cadence. Weekly, monthly, per board meeting.

Step 3. Name the trigger metric

What single number, if it moved, would change the decision? That is the top of your dashboard. Everything else is evidence.

Step 4. Brief the AI with the decision, not the data

Use this prompt pattern:

Tell AI what the dashboard is for, before asking it to build.

In my last Crash Course I uploaded a file and said: "I am the finance manager, I want to understand how to increase sales. I want to build the best dashboard."

I was not asking it to build yet. After one minute I had a proposed set of metrics and a layout, and that was my chance to push back.

One thing I push back on every time: data availability. Before you let AI do anything, walk through each proposed KPI and ask, can I pull this data from my systems, reliably, on the cadence the decision needs?

If the answer is no for even one KPI, change it out or find something different for now.

Otherwise you end up with a beautiful dashboard you can never populate and every month you waste time finding exports to create it.

Step 5. Review the layout before the data is plotted

When AI returns its proposed structure, check it against the decision.

Is the trigger metric the most prominent element? Does every other chart earn its place by supporting that one decision?

If not, keep on improving it.

The CFO of HPE did this, this year

Earlier this year, Fortune published the story of Marie Myers, CFO of Hewlett Packard Enterprise (article here).

Her weekly Monday review used to improve 100+ PowerPoint slides and hundreds of hours of manual prep across the business.

Her team built an AI dashboard called Alfred (yes, after Batman's butler) that led to: A 40% cut in financial reporting cycle time, a 25% cut in processing costs, and 90% of the manual weekly prep removed.

The super important bit? Myers told Fortune: "The first move wasn't to switch on AI, but to redesign the work."

She rebuilt the process around the decisions her business needed to make - then switched on AI.

And if you're thinking "yeah but I'm not Fortune 500" - this is good. You've got less complexity, less politics, and AI that costs $20 a month.

If the CFO of a Fortune 500 company had to start with a blank sheet of paper, so can you.

The One Thing To Remember

My BI team failed in 6 months. An AI dashboard can now fail in 10 minutes. The time saved is not the point. The decision behind the dashboard is.

P&G proved this in 2012, without AI. HPE proved it still works in 2026, with AI. The tools change. The method does not.

Your next dashboard should not start in Excel, in ChatGPT, or in Claude. It should start on a single sheet of paper, with one sentence:

"This dashboard helps [who] decide [what] each [when]."

If you can write that sentence, you are already the most valuable role in finance in 2026 - the person who owns the brief (and the decision).

Best,

Your AI Finance Expert,

- Nicolas

P.S. - Hit reply and tell me - what is the one decision your most-used dashboard was actually built to serve? I read every reply.

P.P.S. - The full walkthrough of how I build finance dashboards with AI is on YouTube - How to Build Finance Dashboards With AI in Minutes. The six-month BI team story sits around the 1:49 mark.

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