I've been in finance for 30 years.

I've seen spreadsheets replace ledgers.
ERP systems replace spreadsheets.
And now AI replacing the work that shouldn't need a human.

But here's what nobody tells CFOs about AI:

You don't need a transformation programme.
You don't need a six-month implementation.
You don't need to understand how it works.

You just need to ask one question:

"Does a human actually need to do this?"

Most finance teams I speak to have 10-15 hours of work every week that the answer is no.

Month-end reconciliations.
Data entry between systems.
Reports that get generated, emailed, and never read.

That work is silently killing your team's potential.

Not their jobs.
Their careers.

The CFOs winning right now aren't the ones who've read the most about AI.

They're the ones who started.

What's one task in your finance team that a human probably shouldn't be doing?

Everyone is talking about how AI makes work faster.

That’s the wrong focus.

Speed doesn’t matter if it doesn’t improve decisions.

I use tools like ChatGPT and Microsoft Copilot to move quickly from raw information to structured insights—but that’s just the baseline.

The real value is using AI to analyze financial data, build reporting models, and surface KPIs that actually drive executive decisions—not just produce reports that look good.

Inside QuickBooks Online, AI helps automate routine work, flag anomalies, and increase confidence in the numbers.

And with Power BI, I’m pushing those insights further—building dynamic, decision-focused dashboards instead of static reporting.

AI shouldn’t just make finance more efficient.
It should make it more impactful.

If it’s not changing how decisions get made, it’s just noise.

Curious—where are you actually seeing AI make a difference: speed, or better decisions?

#ArtificialIntelligence #Finance #DataAnalytics #FPandA #DecisionMaking

I spent months telling my finance team to use AI more.

Then I sat down and saw the problem.

It was the prompts.

Vague inputs get vague outputs. And vague outputs don't help a CFO make better decisions.

Here are the prompts I keep coming back to:

**1. Variance analysis**
"Here is our budget vs actuals [paste]. Identify the top 5 variances, explain likely causes, and suggest 3 questions I should be asking my team."

**2. Board narrative**
"Summarize this financial data [paste] for a board audience. Lead with the headline. Keep it under 200 words. No jargon."

**3. Scenario planning**
"You are a CFO. Given these assumptions [paste], build 3 scenarios: base, upside, downside. Flag the top 2 risks in each."

**4. Headcount review**
"Review this headcount plan [paste]. Identify gaps, redundancies, and flag any roles worth reconsidering before the next budget cycle."

**5. Executive storytelling**
"Turn this data into a narrative for a non-finance audience. Explain why we missed or beat plan and what it means for the next quarter."

The secret is treating AI like a brilliant analyst who needs full context and a clear ask.

Give it both, and the output changes completely.

**What AI prompts are actually working in your finance workflow?**

"I've already automated a lot of my manual tasks, why invest time, and money, into AI right now?"


Every day I speak to finance professionals at very different points on the AI journey. Some are still figuring out where to begin. Some are using it for everything.

But the group I find most interesting - and probably most underserved by the current conversation - are the leaders who genuinely get it, have already built something good, and are now asking: does AI actually add value when things are already working well?

That question deserves a proper answer. Here's how I'm currently thinking about it.

Using AI for automation gets you from 100 to 50. AI as a tech stack does something different.

In an already-automated finance function, the process is still disjointed. You automate the flag, but someone still reviews, interprets, and acts on it. AI can close that loop - using your business context to not just flag a mismatch, but understand it and commit to an output. Input > insight > action, without the manual stitching in between.

Three areas where I'm seeing this matter most:

1) Workflows - A CFO already running a tight, automated function is well placed to go further. Experimenting with AI is a natural starting point, but the real step-change is when it's genuinely embedded into your workflows rather than bolted on top. That's where you move from "AI saves me time" to "AI runs this process."

2) Finance Systems - A dedicated Finance Systems or FinOps lead who's AI-literate can be transformational here. Not just maintaining your stack, but elevating it - building something that's not just automated but self-auditing and genuinely scalable as the business grows.

3) FP&A and Commercial - A Finance Data Analyst, or equivalent, as the connective tissue between data, finance, and commercial teams. Done well, this isn't about better dashboards - it's about surfacing commercial insight that wouldn't otherwise exist, and adding outsized value at a business level.

Right now (almost definitely) probably isn't the moment to tear up what's working. But it is the training phase, and the teams building these capabilities now - through tooling, through the right hires, or both - will have real options when the technology matures further.

Always interested to hear what people think of this.. feel free to share your thoughts below.

Using AI Doesn't Make You Less Brilliant








Less normalize this

People use calculators; no body says they are bad at math.

People use Canva; no body question their creativity.

People use Google; no body questions their intelligence.

So why do we suddenly become uncomfortable when someone uses AI?

AI is a tool
Just like Excel
Just like Power Point
Just like Canva

The thinking is yours
The strategy is yours
The decision is yours

As long as you don't allow it to overtake your thinking abilities.

It definitely can't make decisions for you.

When used properly, It makes you efficient.

AI simply helps you move faster, think broader, and execute better.

As a Financial Analyst, I use AI to Improve storytelling in dashboards

Does that make me less analytical?

No. It makes me more effective.

The best professionals don’t avoid tools.
They master them.

Because the future doesn't reward who works the hardest.

It rewards who works the smartest.


👇 Are you for AI or again AI? Is AI good or Bad? At what point in your job do you leverage on AI?
What is the disadvantages of using AI?

I remain your
Financial Data Analyst Clarity Queen ❤️

A CFO at a startup is usually a team of one.

We have integrations pulling data from a bunch of different systems.

There's consolidation, forecasting, board reporting, year-end close, and then all the co-founder work on top of that (product, customer conversations, hiring, fundraising).

I don't have a finance team backing me up. It's me.

And I think that's actually pretty common at this stage. You look at a company from the outside and assume there's infrastructure behind every function.

But in a lot of growing businesses, the person presenting the board deck is also the person who built it, reconciled the data, and closed the books.

I'm one of the users who needs Pluvo.
I need to schedule analyses that run without me.
I need agents that cross-check my systems overnight so I'm not spending my mornings doing it manually.
I need consolidated reports across entities without hours of currency conversion in spreadsheets.

I genuinely could not do my job without Pluvo. And because I use it every day, I also know exactly where it falls short and what we need to build next, which is a pretty good feedback loop for a product company.

The best part is that every hour I save on manual finance work is an hour I can spend talking to customers.

And talking to customers is my favorite part of this job by far.

I've been using AI heavily in finance for a year. Here's what I've found actually matters:

1️⃣ If your accounting system doesn't connect easily with an external AI (ChatGPT, Claude, etc.), you're using the wrong accounting system. Built-in AI tools in accounting systems like QBO are rarely as capable as the real thing.

2️⃣ Know the privacy risks. My rule: strip out identifying information before uploading any document.

3️⃣ Treat it like a new staff accountant. You have to check its work. You have to train it. It will make mistakes.

4️⃣ Direct connectors to Google Drive or cloud storage are hit or miss. If accuracy matters, export the reports and upload manually to the AI model.

5️⃣ Before you review anything, ask it to double-check its own work first. It catches its own errors more often than you'd think.

6️⃣ Reconciliations and matching tasks are where it shines. Add agents into the mix, and you're automating a significant chunk of staff-level accounting work.

7️⃣ The future belongs to finance professionals who understand how these models think, know roughly what the answer should look like, and can verify the math. Your experience-based gut instinct matters more now, not less.

8️⃣ Cash flow forecasting is where I'd start for most businesses. It's the single highest-impact use case I've found.

9️⃣ For FP&A work specifically, Claude is the clear winner — and worth the paid upgrade.

🔟 Information overload is real. My fix: ask it to cut the output by 50%, then ask it to simplify further. Forces it to distill down to what actually matters.

AI won't replace experienced finance professionals. But it will replace those who don't know how to use it.

#Finance #AI #Accounting #FPandA #CFO

I wrote about AI slop back in December.

Marketing emails that sound like robots.
LinkedIn posts that all say nothing.
AI buttons on every app that make everything worse.

It's gotten worse. Way worse.

It jumped departments.

I'm seeing it in spreadsheets now.
Excel models with 20 tabs and yellow input cells on every single one.

Formulas that reference tabs that reference other tabs. Nobody can trace the logic. Nobody checks if it even makes sense.

It looks professional. That's the problem.

Presentations too. Every deck has the same gradient sidebar.

Metrics cards that never end. Total Revenue. Revenue Growth. Revenue Growth Rate. Revenue Growth Rate vs. Prior Quarter. Four cards saying the same thing in different colors. Accent bars across the top like we're designing a magazine.

And a "Key Takeaways" slide that takes away absolutely nothing.

Then the writing. Em dashes everywhere. "The quarterly results were strong — driven by operational efficiency — and positioned the company for growth." That's not a sentence. That's AI doing jazz hands.

Here's what actually happened. People used to be bad at Excel. They knew they were bad at Excel. So they kept it simple. One tab. One purpose. You could follow the logic.

Now AI builds a 20-tab model in 30 seconds and nobody asks if tab 14 needs to exist.

I use AI every day. I run finance, marketing, tech, and HR with it. I build dashboards, automate workflows, draft reports. I'm not some guy yelling at the cloud.

But "AI can build it fast" doesn't mean it should exist.

The whole point was to enhance what we do. Not bring the average down. Not flood every shared drive with pretty garbage nobody reads past tab 3.

More stuff is not better stuff.

Stop it. Think. Then build.

What's the worst AI slop you've seen at work?

Workflow I teach how to turn raw financial data into a clean, executive-ready presentation in minutes using AI.

✲ Step 1 — Upload Your Financial Data

Start with your core files:

- P&L
- balance sheet
- cash-flow statement
- KPI reports
- variance analysis

Upload them into ChatGPT (or Copilot).

✲ Step 2 — Generate the Key Insights

Use this prompt:

"You are a CFO-level finance analyst. Analyze the uploaded data and extract the most important insights for an executive presentation.

Focus on:

key trends
major variances
risks and opportunities
unusual movements

Summarize in clear, non-technical language."

✲ Step 3 — Turn Insights into Slide Content

Next prompt:

“Convert these insights into a structured presentation. Create slide titles + bullet points for each slide. Keep it concise and executive-level.”

You’ll instantly get something like:

- Slide 1: Financial Overview
• Slide 2: Revenue Trends
• Slide 3: Cost Drivers
• Slide 4: Key Risks
• Slide 5: Recommendations

✲ Step 4 — Generate Visuals Automatically

Use:

- Excel Copilot → create charts from your data
• Power BI → generate dashboards

or ask ChatGPT:
"Suggest the best charts for each slide."

This ensures:

- clarity
- better storytelling
- faster understanding for stakeholders

✲ Step 5 — Export to PowerPoint

Ask:

"Format this into a PowerPoint-ready structure with slide titles, bullet points, and speaker notes."

Then use:

- PowerPoint Copilot
• or copy-paste into your template

Your presentation is 80% done.

✲ Step 6 — Add Executive Commentary

Final prompt:

"Add CFO-level commentary and recommendations for each slide."

This is what separates the two:

- a data report from
- a decision-making tool

Most finance teams spend hours preparing slides…

When the real value is in:

- interpreting the data
- guiding decisions
- highlighting risks

QuickBooks just put AI inside your accounting ledger.

I tested it live.

I've been a fractional CFO for years and worked with over 100 companies.

So when Intuit dropped a new AI chatbot that claims to read your books and surface insights on demand, I had one question.

Is this actually legit?

I ran it through 7 tests. Each one harder than the last.

Revenue lookups. Expense comparisons. Vendor analysis. Transaction level details. Full CFO reports.

And I'll be honest... some of what I saw genuinely impressed me.

The AI pulled numbers I would've spent 30 minutes digging for manually.

It compared quarters and gave me dollar and percentage variances without me even asking.

It flagged vendors I didn't know about.

It generated a full CFO report across all three financial statements and surfaced issues I would've caught myself.

At one point I actually said out loud "oh, that's actually really cool."

Now look.

Reporting on historicals and pulling what's already in the books is not easy.

Anyone who has done this manually knows exactly how painful it is.

The fact that this thing handled that level of complexity without breaking a sweat is genuinely impressive stuff.

Then came the forecasting test.

Build me a projection. Use last year's actuals. Grow each line by 10% monthly. Show me the table.

I rephrased. I tried different prompts. I pushed it.

And what happened next... I really don't want to spoil it.

Watch the video.

I walk you through every single test, show you exactly what worked, what completely fell apart, and what it all means for CFOs and Controllers going forward.

How much time do you think something like this could save you each month?