AI-assisted financial forecasting, scenario modeling, and cash planning

Every finance leader I speak to right now is asking some version of the same question: 𝗵𝗼𝘄 𝗺𝘂𝗰𝗵 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝗲𝗹𝘆 𝗼𝗻 𝗔𝗜?

The pros are real, and they're not hype.

𝗔𝗜 𝗶𝘀 𝗰𝘂𝘁𝘁𝗶𝗻𝗴 𝗱𝗼𝘄𝗻 𝗿𝗲𝗰𝗼𝗻𝗰𝗶𝗹𝗶𝗮𝘁𝗶𝗼𝗻 𝘁𝗶𝗺𝗲 𝗳𝗿𝗼𝗺 𝗱𝗮𝘆𝘀 𝘁𝗼 𝗵𝗼𝘂𝗿𝘀. It's catching anomalies in transactions that a human reviewer would miss simply due to volume. Forecasting models are getting sharper because they can process patterns across years of data instantly. Repetitive work data entry, invoice matching, basic variance analysis is finally being taken off the plate of skilled finance teams who were never hired to do that in the first place.

𝗕𝘂𝘁 𝘁𝗵𝗲 𝗰𝗼𝗻𝘀 𝗮𝗿𝗲 𝗷𝘂𝘀𝘁 𝗮𝘀 𝗿𝗲𝗮𝗹, 𝗮𝗻𝗱 𝗹𝗲𝘀𝘀 𝘁𝗮𝗹𝗸𝗲𝗱 𝗮𝗯𝗼𝘂𝘁.

AI is only as good as the data it's fed and most finance functions still don't have clean, structured, real-time data to begin with. 𝗙𝗲𝗲𝗱 𝗮 𝗯𝗿𝗼𝗸𝗲𝗻 𝗠𝗜𝗦 𝗶𝗻𝘁𝗼 𝗮𝗻 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹, 𝗮𝗻𝗱 𝘆𝗼𝘂 𝗱𝗼𝗻'𝘁 𝗴𝗲𝘁 𝗯𝗲𝘁𝘁𝗲𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. You get bad decisions made faster and with more confidence. There's also a growing risk of teams trusting model output without understanding the "why" behind it which is dangerous in a function where judgment, context, and accountability still matter more than speed. And in regulated environments, "the model said so" isn't an audit trail.

Here's what I think most companies are getting wrong: they're asking "𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝗮𝗱𝗼𝗽𝘁 𝗔𝗜" 𝘄𝗵𝗲𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗶𝘀 "is our financial foundation even ready for it."

AI doesn't fix broken processes. It amplifies whatever is already there good or bad.

Before we talk automation or AI tools with any client, we look at the basics first data structure, MIS accuracy, process discipline. Because the businesses getting real value from AI in finance aren't the ones with the fanciest tools. They're the ones who got the fundamentals right before layering AI on top.

𝗜𝘀 𝘆𝗼𝘂𝗿 𝗳𝗶𝗻𝗮𝗻𝗰𝗲 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗔𝗜-𝗿𝗲𝗮𝗱𝘆, 𝗼𝗿 𝗷𝘂𝘀𝘁 𝗔𝗜-𝗰𝘂𝗿𝗶𝗼𝘂𝘀?

#AIinfinance #FinanceManagement #DigitalTransformation #CFOinsights #BusinessAdvisory

New QuickBooks features for August 2026: Intuit Intelligence automates your books and surfaces important insights, while updates to everyday workflows like bank matching, reconciliation, reports, and bill pay keep them clean.

AI is transforming finance, and I use it every day.

But here’s the mistake I see companies starting to make…

They’re trusting AI to produce financial analysis, forecasts, board reports, and even strategic recommendations without an experienced finance professional validating the output.

That’s a costly gamble.

AI is incredibly good at organizing information, identifying patterns, and accelerating analysis. What it doesn’t do is exercise professional judgment.

It doesn’t know:
• when source data is incomplete
• whether assumptions are reasonable
• if revenue recognition is being interpreted correctly
• whether a margin trend is actually meaningful or simply a timing issue
• when one bad formula can change an entire forecast

The most dangerous AI errors aren’t obvious. They look polished. They sound confident. And they can quietly make their way into executive decisions.

One incorrect assumption in a cash flow forecast can affect hiring.
One inaccurate margin analysis can change pricing.
One flawed financial model can influence an acquisition or investment decision.

The cost isn’t the AI.
The cost is believing it can’t be wrong.

The future of finance isn’t AI replacing finance professionals.

It’s AI making experienced finance and accounting professionals exponentially more effective.

Speed is valuable.

Accuracy is priceless.

Across Financial Planning and Analysis (FP&A), Accounting, Tax, Compliance, and Treasury, Microsoft Finance runs Copilot in Excel in real workflows, spending less time hunting for information and rebuilding analyses, and freeing teams to spend time applying judgement to decisions. They shape it as much as they use it, telling us where it falls short and pushing the product toward the standard their own work demands. By the time it reaches you, it has already been pressure-tested by a finance organization operating at the frontier. 

Today, we’re introducing new features built for financial professionals to continue doing that with skills for repeatable workflows, new financial connectors for trusted data, and improved capabilities for traceability.

Planner Assistant generates anomaly detection and forecasting insights grounded in the customer's own planning models and the Planful Predict engine.

Planner Assistant is built on the Planful Predict engine and the customer's own financial data, which means every forecast and anomaly signal is fully explainable and can be traced back to actual financial data.

Planner is Planful's second major persona-based AI Assistant, following Analyst in October 2025. Together they form a continuous loop. Analyst explains what happened; Planner predicts what comes next.

🚨 Claude Scheduled Tasks changed Finance forever.

I built 15 automated finance routines for UK business owners.

It's an AI agent that runs on a schedule inside your desktop app.

Here are 15 ready-to-use tasks that keep your numbers visible between accountant meetings:

→ Weekly cash flow pulse check every Monday before you start work
→ Daily overdue invoice flag so nothing goes cold
→ Monthly tax position summary so you know what to set aside
→ Quarterly VAT prep checklist that gathers everything before the deadline
→ Weekly P&L snapshot translated into plain English
→ Monthly salary vs dividend data pull based on current thresholds
→ Weekly expense categorisation cleanup so your books stay tidy
→ 90-day HMRC deadline early warning scan
→ Monthly KPI summary built from your actual numbers
→ Weekly competitor and market rate scan for your industry
→ Monthly pension contribution data review
→ Quarterly business health snapshot tied to your goals
→ Weekly lead and pipeline follow-up reminders
→ Monthly subscription and overhead audit to flag wasteful spending
→ Annual year-end tax planning countdown starting 90 days before year end

Each one comes with the exact prompt, the schedule, and a step-by-step setup guide.

These won't tell you what decisions to make.

That's what your accountant or fractional CFO is for.

What they will do is make sure you never walk into a financial conversation blind.

Want the full playbook?

1) Connect with me so I can DM you
2) Comment "FINANCE"

I'll send it straight to your inbox.

If you’re struggling to get started with AI in accounting/finance because you can’t think of real use cases, here’s one I built that actually helps my day-to-day:

I can now build new 13-week cash forecasts directly from bank statements.

Before, just setting up the structure and mapping historical activity was tedious and could take hours.

Now, I drop in bank statements, and Claude:

- Identifies the company’s business model
- Analyzes inflow and outflow patterns
- Translates transactions into Revenue, COGS, and OPEX categories

Then it takes our standard 13-week cash forecast template and:

- Builds out the structure with those categories
- Maps historical cash activity by week
- Ties everything back to the statements

From there, I’m starting with a clean, structured foundation.

Which means I can spend my time where it actually matters:
building the forecast and advising on decisions.

What used to take hours now takes minutes.

That’s the real value of AI...removing the boring low-value work so you can focus on better value-add tasks.

If you build cash forecasts, I’d highly recommend trying something like this.

Worst case, you’ll learn a ton. But you might also end up with something you can actually use.

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?**

#Finance is often seen as conservative, which makes it even more exciting to rethink it, especially with #AI.

I recently had the opportunity to speak with the FINANCE Magazin, a German publication specializing in finance leadership and transformation, about how artificial intelligence is changing the role of finance. For me, AI is not just an effective tool but a genuine matter of the heart.

In my role as CFO at NTT DATA Business Solutions, I already use AI in my day-to-day work. I use it to structure complex information, explore scenarios, support analyses and challenge my own perspectives. Not as a shortcut, but as a thoughtful companion that helps me build a stronger foundation for decision-making.

I am convinced that AI has the potential to level hierarchies by democratizing access to insights and knowledge. Especially in finance, this enables a shift from purely reporting the past to actively shaping the future of the business, with greater transparency, speed and collaboration.

But beyond the many opportunities AI presents, we should remain honest and self-critical. We are all still learning. AI is evolving rapidly and so must we. For me, this is closely linked to the idea of lifelong learning. Staying curious, being open to new technologies and sometimes investing time beyond our daily routines to truly understand what comes next, that is what will make the difference in the future.

What matters most is not the technology itself, but how we use it: responsibly, ethically and with people at the center. AI can become a powerful enabler for better leadership, but never a replacement for it.

You will find the link to the article in the comments. It is in German and behind a paywall, but the dialogue it encourages is one we should continue across organizations and industries. If you are interested, please feel free to reach out.

This journey has only just begun. Let’s take one curious step after the other.

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."