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."

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'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

💻A.I. in Finance | 📊#Day6
🔎 Topic - I used to think AI was a "Tool." I was wrong.
It’s Saturday, and I’ve been reflecting on the recent posts on AI being implemented in Finance.

Two years ago, I looked at AI like a better version of a calculator. I thought, "Great, another tool to help me finish my spreadsheets faster so I can get home by 5 PM."

In 2026, I’ve realized that AI isn't a "Tool"—it’s a Team Member.

A few months ago, I was struggling with a complex forecasting model. I was treating the AI like a Google search, asking it for "answers." I kept getting generic, "hallucinated" garbage.

Then, I changed my approach. I started treating the AI like a Senior Associate. I gave it context about company’s specific risk appetite.
I gave it "Personas" to challenge.

I asked it to critique my assumptions before giving me a result.

The result wasn't just a better forecast; it was a better strategy.

The Lesson for 2026:
If you treat AI like a software, you’ll get software-level results (standard and boring). If you treat it like a collaborator, you’ll get "Intelligence-level" results.

The biggest bottleneck to AI transformation isn't the code—it’s our own ego and our refusal to let go of "The way we’ve always done it."

#WeekendReflection #AImindset #FinanceLeadership #HumanInTheLoop #CareerGrowth2026

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.