Using AI in accounting feels like working with Einstein — except you have to constantly teach Einstein what accounting is.

A little over 2 months into an AI startup as a Controller, I am having more fun than ever automating accounting workflows so I don’t have to do the work myself. I blew through my session limits 3 days in a row this week (consecutively mind you - 11am to 6pm, 6pm to 11am, rinse and repeat) - but I created financial reporting packs in days that would have taken an entire team weeks to prepare and review.

I no longer want to prepare any workpapers in Excel - that’s my agent’s job now. I am only going to review my agent’s work. Anything in Excel can be automated - no exceptions. In fact, this probably applies to the entire Office suite.

With that said, I am learning that the bottleneck is becoming… me. We have gone full circle - I remember back in my audit days waiting for CaseWear IDEA to finish selecting samples so I can begin testing, or waiting for Blackline to batch process reconciliations so I can start preparing reports. But now, I find myself struggling to keep up with all the different sessions and agents that are running in my Claude. Jumping between sessions to review AI output is harder than expected because of constant iterations. You add jumping in and out of meetings on top and it very quickly becomes absolute ADHD level chaos.

Because the AI works SO FAST, you need to speed up your review frequency as well, and constantly iterate to achieve the desired output. Gone are the leave-a-comment-and-wait-for-your-team review cycles - AI does 10 cycles in 1 minute.

Am I the only one hitting this attention span wall?

Claude’s new financial services release shows how powerful AI in finance is becoming. It also highlights what finance teams still need around it.
The ability to analyze data, work across documents and spreadsheets, draft outputs, and support complex finance workflows is genuinely impressive. For small businesses, solo operators, and lean finance teams, this can already be a major unlock.

But when we speak with finance teams in larger and growing companies, we hear a consistent pattern: they are excited about AI, but the real need is not just better analysis. It is getting finance work done safely, reliably, and with the right controls.

Finance teams tell us that the hard part is often connecting the right context: accounting data, approvals, budget ownership, CRM inputs, tax rules, entity-specific logics, and manager decisions. They also want AI outputs they can inspect, verify, and approve before anything consequential happens.
That distinction matters. AI can prepare, check, summarize, and recommend. But when the next step affects a payment, reimbursement, approval, accounting treatment, audit record, or ERP write-back, companies need more than a smart assistant. They need a governed finance workflow layer.

That is where the next phase of AI in finance will be won: connected data, encoded business logic, permissions, approval flows, audit trails, and human control over consequential actions.
Finance teams should start now. The best place to begin is with workflows where AI can safely prepare the work, while the company builds the operating layer needed to execute with confidence.

The companies that benefit most will be the ones that start learning now. Not by handing over consequential finance decisions on day one, but by putting AI into real workflows, understanding where it creates leverage, and building the controls needed to scale it safely.

How do you think about this? Would love to hear more thoughts

🚀 And check out how Moss can support you on that journey! 🚀

80% of finance teams blame their slow close on waiting for data — even after investing in automation.
(Source: CFO Dive, May 2026)

Meanwhile, teams using AI in their close are cutting cycle time by 40-50% and reconciliation errors by 90%.
I used to be in the 80%. Two things got me out: process redesign + AI in the right places 👇
⸻
🎯 First: Fix WHEN you work, not HOW FAST
Stop treating month-end as a 10-day sprint. Spread reconciliation across the month, bank, AP, AR, intercompany — weekly. By Day 1, you're confirming, not discovering.
The industry calls this Continuous Close. It's the biggest unlock for lean teams.
⸻
🤖 Then: Layer AI where it actually saves hours
Here are 3 things I now let AI handle — with exact examples:
Variance Analysis (2 hrs → 15 min)

I export the P&L to CSV and paste into Claude with this prompt:
"Here's actuals vs budget. Flag every line item with >±10% variance AND >$50K impact. For each, tell me: is this seasonal, one-time, or a trend shift? Draft a 2-sentence explanation."

AI returns a flagged, prioritized list with draft commentary in 30 seconds. I spend 15 minutes reviewing and editing — not 2 hours building it from scratch.
Board Commentary (2 hrs → 20 min)

After variance review, I prompt:
"Based on these 5 flagged variances, draft a 150-word management commentary. Tone: factual, forward-looking. Highlight the 3 biggest movements and their business drivers."

AI writes the first draft. I add the context only I know — market shifts, pipeline changes, operational decisions. 20 minutes total.
Anomaly Detection (manual review → exception-only)

Instead of reviewing every journal entry, I prompt:
"Here are this month's journal entries. Flag anything that looks unusual — duplicate amounts, round-number entries over $10K, entries posted outside business hours, or accounts that don't normally have activity."

AI catches what human eyes miss at 2am on Day 3. I review exceptions only.
⸻
⚡ Two process hacks that cost nothing:
🚫 Hard cut-off, Day -2: Departments submit accruals 2 days before month-end. Miss it? Next month. I put a "Late Submissions" line in the CEO report. Nobody appears twice.
📝 Same-hour commentary: Write variance notes the MOMENT P&L closes — not 48 hours later. Delayed commentary = fiction. Same-hour = insight.
⸻
Results:
📉 Close: 10 days → 4
🤖 Variance work: 2 hrs → 15 min
📝 Board commentary: 2 hrs → 20 min
🎯 Board pack: Day 4 instead of Day 12
⸻
🛠️ Tools worth exploring: → FloQast / Numeric — close task management → HighRadius — AI anomaly detection at scale → ChatGPT / Claude — variance, commentary, anomaly flagging (free)

The secret? AI doesn't fix a broken process. Fix the WHEN first. Then let AI handle the grunt work.
Which part of your close eats the most time? Drop it below 👇

#CFO #MonthEndClose #AIinFinance #FinanceLeadership #StartupCFO #ContinuousClose #FinanceAutomation #CFOInsights

I built my first AI agent.

Her name is Cam. She joined the team one month ago.

Her first assignment? Month end financial reporting.

Like most businesses, we're still heavily reliant on Excel. So I started small. Pulled the manual reporting activities from our month end checklist and taught her one report at a time.

We're almost halfway through the list.

What Cam does now:
She creates the reports. The team reviews the outcome.
10 reports that used to take approx. 3 hours during month end... Cam executes in 3 minutes. Tested multiple times. Consistent results every time.

We'll keep building her skills from here.

What I want Cam to become: Right now, she reports. But the vision is bigger with full analysis and commentary. The story behind the numbers, not just the numbers themselves.

She's not there yet.
But she has a name, a soul, characters, and her purpose. She's building her skills every week.

Share your AI agents in your finance function. Keen to know what did you tackle first?

New AI Virtual Assistant reduces manual work, enhances visibility, and powers smarter finance workflows

ATLANTA--(BUSINESS WIRE)--Apr. 28, 2026-- Corpay, Inc. (NYSE: CPAY), the corporate payments company, today announced a new wave of AI-powered capabilities within Corpay Complete, marking a meaningful step forward in how businesses manage and optimize spend. These innovations bring practical, high-impact AI into everyday financial workflows to help companies move faster, reduce manual work, and make more informed decisions.

Designed for finance and operations teams, the new capabilities apply AI where it matters most. This includes eliminating repetitive tasks, surfacing meaningful insights, and accelerating decision-making across accounts payable and expense management.

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.

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?**