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?

First, Anthropic didn’t ship another chatbot for bankers.

→ It shipped a full AI agent platform that sits above every data provider, every Microsoft Office app, and every internal system a financial institution runs, orchestrating end-to-end workflows that used to require teams of junior analysts and weeks of calendar time.

→ 10 production-grade AI agent templates covering everything from pitchbook assembly to month-end close to KYC screening.

→ Native Excel, PowerPoint, and Word integration.

→ Apache 2.0 on GitHub.

→ Citadel, Goldman, BNY, Carlyle, and AIG are already deployed.

And the real distribution play - an infrastructure partnership with FIS $FIS, which processes core banking for institutions handling roughly 12% of the global economy - means thousands of banks will get Claude’s agents without ever signing an Anthropic contract.

Combined with a $1.5B joint venture announced the day before with Blackstone, Goldman, and Hellman & Friedman, Anthropic now has three simultaneous paths into finance:

(1) direct enterprise, (2) infrastructure embedding, and (3) a PE-backed services arm reaching hundreds of mid-market firms.

The architecture, the partnerships, and the market reaction all point to the same conclusion: the value in financial services is migrating from the data layer and the workflow tool layer to the intelligence layer above both.

Anthropic intends to own that layer.

Deeper dive into what it means for data incumbents, fintech startups building in the space, and the firms racing to adopt, or trying to figure out whether they’re already too late is here:

Anthropic shipped ten ready-to-run agent templates for financial services. Not a preview- these are reference architectures you can install today. (Catching up on this one from last week, worth a second look)

The Ten: Research & coverage- Pitch builder. Meeting preparer. Earnings reviewer. Model builder. Market researcher. Finance & ops- Valuation reviewer. GL reconciler. Month-end closer. Statement auditor. KYC screener.

Each runs as a plugin alongside the analyst, or as a Managed Agent running autonomously with full audit logs.

Three things stood out:
1. Packaging is the product. Skills + connectors + subagents, pre-stitched. Days, not months.
2. Context travels across M365. Excel → PowerPoint → Word → Outlook, no re-explaining.
3. Data layer is the moat. New connectors- Dun & Bradstreet, Guidepoint, Third Bridge, Verisk, IBISWorld, plus a Moody's MCP app on 600M+ companies - on top of FactSet, S&P Capital IQ, PitchBook, Morningstar.

Opus 4.7 leads Vals AI's Finance Agent benchmark at 64.37%. Walleye Capital reports 100% Claude Code adoption across 400 employees.

If you run a finance or coverage function- pick one agent (Pitch builder or Month-end closer), point it at one real workflow this week.

Announcement: https://lnkd.in/gAtRiTRE

We're releasing ten new Cowork and Claude Code plugins, integrations with the Microsoft 365 suite, new connectors, and an MCP app for financial services and insurance organizations.

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

Mark Andreessen says "nothing replaces the person who knows what to do."
The avg accountant today generates $175K/yr in revenue.
With the right AI tools, that accountant can generate $500K to $750K/yr.

Everyone keeps saying AI will replace accountants.

I run in these circles every day and I haven't seen a single accountant lose their job to AI. Not one.

I've only seen an accountant shortage that's getting worse YoY.

The average accountant generates roughly $175K in revenue, maybe $250K if they're senior level.

Hand that same accountant the right AI tools and I have complete
conviction that they could generate $500K to $750K (3x to 4x their value).

Two camps are forming right now:

Camp one picks up AI tools and says
"I can do my job better, serve more clients, and be more strategic."

Camp two sees the same tools and does the bare minimum.

High-agency accountants will outcompete the low-agency accountants at every turn.
The decision to actually care is the forcing function.

Think about your own accountant for a second.

When was the last time you actually saw them?
When did they call you to strategize instead of just filing?

You email them and might hear back five days later...

The biggest complaint every client has about their accountant is "I never even see them."

The accountants who use AI to automate the grunt work
and then reinvest that time into:

- Sitting across from clients
- Building relationships
- Actually strategizing

Those are the ones who will 3x to 4x their value.

AI can make the accountant 4x more productive today.

But only the ones who decided that productivity
means more time with people will 4x their value.

Anthropic just launched 10 ready-to-run agent templates for finance.
Here's my read on what matters for operating CFOs.

~ For investment banks and asset managers: this is huge. Pitch builder, earnings reviewer, valuation reviewer, KYC screener - these are the workflows that eat analyst weekends. Templates ship as plugins in Claude Cowork or Claude Code, or as cookbooks for production deployment as Managed Agents.

~ For operating CFOs (my world): three of the ten matter directly.

→ Month-end closer - runs the close checklist, prepares journal entries, produces close reports
→ GL reconciler - reconciles general ledger accounts and runs net asset value calculations
→ Statement auditor - reviews financial statements for consistency and audit-readiness

If your finance team spends one full week of every month on close, this is the release that changes the math.

How to actually try them:

1. Open Claude Cowork or Claude Code
2. Go to the financial services marketplace
3. Install the template you want as a plugin
4. Hook up your data sources (QuickBooks, NetSuite, your ledger)
5. Run alongside your analyst on real close work

I'm blocking a few hours today to actually use the templates.
Will share what I find this week.

Has anyone here already tried any of them?
Drop your take in the comments - especially Month-end closer or GL reconciler. Curious what's working and what isn't before I dive in.

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.

🚨 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 cut task time by 90% in some cases. But I still can’t fully deploy AI in my workflow.

The past 2 months I’ve been experimenting with AI tools and building Agents, but as I take a step back, the same fundamental issues are still there.

It started as a productivity tool. I built a workbook in minutes that would have taken hours.

My mindset shifted. I started using Claude in Excel for simpler tasks and automating workbook rollforward with Claude Cowork.

But it didn’t feel integrated. It felt like I was forcing myself to use this new tool because I knew it was good for me, kinda like remembering at the McDonald’s cash register that the mobile app is faster.

I need the Agent to be integrated with my CRM or spend management tool, ping me or the other stakeholder for review or judgment call, and push the process along in the workflow.

Start with AI and end with AI.

Like when my stomach emits hunger signals, order my McNugget already, and have it delivered. Just let me choose the sauce I’m feeling that day 😂

But today, I’m still exporting data, delivering it to the Agent, generating output, and importing it back.

And that’s just with me and my workflow. Scaling it for the rest of the org is a whole other problem.

Data hygiene. System integration. Team collaboration. Same issues, different tool.

How has your team thought about the integration problem? Not just using AI, but actually deploying it?