We’re Ellen and Simone. After 36 years in finance, we’re ready to share what textbooks won’t tell you.

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READ OF THE WEEK

Let’s start with the reality:
AI-native ERPs are still early. The category is young, the roadmaps evolve fast, and yes there is a kind of hype.

But the shift underneath is real.

For the first time in 20+ years, finance systems are being rebuilt from “scratch”. With modern, global, API-first architecture instead of the legacy foundations behind SAP, Oracle, Microsoft Dynamics & Co.

And this isn’t just about AI.

It’s about systems finally being built for how companies actually operate today: real-time workflows, global entities, usage-based billing, multi-currency flows, and seamless integrations.

And importantly: with a modern, intuitive interface. Not the outdated ERP screens we all know too well.

The gap between what companies need and what legacy ERPs can deliver has simply become too large to ignore.

We’ve already explored several of the new platforms in deep, founder-led demos and the architectural difference is unmistakable.

Investors see it too:
More than 255 million dollars have flowed into AI-native ERPs in just the last six months from the very top of the VC world.

It’s early. But this is a space every CFO should monitor closely.

1) The Funding Wave

These funds also backed: OpenAI, Anthropic, Stripe, Figma, Uber, Dropbox, Notion, Datadog, Snowflake and co. The Creme de la Creme of Top A Investors.

Why they invest: Finance has long been one of the biggest untapped automation opportunities. Today's ERP landscape was never built for modern, API-driven, global business models. It cannot be easily adapted. 

Takeaway: Over $350M raised collectively signals a real shift

2. Why These New Platforms Feel Different

These tools do not add AI onto an old ERP. They rethink ERP entirely.

Modern UX people actually like: Fast, intuitive, clean. Nothing like "classic ERP windows".

Workflow-first design: Built around real finance work: quote → billing → revenue → accounts payable → close → reporting.

Implementation cycles: Weeks instead of month / years. 

Built for global finance These systems assume multi-entity, multi-country setups.

They support:

• multiple entities (e.g. US, EU, UK, APAC)

• multiple countries with local rules

• multiple currencies with real-time FX

• drilldowns from consolidated P&L → entity → account → transaction

• full intercompany flows (allocations, eliminations, FX, reconciliation)

Deep native integrations: Not add-ons core architecture to CRM systems, Payment providers, Banks, HR systems etc.

European requirements are on the roadmap: VAT logic, GDPdU-compatible exports, GDPR alignment. Teams do not need to worry that "a US tool might not understand EU rules". The roadmaps are clear.

Moving toward a more unified platform Not "everything in one ERP", but fewer tools, more consolidation, and likely acquisitions of standalone solutions.

Takeaway: A system that fits how companies operate today – automated, global, API-first.

3. What AI Really Does Inside These Systems

A. True AI (the intelligence layer)

• Document intelligence: Reads invoices, contracts, PDFs → extracts → books → builds schedules.

• Automatic schedules: Revenue, prepaids, renewals – created instantly.

• Chat-based analysis: Why did revenue drop? Show October flux. Which customers caused variance? AI runs the analysis.

• Natural-language queries: Revenue by customer YTD Cash impact if payment is delayed

• Real-time anomaly feed: Flags: wrong VAT, manual revenue entries, missing metadata, duplicates, approval bottlenecks.

• AI-supported intercompany logic: Handles FX, suggests eliminations, flags mismatches.

• AI-driven reconciliation: Matches payments and learns from corrections.

Takeaway: AI does the heavy lifting. Finance teams handle judgment and edge cases.

B. Automation and integrations (not AI, but essential)

• CRM: Native links to Salesforce, HubSpot etc.

• Payments: Native Stripe, Adyen.. connections

• Banks: Thousands of bank APIs already supported

• HR: Employee & department sync

• Spend: Card matching, receipt ingestion

• Workflows: Approvals, triggers, reminders

• Tax: Automated VAT suggestions

Takeaway: Automation removes manual work. AI removes the headache.

4. How Legacy Players Like SAP & Oracle Are Responding

SAP, Oracle & Co. are not sleeping – but they are adapting systems originally built decades ago.

SAP

• Project RISE with SAP: program pushing on-premise customers to cloud

• Joule AI (SAP’s built-in AI assistant for S/4HANA) 

• Strong in manufacturing, logistics, supply chain, regulated industries

Oracle

• Oracle AI Agents (task automations inside Fusion Cloud)

• Autonomous Database (self-managing database layer)

• Strong in global consolidation and enterprise workflows

Important nuance: Both invest heavily in AI, but they adapt legacy foundations. AI-native systems start fresh, with today's architecture in mind.

Takeaway: The real question: Can legacy adapt fast enough or does new architecture win long-term?

5. What CFOs Should Do Now

If you have 6–12 months

Wait and monitor.
This category evolves weekly. New features, new integrations, new compliance modules.
Use the time to:

• clean master data

• reduce spreadsheet dependency

• streamline processes & workflows

• fix data quality issues (customers, products, contracts)
  

If you are a SaaS company on DATEV or currently evaluating NetSuite or the new AI-native ERPs

Don’t rush.

Takeaway: A clean foundation gives full flexibility for any automation or AI use case. Regardless of which systems you adopt.

🔎 CFO Watchlist

1. Ramp raises again $300M at $32B 

Ramp a US spend, cards, AP & automation platform announced its fourth raise in 2025, bringing this year’s total to $2.3B.

The platform now runs 26M+ AI-driven finance decisions/month and is rapidly moving beyond spend management into full-stack automation.

Why CFOs should care:
Ramp’s velocity and capital are unmatched in the category and with an expected European push in 2026, competitive pressure on local players will rise.

🔗 Founder’s LinkedIn announcement:

2. A great example of a modern, API-first finance stack

This post breaks down how a US company rebuilt finance around an AI-native, connected tech stack.

🔗 LinkedIn post

🌐 Finance Collective DACH

Your go-to CFO Network (by Simone)

CLOSING REMARKS

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CFO Playbook reflects our personal opinions, not professional advice.