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The acquisition closed on Friday. The new CFO walks in on Monday morning. Within 48 hours, the Operating Partner sends a message: when do we get the first financials? The CFO opens their laptop and starts counting. Six ERPs. Three different chart of accounts structures. Two businesses that still report in spreadsheets emailed to a shared inbox. One entity whose close cycle runs two weeks behind the others. This is not a failure of diligence or talent.

You close. The lenders want a covenant report in 45 days. Your finance team is running on spreadsheets, three ERPs, and a shared folder nobody has cleaned since 2019. This playbook is the week-by-week plan for exactly that moment — written for incoming CFOs, interim CFOs, and the Operating Partners who place them.

Post-acquisition financial integration is the process of unifying financial data from a newly-acquired portfolio company's ERP into the PE group's reporting layer — without forcing subsidiaries to replatform. The 8-12 Week PE Financial Data Foundation Framework is a sequenced implementation plan, structured across four phases, that takes a portfolio company from acquisition close to live, automated portfolio visibility.

Most data warehouse projects fail. Not because the technology is wrong. Because the design is. Three weeks for a number that should take three minutes. AI agents generating plausible reports nobody can trace. Two ERPs naming the same metric differently. The spreadsheet swamp. The fire drill before every audit. These problems live in the warehouse layer, in how data is modeled, governed, and made available to the people and AI agents that read from it.

These terms show up in every other LinkedIn post about AI. Everyone uses them slightly differently. So here's how we think about them at Keboola, and what they look like in practice.

Every enterprise AI story right now follows the same plot. You pick a system — Salesforce, Workday, SAP, NetSuite — and you bolt an AI agent on top of it. The agent can summarize deals. It can write follow-up emails. It can pull a report without you clicking through five dashboards. It is genuinely useful. And it is not transformation. What you have built is a smarter interface on top of a system designed for humans.

Earlier this year we started asking a simple question to finance leaders we met at events, on calls, and in roundtables: where do you actually start with AI in finance? The answers were consistent enough that we decided to do something about it. We invited 30 CFOs, finance directors, and finance managers to a business breakfast at our Prague office. A morning with peers who are all trying to answer the same question: where do I actually start?

You can build Data Apps inside Keboola with Kai. But what if you prefer working with Keboola via MCP, in Claude Code, Cursor, or another AI-powered editor? Want to build a JavaScript Data App that Kai doesn't support yet? That's what the Keboola AI Kit is for. It's a set of skills you install into your agent so it knows how to work with Keboola - how to query your data, how to structure a Data App, how to deploy it. Here's how to set it up.

Finance leaders at top companies all describe the same frustration: scattered data is blocking them from being the strategic partner their organization needs. This guide names the blind spots — and shows what becomes possible when you finally close them. You know what your P&L says. But do you know what your cash position will look like in 90 days?

Building your own financial data system sounds lean, flexible, and smart — right up until year two. This guide gives you the honest picture most vendors won't: what a production-grade finance system actually costs, what teams almost always miss, and a decision framework built for CFOs who need to get this right the first time. Your data team is confident. The architecture looks solid.