Why Feeding Raw Data to AI Is Killing Your FP&A Accuracy

About this episode

This episode discusses the importance of proper data architecture for AI in financial planning and analysis, emphasizing the separation of computation and analysis for accurate results.

Are you feeding raw financial data straight into AI and wondering why the results are inconsistent — or worse, just wrong? AI is only as good as the data architecture underneath it. For SaaS CFOs and operators running monthly FP&A cycles, that means the order of operations matters enormously. Skip the deterministic compute layer, and your AI narrates garbage. Get the structure right, and suddenly AI can do what no human ever could — synthesize five years of retention schedules and SaaS metrics in seconds. In episode #362, I'll cover: Why separating the 'thinking layer' (math) from the 'talking layer' (AI analysis) is the foundational principle for reliable SaaS financial AI — and what breaks when you skip it The pre-compute-everything rule: why you should never ask AI to calculate cohort retention, ARR, or MRR — and what you should ask it to do instead Why context beats prompts: how structured data inputs dramatically outperform one-off prompt experiments in repeatable FP&A workflows How constraints on what AI can and can't touch produce better output than better prompting — and why your context window size is quietly sabotaging your analysis The right mental model for AI…

People in this episode

Host: Ben Murray

Topics covered

• AI in finance
• FP&A accuracy
• data architecture
• SaaS metrics
• financial analysis

Keywords

• raw data
• AI analysis
• cohort retention
• ARR
• MRR
• structured data
• SaaS CFOs

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