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The promise of AI in finance and accounting is everywhere. Survey after survey claims that 80% of CFOs have adopted AI. But when you actually talk to finance leaders at SaaS dinners or industry events, very few hands go up when asked who's consistently using AI in their core processes. There's a gap between the marketing hype and operational reality. And the reason isn't that finance teams are behind. It's that most organizations lack the foundational data infrastructure that makes AI actually useful.
Before you can leverage AI for financial modeling or automated variance analysis, you need to solve a more basic problem: does your finance team have enough context from your business data to do their jobs well today?
If your best revenue accounting manager is working with composite totals and high-level reconciliations, or if your data lives in PDFs and disconnected systems, there's a limit to what they can accomplish. AI won't magically fix this. Like a talented FP&A analyst, AI needs clean, structured, detailed data to produce valuable insights.
Think of it this way: if you're struggling to produce accurate variance analysis or trusted SaaS metrics with your current setup, AI isn't going to solve those problems. It will amplify them.
The most successful implementations follow a layered approach:
Layer 1: Deterministic Automation Connect to your core customer financial systems (CRMs, invoicing platforms, payment processors). Implement reliable automation that replaces manual reconciliations and calculations for revenue recognition, invoicing sub-ledgers, and AR validation.
Layer 2: Contextualized Data Ensure you have fully detailed customer financial data with proper metadata. This means invoice-level detail with start dates, end dates, SKUs, customer segments, billing cycles, and all the attributes you need to slice and dice your analysis.
Layer 3: AI Application Only after the foundation is solid can you apply probabilistic AI solutions to deterministic accounting data. This is where you get strategic value from analytics and intelligent insights.
1. Audit Trail and System Integration
Can you pick a random invoice from six months ago and reconstruct the full revenue recognition path? The data should flow cleanly from contract to invoice to revenue recognition to P&L posting. If this path is circuitous, manual, or ambiguous, you're not ready for AI.
2. Consistent Recognition Logic
Your controller should be able to clearly explain your rev rec methodology. Whether you have subscription revenue, usage-based billing, or professional services, you need documented policies and governance before automation makes sense.
3. Source System Connectivity
Finance and accounting must be connected to the operational systems where customers transact. Batch shuttles and manual file movements create interpretation gaps that make AI outputs unreliable.
4. Reconcilable Sub-ledgers
Your deferred revenue ledger should reconcile quickly and systematically. If it takes days to reconcile your books, that's a signal that your data structure needs work before adding AI complexity.
Start with the chart of accounts.
Everything flows from proper account structure and segments. This isn't glamorous, but it's foundational for both traditional analysis and AI applications.
Treat your MRR schedule like gold.
It should be based on actual earned revenue from your P&L, not CRM projections. When ARR and MRR tie directly to revenue recognition, everyone trusts the numbers.
Scale your approach to transaction volume.
For PLG businesses, the 3-5 million ARR range with high customer counts is typically when this infrastructure investment pays off. SLG businesses with larger deal sizes can wait longer.
Focus on business value, not buzzwords.
"AI-native" is a marketing term. Evaluate tools based on whether they solve your specific problems, like revenue leakage detection, variance analysis, or AR risk assessment.
The accounting team should own AI outputs. You might need technical partners for implementation, but the controller needs to be responsible for what these tools produce.
Most importantly, the ROI often comes from the foundational automation work, not just the AI layer on top. When you connect accounting teams to core customer financial systems with proper detail, they immediately find revenue leakage, bad debt issues, and billing exceptions that were invisible before. That value arrives in weeks, not quarters.
This blog post was adapted from a webinar with Ben Murray, the SaaS CFO on Preparing your back office for AI.
Former Root, EVP of Finance/Data at multiple FinTech startups
Jason Kyle Berwanger: An accomplished two-time entrepreneur, polyglot in finance, data & tech with 15 years of expertise. Builder, practitioner, leader—pioneering multiple ERP implementations and data solutions. Catalyst behind a 6% gross margin improvement with a sub-90-day IPO at Root insurance, powered by his vision & platform. Having held virtually every role from accountant to finance systems to finance exec, he brings a rare and noteworthy perspective in rethinking the finance tooling landscape.
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