The CFO of a PE-backed SaaS company should have billing and retention data rebuilt 12 to 24 months before a planned sale, with AI flagging suspect customer names and addresses, and a named finance owner approving every correction.
Accordion, a consultancy that sells accounting, reporting, data, AI and exit planning work to private equity sponsors and their CFOs, published a survey with Wakefield Research on November 4, 2025. It polled 200 senior executives at private equity sponsors and 200 CFOs at PE-backed companies with annual revenues above 50 million dollars in September 2025. In it, 81 percent of sponsors want exit preparation to start 12 to 24 months before a sale, and 72% of sponsors say CFOs fall short on exit readiness, with weak data, at 67 percent, the most cited of the four reasons listed. Neither the release nor its report page breaks the answers out by industry, so how far they hold for software companies is unknown.
PwC, which sells deal diligence, wrote in its US technology deals outlook of June 17, 2026 that SaaS companies continue to face valuation pressure as investors challenge per-seat pricing, and that buyers evaluate targets on current growth and retention metrics and also on AI defensibility, acceleration potential and organizational readiness. An EY-Parthenon article of 2 April 2026, from a firm that sells exit readiness services, advises securing data readiness at least 12 months and ideally 24 months, before exit. One example is a PE-backed technology asset being prepared for sale that faced challenges due to historic issues with billing data, and whose remediation meant re-building 5 years of transaction data and cleansing anomalies; EY names no tool used for that work.
The evidence on AI cleaning comes from a preprint by researchers at BIFOLD, TU Berlin and the Hasso Plattner Institute, first posted June 30, 2026, submitted for review, and naming no funder. It tested cleaning systems on names and postal addresses of organizations published on the web, not billing data, with correct values built from a geocoding service and manual postprocessing. An approach that sends each record to a large language model was the second strongest detector of errors. On a sample of named addresses where it was told which values were wrong, its corrections, counted right only when they matched the verified value, averaged a score of 42 percent, against 46 percent for the best system. The authors write that none of the evaluated data cleaning approaches can reliably clean the dataset or even its slices, and the model run needed 8 hours per million records.
Ninety days covers the first pass. In month one the billing manager exports customer accounts, subscriptions, invoices and credit notes for every year a buyer will ask about. In month two the billing manager checks invoice lines and contract dates against signed contracts, while an AI model reads the name and address fields of each customer account and proposes corrections into a queue, where the controller approves or rejects each one and the decision is logged. The paper finds cost rises in step with the number of records, so the CFO should set the model budget from the account count. In month three the head of FP&A recomputes retention from the corrected records and writes down why any figure moved from what was reported.
The AI should stay on those account fields. On library and shopping-center addresses in the same paper, the language model approach missed at least two in five errors and caught under a fifth of every error type except missing values.
