Observed Signal · Aug 12, 2026 · Data Report · Source: a16z · Impact: 2/5 · Sentiment: Neutral
What It Takes to Get Paid (B2B Collections)
This a16z chartpost analyzes accounts-receivable and collections behavior across billions in B2B receivables using data from Stuut, an AI agent that automates outbound collection activity. US nonfinancial firms held $7.2 trillion in trade receivables at end-2025; invoice-payment timing is highly skewed (half pay within ~25 days, but tail invoiced dollars can take many months). Collections outcomes concentrate in a small number of large invoices, most successful collections close after two asks, and automation handles the vast majority of routine outbound asks while humans resolve disputes and escalations. The piece quantifies DSO variability, concentration of overdue dollars, ask-frequency efficiency, and the economics of delayed payments.
Provides quantitative, operational insights on B2B collections and automation that matter to finance and back-office operations; informative for SaaS and payments vendors but not industry-shifting.
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Key Takeaways & Evidence Grounding
- US nonfinancial businesses held $7.2 trillion of trade receivables at the end of 2025.
- About 10% of invoices are paid within one day and 50% are paid within 25 days; moving from 70% to 90% paid goes from day 37 to day 74.
- The largest tenth of past-due invoices holds a median 65.7% of overdue dollars; the smallest 80% of past-due invoices share about 20% of overdue dollars.
- Half of collected past-due invoices close within two outbound asks and nine in ten close within six; the median collected invoice requires two asks.
- 81.7% of outbound collection emails are sent with no human involvement; three in five completed collection tasks resolve fully automatically.
Connected Companies & Entities
1 Entity mapped“This edition of our weekly chartpost series goes inside the most awkward recurring conversation in business: asking to be paid for what you ...”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Unified B2B Data Stack to Drive Revenue
The article is a practical guide for B2B revenue leaders on unifying and orchestrating customer data to improve pipeline and conversion. It describes a five-layer B2B data stack (sources & integration, data warehouse, CDP, business intelligence, and automation/agentic AI), emphasizes bidirectional CRM–marketing automation integration, and warns against adopting agentic AI before foundational layers are established. The piece highlights common failure points (broken MAP‑CRM sync, inconsistent account identity, disconnected intent data, and premature AI adoption) and advises building a quantified business case, sequencing roadmap for early value, and establishing cross-functional ownership and VP-level accountability to realize revenue impact.
Unlock Buyer Insights: Leverage Your CRM Data Wisely
The article argues that B2B marketing teams often overlook rich buyer insights already captured in internal systems—CRM deal notes, sales call recordings, support tickets, NPS/CSAT verbatims and operational metrics. Instead of commissioning new surveys or buying more tools, the author recommends mining existing sources through regular "data archaeology," converting recurring phrases and operational KPIs (time-to-value, churn reasons, expansion triggers, win/loss patterns) into concrete messaging, headlines and targeted content for microsegments inside an ICP. Practical steps include monthly reviews of customer verbatims, creating a shared "Voice of Customer Hits" document, and tagging recent closed-won deals by buying trigger, use case and deal velocity to find distinct content-ready segments.
Month-End Is Now Just Another Day
a16z analyzes bookkeeping data from 56 early-adopter companies using Rillet, an AI-native ERP, and finds that traditional month-end closing is becoming obsolete. Rillet processes transactions continuously so ledgers remain accurate and reviewable in real time; 99.86% of observed journal entries were automated and only a tiny fraction required manual adjustment. In the sample, 87% of companies looked at less than 1% of entries during period-end, though B2B firms and multi-entity organizations still require more human judgment and period-end work. As firms scale from one entity to four or more, revenue-and-billing entries fall from 58% to 38% of ledger volume. The authors note the sample is non-random (Rillet customers) and present these findings as a guide to where back-office automation is headed.
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