MAY 28, 2025

• Invoice Payment Risk Radar: A Prompt to Get You Paid Quicker
• The Case for Redundant AI: Why Smart Teams Use More Than One Model​
• THE HEADLINES
• Build Custom Enterprise Software Sans Developers with OriginAI

Invoice Payment Risk Radar: A Prompt to Get You Paid Quicker

Finance teams often discover overdue invoices only after cash-flow pain hits. Manual reviews of aging reports and scattered email threads make it easy to miss the early clues—customers who consistently pay on day 29 instead of day 30, vague disputes buried in support tickets, or a sudden dip in order volume. Spotting these “silent signals” early lets you nudge customers diplomatically and keep cash moving.

How AI Can Help

Large language models excel at pattern-hunting across semi-structured data. By feeding ChatGPT a recent export of your accounts-receivable ledger plus any related customer-service notes, you can surface hidden risk factors (e.g., creeping payment delays, complaint sentiment, contract-renewal dates) and receive a prioritized action plan. The AI does the heavy lift—sifting thousands of lines, ranking accounts by probability of lateness, and suggesting outreach tactics tailored to each customer’s history.

Copy-and-Paste Prompt

You are a finance operations analyst.
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INPUTS
1. “Ledger.csv” — the last 6 months of A/R data; includes Customer_ID, Invoice_Date, Payment_Date, Amount, Days_Outstanding, and Notes.
2. “SupportTickets.csv” — customer-service ticket summaries with Customer_ID and Ticket_Text.
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TASKS
• Detect patterns that predict invoices going ≥15 days past due.
• Generate a ranked list of the top 20 at-risk customers, showing:
- Customer_ID
- Average Days_Outstanding trend (last 3 vs. prior 3 months)
- Any negative sentiment or dispute keywords from SupportTickets.csv
- Likelihood of late payment (High/Medium/Low)
• For each at-risk customer, suggest one concise, empathetic email subject line and two personalized talking points the account manager can use to encourage timely payment.
• Summarize the overarching drivers you detected (e.g., economic sector, contract terms) and recommend one process improvement to reduce future risk.
​
FORMAT
Return:
1. A markdown table for the ranked list.
2. A brief paragraph (≤150 words) with the drivers and process recommendation.
3. No code blocks—just plain markdown.
​

Why This Works

The prompt combines numerical indicators with qualitative sentiment, letting ChatGPT correlate hard data (days outstanding) with soft signals (support language). Placeholder file names keep it plug-and-play—any finance manager can drop their exports in and get an actionable heat map of who to call before invoices slip into dreaded “90+ days.”

Use it weekly, and you’ll turn cash-flow surprises into a predictable pipeline, without wrangling spreadsheets at midnight.

The Case for Redundant AI: Why Smart Teams Use More Than One Model

Your favorite GPS app gives great directions — until it suddenly reroutes you down a closed road. A second app could have warned you. The same logic applies to AI: one model can feel like magic, but it will still miss turns you never see coming. As more teams rely on generative AI for writing, planning, and crunching data, the risk of blind faith grows.

Redundancy — running two or more models side by side — turns that gamble into a safety net. It’s the difference between driving with a single headlight and switching on high beams.

I was motivated to write this post because so many of the people I talk to about AI use ChatGPT, but they still haven’t checked out Gemini, Claude, Grok, or any of the other emerging models.

In this post, we’ll unpack why that’s a mistake by looking at how redundant AI improves accuracy, sparks creativity, guards against bias, and keeps the lights on when one model stalls.

​Let’s start with these 7 reasons it’s wise to use more than one model.​

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