An AI finance dashboard is only as reliable as the data behind it, and most people never test that. The timing matters. In October 2026, Goldman Sachs surveyed 2,473 graduates of its 10,000 Small Businesses programme in the US. 42 per cent said they use AI for financial analysis and business planning, more than double the rate in 2024. Finance is where small business AI use is growing fastest. With the AI Finance Dashboard 2.0 course live in this community, a lot of us are about to make decisions from numbers an AI assembled. I trained as an auditor at PwC and KPMG. My job was to test whether the numbers on the page matched reality. Here are the five checks I would run on any AI dashboard before making a decision from it: 1️⃣ Does cash match the bank? Pick one date. Compare the dashboard's cash balance with your bank statement for that day. If they differ, stop and find out why. 2️⃣ Is every account connected? Business card, PayPal, Stripe, the savings pot. One missing account makes every total wrong. 3️⃣ Are the categories right? Pull 20 transactions at random. Check how the AI coded each one. More than one or two errors means your margins and spending insights are off. 4️⃣ Is it showing cash or revenue? An invoice sent is not money received. Know which one each chart is using, especially before you hire or spend. 5️⃣ Can you trace every insight? When the AI says "software costs rose 30 per cent", ask it to list the transactions behind the claim. If it cannot, treat the insight as a guess. This is the Underlying Data and Numbers part of the ACCOUNT Framework from my book, Human-Led, AI-Powered Finance. The dashboard gives you speed. You still own the decision. Run check one today. It takes five minutes. Which check did your dashboard fail first? Share it below and I will help you work out the fix.