Why small businesses hesitate on AI in finance, and how BILL is addressing it
- Small and midsize businesses want the time and cost savings AI can offer, but hesitate to hand over their financial operations, so BILL built its new AI suite around three pillars: accuracy, control, and security.
- According to BILL’s Tara Wellington, the company’s own risk platform saw about $1.5 BILLion in potentially risky or mistaken payments last year. For her, that kind of data is what lets the risk agent flag issues at the moment they matter, helping keep operations moving smoothly.
At Money 2020, BILL announced it had surpassed $2 trillion in cumulative payment volume and unveiled a suite of AI tools for finance teams. Tara Wellington, the company’s VP of Product Management, says the launch was shaped by a tension she hears constantly from customers. Small and lower mid-market businesses are eager for the time and cost savings AI promises, but their finances are “at the heart of everything” they do, so hesitancy runs high. For these businesses, a single payment error or missed invoice can ripple through cash flow, vendor relationships, and payroll, so there’s little room to experiment. Which is why trust has to be earned before AI automation feels safe
Wellington notes BILL AI is designed to reduce this anxiety, with the suite built around three pillars of accuracy, control, and security.
The need for control, she explains, comes straight from customer conversations, where trust means being able to see what has been done and decide what AI can do. BILL built safeguards into the product, such as a hub with full audit logs of the actions AI takes, plus the ability to switch AI features on or off.
On security, Wellington points to BILL’s’s Risk Intelligence, which uses the company’s payment data to spot anomalies, such as a vendor that normally bills $1,000 a month suddenly billing $10,000. The AI flags the unusual charge for review before money goes out the door, catching mistakes before they cost the business—while a human stays in the loop on the decisions that matter.
BILL’s own risk platform saw roughly $1.5 BILLion in potentially risky or mistaken payments last year. Between August and September 2026, Risk Intelligence has autonomously detected an average of about $138,000 of mistakes or unusual payments per customer.
Key insights
- Interest in AI is high, but confidence depends on proof that it won’t create costly surprises.
- Audit logs, on/off toggles, and human-in-the-loop options keep customers in charge.
- Flagging mistakes pre-payment protects cash and saves time spent unraveling problems later.
- Payment history helps the platform recognize unusual activity within each vendor relationship.
- Businesses should define what trust means to them and look for solutions that prioritize security, control, and accuracy.