We learned the hard way that finishing a task doesn’t mean completing the outcome. For example, in finance, you can perfectly generate and send an invoice, but an invoice can still be unpaid. It could be missing a purchase order, it could be in a dispute in a portal, or it could be with someone who has the wrong approver. Typical automation will not catch any of these issues. The way we changed our thinking was to utilize artificial intelligence to monitor what happens to items after the handoffs occur. We are now using the Monk order-to-cash platform with AI to automate invoice delivery, track unpaid invoices, automatically check on unpaid invoices, flag issues blocking payment, and help to prioritize which unpaid invoices require your attention. While speed was nice, the biggest benefit of this method was understanding everything that was occurring. If you are utilizing AI tools, can you identify a time where an organization marked an activity “complete” when the true activity still existed? submitted by /u/Devid-smith0
Originally posted by u/Devid-smith0 on r/ArtificialInteligence