AI-Powered Savings for Your Business: Don’t Delay!
There is a hidden danger you need to watch out for when bringing new artificial intelligence (AI)–powered algorithms to an organization: people may feel that the algorithm makes them look bad.
Let me step back and then explain what is going on.
At Coupa, we have many different AI-powered applications. Using these applications can bring large savings to organizations. For example:
- Our sourcing optimization product helped one firm save $300M. This savings came from a single sourcing event with over 6,000 items, 300+ suppliers bidding, and 400 different business rules that the firm wanted to follow. The algorithms within the sourcing optimization product help sort through the bids to select the best combinations while meeting strategic objectives.
- Our supply chain suite helped American Eagle Outfitters transform their supply chain. They alleviated all Cyber Monday backlogs in just a few days, sped up labor planning by 97%, and stood up four third-party logistics (3PL) nodes in six months with the right assortment, facilitating regionalization of inventory, and helping deliver products one and one-half days faster to customers.
- Our Spend Guard product helps customers find an average of $1.7M in duplicate invoices every year. In one case, within a few weeks of going live, it found a $1.5M duplicate invoice that was approved for payment.
So, why would these kinds of savings make people look bad?
Algorithm envy?
Once people realize the potential savings of these types of solutions, they may think that they’ll look bad for not being able to find these savings on their own. Why didn’t they find the savings themselves instead of waiting for an algorithm to come along? Maybe they should have run better sourcing events, had thought harder about the supply chain, or been more diligent in processing invoices.
This is not a fair way to look at it. The best thing to say to them in response is, “No, you couldn’t have found the savings.” (At least not as systematically, thoroughly, or in a repeatable way.)
In the examples above, we see that:
- Sourcing optimization has powerful algorithms that allow suppliers to bid considering their economies of scale. (People won a Nobel Prize for this insight.)
- Supply chain algorithms sort through billions of possibilities. (The math behind these problems is one of the reasons people are researching quantum computers.)
- When looking at duplicate invoices, the algorithms can compare all invoices at the same time and apply learnings from across Coupa’s community data. (Tapping into the experience of hundreds of other customers is impossible to replicate.)
In other words, powerful algorithms and approaches allow you to do things you simply were not able to do before. And they allow you to continue to do these things in a repeatable way.
Once people start taking advantage of these algorithms, their only regret is that they could have achieved this level of savings sooner. This is true. You can't go back in time. However, you can avoid future missed savings by getting started today!
