AI in Supplier Discovery: The Day After Tomorrow

In our initial entry of the series, AI in Supplier Discovery: Today, we discussed how the advancements in usability and computing power have made it possible for platforms to implement better and more powerful search algorithms that can actually make searches useful across wide supplier directories and networks. Then, in our last entry, AI in Supplier Discovery: Tomorrow, we discussed how the inclusion of advanced semantic processing, high dimensional (fingerprint) similarity clustering algorithms, range and "like" search algorithms, and machine learning that can improve the algorithms over time as humans identify "good" versus "bad" matches will allow even better, smarter, more useful searches to be performed in the days to come for the identification of the right suppliers for direct categories and services.

But is that the best we can hope for?

While that is all we can hope for tomorrow, we can hope for even more the day after that. More specifically, when we extend our event horizon out just a little bit further, we can predict that at some point in the future, supplier discovery systems are going to support innovative supplier discovery
(based on performance, need and soft factors) and predictive smart search (based on upcoming projects, performance profiles and real-time community feedback).