by Paul Breitenbach, CEO
I believe that in the near future, many aspects of today’s world will be seen with the same amazement we feel when we contemplate those high school students:
- What do you mean, people couldn’t integrate data within their own company?
- What do you mean, retailers lost a trillion dollars a year because products were out of stock?
- What do you mean, 30% to 40% of the food we produced was wasted?
- Companies can’t integrate data easily because it’s trapped in different systems and formats – essentially, different languages. Now imagine “multilingual” AI that can read virtually any kind of data – and not only understand it but summarize it and draw conclusions from it.
- The second problem is related to the first: out-of-stock conditions that squander revenue opportunity and erode customer satisfaction are a symptom of the siloed data problem. If it’s hard to comprehend all the data in a company, think how hard it is across an entire supply chain. But the same AI technology that can help a company with its internal data problem can also look at data (even incomplete data) from a supply chain; match it to external data about markets and locations; and create highly accurate models of future demand.
- The flip side of out of stock is overstock – when companies produce and ship goods that will never be sold. This problem is especially acute in the food industry because food is perishable. As I mentioned above, a staggering amount of food is thrown away because it becomes inedible before it’s sold. The same demand-modeling AI that can drastically reduce out of stock problems can do the same for overstock problems. Imagine the benefits to society and the environment if all that wasted food was never produced and transported in the first place – or if it was routed directly to people in need.