Sramana Mitra: I happen to know enough about what you’re talking about. I know it’s not simple at all.
Rich Green: Yes. In terms of practical near-term use, these kinds of technology are already under examination by a very large customer. That’s some examples of where we are now.
Sramana Mitra: I’ve done a bunch of startups and we also have startups who are doing similar things. Since this stuff has not really penetrated into the customer accounts, there is a question that is hanging around. Will this work? In the history of AI, there are certain things that are working. We’ve obviously crossed the threshold of that question. If you look at the field of media buying, it has become a fully automated procedure.
You can’t really do media buying without the kind of machine learning and automated infrastructure that exists today. Humans have pretty much become redundant in that workflow. That question has been answered. The field of cyber security is using a lot of machine learning in gauging heuristics of attacks. There are actually more actual live customers, data, and feedback that is already out there.
Your domain, in which you are enhancing the power of the CRM with AI, has been talked about and is being talked about for a while. It hasn’t quite hit the market yet to the extent that there are really solid use cases of customer successes. What would you say? Are we a year or two away?
Rich Green: If I may take a quick detour, I think there are two corollaries to your question. One is, how far away are we from implementations that will solve real world problems? How far away are we from people accepting them? One of the classic issues for machine learning is that it is worrisome. I did an extremely informal poll at a very large gathering about people’s fear of machine learning adoption in the context of assisting humans. There was great fear among people who are not Silicon Valley technologists, but they do work and represent the majority of potential users in the industry.
Where are we? I think we have to take a step back. I know there are a lot of great startups in the space. It’s important to understand that AI has been a magic thing for many years. It is turning into an arms race. There are three of four commercial companies that have the scale, investment, and expertise to really build general-purpose machine learning and narrow AI. That standard list would be Google, Microsoft, Facebook, and Amazon.
When you think about a company like ours that is a $100 million company, there’s no way we’re going to build the underlying machinery to solve these kinds of problems. We’re going to rely on the technology from companies like the ones I listed as the machinery under the CRM technology to do that. I think there’s some sense in the market that people can just build AI themselves. It’s like saying, “You should build relational databases. You should build the equivalent of Linux.” No one does that. They just get it and use it and apply their knowledge to use that machinery to solve that problem.
What is the state of the machinery of these four companies? It’s quite far along and sophisticated. You can even throw in IBM. That is present and useable now. Where does this show up in the CRM world? In a year, you will start to see this push into it. Those things are the edge of the wedge that we’ll see in a year to get this going. Then it’ll accelerate quickly from there.
The adoption is a greater concern for us or me than the build out of the technology. That is because, especially in the world, people are used to doing it personally. Having their judgment questioned by insights generated by large machines will be a learning experience for individuals. They’ll be fearful and they’ll realize the value.