The biggest new product to come out of Salesforce this year is an artificial intelligence feature called “Einstein.”
Einstein basically collects and analyzes a bunch of data to push out “smarter” and more predictive analytics for Salesforce users.
But early reviews of Einstein seem to be mixed so far. Most users agree it’s still at a very early stage and is a couple years away from becoming a mainstream product for large business users.
“Our sense is that the recent launch of Einstein is very early in terms of technology readiness, as well as customer awareness and market adoption…it will likely take another year or two before these features/products gain meaningful adoption,” Cowen & Co. analyst Derrick Wood wrote in a note published Monday.
Wood isn’t the only analyst with a skeptical view of Einstein. Gartner analyst Todd Berkowitz recently told eWeek’s David Needle that Einstein’s a “great starting point” but not as strong as some of the other standalone products that have been pushing forward with AI technology for years.
“…in other respects there isn’t a lot there. The predictive lead scoring isn’t utilizing a lot of external data sources you’d need to do it in the marketing automation platform and it’s not nearly as sophisticated as standalone solutions,” Berkowitz said.
Even some of the customers who tried Einstein seem to have come away unimpressed by its early features, according to a note by JP Morgan. Some of the feedback include:
- “Einstein will take some time. It’s a nice to have. Customers will wait to see some references before adopting it. A bank from the Bay Area was an early adopter and they burnt their fingers with Einstein.” “Einstein is at least a year and a half from being fully baked, but that’s par for the course. Benioff realized they were behind on AI, machine learning, etc.”
Given the difficulty of building a truly mainstream artificial intelligence technology, it’s not too surprising that Einstein is viewed as still being in its early stages. Salesforce’s AI initiative only started to gain traction in 2014 after it acquired RelateIQ, and although it has spent nearly $1 billion aimed at boosting its AI capabilities, including the addition of 175 data scientists, it will likely take a little while before it catches up to some of the more experienced competitors in the field.
As Vik Singh, the CEO of Infer, a startup that competes in the same predictive analytics software space, wrote in a blog, AI technology isn’t something you can just roll out to every business by flipping the switch on:
“You can’t just usher out an AI solution to many business customers at once, although that temptation is there for a bigger company…that won’t work for mid-market and enterprise companies, which need many more controls to address complex, but common, scenarios like multiple markets and objective targets.
After five years of experience in this game, I’ll bet our bank that approach won’t result in sticky adoption. Machine learning is not like AWS, which you can just spin up and magically connect to some system.”