Michael Machado (LinkedIn), Senior Director of Product Einstein Voice and Deep Learning Solutions, Ceverly Strand (LinkedIn), Product Marketer for Einstein, and Julien Sauvage (LinkedIn), Head of Tech and Product Marketing for Einstein – all stopped by Mission HQ for this special episode of IT Visionaries. 

This Salesforce crew is part of the team that is bringing Einstein and voice technology to customers everywhere, and they took some time away from the labs to talk to host Ian Faison about  all the ways Salesforce is doing innovative work in this exciting field. They discuss the A.I. marketplace, explain what deep learning technology actually is, and give us a heads-up about the future tech that has them most excited. 

Here are some of the highlights.

What is going on in the A.I. marketplace?

As artificial intelligence becomes more integrated into new technologies, there has been a shift in focus. We’re all trying to figure out the best ways to use A.I. technology. In the case of Salesforce, there are four main benefit statements when it comes to A.I. – and use cases around A.I. – that the team focuses on: discover, recommend, predict and automate. 

• Discover means to discover hidden patterns in data.
• Predict is at the core of deep learning and AI and helps to focus on leads or accounts that might convert to sales.
• Recommend is used to recommend the next best action or engagement to use. 
• Automate allows for the automation of workflows with AI.

A crash course in deep learning:

Deep learning — and the automation that can come with it — is designed to make lives easier. You are not looking to make employees – or processes – one percent more efficient, you are looking to create an environment –or a system – that will yield the absolute best possible results every time. Sometimes, that will mean taking things that can be automated off of their plates,  and sometimes it is analyzing how they succeed in some areas and fail elsewhere The last one, in particular, is what deep learning helps with: identifying small, incremental solutions by using deep learning, and then implementing those solutions to create monumental effects on productivity. 

The excitement surrounding voice technology:

Voice data – and using voice recognition software and technology for data input – are slowly being adopted throughout various industries. Right now, agents in the field usually take notes by hand, and then never get that data into the system for others to use and get insights from. Or, even if the data is input –  it is not always complete. 

“Voice is helping change things by allowing you to put great data into the system at the moment,” Ceverly says. 

But data input is just one use case. There are more ways to use voice technology, and the variety of use cases will continue to increase as more people begin to understand the possibilities of the technology. 

“People have a misconception that it’s a simple technology,” Ceverly says. “But there’s a lot under the hood and there’s so much you can do with it.”

Listen to the entire roundtable here.