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Kingdom and lord cheat download. Here, Ringman describes a machine learning use case IT has taken on to curb -- a major issue in the call center business. Editor's note: This conversation has been edited for brevity and clarity. As the leader of internal IT operations, how are you bringing artificial intelligence into the enterprise? Michael Ringman: Our internal IT role is to create as efficient a call center with the practices within our organization as we can. So, we've started to apply some of the, I'll call it statistical analytics, to things like our team member attrition.
Can we better help identify that an employee may leave our organization earlier? And can we start to red flag that across the organization so we can have a positive impact on that team member and convince them to stay? In the call center business, that has huge implications. One of the biggest costs is team member training. In most organizations, they're lucky if a call center team member stays in that role for seven months. It's viewed as a transitional-type role, regardless of where you're delivering it from.
At [Telus], because of some of the ways we go about embracing our team members and looking at things like how we can leverage machine learning across our HR portfolio, we can better identify those team members. We now have an agent attrition rate that is half that of the industry average. I wouldn't say the 50% less attrition was all due to, but we are definitely looking at those types of things internally to better help us understand how we can reduce our overall cost and add value back to our customers. What's a challenge your team of faces with this particular machine learning use case? Michael Ringman Ringman: There are a ton of data sources that we could look at in regards to team member attrition -- everything from their resumes when they come on, to how were they onboarded, to how did they go through their initial training, to who is their supervisor -- all of this sort of data that resides out there. So, we go to the HR team, who says, 'Wow, this is great.
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Let's really dive into this.' We're trying to find out how we can be sure that our analytics are properly identifying the team members who are going to attrit. So, we quickly need to narrow our focus and get to a small subset [of employees who could attrit]. Do we start to look at a particular training class, and can we more narrowly define some of the potential indicators?