In the first installment of the CX Pod’s Tech Insights series, Sean Minter, CEO of AmplifAI, joins host Liz Glagowski to discuss how AI is revolutionizing contact center performance. Learn why traditional QA falls short, how to identify true top performers, and what it takes to turn AI data into real business results.
Listen to the full interview
Transcript
Liz Glagowski:
Hi, and welcome to the CXPod. I’m your host, Liz Glagowski of the Customer Strategist Journal.
We’re launching a new series here on the CXPod called tech insights, where we interview tech leaders about key topics in customer experience and in the business. And we’re pleased today to welcome Sean Minter, founder of and CEO of AmplifAI, as our first guest. Oh, thank you very much. Welcome. And this is also our first time doing the podcast as video and audio, so we’re very happy to be able to share it on multiple channels. So thank you for participating.
Sean Minter:
Oh, you’re welcome. Great. Thanks for having me.
Liz Glagowski:
Absolutely. So AmplifAI is a powerful platform that helps contact center employees and leaders really optimize their performance. So before we get into the CX conversation, can you give us a little intro about AmplifAI?
Sean Minter:
Yes. AmplifAI platform that’s really going to focus on bringing in and understanding the data around what your contact center associates are doing holistically from a productivity, quality, compliance, customer experience, sales, everything kind of they’re being measured on, understand what the top performers are doing, and then using AI to persona them and drive actions to get everybody else to do what the top performers do.
Liz Glagowski:
Great. And you partner with TTEC on the TTEC Perform solution, know.
And that really does focus on using AI and other tools to really pinpoint top performers, optimize performance, get information to the leaders and the and the employees when they need it. So just to step back then, how would you define a high performer, a top performer in a contact center? Is that different from client to client? Is it pretty standard? And then has it been evolving as tools change?
Sean Minter:
Yeah. That’s a really good question. And, yeah, the definition of a top performer also changes even for a client.
So our view of top performers are people that can do everything you need them to do at a high level of performance. They can be productive.
They’re reliable, they come to work on time, they provide good customer experience, they generate good customer results and they do things in a quality and compliant fashion. So that’s a lot of data, a lot of measurements to understand for one top performer.
Interestingly, it has to be done that way because there’s a lot of correlations between the different things. Like the person that’s the most productive with the lowest handle time might be your worst CSAT person. So you can’t look at it individually. So AmplifAI creates a scorecard across all the different metrics and clients actually get to weight what’s important to them.
Is CSAT more important today than productivity? Is contact resolution more important as they wait the different things that matter to them in a particular waiting? AmplifAI then takes all the data and says, okay, based on these weightings, here are the top performers. And they can change that waiting at any given time because business changes, right? Sometimes cost savings is important and other times customer experience is important. They can change their weightings to define a top performer at any time.
Liz Glagowski:
So to that end, are there major consumer trends or business challenges that are driving the need to look at that performance at such a deep level?
Sean Minter:
Yeah, I think the primary trend, to be honest with you, is to be able to provide the employees a good experience and be consistent across your environment. Because if you can’t define a top performer, then how does a leader really understand which one of their people is a top performer? You could leave that up to opinion. Everybody could define that for themselves. You could then have challenges in how you’re treating your employees, how they’re being treated fairly and equally, who you’re coaching, what are you coaching them on? It could all become a big variation across your enterprise unless you really understand what is a top performer.
So in order to really get to that level of understanding, you talk a lot about conversation intelligence versus that traditional QA. So can you talk about maybe what’s the difference, why it matters, and where that market is headed?
Yeah, so actually an interesting part about top performer is even though like theoretically you could have metrics that define someone to be a top performer, without good conversational intelligence, you don’t know if what they’re actually doing and saying and if it’s resulting in good metrics that are being delivered are actually the things you want them to do and say. So you have to be able to combine understanding from a conversation analytics perspective, what your folks are doing and saying on a conversation with their end result metrics, and that gives you a holistic view of a top performer.
And unfortunately for traditional QA, with limited manual resources at most, you’re maybe having one or two evaluations per agent per week. Well, an agent is taking seven fifty to a thousand calls in a month. The sample size is not enough to really understand whether somebody is doing a good job or not. And so much action is taken off those one or two evaluations.
It’s really not a good way of using time, but unfortunately you can’t throw labor at that problem because it would require too many people to listen to all the conversations to get a decent sample size, which is where AI can come in and actually augment. Nobody can say you can eliminate the quality team because somebody has to then train the AI and oversee it and how it’s doing. So now the job of the quality team changes to not actually monitoring what the frontline agents are doing, but monitoring how the AI is evaluating frontline agents and tuning it.
So you’ll never get, you might reduce some costs on that side, you’ll get higher volumes of interactions, but you’ll have to have your quality team now focus on improving the AI, not improving the agents.
Liz Glagowski:
And you really get that deeper level of insight because of the scale that AI.
Sean Minter:
One hundred percent, like you’ll be able to really understand what’s going on in your environment with higher levels of confidence in your data versus only sampling one per person per week.
Liz Glagowski:
So then you’ve identified these top performers, you’re making improvements to others to kind of emulate those top performers. When you’ve got that level of performance improvement, how does that impact the business and how does that impact customer interactions for customers and people actually calling into the contact center?
Sean Minter:
One of the core problems we’re solving in doing this is reducing the variation you as a business will see based on what one of your agents might take a customer interaction. Because you may have good average performance, for example, but you may have some really, really good agents and really, really bad agents and they average out to average what you want it to be, but half your customers are still getting a negative experience. So the goal of AmplifAI is once you understand this top performer view and action it, you can reduce the variation between your population of people. So you can be confident that every customer conversation is generating a level of experience that you want versus having a big variation and you could have really, really good and really, really bad, but that averages out to okay.
Liz Glagowski:
Right, right. That’s great. We’re at the beginning of the year, that’s a question everyone asks, what are you most excited about for 2026?
Sean Minter:
I think the ability to use AI to understand what’s happening in the data, there’s bad and good. AI is generating so much more data, so it’s causing people to not really know what to do with it because people have invested in all these AI projects and now they have all this data and now there’s not enough people to go understand when we go action.
Being able to use these technologies now to overlay that activity and understand how to action that data, which is I think the next business challenge for AI is now I’m generating so much information about what’s going on, how do I action it properly? Because I can’t just throw humans and looking at all the data and reporting, but that’s now going to be more cost. So being able to leverage AI to be able to understand how to use that data in a way that actually drives a business result, because there’s no point in generating data unless you can get a business result from it. Otherwise, you’re just generating data for the sake of generating data.
Liz Glagowski:
Right, right. So because this is our Tech Insight series, I’m going to ask everybody the same question and so you get to be the first one to answer it.
If I talk to you this time next year, what would we be talking about?
Sean Minter:
I think we’ll be talking about like, I think from last year or this year, the big difference has been AI had a lot of hype and that hype is now going away because people have piloted stuff and it’s not really delivering what you want. I think next year, we’ll be able to talk about some real use cases that actually worked. Because it’s a lot of deployments happened and it was over hyped, just as it’s normal technology cycles, things come out and people overhype it, but then ultimately it starts working in use cases that make sense. And I think next year we’ll be able to talk about some real use cases that are working very specifically and generating some real value for businesses versus just pilots and POCs and how are they going?
Liz Glagowski:
Yeah, well, I look forward to having that conversation next year. So thank you so much, Sean. Great conversation. You’ve really started us strong with some tech insights. So good luck in the new year.
Sean Minter:
Thank you very much.