Welcome to Working Smarter, presented by Calabrio, where we discuss context centered industry trends and best practices, as well as sharing success stories and pain points with some of the most innovative professionals in the industry. We're glad you're here joining us to learn and grow together in order to provide world class customer service to each and every one of our collective clients. My name is Dave Hoekstra, Product Evangelist here at Calabrio, and I have two guests today.
I'm joined from AWS. I'm very excited to have Mike Gillespie. Now Mike is a Principal Solutions Architect at AWS joining us and with us again for the second time on our podcast is Shalima Bala. She is the Global Lead for Partner Development at AWS. Now I assume pretty much the whole world is of AWS. pretty aware of what AWS is and what we do. And so our goal today, what we really wanted to spend some time talking about is the super exciting world of AI. Has it, have we, have you heard of AI?
Do we even know what this is at this point? Absolutely. We all have, right? We've all talked about it quite a bit, and it's starting to really influence what we do. And AI in the context center has. Let's call it let's say it started to mature a little bit we're still very early in the stage, but we're starting to see a little bit of collection of that going and You know what?
We want to focus a little bit on the reporting side today but I really want to start with and either one of you can answer the question here, but You know when we talk about AI in the context center how has it started to work its way into the functions of the context center? And, what can context center users expect to see out of AI today and maybe tomorrow?
Yeah I'll take this one. So first I'd like to jump into a little bit of why, like why is AI all of a sudden such a big hot topic? Why can it now do those things Do before, and it's really a confluence of a number of different industry trends. And first followed the cost of compute and storage has gone way down. So to do the calculations needed to do the AI used to be prohibitively expensive. And now the cost has gotten into it being very economical.
And another aspect is there's a lot less friction and how to track that data. So instead of having to take your call logs and write how long the call was get the sentiment of each and every call get feedback from the customer, those are all automated now. So that data just comes along with the ride. So combining those two things, you have a much richer set of data that's easily collected along with a lower cost for compute and storage.
Now we can actually do things that are predictive in nature that help the efficiency of the contact center. So to do those things are, like, how do we reduce errors? How do we shorten our contacts? When you, as an end customer, when you call the contact center, your goal isn't to have as long a call as possible. It's really, how do I get what I need as quickly as possible?
So to be able to collect data and bring that data using AI right in front of the agent, Or through a chat bot to get to that resolution faster. So those are all the factors that are driving like why the AI is such a big topic. Now And what that end result does is now those data. The business is now more data driven. You have more metrics to track against, and that's where the reporting comes in. So now we have a richer set of data.
We have a I integrated in to help collect that data and make predictions on that data. Now we can make reporting that gives very keen and clear metrics on how we can improve the call center. It was saying that if you can't track it, you can't improve it. Now we can track it and now we can improve it and develop technologies around making improving the experience for both the users or the end customers, the contact center agents and the leaders of the contact center.
Let me unpack that a little bit, Mike, because are you telling me that I do not have to write SQL queries anymore? Is that what I'm hearing?
Yeah, from a generative AI standpoint, that's one of the most powerful components is it makes that data much more accessible. So instead of having to know the data structure or the SQL query language or how to use the reporting system, you can just ask questions.
What was the average call time over the last seven days and it will in the back end knows the data knows how to query that data will generate the query and report the answer to you and you can do things like make a pie chart that shows the call disposition over the last five days, things like that. So it just makes it much more accessible and reduces that ramp up time that learning curve to be to make that data accessible to you.
So what I'm hearing is that. This is available now, because I know a lot of times when we talk about AI we really blur the line between today and five years from now, right? A lot of times it gets really murky when we talk about those kinds of things. These are things that we can do today where I could literally take a data set and just type in a question and have it give me an answer.
Absolutely. Yeah, you're absolutely right. A lot of times when you read these articles in the newspaper, it's very spectacular. Speculative in nature, that's Oh, we might be able to do this in the future. This is a case where you can do that today. So for example, an AWS RBI solution, a quick site allows you to just ask questions about your data. So you can ask those questions that I talked about earlier, or you can build charts and graphs just using natural language.
To do and that's called Amazon queue within QuickSight.
That's pretty awesome. I, when I started working in contact centers back in 1999 or let's, I got into management in 1999. Let's put it that way. I literally had to build a report in Microsoft access like an entire reporting database. And it took me weeks to even get little things going and. What's really funny is how revolutionary it was to everybody that I could, you could click a button and get the last week's worth of call volume reports. So what I'm hearing is we've come a long way.
Have you come across any new connections in data that potentially that would get people really excited? I know it's pretty basic to say, how many calls did I get? But have we seen new ways of data that can connect?
And that's where, this technology is really impressive when it's so conversational. So you'll talk about, show me the data for the last seven days, and that's just a report. But really what it comes down to is when you can start telling it to ask questions of the data, like things that you may not have seen, you can ask it, how is this data correlated to another piece of data or, Tell me any anomalies that are occurring in this data. What are outliers that we're seeing?
And it knows the context of the conversation. So instead of having to run the report again and put it into Excel, et cetera, you can run that, those types of analyses right in line with your conversation with the data. That's one area where. It becomes much more accessible to non technical analysts than it would be someone that is an expert in, Microsoft Access or whatever tool.
Wait, people still export data to Excel? That still happens? Every day. Every day. Every day. I still maintain, it is my own personal feeling, that Microsoft Excel is the greatest soft, piece of software that has ever been invented. And we are. We are hoping to get away from it, but everybody's infrastructure is so built on that, that it's an adventure, right? Excel
is probably the most used and abused piece of software ever written.
Oh, that's such a true statement. And we all have a special place in our heart for it, but we all have, there's a special place somewhere else for it too. Sometimes. That, I, it excites me especially to think about how daunting a new reporting data set can be for people and not just for people who know data already, even for people that are new. Do you think AI, this kind of AI process is really going to help us onboard new people that much more quickly?
Absolutely. That's one of the key. Benefits of this conversational technology is it puts the train the learning curve down significantly, so you don't have to be an expert. You don't have to have a Ph. D. in data science to be able to answer these questions. You can just be very conversational and be able to drill into that data just based on the things that you see in front of you. And the. The trends that it identifies and that you identify in the data.
That's awesome. And I'm looking very much forward to seeing that continue to expand in, not just in the contact center, but in, in all the software that we use the number of times that data is presented to us, but it's still just so up and throw up for lack of a better word, right? It's still just dumped in front of us. And then, I'm a little worried because I've made my entire career out of being able to interpret that data.
So I'm wondering maybe, and you're gonna, hopefully it's going to turn us into better analysts as opposed to no analysts whatsoever. And that's a little excited. Now you mentioned. That, and this question could be for both of you however you'd to tackle it. You mentioned that AI used to be prohibitively expensive. And it's gotten much, much more accessible and cheaper, but I've still noticed it ain't free, right?
We're still using it as a methodology to either recoup some costs or find a way to get our hand when we want to get our hands on that next level of technology. It's usually not just turned on. We usually have to go out and find it. So are there things that. These contact centers can do or myself that can help the kind of these next level functions pay for themselves in a lot of ways.
Let me take that. Let's face it, any technology, not just gen AI or AI in general, the implementation of those technologies can be really expensive. However, when implemented strategically, the cost savings and efficiencies that you gain from these technologies, especially artificial intelligence can outweigh its initial investment, essentially making technology pay for itself. For example, AI can automate tasks.
And identify inefficiencies pretty quickly and make better decisions leading to significant cost reductions. Let's take an example from contact center. Jenny, I can automatically craft a call summary at the conclusion of the interaction, dramatically reducing the after call work for an agent. So by removing the time agents spend manually and summarizing a call, organizations can save millions of dollars and if done right, it can also help with the revenue generation.
So by enhancing customer experiencing personalizing marketing campaigns and developing new products. A. I can generate additional revenue streams for a company. Let's take a basic example. Virtual assistance, right? So if we implement it correctly, it can reduce the inbound call volume for a contact center and also enhance the C set. So it can automate the conversations with customers across digital and voice channels.
E. I. At its core can analyze large amount of data sets to provide insights and predictions. So if we power our agents with the AI, it can listen to the conversation real time and help agent to offer new products to the customers and enhance. The the value that you get out of a particular interaction. In turn elevating the performance and also delivering significant ROI. Now you must be thinking that all of this sounds great, but how quickly can. Customers expect these returns, right?
And the timelines can be very depending upon the complexity of the implementation and specific use cases. But what I have seen that many businesses start seeing these positive otherwise. Probably in six to 12 months. Okay. Done correctly. And so are
you talking about with the, the voice assistants or the agent assist pieces, is that specific to just that, or is it any kind of any timeline where AI has been brought in to assist
on an average?
Okay.
On an average. Yeah. Because mining the data, implementing the technology and learning that especially Jenny, I learning that data, it takes six to 12 months. And also when you're implementing gen AI particularly, it's not about just taking the technology and just replace it. What do you have currently, right? You have to clearly define what objectives you're trying to solve. What is the outcome you're trying to achieve and also most importantly, quality of data.
And you have to have the right talent in your company to monitor and optimize it continuously. So that is really important as well.
Yeah. The, what the challenge that I've seen to phrase it in a different way is we're we've got hundreds and hundreds of years. Of teaching human beings how to do this process. We've learned over many generations to say, okay, if you want to teach someone how to do something, you sit them next to each other and you haven't one person watched the other person and learn, and eventually they get good enough to go off on their own. And AI doesn't work at all like that.
AI is much more you have that person sit down and map everything out before you even. You're even allowed to have it look and that, that's the difference is we have to learn as a race, as a species, how to teach AI to do things for us. And that is that's an, a bit of an adventure because we, our brains aren't wired that way. That's what I've noticed for a lot of people is that I don't think like that.
Learning how to think like that, Calabrio, we just launched a new process that uses AI to to answer quality questions. Did the agent did the agent use the greeting correctly? Something like that. And as we're trying to teach our customers how this works, they're so used to just having a human being listen and go, good job. Without ever having to quantify what that means. They just, they're just good job or bad job. And now we're trying to teach people how to quantify.
Have you ever seen that video where it's somebody says, teach me how to make a peanut butter and jelly sandwich. And then they say, okay, you take two pieces of bread and they're like, I don't have any bread. And they're like Oh, okay. You have to go to the store. But how do I go to the store? Okay. You get in a car. What's a car. And it's trying to teach people. This is how computers think. You can't just say to the computer, take two slices of bread and stick it together.
You have to really, you have to define every minute step. And that's what it really feels like with AI. So I think bringing this to light for a lot of people is really important. You cannot just turn on AI and have it magically work. There's a fair amount of prep work that goes along with it. We're seeing that across everything.
Yeah. One other aspect of it, which is unique is in traditional applications. If you have the same inputs, you'll have the same output. It's very predictable where AI is a little different. There's randomness. So you can ask the same question of an AI. And exact same question and get two different responses. And they could be very different. So building your systems to be resilient to that variability in those responses. Now, the responses may be more accurate than their comparable human.
There's a benefit there, but you do have to build that recognition that you have to check your work and you have to check the eyes work as well.
Nobody wants to do that. Mike. Nobody wants to check their work. No you're right. And it's it's funny because we were starting to see that with some of the large language models that are coming out where, you start to see things pop up where someone says like, How many ounces are in a gallon and you might, honestly, sometimes you might get different answers.
And then we're starting to see things being taken as fact where it's really because bad source data, because the large language models are mining incorrect data. But that's the thing. AI doesn't know what's wrong.
Yeah.
And so I don't think we should be that worried about Skynet. At least in the near future, right? That's the takeaway from this. Last question, either one of you can handle this. Let's say that I am a contact center, or even just an organization, doesn't have to have a contact center. And I'm looking into these tools and I know that more than likely in the next 12 to 18 months, I'm going to purchase some sort of solution. What should I do today?
To make sure that I'm ready for when the implementation calendar starts on that type of solution. So it could be voice assist, it could be interaction summaries, it could be whatever the case may be. What should I do today?
I'll take a first pass and then Sheldon can jump in. But I think having a good understanding of what those metrics are that really drive success for your business. Because what AI will do is amplify what you're trying to, those metrics you're trying to manage. And give you tools to help improve. Whatever dimensions make the most sense for you.
Without having a good understanding of how that, that business works, you're not going to be able to use that amplification as these new technologies, roll into the mainstream. The other part is, even outside of, these packages or these solutions I would say, Use the publicly available AI, chatbots get used to how they, how to interact with them. There isn't a skill, we call it prompt engineering, but it's really how to talk to a computer.
Yes, it's natural language and you're using English or whatever language. But there is a certain style that you communicate with the computer and being fluent in that. And you don't have to be a computer scientist. You don't have to be an expert, but just getting comfortable with having those conversations will go a long way to helping you understand how they react, how they think, and then how to utilize them in your business. That,
that so reminds me of when I was trying to teach my kids how to search the internet and they would sit down and type in a long string of, or they'd ask a question what's the best video game? And it's and it hurt my head because, there's just Oh, that's not how these things think. And you have to be more specific, but it, and then you look back and you realize, how would they have known that this is the way, right? It's my job to make sure that they know how to do this.
So teaching them how to correctly query and correctly prepare for these kind of things is such a huge thing. I think that's a great piece of advice. Just because something can do it better, faster, stronger, doesn't necessarily mean that the old way is going to completely go away. Because we're, there's the world finds that niche, right? It finds that area. And I agree with you 100%.
It will actually in the long run, create more jobs because we're going to have time to create new and amazing things and new and amazing technologies that supplement what's available out there. So I agree with you wholeheartedly. And I think anybody who's scared of AI I don't think you should be. The benefits of AI are going to far outweigh any kind of negative effects of it.
It might affect certain industries or certain areas a little bit more than others, but Mike, anything you want to contribute?
No, I think that pretty much hit it. The awesome, the thing I'll add is that, the. In the long run, the companies that are in organizations that are most successful utilizing these technologies will be the ones that are most successful in the long run. So embrace it learn it, play with it immerse yourself in it, and it will be time well spent.
But I can do this all in an on prem environment Mike?
No comment.
No comment. Oh, this has been fantastic. You guys. I learned so much. It's always great to talk to some people who really understand this and get a chance to immerse themselves in it day to day. I'm not necessarily one of those people. So thank you guys so much for spending some time with us and really educating on what's going on. It's been great to talk to you both. For the rest of the audience, as always.
Thank you guys so much for spending time with us here on the collaborative working smarter podcast. It's great to spend some time with you and looking forward to having many more episodes. So thank you guys so much. Charlie, my Mike really appreciate you jumping in and giving us your thoughts here on AI reporting context center and all that fun stuff that we have to deal with on a day to day basis. So thank you guys so much. And to our listeners, have a great rest of your day. Have a great grit.
Rest your weekend. We'll talk to you again on the working smarter podcast from Calabrio. Thanks everybody.