HITT- Harnessing autonomous agents in customer experience- Dec 10, 2024

The video discusses the transformative impact of autonomous agents in customer experience (CX) technology, highlighting their ability to deliver hyper-personalized interactions and enhance productivity. Sam Nelson, the VP of CX at Telarus, shares insights on how these intelligent systems can handle a significant volume of customer inquiries, thereby increasing efficiency in contact centers. Examples like Roomba and Netflix illustrate different types of autonomous agents, showcasing their learning capabilities and adaptability. While AI can automate routine tasks, the importance of human empathy and creativity in sensitive situations is emphasized. The session concludes with a focus on the accessibility of AI solutions for small businesses and the necessity of data cleanliness for successful implementation.

Transcript is auto-generated.

Introduction to Autonomous Agents in Customer Experience

Today’s high intensity tech training. It’s a look at the cutting edge world of autonomous agents in CX technology.

These intelligent systems are revolutionizing customer interactions, delivering hyper personalized experiences, and supercharging revenue streams.

Your clients simply cannot afford to ignore these strategies, and today is your opportunity to gain the insights needed to be able to help them. We’re joined today by Telarus VP of CX and host of the wildly popular minute snippet feature available on LinkedIn and wherever you get your snippets.

Sam, welcome back to the Tuesday call. How are you doing?

I’m good. How are you doing, Doug?

I’m doing well. Thanks.

Great to have you here.

Great to be here as always.

Right on. So let’s go ahead and dive into it. Let me just, first off, share what I’ve got going on, and we will get things started. Okay. Cool. So let’s make sure, though, that the technology is in fact working.

Slide slide slide.

Hold on one second.

Boom. Boom. Boom.

Okay. Alright. So, first things first. Doug, can you see my screen with the agents of change title on here?

I just wanna make sure everything works.

Absolutely. It looks terrific.

Amazing. Thank you so much. So, as Doug had mentioned, today, we’re gonna talk, about autonomous agents, officially called agents of change, really how autonomous agents are revolutionizing c x. So I’m so glad that you’re all here with me today, to join this this session. This is gonna be pretty interesting, and I am looking at the chat.

So if you have anything pop up, anything come to mind, do let me know, and, we will go ahead and hop into it. So first things first. Okay?

Understanding Autonomous Agents

Let’s look at these primary outcomes sought in adopting AI to accelerate the business. Okay? So I’m gonna do kind of like the weather person thing here. So I’m gonna step aside.

But when we look at just the primary outcomes, again, stop by, you know, these businesses, the primary reason is actually, well, productivity.

Right? And when you look at just some of these other different outcomes, you’ll notice that headcount reduction, which is typically, you know, something that humans are very worried about. Right? Like, oh, robots are going to take my job, things like that.

That’s actually not a primary. It’s actually one of the, least common outcomes that companies are looking to solve for. It’s not really about headcount reduction right now. It’s really more about helping everybody become more and more productive.

Okay?

And then next, of course, comes, like, improved CX. You’ve got the automation, the innovation. Right? Like, just the ability to say, oh, we we use AI internally, at our business. Right? Modernization, cost reduction, all that good stuff.

So what I wanna focus on really is up here in the productivity and improved customer experience components, of the businesses. So let’s go right into it. Right? K.

Types of Autonomous Agents

What exactly is an autonomous agent? And it’s not what you think. So when you think of agent, sometimes a lot of us think of, well, it’s people. Right?

Agents are usually people. They they’re sitting in some kind of location. Maybe they’re answering phones or chats or what have you.

Well, now you have to consider in the context of artificial intelligence that the term agent is actually something completely different. And what makes it autonomous is is really this. Right? It’s this as it says, the sophisticated AI driven system that ultimately performs tasks, but it does so independently.

Right? And there are so many different ways that it that it does this. So think of, again, it’s a system. Right?

Not necessarily a human, but it’s using these different advanced algorithms, to learn from data and then it improves its actions, its knowledge over time. Alright? So, again, when you think of the word agent, don’t think of it as a person. It’s a system, right, that can augment a particular process.

K? Now let me give you some examples. Alright? There are many different kinds of agents.

So just, again, to harp on the fact that it’s not necessarily humans. So I have one of these. Again, we’re gonna go into story time a little bit. Just a little more memorable that way.

But I have one of these. It’s the Roomba. So this is one we call a gold based agent. Right?

I turn it on, and it’s responsible for sweeping, vacuuming the floor. I know there are other mops out there that do other things, like, also the vacuum and mop at the same time.

But the general kind of Roomba that we’ve seen over the last, you know, few years or so I mean, I think it’s a decade now. Right?

But what it’s able to do is actually combine things like foresight and strategic planning.

Right? Funny. You think a robot this little robot vacuum does all these things, but it does. Foresight or strategic planning to actually navigate towards specific outcomes that you tell it.

So in, like, the room app, for example, I can say, okay. Do a survey of my downstairs, and it will literally survey all of the vacuuming areas around the dog beds, around the couches, the tables, etcetera. And it says, okay. Here’s the area.

What do you want me to do? And if I say vacuum all of it, it goes ahead and works its way around, vacuums everything, returns to home base, and empties out all the stuff. Right?

So that’s goal based agents. I give it a goal and it executes. Alright? Another one is called model based reflex agents. Right? So, this is really neat. I know our CEO is a big fan of this company as well.

But, basically, what it does is it’s making decisions about potential unseen parts of the environment. In fact, it’s smart enough to actually anticipate future conditions. And so one example of this is, Waymo. Right? These are the self driving vehicles that you get into, and it drives you around just as if it were a a normal person.

The other day, I drove into San Francisco for an event. I saw a big pot of these, coming down one of the main streets of San Francisco, and then they all it was kinda creepy, but they all kind of just dispersed from a pod.

Now I thought, okay. That’s a little odd. And I drove up to one, and I looked inside, and there were no humans in it. Right?

You could just imagine my surprise. But what’s even more interesting is the interruption that happened afterwards. I was behind one of these guys. Right?

And, actually, before I even looked inside it, and an ambulance was coming up behind me.

And I’m thinking San Francisco, really crowded. How is this gonna work? I hope the person in front of me, this Waymo, moves. Right?

And, sure enough, it turned on its signal to move off to the right. It moved off to the right in an open spot. I did as well. The ambulance went by.

As soon as it went by, the car in front of me, turns on the blinker to go left, and it comes back onto the road and proceeds. And then that’s that moment when I drove up to it and looked and thought, holy cow, there is no human in there. Right? So it is now smart enough not only, to detect sort of what routes to take based on, say, traffic, but it’s, smart enough to know how to react in, again, these unseen parts of the environment.

What if x y z happens? What do I do? And it’s smart enough to make those decisions. K?

So that’s a model based reflex kind of agent.

The Role of Learning Agents

Another one that we, probably interact with every day is the learning agents. Right? And these are ones that can it says here, like, adapt, improve over time based on your experience, and it essentially kind of evolves its own behaviors and strategies. So I guarantee that like, let’s take this example.

If I were to log in to your Netflix account and you were to log in to my Netflix account, they would look very, very different because, we have different preferences, and an application such as a Netflix is going to constantly change, what the recommendations are based on your likes. Right? And so, when we could we take that into account, these particular agents, really, it’s the platform that’s learning and then providing you with the content that you may may not potentially like. Right?

So another one. K. Virtual assistants, virtual agents.

This is one that we, again, are most commonly involved with. So when we look at, like, using us using Siri, you know, you’re on your Android and whatever that one is. Right?

Or any, let’s say, advanced voice mode, for example, as well with chat GPT.

And, Frank, I do see your note in there. We will talk about contact center examples in a moment.

But, yes, virtual assistants, virtual agents, you can now simply talk into the phone. Right? And it will go ahead and move you on to, you know, whatever help you out with whatever you need there, with regard to just using your voice and it completing actions based on that. The unique part of this, you’re thinking, okay.

Well, if I just say, hey. This do this. It’ll do that. Really easy. Right? Wrong.

It’s actually taking to account how you say things, and it’s further building on what you say. Right? So not only do you want an action, but it’s going to start trying to figure out what else you want to do after that action. In other words, predictive, AI as well.

Right?

So, technically, how does this work? Okay. I’ve covered this with you before, but let’s just revisit this that these different technologies in this agent world, right, is really leveraging two branches of AI. One, it’s machine learning.

Two, it’s NLP or natural language processing. So what do these things mean? Well, the machine learning component is basically just ingesting volume large, large volumes of data. Right?

Whether that’s conversations, it’s, finding patterns in that data. It’s also, like I mentioned, making predictions.

But from the LLP NLP perspective, it gets a little more granular, right, where it’s actually taking, like, these mathematical relationships between words and phrases. Think of, like, beautiful mind stuff. Right? You’re just drawing lines from different words and code and what have you.

Right? But the reason for all that is because it’s actually trying to detect the how or the why. Right? Your intent, your sentiment.

If I call into a contact center and I say to the IBA, to the virtual agent, I say, I just want to pay my bill versus I just wanna pay my bill.

Right?

I said the same exact thing, but I said it completely differently. And so, for example, in a here you go, Frank. A contact center example, the person, first version of me, might just route me to the payment system. Right? No problem.

Automatic automated payment system. The second version of me may actually route to a customer service agent who specializes in retention because they’re you know, the system sense that there was some kind of strain or maybe some kind of negative sentiment there. And they want people to thanks, Scott. They want people to work on me from a loyalty perspective because maybe Sam’s not happy.

She’s not happy with the service, and we wanna make sure that she is happy. So providing more of that white glove service. Right? So just another example there.

Use Cases for Autonomous Agents

Okay. Let’s head into the use case. Right? And so and good point, Asim, NLU, NLP, natural language understanding, processing, you name it, lots of different flavors out there. But for the sake of this, we’re gonna keep it super simple. So use case number one, NCX, right, is overall enhancing your customer interactions.

So in the term of AA here, we’re we’re referencing autonomous agents.

Believe it or not, it’s actually transforming customer service efficiency. No surprise there. We talked about productivity. Right?

Efficiency does tie into that quite a bit, because these autonomous agents can actually handle, now up to an average of eighty percent of inquiries. And that’s taken across the board, you know, across several companies today. But here’s exactly how they’re doing it. Right?

Let me dive into this.

When we look at response times, how long does it take a human to get back to someone versus, an autonomous agent, right, that can also answer the question.

So let’s say a company has a knowledge base full of how to documents. Right? An autonomous agent can leverage that particular knowledge base of information, and provide those answers directly to the customer really, really quickly.

When we look at personalized experiences, maybe my particular profile, calling into a business or interacting with a business is going to look very different than if you were to talk to a business, whether that’s a customer service line or even the sales line. Right? Example here.

Let’s say I am inquiring about a T shirt that I really liked, and what it’s going to do is, or a T shirt that I ordered, it’s going to do is going to, recommend maybe a pair of shoes that go well with that shirt, but that’s also based on the different kinds of shoes that I purchased in the past and what colors of those shoes I also preferred.

Right? So you could see just how detailed and how targeted so many businesses can get around personalizing your particular experience. Again, just like our Netflixes and how they’re different, mine’s going to look very different than yours.

Another one is around proactive support. Okay? So think of a company, let’s say a bank. Let’s say they don’t have autonomous agents, whatsoever.

Right? When we think about proactive support, versus reactive. Right? So, say, for example, I call in and I’m calling in about fraud on my account and I work through the IVR, work through the menu, and finally, I get to a person. Right?

But, like, proactive might be something like someone calls me. Hey, Sam. I noticed this fraud on your account.

You know, what do you wanna do about this? Was this you? Right? We get that via text. We get that via email. We can get it via phone call, whatever my preference is.

And, also, like, if you think about it, we want our stuff handled.

In other words, when I call in about a balance inquiry or I interact with my, banking app about a balance inquiry, no problem. Right? But as a consumer, if there’s fraud on my account, like, I want someone to handle that, and I want someone to handle that, like, yesterday.

Right? Like, I want those funds readily readily available again. I want a confirmation number, all those things. And so, essentially, what autonomous agents can do as well, is really free up a lot of those humans, like, actual humans, to deal with more complex issues that are going to lead to overall increased customer satisfaction.

Right? So just one example. K.

Another example. So we talked about personalization, but this is a much bigger use case than I think a lot of people realize.

And so when we look at how autonomous agents are using data, k? This is really, really important. There’s so much data out there. And so what it’s doing is it’s able to not only analyze but apply different customizations to a particular client based on those clients’ behaviors as well as preferences.

Right?

And how is it doing that? Well, just talked about that, the underlying technology, ML, n l u n l p. Right? But also the amount of data. There’s a lot a lot of data out there. In fact, we’re gonna talk about some challenges, in this session around, autonomous agents and implementing those in companies today.

But when we look at customization, personalization, it’s not just about support.

It’s also about sale. Right? So, again, back to my t shirt example. I order a t shirt. I’m gonna start getting some notifications about pants or maybe shoes that match the t shirt or that go with the look. Right? And so, from a lead gen standpoint, autonomous agents can actually help from, a data consolidation standpoint, but also turning all of that into a consumable and preferred communication strategy for individual customers.

Think about that for a moment. I know I just said a lot in this one sentence. Right? Promise it’s not a run on sentence.

But it’s taking all of the data, not just about me, but also data from other people like me, and it’s gonna start testing me with all of those different suggestions.

Right? So it’s a lot bigger picture, bigger story than what we see on the surface as consumer. Right?

So there’s that use case.

K. Third use case, we talked about productivity being the number one, goal in implementing AI. Right? So as we as we predict, autonomous agents, what they do is they automate all of those routine interactions.

Enhancing Productivity with Autonomous Agents

In fact, they can do it twenty four seven. They don’t need breaks. There are no really, say, compliance, issues around, you know, taking, lunches, time off, PTO, what have you. Yeah.

There’s no breaks or overtime pay. Right? So, let’s say, you know, this stat is an interesting stat I pulled, but, like, the daily increase in contact center agent productivity, is one point two hours, and that’s very, very significant. Again, helping make these eight human people, more productive.

Right? And so the productive, productivity goes through the roof with autonomous agents.

Use case number four, scalability.

Okay. So we think, alright. We’re talking to a person. That’s a one to one conversation.

Right? We’re talking to a human one to one. But if we’re thinking about autonomous agents, they can handle thousand. Right?

Thousands of simultaneous interactions at any given time. So for the company, it actually helps them maintain really high service levels or SLAs. Right?

How do they do this? Well, there’s there’s no limitation to it. Right? It’s it’s all tech.

And so there’s this thirty percent overall reduction, in need for human resources during busy periods. And good point, Wes. The one point two hours of increased productivity, that is that is on a daily basis daily basis. Thank you.

I’ll let that one out. But, yeah, when we look at the reduction in need for human resources during those busy periods, from a scheduling perspective, it definitely helps with that component, right, where autonomous agents can take on a lot of, that stuff. And so feel like this is great. AI is awesome.

Right? All of my customers need autonomous agents or they need some form of agent or AI or what have you.

Identifying Challenges in AI Implementation

With something as big as AI right now, there are several, several challenges to consider.

And so as you have the AI discussion with customers, this is really where the rubber meets the road, right, is you have to look at potentially what roadblocks you’re going to see. And the value here that I’m going to share what we are seeing the most when it comes to implementing something like artificial intelligence in whatever capacity. Right? But AI into businesses these days.

The number one challenge that we are seeing in almost every single almost every single opportunity is the data.

K?

So we come across a lot of data silos, and this occurs especially in larger organizations when you’ve got, multiple departments using multiple systems. Right? Whether that was due to an acquisition or, you know, they just kind of do the shadow IT thing where one department says, oh, we’re using this. By the way, we’re using this.

Surprise. Right? And keeping that data separate is is really easy to happen. Now, the other thing is data cleanliness.

A lot of the data out there floating around can be extremely inaccurate, and we see that pretty much in every business. Right? Like, not everybody’s phone number is correct or email or maybe there’s multiple profiles for people. Right? And so, like, what do you do? How do you kind of approach this conversation with customers?

And you have to start with this conversation of, hey. Like, what does your data hygiene look like? Right?

And you have to talk about, like, potential data integration tools that might be required, because the AI, at the end of the day, is only good as the data that you give it. K? Again, AI is only good as the data that you give it. And so if the data is not clean, you won’t get clean AI results, right, or usage.

So the data integration component is key, when we look at making sure that all of that data is talking to each other and also accurate in all the systems.

Another one, setting up really strong data governance policy. Okay? So this is where we talk about, oh, like, you know, how and what kind of data are people putting in, even all the way down to, like, the nomenclature of the data. Right?

Things of that nature. And so what I would encourage you to do, if you’re not too familiar with the data conversation, is you have to have to have to talk to our cloud and our cybersecurity guys. You gotta talk to Kobe Phillips. You gotta talk to Jason Stein, as well as our supporting solution architects as well as solution engineers, because these are the ones who are going to have the conversation around the data.

K?

The Importance of Human Touch in AI

Alright. Challenge number two, maintaining the human touch.

The reality is humans are not going away. They are not going away.

When we look at the, empathy, the creativity that we have as humans, and then we can still handle, like, complex skills. Right? Things like negotiations, sensitive complaints, emotionally charged situations.

So, for example, on the weekends, because, I guess, work is not enough. No. I’m joking.

On the weekends, since COVID, actually, I am a volunteer for nine eight eight, which is the official suicide and crisis hotline.

And every time I take a call, literally in my mind, at the end of that call, I think, thank goodness a robot or artificial intelligence did not take that call because there are so many situations in where you have to be able to pivot when you’re trying to talk somebody off a ledge. Right? You have to.

And just having that empathy from a human standpoint, is really, really important. So, just know, I’ve been there. I’m in that contact center every weekend for four hours.

But, it’s already an emotionally charged situation just being on that line, not just, of course, someone calling in, but from, a human agent perspective, and that, yes, we can benefit from AI making suggestions on what to say next, but the reality is, it will never know, that, you know, it it will never be able to to say, the right things to the right people and understand personality within the first two to four minutes where, you have the impact to change someone’s life. Right? So that’s challenge number two.

Looking Ahead: The Future of AI in Business

To kinda sum everything up, the future is bright, and I had to say that future is bright and autonomous. Right? It’s a really, really exciting opportunity. So kind of what do you do next?

Right? What I’d recommend is, like, get to know AI. Get to know exactly how AI fits though in across several suppliers. So, just about every supplier we have in the portfolio has a different flavor of artificial intelligence.

And, yes, like, we can certainly help with figuring out what path to take based on the conversations that you have with customers.

But just your own general knowledge, like, just get to know, okay. They do this flavor of AI or that. Understand the use cases so that you can educate clients, in number two, just about how they can leverage this concept of autonomous agents, to impact to enhance just the overall stack.

And then last but not least, like, leverage this AI adoption road map that we’ve built to start your conversation. So probably, like, how do I get there?

Here is the QR code. But if you don’t snap it ahead of time or or you’re maybe you’re listening from your mobile, no big deal. We will send you the resource immediately afterwards, and that way you can get it in your hands.

And then, last but certainly not least, we’ve got all of our supporting stuff in Telarus University. So if you haven’t taken our CX track, our AI track, start there, and we’ll actually be releasing a lot more content in the new year. So look forward to, starting that up. And if you don’t have access to it, just head over, to your email, email admin at if you have any issues there, and let’s get you in.

And that way, you can get started on on selling more CX as well as having that AI conversation with your customers.

So that’s all I’ve got, Doug. I know we’re gonna get loaded with questions. I can already see so many popping in, but hopefully that was helpful.

Sam Nelson is here, everybody’s favorite, today discussing autonomous agents and how they’re revolutionizing CX and pretty soon most of the other industries as well. We do have a lot of questions that have come in. You’ve taken a few of the, the main ones so far. Let let’s go back and talk about data for just a second.

And and this is my own question I wanna throw in before we handle the, the other ones that are there. I’m really concerned about data because internally and at every company, we focus on the cleanliness of data, the the accuracy of data. And I always worry about if there are data which enter into the system, which are incorrect or bad or in some other way negative, and they persist either because they weren’t caught, they weren’t cleansed, whatever it may be. How does a company go about then trying to reverse engineer those out and restore the database to what it needs to be?

Seems to be a huge concern.

It is a huge concern because if you think of, like, just humans in general, how much hours we have in the day, and imagine having to go in to the datasets and modify that manually.

That’s just a real pain. Right? And there are I mean, that millions of probably billions of components of data out there, how does a company I think it’s what you’re digging on. How does a company kind of deal with the fact that, yeah, data hygiene may not be the best?

So when we look at autonomous agents, there are, AI capabilities out there that will literally help kind of scrape and clean up that data, and that’s where we go into more of, like, the cloud infrastructure technology, even, like, the cybersecurity technology of creating those parameters around what can and cannot be done and what data is protected versus what is not. When we look at data cleanliness, you have to look at not just the accuracy, but also how it’s protected, how it’s transported, all of those good things in addition to how it’s actually input. So the good news, Doug, is that, there are solutions out there that will actually help with the data cleanliness component, whether that’s going back into it and fixing what was already done, or and or figuring out what the process is moving forward.

It’s It’s a good thing because regardless of what Netflix says, I did not watch those twenty seven Hallmark Christmas movies. I didn’t.

No. But you need to be honest. Has a great question that I wanted to bring up. He makes a great point here. Often when we talk about AI, we get concerned about replacing people, and you did a great job of addressing that. He, makes the, the statement here that it’s often about turning three people into four, and I loved that analogy in terms of maximizing, efficiency and scalability and so forth. He asked a great question, though.

AI Opportunities for Small Businesses

What sort of advantages are now available and will soon be available to more of the small business, the SMB customers, in really meaningful ways? How can they best use it now and in the future? Many of our, partners and advisers are working with those clients.

Yes. It’s a great point. And I get this question all the time. It’s like, hey.

I work with a very small business. How can they leverage something like artificial intelligence, just generally? Right? Because a lot of, I think, the AI solutions that we’ve seen mainstream are really for enterprises.

The good news is that this is a this question is growing in popularity.

And so that’s actually why I encourage, all of everyone here, TAs, everybody, to look into exactly how each supplier applies AI into their solutions.

Because even a lot of those players who participate in SMB conversations and opportunities, now have AI components that you may or may not know about. Right? So that’s step one. Step two, however, I’m probably doing an overshare here.

I guess this is being recorded, so here we go. But we are, here at Solaris, looking more and more into, how to, give customers paths safe paths to leveraging large language models. Right? And not just for enterprises, but especially for SMB, ones that are looking to establish baselines, in within the industry to figure out how they grow.

Right? All that data is out there, how it’s being done. SMBs can now leverage that data to improve their own processes to help establish baselines there to be more successful and grow. So there are quite a few things, but long story short, start with each supplier, that you work with, figure out, you know, what AI components they have, and then leverage those for immediate return.

Integrating ETL Tools in Data Processes

Tom Troiano’s got a great question on here about ETL tools. These are the extract, transform, load tools that automate data movement, to various destinations.

He’s asking, would you would you tend to include those tools as part of a total data integration process, and what sort of questions should we be asking around that?

Yeah. I think the question well, okay. Let me start with, the first answer, which is I think I think you would have to, but it also depends on, like, the scope of the project. Right?

The size of the project. Is it an easy lift, or is it, like, are we talking enterprise where this is something that they’re used to using or, you know, they have to use? But it’s part of the bigger scope of the project, right, where, you absolutely have to say, okay. Here’s the end goal, and here’s exactly how you’re going to leverage something like AI here, and then work your way backwards.

And more likely than not, you’re gonna find out, we gotta start with the data before we can get there. Right? So the first question really is, around data cleanliness.

Right? Are you managing your data today? Things of that nature to get to that conversation and then incorporating the tools you need to get to the end goal. Right? So you’re, like, going backwards in order to go forwards a little bit.

One more question.

Les McKenzie, is asking if there is a way to, use a white labeled AI and package that with other services for our clients. What sort of resources do we have to try and build those together?

Yeah.

So we’ve got a lot of a lot of resources. Right?

In terms of white labeling AI, I mean, we have to kind of look at, what the use case is, and I think that we start to jump to a lot of capabilities, and functionality before we figure out what the end goal is. So if you are in fact looking, you know, creating an option to white label AI to package with your particular services, most likely possible. Right? We have to look on a supplier basis, supplier by supplier basis. But, again, it you have to start with the end goal and then figure out, okay, working our way backwards, you know, how do the mechanics work? Logistically, how does this work out on paper?

White Labeling AI Solutions

And we can certainly take that one offline too.

That that’s a great question. Really enjoyed it. We are out of time. I gotta run. I wish we had another hour, but last word to you, Sam.

No. Thank you. I appreciate the time, everyone. I’m seeing a lot great questions in here. I’ll respond here and there. Feel free to hit me up, but, you will absolutely get some stuff from me. AI readiness, don’t forget.

And as always, me and my team are here to help with any CX and AI opportunities. I’m really looking forward to working with everyone.

Phenomenal presentation. Always interesting. Always a great time. Sam Nelson, thanks so much for today’s HIT training.