In today’s high-intensity tech training, the focus is on the challenges of AI adoption, particularly the high failure rate of generative AI pilots due to execution breakdowns and misaligned strategies. Sam Nelson, along with presenters Meagan Thai and Mikey B, introduces the AI readiness assessment, a tool designed to evaluate client preparedness for AI implementation by examining strategic alignment and data infrastructure. The discussion emphasizes the importance of outcome-based selling, where features must be tied to measurable business outcomes rather than just technology. A new monthly event, AI Power Hour, will facilitate further engagement on AI solutions, highlighting the need for organizations to start conversations about their AI readiness. Ultimately, the goal is to help businesses leverage AI effectively to solve real problems and improve ROI.
Transcript is auto-generated.
Well, today’s high intensity tech training begins now. Everyone is spending a lot of time on generative AI, but only a few enterprises are providing ROI. Most AI pilots fail, and it’s not because the technology doesn’t work, but it’s because execution breaks down. It’s what we call the Gen AI divide. It’s that growing gap between the companies who are experimenting with AI and those who are scaling it into measurable outcomes. Today, we learn about moving from AI features to outcomes and how to make AI adoption stick, building platforms, not pilots, and having and aligning AI initiatives with governments governance compliance and, of course, with security.
As always, your comments and questions are welcome in the chat window to which today’s presenters will respond both during and after today’s event. Today, we’re happy to welcome back to the Tuesday call Teleris VP of CX, Sam Nelson, along with director of CC at UC Solutions, Meagan Thai. She says AI is in my name and solutions architect for UC and CC, Mikey b, Mike Baillargeon, who also says, Meagan. It’s also in my name. For those of us whose name it is not, welcome Sam and all. Today’s topic is not the great divide that I learned about in school all those years ago. This is something completely How you doing?
We are great, Doug. We are so excited to be here.
We’ve been on the the line for, like, the last fifteen minutes or so, so we’ve just been, like, trying the who shows up two minutes before.
What?
No. We definitely beat you to it. But, like, as Mikey b said in the chat, they call those themselves AI nerds, but quite honestly, they’re the best of the best. So and they do you’re right.
They do have AI in their names. So Yes. Zachary, I I may go to city hall and change my name to Sam AI and the I like that. I like that a lot.
So many of I love it.
I love it. Okay. Alright. So let’s go ahead and kick things off.
So if you wanna pin me to the webinar perfect. Alright. So everyone can see my screen here. I’ve got my light board up. We’re ready to rock.
A few housekeeping items, though. As I’ve mentioned, keep the chat rolling. I do have my eyes on the chat. Yes.
I am stalking all of that. But even more importantly, Mikey b and Meagan are there to answer any and all questions, but also love to stir it up. So as we are going through this, session here, please feel free to stir it up. So today, as Doug mentioned, we’re talking about is closing this GenAI divide.
What is it? What is it all about?
How do you turn AI projects into, like, actual business outcomes? Now y’all know that I’ve been talking about outcomes for a very, very long time. It’s like for the last, I don’t know, maybe three to four times even on the Tuesday call, it’s all about outcomes. So let’s talk about this Gen AI divide thing.
But before I do, y’all know I love to know who’s in the meeting. So how familiar are you with changing it up, Selling artificial intelligence or AI. One, not familiar at all. Two, a little.
I wanna learn more.
Three, I’m an AI selling rock star.
Alright. I will take one point ones, two point twos, two point sevens. You know?
We can mix it up a little bit just like they do in the Olympics. There you go.
K. Two point five, one point five. I love it.
Alright. So I’m digging it. I love the the participation in the chat, and that’s exactly what we want, folks, is we are gonna keep it high intensity as it says. So really great to see that there’s a whole mix of you in here.
Alright? So today, like I said, GenAI divide. There’s also gonna be going to be a lot more resources coming out around this, like an ebook Spoiler alert. Okay?
This is just a little teaser for you. But if you have been watching my minute snippets on LinkedIn, you’ll kinda know what I’m gonna cover today. Alright? Just a preview.
So today’s agenda. Okay. Number one. What Is it? What is the Gen AI divide? And why is it such a struggle?
We’re gonna talk about a brand new AI readiness assessment. We’re gonna go into defining the outcomes, just a quick reminder as far as why it’s so, so, so important. And then we’re gonna talk about the bottom line, and last but not least, call to action. Alright.
Excellent. Yes. And I try to keep this pretty high-tech. No drawing today because that ShamWow moment takes way too long, y’all. Alright. Story time.
You know, I love story time. Actually, Chandler, who helps kind of work all this together, he’s the real rock star here in marketing. He actually recommended that we have, like, rugs to, like, you know, like, in kindergarten to pull out on the floor and everyone sits on one because it is story time. You know, I love to to do this.
So Story time. Check this out. Ninety five percent of generative AI pilots are companies are failing to to deliver measurable r I or ROI. Right?
And that actually comes from MIT. Alright? So granted, or they’re failing to deliver it. And so why?
There’s a few different reasons. Right? And it’s messy data. It’s no strategic alignment outcomes, and it’s poor implementation integration.
It’s quite a few things.
And so when we look at what exactly the Gen AI divide is, this is really, really important to understand, folks, is that everybody is doing it, but few of them can actually prove that it works.
Okay? Few of them actually prove that it works. And when we talk about a divide, there’s actually two different sides. Alright?
So on one side, there’s this experimentation without impact. In other words, to give you an example, you hook up a few people with maybe a GPT application, and you have them test it out a little bit, but you’re never actually tying that to how effective those tools are making those people or how productive it’s making them. You’re not actually tying them to any defined outcomes. Alright?
That’s one side. The other side is actually called execution without scale.
Alright? So what this means is, yes, you hook up maybe a department with particular AI tools, but it’s not fully integrated. It’s not integrated across departments. It’s not scaled out, and it doesn’t follow any particular workflow or framework, and then it just starts to break down.
Like, think about it this way. If, for example, let’s just let’s let’s be really biased here and talk about, like, something in CX. Okay? Stay with me for a minute.
So if I am an let’s say, a call center agent and I am handling the call and I’m leveraging AI to You know, create a a call summary afterwards. But that call summary, let’s say that call summary doesn’t go anywhere. It just, like, is there, and it it’s sitting there. And that’s cool, but it’s not actually connected to any CRMs.
People don’t have act certain people in the organization don’t have access to it.
Like, what’s the point? Right? It’s cool, But is the execution there to make it effective? You get what I’m saying? So this is the Gen AI divide.
Alright.
Okay. It looks like we’re having maybe some issues with the pinning me. So, yeah, it’s it’s all Doug’s fault. That’s what I I agree, Bernadette.
There you go. Alright. So why is there this struggle? Let’s dive into this. Okay? So number one is a misaligned strategy.
So we when we look at AI, there’s a lot of cool stuff out there. Really, really cool features. There are things that can generate emails for us. It can change the tone.
It can change language. It can You know, create PowerPoints. It can create graphics now and even videos that look super real. In fact, any of you on social media, you know, especially things like Instagram and TikTok where you see those reels and you see the little Sora watermark, Right?
Those Sora videos are now looking really real. There’s one, I think, famous one where there’s like a kid on the porch and there’s like an alligator right next to him and kids feed in the alligator candy. Like, look, mom. And the mom’s like, get in here.
Right? And so, there’s a lot of cool features. Right? But what we’re failing to do is is p The actual outcome that it solves for in a Business.
In a business. Right? So, like, the video one, for example, is actually pretty simple. Look.
If I don’t have the time or resources to create videos, but I have, like, screenshots of me, you know, I could go ahead and send it to the tool or Sora and have Sora create a video on my next Snippets. Right? Potential. I don’t do that, but potential.
Right? That’s an outcome.
Or also, let’s say in a quality assurance, quality management situation, right, where I’m implementing that technology to, you know, you know, ingest all the interactions and analyze them and surface the right insights for me. As a sales leader, I could use that to identify things like competitors, what are my salespeople saying, not saying, and the outcome essentially at the end of the day is revenue growth. Right? So misaligned strategy, that’s a huge, huge struggle these days.
Low adoption as well. I talked about this a little bit just, like, three minutes ago, four minutes ago. Resistance from broken workflows. Because when people see that, oh, it’s not actually helping me, they’re like, well, why would I use this?
Right? Or, like, maybe there’s something better out there that they like. Right? And it’s a change management problem.
Yeah. They’re gonna go ahead and use that instead. Right? Don’t lie. We’ve all been there.
Alright. Then the last thing is fragmented platforms. No integrations, too many point solutions. Look. I’m not saying point solutions are bad.
Point solutions can be great, but they’re not effective if they’re not integrated the right way. So at the end of the day, you gotta back up. You gotta talk to the business leaders and say, hey. What it what exactly are you looking to accomplish here in the next twelve to eighteen months?
And then let’s talk about how AI can get you there.
To get you there, I’m excited to talk about this AI readiness assessment. Don’t worry. We’re gonna send it out to you afterwards. But if you wanna take a screenshot or scan this QR code, there it is.
We do work in CX. Jason Stein would never approve of this cybersecurity, but your your CX folk like me, we are scanning away. So go ahead and scan away. That is the link to the document, but I’m gonna review it in just a second here.
What in the world is an AI readiness assessment, Sam? Okay. This is different than what you’ve seen from us before.
We have a checklist. We have a road map. Now the AR readiness assessment, exactly what it says. It’s it determines just how ready for artificial intelligence your client is. But most importantly, it covers five very key areas that are absolutely crucial to having that artificial intelligence conversation. So everything from strategic alignment where you’re talking about the overall business goals, is everyone at the leadership level on the same page or not, and that’s okay. Right.
Data and infrastructure. How is the data organized?
Is it in fact ready? Is it clean? Is it, you know, garbage in is garbage out? Right?
Things like that. And so oh, and again, don’t worry. We’re gonna send it to you all afterwards.
On this will, you know, participated. We’ll send it to you. Don’t worry. Don’t miss the QR code. Don’t worry about it.
But, yeah, when we talk about the data conversation, it’s super important. Like, how are you leveraging the data? Is it connected? Right?
There’s a lot of data out there that needs sorting before you can even start having that AI conversation. But, also, there’s AI that can help with things like sorting and cleaning data and things like that. Right? So every single one of these pieces is a great segue into the AI conversation.
Business impact. We talked about outcomes. What value are you actually looking to get out of artificial intelligence? And guess what?
Maybe they don’t know. Right? Maybe you have to teach them. And there’s processes, governance. Don’t forget about the security piece, y’all.
Like, Stein would be all over me for this for not covering this, but really, really important is that when you think about all this data running around and the AI tools available out there, how is the company protecting all of that information, And what are what do the processes look like? Are people taking screenshots of of sensitive information, willy nilly posting it in chat GPT and asking for recommendations.
Y’all, it is happening, and it’s happening in finance. I hate to tell you that, but I’ve seen it. People taking screenshots of customers’ portfolios and putting it in chat GPT and saying, hey. What should I recommend as a financial adviser to do next?
Right? Now we’re not saying that’s a bad thing, but what we are saying is it’s not secure. But there is a secure way to do things like that. And the last piece is people and skills.
Do you have the right talent in place? Do you have training programs? Because guess what? AI is also only as good as the people who are able to use it and execute on its capabilities.
Right? If they’re not using the tools, they don’t know how, or they, refuse to do so, right, then you’ve got a people and skills problem as well. So once the assessment is done, there’s two questions to each one of these in the assessment. It’s very short.
Right? But each answer actually has a score to it. And what you use that score for is exactly this. Right?
So as you could see, the scoring here. It’s this. It’s a scoring framework. So let me step out of the way.
I’m gonna be the weather person again. Alright. It’s Doug’s favorite moment. So there’s a scoring rubric.
Alright? So score is zero to ten, eleven to twenty, etcetera. Like, you get what I’m saying here. Right?
So based on how your client scores, it tells you where they sit as far as AI readiness. But even more importantly, it tells you what your focus area should be in working with that client to get them to the next step. So for example, okay, let’s start with the folks in the zero to one section here, where they don’t have a strategy, the infrastructure is super minimal, maybe they’re just, like, kinda dabbling with things. They’ve used ChatGPT a few times.
They know AI is out there.
And in this realm, typically what you as an adviser need to do is you need to focus these people on a few things. One, you gotta educate them. Educate them on, like, what are the AI capabilities? What are the AI use cases that are most relevant to their businesses? It’s getting that executive buy in. It’s, again, data foundations.
What’s the deal with the data? Is it ready to go? Are you actually ready to leverage AI to use with the data that you have? Alright?
And then going into, like, the next one, you could see there’s a progression, right, based on where they land. Now let’s say they are, like, a super optimized AI enabled organization. By the way, I have, like, yet to see this maybe in, a couple enterprises, but still, like, they have the struggles, believe me, where, like, AI is embedded into a lot of workflows. They’re, like, measuring ROI across a lot of things.
Like, what do you do with someone like that? What you can do is go to them with things like continuous improvement. What are some other things that we can do? How do you further scale this in the business?
We talk about advanced use cases, and that’s, Gosh. Anywhere in here is where you can really bring us in to help you have the conversations. But the purpose of having that type of assessment at your fingertips is really, really helpful because what your next step today, Well, right now, if you want to, should be, hey. Reaching out to all the clients you know and saying, hey.
I’ve got an AI readiness assessment that we need to have you complete. We need to have a conversation about this. It’s crucial. You gotta have AI within your technology stack.
Here’s why. Let’s get you started with the conversation.
K?
Alright. So beyond that Beyond that.
Coming soon. The AI pitch deck. Alright? So this is something that I’ve been getting a lot of requests for, and it’s something that we absolutely want to arm you with.
So in addition to an a AI readiness assessment, we are coming out with the pitch deck to enable all of you to be able to have that conversation and position yourselves as AI advisors. Alright? It’s the next phase, folks. I am so, so, so pumped for this stuff.
It’s unbelievable.
And so we’re gonna be able to arm you with things like, gosh, different use case slides, like conversational AI and health care and finance.
Right? Identifying your business priorities with your customers. Next steps, right, into an AI readiness assessment, kind of state of the union stuff. And it’s all in neutral color, so that way you could fully brand it.
Alright? So it’s not available yet. Like, see, it says coming soon right there. So it is not here yet.
However, it is coming. So look out for that. I would say within, like, the next two to three weeks, putting myself on a hard deadline, but we’re gonna get it there. So very, very excited about the AI pitch deck accompanying those readiness assessments that we are releasing to you.
Alright? So I I am going to die on this hill of outcome based selling because I wanna go back to defining outcomes. Because, guys, the reality is if you’re pushing out the checklist, you’re pushing out the readiness assessment, the road map, you’re having a discussion on AI maybe with your own AI pitch deck, it doesn’t matter if you have not defined the outcomes desired by the business. Alright?
So going back to my last three to four hits on these Tuesday calls, outcome based selling is the way to go. It is the way to win with selling artificial intelligence. You always, always, always want to anchor measurable outcomes. Do not leave with features.
Now here’s something that came up to me the other day.
I had an adviser who said, hey, Sam. You know, I I I I tried. I tried really hard to start with the outcome, but my customer was so enthralled with a particular feature. They thought this feature was great, and they were so confident that they needed this feature.
What do I do?
Alright? And here’s what you do. You have to tie it back to the outcome. A lot of times, customers will already see the bells and whistles.
You have to tie that back to what’s important to them, And it also makes you look really, really good. So let me give you an example. Okay? This feature here that this person was actually real life that the TA was talking about was AI meeting summaries.
Alright?
And they said, oh, the the customer really loved the fact that AI handled the meeting summary. It spelled out all the different tasks for the person, and it sent out, like, an email to the end customer with the meeting notes, things like that.
It’s really cool, and now I can’t get to the outcome. How do I do that? So the feature is AI meeting summaries. Work your way backwards. The impact is that it’s auto generating and distributing meeting notes automatically.
Right? That is the impact. The outcome here is that it’s essentially saving ten plus hours a week leading fast leading to faster follow-up, leading to more deals closed, leading to increase in revenue. Right?
You see what I did there? So I was able to take the customer back from okay. Yeah. You like this bells and bell and whistle.
Right?
But how do we tie that back to exactly what you’re looking to achieve holistically? In other words, how does it tie it back to getting you paid and or promoted, the outcome. Right? See See what I did there?
So tie it back to the outcome to make it relatable, but also making you look even more like an expert. Alright? So just don’t forget to define the outcome. So let’s talk about the bottom line here.
AI success is not about Technology. Believe it or not. It’s not. Not about technology. Folks, you guys have been selling a lot of you have been selling technology for so long. Right? But the success actually lies in the performance.
Right? It’s not it’s not about, hey. Here are the flashy AI things that you can do, and that’s that’s cool. Right?
But at the end of the day, it’s about translating the promises into actual performance. What kind of an impact is it making? And then are you tracking return on investment? Right?
Again, back to that MIT that MIT study finding that ninety five percent of Gen AI pilots are failing due to lack of identifiable ROI. Right? So tie it back. So what can you do?
You can do quite a few things here to help enterprises. Right? And when I say enterprises, I mean companies. Right?
That includes a mid market. That certainly includes the SMB, but connecting the pilots to the outcomes. You’ll oftentimes come across a business who has something in play, but they’re just kind of testing it out. What you wanna do is say, cool.
Like, what are you looking to accomplish with these people trying this out? Like, what are your desired outcomes of this, like, kind of mini pilot that you kicked off. Right? And help improve that measurable ROI because if you’re able to do that, that’s where you’re bringing the true value.
Because think about it, Anybody can go and download some kind of AI thing or or turn up ChatGPT and start popping stuff in there. But if they don’t have the outcome, what’s the point? Right? Your value is driving this home right here.
And even even more importantly, helping those businesses scale that project safely.
Right, cybersecurity month, safely and effectively.
Okay? So AI is not the destination Business Outcomes are. Alright? So this is the time, folks. This is the time. Guess what?
We’re all in this together. I know it sounds super cliche, but customers, they have no idea. Right? They have no idea.
And so we get to help them pave the way in figuring out how to put AI into the technology stack and helping them grow in incorporating it safely, securely, effectively. Alright? And, yes, I do wanna point this out. I didn’t put it in my deck here, but in the chat, Heather Conaway did put a link in there.
I I do have a new monthly AI. It’s called the AI Power Hour with Sam Nelson. First one kicks off on December tenth. It is recurring.
So every second Wednesday of each month, we’re gonna do a livestream, talk about all the cool new stuff having to do with AI, how businesses are using it, and how you can also use it, to be more productive. Alright? So the call to action here, folks, engage the Teleris team. Engage us.
Right? Leverage our AI and CX solution architects, literally the ones that have AI in their names, Mikey v and Meagan Tsai. Alright? Get to know the key suppliers who can make the most impact in the customers that you have in your base today as well as the ones that you want to get after.
Alright?
Stay up to date on the latest trends and news. There’s lots of different resources out there, almost too many to name. I’m a big fan of the neuron. Lots of memes in there.
And even something like morning brew just to get you going. Right? But, yeah, AI specific, lots and lots of stuff out there. So wanna bring the team back on.
Doug, what have you got for us? We got a lot going on the chat. Honestly, I had, like, so much going on here. I didn’t get to look at the chat.
So much going on. Mikey, being Meagan Tiles. Turn turn it on. Let’s go. First of all, great presentation.
And if this whole telecom thingy doesn’t work out, I think you do have a future.
You are the best weathercaster in the business. So thanks for that.
No. I I think the theme over here, and you emphasized it so many times, was this has to be an outcome based situation. This is not about the technology. One of the best comments I saw in the chat going through this was from Tom Cross where he said, you know, this should be about solving business problems and not just creating gimmicks. And it seems like every time we’re not pursuing a legitimate outcome, that’s really all we’re doing is showing off a new toy or creating some kind of a gimmick that’s designed to get someone’s interest. You mentioned, several of the the problems that come along here with misaligned strategy, low adoption, and fragmented platforms. Let’s talk just a little bit more about how each of those affects our ability to perform outcome selling properly, why it’s too easy to get hung up on things that don’t matter.
That’s a good one. I’m gonna open it up to the team. I don’t know if you guys wanna take that one.
Well, I I think I love that Sam keeps driving this because every single day we’re getting inquiries about, you know, customers.
We we want AI, and Mikey and I stopped and were like, that’s great. We all want AI. What what are we trying to do here? Right? Like, what what do you mean you want AI? Let’s talk this through.
So I think it’s they might they’re just thinking they want AI because it’s all the buzz.
Does it really matter? Is it going to impact their their business? What are the outcomes? Right?
So we just get this request all the time, and it’s all about AI in a specific vertical. Why? What’s it for? So I think we have to just pull back the reins and talk through it and help our our TAs talk through it with our customers to say, why exactly we’re looking for AI other than it’s the latest buzz?
Right? And just dive deep into it. So you’re right. It just it may not matter.
It may not be impactful. They just know it’s AI because everyone is talking about AI and everyone’s using it, so they need to as well, which I again, not don’t get me wrong. I think it’s a good step, right, as we’re talking about technology and making changes. But, again, we have to just dive deeper with them to understand why are we talking about it.
Why does it matter? Does it matter? Right?
That’s my When we talk about misaligned strategy, I think people misinterpret that a little bit.
I I think where you’re going with this, and correct me if I’m wrong, is that the the goals of the organization and where they want to be after a certain period of time does not align with the direction that they’re pursuing on adoption of AI. It seems to me that certain companies are so concerned about getting all of their people familiar with it and using it that they don’t know why they’re doing so.
I think what’s interesting is many companies think AI is the silver bullet. It’ll fix all my ills. Everything wrong with my company can be fixed by AI. Simply not true.
You look at Karna. Right? So Karna, they’re a buy now, pay later type company, and their CEO fired all the humans, basically, in the in the company, in customer service, in dev, and everything, and said, we’re just an AI focus. After his stock tanked, six months later, guess what he did?
He hired everybody back. Right? So AI can’t fix everything. AI is not just a bunch of robots on the manufacturing floor putting things together.
It has to be human driven initiatives. And remember, AI is really for high volume and humans are really for high value.
Right? So let AI handle those monotonous task after call summaries that no one really likes to type up or or slows things down.
The upfront AI for, hey. I need to cancel my appointment. I need to change my appointment. All that is great for AI. What AI is not always great at is empathy and sympathy and complexity.
Although, I will say with AgenTic AI, which is AI that can reason beyond conversational, you can actually type in, I’d like a southern woman with a southern accent who showed great empathy. Oh, Darlene, I’m sorry you broke your arm. Let me get you that appointment right away. Right? And and this is a real thing.
Apologies to all my southern friends. As this Bostonian try the southern accent. Never goes well.
But these are things that we’re seeing more where it’s gonna sound more empathetic. But at the end of the day, you’re always gonna need that human to human contact to really get the final job done.
I I’m just over here praying that somebody sampled that and we can use that later on.
Oh, w d was all over it.
That’s right. That’s right.
Let’s talk about the low adoption rate.
This can be among individuals. This can also be among enterprises. As our advisers are speaking with their clients and they run into low adoption type situations, what are some of the best tools and resources that can be used to try to narrow down how it could be more effectively used, more widely adopted in these organizations?
You know, it’s funny. I you bring to mind, I a conversation with a customer not long ago, and he was one of the new, I think, was CTO, CIO. He’s one of the executives who came in last year, and he wanna make an impact. And he immediately brought in AI, Made a bunch of changes.
It was very impactful. It increased numbers. It, decreased the number of open tickets and cases that they had just floating around out there. So from a business standpoint, it really made a positive impact.
When we talked to him, the biggest challenge was adoption from his users.
These are folks that have been there for twenty years, and all the information is in their heads. And they’re thinking, why do we need to put all this into AI and have the have AI do all this for us?
So we had to come in and help him. Again, taking a totally different angle to say, okay. You’ve got the tools already there. Now what you need help is with is adoption, and change management from an AI perspective. So it was so different and interesting. We’re able to bring in a a vendor or two that could help with that change, but it’s something you don’t think about.
Is that adoption and and how do you get people to accept it and not be frightened by it? Because his employees were afraid that it was going to replace them. Right, Mikey, to your point. And we’re saying, no.
No. No. It’s gonna make you so much more efficient and and bring you on to the next stage. So we’re gonna help him in the next year with his initiative to help with that change management and adoption from the users.
So, again, you don’t think about this. Right? AI is so all encompassing. We don’t think about the important stuff that’s gonna be like, okay.
You’ve got the tools in there in place already, and it’s making impact.
How do you now get everyone to accept it and and move further and enhance all the AI tools that you’re bringing to place? So anyhow, Doug, that just came to mind.
Look at the chat. How many AI bots are in the chat? Right? Probably twenty or so Yeah.
Every week. Right? So, I use a tool by Krisp, one of our great suppliers. And I’m the human on the call.
I’m doing my human interaction. And then at the end of the call, I go back, I look at my analog notes. I’m still an analog guy like you, Doug. And I compare it to my AI notes, and and it’s that blend that I think really rounds out the call.
So I really think AI can augment, complement, supplement what the human does in a very positive way without replacing my job with a mini Meagan, you know, which is my my fear, actually.
We’ll call Meagan one time. I’ll be out of a job.
But on a on a side note, you know, if you talk to any contact center leader, the number one thing holding a contact center from growing is attrition Churn. Humans, they can’t hire enough. So when we talk to a call center, we don’t let we don’t start with, you know, AI is gonna replace all those idiots on the phone. Right?
You’re gonna save so much money. No. Hey. You can’t hire enough people. What AI can do is kinda right size your call center.
Now you can use AI to get you to the number of staff you really need. So So So last week on the call, we rolled out the out me calling, call center agents idiots, please. Appreciate it.
We’ll we’ll edit it in post. Don’t worry about it. We’ll fix it all there.
So last week here on the call, we rolled out the, the cybersecurity playbook.
This week, we’re rolling out the AI readiness assessment. So the, Teleris tools tsunami, and those are three t’s, not an s, continues to roll out. This is extremely exciting, and, of course, everybody lit right up in the chat. Sam, tell us just a little bit more about how this is going to be used and how advisors can use this to further their objective of outcome based selling in AI.
Yeah. For sure. So the goal with the readiness assessment is, well, it’s multifold because you can actually use it as a great prospecting tool just to start the conversation first off. Hey.
Wanna learn more about how to leverage artificial intelligence in your technology stack today? Great. Let’s talk about how ready your organization is. In other words, wouldn’t you like to know?
Right? And we all wanna know. We’re all in a need to know basis whether or not we think that, but we all are. Right?
Especially businesses today who are considering artificial intelligence. But with the clients that you have today, folks, like, this is new stuff. It’s another channel of revenue for you. And whether or not you want to have that conversation, you have to.
Because guess what? If you’re not, someone else is. Right? And so, this, like, slew of tools that we’re coming out with is enabling you not just to prospect and expand your book of business, but go even deeper with the clients that you have today, right, and talking more about AI and just how ready they are and having those next steps at your fingertips, knowing what what you should do next on that journey to AI transformation with the client.
I I see a number of questions in the chat that have to do with who are the best vendors, who are the best suppliers for AI products, services, and so forth. And I do wanna take just a second to clarify because we’ve mentioned a number of times on this call and in other places that AI is not necessarily a product or a solution. It facilitates other products and solutions. No one walks in and says, know, I’ll have five orders of AI with a side of fries. But talk a little bit about how we determine which of the vendors and suppliers can be most helpful when there’s a desired AI outcome that we’re pursuing.
Okay. Doug, before we answer that question, because we’re let Meagan and Mike do that, but I know several people on this call probably have that screenshot of me with, like, my head turned to the side with, like, a sad face because whenever someone asks me who the top AI solutions are or all the top AI vendors are, yes. It is literally like a face palm exactly what Mikey b is doing. It’s like, folks, like, can’t ask me who’s doing the top in AI, and it looks a little something like this.
Right? Because there are so many different use cases, and all of these different solutions have different ways of doing them. So, when we when we look at the experts, I would turn it over to them as far as how we could figure that out. Who is best for what particular outcome and or use case, assuming that we have uncovered that first.
Yeah. I so Mikey and I did some recent webinars where we talked about the different phases of AI.
Right? The preinteraction, the live interaction, the postinteraction, and that’s where we can kinda put the pieces together. We get asked all the time, all day every day, Sam, to your point. I’m working with a x vertical market.
You name your vertical, manufacturing, logistics, health, whatever. And I need the best AI provider for that. We’re like, oh my gosh. What?
Stop. Hold on. Let’s have a conversation about this. We need to know what is the outcome, what are we trying to do.
Because we have different AI vendors for different phases of the interaction. Right? So we have voice AI agent or AI voice agent or, virtual agent, whatever you wanna call it, conversational AI. There’s so many words for it.
We get that asked for a lot. So it’s from the front end. Right? Anything that’s preinteraction.
So anything that can help with self-service is it’s a human sounding voice that can help through that interaction. And again, it it takes the workload off of your live agents, but can escalate to a live agent as needed. We get asked that quite a bit, and so we have a slew of different options for you. But it depends on the volumes of interaction.
If it’s an SMB, a mid market, an enterprise play because a lot of these vendors have minimum requirements. So we don’t want them to just throw the name out there and have you guys start talking to them, and then they they decline the opportunity because they’re like, oh, no. You don’t meet our twenty, you know, million minutes or whatever it is a year, minimum commitment. So we have to vet that out for you.
There in the live interaction, there’s agent assist. Mikey and I get a lot of inquiries for, hey. How do we they they don’t say agent assist because we don’t know they don’t know that terminology. Right, Mikey?
So we have to figure that out. They’re saying, well, we want something that’s gonna help, gosh, our live agents. You know, how do we access knowledge bases and give them that information so they can interact with our customer in real time and have all the answers at their fingertips? And then also, how do we how do we do quality, assurance on this?
Can we score them? Can we use AI to score them? So all these things, we have to talk about the use cases. I always say, I’m not technical.
I’m just the queen of use cases. And Mikey’s a king of use cases. What we’re doing is trying to listen to what are we what’s the use case and what are you trying to to help solve for, and what’s the outcome that you’re looking for? And then we can bring in the vendors for those pieces.
And it it might be one vendor that can do it all. It might be pieces and parts of different vendors. And, again, what’s what’s a priority for the customer?
So we have lots of options on vendors. We just have to figure out where the right fit is, and Mikey and I are here to listen. And we do a lot of vetting before we even come out with a recommendation. Right, Mikey? It can take time. We don’t necessarily do this on the fly.
And and we don’t do this, during a a live discovery call either. At least I don’t.
You know? We never mentioned vendors on the discovery call because couple things happen. One, the customer goes right to that vendor site, creates a direct lead, and then we have to fight to get that lead. That happened to me recently where a TA is like, oh, we were we’re gonna bring in, you know, Billy Bob AI. And guess what happened? They went to Billy Bob AI, and then I’m fighting with the channel manager now to get tagged to that lead.
Here’s the other thing is you’re gonna meet a lot of great suppliers out there, lot of great channel managers, and it’s all unicorns and rainbows.
And they’re all gonna say, well, you know, just bring us an opportunity and we’ll figure out if it’s a sweet spot. So I had a TA do that recently with, I think, probably our best quality voice IVA supplier, but enterprise and talk to the customer, waste the customer’s time for an hour and a half, and they came back with, yeah, they’re too small for us.
Come to us first. Right? We know who handles low volume, medium volume, and high volume, how well they work, and how they like to work, and we can really help you out. It doesn’t cost you anything.
And I promise not to take any more vacation till the end of the year.
So keep those cards coming.
You heard you heard it from Mikey. Hey, Sam, just to bring this around, we we began by talking about this great gen AI divide that exists and we talked about experimentation without impact. We talked about execution without scale. Experimentation and execution both extremely important but if they don’t result in the outcomes that we’re looking for, then they’re somewhat fruitless.
This is a great way I think for advisors to be able to approach this subject with their clients and find out where they are along that spectrum. Just to tie this all together here at the end, best things that you would suggest to advisers to try to help clients overcome this divide.
Yeah. Folks, have the conversation, And you can open it up now with something like the readiness assessment. Right? You now have a tool to be able to determine where your client’s at.
But even just to start the conversation, say, hey. How are you using it today? How are you using AI today? And uncover where in that divide they may be sitting.
They guarantee you they’re probably sitting in one of those spaces. Right? And you have the power to bridge this divide. That’s the whole concept of today.
He’s giving you the tools, the talk track to be able to bridge that divide, connect the dots, make sure that companies are rolling out artificial intelligence in the right way that’s most effective for the business. And I did see a comment in the chat that I actually wanted to address here was, yes. When you have a client who is really interested in a very particular feature. Right?
You are essentially stuck in that solution selling situation where you almost have to sell that customer on that feature that they’re really excited about. But most importantly, transform. Form that particular solution selling motion into outcome based. Once you get there, you’re going to be able to uncover so many more opportunities beyond just what the customer thought they may be interested in.
So just a little food for thought there.
Great summary. I’ve gotta run. We’re up against the clock. But, Sam, Meagan, Mike, thanks so much for the, presentation today.
Great work. Ordering the machine, Doug. We paid for the full hour. What’s going on here?
I I’ve gotta give away Patrick Oborn’s car. We’ve got the big raffle coming up.
Alright. Amazing.
Thank you. Bye, everybody. Hey.
Thanks, everybody. Great presentation.