Shawn Cordner (00:12.952)

Hi everyone and welcome to the Amplitude of Tech podcast. I’m Shawn Cordner, Chief Marketing Officer of Amplix. Today we had Cobus Greyling and Carl Katz on the podcast, our first double guest episode. Both of them are from Kore.ai. We talked about the horseless carriage problem of trying to shoehorn a new technology into existing structures and the tension between moving urgently but thoughtfully with AI. This was a great episode. Hope you enjoyed as much as I

 

Shawn Cordner (00:42.968)

All right, Cobus Greyling and Carl Katz, thank you for joining the podcast today.

 

Carl Katz (00:47.778)

to be here.

 

Shawn Cordner (00:49.218)

Happy to have you. This is our first podcast where I’ve had two guests on. So I’m feeling a little bit outnumbered and unsure of myself. Feeling a little defensive. I’m trying not to gang up on me too much, but I’m really looking forward to having this conversation with you guys. But I thought let’s start off with some introductions. So maybe starting with you, Cobus.

 

Cobus Greyling (01:08.056)

Thanks Shawn. So my name is Cobus Greyling. I’m an AI evangelist with Kore.ai. I’m South African, born and bred still in South Africa. And something I enjoy is just like continuously researching and writing on how things are unfolding in the space of AI.

 

Shawn Cordner (01:26.318)

Excellent. And then Carl, how about you?

 

Carl Katz (01:28.162)

Hey Shawn, happy to be here again. Carl Katz, the vice president of global technology partners for Kore.ai. Been here for over two and a half years working with different types of partners globally to evangelize the Kore platform and ecosystem with partners. So yeah, that’s me.

 

Shawn Cordner (01:48.152)

lot of evangelism happening over there.

 

Carl Katz (01:51.182)

It’s important to evangelize when you’re talking about AI, right?

 

Shawn Cordner (01:56.59)

Yeah. Perceptive viewers of this watching on YouTube will see that my camera just changed up. This podcast has been plagued with technical problems. is our second time trying to record in Riverside had an outage, which is the platform that we use. And now my camera appears to be having an outage. So you can enjoy the view from my MacBook camera if you’re watching on video. So apologies for that. So I thought maybe we could just kind of start off with people that might not be familiar with Kore.ai and you know, we don’t like

 

these podcasts to be a commercial per se, but if you could just give us the 30,000 foot view of what Kore.ai AI does.

 

Carl Katz (02:30.318)

All right, well, do that. So, Cordai AI is a, is an ecosystem basically. So we provide businesses, enterprise businesses with the cutting edge of AI products, including generative AI, agentic AI, and advanced AI automations and orchestrations. We were founded in 2014, by Raj Canaro. And I would like to say that we are the oldest startup.

 

Cobus Greyling (02:30.926)

I’ll

 

Carl Katz (02:59.438)

in the industry. And I say that because we’re very nimble in our approach and we’re always developing newer technologies. We work extensively with the customer and employee experiences and creating really great empathetic and human-like conversations that are well advanced, you know, for our customers. So we have a global presence. We operate in 130 different languages and dialects.

 

And we have offices throughout the world. So that’s some of that Kore.ai.

 

Shawn Cordner (03:34.04)

think your marketing department would be proud of that response, but what do really do? Let’s talk about just kind of some use cases that people would understand.

 

Carl Katz (03:41.27)

Yeah. So we really focus on several verticals. We do really well in financial services, banking, healthcare, and retail. Those are core industries and core verticals. And, you know, we actually have a really faster time to market than a lot of AI companies because we have accelerators within those verticals that allow us to, when we sell our services, allow us to turn service up quickly.

 

because we have extensive experience in those different verticals. So if you’re selling into a bank, we already have the call flows, the integrations complete, and even the agentic flows already created and pre-created to facilitate a quick engagement and a quicker time to ROI. So those industries are our primary industries. Of course, we’ve worked with other industries as well, but creating that customer experience,

 

from the actual virtual assistant to the agent is what we accelerate at. And so I would talk about really our agnostic ecosystem. I’ll give that over to Koba to talk a little bit more about.

 

Cobus Greyling (04:54.062)

Great. Thanks, Carl. Yeah, Shawn, just to maybe put it into like, you know, know, practical terms. So we try and be, we are platform agnostic, but also model agnostic. So I think what you’re seeing currently is that, you know, and that’s something in video. Carl, I was just speaking before we went on record about NVIDIA GTC. And so something in video is really, really advocating

 

is multi-model orchestration. And recently, I don’t know if it was on purpose or not, but OpenAI revealed the mechanics behind their deep research API and also ChatGPT. And for us ChatGPT is just this very simple user interface. But what they actually revealed was that multiple models are orchestrated under the hood.

 

And, you know, multiple models play different roles. So that’s something we’ve been focusing a lot on as well as model orchestration. And so not one model to run the whole environment. And to be able to do that, you need to give your customers access to open source models. Obviously from a price point perspective, and there’s also specialized models. So we put an environment where you can have access to, I think, close to 200 open source models.

 

But it’s also integration to a hugging phase that gives you access to potentially thousands of models. And that really gives you the freedom to build a highly granular, a GenTech environment where you can orchestrate these different models within a workflow. And I mean, we really see that as the way forward where you don’t have one single model that solves for everything, but you have specific models that run, you know, specific places in this GenTech workflow. If that makes sense.

 

Shawn Cordner (06:49.358)

Yeah, that makes total sense. I think it’s more relevant now than it was a couple of weeks ago, just because of the changes that Anthropix made with Claude and token usage, right? So they made some changes there. I don’t know if I can articulate exactly what they are, but what I do know is I’m hearing from people that all of a sudden they’re hitting their weekly token limits in a day. Whereas, you know, they weren’t hitting those limits before. So I think it makes it more important to not be locked into any one model because

 

It reminds us that the underlying economics of the companies that we’re using in our AI solutions could change at any minute and that could totally disrupt the economics of your model.

 

Cobus Greyling (07:27.714)

Yeah, mean, just on that, the other day I made a list, list just for myself on the models that’s being deprecated. if you look at the big model providers, say there’s a constant flow of models that’s being deprecated. So, I mean, if you pre-miss your application too much on the behavior of a specific model, then you’re in danger. I think there’s another dimension to that is something that’s been documented as model drift, where models actually change under the hood.

 

So the behavior, the API changes. just to, as a third point to what you said, Shawn, about the token usage, I mean, like I use Claude code extensively, I think Opus 4.6 and I hit my daily limit frequently and then I hit my weekly limit, right? Before the end of the week. So those are all challenges. And I think people are really looking for alternatives. I mean, I’ve been looking at something like the Nvidia TGX Spark.

 

where you could run a model locally and they are highly capable open source models you can run locally. I think really, I mean, not to go off on a tangent, but I think the newest buzzword is harness engineering, know, AI harness. And I think that’s something we aspire to, to be able to give that flexibility. I also think of it, I also like the term bounded autonomy, right? So, mean, when Carl mentioned

 

You Kore comes from natural language, understanding models, NLP models. There you had a problem of scarcity. You really had to be innovative to give like a natural conversation to your customers. And now you have the problem of abundance. You go from fast to feast really, when it comes to the technology. And so I like this whole idea of harness engineering and bounded autonomy and try and control the workflows.

 

And especially the user experience. I think Carl alluded to it, but especially because we find ourselves in the financial sector and health services. So it’s highly regulated environments. You need that level of, of when it comes to the applications, but just to book in that definitely to have the ability to swap out models and orchestrate multiple models as you choose to do.

 

Shawn Cordner (09:49.25)

Well, I think you can also think about the outcome as being a driver for having multiple different models too, right? Because different models have different strengths and different weaknesses, right? So you really need to kind of understand what is the function that you need that model to play in the overall workflow of the AI. Also, each model has different, you know, cost implications, right? So something that’s doing a lot of deep thinking that, you know, that you want to use that.

 

for let’s say deep research and not necessarily for just rewriting a paragraph in one of the deliverables or the outputs of the task that it was given. And there’s cost implications depending on which type of model you’re using, I think that also kind of speaks to the skill set that’s needed in what we could maybe call the next generation.

 

of AI because you’re right, large language model and generative AI that came out of nowhere almost it seems like for most of us at least. And, and it blew up and it’s almost like we got ahead of ourselves or out over our skis because that’s not actually the end state of AI, right? Because it doesn’t do anything. It tells you things, but it doesn’t do anything for you. so now that you’re seeing the adoption of agentic AI because

 

That’s where the rubber meets the road. That’s where things can actually get done and maybe cost savings or productivity gains could actually be realized. so it’s going to require someone to architect these things. If you’re working in an enterprise that’s building their own, you know, models or their own type of solution using different models, they need to, to know this. They need to know what are the available models and what are the strengths of those models and what are the economics of those models. And you need to have.

 

explainability and observability built into that to make sure that you don’t have that model drift and that things are operating the way that they’re supposed to be. And so that’s a long way of me saying that, and I spoke to a, an AI expert that we’ve had on the podcast yesterday. And he was saying to me from a skillset perspective, he thinks that too much emphasis was put on prompt engineering early and actually where the skills that needs to evolve to is in orchestration.

 

Cobus Greyling (12:06.136)

Yeah, that, I mean, think the orchestration layer is definitely something that’s been, I think largely neglected and I fully agree with that whole idea of, having too much focus on, on prompt engineering, and not all on all the different aspects that goes into building an enterprise, solution. But I just wanted to just on what you said about the skill set.

 

Something I really find interesting is that people know what good AI looks like. I keep on thinking of that MIT piece that coined the phrase the shadow AI economy. we’re like the official AI tools that’s being supplied by people, supplied to workers. They opt not to use that, but they choose to bring their own AI tools to work. And hence the shadow AI economy, because you’re not using tools that’s

 

you know, officially is applied, but it’s, the tools you choose to use. mean, I think there’s a, when you speak about, in a skill set, there’s definitely some kind of void there, but yet people know what good AI looks like and they know that AI they want, they choose to use. and then maybe secondly on that, you know, I also think there’s, there’s kind of like an attribution problem currently when it comes to enterprise AI.

 

And I was listening to an interview a while back and from A16Z and they spoke about how internet advertising had an attribution problem. Like how do you attribute user behavior in terms of clicks to your actual advertising? And the moment they got the attribution drive for online advertising, the business model was there and advertising could be sold.

 

And then they equated that to the shadow AI economy that’s happening currently. And AI use are not really attributed accurately when it comes to enterprises. So that’s something I found interesting. Just our idea of people, they’ve got set ideas on how they want to use AI. And the tools have been given officially aren’t they, you know, the tools that they’ll…

 

Cobus Greyling (14:29.518)

the tools that they prefer.

 

Shawn Cordner (14:34.168)

So what you’re saying is that businesses don’t necessarily know which AI is producing whatever outcome or result that they’re seeing in the business, right? So in the ad attribution model that you’re talking about, there’s different standards that you can use to try to figure out where your revenue is coming from so that you can attribute them to the right channel because that lets you know where you should continue to invest. That’s how you make the ROI statement. And so the ongoing challenge in

 

the advertising marketing space is, we don’t really know. There’s no, there’s no science to this. There’s the first click model. There’s the last click model. what we do know is that when people make a buying decision, it takes something like 14 or 17, interactions with that brand. There’s a lot of things that go into it. So where did they hear about you first?

 

What are the subsequent 17 touch points? Which one of them pushed them over the threshold and got them to buy? There’s probably not a clean answer. It’s the aggregate of all of those interactions that they’ve had with the brand and with the advertising. And, and so I guess the challenge in the AI enterprise AI space is you may have rolled out a handful of tools to your team that are approved. And then you have your team bringing their own AI.

 

tools that you have no visibility to and they’re using some combination of the approved and the unknown tools and who the hell knows what they’re using more and what they’re using it for and but what you do know is that you’re either seeing a productivity increase or you’re seeing a cost getting lowered or you’re not seeing anything. It really kind of leaves a variable in the equation that leaves you kind of, you know, sitting on your hands like, okay, now what?

 

Carl Katz (16:26.412)

Yeah, I just want to from my experience, know, businesses are in different phases of their AI journey, right? they, know, you have some businesses that are actually considering AI for the first time and implementing AI solutions, even though they’re in, you know, they could be in highly regulated environments with compliance concerns that by the way, we can meet globally from that perspective.

 

Cobus Greyling (16:30.104)

Cover it.

 

Carl Katz (16:55.438)

but you have other companies that are really advanced in their AI journey and that are initiating AI and creating even like small language models to facilitate their engagement as opposed to, you know, leveraging the large language models that might be a little too obtuse for what they require. So, you know, the best would be Kore.ai orchestration, you know, an automation platform is that we can facilitate multiple

 

large language models were agnostic as mentioned, we can facilitate multiple large language models into our ecosystem. And depending on the type of query within a conversation, we can pivot. So if you want to use, for example, you talk about token costs and such, you know, from like, for example, healthcare loves Azure, you know, they love the Azure LLM. Then you have others, then you have a query that might be something on a website or some other Excel spreadsheets somewhere else.

 

But you might want to use deep seek, right? Because the cost is significantly lower. So we can, we can facilitate that type of engagement on our platform to where token costs is not really an issue. as much as let’s say just leveraging chat GPT or open AI 5.2, whatever it is, for all your queries, then, then it gets a little outrageous. Honestly, it gets a little nuts there. So.

 

We can work with you, your customers, to create an overall AI strategy and mitigate costs for token usage utilizing several elements of our platform.

 

Shawn Cordner (18:31.574)

Yeah, I think that brings me back to the buy versus build conversation, right? Because I think a couple of years ago, that was the entry point was buying and we saw it mostly in the CX space. was low hanging fruit for business to, well, to back up, you know, that you were in a situation a couple of years ago where boards were saying, we need AI, go buy us AI, right? And nobody knows what that means, least of all them. And so that’s, think what made it.

 

attractive to go the buy route because you don’t really have to know everything there. There’s probably less risk by doing that as well, but it satisfies that, that mandate to buy AI or to do AI as, as the uninformed would say. But I think what we’re seeing now is pilots are starting to get built and most of it is agentic and most of it is around workflows supporting.

 

processes that already exist. So, you know, I, I wonder a platform like yours or maybe not yours specifically, but just in general, that kind of, you know, you’re, I’m, paying for a platform that has these agented workflows kind of pre-built. Is that a, is, is that a half step or an evolution towards the build? I guess I don’t, I don’t know personally, tactically what it means to leverage a platform that already has agentic.

 

AI workflows built, I imagine that there’s some level of customization there. So are the people that are your clients or customers, are they getting more hands-on experience? Are they learning something in that process that can then be applied for them to build in the future?

 

Carl Katz (20:13.814)

Yeah, it’s so much, it’s so complex. And I don’t know if you’re aware of this, but 80 to 85 % of all pilots fail within the enterprise business space. That is a, that is a true number. And the reason they fail is because they, in many cases, they don’t know what their requirements are per se. And when you get involved with the agentic flows and stuff, never, never quite meets requirements. And then, you know,

 

Most of these companies that build their own are not AI companies, obviously, they do something else. But at its Kore, our platform brings together multi-agent orchestration with a full spectrum of development tools that businesses just can’t allocate themselves. I mean, we leverage no code, low code and pro code. So teams across the organization can build, deploy AI agents at scale. Kore.ai ecosystem is powered by advanced search, data AI.

 

and also robust to engineer AI engineering tools and, really full stack of observability all throughout the journey, to monitor and optimize performance. This is not something that we can, that organizations can readily leverage, right? Especially from an enterprise perspective, because this needs to be built out. That’s why being a 12 year old company with this type of, you know, with this type of technology.

 

And the advancement technology, advancement of the technology truly differentiates us from other AI companies or organizations that are trying to build it on their own. We have one airline customer that tried to do that. I won’t say who they are, but airline customer that try to do that on their own. were seeing, there were no guard rails on the conversation, which is an issue by the way, obviously hallucinations and other types of issues. A large to the large language model.

 

of the more latency associated with that language model. Right. And also the more opportunity for hallucination within the model as well. So like be on an airline to try to do that. And they said, Hey, we’re using open AI. Cobus knows who I’m talking about. We’re using open AI. We’re doing a great job. You know, we’re an airline and we’re going to build this great system. And guess what? They weren’t giving bereavement fairs. The rates were wrong. The experience was bad. It just wasn’t a great experience. And they got sued actually. they lost and.

 

Carl Katz (22:30.048)

Now they are happy Kore.ai customer. So there’s an example of where, you know, where I guess working together, right. With businesses to understand and then bring them on to our, into our ecosystem is the game changer for them. And that’s where we’ve celebrated.

 

Cobus Greyling (22:47.65)

Shawn, if I can just add to what Carl just said, Shawn, mentioned the build versus buy part of it. So definitely something that exists in the market is like this disconnect between marketing and what’s happening on the ground. Like you spoke about when the rubber hits the road. So there’s definitely that disconnect. There’s this marketing hype, technology that exists, but what’s really happening on the ground. And if you could look…

 

on archive, there’s a whole new genre of studies that looked at what developers are experiencing, you know, in building agentic systems. What are the biggest pain points? So they went to, they use platforms like Reddit, Substack, which is still being used, GitHub, just to quantify and categorize what developers are struggling with. And the biggest impediment for developers they found.

 

when rolling out technology is technology churn. They just need to run constant updates, patches, the technology churn keeps them busy all the time. And apart from that, there’s lots of granular, especially when comes to search and vector databases they struggle with. this, this study is just pointed to one fact and that is that it is very hard to keep up with technology at scale and

 

together with that deliver solutions. know, when, when Carl spoke about this environment we, we, we building, I really see it as this abstraction layer. So we try and abstract away all that technical overhead and all that technical PT and have that under the hood and just surface AI primitives with which people can build solutions with, you know, so

 

I mean, there are companies that want to tinker. One of our biggest customers are really like building good prototypes in-house. And that’s a good way of getting a grasp on technology and a complete understanding of what’s happening. But, know, these studies just pointed to the fact that you need to abstract away that overhead of technology churn and hand it over to someone that focuses on that and then have the

 

Cobus Greyling (25:13.102)

the no code primitives or the abstraction layer you can make use of to build the solutions. I think it’s becoming near impossible for a non-tech company to keep up with the technology trend, and always have access to the latest technology that’s available.

 

Shawn Cordner (25:34.796)

Yeah, I, I have a sense that, for the most part, we’re still in the very nascent stages of this for enterprise adoption. Right. And it’s just getting started. cited a stat. I think you’re probably alluding to the MIT research, study that they put out. And I think it was 95 % of pilots failed to produce an ROI, but that’s a contentious study because I

 

I think what it fails to take into consideration is a lot of those pilots weren’t intended to produce an ROI. There were people learning and so it was an experiment and it was an exercise in wrapping their hands and head around what was going on. But I’ve seen some demos of some people that are leveraging agentic AI in a more advanced way than I think your average, certainly your average individual, let alone your average enterprise would.

 

be able to do and, and what I’ve actually seen and demoed is, you know, just guy building this in his basement on a Mac mini and he’s got a, a army of agents that are writing code for him. Right. So he just, he doesn’t even type, he talks to Claude and Claude kind of orchestrates it and it works with hundreds of other different models. And he says, okay, what I want to do is this, this, and this, this is the objective. This is the.

 

data that I want you to access. And it just went through and I think you, called it a bounded autonomy. I’m pretty sure this is what you’re referring to. So he’s got guard rails built around the whole thing so that as it goes through each step in the process, it tells him where he’s at in the process. He’s got a, a pane of glass that shows what the AI is doing and why it’s doing it. And then he’s got another pane that is logging it. So he can explain it later if he has to. Right. And it goes through each of these steps.

 

until it produces code, tests code, and commits code. And then he is basically done. And then he’s got human in a loop in each of the steps so that he gets an opportunity to review and approve or send it back for revisions if he wants to. And so what I thought was interesting about that is this, can tell it to integrate with business applications. He doesn’t even have to do the integration. He can tell it to integrate with

 

Shawn Cordner (27:59.008)

Salesforce or an ERP or something like that. So what he said to me is I can see a time in the future where software as a service, as we know it today dies because it’s, it’s not the portal. It’s not the pane of glass that you really need to access. It’s not the user experience of that software. It is the data that that software houses and that data becomes a piece of a broader picture of an agent tech workflow.

 

And it could be interchangeable in that you migrate from one CRM to the next CRM. There’s no real learning curve because people aren’t engaging with the data that way. Right. So then you don’t have vendor lock in like we’re, we’re a HubSpot shop. I can’t leave HubSpot no matter what they do to me. And believe me, they try to make me leave HubSpot, but I can’t because where am going to go? And I’m going to have a learning curve for my team to get up to speed on a new platform. so the vendor lock in becomes.

 

less of a thing if it’s not about, you know, our producer, this podcast is Brandon. It’s not about Brendan learning how to use HubSpot or learning how to use a HubSpot competitor. It’s about just plugging that data and that functionality into our agentic AI workflows. Right. So, you know, I don’t think SAS is dying overnight, but what does it look like in 10 years? I don’t know. What do you think of that?

 

Cobus Greyling (29:20.224)

Yeah. So Shawn, I’ve got this construct in my mind, when we spoke about the future. So three sides to this. The one is like software is expensive to create traditionally or to produce. So software had to be durable. You had to license it. You’ve got to have like future proof and all the rest of it. Now all of a sudden software is becoming ephemeral, like that guy in the basement.

 

You know, I use Claude code to code all the time now. It’s just in CLI, right? So even the IDE is being abstracted away. Like you didn’t have an IDE anymore, VS code, you just do it in the CLI. So that’s one side of it. Software is less durable. It’s more ephemeral. It’s generated almost as a utility when you need it and then it can be discarded. So that’s one side of it.

 

I think when it comes to the enterprise and implementing software in enterprise, are two sides. So the one is the more recent, you know, it’s closer to where we are currently. And that is trying to just give employees to give workers knowledge, AI tools. So I’ll give you an example. think Carl knows this much better than what I do, but I was recently at a

 

Gartner IT Symposium and a CIO came to our booth and he said, he’s in shock seeing how many people are working on their laptops during presentations, uploading docs to chat GPT to Claude. So a lot of our customers don’t have access within their day-to-day working environment on their laptops to AI because for obvious reasons they upload docs and stuff.

 

So a big part of our business is giving enterprise customers alternatives. So, you know, we call it AI for Work. So it’s an alternative to ChatGPT or to Claude. You know, it’s all the data governance, model sovereignty, data sovereignty, data flows managed. So that’s one part of it. So you’re currently within the current organizational structures.

 

Cobus Greyling (31:36.298)

Spanish bank recently, one of our customers took away all their, all these AI tools because of the risk. then you’ve got to give the workers, you know, similar tools or better that complies to all these things. So that’s one side of it, like very granular giving people these specific tools. But then Shawn, there’s an other side that fascinates me. So currently AI is augmenting enterprises, but somehow we’ll have to get to the stage where

 

enterprise is transformed. so recently there was an interview with Jack Dorsey, you know, the founder of X Twitter and Rulof Buita from Sequoia. You know, they spoke about the enterprise of the future, the AI enterprise of the future. And the best way for me to articulate that is, you know, when we used to have horse-drawn carriages and the internal combustion engine was developed, you know, this engine replaced the one or two

 

horses, but the engine was just placed within the carriage, right? And it was used to propel the carriage. There was opportunity to redesign the carriage completely because the carriage is built around the horses. But it was just seen as a horse-less carriage and the internal combustion engine was just slapped onto the carriage and, and, and off they went. took, you know, quite a while.

 

for the actual carriage to develop and change around the engine. The engine became an integrated part. And I think AI, when it comes to enterprise AI, it’s very much in that state where Jack Dorsey speaks about enterprises hierarchies develop because we had to pass data up and down and filter data and manage data and…

 

manage people and so that’s why we have the enterprise structures we have. And so we, at this early stage now where we plugging AI in, augmenting humans, but it’s got to get to a stage where a company or an organization is completely transformed in the way it uses AI. And I think that’s a state, I’m not exactly sure what that will look like, but that’s a state we need to get at.

 

Cobus Greyling (33:58.632)

and they, mean, created a manifesto. It’s actually a very good interview to listen to and how they want to completely reshape block into this, you know, AI orientated enterprise. So I think, you know, in that sense, AI is going to look much different than what, what we know it today. Agenetic AI, always say, genetic points to agency, a level of agency and freedom to act on your behalf.

 

obviously within the guardrails you’ve put down. So I think that’s going to be very exciting. And like you mentioned it, Shawn, like we didn’t know even what’s going to happen in the near future, but I think that’s the exciting part where enterprises will be transformed. I think that the problem, a big part of that problem is distribution. Like you mentioned HubSpot, you you’re stuck in HubSpot. If you think of other systems of record, they’ve got the distribution and the presence within the organizations.

 

So they’ve got that advantage software as a service. People say it’s dead, but if you’ve got distribution, then that’s, you know, it’s going to be hard to remove that. And maybe just to bookend this thought. I mean, that’s why I think, you know, Elon Musk bought Twitter distribution, you know, to have that distribution, the distribution for open AIS chat, GPT, and obviously Microsoft has got distribution. So I think.

 

that traditional distribution will help you to get your AI in. But I’m very curious to see how this future will look like where enterprises are truly transformed and we then sit with this horseless carriage scenario.

 

Shawn Cordner (35:39.136)

Yeah, I love that topic. That’s, it’s a hard one to have, I think when you’re a technology leader who’s trying to keep the lights on and keep up with the day to day. And, and do you really have the time to sit back and reimagine what the enterprise would look like if you could start with a green field, right? But it’s probably important to dedicate some time to think about it, at least in specific ways. And I’ll give you an example right before this podcast recording.

 

I was on our executive call, we have a weekly executive call and we have some AI initiatives that are in the pipeline. And our CEO said, just remember, we’re not trying to be bad faster. We’re trying to be better. And that really resonated with me. He’s got a way with words. He’s a plain speaking Boston guy. But I love that, right? Because don’t just put AI into the process as it exists today.

 

evaluate the process and understand if there’s a better way to do the process because this is your opportunity. You don’t want to inject automation that comes with some risk, right? Into a process that’s already bad or not creating the desired results. But you said something that I noted. said you have the, they had the opportunity to build the carriage around the engine once the engine was invented. And I think I would argue that it’s.

 

Not about the opportunity, it’s about the necessity, right? People aren’t going to do anything. They’re not going to change unless the pain of staying the same outweighs the pain of that change. when, and I’m speculating here, but when engines got so powerful, or at least they had the ability to be powerful, that the carriage started to break down around it. Or when the thing ran into a pole or something and it just disintegrated.

 

The pain of staying the same design as a carriage started to exceed the pain of designing a better carriage to go around the engine. don’t know. What do you think? What’s going to be the catalyst for that level of change in your average enterprise?

 

Cobus Greyling (37:48.462)

I don’t know if Carl’s got some thoughts, but maybe I can kick it off. you know, I think the necessity or pain, I mean, I’m thinking of a few years back, I read Lou Gershness book who says elephants can’t dance. And you know, when he took over as the CEO of IBM, IBM was done. Like, I mean, was written. It was like a fact that IBM will stop existing. And then the only solution he had was to re-engineer the whole business.

 

to remake IBM. And then he said he had to re-engineer all the processes to save the company. And then he said, one thing he said in the book was like, you know, re-engineering the processes of IBM was like setting your hair on fire and putting it out with a hammer. know, what is worse? So I guess like there’s a, I guess there’s a, there’s an aspect of necessity. I mean, like, you know, again, thinking of that interview,

 

with Jack Dorsey, think, you know, block can be like a blueprint of how to really rapidly change an organization. and having have, have it managed with AI in the center. I think most other companies will have an incremental change in the way they do things. You know, when it, when it comes to, to employing AI.

 

But I mean, I think there’s people need to realize the power. I mean, recently, full disclosure, I recently, I started coding with Claude code. was using Croc. I mean, I was just like blown away by the way Claude CLI can orchestrate my MacBook. I mean, the way it can log in, go to places. So I think you need that realization of what’s the actual power. And I mean, like.

 

Currently, like everything is measured in tokens. You mentioned it like this, there’s a cost involved in everything. So currently like, I mean, we didn’t talk about electricity. We talk about utilities at home. So currently we just talk about AI and tokens, but that’s going to be, we’ve got to get to a stage where that’s abstracted away and we speak about functionality and tools we’ve built. know, so that’s like a…

 

Cobus Greyling (40:12.748)

the next step we need to get to. I think some companies will, to your point Shawn about necessity and pain, think some companies like Block, Jack Dorsey, it’s just going to like really just go like extreme. But I guess most organizations will have more of a stepwise gradual approach in transforming.

 

Shawn Cordner (40:34.284)

So Carl, in science, have you ever seen the movie Contact?

 

Carl Katz (40:40.3)

Yeah. Yeah. Jodie Foster.

 

Shawn Cordner (40:41.804)

Yeah. So I don’t know if you remember this specific scene, but there’s the head of science. don’t know. I can’t remember what his title was, but he’s the head guy, Drumlin I think was his name and he’s talking to Jodie Foster and he’s going on this giant tribe saying that science should be about producing results. We shouldn’t be spending money on, we shouldn’t be spending public money on scientific efforts that aren’t going to yield a measurable good to the public that are funding it.

 

Right. And she says, what? So there’s no room for pure experimentation in science anymore. And I think if you know anything about the history of science and the scientific method, you really do need to sometimes just experiment for the sake of experimenting. And then it’s the aggregate knowledge of all of science that leads to new innovations. That’s why we government funds DARPA, right? It’s, it’s, it’s money that they spend to, you know, to,

 

do the moonshot experimentation, right? And that eventually does trickle down into the economy. So where I’m going with this is, Carl, do you think that, how do you think a technology leader can go to the finance department, can go to the board and say, I need money to play around with AI? I don’t think there’s going to be an ROI. As a matter of fact, I’m quite certain there won’t be. I’m quite certain we’re going to fail. I’m quite certain that we’re going to…

 

make mistakes that cost us even more money than I’m telling you that I need for this. But we need to do it anyway. How do you sell that?

 

Carl Katz (42:14.016)

Yeah. I mean, part of it is keeping up with the Joneses, right? your competitors are all getting involved in AI and offering, a more consistent experience. Things that actually, have evolved it, interestingly enough, you know, we started with onshore contact centers, for example. Then we went near shore, then we went offshore, right complete. And then now things are pulling back in and going AI and then onshore.

 

right for those calls that can’t be handled by AI. So really discussing the entire customer journey and how effective automation orchestration can be in that journey, right. With a more consistent results, right. And more consistent at the end of the day, it’s all about customer engagement, customer sentiment. So talking about their NPR sKores, right. Talking to them about their

 

there are challenges from a customer engagement perspective, you know, obviously ROI is always involved in the conversation, right. And costs, you know, associated with that. So I hate to say this, right. And we talk about human, but companies are looking to, and we’re seeing that with BPOs even, right. Where BPOs are actually, cause I manage the BPO, organizations globally as well. We’re seeing BPOs going to the AI module, right. They’re leveraging Kore and other companies to facilitate that.

 

because they realize they need to get on board with that, not only from a cost structure, but for consistency, net promoter scores, also, you know, and also from a ROI perspective, right? So it’s really easy conversation to have because everybody, as you mentioned before, everybody’s hearing about AI and different organizations are.

 

in different sections, you know, are different parts of their AI journey. And they want to know more, but really they want to know what the other guy’s doing at the end of the day. How is the other bank doing this? How is the other insurance agencies doing this? How is other healthcare institutions leveraging AI? So they want to learn and they want to implement. And in many cases,

 

Carl Katz (44:40.43)

It’s a land and expand methodology, right? Where I’m like, you know, I mean, I’m not going go too much about it, but at the end of the day, we are agnostic. So, you know, somebody says, I got Watson X. Okay, great. Well, you know, we have a more agnostic platform so we can start there and work in conjunction with Watson X or.

 

Or, you know, I only want to implement sometimes it be one of like, you know, take a bite before you eat the whole cake and you say, okay, I want to implement this segment of AI. And it could be like the low hanging fruit, like just a chat virtual assistant. And then they want to move into advanced. Agenda applications. We’re not seeing companies that are starting with AI looking, going right and diving into the deep end of the pool and saying, you know what? I want all channels.

 

I want these agentic flows, right? Here are my orchestration agents that I want to be implemented, right? Here are the flows that I want to implement within the orchestration. We’re not seeing that too often. We’re trying, but that from my perspective, being in the sales side, that increases the cycle significantly. We find that if you give them something and believe it or not, a lot of times before the end, the sale is made.

 

we’re actually going deeper into the conversation than they believe they would go. So you’re right, there is pushback. But once the conversation started, okay, and they understand the benefits associated with it, with all these things I mentioned, it’s an easier conversation to have, especially today. Everybody’s using AI every day for something.

 

Shawn Cordner (46:21.24)

So the two things that I pulled out of it, two themes that I think your response circled around, if not hit directly on is we can’t get left behind. And so if we don’t get in the game now, we are going to get left behind because everybody, and that is powerful. And I think that’s actually, that’s a reason enough to get in the game and start spending, experimenting and spending some money because this, we don’t know what’s going to happen with AI next week, let alone two years from right now. Right. So.

 

There, there could be a leapfrog moment that happens here that is predicated on having a certain skillset, having certain governance frameworks, having certain education and transparency built into your, your enterprise to be able to leverage that next technology when that moment happens. So if you’re not accumulating that intellectual property or that intellectual capital, mean, now it’s too late. Like, what’s that saying? Like don’t dig the well when you’re already thirsty.

 

Dig it before you’re thirsty, right? So now’s the time to start digging. But then I think you hit on something that’s, that’s actually equally powerful, which is you as a technology leader have the opportunity to frame this conversation with your executive team and with the board. Right? So start framing it around soft metrics that tell the story, develop the narrative that you need it to develop, not in a deceptive way, not in a manipulative way, but in a way that you’re quantifying.

 

the soft immeasurable value to the business and starting to report on that, right? Because ROI doesn’t always have to mean dollars and cents. It does if you’re talking to the finance department, but if you get to a point where your CSAT scores are higher because you’ve implemented AI in a really smart way, right? Or if you’ve built

 

the skillset that allows you to eventually implement a Gentic AI workflows in aspects of the business that do reduce overall costs. Then you do have that ROI story to tell, but you can’t get to that without having that understanding. First, alternatively, you could go and hire a CAIO or a CDO or whatever, somebody that’s going to head up the AI initiatives in your business and own that, but that’s a

 

Shawn Cordner (48:38.018)

pretty expensive proposition in itself. And I think we’re in a situation right now where it’s like in cybersecurity, where you’ve got more demand for people with the skill sets, then you have supply of people with the skill sets. And so those people that really do understand it are a significant expense if you can find them.

 

Carl Katz (48:55.842)

Yeah. I mean, just a few years ago, we were talking about robotic process automation, right? That was too long ago. I mean, natural language processing, understanding, moving into the language, large language models and generative IT and now agentic IT, agentic AI. And very soon we’re going be talking about artificial general intelligence. Right. That’s going to be the next iteration, right? And that’s coming out within a couple of years. And, you know, we’re, developing that as well as an organization.

 

So, you know, you also want to be on the latest and greatest. You don’t want to be at the back, on the back end of the curve. You know, you want to be forward and working with an organization, whether it be Kore.ai or somebody else, hopefully Kore.ai, you know, it’s going to allow you to, you know, to get involved in an ecosystem that’s constantly evolving and constantly changing to benefit the customer experience. mean, there’s a lot of, I mean, you know, we have hundreds of engineers working on our product every day.

 

to maintain and advance it. Hundreds of them were an AI company. What businesses are telling me when I talk to them is that we don’t want to be an AI company. I did meet somebody who was very astute in building his own AI platform, very large company, Fortune 10 company. I was speaking to them and they have their own AI division, they’re doing their own agentic flows, and that’s what this guy lived for, et cetera, et cetera. So, you know, he thought he was doing a good job, but going back to the MIT study.

 

Okay. I think it was like 67 % of all in-house builds pilots failed. Well, think 30 % of the, so the other 30 % that were external did well or something like that. So if you leverage an external source, a company who specializes in AI, you’re more apt to have a favorable experience than creating something on your own. And not only that is just future-proofing.

 

You talk about compliance and, you know, and, and, you know, building your own ecosystem and such. It’s not always forthright. So do you want to keep track of the compliance changes within HIPAA, within GDPR, within these different, compliance rules that are always evolving? Co-Bus always posts something about the evolving metrics of compliances. I see that online and that’s…

 

Carl Katz (51:23.008)

always changing as well. So you don’t want to be caught with not being compliant because there’s some significant downside to that obviously. So I think that I hope that answers the question long-winded.

 

Shawn Cordner (51:34.71)

No, I think it answers it as well as you can. mean, there’s no, I have a lot of empathy for people in this situation because this is the constant fight that a CMO has over spending money on brand initiatives. What’s the ROI on your branding efforts? I don’t know. Nobody knows. I was on a Gardner webinar over the summer and the topic was, you know, selling brand initiatives to the board and

 

I thought, wow, maybe they have an answer. Maybe they’re going to tell me how to show an ROI on my brand spend. And they didn’t bring it up. so at the end I asked the question and they’re like, we don’t know. That’s Gardner. Nobody knows. can’t quantify. It’s very, very difficult to quantify return on investment with the brand. I think like Kogus had said earlier about good AI, know, your brand investment is working.

 

when you see it working, but you can’t really put your finger on it, right? But that doesn’t mean that you can’t do it. And so I think that’s the situation that a lot of technology leaders are in right now. And the buy versus build conversation, I think, comes back into this because you can buy, you can find a partner. Finding a partner doesn’t necessarily imply buy either. You can find a partner that will build along with you. And then that can be a transfer of knowledge as well.

 

I think the message is you need to get in the game. Cobis, someone sitting there listening to this and saying, okay, I need to get in the game. Would you say that most enterprises today are from an architecture perspective and from a data perspective and from a governance perspective, ready for this, especially for agents at AI? And if the answer I’m quite sure is going to be no, what can they do?

 

Cobus Greyling (53:25.314)

Yeah, I was, I love the build versus buy discussion. was recently at one of our biggest customers and I asked them, asked someone there, what’s your view on build versus buy? And they said that they, they buy for table stakes and then they try and build for differentiation. And what was interesting, they didn’t get to that, right? So that they didn’t get to the build for differentiation. So what they

 

do is they’ve got like an incubator, a, you know, people build prototypes and, you know, systems in-house and then once a year they’ve got an AI day and people showcase what they’ve built and what they’ve learned. And I really liked that approach, because they’ve, they’ve bought a platform, they are actively implementing, you know, AI solutions, but also.

 

they building their own understanding. And I think a big part of like advancing AI in enterprises, if people have a very good understanding of technology and how things fit together. we like to speak about value engineering and so proving value. It’s not always that easy. So we, you know, we’ve got people that look at ways of how value can be.

 

calculated and represented. So think there’s a big part of value engineering that helps with this problem of attribution. But, you know, it’s great to have conversations with people that try to build something. The best way to build knowledge is to try and build something. You know, if people try that, they get a better understanding of how the technology fit together. That really helps a lot. I’m very apprehensive when people’s understanding is too opaque.

 

there’s not a clear understanding of what they want to achieve. I’ll give you an example. So a while back, I was speaking to a CIO of a big leisure company and he said to me that he’s made three mistakes in acquiring AI software, three failures. And he said like, so nice speaking that we were just having a conversation and he said to me, well, I don’t want to make that mistake again. So I don’t want to be tied to technology. And I said to the team, like you have to, you couldn’t, it is an, I don’t want to be tied to a framework.

 

Cobus Greyling (55:48.46)

And I said, you have to like, can name dozens of frameworks, different programming languages, different models. You’ve got to be tied to technology to have that solution. So it’s almost like this fight or flight, you know, you went from one extreme to another extreme where, know, it’s just, I mean, you can’t, you can’t not align with technology. So I think, you know, enterprises, if they build their own understanding of what they would like to achieve.

 

then that really goes a long way in achieving success. If it’s too opaque, I think if you just throw money at the problem, think that’s a recipe for disaster.

 

Shawn Cordner (56:34.712)

So I agree. think the best way to learn something is to do it and just learn on the job. I started cooking professionally at 14 years old and I eventually went to culinary school, but I had already been cooking for four years at that point. I learned because I was a dishwasher and I was looking over their shoulders and they would let me start to prep things and they would let me, you know, mess around on the, the fry machine and then the saute and,

 

I learned a lot faster than I would have if I had just started in culinary school. So I think you start and you learn as you go. We had a AI expert on the podcast a couple episodes ago and I asked him question. If you’re going to start tinkering, if you’re going to start dabbling, if you’re going to develop a pilot, do you treat it like a true experiment in that you develop a hypothesis?

 

And then you develop your parameters and then you test the hypothesis and then you evaluate your results. And so, you know, you go into it with an idea of what you think you’re going to be able to do and what you think the outcome is going to be. And you have some sort of KPI that you’re measuring on, you know, from one end to the other end, treat it like a science experiment. And he said that felt burdensome and probably unnecessary and to just get started. So.

 

Fast forward a little bit and I had my team work on a list of priorities for us to implement AI. A way to improve our productivity in the marketing department here. So we all individually developed our use cases. We started to look at what are the repetitive tasks that we do? What are the things that are time consuming but not valuable and not necessarily accretive to our department?

 

And then we looked at the commonality of them and then we did some chunking and turned those into initiatives. And then we prioritize those initiatives against risk and investment and dependencies. What can we do without the IT department or the security people, you know, so that we could be autonomous and we kind of stack rank them. And so then I developed this list and I sent it to a different AI expert and he said,

 

Shawn Cordner (58:47.768)

This is great, but what you’re missing is you’re not treating like this, this like an experiment. You need to have a hypothesis. And he basically went through the same thing, right? So I’m getting conflicting answers here. And the answer is probably somewhere in the middle because I can tell the two different personality types of the individuals I was talking to, but I don’t know. What are your thoughts? What’s the appropriate level of managing your experimentation versus just getting your hands dirty?

 

Carl Katz (59:12.334)

Cobus that’s probably more up for you, but I mean, um, you know, from my perspective, know, experimentation, really, you need to have all the right tools and you have to the right people. You have to have the right consultants, the right business partner, right. Before you consider any, any type of experimentation, because you don’t want to get an up suboptimal result, right? We do have a lot of,

 

companies that have tried their own experimentation and play around with it. And then what they, what they get is basic usage. Kind of like somebody coming in to use a chat GPT just to write letters or something, you know, how deep can we go down this rabbit hole to increase productivity and, and help you get in competitive edge and a more consistent customer experience. Why experiment when you actually have businesses out there that are leveraging the technology already.

 

many of the same use cases you have and you’re vertical. you know, cause when we discuss experimentation, a lot of times people are doing this on their own with their own platform or trying to create your own platform. Then it’s more than experimentation. Then it’s getting the right tools, getting the right, you know, you know, creating your own ecosystem. Right. And then, and then only then when you have the right ecosystem with a comp-

 

right orchestration or right from client season. And then you put it in your use cases. Only then can you actually have an accurate, you know, an accurate experience, right? Why do all that? And that’s, that’s what Cobus was mentioning, you know, this is supposed to buy BERT versus build. That’s the advantage of dealing with, with working with a company who’s been around for a while and understands the,

 

you know, the use cases that what other companies are doing and say, have you tried this? Have you tried that? And, know, they’re going to build something and are going to say, well, you know, I didn’t have that information. then, you know, if you want to get that, I mean, they can, you can invest millions and millions of dollars into this, the creating your own ecosystem. And it’s probably still not going to be right. So, I mean, that’s my thought. Kovac, think anything differently?

 

Cobus Greyling (01:01:30.306)

Yeah, I think call what you mentioned is true from like a macro enterprise level. But if I think of on a like more personal level, it’s I’m very outcome space. Like every day I’ve got tasks I want to achieve. to the way I got into programming was like, I would write utilities for myself. know, Python utilities or just bash utilities to transform information, gather information, just to make my life easier.

 

And then I keep those utilities, those little programs and just run them. And I tend to use AI in the same way. You know, like when you spoke about the hypothesis and like what a success look like, I know what I want to achieve on a personal level every day. so, you know, I use AI to expedite that and to get closer to, or get to that outcome faster.

 

And whatever I use differs from day to day. think a big, I wrote a blog in this recently, but you know, dog food in your own AI. think a big indication is how to what extent does AI companies use their own technology internally? And I mean, we do, but it’s a shockingly low percentage of AI companies that’s using their own AI. It’s like a vehicle manufacturing company.

 

and no one drives their own vehicle. You know, they drive some other brand. But I think that’s for me, Shawn, that’s like the main focus too. And I think that’s a good flywheel for, like you mentioned, you’re a small marketing group, know, small group of people. That’s a good flywheel to see which tech works. How can you spin that out? That’s just on a personal level. And I love the way people compare notes and especially on Reddit, what works for them and what don’t.

 

does not work for them. think one of the sort of going off on a tangent here, but I think that’s why the DGX Spark of Nvidia so popular, that small GPU unit, because people run open source models on that. it’s, you know, it’s no token costs, you know, it’s just the price of the device, the hardware.

 

Shawn Cordner (01:03:45.442)

Yeah. Yeah. I could argue both ways probably because to support Carl’s position, you know, people aren’t going out and building their own telecom infrastructure. They’re leveraging a telecom provider and you know, they’re not building their own computers. They’re buying computers. And so I get that. But at the same time, I think the business, if it’s going to seriously leverage AI in the future, needs to start building a

 

foundational skillset and institutional knowledge for the business about AI. I think doing these kinds of, that kind of experimentation yourself and building yourself also kind of forces the issue of education and training and governance and data readiness, which probably applies both ways. You know, these are disciplines that, that the business needs to develop and the business can’t develop them if it doesn’t have the opportunity or the necessity like we talked about before.

 

to develop them and I view it as an investment, right? Brendan, again, on my team, he’s a really smart, tech savvy guy. He knows his way around HubSpot. knows his way around everything, right? He’s a generalist. But by telling him to get involved in building some AI initiatives for the marketing department without third-party help, he’s educating himself, right? So I’m giving him license to spend company time.

 

to mess around on Claude and start figuring things out on his own. And that is a skillset that he’s going to develop and grow and leverage here. And he’ll be able to leverage it in his career in the future as well. And I’m learning as a by-product of that as well. Right. So I’m a Gen Xer, technically an Xennial, if you know that micro generation, but you know, I’m

 

48 now and I’m finding myself being a little grumpy about technology. So I’m being dragged kicking and screaming into this. you know, I need a, a forcing function for me to actually get my hands dirty. I can have these conversations. I understand the business theory and necessities and outcomes that we’re trying to achieve with these things. But you asked me to go build something for you. I literally don’t know what.

 

Shawn Cordner (01:06:04.27)

button to push first. have no idea, you know, so I have no hands on experience in this kind of thing. So I think a business does that, but that leads me to the last topic that I wanted to touch on here guys, which is let’s fast forward a little bit in your AI maturity as an enterprise. And let’s say that the people listening here, maybe they’ve got no AI agents today. Maybe they’ve got a handful because they’ve been doing some of this experimentation and that let’s fast forward a year and let’s say that they’ve got a hundred AI agents.

 

Let’s talk about Char for AI, not AI in HR, but Char for AI. Ultimately, if you have 500 AI agents running in your enterprise, who manages them and what does that org chart look like and who’s accountable for what they’re doing? How does the human workforce start to work alongside of the AI workforce? And what does that look like from an organizational and a management perspective?

 

Carl Katz (01:07:00.654)

Yeah. So that’s a good point. And that’s why organizations need to have card rails on AI usage within the organization. They just shouldn’t let everybody utilize different AI solutions. Like internally, one of my part of sales directors is leveraging Claude and several other AI platforms like Notebook and things of that nature to facilitate creating.

 

you know, creating these, account overviews, right. scenarios. And I said, yeah, that’s okay. You can leverage it for that. he’s also leveraging our AI for work product, which is our customer experience, employee experience product to facilitate the information instead of like a chat GPT, et cetera. So they’re all working together, to facilitate this output. Right. But it’s important to have the guard rails on.

 

What AI usage is happening at work, right? And organizations need to be in sync and lockstep. And what AI they’re utilizing, how are they utilizing it? What are the compliance standards associated with that? Right? What are the risks associated with that? They really need to nuance it significantly because when you open things up, then it gets more, things get more convoluted and then actually you’re risking,

 

to risking the organization from a compliance perspective, you know, and also, you know, you don’t want people using AI for certain tasks, right? Writing a letter is one thing, but actually created doing my job description, right? Leveraging AI, you might not want to do that, right? Whether you’re like a insurance adjuster or something of that nature. I mean, you can actually create AI to facilitate the output on a claim if you wanted to, right? Without human allude.

 

Right. And you mentioned human loop. think human loop is critical, especially when you’re judging the output of information that is going to cost the organization’s significant amount of money or whatever it may be. could be money. could be something else. Right. Human loop is critical and people don’t realize that that’s an important part of what we do here at Kore.ai. And, and I think especially as it pertains to like banking decisions.

 

Carl Katz (01:09:27.904)

insurance companies, loans, and things of that nature. You just don’t want to leave it in the hands of AI. So keeping track of how AI is utilized and what AI tools are being used in workplace and put on people’s laptops and how they’re leveraging it could be bad, but also it could be good because you could leverage and you could say, wow, well, Joe is using AI for this and this could be a really good best practice within the organization. So organizations can learn from their employees because

 

You know, different employees have different skillsets with an AI and they can create amazing things to benefit the organization. Of course, from a productivity perspective, but obviously you have to know, you know, place guardrails on that, you know, and no, no, no limitations, no limitations. And by the way, from my perspective, when I walk in to a very large organization and they’ve built their own CRM, for example, that’s a flag.

 

Like we could do an API integration into the CRM if that’s open, open source, right? Open API. And that’s great. But to me, that shows that the organization is trying to be progressive. But I will tell you that in many cases that that CRM is outdated by the time they build it. Okay. Because something happens, some change happens in the organization that they haven’t accounted for.

 

And then using their own builders and they’re constantly cycling through builders, know, engineers, and it just, just the changes, the CRM doesn’t work. So you’ll see people moving the Salesforce. Right. From their own tools. You don’t see it the opposite way as much. Once a lot of times these, these CRMs are legacy CRMs that they’ve been using for years. know, they’re using these legacy CRMs for years and they’re just afraid to get off, but they know it’s not doing what they need to do. The same.

 

for AI, right? You build this legacy tool and in two, three years from now, it’s not going to be what you want it to. You don’t have the support. You don’t have the technology group. You don’t have the expertise to take it to the next level to satisfy what your requirements are now. So that’s why I would look at AI and legacy technology.

 

Shawn Cordner (01:11:46.284)

Any thoughts, Koguz?

 

Cobus Greyling (01:11:47.576)

Yeah, I fully agree with what Carl said. And just when you, on what you said, Shawn, about having multiple agents and this problem of agents sprawl. something that’s happening now is it’s just technologies evolving, you know, as adoption evolves. Like, so one of the things is this whole idea of agent control plane. And you’ll see a lot of vendors are speaking about this.

 

The agent control plane that handles this agent sprawl and it gives you inspectability, observability, discoverability. So I think, you know, like this whole problem of all these rogue agents, different products will come along like an agent control plane where you can have like a rich set of agents and you can monitor and manage those agents. A second thing is what Carl mentioned about human in the loop. Obviously you want.

 

you know, confidence thresholds and if confidence is low or if it’s a risky transaction, you want to involve a human. Recently, Andre Capati spoke at a Y Combinator event and he was a founding member of OpenAI for many years, the lead on Tesla, you know, autonomy, autonomous vehicles. And he spoke about an autonomy slider, right?

 

He said like we live in this world of all or nothing, no autonomy or full autonomy to AI. And he actually spoke about the autonomy slider and depending on the use case, you want more autonomy or lower autonomy. And that came to mind when Carl spoke about, you know, the whole human in the loop idea. And we actually constructed this whole grid of different tasks and risks and domains. And in each one of those, you want a different level of autonomy.

 

and a different level of human involvement. So I think that’s really important when it comes to implementing AI agents or agentic AI in any software with agency, where you understand the task that’s being executed and understand the level of autonomy you want and the level of human involvement. And just to close that off with, you know, open claw or claw bot, you know, that’s, I mean, that opened a whole new attack surface when it comes to the various attacks.

 

Cobus Greyling (01:14:07.124)

At least that showed us that people have a desire to automate their lives. that was a good confirmation. But that just came to mind when you spoke about these rogue bots running around unmanaged.

 

Shawn Cordner (01:14:22.978)

Well guys, RogueBots running around sounds like a good place for us to stop.

 

Carl Katz (01:14:27.406)

you

 

Shawn Cordner (01:14:28.91)

So, Cobus Grayling and Karl Katz, thank you for your time and expertise today.

 

Cobus Greyling (01:14:32.526)

You’re welcome.