Latest / 10X AI with Julius Neil / AI’s Hardest Problem Isn’t What It Can Do — It’s What It Leaves Alone

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AI’s Hardest Problem Isn’t What It Can Do — It’s What It Leaves Alone · 10X AI with Julius Neil

• Charif - DataChi: the hard problem is the silence, okay? It's not the detection. But deciding what not to tell you is the hard part. Is it really a human? Is Julius Neil Buenconsejo: Yes. Charif - DataChi: becoming a robot as well. the applications is a set of good tools, but the tools are waiting for you, And the teammate does the work you did not watch Julius Neil Buenconsejo: good day everyone I have here Mr. Charif Mouttaki one of the team members of Data Chi. previously I already interviewed their founder and CEO of Data Chi, which is a virtual teammates. This is their positioning. So Charif, can you tell me more about your experience and your background? why are you helping DataChi now? Charif - DataChi: Yeah, yeah, very good question. Thank you, Julius. So my background is mainly sales. Okay, at the beginning of my career I had a brain and I was a pre-sales engineer, but I switched to the sales full sales role. And as the rest of the team at Datachi, we are all sales so to say. So in fact, very quickly we all had big big blue names like IBM, like Oracle, Fujitsu, Broadcom, Cisco. So that that's so to say big shops. So we experienced quite rude conditions with very promising stacks, very appealing application landscape but not delivering. So that's the DNA of the sales team and the team. core team at Datachi. So also very quickly, at DataChi either you have people building the thing or you have people selling with the thing. So their is no layer in between. So their is no product people writing rules for a job they haven't never done in the past. Julius Neil Buenconsejo: So you mentioned about this, blue chip companies like IBM. So Fujitsu, worked in Fujitsu way back before when I was starting, but it's more on the semiconductor side. currently, so how did you teach this virtual teammate with Fujitsu for example. This is Japanese company, they're very structured they know what to do. now a foreign company, which is not a Japanese company, is coming in turf. So how did you convince them that Datachi is good for their system? Charif - DataChi: I would use the angle that we we developed at DataChi, that is the angle of virtual teammates. So that word is very good and I would make some comparisons here. I was just mentioning the applications. So the applications is a set of good tools, but the tools are waiting for you, okay? Whereas the assistant is answering to you, okay? And the teammate does the work you did not watch in fact. That's the the real difference. So and the teammate Julius Neil Buenconsejo: you Charif - DataChi: versus all the bench of agents that are produced right now in the industry. since it will come back later in the interview I'm sure, but I will massify all my pitch on the the teammate that has to answer to someone and an agent that's just to execute. Julius Neil Buenconsejo: based on your background, when you joined Data Chi, so what is your primary purpose? is your goal to be able to, it's product or sales, but in your KPI, what's number one? Charif - DataChi: Mm-hmm. So let's then use the VTM for instance. my role Julius Neil Buenconsejo: Yes. Charif - DataChi: was to position the new paradigm that we were developing at the DataChi and currently with the the alpha that will be released in the coming weeks that will be implemented also. So my role was for instance to shape this VTM instead of using a specific agent, okay. And having said that their is a lot of work behind because the world itself does not sell, you know. The moment you sell teammate, okay, the buyer starts to compare it to a real person. And the the question he will ask is, is it good as someone, as someone I could hire, you see? So On this competition you cannot win. Okay? It's not how you win. But the teammate itself, the virtual teammate, is how the buyers will understand once it is in the team. So Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: they understand the concept and then start the other part of my job. It's why they will sign. So preparing all the documentation and all the path to sell. Julius Neil Buenconsejo: so practically you're selling, a new teammate to a company, which is a virtual teammate who never sleeps, never forgets a follow-up, updates CRM, prepares your meetings and finds prospects. Right? Is this something that you're selling to your clients? Charif - DataChi: It's if you don't mind, I will let I would just try to do it the other way around, okay? Let me do it as a reps day rather than as a product itself, okay? So so you understand where we position ourselves because the paradigm is Julius Neil Buenconsejo: Okay, yes, no problem. Okay, yes, yes. Yes. Charif - DataChi: quite unique on the market. There are very few companies, but that's our chance also because we are launching our product in twenty six, so we are really, really able to address this. So Let me come back to this approach. Julius Neil Buenconsejo: Okay. Charif - DataChi: Before let's take a day for a rep, okay? Before they speak to anyone, the question is who who to speak to? Okay, which account looks like the ones that already had a success or worked in the past, and who does the buying at that account? what are the contacts details in place in my CRM and in my DB, in my database, and are they still a real, you know? That would Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: take one hour, maybe more, maybe less, but roughly we are in this magnitude, okay? And but that's for just to give you the the pitch now, none of this is selling, okay? Then start the timing. Someone something changed in at at the account that had been selected. For instance, their is a hire, their is an office that opened or closed, or their is an announcement in the group. Okay? Knowing which of those matters is a full-time job. But nobody, no rep has the has the time to to to to spend to do this intelligent, to to do this prep work. And so most of the rep, what they do, they they are they they call in the order of the list is in, you know. Which is in fact a n an order of nothing. Okay. So having said that, we have Julius Neil Buenconsejo: Okay. Charif - DataChi: the preparation, we have the timing, and now we have the writing. So the for the writing is first message, first message with the account, then you have the follow-up, then d you have the the message after the meeting. So the meeting roughly twenty minutes, forty-five minutes to to prepare to prepare it, ten after to capture, and Then enter and fill the CRM. Once a week you will notice what leads had been quiet the entire week, and then you will act on it if you have time. And to add up to add it up, most of Reps Week is is not the conversation. See? It's Julius Neil Buenconsejo: you Charif - DataChi: everything standing before and after the the meeting itself. So what should the machine do in fact? That's where we are we're coming now to the concept. What should the machine do in twenty-six? All of it? Really, should use all of it. But what stays human here? The conversation itself. And their Julius Neil Buenconsejo: Okay. Charif - DataChi: are two decisions. I will come back to this because I I I read your last arc article. I need to complement it. So their Julius Neil Buenconsejo: Yeah, Yeah. Charif - DataChi: are two decisions. When a mistake is really expensive, okay, then the human should intervene. And when the right move is worth real money. That means you you you need to keep those two. apart from that, I'm thinking loud now, their is nothing else. So the machine should take care of everything I I mentioned, make the decision r in the hands of the custom of the rep, the human, only for those two cases. Otherwise do do everything. Julius Neil Buenconsejo: Okay, okay. So it's a very, it's very interesting, you know, because when, when I had an interview with Ray, he described this as a virtual, you know, virtual teammate, but I never really understood how are you going to position that in the organization, especially in the sales funnel, in the sales process. So now it's very, it's very, it's very clear to me now their will still be some, you know, human intervention. You need to have the conversation and then from that conversation, you'll be able to start the process. So now. since it's clear to me. So how did you pitch that to the companies like IBM or Fujitsu or some Cisco? Because an AI teammate, they might mistake this as an AI agent as well. So what's the difference between an AI teammate and then an AI agent that we can use or we can develop inside our company? Charif - DataChi: Mm-hmm. So as I mentioned already, the the engin the the agent will interoperate with the the the human, okay? The agents will in fact be on a reactive mode, whereas the teammate is having high percentage, high level of autonomy. That's that's the main difference. So I will come back to it, but behind the scene you have a lot of work that had been done on the playbooks, okay, dedicated for us at Data Chi We are focused on the the sales function to help the company, customers to develop their revenue. So focused on the sales with the playbooks working in the background. Julius Neil Buenconsejo: Okay, so just for our viewers and listeners, so for just context, are you selling a productivity capacity or a new worker? Charif - DataChi: It's a productivity capacity to be honest. It's a human that is enhanced. in my background you see you can see that the motto for the company is more human than humans. So we keep the human in place and we are just helping them to be really productive in the nowadays situation. Julius Neil Buenconsejo: I remember before with my conversation, he mentioned about a different type of structure. There is, for example, the chief executive officer and then their are the PTMs, the virtual teammates. Is it still the same process? It's still the same process. Charif - DataChi: Yeah, yeah, yeah. The this is this is the backbone actually. we won't change it. we have this chief of staff that is supervising Julius Neil Buenconsejo: Yes. Charif - DataChi: the other team virtual teammates and then you can either interact directly with the teammates or having your your peer who is in fact chief of staff summarizing and focusing the right actions to do. Julius Neil Buenconsejo: How do you integrate that into the legacy systems of different companies right now, your clients? How are you going to, are you going to have a layer on top of the legacy systems or are you going to have it a standalone system for them to use? Charif - DataChi: You see me smiling because obviously you you provided me with a script and we are totally out of the script. It's a very good question. I like this question because in fact Julius Neil Buenconsejo: Yes, yes, yes. Charif - DataChi: I mentioned the the year, we are in twenty-six. Okay. So in twenty Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: six the promise is not adding a new layer on top of the existing stack. So what we do in fact is whatever is the complexity or the call it richness of the stack. We are having an overlay. that is in fact little by little being the unique interface. So I give an example, extreme example. A company that has no CRM or not satisfied of the CRM, we just need the DB. Okay? And we will play the role of the CRM. There is a CRM in place, everybody can understand this example. We will feed the CRM, that's it. So we are not adding something. Julius Neil Buenconsejo: clear. Charif - DataChi: Okay. Julius Neil Buenconsejo: Okay, that's nice. So it's very easy. It's very easy for companies to just adopt it into their systems. So that's very clear now, clear enough for our audiences because some of them will be, maybe they're asking right now, how do we integrate this? Because it's a new concept. You created this and you said that it's very new, like you are doing like a blue ocean strategy. Very few companies, I don't know if their are a few companies that is already doing like yours or maybe you are one of the two or the only one in the industry. Charif - DataChi: Okay, to be honest, I hope that my researches because I am also part of the team that is doing the all the intelligence in the market, are still correct and the other we identified on market, very few, are still alive because yeah, we are doing the same. what we are doing different, obviously we are in Europe, you know, and you ask me where I'm located, it's the same Julius Neil Buenconsejo: Yes. Charif - DataChi: for the servers. They are in Europe. So their is a high level of sovereignty behind. We can come back to this later on, but The idea is that the market is demanding this kind of approach now. That is, in fact, whatever is the ecosystem the company already invested in, we will just interconnect with what is in place. And if it's super secured, guess what? We will impersonate via the browser of the user and just virtually play the role of the user and access so long he gives us the access. To the same complex and super secure the platforms, it's easy. Julius Neil Buenconsejo: Okay, so let's go to the real use cases. So can you give me one real example that our, you viewers and listeners should be able to understand in their organization? So for example, let's start with the prospect research and prioritization. Charif - DataChi: Mm-hmm. Okay, then you are now you you got the concept of the the virtual teammate. So in fact here we are talking about the the the the playbook now. So imagine that first you you understand also that at DataChi we are focusing on the sales function within the organization. So the Julius Neil Buenconsejo: Yes. Yes. Charif - DataChi: sales has a purpose is to sell. Easy. Easy to say that. And the purpose is based on a series of tasks. Okay. So first all the tasks will be automatized. Okay. And we won't bombard the cells with a lot of questioning and to having in the loop to to interact with all these result or outcome of the executed tasks. Okay, so that's in the background. But when we will come to the human. Okay, to the sales, it's to either to judge, to say, okay, let's proceed, or what do you what would you do at that moment now? So the sales play, to make long story short, the sales play will adapt dynamically based on the targets of the company. I give you an example. you have a target of of one million for for you and your team, okay? So that's Julius Neil Buenconsejo: Yeah. Charif - DataChi: the one million for let's sell f let's say half of the year. Okay. And after one quarter, you only you on your team only reached three let's say two to two two quar let's say half of the target. Okay. So the sales play will automatically adapt the the level of actions towards the fishes you have possibility or chances to close based on the history instead of continuing trying to have other fishes bigger or long longer sales cycle to try to try to cope with these discrepancy between the obtained numbers so far and the real target that is one million. See? So that's dynamically adapted in the background. That's an example. Another example will be based on some signals detected on your patch on your set of customers because they opened one of your customers opened a branch or hired certain specific skills in the in the last days it will prepare a set of communications or post even and just suggest to sales Julius Neil Buenconsejo: you Charif - DataChi: to to sales rep please what do you think can I or Will you proceed with the posting or the the sending? That's the kind of action that will kom up in the team Mate interaktion. Julius Neil Buenconsejo: Okay, so it's very interesting to see that, like you are correct, most of you have this sales background and you know that a lot of these are overlooked when it's not automated and there's no system behind that. And then there's a big, there's a big loss for the company if we don't follow through with all of these gaps. Right? for once, for example, the timing. So how is the timing going to be? For example, the yearly, you know, the daily KPI or goals for sales for this year is fiscal year is around 1 million euros. And then when is the first, you know, check of this virtual teammate that you are lagging behind? Charif - DataChi: Okay, okay, then Julius, I will answer up to a certain moment. You you can understand that I can start with a Julius Neil Buenconsejo: Yes, Charif - DataChi: basic sales pl playbook that had been implemented, Medic, for instance. Medic is a big standard, okay? That's that's not secret. But Julius Neil Buenconsejo: Okay, yes. Yes. Charif - DataChi: the rest is part of the je ne sais quoi, the secret recipe. I won't but you can imagine Julius Neil Buenconsejo: Yes, no problem Charif - DataChi: that this is where we are putting all our efforts. Really, even in the phase of detecting the signals, because you would say you would can you could ask me, okay, where how do you compare a signal between sorry, how that can you detect and raise a signal in the ends or to the attention of the rep, whereas the rest is just noise. Okay, I could argue on this. So this is also play based on the p on the sales book, the playbook, sorry, because It's okay. It's really okay. I can give you a number. If you go on our website for the moment we have sixty nine, but we think that we will end up with well more than one hundred playbooks just for the Julius Neil Buenconsejo: Wow, Charif - DataChi: sales. Julius Neil Buenconsejo: nice. It's a lot of work that you're putting on this. it takes a lot of that you have. So where do you get those data now? know, the qualified data that you're using. Is this based on your experience or you're still, you have a research and development team on the side doing all of the data gathering? Charif - DataChi: Correct. Correct. Correct. Correct. Okay. Obviously we will do as all other big large company do that is in fact subscribing to official data sources, okay. And also use the usual suspects plus the information system of the customer. But mainly we just need to connect to the database of the customer. That's it. We do not need Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: really the C CRM is is is a layer that we are not requiring but if the CRM is their we can extract, obviously interact, feed the CRM but mainly that's our our sources. There is no we are not inventing data. Julius Neil Buenconsejo: So obviously we have basically the sales processes like standardized across different industries. But their are different products as well, right, that you are selling. Is this going to Charif - DataChi: Yeah. Julius Neil Buenconsejo: be one size fits all? Any industry, any product, services, or is it more effective in one or two industries first before you go into different industries? Charif - DataChi: Okay, very good. it'll it it will be auto adaptive. But during during the onboarding phase, it's very cra it's very straightforward, you mention your website, you mention your email address, and obviously you just mention your role in the in the company. And the system will automatically figure out which industry, which target, and start to interact with the with the user. So We are trying to make the onboarding which is leading to your answer the most efficient possible, the less intrusive possible in terms of appropriation, so that the user gets like you know, reward feeding the system and seeing like an echo that the mach the system is understanding his industry, his challenges, and at the end of the process. We were we are proposing a set of playbooks and those playbooks will be adapted by the end user, you know. But along the usage of the applic the virtual teammates, all the playbooks will show up. Okay. Obviously, I just I just did not mention it again. You can put it fully silent, you can hide it. for instance the sales manager of the the head of sales could say I want to hide it from my team. I will manage those see? So this is totally open. Just to address your point that is one product fits all yes but it's fact at the end of the day it will be as much as product as we have customers. Julius Neil Buenconsejo: You mentioned about the onboarding. How is the onboarding process? Is it going to be all online using your website? Go to the website or their Charif - DataChi: Yeah. Yeah. Julius Neil Buenconsejo: will be still human in the loop during the due-trict end boarding. Charif - DataChi: Yeah, the human will feed three to four. we are not finalized yet on the number of screens, but after there's four, that's it. The onboarding is is ready. So we want it very, very quick. But at the end of the process, as I mentioned, the system must demonstrate that it understood already the industry, the main targets, the challenges and interact with the person who is doing the onboarding to to say, okay, am I right or totally wrong? Can you correct me? Or on on on the point, you see? Julius Neil Buenconsejo: Yes, so you mentioned all these big companies earlier. So their might be some businesses, know, small businesses, not as big as they was listening to us right now. Is this going to fit also to small businesses or this is only enterprise level thing or, you know, visual team? Charif - DataChi: Very good, very good. Yeah. That's this this is all about the positioning. So the idea is to target first we need to target a company that is feeling that they are losing time during the sell cycles, okay? That they are leaving some money on the table because of lack of time, because of lack of efficiency, name it, of the system or interaction, name it. So we need to have someone a company that is suffering, that is conscious that they can improve, they can have more revenue with the same number of people in the in the company. Whereas we are also targeting for the moment companies not big enough to have a huge data team, you know, because if you have a a a bunch of data scientists and data team dedicated to the sales team, like you mentioned the big the big numbers but the big names Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: but You have these in all industries at for instance the the the the SP five hundred. They won't use our product for the moment because they have a bunch of data specialists feeding the cells with the right actions Julius Neil Buenconsejo: you Charif - DataChi: to do with the most relevant action to be defensive on the market and so on and so on. And the Julius Neil Buenconsejo: Okay. Charif - DataChi: the the third criteria I would say is to have a company. That is ready to try to change something without hiring someone. See? Enhancing the existing team, want really wanting to enhance and maximize their pipeline, but not ready to hire someone. That's the sweet spot for us. So you ask me about the size of the company? Any any company. Julius Neil Buenconsejo: Any company. Yeah, so another thing is if I'm business owner, SEO of company, so how would I ensure that I can trust whatever is being given to me during this process? Like I'm a first time user, so of course I will have a lot of questions, but on the backend, everything is going to be automated and you will give me a playbook how to do that. And then what is the trust process? How you get the trust of the person who is using that? Charif - DataChi: Okay, you're okay, that's super good point because in fact I would just question the ladder of trust itself, okay? Autonomy Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: it's it's it's not about one dial, you turn up and trust has grown, you know? As trust grows. autonomy on a small account, so to say, is like a feature. See? Okay. But the same autonomy you allowed, okay, on your biggest account. Is not a feature anymore. It's a risk. Okay. So on most things, all of it, and on few things, none. The skills is knowing which few, in fact. See? So everyone else here, everyone here is asking how much can I can it do to the industry? How much can it do? Pushing to the limits. But we are data chi, we are asking how little. Can it show up? Can it show you? See? And sorry, I have to come back to your article because you wrote it yourself last week, okay? A human in the loop Julius Neil Buenconsejo: Yes. Yes, yes. Charif - DataChi: isn't the same as a human judging. Exactly. So yeah, okay, Julius Neil Buenconsejo: Human judgment. Yes, correct. That's why I'm asking. Charif - DataChi: we did not prepare this one. So Julius Neil Buenconsejo: Yes, yes, it's okay. That's good. Yeah Charif - DataChi: For me, because I I like this article, it was resonating with the architecture we are putting in the product. It's exactly right because I would add that the part that decides which one you you you are you are getting, a volume, in fact. give a name give a named owner three decisions during a week, they will all I mean it's human, they will Judge every single one, okay? But give them 300 to approve all of them, to to to be approved or to be judged, Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: they will automatically approve it, you see them. So and the the records Julius Neil Buenconsejo: Yes. Yes. Charif - DataChi: we we analyzed based also on our experience looks the same either way you take it. So the hard problem is in fact, and that's Not something we have issues we are thinking about this point, but the hard problem is the silence, okay? It's not the detection. I think all the agents even are able to show you that detect a lot of things and this had been implemented like the basis in our product. But deciding what not to tell you is the hard part. See? And this is something we cannot demo because We will show empty screen and it looks like nothing is happening, but in fact an empty screen for us is where the product is working good. So the the Julius Neil Buenconsejo: Mm. Charif - DataChi: this is for me to resonate with the article and I will put a last comment about the adoption barrier itself. Because if you look at the adoption barrier, it is the same story. People aren't waiting for the machine being wrong. Because everyone expects it it expects that the machine will be wrong, okay? They are just afraid of being the person who is signing. So you start to to to be successful, you start where being wrong is very cheap, okay? And trust gets built by watching it work, but not by reading a benchmark. So what a lot of organizations make wrong here is that to maximize the ROI. They start on their biggest account instead, and someone in the organization got a bad message and that will kill the pilot on the point. So not because of the tech tech fail, but because someone got embarrassed. So that's to cover your point. Julius Neil Buenconsejo: Yeah, I like your point about, you know, it's not about detecting what is wrong. It's, you know, the silence, you know, the silence that you are worried about. So can you elaborate on that? So our viewers will be able, our listeners will be able to understand what is that silence you are talking about? Is it silence Charif - DataChi: Yeah. Julius Neil Buenconsejo: not to say what mistakes or errors, or is the silence is like, because nobody knows what they're doing. Charif - DataChi: No, it was just to mention to to to finish your analysis of your article last Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: week because in fact that's that's the the point. If you are flooding with a lot of tasks been done that had been done that had been detected, all this noise had been trapped, okay, great. But me I'm just a human. I need to take a decision. And based on this, I'm flooded, I cannot I I trust you, let's automatize it, let's let let let you do that. So for us, all this can be as soon as the customer the the sorry the employee the sales is confident enough, he put automate it put the the demur to automatic automation. this is my bad English now. Automatized tasks. So Julius Neil Buenconsejo: huh. Charif - DataChi: this cannot we do not need to show what is done in the background to to the user. We just need to come up with a strong signal. a decision as I said either we feel that the system feels that their is a risk and if we make this the risk on the wrong part on the wrong side it's a loss it's a lot of money it could be reputation it could be money could be an impact on the sales cycle so then we raise the information so that the sales can take a decision and on the other way or also when We detect something that is really, really the up the the right moment to to to do upsell or to shorter the sales cycle because we detected that the customer is very, very in demand of this product or the solution the customer is selling. See? Julius Neil Buenconsejo: Yeah, so, okay, so I would like to have a contrast about detection of error and then how much are this company losing when you made the wrong decision. And based on the, do you have a study, for example, you are targeting this problem. Do you have a study that if they don't do this, they don't apply the VTM, they are going, in this industry, they're going to lose this much. billions of dollars in certain industries. Charif - DataChi: Mm-hmm. Okay, so in fact, we we don't have study to be honest, we don't have study. Julius Neil Buenconsejo: Or any article or any news from the mainstream media that's not following up, not being able to track down all those KPIs monthly and then missing out on your fiscal year targets will affect the organization in terms of financial and economics. Charif - DataChi: So now it's not an article, it's based on the bunch of colleagues at Datachi who are also sales, experienced sales, and we all at the end of our days experience this money we left on the on the table. And I mentioned a series of reasons why even if we were doing correctly our job, we were using correctly, feeding correctly the CRM, we were missing those opportunities. And we all had let's say Some frustrations because even Julius Neil Buenconsejo: Yeah Charif - DataChi: eating the numbers and believe me in the team we have very strong sales persons, they they were overraschieving the the numbers, even though their is a frustration because you can do way better afterwards, you know. So their is no article, that's a good question, but we implement it and we believe strongly in it because yeah, it's a product designed by sales. Julius Neil Buenconsejo: It's based on your experience, yeah, we have that. Charif - DataChi: Who suffered of this, yes. Julius Neil Buenconsejo: Okay, so another thing that I'm thinking since this is going to be a virtual teammate. So if a virtual teammate helps a salesperson to close the deal, then who closes that? Who is KPA? that? Is it the salesperson, the human or is still the VTM? Charif - DataChi: Mm-hmm. So very, very good question. it's still the human. The human is signing, okay? their is no hesitation to have here. We want to have the the the in fact in our model it's always the the human who will sign the the deal, okay. But having said that, you remember the two moments I said earlier? Julius Neil Buenconsejo: Yes. Charif - DataChi: the Rep will be more in control to decide when acting and when accelerating a deal or when putting some distance and being more cautious based on real facts and yeah, he has experience, but it's let's say more comfortable to approach his account or close his his opportunity with real facts that were judged. like low signals or in a dead angle not available because he had no time to review those. So yeah, the for us the human is always in in in charge of signing, yes. Julius Neil Buenconsejo: Okay, so we're talking about from features now to outcomes. your RAI, for example, can update the CRM. Then you get your selling time back, correct? Charif - DataChi: Correct, yes. Julius Neil Buenconsejo: Is this always about inbound marketing or outbound marketing or sales? Both, okay. Okay. Charif - DataChi: No both. Bus, both, both, both, both, both. Bus, both, boss, yes. Julius Neil Buenconsejo: So how is the system works when it comes, the difference between the inbound process and then the outbound process, and then the playbooks that they're going to use. Charif - DataChi: Mm-hmm. So in terms of sequence, I won't name the Julius Neil Buenconsejo: Yes. Charif - DataChi: modules that are working, okay, because I do not want to make this too dedicated to us. I want it really to have this conversation about the new philosophy we are introducing on the market. But yeah, we are Julius Neil Buenconsejo: Yes, yes, Charif - DataChi: catching let's say Let's make it a little bit different. You already experienced making a cold call in your life? Yes. You liked? Julius Neil Buenconsejo: Yes. Charif - DataChi: Obviously, yes. Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: You see my point. As as far as I remember, nobody likes to make only cold call. Okay? So all what we do, whether it's inbound or outbound, is based on making the call when it will happen the warmest possible. Okay. We will disturb the cells with let's say an information, an action to to to to to to do, to proceed with the most information possible on the contact, on the company, of the sector, on the industry, so that it will have really facts and figures. confidency to to start even if it's the first contact. I would say even you, Jolice, for instance, the Julius Neil Buenconsejo: Okay. Charif - DataChi: first time you will be contacted by one of the rap using Datashi, okay, Julius Neil Buenconsejo: Yeah. Charif - DataChi: would be you made this, you published this, you you wrote this, and you are apparently targeting this. So it creates from the from the very first contact already intimacy. Even out of the blue, this person is reaching you. So that's to give you the idea. Obviously, we have a module that is continuously scanning the market on different data sources that I already mentioned to grab the triggers. That's making a picture to interact with the employee, the cells, and then we have a dynamic scanning. We in fact we call it sonar. Okay, I I said I won't mention the module, but we have a module called sonar. And this sonar will based on the picture that had been agreed, adapted, and corrected by the human, will constantly continuously scan everything to see if changes occur in if it's valuable or if it's worth it to disturb the human. I hope it answers. Because I wanted to make Julius Neil Buenconsejo: Okay, okay. Yeah, yeah. Charif - DataChi: some distance with the usual inbound outbound. It's not only that. It's it could be, you know, I don't know a ticket that is in suffering in in the support team, you know, a ticket, a complaint, for instance. That happens, you know. Julius Neil Buenconsejo: Yes. Yeah. Charif - DataChi: A complaint. So when you make your call, maybe it's it would be interesting to be informed that the the the person you are contact or the company that you are reaching out to is having an issue, a ticket open for Weeks or days and that is super important. So the rep can have this information and make it make a play with it. You know, we have a feature in our new release that is solving this issue. Don't waste your time to fix it. Just try our new product, for instance, see? Julius Neil Buenconsejo: Yeah, yeah. So moving into your positioning. So when you're marketing an AI teammate, are you marketing an AI or are you marketing what humans get back? Charif - DataChi: Okay. So in fact it's about now who will buy the AI teammate itself, okay? Yeah. Julius Neil Buenconsejo: Yes, yes. Charif - DataChi: So as I mentioned, that's that's a good point because the the the target for us must be steady. As soon as you cut you start to change this, you will have to change the entire philosophy. So for us, we are targeting the the sales leader, okay. And with a team that has no data data team behind the sales team to feed to help and that is big enough to to fill all the deals all the hours all the business that has that that is lost okay and that is too small to hire a dedicated person to fix all of what they feel that's the perfect Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: target and for them we have adapted our pitch to sell them The AI teammate to be tested, yes. Julius Neil Buenconsejo: Okay, so now your tagline, more humans than humans. So that's a very provocative claim. So what does more humans than human actually mean by the standards Charif - DataChi: Yeah. Julius Neil Buenconsejo: or by the positioning or the message of DataCheap? What does that mean as a product marketing? Charif - DataChi: Yeah. Yeah, that's good good point. Okay, okay, this is coming out of I invite you to to to go and check the reference. It's the first version of Blade Runner. So Julius Neil Buenconsejo: Mm-hmm. Mm-hmm. Charif - DataChi: if you recall it, in this movie that was the the the sentence for the replicant. And in fact, the idea we we keep in mind at Datachi is not replacing the human. We are trying the best as we can with our solution to make the human able to continue not only delivering, but to continue to be super relevant in their function of sales. So yeah, we are enhancing the cells for the moment because Datashi is also having some ambitions, we can come back to this later, but for the moment we are focusing on cells and we make the cells more human than human, yeah. Julius Neil Buenconsejo: Okay, what do you think is the parts of the sales process that cannot be automated? Charif - DataChi: the the conversation the conversation itself really we we already tried to to make even auto this conversation auto negotiation but yeah we want to have it as as much as possible with between humans and for us to to to to to to be able to put at the last centimeters of the closing of the deal, the maximum of chances to to our customers, we invite them and we will do everything to have their sales continuously making the the interaction hum between human, you know. That won't be replaced. Julius Neil Buenconsejo: Okay. Charif - DataChi: I don't I don't think so. Julius Neil Buenconsejo: Okay, so maybe, of course, lot of our listeners will be asking, how much should be automated when it comes to, how much of the AI should be allowed to do without asking? What do think? Charif - DataChi: Mm-hmm. So we all agree that yeah, we are all agree that all the tasks should be automatized. I mean that their is no way to to have. By the way, coming back to more human than humans, if Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: you have a human continuously performing tasks all day long instead of selling, easy easy. Julius Neil Buenconsejo: Yeah... Charif - DataChi: Is it really a human? Is Julius Neil Buenconsejo: Yes. Charif - DataChi: becoming a robot as well. So now back to Julius Neil Buenconsejo: Yeah. Charif - DataChi: your to your point. I think that first the technology is totally capable to do all the automation for the tasks. Okay. But with having all these tasks automatized, for good, once for good, we have the human really in control of taking the right decision. That's the decision at the end of the day should remain in the hands of the cos the the humans, yes. That cannot be automatized. Julius Neil Buenconsejo: Okay, and then I'm anchoring into that, so some of the things will be automated. So for example, if an AI virtual teammate sends the wrong email, then who owns the mistake on this one? Human. Charif - DataChi: human human and that will happen. That will happen for sure. That will happen. we are minimizing as maximum as we can this kind of mistake. But you know the mistake can be we are dealing with with deals, you know, so it's a question of emotion in in in most of them. So even if we are in business to business, that can happen. So we are architecturing our solution to minimize at its maximum at s at its minimum possible. I give you an example. A good email with a good content, but arriving at the wrong moment. This is a mistake. At the same time, Julius Neil Buenconsejo: Mm-hmm. Mm-hmm. Yeah. Charif - DataChi: the right moment, but it's a Mr instead of a misses. This kind of mistakes, you know, you you already experienced in the past. Okay, I gave you a very dummy issue that obviously the system is correcting, but it will happen for sure. And the human is responsible, yes. Julius Neil Buenconsejo: Okay, okay. In terms of transparency, do you think the human should know exactly why the AI took an action or it's okay for them to trust the system right away? Charif - DataChi: Mm-hmm. We are coming back to the ladder, huh? So I think at a certain moment the confidency is getting higher. Why? Because the revenue is increasing, you know. So when the mistake is happening, the the human is considering okay, maybe their is a mistake, but look, we multiply the revenue by six times, for instance. Then coming back to your points, it will I think each time their is a an error in the system coming, it will come back and say, Okay, I I I can reduce the level of automation and look if I can judge more on what is being made in the in the in the backstage. Julius Neil Buenconsejo: Yeah, so before I went and had our conversation with Ray, he mentioned about the Maya principle, which is the most advanced yet acceptable. So Charif - DataChi: Mm-hmm. Julius Neil Buenconsejo: in terms of the learning curve for this system. How steep is the learning curve? Is it very easy to understand, very easy to onboard people using your system, or there's a certain amount of time that they need to focus on this system to be able to understand how to maximize and optimize their work in terms of adapting it to their daily task. Charif - DataChi: Okay, this is a key question. Very long question, thank you, but it will be very short answer because you you saw me Julius Neil Buenconsejo: huh. Charif - DataChi: you saw me hesitating between three and four screens. That's Julius Neil Buenconsejo: Yes. Charif - DataChi: it. That's it. After that, you're up and running. Julius Neil Buenconsejo: hey. Charif - DataChi: It's it's done. Julius Neil Buenconsejo: Okay, okay, this. Charif - DataChi: It's done because the first playbook will be okay. this is your email. Do you allow me to connect to your email? Yes. Then do you have a CRM? Yes. No. Okay. If yes, can I connect? There is the connector and so on so see it will be pff and if the user con don does not want to connect, does not want to do anything, and start to just interact with what the system already detected, fair enough, it can. So You see, we we want to make it able with different angles. So that's why in twenty-six it's gonna be the expectation. Julius, the level of expectation is Julius Neil Buenconsejo: Yes. Charif - DataChi: going to most possible to the simplification for the onboarding and the digestion Julius Neil Buenconsejo: Mm-hmm. Yes. Charif - DataChi: of the interface is unique. Ray explained this Julius Neil Buenconsejo: Okay. Charif - DataChi: Maya cont principle. And I like that because at the beginning we had very, very, very original interface that was in fact like an Alexia on the screen and you were just talking and it works, but it's it's too shocking for a lot of people. So we keep the screen so far. But my point is the the the one I mentioned what will be in the screen depends on the maturity of the user. That's it. Julius Neil Buenconsejo: Okay, so going back to your tagline when comes to the teammate. Should an AI teammate have a name? Can the people put a name on their AI teammate? Or it's just a standard name for data cheek? Charif - DataChi: Super good, super good. In fact, our VTMs or our VT our team members, teammates are already coming with names, so their are names. But good point. Julius Neil Buenconsejo: Mm. Charif - DataChi: I will ask if we can change and adapt. Julius Neil Buenconsejo: You Charif - DataChi: you can also mention them, but yeah, they come basically with with their their surname so that you can recall, you know, easily. Julius Neil Buenconsejo: Okay, another thing is should an AI teammate also have a personality based on the company culture or the base of the person who's using that? Charif - DataChi: Okay, now we are entering in the interaction, in fact. Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: okay, first personality, it's very, very vast concept. Okay, you know that the the big LLMs are working on it, and behind you have all these concepts of intention, detection, and also the principle of dreaming and more and more making the AI looking like human. But because it's just efficient, it's super efficient to to to to reproduce our brain. But here what I would say right now is that the more you interact with your virtual teammate, the more you will see that the teammate is closer than to from you and able not to anticipate but suggest you exactly what you were even not expecting but you will like to to proceed with. Julius Neil Buenconsejo: Yeah, so since this is a teammate, should this AI teammate should be evaluated, you know, their performance evaluation, for example, annually or yearly like a human employee, or there's going to be a hybrid evaluation between the human using that and then the AI performance. Charif - DataChi: Mm, it's a good idea. you allow me to suggest it to the team? Because for the moment we did not think about that, yeah, but that's a good idea, yes. Julius Neil Buenconsejo: Okay, all right, all right, all right. Another thing is, should an AI teammate should have a manager or it's going to be, you know, it's going to be, it's going to be autonomous or should they have, yeah. Charif - DataChi: Okay, first okay. First of all, the the cells, the human stay the manager, stay the the owner on the hierarchy of virtual teammates. But we have chief of staff that Ray already pre presented you with and this chief of staff very quickly is like VTM manager, coordinator, supervisor, name it, but it can be also just the peer. of the other teammates, Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: but your preferred point of contact. Julius Neil Buenconsejo: Okay, okay, okay. Another thing is most people are very, especially the normal people who are using AI, they are worried about the hallucinations of the answers, you know, all Charif - DataChi: Mm-hmm. Julius Neil Buenconsejo: this, for example, what they're getting. So shouldn't AI also be allowed to say, don't know? Or is your system allowed to say, okay, at this point, I don't have this information, but I'll get back to you after I do the analysis and all this intelligence. Charif - DataChi: So very quickly, okay, I'm not a super specialist in AI, you know, But what I can tell Julius Neil Buenconsejo: It's okay. It's no problem. Yeah, but based on your Charif - DataChi: you is that for sure what I understood so far is that the AI system does not know. There is no LLM that is knowing anything. It's just a question of statistics Julius Neil Buenconsejo: Yeah, yeah. Charif - DataChi: and then complementing and guessing with a super educated guess, okay, but it's not based on a knowledge, okay? That's difference. You say, yeah, is it possible that the AI does not know and then hallucinate? Yeah, but in fact, they are only Julius Neil Buenconsejo: Hmm Charif - DataChi: doing prediction and calcul they are computing the most valid continuation of the question or the missing word or the missing senten text or sorry sentence. But here what we put in place. Julius Neil Buenconsejo: you Charif - DataChi: In fact, for very, very simple task like auto feeding an agenda or optimizing a schedule, we use a very small AI that that cannot make any mistake. I mean for frankly, and to make a mistake here it's it's so for that we use a very small one. And the question is for strategic questions. Okay. For strategic questions then it's most likely Very important that you get with something that is actionable and a good answer to your strategic question. So, what we made is that we put three LLMs in the same room. Could be at that moment Gemini plus Cloud plus Gork, okay, all together. Submit with the question and they will fight, they will discuss together, okay? And they come Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: up Most of the time they come up with one answer. So that's the way for us to reduce the level of hallucination or answer to make us super pleased with an answer. But also we this we discover that because we strengthen the way we submit the strategic questioning, we are coming more and more with two options A, B. And you, Julius Neil Buenconsejo: Hmm. Charif - DataChi: Mr. Human, you have to decide. Whether you go for A or B, or Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: sometimes you say, Okay, A B good, but I will make my C now with the team, you know? So that's the way we Julius Neil Buenconsejo: Okay. Charif - DataChi: more and more try to address your point. Julius Neil Buenconsejo: Okay, so now I'd like to visualize it for our listeners and viewers about us. Traditionally, the organization would have a founder, the VP sales, then sales manager, and then 10 reps. So with Data Chi, this is going to be like the founder, VP sales, sales manager, 10 humans, and plus 10 AI teammates. That's going to be their support. So why do you think this works better than the traditional hierarchy or structure in the organization. Charif - DataChi: Okay, because simply your 10 people plus your head of sales are more and more surrounded by information they they miss simply. They are in the Julius Neil Buenconsejo: Okay, okay. Charif - DataChi: they are just digging into the noise. The market is moving so quickly, they have new competitors, they have new customers potentially because they can just pivot. and address a market that it's brand new for them. So the effort to mobilize all the different departments is huge, huge. So yeah, we just bring all possible in every single rep hands. Julius Neil Buenconsejo: Okay, so one thing I wanted to clarify with you is also about sales is different, right? And then marketing is different. Is data achieve going to be only sales or their will be also marketing side in the future or you already have that in your system? Charif - DataChi: Okay, that's a good question. Because I Julius Neil Buenconsejo: you Charif - DataChi: kept saying that we are focusing on sales function for the moment and yeah Julius Neil Buenconsejo: and yet Charif - DataChi: most likely the next the next role, the next persona we will address is the marketing team. But the same philosophy, helping Julius Neil Buenconsejo: Nice. Charif - DataChi: them to be more relevant within their organization or with the end customer directly. Julius Neil Buenconsejo: Okay, okay, okay. This is just for my curiosity. Does it correlate that if you have a good marketing strategy, you will also have a good sales strategy? Or if you have a good sales strategy, you will also have a good marketing strategy? Or vice versa? Charif - DataChi: That's a an excellent point because you you mean a company that is putting the same Julius Neil Buenconsejo: Yes. Charif - DataChi: effort in both departments and you are Julius Neil Buenconsejo: Yes. Charif - DataChi: wondering where the investment is is is is most relevant currently? Julius Neil Buenconsejo: Yes. Yeah. Charif - DataChi: Okay. So I would say that the most by far, by far the most efficient money you can put in any company right now is the sales function. But the sales ready for twenty six. Not the sales of even two years ago. Julius Neil Buenconsejo: Okay, okay. So this is going to be my last question before we go to the 10x round. let's Charif - DataChi: Okay. Julius Neil Buenconsejo: jump into five years forward. So if AI teammates, like for example, if we, I'm very positive about this, if they touch, it become a unicorn company in Europe, right? And AI teammates becomes normal. What becomes a new normal? Charif - DataChi: Mm-hmm. Mm-hmm. Okay, very good. in fact, if we are focusing on the humans, okay, in five years from now, my vision is simple. You will even you will move, you will change. Okay, you are in company A and you will go to company B. Okay. So in fact, your skills of cells will be also a skill or set of skills based on the virtual teammate you have in your toolbox. So on your CV, on your presentation or your introduction, you need to specify how you cope with AI and how productive you are. See? So in five years from now, the companies will offer a space to intercorrect to interconnect sorry to interconnect your tools with the ones of the company. See? So you will come with your teammates, one teammate, ten teammates, and you will plug them into the information system of the company. Julius Neil Buenconsejo: That's what's, it's very nice. Imagine that, you know, when you keep coming to your, into the company, you already have this huge pile of data in a huge pile of history, huge pile of, you know, of clients that are already on top. And even though that person moves on to the another, to another company, you will have, you know, when you start, you don't have to start from zero. You don't have to say, okay, give me all the files. And then I have to review it because you have your AI virtual teammate already embedded in your system in the company. and that is really really great. I like that, I like that. Charif - DataChi: You you mentioned something, Julius, key from ages now, from maybe the seventies, data. One Julius Neil Buenconsejo: Thanks Charif - DataChi: thing is constant in what you said in five years is the data. Even to make an hallucination, the AI needs and data, okay? d a reliable, Julius Neil Buenconsejo: Yeah. Charif - DataChi: trustable trusted data at the at the source, okay? So in five years, what we will need is just the data. Data of quality and then the rest is yeah. Julius Neil Buenconsejo: I like that. I like that feature. Thank you for that Sharip. Thank you for that. All right. Thank you for that very rich conversation with you about the product and marketing and sales of DataCheat. So we are now in the final punch. This is 10 questions, fast-fire round about AI personally about yourself. So what's one AI tool you use every day? Charif - DataChi: Okay. I use two, honestly, a general assistant, to to Julius Neil Buenconsejo: Okay. Charif - DataChi: really because I love to thinking to think to think loud, out loud, you know, before I write anything or before before I execute anything. And obviously our own alpha version of Datashi because you're you're building this and not using it yourself, it would Julius Neil Buenconsejo: Yes Charif - DataChi: be it wouldn't be well called. Julius Neil Buenconsejo: Okay, okay. All right, and then number two, what's one task AI has already 10x in your life, like exponentially improved in your life? Charif - DataChi: Hmm. Okay. I'm I'm quite having now some years, you know, I'm quite old, so to say. So really for me this is reading. Not the not the speed, judgment, but the the getting all these through all these materials I use to skip entirely now and I can just focus on Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: summary. And then can question on the summarized content. So reading by Julius Neil Buenconsejo: Okay. Charif - DataChi: far, by far. Julius Neil Buenconsejo: Number 3, what's one human skill AI can never replace? Charif - DataChi: knowing when to say nothing. Julius Neil Buenconsejo: Okay, okay, good. That's a point. Number four, if you could automate one annoying thing forever, what would it be? Charif - DataChi: Getting back to where I was after any interruption. That's for me the the most costly thing that happens during the day, during the week, and nobody is able to measure it. I think that their is a study that is in the air di demonstrating twenty-two to twenty-three minutes of interruption, but for me this is May because maybe because I'm getting old, this is the p the by far the the the the the the the the automation I I dream, yes. Julius Neil Buenconsejo: Okay, yeah, I totally agree with that because sometimes you're doing something and you focus on that and then suddenly something came up. There's an urgent this and that and then you come back after 30 minutes or 45 minutes. It's really hard to go get into the groove again and get into that into the rhythm. So yeah, I understand that. So what's the biggest misconception about AI right now? Charif - DataChi: Well that the the hard part is what it can do, okay? And the hard part is what it leaves alone. Julius Neil Buenconsejo: What advice would you give someone who wants to 10x or exponentially improve himself or herself starting today? Charif - DataChi: Mm-hmm. Mm-hmm. This is a this is an excellent exercise. Julius Neil Buenconsejo: you Charif - DataChi: write down what your job as a I don't know what is the role of this person in this organization, but write down all what your job is actually for. Okay, then you look at your entire week and the gap between the job, the the the the two, the the the the job itself and the week. That's where you start to build it. Julius Neil Buenconsejo: Okay, so what would you tell your younger self about the future? Charif - DataChi: Easy. buy bitcoins and lock it in a drawer for twenty years, and that's the honest answer, okay? But the serious one, Julius Neil Buenconsejo: Okay Charif - DataChi: the serious one, don't get attached to the tools itself. Because IT never stops surprising you. for me actually, I'm still a kid with the technology improvements. But also the tools are gone in One or two years. So the questions that is underneath all them don't change. And nobody ever taught me the to look at those first. The questions, that's the most important, not the technology. Julius Neil Buenconsejo: Okay, what's one book, podcast or resource that shaped your thinking about AI or innovation? Charif - DataChi: I like the question, and then you will understand that I'm old because for me it's the end of work by Jerry Rifkin. Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: I read it years ago, you know, it was nineteen ninety-five or nineteen ninety-six book, but I had the chance to meet with him in Luxembourg. That was in twenty in twenty fifteen or twenty sixteen because he ha he ha he had a mission with the government, the Luxembourg government. And I asked him Julius Neil Buenconsejo: Hmm, that's nice. Charif - DataChi: if he he got the timing wrong, you know. I was like and that was before before the AI wave itself. So I Julius Neil Buenconsejo: Yeah. Charif - DataChi: said, Maybe you made a mistake, maybe it will be sooner, you know, for the white or the blue colors. And he looked at me because he was dedicated and he was signing the books and he said Julius Neil Buenconsejo: Mm-hmm. Mm-hmm. Charif - DataChi: well we will see. But thirty years on his his real point still stands. Okay. The hard question about work was never technological. It's social and political, that's it. So that's for me the reference I'm spotting on with the team when we brainstorm about the future. Julius Neil Buenconsejo: Okay, that's a good point. So it's social and political, right? Okay, so if you had to bet on one AI trend that will be massive in three years, what is it? Charif - DataChi: So system judged on what they don't do, okay. And right now everything is measured by output. So that's gonna be how do you say that will invert or flipped? That will Julius Neil Buenconsejo: Mm-hmm. Charif - DataChi: that will invert in the future, yes. Julius Neil Buenconsejo: So in one sentence, what does 10X mean to you personally? Charif - DataChi: That's our motto, huh? Same team, the same week and doing ten times more of work and only what they can do. Julius Neil Buenconsejo: Okay, all right. Thank you very much for that very fruitful, insightful conversation. Over an hour of conversation, really enjoyed our conversation. That's like what I told you in the email. I was excited to talk to you about the product side and the product marketing side of Data Chi because I already talked to Rai, your visionary guy, who explained to me what's going to be the product, how is it going to change the landscape of the sales and marketing funnels of the different companies. And I'm really very happy that you are now getting a lot of traction from blue chip companies like IBM, Fujitsu, Cisco and these are things so I hope that you become one of the unicorns in a billion dollar companies in the future. So thank you very much again Charif again Charif - DataChi: Thank you for the exercise. Julius Neil Buenconsejo: maybe yeah maybe next week next year we will have you know a follow-up of what's happening now with the data cheat so I would would be happy to have you again. Alright yeah Charif - DataChi: With pleasure. Thank you very much, Joris, for the exercise. Let me go back to my barbecue now. Julius Neil Buenconsejo: Okay, thank you for everyone and thanks for watching 10xAI. Remember, the future doesn't belong to AI, it belongs to the people who use it. See you next episode.