Michael Hartmann: 0:23

Hello everyone. Welcome to another episode of OpsCast brought to you by MarketingOps. com, powered by all the MoPros out there. I'm your host, michael Hartman, joined today by Mike Rizzo. Say hello, mike.

Mike Rizzo: 0:34

Hey, what's happening? This is a new thing for us. We're going to do video guys.

Michael Hartmann: 0:38

I know right.

Mike Rizzo: 0:39

MoPros, all you people out there, we're going to use video from now on.

Michael Hartmann: 0:50

I don't know how this is going. I've got a face for radio, you know. So tying into the music talk we had before we started recording, all right. So joining us today to talk about the potential impacts of AI in business operations is Kobe Stock. Kobe is a serial entrepreneur with two decades of experience in building software. He currently leads Forward AI, an automated data science platform built for RevOps. Previously, he was with WalkMe, where he led the global product strategy with a focus on scale, growth and innovation. He joined WalkMe through the acquisition of Abbeyio, a mobile AI company, where he was co-founder and CTO, leading the product and technology. Prior to WalkMe, he was the founder and CEO of a consumer music tech company with millions of users. Early in his career, he was a software engineer, architect and a manager at multiple startups as well as SAP. So, kobe, thanks for joining us today Late in the evening for you.

Kobi Stok: 1:37

Hello, hello. It's a good thing that I just changed my camera as we're doing video, so full HD here. There you go, full transparency.

Michael Hartmann: 1:46

If I showed my background fully, you would see the in-process taking down of our holiday decorations.

Mike Rizzo: 1:53

So I had to move a few things before Nice Stuff that I haven't had to deal with in the past.

Michael Hartmann: 2:00

So all good. So you founded Forwardai not too long ago. It was recently. You started it. You call it an automated data science platform. So for us and our listeners, what does that mean to you and how should we be thinking about that?

Kobi Stok: 2:16

Yeah, so actually every time. So that's my third company that I'm starting right, and every time that I start a company I try to, before I check the market and check the need, I try to solve my own problem. That's like number one. And in my previous role we were trying to build a model that will predict churn based on product usage. Think about it we work for a company collecting tons of data and we're trying to build a model that will basically use this data and actually surface accounts that are at risk. So we get a heads up and we can create some sort of a mitigation.

Kobi Stok: 2:59

And it took us a year to do it A year. It took us a year, one year to do it. It and it was complex. We needed to involve like at least 20 people. Half were technical, half were from the business.

Kobi Stok: 3:19

You know we've talked about it so much and the process was so long right, but when we finished it after a year plus even and we launched it, then what we did? We basically surfaced those insights to the customer success team in our case, and the impact was amazing. It really changed how the customer success team operates on the day-to-day, not on a specific moment, and even afterwards, we modified the compensation of the reps based on the model. So I saw the impact and I saw the investment and I said it's too long to make those investments. I mean, most companies won't even start. And if you think about it, companies are collecting so much data these days and the data costs money. It takes time to arrange it, it takes an effort to arrange it. They're building tons of dashboards right, but by the end of the day, people are not really data driven in the day to. People are not really data-driven in the day-to-day.

Michael Hartmann: 4:31

No. It's just something I think a lot of people either claim that they especially marketers claim that they either are or want to be data-driven. I'm not even sure they know what that means. Yeah.

Kobi Stok: 4:43

The best-case scenario is that companies have good management dashboards that provide visibility on the C-level, right On the leadership team, but when you go down to the people who actually manage the business right Manage the product, the day-to-day with the customers, support finance they don't have this information right, and they don't have this information because it's really hard. Yeah, and let's imagine that a company has unlimited resources of data science internally, then, using data science methods, they wouldn't be able to surface those insights or data or whatever, right, but they don't have it. So automating data science means that we are selling you a piece of software that acts like hundreds of data science professionals that are working on your data to build models that are fueling business processes in your organization, to make them all automated, to surface insights or signals that will help the team to perform better, to surface allots or emails right, with specific call to actions to improve forecasting and basically every business KPI that you can imagine. So that's what we do today. That's interesting.

Michael Hartmann: 6:30

What really strikes me as interesting is that when I first made my transition from doing IT management consulting mostly with accounting and finance, real estate type environments into marketing, it was to do database marketing so data warehousing into marketing. It was to do database marketing so data warehousing. And we had a team of today that would be called data scientists but you know statisticians who did things like churn prediction and likelihood to convert and all those kinds of models. This was a big telecom company and it was. My job was to build a database to help with go-to-market activity, so a 50 million household database and the data processing and the amount of data that we had to acquire and then analyze was huge and it cost a lot of money. It was a huge effort. I mean what you're describing. It's almost surprising to me that that is still a challenge at many places.

Kobi Stok: 7:29

You know what I mean Very, very big challenge. I think that data became more complex. So every time that someone, any company, improved something, then another complexity layer thing happened and then it became more complex. Improved something, then another complexity layer thing happened and then it became more complex. And I think that in the past, let's say five to seven years, we're in a race. We were in a race before that of hey, I need to collect data, right, everyone was in a race, I need to collect more data, I need to, right. And now everyone stopped collecting data. They have enough data. Now, how do I get ROI on those huge investments that we just did? And we bought Snowflake and we bought Salesforce and we have HubSpot and we have Marketo and we have Segment and we have product analytics, like so many things that essentially so much value, right, there's inside so many things you can drive, but you just don't have the resources and it's so complex to even start. So my goal was how do I make people start?

Kobi Stok: 8:44

Forget about the price.

Mike Rizzo: 8:47

Yeah, yeah, forget about right. Yeah, yeah, I think, and it's so, um, it's so interesting to be at this intersection of this like data aggregation and collection phase that we've been going through for the last couple decades. Where it was it, I don't know. I think for some companies it's going to feel a little bit like a retirement plan, like you've been piling this data away for a while and eventually it'll have a payoff Hopefully a good one, exactly and for others it'll feel like let's just scrap the last 10 years and then take the most recent five, and you know so we'll look at the last 15 and take the most recent five and roll with that, because our business changed a lot. But there's this, the. The opportunity now is everybody went.

Mike Rizzo: 9:45

It's a little bit of a cop out. I don't know, maybe, maybe, maybe you don't feel this way, kobe, but it's a little bit of a oh well, now I don't have to think quite as hard Like AI's here. So cool, let's figure out how to use the data now, which I think is great, right? We've all kind of just been waiting for this moment to go. How on earth am I supposed to get my arms around this massive mishmash of information? And today we finally, like for a number of us, we're going oh, this is the way right, we're doing a research project right now in marketing ops.

Mike Rizzo: 10:29

We've We've decided to break apart our state of the mo, pro research, to go deeper on some subjects, and one of them is on this area of the state of go to market data. And, to your point, michael, it's fascinating that, like we're still faced with so much of these challenges so far, the responses to the question of the top three priorities in the next 12 months. Number one is unifying data across platforms, so like data integration in general, which makes a ton of sense. Here we are. Number two is I'm sorry, data quality and hygiene is number one. Number two is unifying data across the platforms. And number three, so far in the state and the research, is automating reporting and dashboards. Are you kidding me?

Mike Rizzo: 11:14

This all sounds like 10 years ago.

Michael Hartmann: 11:16

So a thousand and ten, yeah, that was like what?

Mike Rizzo: 11:19

We've been doing this for a long time. Why are we still worried about automating? Like that's crazy and yet it's still a priority. And like I'm not dissing anybody for not having figured this out. I mean, I still pull manual reports too, but I think that's exactly why you're trying to to work on forward, right, like you're saying hey, let me help you get started, let me start showing you a path, right?

Kobi Stok: 11:42

yeah, yeah, exactly I, yeah, I think that you're right and I think that the intuition that we had as operators seven years ago and 10 years ago, I think that the intuition was very good. Yes, it's like like when you're calling customer service today, uh, you get like this message hey, we are recording this call to improve our service. Blah, blah, blah. Right, no one is going through this call to improve right.

Kobi Stok: 12:16

But now, but now, maybe right Now, they have the ability to actually do that. So I think the intuition of collecting data right, it's like me buying sneakers of collecting data right, it's like me buying sneakers. I don't know when I'm going to actually wear them, but I'm buying just in case and I will find, like the best you know event or occasion to kind of use them. So I think that people used like the intuition was right, let's collect data, we'll figure it out later. Right, but I think that the complexity, um, also grew, yeah, and I think that and the volume and the volume grew the volume is done.

Mike Rizzo: 13:04

Yeah, the integration layers got easier to make, it became easier to add more right, and everybody said I mean we know from our research year over year martechorg has talked about it, scott Brinker's talked about it, our state-of-the-moment research talks about it Integration is the number one thing and it's proven to propagate this big challenge of you know. Okay, now there's even more for us to access but big challenge of you know.

Michael Hartmann: 13:35

okay, now there's even more for us to access, but what do I do with this stuff? Well, so I have my theory on why a lot of companies struggle on this, because we've talked about a lot of reasons why, but I think it ultimately comes down to you in your point, mike, about data hygiene and quality being the number one concern, is that this the fact of it is it generally doesn't get the kind of attention because of at least two reasons. One it's not. It's not, uh, uh, immediately obvious why it's valuable, right, so doing that work on cleaning the data doesn't generate immediate and obvious results, so it's taking you away from doing other stuff that is more obvious, like building more emails, building more campaigns, et cetera, et cetera, right, so there's this like just like, how, how visible is it? Is one. The second part of that to me is I have yet to meet anyone who goes our data is great, right. So I think there's this fear when you say, oh, when you go like, my data is not clean. So therefore, I probably need to do that before I start doing any kind of reporting, because I don't trust the data. The data is not complete, not, right, whatever the term you use is, which I keep coming back to, going like, first off, that assumption that you can't get any value out of the reporting until your data is quote right, right, is a false one, right, I think, like by the nature of just doing reporting it's gonna, it's gonna really highlight where there are issues, which then gives you the ammunition or the case to go like we need to go fix and clean the data. And it could be an iterative process, and I think I think that's part one.

Michael Hartmann: 15:05

Part two is this desire for dashboards versus something that's going to give me insights that I can make a decision about. I don't know that dashboards are always great at going. They may give you a good like how do I feel about the general health of the organization or what I'm doing for go-to-market, but it doesn't really go like. What should I do next? What should I cut? What should I add? What should I? You like? What should I do next? What should I cut? What should I add? What should I? You know, you know how should I adjust, and I don't. I think this, this cause you go into like I've gone into organizations like, oh, we want you to do part of your job is going to be build dashboards. I'm always like how about we do a few reports first, like let's get those right, see if they'd help us, before we start going to build a dashboard. That is not going to probably help us and it's going to be painful, expensive.

Michael Hartmann: 15:50

Yeah.

Mike Rizzo: 15:52

And the context that's needed. I've been in countless SaaS organizations that have, you know, both a sales led motion and a PLG led side of the business, and you know you're trying to track free trials and starts and stops and crossovers between lifecycle stages and you inevitably get into a state of well, is that person on a paid account or not? And, like you know, dashboards actually create more questions, which is good. I'm not saying it shouldn't happen, but most of the time if you don't have somebody trying to filter out the noise and just say like, if I have to explain where the data came from every single time, then yeah, that's a problem. That's a data quality and hygiene issue that needs to be solved yeah, right, I I yeah, sorry, mike, no, you're fine, you're fine, I just I.

Mike Rizzo: 16:45

I was just like thinking through how AI's impact on our ability to get to maybe some more focus and support around. You know, I've got a couple of core priorities and what are the? What are the sort of key assets that I have in my data lake, data warehouse, whatever my integration layers, layers, what are those that can help point me in the right direction?

Mike Rizzo: 17:11

yeah and I think that's the thing that's most exciting is to try to like, use the thing, use the phrase that I say all the time right, aim small, miss small. Right, like, let's just like focus on this thing. Um, yeah, I don't know your thoughts, yeah, your thoughts, kobe, on the state of all this stuff.

Kobi Stok: 17:29

Yeah, first of all, I think that I have so much to say about dashboards, by the way, but I think that when my team, my customers, whatever, define an initiative or a task, I try to divide it into two classes, let's say Strategic or tactic. I think that for strategic initiatives, dashboards are, in some cases are the best output and people need to see visuals, people need to see trends, people need to see stuff right. But because dashboards are complex in high-velocity businesses, now a very smart person needs to define the dashboards so they are readable. It's not a simple thing to do To build a dashboard. Forget the technical aspects, but the usability. It's not a simple thing to do To build a dashboard, forget the technical aspects, but the usability, it's not a simple thing to do. And second, to read a dashboard, it's not a simple thing to do. To tell the story behind it, it's not a simple thing to do.

Kobi Stok: 18:39

For example, me and Michael can look at the same dashboard. Michael can decide let's fire 50% of our workforce and I can decide, thanks for fire 50% of our root force. And I can decide Thanks for putting that on me, you know I try and I can decide let's hire 50% more on the same dashboards. Why? Because we saw two different things. Because it's not deterministic. We need to understand what's going on right. So this stays on the executive level, but on the operational side of the business, those decisions can't be made Right. You want people to make decisions on the tactical fragments of the business.

Kobi Stok: 19:29

I have a chain problem. This customer can upsell right, this lead may convert, this opportunity is at risk, so on and so forth. Those stuff are today unsolved, agreed. Unsolved Agreed. Companies think they solve it, but they just define a set of rules that they think that are the reality, but in most cases, they don't know. And I think that I also think that there's the approach of trying to prepare your data before you know what you want to do with it. It's a problem. People need to change, to flip the cassette. Going back to our music before, right. I think that now people need to be target-oriented rather than think about how they would solve it.

Michael Hartmann: 20:31

I want to make sure I understand what you mean by that Target-oriented versus how they want to solve it.

Kobi Stok: 20:37

So let's say that we have a problem in the business, let's say churn, whatever, and we want to figure out what's causing churn, instead of trying to guess the data points and whatever. Let's first understand if we know how to identify churn in our data, ie if we have a field that's called churn, yes or no or whatever the case is. Obviously you don't have this, but I don't know like renewal opportunity, closed loss, whatever. Let's make sure that this process every time that someone churned, we have this in the data, we know it's churned. Right, when we think like that, then we can analyze it better, right? Then what we need to do, what people need to do, is reverse engineering and not forward engineering, meaning let's trace backwards what caused this specific event that we know how to measure.

Kobi Stok: 21:42

Obviously, it's hard. It's CRM. People will tell you hey, listen, it's CRM's crm. People will tell you hey, listen, it's crm. You know reps are not filling the data correctly. You can solve this as well, but because it's so much data today, so even if 20 are not filling the data correctly, or 30% and 70% does this right, you will get the answers right Because you have so much data and every time that a customer or prospect survey, are coming to me, to us. Everyone is like look, my data is shit. I know that, I know that for a fact and I'm telling them for sure. The question is what's the ratio?

Michael Hartmann: 22:31

Yeah, you bring up such a good point.

Kobi Stok: 22:33

What's the use case right?

Michael Hartmann: 22:35

I love that you bring up this point of you pick the ratios 30% bad, 40% bad but you need to. I think this is like the mindset shift of going like but that means that 70 is still good, or 60 is still good or 80 like, what, like that's the part that I think people get caught up and he caught up in the. This is the portion that's questionable, bad, invalid, whatever, as opposed to how much is right, and I think to me, this is what I get again get this data quality and going like this is what's stopping people from moving forward with this kind of stuff.

Kobi Stok: 23:08

Yeah, because the problem is that people can't measure it today. They don't know how much. How bad, slash good is your data? By the way, my enterprise data can be good for leads closing accounts, going in, bad for return whatever, right. So even today, when you have so many touch points, you have PNG, right. You have the calls all recorded, right. You have the emails with tracking calls all over the place. You know everything.

Kobi Stok: 23:39

So if you're using like simple platforms, right. If you use SSDC, HubSpot, mercado, you already have most of it and people don't utilize it. People pay license, people pay big money and they don't utilize the data. And then they come to us. They say we have a problem, right, and then we kind of get solve the problem faster. It's not like we kind of get solve the problem faster.

Kobi Stok: 24:08

It's not like we invested anything new, but we just automated like a workflow that took so long, it was super complicated. We just we broke it down to pieces, to 80% pieces and we just simplified it. So, instead of 10 people and nine months with a failure rate of 90% by the way, the fail rate at enterprises of internal data science projects is 90%. That's crazy. It's crazy. We had a prospect. I won't mention their name and they are figuring out health score. Health score is like an indicator that indicates the health of a customer right and they spend six months, two people, building a health score, two data scientists. After six months they stop and they check the correlation of the health score to churn no correlation. They're like, oops, six months, two people.

Michael Hartmann: 25:06

They're like oops, six months, two people Plus technology and other resources.

Mike Rizzo: 25:10

right yeah all kinds of layers.

Kobi Stok: 25:15

Even that, even that, right Today, with the public markets, with AI, with competition, you can't afford that In 2021, in 2018, like you know, I can throw 10 people in a problem. If they want solve it, no worries, I have like it's, it's really better right, yeah all good, zero interest, I'll get more money from my vc. But now people need to change how they think and and if people want hyper automate, not automate, hyper automate, right. If people won't understand how to consume and how to utilize their data, they will lose the battle.

Michael Hartmann: 27:07

Yeah. So, kavi, to me, this is what I'm really interested to hear when we start talking about what you're doing with Forward. I've always thought about this as like to get reporting analytics in go to market activities, in particular for B2B, where it's complicated and messy and incomplete, all those things we talked about. There's sort of two components that I've been hopeful that AI would be a part of solving. One is you know pulling, helping to pull that data together and normalize it and clean it up kind of in an automated way. That right now requires usually typically a lot of human capital and technology, and you know exception rules and all that kind of stuff right and getting it cleaner over time.

Michael Hartmann: 27:51

The other part is, I think what you just touched on right, you hired two data scientists to to to build out a hypothesis and then go test it, as opposed to you know letting and this is my ideal state, I think is that you have ai that can go. Rather than testing a hypothesis, it can go like identify the patterns that you might not see or be able to come up with on your own, to then test, and that might be able to do that more effectively. I still think there's a place for humans in this process to be able to make sense of what is generated, because it still may be nonsense or something you can't action on but like are you seeing? Is that are you addressing one or both of those generic sides of this problem?

Kobi Stok: 28:41

Yeah, good comments. I think that we're solving all of that, from raw data to predictive models, to segmentation models, to forecasting models up through activating your CRM and creating alerts and all of that and the reason that we are doing everything, because the composition and having everything in one piece, that's the value that we bring to the table. That's the value that accelerates the process, because if we were doing only one thing, we didn't get to the finish line. It will be stuck in the middle in the organization, right? So I, with ai, the, the data modeling piece, as you're referring to multiple data sources, how do we integrate the data modeling thing is really um in a way that it will be much more simpler not fully automated, but much more simpler, right? So, mike, could write, like you know, a prompt, maybe drag some elements on the screen and set to okay, now, in every step that you are doing as a builder, right, you get the human in the loop. In every step that you're doing as a builder, right, you get the human in the loop, meaning you're validating every step, because in some cases, you would need to tweak or tune the initial output that the AI did.

Kobi Stok: 30:11

In some cases, you'll be able to tweak it using prompts and in some cases prompts won't help you, because when you go low resolution, in most cases prompts will kind of um, it's like um I'm trying to find an uh analogy, but it's like in golf, right, when you're so close to the hole and you're and you're trying to make it, maybe the ball will be much far from the hole. Then you started. This is how I, this is how prompts are, with really small refinements, right, and in some cases you would need to do those small refinements in a UI, right In a dropdown or in a box or in a drag and drop quiz, whatever, right. So that's number one. Right On the data modeling piece, number two what you asked on the actual like human in the middle, of course, the AI, or your data or statistics or math don't know anything about your business or statistics or math don't know anything about your business. They don't know that. The field that you've used in AppSpot this is just a copy of another field that's used for unit testing, whatever.

Mike Rizzo: 31:34

I've been trying to say this to people for so long. You articulated that really well, great example.

Michael Hartmann: 31:41

I was just like. Every Salesforce instance I've been involved with has had some existing field that is supposed to be used for one thing be used for something else because the process of adding a new field and all the integrations was so painful. But if you didn't know that you could misinterpret that data?

Kobi Stok: 32:03

Yeah, but the thing is is that you are now in a position to validate the data and to validate the points much better than you were before, and it's crazy. It's crazy. Now you can actually do it. I see people like marketing ops, rev ops, rev ops, cs ops making an impact, like one person making an impact of 10. I'm seeing it day in and day out. It's really crazy what you can do with utilizing new technology and rethink and think different about how we would solve it.

Mike Rizzo: 32:48

To use it, and that's yeah, and that's the part that, like I've been definitely on the pedestal about, without a doubt, is the opportunity for folks in this type of a role marketing operations, rev ops, sales ops, cs ops, whatever ops role you have. I love the opportunity for the marketing ops folks because, in large part, you tend to have a lot more access to a lot more touch points than the other functional areas yeah, more touch points than the other functional areas. Yeah, everything from you know anonymized, de-anonymized, visitors and tracking at the top of the funnel through. Did somebody become a closed one customer or churn? Yeah, oftentimes you get to interact at some level with product data as well, because product marketers want to know usage and adoption of features and all those things. So and I think you made a comment earlier, kobe, about you know, starting with this, ensuring we understand the steps and the process to identify churn right, and that we're all in agreement that those are the things that we think or need to have in place to identify churn to then go after solving solving the problem, you know later, um, and that is no one else, like, no one else can have these conversations like, like, really, truly no one is as close to the systems as you are in marketing operations and revenue operations in general, using that term broadly.

Mike Rizzo: 34:32

All you ops folks out there, yeah, it is a really, really exciting time for you to go make an impact and I love that.

Mike Rizzo: 34:39

You just said you had one person effectively making the impact of 10, right, and I think right now is the time to you know whatever, whatever phrase you want to use, grab the bull by the horns or seize the day or whatever. Absolutely sink your teeth in to the art of the possible and I'm going to say again start thinking like a product manager and you know you're going to hear it over and over again from our community and for me, and you're all going to probably get annoyed with me at some point but you are the product manager of the go to market tech stack and your job is to figure out how to best leverage all of these pieces of information to enable your internal teams to go to market, to enable your buyers who are buying your products and services to engage with your brand and your internal teams in a meaningful, effective and scalable way, and you also, unfortunately for you have to be aware of is the brand being represented properly and are we legally compliant?

Mike Rizzo: 35:53

Those are a lot of fun things that you have to manage, but they're all stakeholders that you have visibility across and it sounds daunting, but for those of you that are in this role and you're having fun with it it's actually really exciting, yeah.

Michael Hartmann: 36:07

I would add a third one.

Mike Rizzo: 36:08

That's how I translated for Kobe yeah, go ahead.

Michael Hartmann: 36:11

Yeah, I would just add a third one to your list there, which is being an advocate for the customer, because I think sometimes that gets lost. I mean, I don't know about you.

Mike Rizzo: 36:20

Sorry if I didn't say that clearly. Yeah, you're absolutely right If I didn't say it clearly enough when I said hey, you have to service the internal team members as well as the customers that are buying your products and services. Yeah, that's the part where you need to hone in on that customer experience. How are they able to actually engage with us in an effective way? That's also brand safe and compliant right. Brand safe and compliant Right.

Kobi Stok: 36:43

I would even add two more things in general that I think that are must-haves for every ops person out there, and especially these days. I think the number one is think like a builder. Think like a builder. That's what you need to do. That's the mindset that you need to be in in order to be very successful. Right, you need to build stuff. You don't need to build another like, build like. You need to orchestrate those systems together. So, yeah, right. And second, it's really work out, because I think that, again, doing the impact of 10 people, if you are successful in achieving that impact, I think that's the whole grand for everyone at Ops. And I think that Ops gets more like a center stage not necessarily the singer of the band, but maybe the bass player or the lead guitarist still referring to our earliest conversation Yep, but that's as center as they would, as they ever end and currently and I think it will be even even further down the road, because this is the platform that the business runs on yeah, Without it, it won't run.

Kobi Stok: 38:10

It won't run without it.

Michael Hartmann: 38:11

Yeah Well, I think it's interesting that you're seeing a 10x productivity gain using some of these tools, specifically around data analytics and science reporting, whatever you want to call it, because I've believed for a long time and this is why I've been really hopeful that AI would play a part in making this I don't even want to say easier, but more scalable is because I believe for a long time that it is an effort-based thing to do right. It takes time, effort and some expertise to be able to do effective data analysis and reporting and to get insights that can then be used to impact a business and so go ahead. No, no, I think that's what I'm really excited about is hearing that this is maybe something that's actually happening.

Mike Rizzo: 38:59

I think it is, and it's funny here a word that we very rarely use in our uh, sort of day-to-day world here I suppose I don't think I hear it all that often is creativity.

Mike Rizzo: 39:14

And and this is an interesting time because, while we don't fall into that creative marketer category, the one that we all think of traditionally, right, right All, the fun ads and the design and the and the cool campaign ideas Although I will say there's a lot of marketing house people that come up with great ideas like campaign wise. Okay, um, this type of creativity is it's a jigsaw puzzle that you are, you are stitching together, you're actually making the jigsaw puzzle that you are, you are stitching together, you're actually making the jigsaw puzzle while you complete it, like all at the same time. Yeah, and it's, it's fascinating, right, it's like you're seeing possible, you're asking questions that could lead to down paths, that that could answer questions that the business never even knew it had. Right, and I just I think it's a new type of creativity that, um, in some ways, is very freeing it keeps coming.

Michael Hartmann: 40:15

I can't go back to one of our earliest guests, brandy sanders, talking about how, like thinking about this like chess, right? So I didn't even go beyond a puzzle, right, because it's it's a puzzle that has made probably multiple dozens of potential solutions and I think that's, yes, how I think about it. Um, but here's my, here's my concern. It could be maybe you can chime in here too, because I've said this many times here, but I love the idea that the technology could make it so that someone who just doesn't have the bandwidth to be able to provide those insights, but has access to the data and understands it and the flaws in it and everything else is that I am concerned that there's not enough knowledge or expertise on how to actually interpret the analytics and statistics, or whatever you want to call it, across most organizations and even in inside ops. So are you seeing some of the same stuff with your clients and customers or in your own experience, and do you have any thoughts on how to address that?

Kobi Stok: 41:17

yeah, that's that. That's um. That's a very interesting uh um thing that you said just, Michael. I think that talented people across any profession are really hard to find. That's in general. I think that analysts and data scientists are really unique because they need to understand. They need to have technical chops right, but in order to really find insight, they need to understand the business. The combination of one person being super technical and understanding the business is unique. What were my theory right? And we are building the product as we built an AI agent inside of the product. That's an analyst. We built an AI agent inside of the product.

Kobi Stok: 42:17

That's a scientist, Meaning we kind of lead the ops person to a journey where we ask them the questions. Right, there's a think about a very precise process that they go through and then the AI execute the job of an analyst and scientist, because the problem is that in most cases, you don't have enough of them at a specific point. So every question that you ask today let's say we don't have AI let's go back three years or four years Every question that you ask on the go-to-market front, you would need someone else to help you around. You couldn't answer questions on your own.

Michael Hartmann: 43:23

Yeah.

Kobi Stok: 43:24

I mean, it's so complex, right, in most cases you need to export data, then you need to have a Python notebook and then blah, blah, blah. It's really complex. Yeah, right, but today the same process is happening, but someone else is driving the wheel and it's not a person. So what we're seeing is that we are freeing, we are freeing the organization from those headcounts that are expensive and can't operate in the two fronts of the data and the business, and we're enabling the business to run it. Now a little kind of clarification here For the very strategic initiatives, ai is not still there. But for the tactical initiatives, let's understand why customers churn, let's understand apps Like all those. Ai can definitely do this, and we do day in, day out. So I hope that I answered the question, michael.

Michael Hartmann: 44:35

I think so. I'm sure I think we could probably carry on for quite a bit longer, but I think we're going to have to wrap it up here. Kavi, if there's, if folks want to keep up with you, connect with you or learn more about what you're doing at Forward AI, what's the best way for them to do that?

Kobi Stok: 44:53

Oh, so maybe before I answer that, I want to add one sentence on the data hygiene piece that Mike mentioned. So while we're implementing forward, we automate it in reprised. So there are many problems that we see along the way. What Mike said about data quality or data hygiene, that's the number one problem that we see as well in every case out there, Every case out there, and I'm excited to that's the first time that I talk about it but we are launching an agent that puts data hygiene on autopilot.

Mike Rizzo: 45:40

Okay, I like it All right.

Kobi Stok: 45:43

Okay, I like it. All right. So every data hygiene problem, you can use the agent to solve it. I'm not sure it can solve everything right, but we mapped 200 problems that we have heard from our customers and prospects and we solved all of them. Not saying that it will be able to do everything, but that's just that. That's a new product that we're launching soon. We are super excited. We have better users and the results are amazing.

Kobi Stok: 46:25

If you want to um kind of um, uh, I'm learning about forward, so it's forward or the I, without the a, um and um. You're welcome to follow us and myself on linkedin where we talk about ai and the difference between an LLM to machine learning and I think that educating the people around what is AI because AI is big, it's from the 50s, it's like you know, it's like guitar. Innovations are happening across the board and people don't understand what AI is, what LLM is, what OpenAI can do, what they can't do, what the difference between an OpenAI to an entropic, I don't know. Deepseq, all of this people need to. And again, ops I encourage Ops people to really be curious and learn more about it and I invite you to follow us myself on LinkedIn, you know, on all those channels.

Mike Rizzo: 47:23

Fantastic. Yeah, I'm really excited about what you're doing at Forward, kobe, and I'm glad we were able to reconnect this year and have you on the podcast, and we'll probably, I know we have a few more things lined up to in terms of educating the audience. So, for those of you listening, stay tuned to what Forward's doing. It's F-O-R-W-R-D, that's how you spell it. Uh, you can go visit the website at forwardai and um and then pay attention to some of the other posts, emails and slacks and social things that we're doing on the marketing upside, because you're going to hear a few more different renditions and going deeper in different areas around AI and data that forward's sort of helping to solve. And then Kobe I got one of these days when we get a chance to sit down for a little longer. I got to show you what I was doing with audit hub as a product that I built. It was totally around this data hygiene thing and I feel like you probably already solved it, but I'm so excited by what you're what you're telling us.

Kobi Stok: 48:24

I am too, we.

Michael Hartmann: 48:26

but I'm so excited by what you're telling us, I am too.

Kobi Stok: 48:28

We will meet and you will show me Promise that Sounds good, kavi, thank you.

Michael Hartmann: 48:30

Thanks for staying up late for us and, as always, mike, thank you for joining Absolutely. And thanks to our audience for continuing to support us. And if you have suggestions for topics or guests or want to be a guest, feel free to reach out to naomi, mike or me. We'd be happy to chat with you about it until next time.

Kobi Stok: 48:46

Bye, everybody bye, bye guys.