Dave Hengartner rready - How Employee Driven Innovation Transforms Corporate Ventures

It's basically innovators within a large organization. Let's say you work in a large corporate and you really want to bring the company forward. You have ideas then uh we basically provided the tools, the resource, the space and support to give these people wings so they actually get the chance to to execute their ideas and and bring the company forward. And it's actually another new new thing. For example, Steve Jobs, like 40 years ago, we called the Macintosh uh team an entrepreneurship team that he put into the garage, gave them the space, and let them build something new. >> Hi, my name is Demetri Bonichi and I'm a content creator, agency owner, and AI enthusiast. You're listening to the AI agents podcast brought to you by Jot Form and featuring our very own CEO and founder, Idkin Tank. This is the show where artificial intelligence meets

innovation, productivity, and the tools shaping the future of work. Enjoy the show. Hello and welcome back to another episode of the AI Agents podcast. In today's episode, we have the CEO and founder of Ready AI, Dave Hengardner. How you doing, Dave? >> Very good. Thanks very much, Demetri. Thanks for having me. >> Yeah, I really appreciate having you on the show. Um, I think it's another unique interesting product and we're just really excited to to get chatting about Ready AI. what is the kind of background of you? You know, like there's there's so many different stories of how people um get into business and get into AI. So, just tell us a little bit about how you got to this point. >> I'm actually an entrepreneur who joined a large corporation 10 years ago and um okay innovation team and my job was open innovation

to collaborate with startups to find ways to make Swisscom like the largest Swiss telco more innovative. And then um basically I discovered an innovation methodology for entrepreneurship um I liked a lot and brought that into the company and then like over the years rolling out scaling the program and eventually turning into a corporate startup. So what we have built kind of attracted a lot of interest in the market and we we start to sell it first as um employees of the of the corporate and eventually four and a half years ago we got the opportunity to spin out to find venture capital in the US actually lead investor from San Francisco and some venture capital and angels from Switzerland and now we're four and a half years into that into ready.com and ready AI and with three other co-founders and super exciting as well coming

from innovation trying to solve the big challenges the big problem of innovation with our software tooling. >> Very cool. Um you know I never even heard of that telecom. So that's the that's the large that was the largest or is it still the largest? Uh okay. Swisscom is still the largest >> AT&T in the US. >> Very okay. Interesting. Nice. And then you know it seems like you know about four years ago you got started with this with um with ready >> we had the chance you could start in a safe corporate environment and then take out some revenue some customers some IP and employees over and start the ready journey. >> Oh yeah very cool. Um and I guess just for that experience you know um you know you started as I guess an entrepreneur and then you said you got back then you

were at a company and now you're back as an entrepreneur again. Um, what's kind of the experience been like building this company during the, you know, I mean, everyone I feel like in to some respect who has AI right now or like has a new AI product or like framing they're like oh we were always an AI company. I'm like well in the not maybe in the same way as it is as it is now but you know what what has the journey for ready been like in general like where did you guys uh what did you first start doing? We started in the entrepreneurship area where actually we have developed the IP and methodologies at Swisscom. But when we span out uh four and a half years ago, we already started to rebuild our architecture to be very flexible. We uh really had this

big goal of solving the challenges of innovation and that's not just entrepreneurship, right? If you want to innovate as a large organization, you need to do it with multiple tactics, multiple programs. And we basically build the the architecture the back end to deliver solutions, deliver software for different ways of innovation like like for example you want to do idea management more kind of improve the company maybe you want to do in typical portfolio management for the big innovation projects you want to do trend management you want to innovate with startups and we can serve kind of with solutions for all of these um programs. So nowadays we really broad if you look at our website we're basically um a SAS uh provider for all these type of innovation but it very much started in the entrepreneurship area. >> You know entrepreneurship is an interesting concept.

I don't think a lot of people could you kind of like walk us through what that means because I don't feel like a lot of you know a lot of employees don't think about what that means uh to be frank right like I mean I mean you know the ones that are entrepreneurs would think like that but yeah just what does that mean to to everyone who was probably curious hearing that word >> it's basically innovators within a large organization let's say you work in a bring the company forward you have ideas then uh we basically provided the tools the resource the space and support to give these people wings so they actually get the chance to to execute their ideas and and bring the company forward. And it's actually another new new thing. For example, Steve Jobs like 40 years ago, we called the

Macintosh uh team an entrepreneurship team that you absolutely garage gave them the space and let them build something new. >> Yeah, that's a great point. And I you know what's interesting um about that is uh I believe it's maybe it's not common knowledge but it's like common it's been statistically proven over and over again most of the time when people are dissatisfied at work it's actually not due to their like lack of pay or oh being overworked it's usually because they don't have like uh self-fululfillment sort of right and the entrepreneurship seems to I guess solve that right for lack of a better right >> it's a big culture program as well that it kind of culture transformation kind of the breaking up silos and educating people in lean startup and stuff like that as well. >> And what kind of like size companies were

you working with uh you know early on already? >> Always with the large multinationals like at least 10,000 employees I think sweet spot 10,000 plus. >> What what kind of made you want to go that direction? I think at the end uh as a software provider actually the reason why you need a a software platform is if you need to have enough employees you need to have enough ideas right otherwise you don't need a you don't need a software platform to manage that >> what like you know it's it's interesting I um I feel like with the advent of AI that's had to have been an interesting like shift so when when did you know obviously ready.com is like the the main domain you guys had when did you start this initiative for ready AI. I think when AI emerged like to with the chachi

PT release obviously we start to play around a lot because it's interesting that uh innovation as five years ago is still a very very important and discipline for each company like just the recent Boston consulting report showed that 83% of companies rank innovation as a top priority and that's very in a massive contrast to 3% which is actually what companies deliver on their goals. So from 83% saying it's important just 3% delivering it's a massive innovation gap and it's just because the output of innovation is not not big enough and we also identified since years the key problems around that for example they're not enough relevant ideas uh being um pushed forward like or don't people don't have enough time to innovate or you don't have proper reporting or you don't have datadriven validation and there are many many problems and when the AI AI

emerged, we quickly realized that that new technology can tackle many of the problems we've been observing for the last 10 years being in the field. And we start to experiment very quickly with ways to generate more relevant ideas, ways to execute faster. If you can execute 10 times faster, then you solve the problem of not having enough ideas. And all these experiments that started like two, three years ago internally testing with customers. We basically brought them to the market with the ready AI value prop which for us at the end AI is a technology like like blockchain like any other technology and we really want to solve problems shouldn't be just a flashy word of AI. We really want to solve problem build it into our product that we believe it does make a difference and improve the output and throughput of innovation. >> What

do you feel like is some of the biggest issues that companies are facing that you're helping them solve for like on a consistent basis? >> I think restart at the beginning. I think if you look at innovation kind of you look it as a as a funnel like the jobs to be done that >> source ideas you evaluate ideas you execute and you report right and at the end all of that should lead to topline and or bottom line impact and it really starts with sourcing. If you don't source enough ideas you will will never get enough output. If you think of innovation like a portfolio, think of the startup world for example, everybody can start a startup, but only very few make it to success. And often corporates have very limited the amount of ideas they're testing and sending to the race. But you

have to send many many horses to the race in order to have one unicorn coming out of that. And I think that's one major problems we have been observing over and over again. So how can we increase the amount of ideas 10x? How can we make sure these idea are actually relevant for the company to increase the chance to have an a big big output at the end, right? >> Yeah. So you like almost you're helping them like earlier and more systemically. >> Yeah. >> Interesting. Um >> that's one thing where AI plays a massive role, right? Because for example in the past you were mainly relying on the creativity of employees. Like when we did entrepreneurship we asked employees do you have ideas? Some had ideas, some were good, some less good, >> some were not good. One is how can we supercharge employees

in ideation. So we have whole agentic workflows that are guiding employees from a business challenge for the corporation all the way to a sound idea which tackles the problem we're solving and which in the past either we didn't have the structured approach or it was much slower and with the use of AI we are much much faster and supercharging employees in the ideation journey and that's just one part of ideation like the humanpowered ideiation and we also through AI through our agentic workflows. We added other layers. For example, tapping into market signals, looking at what what competitors are doing, which trends are in the market, which signals are out there, and our system is picking them up and sending them through a structured validation to really have relevant ideas based on market signals. And you could even do it one step further and tap into

internal data. So we look into ERPs, CRM like the big system like a Salesforce like SAP and see if there are any ideas any opportunities or problems around based on real-time data and again you can generate relevant ideas based on that data. So at the end what we do on the just talking about the sourcing part we supercharge the employees to be faster and more tailored with their ideation. We tap into external signals from market and we tap into internal data and with that we can solve the big challenge of having not enough relevant ideas right. Interesting. Yeah. So I was going to ask like do you find that this is being because you know you have your own workflows like you said right now. Have you found that in the last few years this has increasingly become more effective and more efficient and more

innovative because of the improvement in you know uh language or well LLM's ability to to reason think and kind of expand on ideas or how do you feel like it's changed for your company the last couple years as like the tech has uh improved? I think what the different ways we could improve it. One is like using AI for uh semantic search like let's say somebody submits an idea or the system generates an idea. You could use certain prompts certain flows to analyze >> what's similarity contextual similarity to existing ideas to make sure you don't do the same thing over and over again. That's one example you could use. There are different kind of levers in the system that help um help us to make our customers more effective in innovating. Another example now recently we launched that our customers can bring their own LLM

like let's say you develop your own GPT train with your company data. Now with just one click you integrate this kind of API key to our platform and all of a sudden the the ready AI is much more smart because you really tap into the relevant uh models trained by our customers. >> There are different ways of how we deliver the AI to the employees to increase adoption. I can tap I go into that a bit later but I think there are different ways with evolvement of the models like these new MCB protocols or like they really help us to get even better even more effective as a platform. Uh just out of curiosity, you know, like what would you say has been the biggest concern or like hurdle that companies need to get past mentally on the adoption side for whether it be AI

at their own company in general or you know I'm not not to say that that your product wouldn't produce great ideas. I'm sure it does. Like has there been like kind of like this this hurdle of convincing companies that that would be the case though, right? Like it's kind of a I'm curious like with larger companies maybe they're harder to sell on like oh yeah I can fix XYZ thing. I'm just I don't know what the state of a company's mindset would be like when working or starting to work with you and what like you have to kind of overcome. I don't want to say objectionwise but sort of >> I think one challenge obviously always the the privacy topic with more like >> increasing right yeah more you but in general that's a big big challenge that people a company have to overcome >>

the governance data privacy topic then on the adoption side I think that to let employees use it and make sure delivering AI the right way so employees actually enjoying to use it right if it's too complicated Sure adoption is very low. So you have to make sure you deliver it to your employees in the right way to really um uh have an impact. Right. Do you uh feel like on the level that you're working at that you are able to solve systemic problems that are you know like so large that you know you're you're able to kind of impact the trajectory of either like growth or like hiring requirements and stuff like that. So on the sure at the end if you look at that the impact the data sure then we do have impact top line bottom line like new business cost saving which

is substantial and also that's also again what convinces customers if they look at our case studies and data we we we can share that we achieved usually that also is kind of a convincing factor. No, absolutely. Yeah, I'd imagine. Right. Like as we and and like where do you think this is going to continue to go, right? Because this is you're just at at a level where so in a last couple years you've been doing this, right? So, >> um where do you feel like the the ceiling is for for what you guys are doing in regards to to practically helping companies out, right? Are you are you going to completely remove every uh everyone with it with the thought that their ideas are better or you know like what's what do you think the the ceiling is for capabilities on your product? I think

very interesting will be right now we also launched a new product we call it AI hub because like 20 20 years ago the digital transformation there were a lot of grassroot bottom up initiatives all across a large orc same happens now with AI that a lot of people are doing a lot of things decentralized and we now bring that together to one central place so our customers can manage the whole AI transformation and in a very structured way again like a portfolio let's say you use certain models you want to solve certain problems. Let's say you want to use AI in a call center in sales process. We offer our product to streamline and manage all of these PC in a very good way as a step one. But in the future even running such workflows flows on our system because our our platform is

a very flexible um system. It's basically a workflow system where you put certain agents in the workflow and I think at the end we I think one now as the AI transformation is progressing at the large organizations we're seeing amazing opportunities how we can support these organizations on that journey. >> Yeah. And are you seeing like I I'm you know are you kind of a unique it feels pretty unique to me because I haven't quite seen a company like this. Is there a lot of competition in this type of space right now or do you feel like you're kind of in a great position to to offer something like very unique in comparison to the rest of the the marketplace? >> There are there are quite some platforms in the innovation idea management space who are kind of also coming from from that side but

I believe we have really we are coming very much from methodology very kind with very very flexible architecture now. So we can really tailor now very much to the to the emerging needs of of our clients. Now in AI for example now we have our own AI agent builder our customers can put the right AI in the right form to certain users could be like let's say you're filling out certain parts of of an innovation then we are either enabling directly into kind of writing into fields having chatbot interfaces having side panels and I think the the flexibility of our platform we actually did a complete rebuild over the last years that played very much in our favor looking at the market as well so I think That's one side of competition. On the other hand, I think consulting is also going into a lot

of kind of consulting side in the AI transformation which probably also um goes kind of sometimes hand inhand, sometimes very complimentary the offerings, but I think in that space there's kind of this kind of competition. And you know, as we've kind of reached a point where every single day I feel like there's some new improvement on the capabilities of of AI from a reasoning standpoint, you know, it kind of leads to a lot of information, right? And there's a lot of different ideas getting thrown around. So when I when I hear like you're having you know AI ideation and on all these different you know like you know like the AI hub all these different things I'm seeing on your website would what would you say to people about the organization aspect right because I'm imagining if you're working with large companies they're going to

want this type of stuff to be integrated with their current tool set to a certain extent because having just another thing um is kind of is kind of a a lot right like having so many different things in a company to where you know you have un ungodly amount of list of tools, right? How do you really make sure that you're integrated into a company's like current ecosystem so that it's like an easy fill in? >> Yeah, I think it's a very good question. Nowadays also the trend towards goes more towards kind of consolidation there as well and we our platform is fully API first. So we can fully integrate to >> okay that's good >> the enterprise architecture sure obviously like the Microsoft the the single sign on active directory all that kind of stuff but also we have different kafka pipelines for kind

of data pipes to integrate to the data environment of the clients we can through MCP protocols integrate to other platforms or systems who are kind of MCP ready. So at the end we see ourself also as well more and more as this kind of intelligence layer that we kind of we can build these agentic workflows using tapping into data tapping into tools are already there and we have multiple cases of clients integrating to their safe environment to other agile kind of organizations to make sure it's not just one more tool. It's very a seamless experience for the employees. >> Yeah. And and that's got to be the biggest thing for me, right? Because if I if I was working at a big company and it wasn't that seamless and it kind of takes like what the technical knowledge right is always a hard thing like

is this something that's able to be used by you know pretty much anybody at the company with a decent amount of technical knowledge because that would be like my main concern right like if we don't have uh you know someone who's very technical it's going to be a hard thing to kind of integrate into the company like what what does that look like from that standpoint? >> That's a very good question. I think you have more like our our direct customers a bit more techy like then we have this >> administrational environment where you're doing all the pre-prompting we can do kind of up to 20,000 um uh kind of sign pre-prompting you can train your agents with certain data with certain files you can do certain kind of suggested prompts do that's more the techy side which we are assisting or our clients are

doing themselves and at the end the delivery for the for the employee who sometimes doesn't have lots of exposure to AI. It's just a oneclick thing. A bit like the Amazon oneclick purchase. If you remember in the e-commerce times click AI delivery. Let's say you are in a certain phase in your innovation workflow and you you get your AI support. >> It's basically one click >> and AI executes. But behind the scenes, it's a lot of pre-prompting and and and workflow stuff happening. But the employee doesn't know anything, doesn't have to know anything of that. Again, AI being a technology that should just work shouldn't be kind of a lot of work for the employee to to make it work, right? And that was always our goal to to have a very simple delivery because I believe only if you have the simple delivery at

the right moment, the right AI, then you can actually really uh generate the adoption. And we recently saw this MIT study that I think it said like 95% of AI pilots fail. I believe it's because it's just not well integrated in workflow. If you just throw an AI tool to an employee doesn't know how to use or she doesn't know how to use and it's probably low adoption. It's wrongly used. So we really tried with our clients find crack that crack the problems you want to solve the way you want to integrate so employees only have to do one click. >> Yeah. And how do you think these companies just on a practical level? Obviously, your product seems to have it sorted out, but how how on a practical level of do you think companies in general can do a better job of of creating

quality AI adoption at their companies, right? With with not only your tool, but like any tool in there, >> I think you need to have a very structured approach. I think clear educational part of the employees, the structured governance kind of really to I think it's make sure it's it's a coherent way across the company how to use it. Employees need to see it as an opportunity not to as a fear. I think that's also a risk. If if employees perceive it as a cost cutting initiative, it shouldn't be. It should actually be an assistant to make you more productive. I think if you communicate the right way, set up the right governance, set out the right delivery system, then I think you will be successful. And also we have to tackle kind of the right workflows. You have to analyze as an enterprise where

can we bring AI in which way? Which again brings him back to the AI hub where we provide a tool so companies can in a very structured way analyze where to use AI and how and how can you replicate maybe it works here can we use the same workflow here and I think this very much key to success not just randomly everybody trying whatever and nobody has an over overview of what's going on. Do you feel like with the you know addition of all these different tools that have come out in the last few years right obviously first it was just chat GBT do you feel like to a certain extent we've kind of not done nearly enough I mean that study was interesting we've not done nearly enough to to hone in as companies on the power of AI you feel like a lot

of companies are still like behind the eightball so to speak >> I think it's so fast it's development is crazy to stay on top what is going on and the >> the newest model or update or tool it's crazy also discover all the time new stuff which impressed me a lot like now like the whole vibe coding topic like in the past when when 10 years ago building an MVP or or a website was quite some work and now you just use replet lovable or any of these tools you quickly build your MVP to test with users and I think it's just so much going on a lot of education happen so So you need to be stay really on top. You need to dedicate time and probably our clients the innovation teams they are dedicating the time. It's their job but you cannot expect

that an employee who has who works in sales. It's not his job to be on top of AI. So, it should be the dedicated AI team being on top and make sure that the employees can use the state-of-the-art tools and and in corporates, there's always these kind of lot of lot of uh uh rules and and and hurdles there, right? Because for you us as private people, we can just try the new stuff and try them out and play around. As a startup, you have a bit less hurdle, too. But the bigger the company, the more governance, the maybe you only are allowed to use co-pilot of Microsoft, nothing else. and all of a sudden a big part of the world is excluded. So how can you be so deep in unless you're very interested right privately to really stay on top of it. >>

Yeah. And that's got to be why you've you know kind of put such of a big emphasis on data security and stuff like that because I'd imagine there's a there's a litany of companies that maybe you would have wanted or you know if you didn't have that be the case that you would have wanted to work with that they would have just kind of brushed it off because of that very thing. Right. I'm guessing they're going with co-pilot or whatever it is because of the uh safety as it were. >> Yeah. No, no chance you would get in. You have this kind of ISO certification for for data and all these kind of stamps we have to be an enterprise ready platform because let's say and as an enterprise I want to use the newest AI tool I found probably you do it illegally. So

if we come in we are always vested. We have our AI tools are kind of um safe to use and also we have among our customers we have banks and and insuranceances and very kind of companies a lot of very highly sensitive data and I think so it's key to bring that security uh so enterprises kind of start engaging with you. Yeah, I mean it's just it's really true. There's just I don't think there's really any opportunity for people out there to do enterprise level deals without significant significant amounts of certifications like you said the ISO the um so what is it SOC what's that one I'm forgetting but yeah there's there's a list I I'm seeing the list every everyone that's ready has got the list you know on their homepage >> um so that the enterprise level customers are aware >> um and

I think it's very good that you know companies are more and more kind of implementing this because it it's a hard space and I I I get the the data privacy thing. So, it's it's definitely for sure something that everyone needs to worry about and I guess some other things people are worried about just to just to be honest, right? Like this is a great product. It has a lot of really amazing capabilities. Some people have concerns obviously with data privacy which is which is totally fair on the enterprise level but some people also have concerns with it on on a personal level with like job growth and like what what is this growth of product and tool capability mean for the job market? where have you kind of seen um how that's going from an enterprise decision-making level with the companies you're working with

and and what do you have to say about kind of that aspect of things with uh the amount of jobs that kind of exist uh and will exist so to speak with this >> I think in in general I think entry level jobs are at more risk looking at lawyers developers absolutely consultants I think all these analyst jobs which are re research heavy look just GPT deep research search if you connect some systems to it you get quite far it's not there yet obviously but it's improving so I think entry-level jobs are at risk and um yeah and I think it general I think it al depends if a company is in a growing or shrinking phase like let's say if your company's under a lot of attack and you need to save money then you are at a higher risk that AI could come

for your job if a company is growing I just had a conversation with a with a colleague who's in a in a growing enterprise and she I mean look for us it's an amazing opportunity for the employees they love to use it they know if they can be more productive it can grow faster and everything so it's not really perceived as a threat I just ask her this very very question actually where off in other organizations obviously it it is at the end on on a board level on a CFO looking at the number and if you need to shrink if you need to be more effective it is a risk and then unfortunately that was probably affecting most entry-level positions >> yeah no I think that's a fair point like the nature of where we're at is a little bit I mean you can

only really analyze it based off of you know the company you're in to some extent like on a macro level sure maybe there might be a and this is the hard thing right like because enterprise companies hire so many people like maybe they lay off a bunch of people because they're trying to you know do the the natural big layoff and then there's this opportunity for agentic and AI and AI stuff in general to save them time and money but you know on a low and a on a business that's just like growing a small business. Maybe they don't have to hire as quickly, but if they're a really rapidly growing company, they're still going to want people, you know, they're still going to want people to come in and know what they do because um quite frankly, like I just don't I I I

personally see it as maybe it'll get worse before it gets better, but we we really don't know what the the final outcome will be with all this stuff. I think entry- level jobs are definitely at risk in a lot of ways and that's a concern for maybe a certain segment of people right the the younger uh kind of people who are looking for it but maybe this will lead to different adjustments in jobs because they're you know like yeah sorry you were going to say something no I fully agree I think at the end it's adoption is alo key for companies for me as a kind of a at ready for example I'll encourage all employees to actually tap into the potential of these AI tools out there I think if you if you can make your chop uh kind of maybe you can get

rid of some more annoying parts of the chop, you can get get more output and then we actually we encourage people a lot to use AI. We just recently had our annual offsite and we did a hackathon or a workshop in this in this um offsite with the business challenge like how can you use AI in your job to be more productive to make your job more enjoyable and then everybody spent like two hours of thinking around that challenge. We actually applied our own ideation methodologies and everybody thought how can I use AI? Which tools could I use? How could I make my job more fun and we had 30 amazing ideas that people are testing now? And it's not about saving people are saving saving cost. No, it's about really leveraging what's out there and using these tools. And I think as a as

an employee you always should make sure you you're staying on top what's going on trying out and I think the people who don't touch any AI at work they're actually more at risk because at the end there is more like if you have a if you have a calculator for doing math you should use it and if you just refuse to use it eventually will bite bite back right >> yeah if you're in accounting in the you know the 60s or 70s when you know I'm sure or 80 I'm trying to remember I don't even know when the calculator was invented, but I'm trying to think of an example. That was my guesstimate. I don't know when it was invented, but you know, when was the but uh if I Oh, you know what? That's crazy. But the pocket-siz devices and it emerged in the

60s and 70s. That was a great guess. >> Nice. But in the 1960s to 70s, if if you you know, in accounting and you're just like, "No, I'm not going to use that. That's a ridiculous thing. Why would you why would you not?" Um, and there's a lot of similar like parallel arguments now, right? Where people are like, well, you know, you want to make sure that you have your in this case it would be math skills up to date, but you know, maybe your your writing skills up to date, all that kind of stuff. Something I like to think about with this topic is entry- level people are the most at risk mainly because they don't they haven't acquired the skill set uh to to do a lot of this work. And essentially what happens is like a slightly more senior person will tell

them to do sort of like grunt work level things and those grunt work level jobs are just if then pattern recognition with uh minor variations based off of you know low-level decision-m and it's a I worded that in an unfortunately accurate and blunt way. If you look back at your first job or my first job like it's just basic pattern recognition with like I got some help from my boss like true but like sorry but you know people need to go through that to learn stuff and what I find interesting about the AI now is that it has the ability to to become agentic and learn those types of patterns and learn those types of things. So people are more and more open to the idea of these of these agents taking over these roles especially you know as things expand. Like what I found

so interesting about what you said with Ready AI is your API first, right? Um we're truly I don't actually know if we're limited by compute or reasoning at this point. I think we're limited to the connections whether it be via API or whether it be the click on screen capabilities of a tool, you know. >> True. And I think if you if you crack these two things, having make sure people know where to click and if it's just one click and not writing a massive prompt and getting access to the right data which at the end has to be structured the right way. If the data is not structured AI is struggling as we know >> if you crack these two things I think that will be a game changer and also we betting on that right with this API first platform we're betting on

on these enterprise platforms opening it up beginning of the year we put it as a hypothesis in our website by now I think most of these platforms did open up and we also see a lot of kind of this problem mining opportunity mining in big companies where uh the organizations try to tap into that data make sense of data And there I think as these two hypothesis kind of manifest more and more I think we have really right to play as this intelligence layer to really um create >> kind of make innovation a profit center not a cost center as we say. >> Yeah and you know you were talking earlier about MCPs a little bit and the opportunities there. Could you speak to a little bit of how you know you guys are trying to slot into that opportunity and kind of maybe just

give a brief explanation of what it what it means in general to those who might be a little bit uh less knowledgeable on MCPS. >> It's more like that that's kind of the two AIs can talk together. So basically let's say you have an innovation project and they want to find relevant trends then you can basically our AI can talk to a trends database with MCP and then the two the two agents talk together and they make sure they get the right trends for a certain innovation out of the system and pull them into our system. Let's say you want to find scout startups very similar like CB insightes a big a big startup database is MCP ready and is much more powerful than the API because you really can kind of more in a gentic way find find the right content and pull them

out of the system. >> Yeah, I mean it it is such an interesting opportunity because I see all of these tools going on this protocol. Yeah, I think it started what four or five months ago? Like I don't feel like it was that hasn't been that long, right? >> Opening it up was quite recent especially I think. >> And Claude was Claude kind of the the catalyst for this. >> Yeah. I think there was a very remember when we um looked into it they just started a beta beta closed beta I think if I remember right. >> And now with adoption is going up I think. So I think it's a great opportunity there. also MCP on platform as well that we can even run this protocol on our platform to for example compare all ideas against each other to to kind of also analyze

more the similarity or or opportunities based on all the data set we have on our platform so we have kind of MCP within platform MCP towards external platforms I think that's a a nice opportunity also emerging more and more obviously right >> and just to tap in a little bit more to explain it at at a as basic of a level as you can to to the lay person when you say they're able to communicate with each other right on an easier basis because I understand you know with APIs and stuff that's kind of what they're doing as well with specific endpoints but how does it how does it communicate to to each other in in a different way than how like uh you can make call requests or put requests and get requests etc through APIs like what's the kind of difference there I

guess to for someone to understand >> I think API have to map certain fields I think like title description like certain elements yeah there's always yeah there's choice fields there's an end point >> say okay this end point the description of this article the title should relate to the title of of >> correct yeah >> and this is more like a protocol where you basically the data exchange between two kind of um platforms with AI basically you really have this these language models and you basically ask can you find certain tickets in Jira with certain characteristics and then it will actually talk to the MCP of Jira and then kind of using the Jira AI in a way to find the right content and pull it over. Not this very >> kind of one that >> structured >> much more simple one with APIs, right?

If a much you have much more opportunities with the MCP to make sense of these massive data pools, data lakes uh in different systems. >> Yeah. Like for example, I'm pretty sure Claude itself in the last few months implemented a Gmail integration uh like a Google calendar integration, etc. And you can ask and maybe this is this kind of in the same category of what it's doing. You can ask questions to claude now about your inbox and about your um your calendar and stuff like that and it's able to get the the mass amount of data that just exists within Gmail or Google calendar and it's able to give you natural responses of that that data and what's in there. Is that fair enough? Right. >> Or notion if you know notion this kind of note takingaking tool it's not fully integrated. >> Absolutely. I

love it. YPT is an easy example. You can now connect with with with your uh Gmail with one drive with whatever tool you need and then you just do research and say find me blah blah blah and it goes through your emails goes through your drive goes through your calendar all the the channels that you can connect to which is increasingly notion does the same and all these platforms are connecting and the question would be who's the master platform will it be open AI integrating everything notion launched their own AI notion AI for example but now you can >> I remember all the notion data on chat GPT. So, do you still need the notion AI? Like that's this question which I'm curious to see if open AI will become this big monolith. Now we have this kind of glorious seven seven tech companies and

I feel open AI is going for all of them trying to attack Google on the search, Apple on the product with Jonathan Ice. It attacks Microsoft and enterprise and let's see maybe in five years from now we have one massive player integrating to everything else and let's see how this develops. It's very exciting. It's going to get even more interesting when uh have are have you familiar with the theory that well not theory there was reporting on it that seemed pretty substantial like Perplexity wants to buy Chrome. I don't know if you heard have you heard about like how companies want to buy Google Chrome because there's questions as to whether they have like a monopoly. Yeah. >> Um, yeah. Right. >> I try to come com Comet I think the browser of perplexity where it's a kind of a gantic browser. >> Correct. Yeah.

>> Makes sense. Kind of really attacking the Google the big kind of since many years being that tech company the internet basically now being attacked from all sides from open AI from perplexity. It's interesting how fast it can go. >> Yeah. I mean yeah >> years ago nobody would have that Google is so easily attackable I guess right. >> Yeah. But the the concern basically is coming from the fact that you know Google has the you know they have the search platform they have the browser they have the cookie still they have the ad platform that they've I worked in Google ads for a long time >> I tell you what last couple years before I got out of that industry they are price gouging people on that stuff it it's like the cost per clicks are just like going through the roof and I

don't think there's necessarily a correlative value like uh maintenance there. So, um it's it's very interesting where that's going to go. And you know, just just to speak on the MCP thing for a moment one more time, like yeah, I know that OpenAI's got the capabilities, but was really interesting like this is just a use case I saw the other day like I'm trying to make sure that people I'm trying to get people on more podcasts. Like we have this podcast. I'm trying to like help founders get on more podcasts. And just as like a practical question that I could have given to somebody, this is an example of where I think it's going to impact the workplace on the margins and then lead to you maybe less hiring because you don't need as much hiring. I was like, "Hey, we get a lot of

podcast guests on the podcast. I know we're doing our own outreach. I know there's some PR agencies that keep just giving us more guests, right? um can you to my assistant I was like hey could you just go through the email and find all the PR agencies that have like been in the inbox and he's like okay I'll do that probably take a little bit of time cuz it would if you go through a Gmail with a bunch of emails right about a bunch of different topics and then I I went and I opened up Claude connected to Gmail and asked find me the list of the PR agencies people their emails etc and all the info and guest that they had us booked on, please. 5 minutes later, it pops up and and I know that sounds like, well, it's kind of took a

lot of time. Like, >> well, it just searched through my entire inbox that gets like hundreds of emails a week because it's like my my inbox for the company and everything kind of like goes through. It's like my hello email address, right? So, it really gets a lot of stuff. And I was like, this is incredible. And if we start getting that kind of data cohesion with this MCP protocol, I I I think people like are blissfully unaware of the capabilities uh that'll come out of it in the next few years, you know. >> Agree. It's crazy. I think it's so impressive. It's just the beginning. It's not perfect, but I think it's already impressive what it can achieve in very short period of time. >> Yeah. And it's not full there's not a we're not a full adoption yet from the companies that are

able to implement the protocol, let alone people, right? So once there's mass company adoption and mass people adoption, it's just going to get better and better and better. So it uh you know that that was a big eye opening moment for me because I'm like I'm a big automation person. I'm like a very nadmake.com call me an expert. I I it's one of the few things I'll be like I I know what I'm doing there. Like I we have millions of we have like I literally have like 300 automations running at the company right now. So it's like okay I do a lot of that. I'm like so I know how to do this. Like what's the point of an MCP though? I get that like it's supposed to be more free flowing with the data. And then I just typed that in and I

went okay. I was like now I get it like that that's going to be a big deal when even at an automated platform you start having MCP endpoints so to speak inside of an automation platform. And then that was like the big aha for me. I'm like, "Oh, could you imagine if there was recurring instances of of that thing because everything starts with manual and then can go to the the autom and go to the automatic and the recurring capabilities." So, it's like we we got a we got a future in store for us. Um, but yeah, man. I I guess just last thing I' I'd have to ask is like what is what has got you most excited about your company um in in the next couple years before we kind of close this out? what what are you really looking to tell people

about that you guys are working on? I think it's super exciting where this aantic future goes now in the enterprise area where we are focusing on. I think it's things are moving at very fast speed and I see crazy results that some companies already adopting achantic workflows in their companies kind of managing the AI transformation and I believe that's a very exciting field to be in. I think the way we are developing there together with our clients, it's kind of also new because development is so fast for us for clients and I think jointly this develop there and the conquer this new world is super exciting. I think it's a great great time to live. I think I agree. So for everyone who is you know looking for what uh Ready has to offer, make sure to go to ready.ai. That's spelled ready with two

Rs. RR Eddie.ai. AI. Um, and yeah, we really appreciate having you on the show, man. It's been a pleasure, Dave. >> Thanks, thanks for having me. >> All right, guys. Thank you for this episode. Please make sure to leave a like, comment, follow, leave a review, and we'll see you in the next one. Peace.