While new generative ay, I models like dolly too and Emma Jane, have recently brought broader awareness and excitement the space s eyes. Already changing how we work and the software we use across the enterprise. And this episode from october, twenty nineteen people. I found her and seo. Oleg regent, ski, an agency partner, Peter lawton, discuss how a eyes making its way into the enterprise, including free, march for how to think about where I automates knowledge work like sales and marketing put alike, cause autopilot mode and where it enhances productivity or copilot mode. How startups can spot new, ai opportunities and more learn about the latest in a high and now data and how enterprises are working with these technologies, good, a future dot com such data-

The content here is for information purposes, only should not be taken as legal business, tax or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any a sixteen see fund for more details, we see a sixteenth eda com, slash disclosures everyone welcome to the eight sixteen see podcast, I'm dos rush in this episode. We talk with oleg region, ski the founder of people? I a platform for sales and marketers, but the broader question or tat is beyond just sales and marketing truly how air is taking data from across the enterprise to change how we work, regardless of our function or industry. Also, joining us in this conversation is peter out and from the eight sixteen see, enterprise investing team. We're gonna cover everything, from how to design a I'd, be a co pilot for knowledge workers to the founders play. four spotting the next day I opportunity and then how to take a product market once you do, First, we begin with why I and be to be that his business to business is so different from business to consumer

in the last five years. More and more areas of human existence activity are adopting. I an impact Colombia, iced finally, proliferating be to be enterprise world, which is very different to work with them. Yet the classic bittacy scenario b to seize much more react. You build something you throw it at a million users in australia and see what happens and if it does work you can pull back would be to be. You can Do it because you have five customers that each one of them is being you, so you better go ahead and do it research in advance and make something enterprises one, and so with that, it's a very different level, risk, but also very different level of data instead of having a large number of users out there that all contribute loop. Data to you. You have very few users that control a lot of very valuable, very private, secure and needle moving data base, much harder because you have to

those companies very comfortable with the fact that the data will be anonymize, aggregated will not be shared with competitors will not be leaked. If you get hacked in one of the big calculations, particularly big customers would be doing, is how do I compare the gains of machine learning being applied this way against the costs of actually doing it in so, if you think about the costs of, collecting cleaning and maintaining the data like that. Certainly gotten cheaper overtime, particularly a decade with cloud computing native you as tcp hazards, But there are some areas where the value out of the data you get. Maybe it's obviously worth the cost of actually collecting it in. So, when we think about like how this generation technology will be deployed over even the scale of decades. A lot of it is where is the greatest, yielding application of these technologies by industry, and then, let's aim or efforts there.

What's the next best industry, so it's also a function of just how valuable is the promise you're making to customers against their of being two it interesting! So that's how companies are evaluating I, but what about? On the start up side? How should an entrepreneur look at the market? For I for aspiring entrepreneurs? There is a pattern that if there is an area of human activity, existence, the generates activity, data of how humans do something and the data is not being captured today. That is a ripe opportunity. There isn't I'm serious activity stream, someone doing something moving shipping containers are recording readings from wind farm recording emails from a cells, person, recording location of ober driver? and as long as you can collect, reliable, comprehensive non manually entered high volume activity.

It out from many wind farms or from many containers and ships from many salespeople in one place, aggregated start seeing the big picture and then use the big picture to analyze the micro trends and predict what the best next action for the operator, the cells person, the driver is that's where it brings in network effects and significant acceleration of growth. So my fear is that every industry is going to switch into this business model collect activity, data understanding at scale and turning into best next sections in the next fifteen years, you mentioned network effects there as an accelerator of breath. What exactly do you mean by those network affects the more sensors edge computing devices salespeople Ober drivers, you haven't the network, the more data you collect. the more the patterns of behaviour you see when you put them to one central as graph these

Part of this graph becomes for everybody, the better the predictions it can produce above the best. Next actions. Now this is where the second loop of these never give starts the better. The predictions are the more money over drivers make them more money cells people make the less wind farms break so more and more Where's will be lowered in with this better efficiency to become part of the network. Contribute more data into this shared gruff becomes kind of this virtuous cycle. Actually imagine your wind farm operator and you. We know that european firms are gonna break after they broke and that has been found. Next, to you say same everything, but they come in and practically fix them. I have zero downtime because they started collecting the data. About their inference breaking five years before you if your computer outcome, It is a business process. Eu did not your disagreement

but you can go to the same vendor and by the same assembly line and catch up. If you miss the boat, the results are very different. If you did not collected data early enough there's nothing. You can do to make your I better than your competitors, even if they do is shared. The EU has been trained on your particular behaviour and the market response to it for three years longer it seen more examples, more samples is just smarter and so on I got donation like industrial revolution from a hundred years ago. A lie. arms race is a zero sum game. So my pretty is the ten years. From now fortune, five hundred will look very different now because some, companies did not get into collecting data and training their machine on immortals early enough, is there a tipping point where this balance of power around the data shifts from the customers?

I get up to to the vendors because your ai your model becomes such a competitive advantage that they're going to have to play by your rules, they're going to have to allow you access to that data and to reuse that data for training. The competitive dimension, part of that is really interesting in theory, if one customers contributing their data to that corpus. Certainly there data's anonymize and no one else in the customer base would really have access to their data, but in theory, anything you observe at one customer is informing generalise models that all the customers benefit from inciting port It is just being super honest in the sales process that, yes, of course, that's how it works by it. I think the calculation that most of the customers are doing is ok, that's and I realize I'm helping everyone else out a little bit, but you're bring me so much younger than I am willing to contribute a little bit of the signal from my data to help everyone else. It's gotta, like you, push the boulder upheld getting first customers, but then

you, roll down hell with the network effect becomes so strong and the value of joint data said joint knowledge becomes so big that people just go ahead with it and so reaction to how nervous customers can be around oh If I want other people. Have it from my data at all, is that's kind of analogous to people writing contracts it You can only sclusively, sell your software to me because in abstract these are just tools. acknowledges that everyone's gonna get access to eventually in so anyhow, the tastes in having customers have about it, that's going to be a relic years from now. One trend- and actually I think I heard from from mark recent here- is that when data model changes, systems of record die So they were systems of record like ceramics built on not relational, but on hierarchical databases in the seventies. Do you know of any today the longest exist anymore. Then

Thanks to the model that happened was on prime sequel sibyl, then the cloud sequel happened and now it took about sales force. The next generation of data mortal is likely to be a graph which is the date, the model that allows you to train a I in the best way possible federated shared graph of data together with data and well shifts the way you consume. The software has been changing. Instead of you pulling data and looking for it in bunch of excel reports, sales force reports or websites, it's actually to be pushed to you in a pre packaged, we call it personalized, actionable insights way where everything you need to know to complete. This action is right here, pushed to you through the channels through which are most likely to engage with it. Most of the systems of the future will have this. He'd or minimum choice, a maximum focus on one thing in front of you, and once you completed

next thing will common and the next thing will come and debt will make you much more productive and much more focused at what you do So you mentioned that the way we consume software is changing, say a little bit more about that. What does that change mean for product design? Is not the case or the product is simpler? Is that the works is designed such theirs. level of intent that you can observe from the user? you don't really know what they're trying to do. Such you dont have to expose the entire complexity of a product to the user and overwhelmed them. To say, or quite certain. This is what you actually want to do at this stage of the product that org show you and you're gonna be a very efficiently execute that function. It sounds like the prediction here: is this next: wave of ai companies this next generation of enterprise software. It starts to make worker lives easier because it gets simpler and gives you stabbed and chrome, totally order. That is just the interaction between how do you map you

send design against this, like increasing orchestration of the knowledge worker labour force. Does that mean men for kind of the enterprise worker of the future? It seems to me like, in that scenario, you're getting workers to be increasingly focused. Does that mean they're also getting increasingly specialized? I think it's two things they are able The focus on what really needs human judgment really complex, rare situations that may be there's not a sufficiently big data corpus to make an interesting in france on women wearing application in. So what you get is huge not only do the hardest iq work, they compute can't really replicate yet, but also they do become more empathetic york spends more time thinking about your specific needs and how they communicate with you verses them. simple math the two modes which operates with people we call a one hour to pilot and another one is copilot. So autopilot is when

you're doing as a human you're working on something mundane, some repetitive low value, our task and so on. can easily out to me that, for you so think of our burn analogy of them receive. iphone called typing in the computer. Picking up the ham, radio delivering it to you that just not the best use of all. billions of neurons in your brain, so The items in an out two maids those functions, freeing up your time to do something much more product to a much more effective. The second bucket- and we we see a I played, the role is overcome, copilot in copilot cases a. I is augmenting your ability to make decisions Think of you are in the re, scarring. There's someone next to you that no, What's around the curve in his whispering in your ear, what to be ready for? What's the best next thing you could do while other pilot makes a super productive copilot allows us to

because on more human, more face to face more each, you driven things that machines will not be able to do for a long long time all exemption is a lot of these companies that have some sort of co pilot go there. some sensitivity around the employees and the user not wanting to be tracked, for some people did robin the internet? They say, oh, the violation of my privacy I dont y. All. The magic of how I deliver my sales and to be If I have some really good weeks- and I have some bad weeks like- I don't want them to know about my bad weeks, how do you get the end user, comfortable, recorded tat actual you have to be? There jolly in a very simple explainable way, promised in delivering ten acts, the value of being on this system, then being off the system now to simplify that go to customers was so great

let's not put you on the platform, let's ab test and see what happens at the end of the quarter when the guy next to you or the girl. Next to you, did tennis your results, the only variable we have, as you were, afraid of being a user of a modern system to break them down for me, a little bit more specifically gear making this technology for sales at marketers, What are sales and marketers doing today that they're, not gonna, have to do five years from now and house, Hey I going to change the way that they work. Is this just about their efficiency, or is it going to fundamentally shift how those industries work? It will definitely shift holiness, does work. So, let's think about the day of a salesperson in an average week, salespeople spend about a third of their time on manual data entry. Not a third of their time is always supposed to be spent on prospecting, so finding who else looks like My current customers, which, from computer science perspective, is a very classic luca like problem with salt thirty forty years ago, and then only

that time is spent in face to face meetings, actually selling actually building relationships. Now, the first two parts I know that the entry of which should be gone. It should be fully automated, threw out the pilot capability. The second part is who should talk next is also very easy to solve. With the help of look modeling machine learning, pointing you two very I value, add prospects of customers and actually helping you out to made the outreach to them and then a third part which is being face to face with customers. Truly bitterly relationships is where she's, cannot replace human so far ten years from now salespeople or any white collar kind of knowledge, workers will be actually focused on a cue driven relationship, building activities still with co, pilots help because by then copilot will recommend how to better builder relations,

See you can think of in particular, of the sales of the future is really high EU people showing up and then compute is aiming them at where their way, is best suited. I in this all sounds really fascinating, but then I think the older generation of sales force out there being told they have to use this new tool they ve been. Lying about their methods of selling and they ve got their kind of day to day. How do you go about with that habit? Change for those sorts of work earth, or is this a generation of shift in terms of how I technologies get adopted in the enterprise? If you think about it, the cells people themselves their ingrained in their habits because something they ve done over and over again worked. What autopilot does it frees up a bunch of their time to experiment more, and so, if we approach any problem that involves a I with autopilot, freeing up your time and co pilot teaching you the new ways, eventually you be able to retrain the people.

brain drain in their old ways into new ways by showing them that try and new stuff actually yields results. This training at the eu- point exactly where the knowledge worker is doing their job or say these are the next top priorities for you, human, can learn from that and they, There are two understand. Okay. This is the best way to sell the way that this is playing out. It's kind of unbundling, these horizontal, like learning management systems of two decades ago. This kind of dance between the knowledge worker in the software, the knowledge worker in the machine, learning, algorithms, trains, and live on. The job you learn in software. While you do your job, we hear a ton about, we have to retrain the workforce, but this is, I think, the first time I heard at our violated that some of that retraining gets built into the products, especially at the point where it sounds like the products are augmenting. What humans do so how our products gonna look different. What is the product of tomorrow? Look like,

for me. So, in the next fifteen years, every area of our existence, they go through transition and will be instead of being inundated by bunch of chrome tabs open inundated by a bunch of feeds that tell us what to do. I think they're cycles of bundling unbundling playing out here already in like the latest wave of machine learning applications. The use Caesar, getting increasingly precise and tailored to the end user, and one of the consequences of that is, as he says get richer and deliver more promises to the user and then help them do their job in more more ways. I think you just get much fatter, workflow applications, and we see that already happening in b to see the bundling of services, as they figure out the right, workflow. Think of goober used to be just rides, notes for delivery, notes, freight and stuff. Like that, everything from the same up you can ship something you can

somewhere where you can get food delivered or whatever you want to have delivered once the company finds an optimal user interface that allows for suggesting best next action then it just makes sense to bundle in more and more functionality and take over more and more of their attention span of the user. I think this actually is, question that I'm really curious about two, which is like the broader trends of how these products are coming into the market. Are you finding that you have to really drive that adoption at the enterprise through your user first or is it still more of a traditional top down your go it in your selling to somebody because they want to automate what their sales force is doing. So there has been interesting point for us, because we want to build a bottom up approach right now, a top down. We come in through a standard procurement channels. The part that is tricky there is that you have to make the organization comfortable with your security,

It's true your privacy post trip and so having. That is what slows down the bottoms up approach. You cannot give someone an ai that is going to learn from individual user without checking in with a company. First it'll be a very tricky balance played out over the next few years, where the users will be demanding more and more powerful and data hungry products, while the surprise will be saying. Well, I still want to be in control of it. I think there is a distinction between the types of enterprise apps, the absolute build, a I based and user behaviour, and do not require merging of that behaviour, data with propriety enterprise data, so it's zoom or slack, those will have much easier time with bottoms up adoption, because they just need a user to login and do stuff it's a word or utility tool that is, a player can use any single player mode. The flip side is when devalue ukraine.

It is based on our- is significantly amplified by the historical proprietary knowledge of the company. So, In our case of people, I we need to know what's in your serum, in order to not give you random suggestions and so having access to proprietary information that the company has under the lock of by their security, and I t is where you have to go and be very transparent and open with the kind of top down ity security in for sec teams and and work with them to get access, but also work with

and to explain to him the value that the end users are going to get you've both been working around these ai technologies. For a few years now, I know, like you've, gone through a few different companies and iterations getting to people ai. What have been the biggest surprises for you in the space and what have been some of the biggest lessons learned. So the first company to join us started my career as an inside salesperson back in two thousand, and six like I was bounding, the phones before linkedin existed and before twitter existed smile and dial style. The company that I was at went out on the toronto stock exchange right before the downturn. So the first lesson I learned was timing is everything and if you get a opportunity to move forward. Aggressively second lesson learned was with samantha the company historian, twenty eleven. That's where realized, why the size of the market we matters, we started a sentiment and also be I beautiful technologies, scales, nice de the only problem is, there was only twenty thirty come.

Is it really needed in the world to Samantha end up being a very small market and we end up owning, probably eighty percent of the market within three years, whilst still doing single millions of revenue, which was kind of crazy. It's really striking that you mentioned you ve been an inside sales on the smile and I'll, even a marketer. How much did your personal experiences inform your product development, a hundred percent? I will never forget the moment. in two thousand and seven when ceo of the company, you was actually that, when leading in cells team grounded me whole team in a room in a pre small sweaty conference from for a week having as go and clean sales force record by record and then a week later, we came We used the new clean amazing cells was for a week and by then of the month it was just as bad as it was before, and so there was

but the first format of experience when you something is really wrong with how we do sales and marketing today, and then I had to run my own esteem and was writing my own sales team. I could not get the data I liked data, I understand it. I could not understand. What's going on the sales team and my my people were working really hard, but I could not pinpoint why it's not working out why we are losing deals, why we are not and think weekly. Why we need to hire more people, why the seemingly our productivity seems to be fine, but we're not doing enough? And so all these, why questions dead? Your support to have the data in the serum, but you never actually have it led me to really start thinking about starting people. I I love that. The way that, like the product development, has been informed by your personal experiences, I think that's a beautiful tie and just how you can have all the data in the world, but there is still something about human experience, human empathy

that you really can't replace with a computer data set. I get to spend almost every single day of my life, meaning founders on the frontier building products for knowledge workers, and there is a huge disconnect between their optimism for these people again what you here in the policy realm and all this doomsday thinking like this whole way, eve of automation, augmentation, that is making people pretty nervous at macro picture, I the oldest micro level, evidence that is just really transforming the workforce, in view really give more meaning out of their jobs, and they really start to love that they can focus on exactly what they're good at you're really good at. Something like you want to be. The best musician you want to be. The best part is the best computer scientist. That's what deep inside were also striving towards, and that self mastery is what a I the copilot specifically helps to amplify,

the outer pilot takes away the time that you spend on staff. They didn't care about on your way to mastery and then the co pilot actually use your guidance on how to become better, more effective, learn, foster unbecoming at my maastricht. What you do so. I think that these side effect or the primary effect of a I ventured is gonna. Be that a lot more people will be insanely good at dead specific thing great. I think that a fantastic hastigt now turned on. So I want to thank you all for coming in and thank you peter thanks for whom we hear this bill. A lot of fun has been a phone, been a conversation.