a16z Podcast - AI in B2B

Episode Date: October 24, 2019

Consumer software may have adopted and incorporated AI ahead of enterprise software, where the data is more proprietary, and the market is a few thousand companies not hundreds of millions of smartphone users. But recently AI has found its way into B2B, and it is rapidly transforming how we work and the software we use, across all industries and organizational functions. In this episode, Das Rush interviews Oleg Rogynskyy, founder of People.ai, an AI platform for sales and marketers, and Peter Lauten from the a16z Enterprise investing team about what the rise AI in B2B means for enterprises, workers, and startups. They explain why AI provides a strong first mover advantage to enterprises that adopt it early; how it can automate lower level tasks, maximize our focus, and, ultimately, make our work more meaningful; and for startups, they provide a playbook for seizing the next AI opportunity.

The content here is for informational 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 A16Z fund. For more details, please see A16Z.com slash disclosures. Hi, everyone. Welcome to the A16Z podcast. I'm Doss Rush. In this episode, we talk with Ola Griginski, the founder of People AI, a platform for sales and marketers. But The broader question we're tackling is beyond just sales and marketing, truly how AI 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 Lauten from the A16Z Enterprise Investing

Team. We're going to cover everything from how to design AI to be a co-pilot for knowledge workers, to the founder's playbook for spotting the next AI opportunity, and then how to take a product to market once you do. First, we begin with why AI and B2B, that is business to business, is so different from business to consumer. In the last five years, more and more areas of human existence or activity are adopting AI. And in particular, AI is finally proliferating B2B enterprise world, which is very different to work with than your classic B2C scenario.

B2C is much more reactive. You build something, you throw it at a million users in Australia and then see what happens. And if it doesn't work, you can pull it back. With B2B, you cannot do it because you have five customers and each one of the of them is paying you. So you better go ahead and do the research in advance and make something enterprises want. And so with that, it's a very different level of risk, but also a very different level of data. Instead of having a large number of users out there that all contribute a little bit of data to you, you have very few users that contribute a lot of very

valuable, very private, secure, and needle-moving data. It's much harder because you have to make those companies very comfortable with the fact that their data will be anonymized, aggregated, will not be shared with competitors, will not be leaked if you get hacked. And one of the big calculations that particularly big customers would be doing is how do I compare the gains of machine learning being applied this way against the cost of actually doing it? And so if you think about the cost of collecting, cleaning, and maintaining the data, Like that's certainly gotten cheaper over time, particularly in the last decade with cloud compute and AWS, GCP, Azure, et cetera. But there are scenarios where the value out of the data you get maybe isn't so obviously worth the cost of actually collecting it.

And so when we think about like how this generation of technology will get 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 our efforts there. And then 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 cost of being able to achieve it. Interesting. So that's how companies are evaluating AI. But what about on the startup side?

How should an entrepreneur look at the market for AI? For aspiring entrepreneurs, there is a pattern that if there is an area of human activity, or existence that generates activity data of how humans do. something and the data is not being captured today, that is a ripe opportunity. There is a time serious activity stream, someone doing something, moving shipping containers, recording readings from a wind farm, recording emails from a salesperson, recording location of an Uber driver. And as long as you can collect reliable, comprehensive, non-manually entered, high volume activity

data from many wind farms, or from many containers and ships, or from many salespeople in one place, aggregate and start seeing the big picture and then use that big picture to analyze the micro trends and predict what's the best next action for the operator, the salesperson, the Uber driver is. That's where it brings in network effects and significant acceleration of growth. So my theory is that every industry is going to switch

into this business model of collecting activity data, understanding at scale and turning into best next actions in the next five, ten years. You mentioned network effects there as an accelerator of growth. What exactly do you mean by those network effects? The more sensors, edge computing devices, salespeople, Uber drivers you have in the network, the more data you collect, the more of the patterns of behavior you see. When you put them all into one centralized graph, the smarter this graph becomes for everybody,

the better the predictions it can produce about the best next actions. Now, this is where the second loop of the network effects starts. The better the predictions are the more money Uber drivers make, the more money salespeople make, the less wind farms break. So more and more players will be lured in with this better efficiency to become part of the network, contribute even more data into this shared graph. So it becomes kind of this virtuous cycle. Exactly.

Imagine you are wind farm operator and you know that your wind farms are going to break after they broke. and there is wind farm next to you, same wind, same everything, but they come in and practically fix them and have zero downtime because they start collecting the data about their wind farms breaking five years before you. If your competitor automated the business process and you did not, you are at disadvantage, but you can go to the same vendor and buy the same assembly line and catch up.

If you miss the AI boat, the results are very different. If you did not collect the data early enough, there's nothing you can do to make your AI better than your competitors even if the data is shared, the AI has been trained on your peculiar behavior and the market responds to it for three years longer, it's seen more examples, more samples. It's just smarter. And so unlike automation, unlike industrial revolution from 100 years ago, AI arms race is a zero-sum game. So my prediction is that 10 years from now, Fortune 500 will look very different. And now, because some companies did not get into collecting the data and training.

their machinery models early enough. Is there a tipping point where this balance of power around the data shifts from the customers you're serving it 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 customer is contributing their data to that corpus, certainly their data is anonymized 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 generalized models that all the customers benefit from. And so I think part of it is just being super honest in the sales process that, yes, of course, that's how it works. But I think the calculation that most of the customers are doing is, okay, that's fine. I realize I'm helping everyone else out a little bit, but you're helping me so much, then I'm willing to contribute a little bit of the signal from my data to help everyone else. It's kind of like you push the boulder uphill getting first customers, but then you roll downhill with the network effect becomes so strong and the value of joint data set, joint knowledge becomes so big that people just go ahead with it.

And so one reaction to how nervous customers can be around, oh, I don't know if I want other people to benefit from my data at all, is that's kind of analogous to people writing. contracts that say you can only exclusively sell your software to me because in abstract these are just tools and technologies that everyone's going to get access to eventually and so any hesitation I think customers have about it that's going to be a relic years from now one trend and actually I think I heard it from mark and recent here is that when data model changes systems of record die so there were systems of record like CRMs built on not relational but on

hierarchical databases in the 70s, do you know of any today? They don't exist anymore. Then the next data model that happened was on-prem SQL, Seabell. Then the Cloud SQL happened. And now we're talking about Salesforce. The next generation of data model is likely to be a graph, which is the data model that allows you to train AI in the best way possible, federated shared graph of data. Together with data model shifts, the way you consume the software has been changing. Instead of you pulling data and looking for it in a bunch of Excel reports or Salesforce reports or websites, it's actually being pushed to you in a prepackaged, 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 channel through which you are most likely to engage with it. Most of the systems of the future will have this feed or minimum choice, a maximum focus on one thing in front of. of you, and once you complete it, the next thing will come, and then the next thing will come,

and that will make you much more productive and much more focused at what you're doing. 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? It's not the case that the product is simpler. It's that the U.S. is designed such that there's a level of intent that you can observe from the user. You should generally know what they're trying to do, such that you don't have to expose the entire complexity of a product to the user and overwhelm them. You just say, we're quite certain this is what you actually want to do at this stage of the product, that's all we're going to show you, and you're going to be able to 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 the last tabs in Chrome. Totally. And part of that is just the interaction between how do you map UX and design against this like increasing orchestration of the knowledge worker labor force. So what does that mean then 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're able to focus on what really needs human judgment, really complex, rare situations that maybe there's not a sufficiently big data corpus to make an interesting inference on with a machine learning application. And so what you get is humans can not. not only do the hardest IQ work that compute can't really replicate yet, but also they do become

more empathetic. Your accountant spends more time thinking about your specific needs and how they communicate with you versus them doing simple math. There are two modes in which AI operates with people. We call it one autopilot and another one is co-pilot. So autopilot is when you are doing, as a human, you're working on something mundane, some repetitive, low-value ad task. And so AI can easily automate that for you. So think of our Uber analogy of them receiving a phone call, typing in the computer, picking up the ham radio, delivering it to you. That's just not the best use of all those billions of neurons in your brain. So AI comes in and automates those functions freeing up your

time to do something much more productive and much more effective. The second bucket in which we see AI playing the role is what we call co-pilot. In co-pilot cases, AI is augmenting your ability to make decisions. Think of you are in a race car and there is someone next to you that knows what's around the curve and is whispering in your ear what to be ready for. What's the best next thing you could do? While autopilot makes us super productive, co-pilot allows us to focus on more human, more face-to-face, more EQ driven things that machines will not be able to do for a long, long time. Oleg, I'm curious, a lot of these companies that have some sort of co-pilot goal, there's some sensitivity around the employees and the users not wanting to be tracked.

For some people who didn't grow up on the internet, they say, oh, this is a violation of my privacy. I don't want Oleg to know the magic of how I deliver my sales. And to be honest, 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? Yeah, recall the 10x rule. you have to be visually in a very simple, explainable way, promising and delivering 10x the value of being on the system than being of the system.

Now, to simplify that, we go to our customers, we say, great, let's not put you on the platform. Let's A-B test and see what happens at the end of the quarter when the guy next to you or the girl next to you did 10x your results and the only variable we have is you were afraid of being a user, of a modern system. So break that down for me a little bit more specifically. You're making this technology for sales and marketers.

What are sales and marketers doing today that they're not going to have to do five years from now? And how is AI 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 how the industries 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.

Another third of that time is, or is supposed to be, spent on prospecting. So finding who else looks like my current customers, which from computer science perspective is a very classic look-alike problem we've solved 30, 40 years ago. And then only a third of that time is spending face-to-face meetings, actually selling, actually building relationships. Now, the first two parts, the manual data entry, it should be gone. It should be fully automated through autopilot capability. The second part is who should you talk to next is also very easy to solve with the help of

lookalike modeling, machine learning, pointing you to very high value add prospects of customers and actually helping you automate the outreach to them. And then a third part, which is being face to face with customers, truly building relationships, is where machines cannot replace humans. So five, ten years from now, salespeople, or any white-collar kind of knowledge, workers will be actually focused on EQ-driven relationship-building activities still with co-pilots help, because by then, co-pilot will recommend how to better build a relationship. So you can think of, in particular, the sales worker of the future is really high-EQ people

showing up, and then compute is aiming them at where their labor is best suited. I mean, this all sounds really fascinating, but then I think about the older generation of Salesforce out there, being told they have to use this new tool. They've been going 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 workers, or is this a generational shift in terms of how AI technologies get adopted in the enterprise? If you think about it, the salespeople themselves, they are 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 AI with autopilot freeing up your time and co-pilot teaching you the new ways, eventually you will be able to retrain the people who are ingrained in their old ways into new ways by showing them that trying new stuff actually yields results. This training at the endpoint exactly where the knowledge worker is doing their job, we'll say these are the next top priorities for you. The human can learn from that.

And they start to understand, okay, this is the best way to sell. The way that this is playing out, it's kind of unbundling these horizontal learning management systems of two decades ago. This kind of dance between the knowledge worker and the software, the knowledge worker and the machine learning algorithms trains them live on the job. You learn in the 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've heard it articulated 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 are products going to look different?

What does the product of tomorrow look like for me? So in the next 5, 10 years, every area of our existence will probably go through the transition and will be, instead of being inundated by a bunch of Chrome tabs, it will be inundated by a bunch of feeds that tell us what to do. I think there are cycles of bundling and unbundling playing out here already in like the latest wave of machine learning applications. the use cases are getting increasingly precise

and tailored to the end user and one of the consequences of that is as these systems get richer and deliver more promises to the user and then help them do their job in more and more ways I think you just get much fatter workflow applications and we see that already happening in B2C

the bundling of services as they figure out the right workflow think of Uber used to be just rides now it's food delivery now it's freight and stuff like that everything from the same app you can ship something you can get somewhere or 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 the attention span of the user. I think this actually goes to a question that I'm really curious about too, 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? You're going in, you're selling to somebody because they want to automate what their Salesforce is doing. So that has been an interesting point for us because we want to build a bottoms-up approach right now. We're top-down. We come in

through kind of standard procurement channels. The part that is tricky there is that you have to make the organization comfortable with your security posture, your privacy posture. And so having that is what slows down the bottoms up approach, you cannot give someone an AI that's going to learn from individual user without checking in with the company first. There'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 enterprise will be saying, well, I still want to be in control of it. I think there's a distinction between the types of enterprise apps. The apps that build AI

based on user behavior, and that do not require merging of that behavior of that behavior data with proprietary enterprise data, such as Zoom or Slack, those will have much easier time with bottoms up adoption because they just need a user to log in and do stuff. It's a workflow or a utility tool that a single player can use in a single player mode. The flip side is when the value you create is based on or is significantly amplified by the historical proprietary knowledge of the company. So in our case that people are, yeah, we need to know what's in your CRM in order to not give you random suggestions. And so having access to this proprietary information that the company has under the lock by the security and IT is where you have to go and be very transparent and open with the kind of top down IT security infasac teams and work with them to get access, but also work with them to explain to them 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 Oleg, you've gone through a few different companies and iterations, getting to people AI.

What have been the biggest surprises for you in this space and what have been some of the biggest lessons learned? So the first company I joined, I started my career as an inside salesperson back in 2006. Like I was pounding the phones before LinkedIn existed and before Twitter existed, smile and dial style. The company that I was at went out on Toronto Stock Exchange right before the downturn. So the first lesson I learned was timing is everything. And if you get an opportunity to move forward aggressively, second lesson I learned was with Cimantria, the company I started in 2011. That's where I realized why the size of the market really matters.

We started a sentiment in analysis API, beautiful technology, scales nicely. The only problem is there was only 20, 30 companies that really needed it in the world. So Cemetery ended up being a very small market, and we ended up owning probably 80% of the market within three years while still doing single millions of revenue, which was kind of crazy. It's really striking that you mentioned you've been inside sales, you've done the smile and dial, you've been a marketer. How much did your personal experiences inform your product development?

Oh, 100%. I'll never forget the moment in 2017. seven when CEO of the company, where I was actually at that point leading inside sales team, grounded me and my whole team in a room in a pretty small sweaty conference room for a week, having us go and clean Salesforce record by record. And then a week later, we came out and we used the new clean, amazing sales force for a week. And by the end of the month, it was just as bad as it was before. And so that was one of the first formative experience where I knew something is really wrong with how we do sales and marketing today.

And then I had to run my own sales team. And when I was running my own sales team, I could not get the data. I like data. I understand it. I could not understand what's going on the sales team. And 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 ramping quickly, why we need to hire more people while seemingly our productivity seems to be fine.

we're not doing enough. And so all these why questions that you're supposed to have the data in the CRM, but you never actually have it led me to really start thinking about starting people AI. I love that, the way that like the product development has been informed by your personal experiences. I think that's a beautiful tie-in to just how you can have all the data in the world, but there's still something about human experience, human empathy, that you really can't replace with a computer or a dataset. I get to spend almost every single day of my life,

meaning founders on the frontier of building products for knowledge workers. And there's a huge disconnect between their optimism for these people against what you hear in the policy realm and all this doomsday thinking. Like this whole wave of automation and augmentation that is making people pretty nervous at the macro picture, I see all this micro-level evidence that is just really transforming the workforce. And people really get 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 artist, the best computer scientist. That's what deep inside we're all striving towards. And that self-mustery is what AI, the co-pilot specifically helps you amplify. The autopilot takes away the time that you're doing. spend on stuff that you don't care about on your way to mastery. And then the co-pilot actually gives you guidance on how to become better, more effective, learn faster on becoming a master at what you do.

So I think the side effect or the primary effect of AI eventually is going to be that a lot more people will be insanely good at that specific thing. Great. I think that's a fantastic note to end on. So I want to thank you, Oleg, for coming in and thank you, Peter. Thanks for having me here. This has been a lot of fun. Yeah, thanks to us. This has been a fun conversation.