Do CRO Robots Dream of Sales Funnels? How AI will change the life of CROs forever with Heidi Messer

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Production. Hi, I'm Warren Zenna, founder and CEO of the CRO Collective, and welcome to the CRO Spotlight Podcast. This podcast is for Chief revenue officers, aspiring CROs and CEOs who are looking to hire or support a CRO to succeed. So join me and my expert guests as we debate, discuss and tackle today's complex revenue growth challenges and provide practical, practical insights to help CRO succeed in the role. We're really excited to have you with us. Now let's get to it. All right. Hello, welcome to this episode of the CRO Spotlight Podcast. This is Warren Zenna. I'm the founder and CRO of the CRO Collective and we've been some really great guests in the last month and I'm really excited about today. Going to talk about AI, of course, right? Who else isn't talking about AI? Everyone's talking about AI, so we might as well talk about AI. I'm seeing how this whole craze, I probably get seven or eight AI based emails or inbound messages a day from various people all over the world offering me some newfangled way to use AI to improve this or increase that, or reduce this or do that. And I've been playing around a little bit. As a matter of fact, my guest today gave me some tips on it. But we'll get into it because this platform, you know, it may or may not have an impact on Chief Revenue officers. You know, I'm. I'm sort of weary of these spates of excitement that happen with these things. I mean, I think you look at AR&VR, everyone was freaking out about it and nothing happened with it. That doesn't mean it's not going to be huge. It doesn't. I still think those things probably will be, but these things take a lot longer to seep into the regular flow of things. And it's usually people that are on the edges, who are really innovative, that are out in front, that have the most to say about it. And also there's a lot of misinformation. I hate using that word, but there is, there's a lot of stuff that's going on. I hear fear is a big one. I have a client of mine who has an AI platform and they're telling me that clients are worried about adopting their system. They're worried it's going to take away their jobs, like it's some evil creature. And I think that. What I want to talk to the guest today who is Heidi Messer, she's the founder of Collective I. We'll Talk about that in a second is she's really smart about this and I think it should be really interesting to talk about the implications of this stuff from perspective of revenue forecasting, et cetera, et cetera, and using AI to better provide intelligence. Maybe we need to do this differently. So anyway, Heidi, welcome. I'm so glad you're here. It's great to see you.

Thank you. I'm so happy to be here, Warren.

Yeah, well, great. So I'll tell you a little bit about Heidi. So Heidi's been an active entrepreneur investor in the digital economy since the commercialization of the Internet. She's like me, she's been around for, you know, since there was a thing called the Internet. We both have a lot of stories we can tell. She's the co founder and chairperson of Collective I, which is short for Collective Intelligence, which we'll get into. The platform uses artificial intelligence to help businesses forecast, manage and grow revenue. So I'm really excited about this and I'm going to kind of end there in terms of all her background because I want her to talk about it more. But Heidi. So you know, Heidi and I bumped into each other. It turns out that, you know, we kind of like have some similar backgrounds and people that we know and got talking about these things and what it is that Heidi's doing is very fascinating. So I'd like to talk to you a bit more about yourself, your background, how you got to where you are today, what evolute, what the evolution was that got you to the point where you found this sort of early adoption of this thing and built a company around it. And then we can get into a little bit more about this newfangled AI stuff and how it could impact industry that you and I are both floating around. So tell us a little bit about, more about your how you got here.

Oh my gosh. Well, Warren, as you were so gracious in saying, we, we have a lot of experience in this area. It's a nicer way than saying we're. We've been in it a long time. And I think for my part I've seen the evolution of the digital economy starting in the 90s when my co founder, my brother and I actually started a company that was one of the first ad networks. And it's interesting just to bring it back to sales. At that time, everyone's objection was no one's going to give their credit card online. No one's going to buy goods and services over the Internet. And so that sort of subterfuge gave A lot of people, the excuse not to adapt to things that were, in my opinion, inevitable. You're sort of at a similar juncture today where you can see all the signs that buyers have evolved faster than sellers. And the real benefit that I see in the technology that we're about to discuss in artificial intelligence is that it's a way for sellers to leapfrog and catch up. And I'll just give you a couple of statistics that are particularly upsetting. One is 83% of buyers who go through a successful sales process are dissatisfied. So 83% of people who actually go through the motion that we've put them through as sellers come out on the other end and say, I bought in spite of the process, not because of it.

It sounds low. I'm surprised.

Do you think it's 100%?

Yeah, I would say so. I mean, I don't know anyone that's happy with the sales process. But anyway. Ok, that's. That's actually kind of, in a way, like, positive. But it's still bad. But it's. I thought it'd be worse anyway.

Yeah, maybe. Maybe the other 17 aren't admitting it.

Yeah, they're just not thrilled. Right.

Just not thrilled. Right. There's nobody standing up and saying, I love this process.

I. I mean, I hate. I hate sales processes. We'll get into this because there's a whole topic on that.

Yeah. Because you and I, over this, you know, about, about the need for really quality over quantity. And then I think the second thing is that, you know, you've looked at productivity rates of sellers actually plummet over time. And so you have a situation where sales management and, you know, CROs all the way down to the people that work for them are spending more time looking inward than they are looking outward. I think the last statistic I saw was something like 30% of time is spent with buyers, which is an unbelievably low statistic and could also explain, you know, your 83 to 100% of dissatisfaction rates that are there. So you have people working harder, not generating results. You know, every other statistic is dropping, by the way, win rates are dropping, average contract values are dropping, and people are doing more and more to get less and less. And so that's really when my co founders and I looked at this space and said, is there a place for a technology that could radically improve the life for people who are operating in the profession, but also the life of people who are influenced by the profession? That's what led us to create collective Eye.

It's fascinating because you're talking about something that I talk about a lot, which is again, we're looking at a chief revenue officer who has this really complex job and they're managing this revenue operation which not only has a sales operation but also has a marketing function and a customer success function. And there are technologies that come with all these different functions that all have to interoperate, you know, and the stack gets bigger and bigger. There's more data. The more data is the more garbage. There's less intelligence. Even though there's more data, there's less intelligence. Companies are struggling to figure out ways to make sense of all the data that they have. They don't want to get rid of data, they're afraid that's the good data. So I just keep it, you know, so they leave everything lying around and try to make sense of garbage. And as a result, you've got, like you said, more and more constraints are put on people within these different departments to look at dashboards or use tools or attribution platforms or whatever they're doing to try and provide more and more and more intelligence to everybody in the organization. So everybody knows, knows what's going on and it feeds the wrong purpose. It's always very inward thing. You've got the CEO who needs to report to a board that needs to give the board numbers and the board needs numbers. The numbers come from the technology. If the numbers aren't good, they go back and look at the technology. And I've been in this, and you've been in this situation thousands of times. And so what's happening is the thing that's never mentioned ever is the customer, the customer suffers from this because they have a lousy experience when they're trying to deal with a company and just trying to buy something and then extract the value from it. And then the other part of it is what I'm hearing a lot, and this is something I wrote about recently, is the neverending top down projections that come from the board or the CEO that they're pushed upon the organization. The CRO sort of in this devil's, you know, deal where they have to say they're no, I'm not accepting these numbers. They don't match up with what's going to happen. But if they say no, they're not a team player so they accept the numbers and then of course it doesn't work and they wonder why was the forecasting off, you know, what's going on. So the big sort of genie in the bottle Is how can we get forecasting working? How can we predict better? How can we use data to make better predictions? And that's where when you and I talked about this, you explained how artificial intelligence can act as a way to maybe alleviate some of that ability to look into the future so that it can help understand better how to do things and what the right things are. But before I want to get into that, before I do, maybe just for some people who don't get this stuff as well, I know the differences with these things. But if you could explain the difference between, let's say machine learning and AI, how do you break up all these words that are being thrown around right now so we understand what you're talking about?

Machine learning is a form of AI, it's just an older version of it. The newer versions that you see are things called neural networks and deep learning which actually go one step beyond and replicate human thinking, right? Human reasoning. So when you see the difference between say for example, like a traditional search versus a ChatGPT search, you can actually have a conversation with ChatGPT because the way that it's using data and ingesting data is actually designed to replicate the way you and I would have a conversation versus just generate a search result. So the reason why people like me are so excited by this kind of artificial intelligence is because once you have that ability to replicate analytical thinking that human beings do, you can remove a lot of very time consuming work from people and allow them to get straight to execution, straight to demonstrating their skills. It's a much different, it's a much different output. So like say a traditional algorithm that used rule based training for, for the algorithm. So it says if a deal is at stage discovery, the odds are 25 to 30%. So when that machine generates a forecast, it just looks at that stage and says those are roughly the odds that that opportunity falls into A more advanced form of AI would actually be looking at a much more massive set of data. So it would be looking at all of the activities that a seller had done, all of the activities that a buyer had done, buyer patterns, seller patterns, economic factors that might influence things. And say, gosh, if I were an economist and I were a PhD, and I were an analyst and I were to look at all this data and be able to process it in real time, what would I do predict the odds would be? And it's a much more precise way and a much less biased way of predicting outcomes because a machine is actually ingesting huge amounts of data in order to make that prediction. Now that's the rub. Why hasn't everybody adopted this form of AI? The amount of training sets of data that you need in order to be able to utilize and access this AI are massive. And no company on its own will be able to use their own data to replicate that. And very few companies have gone the way that collective I has to aggregate that data. So there's a training set of data that's large enough that can enable those kinds of predictions to be made.

So, okay, that's interesting. So. Because when you were telling me this, I was thinking in my mind, well, where is all that data coming from? I agree. Like, it would make sense to look at everything, right? All these disparate factors that go into a sales cycle. It's not just whether the person said yes or not, or it's the third phone call. You know, it's a lot of things. And it gets really interesting, Right? It could be the quality of the conversations. I know that there's platforms that can even detect the tone of the conversations that you've had, whether they seem positive or negative and those things become a factor. Or certain keywords are being used frequently by the client that indicate patterns of, you know, receptivity or interest. Right. And you're right. Economic factors, et cetera, and all that stuff. But where does that data come from? And how does your system analyze it? And how does a company set themselves up so that they're able to take that data, collect that data in a way that is useful?

So the beauty of it is with our system, you don't actually have to set up your system to collect that data in a way to be useful. Because one of the things that we do is we actually automate data capture into CS CRM. So maybe it would be helpful just to go through sort of the status quo of the way most organizations operate today, and then sort of the change that comes afterwards. So today we've done a lot of things manually. We've asked sellers to do things. In fact, they used to have names. They would put really, you know, the world of CRM is great at putting labels on things. You know, rigor. You may recall that that was where you said, you know, a great seller had great rigor, meaning they were really good at data entry into CRM. Anyone who's been a CRO knows that there's almost an inversely proportionate relationship between the people who are great at entering data and CRM and the people who are great at selling your best sellers. Just say, I'm not going to do it.

I'm not going to do this. I want to have conversations with people. I was that guy, I was the one that the CFO used to chase around the office all the time saying, warren, we don't have any information in your system. Like I know, because I'm talking to people, leave me alone. You know, and you know, it's just, I, I get it, I understand the need and the hunger that an organization has to collect information so they can make analyses. But it, it puts such constraints on someone like myself who just likes to be out in the marketplace having conversations with people. And I, I find perhaps maybe where you're heading here is how this could supplicate that so that people like me and you, I assume, don't have to sit around in front of a dashboard all day long because it's being done in a different way. I mean, maybe. Is that it?

It's been, it's been a ridiculous choice that organizations have had to make, which is, do I sacrifice the productivity of my most valuable asset, my sellers, right, in order to get information that helps me understand what they're doing, or do I forego having that information and have no transparency into anything that people are working on and where they're spending their time? And for the longest time people were willing to sacrifice that productivity, but the problem was they weren't even trusting the data that was being input through rigor, right? So that led to all day long pipeline reviews, right? So imagine this. If you're a seller, somebody said to you, I don't want you talk, I'd rather you sacrifice time in front of buyers to input all things you already know, like what you've been doing all day. And by the way, I don't trust you when you do it. So now we're going to have an all day review of everything that you typed in manually into CRM system so that I can critique it. And then on top of that we're going to add in an all day long forecasting process. So what you have is a situation where there's really, when you nail, when you go down to it, there's one person who has a bunch of information that everybody wants to know. And so we've now created three days worth of work in order to get that information. So here's the first step of where AI can be very useful. So one of the things I said, as I mentioned, we automate data capture in CRM. So as you're working, literally the machine is recognizing from the metadata, meaning the to and from and you're emails, who you're talking to. That's a buyer. It's taking a snapshot of that and it's throwing it into CRM so you don't have to. Easy, right. Once you eliminate that, suddenly you also clean up CRM data which nobody trusted and not wrongfully. So because you know, sellers are busy sometimes sellers forget to put things in. Sometimes sellers intentionally omit things.

Like when a deal, it's just sort of like, just to understand it better. More from like a really on the ground. Is, is it just like a listening platform that's just hearing and watching what's going on and making assessments automatically so I don't have to do anything. Is that sort of like a surveillance in a way? I don't use that word. I know it's a negative word, but it's sort of like that. It's like a, like a, like a, like an Alexa type situation.

It's less like that and more of a screening mechanism. So if there's nothing in the metadata, you know that to and from section that has buyer or seller in it. We don't even want to see that email. We don't even want it entering our system. We don't, we don't. You know, so like an Alexa can listen to you all the time. This is like if it's not related to a sales motion, we, we do not want it. We don't want it.

So in your, in your case, just so I'm clear, you're talking about right now, are you looking only at email?

So any source of, of seller activity that an organization wants to connect us to, so they might want us to connect to Zoom, for example, Slack, Text.

And all that stuff.

Right, so. Well, yes, as long as they're company devices and, and Slack is, is less typical because that's usually internal communication. But any, any system like, you know, could be DocuSign, Adobe Sign, it could be Box, Dropbox, anything that are sort of reflecting sales activities. By the way, they're, they're not limited to sales activities by sellers. Anyone who's in sales knows legal has a tremendous impact.

Sure. A lot of people get involved in.

A lot of different communications and so, so this notion that a seller is solely responsible for an outcome I think is getting debunked, I'm hoping is getting debunked when you start to have the ability with AI to understand everybody's participation, the role everyone place. Because if you ask me my opinion, entire organizations should be accountable for revenue. Not a single salesperson, 100%.

Correct. So let's talk about that for a second. So, because again, the CRO needs to align multiple functions in relation to revenue growth. Right? What's marketing's contribution to the sale? What's the customer success? Contribution to selling. What's sales? Contribution to selling. What's product. You know, contribution to sales. Right. So would an AI system be able to monitor all those elements and look at the whole picture from the entire system and see not only what types of communications were you having the client, but also how are they impacted by the marketing messaging and what do they respond to? I mean, do these factors get played into this, or is it all mostly relegated to sales conversations or.

No, any relevant factor plays into an AI assessment. So that's what I mentioned about. It's like having. It's like having a thousand.

Almost like Jarvis, right? Jarvis is, you know, Tony Stark's robot, you know, that was pretty much ubiquitously there all the time and collecting all the data all the time, and was able to. No matter what he did, Jarvis was there. Temperature, your heart rate, what you just ate, who you're talking to, what time it is, how long that bomb's gonna go off, background information on the person you're talking to.

Yeah, I mean, it sounds more nefarious when you put it that way, but it's. It's more designed to. Like, if you could. If you could access a sales assistant. So if every. If you took every seller and said, I don't like to input data into CRM, can you do it for me? It's your sales assistant. For a sales professional and a sales manager. It's an analyst, Right? So it's an analyst that's looking at, you know, an economist and a PhD, like, literally looking at the market and saying, where should you focus today? Like, where are the risks in your pipeline? And then the transparency piece that gets coupled with it. Meaning, like, can we show people in a feed everything that's happening related to an opportunity alongside of these insights that this AI assistant has produced? The idea behind that is to say, okay, if a lawyer, for example, is sitting on a contract for a week, right? And we're sitting there telling the seller, time kills all deals. Time kills all deals. But this other person is adding time to the sales process. Like, is there a way that that can become transparent to everyone and you can see its impact on the odds so that everybody gets enlisted in the process of bringing things to the finish line.

Got it. This is really cool. So, I mean, I'm curious because you and I talked about. We spoke last. My sort of, I guess, maybe curmudgeonly sort of general attitude about new things. I love new things. I love tech. I always download the new app. I always play around with the new software. But I'm also skeptical. When I see a lot of people excited about something. Every time I see everybody excited about something, the first thing I think is up, okay, this is the new thing. So we'll see what it really goes. A lot of it is, as you know, AI has offered the media something to talk about, right? It's really interesting conversation to have. It's provocative. It's also something that a lot of people don't know about, so you could fill the topic up with any opinion you want and sort of be right, you know, and so it's an interesting place where I see maybe I'm. And what you and I also discussed, and I agree with this, is that we're at a different time, right? I mean, when the Internet came out and when the iPhone came out, things weren't moving as fast. And they were moving. Moving fast, relatively, but they're moving even faster now. I mean, I see a new AI system come out every two weeks that seems to be better than the last. And so I'm aware of how these sort of, you know, Moore's Law stuff works. So I'm curious to know where you think this is really headed and what, what's the adoption? What are some of the things that you're hearing that people are resisting? And how do organizations overcome that resistance? What's the actual real use that an organization can have today with this stuff instead of just like poking around with a bunch of new dashboards or make it really useful?

Such a good question, Warren. So I think, first of all, your first question, sort of understanding the nuances between different versions of artificial intelligence. So, as I mentioned, deep learning is a subset of machine learning, right? But it's a much more advanced version of what older forms of machine learning could. Could actually do. So the first thing is understanding that not all AI is the same. And one of the questions you're going to want to ask is, what is the training set of data that you're using to produce these insights? And here's. Here's the downside of AI. If the training set of data that you're using is biased, then the outcomes it predicts will be biased. So if I could. If you could indulge me in the forecasting process. It's like you and I had a mind meld on this, which is if you look at today's forecasting process in most traditional organizations, it's very much based off of human opinion, even the words behind it, you know, commits stages. So a seller commits to closing a deal, they pick the stages that it goes with, then the stages become the predictor. So if you want to talk to any seller who's manipulating a forecast, whether they're using, using it with a SaaS piece of technology to help them or they're doing it through Excel spreadsheets, literally, they just change the stage in CRM and suddenly the odds of that deal closing go up. If it starts to go south, they've got in the back, their back pocket two deals they're going to swap it with. Right. So now think of, you're the manager and you have to go to that board. You're the CRO. You, you have to commit to a number and you're going to get opinions from all your salespeople of what they're likely to do. And you're going to sit there and say, is that person a sandbagger? Does that person have happy years? I mean, this whole process is literally, it's all emotional. It's almost very emotional. And the process has become emotional. And so to answer your question about what can organizations do to modernize, I think the first thing is to be aware of when a process is built around emotion, bias, inefficiency and be willing to part ways with it. And, and that is so hard. I've, I've heard so many CROs say to me, my forecast is my brand.

Yep.

You know, my ability, think about it. My ability to predict the future, which no one can do. My. Is how I value myself.

Yep.

And that's actually, you know, the real value in my view of a CRO is they're an incredible coach, they're an incredible strategist. They're people that can look at changing markets and help an entire team adapt. They can bring out the best in the people that they, you know, they manage. They can optimize revenue so they can take their strategy and convert it into the optimal results. And this process that has become very emotional and embedded in organizations has actually superseded all of those human skills that are way more important than the ability to go to and poll your people and get an accurate result from a poll.

Yep.

Like AI should do that for you.

Yeah, it's great. I mean, it is fascinating. Everything you're getting into now, which I think is cool. We're to going of getting into philosophy, which I Wanted to, because this is really philosophical. So the problem we're having with business in general is it's people, I mean, people, it's not machines. Right. And so people, of course they have all sorts of biases. I mean, aside from survival, which I think frankly is really primarily what's driving most of this, it's survival. Right. Like you just said, I mean, if I'm going to build a brand around a certain thing that I'm able to do, that's my survival, that's my sort of brand in the marketplace and I'm going to try whatever I can to burnish that and make it even more prominent so that I can gain more followers or more, you know, prestige around that thing, particularly if I tend to get it right enough. And I think frankly, you and I both, both probably know that forecasting is like gambling. I mean, if you get it right a lot doesn't mean you're a better gambler, just means your luck was better, you know, and I'm not trying to denounce the skill, but you know, there's so many variables, it's really hard to predict this stuff. And I see it happen all the time. But what we're talking about too is, you know, we're talking about people who have motivations, they made investments, they want to keep their jobs, they want to look good, they want to. I mean, there's just so many factors here that if you're having human beings that are creating what you call these training sets. Never you heard that term before, but I understand what it means. They're going to be inherently biased because people produce them. So I mean, I look at what's going on right now with some little prompts I've put into chat. GPT just more as like an experiment, you know, and I ask it questions that I know are provocative and you know, you could see the output is biased and you know, okay, so I understand that, but how does that get removed from the system? How do you get people to. How do you get a company to be self aware of its bias in order for it not to be? I don't know, it's almost saying how do you make a company not human? Because I don't think it's possible for human beings to not be biased.

Right, well that's the problem with forecasting off of sales history, right? Because sales history is a compilation of what your organization, organization has done in the past, perhaps tempered by people managing expectations. So was the forecasted revenue that you produced the optimal amount of revenue or was it designed to attain a certain number that was forecasted. And 90% of the organizations that we talk to, they're measuring their forecast on attainment rates, which means, you know, people are discounting at the end of a quarter, people are swapping in deals, they're doing all sorts of things to hit a number. Some of it is healthy behavior, some of it's less healthy behavior. So I would say the big juncture where we come in and change that paradigm is there's what people say and then there's what people do. And all of the activities that our system is analyzing to determine a daily forecast is based on patterns of what both buyers and sellers are doing, not what they're saying they're going to do. It doesn't have any commits are irrelevant in our system stages are irrelevant in our system. It's literally watching all the interactions and saying, I'm observing what's happening in the economy. I'm observing what's happening between this seller and buyer, and I'm observing what this buyer's done and other, other selling context. And here's what I predict probabilistically is likely to happen. What we then do is we say, look, AI is not omnipotent. It's, it's, it's not. It can't, it can do the job of many humans, but it can't do superhuman jobs, Right? Like, it can't do things that we can't do. It can think on a much faster plane than we can ingesting lots more data, but it, it can't predict the future. So if it could, then all us would be retired and living on a beach somewhere.

So that's what everyone, that's what everyone's afraid of. I don't think people think about being retired. I think people think about not being employed. I think that's what people are afraid of.

Yeah. And look, I think I'll steal an Internet meme which is, AI is not going to take your job, but someone using AI will. Which means if you rejected the help of all those analysts and all those assistants and you said, nope, I'm going to be a lone ranger, I'm going to go it alone, you're going to be slower and less informed than somebody who has the help.

You have to become a cyborg or else cyborgs are going to take your job. Basically, yeah. Yeah.

A lot of people refer to it as like, giving people superpowers. Right? So, like, I don't know, do I want to go try to lift weights next to Superman? Like I'm going to lose, you know, because Superman has superpowers. So. So I would look at AI more as a superpower and less as, like, threatening job security, because what it really does well. And this is where I think CROs in particular benefit from AI. There are certain jobs where, you know, I think if you're doing rote tasks all the time, yes, like, then it's likely that AI is going to replace you. But for CRO, so much of sales is. Is about that, like, layer of skill that CROs very rarely get to exercise. You know, the best CROs I know could coach a professional sports team. Like, they're that good at coaching.

Sure. Agree.

You know, they have a network of relationships that's worth more than, you know, any sort of line item on their resume. They just know people. People trust them, people follow them. They have good strategic skills. How much of that, and I'd ask this back to you. How much of that do you think gets exercised on a daily basis when you're a CRO versus how do I prepare a board book? How do I generate a forecast? How do I make sure that I understand where? What surprises lurk in the pipeline that I'm not aware of? Most of their work now is going to be great.

Maybe 30, like, same thing, like 30% of the time. And you're so right about this. It's great you're talking about this because you look at the CRO competencies and you say this, and the people I speak to agree that you need to be a great leader. And no one doubts that the job comes with an incredible amount of communication skills and persuasion skills and motivation skills and coaching skills and empathy and all this other stuff. But at the same time, too, the CRO is responsible for growing revenue of the company. And that always takes precedent over everything else. And there's always fire drills and there's always data that needs to be looked at or things that need to be fixed. And so all those skills that would value or benefit the company are not being taken advantage of because the person's mired in all these other things that people think are more important because they're more urgent, you know. And so this is where I think the opportunity lies potentially in tools like this, where if I as a CRO knew that I had something that could assist me in me having to do less of that stuff instead of hiring a whole staff to do it for me, which might be another solution so that I could stick to the things I know that really matter, that would be amazing. So Again, sort of like the question I asked before is, I'm curious to know, like what are some ways that that, is it something that can be done today or is it still sort of, we're not there yet. Like what are some things that are actually achievable now as opposed to what might be achievable? And your second part of the question is, how soon do you think this stuff really will be ubiquitous? Like what, what's the, in your prediction? I don't want. Now you're not, you're an oracle, but you're in this, so you might have a sense of what that looks like.

No. So I think, is this ready today? It is, it's here, it's now. I would say if you have not automated CRM data capture and automated forecasting, you're behind. You know, if you're spending a day, a week forecast Fridays and you've got, you know, competitors who are literally clicking a button and getting an up to date scientific AI enabled forecast, you're eight hours behind in execution. Like they're, they're spending those time, that time training their people, people better or being in front of buyers more or doing something that is not going to help your cause. So I think getting to basic automation and then starting to do the change management that comes along with AI, which is, okay, maybe my forecast isn't my brand, maybe my brand is how well trained my sales people are. Maybe my brand is how satisfied my buyers are. Right? I don't have 83% of buyers who are unhappy at the end of a process. They felt like they encountered a trusted advisor.

I would assume though, the only way that somebody would be able to shed that forecasting brand would only be if they knew that they could substitute their forecasting with something else that was equally or better because they'd have to hold on to it because it's so important. Right? So that's why you, you need a substitute, you know, so it takes two.

To four weeks to get up and running with Collective I. And one of the things we do is we say look, if you're, if you're nervous about that, run your traditional forecast for another quarter and look at what happens with ours and look at what happens with yours. And then when you feel comfortable that you can trust the machine generated forecast, it's a lot like we often use the analogy of Waze. I don't know if you've used Ways or Google Maps.

Of course, I use Ways almost every day.

So you, you probably can remember a time when you said you know what? Screw it. I know how to get where I'm going. I'm not going to turn ways on. I'm just going to use my own intuition and, and then all of a sudden, you get in the middle of a traffic jam.

Yep, yep. Happens all the time.

Right.

And my wife and I argue all the time. If I'm in, if I'm using Waze and I'm in a traffic jam, then she says, way sucks. You shouldn't have used it. You see, And I'm thinking, yeah, but the odds are that if we didn't use it, we'd be in traffic jams more often, you know, so it's, it's, it's, it's funny how these things work.

I, I, you have the mindset for this.

I mean, I love it. Like, for me it makes sense because I'm willing to take that risk and have that traffic jam every once in a while, knowing that it'll happen less. Like, to me, that's what's really more.

Important way to think about an AI. Yeah, forecast. So, so my point is, if you're, if you're worried, you know, take both sets of directions with you. Like, look at the forecast that you did manually and look at the forecast that AI.

Your, your, your science experiment would be great. It'd be like my wife and I taking two different cars and I use ways and she uses her, and we'll see who gets there first, you know, and yeah, probably most of the times I would get there before her. But, you know, whatever, it's, that's, that would be a really good experiment that might end the argument right there.

So I'm glad I can help you in that vein.

But yeah, no, it's fine. I know I have to work that out myself, but I like the idea. It's brilliant. It's saying, I tell you what, let's run your experiment against our experiment side by side. And then the only kind of barrier to that is like, well, what if this was an anomaly? Because it was sort of a 5050 thing? But I think that there's probably ways you can indicate as to how your, your system was able to.

What's funny about that, Warren? I'll just say so, you know, when you say about the anomalies, like, yeah, AI gets it wrong sometimes for sure.

I mean, everything does.

Yeah, but everyone's assumption that humans don't, like suddenly we, we trust people all of a sudden in a way that we never do. And there, there is no CRO that I've Met. That feels good. Week one of a quarter with their forecast.

Yep.

Not week two, not week three. You know, where they start to feel better is like, I don't know, week seven or eight when they are kind of can see the line of sight into they're going to make their quarter. AI should help you feel better about that because the time that you're taking and feeling insecure for almost half of the quarter plus, it's removing that time and allowing you to actually be executing during that time instead of spending time and still feeling like you don't know.

So, you know, that's really interesting because it seems to me then that the adoption of something like this, a predictive modeling AI, will accelerate when it's done enough times like ways. Okay. That it just works more often for more and more people. So collectively everyone realizes that, you know, this is just better. And there's enough data for everyone to agree that, yeah, you know, I guess on average if I go to a party and I do a little, you know, like a poll everybody at the party, and if 90% of them say, yeah, ways gets me there, usually better, then okay, that's sort of proven. Right. So what's the timeframe for this? Like, so I'm a company, I've been in business for like eight years, you know, whatever, and I've been running my traditional predictive models that I do now, you know, the traditional stages, etc. And then I adopt an AI program. How much time do you think it needs to take for a company to go, okay, we're bought in. This is something we're going to do from now on. Is it like a quarter? Is it a year?

So there's, there's the time it takes to implement our technology, which is two to four weeks.

Yeah.

Then there's the time to address the change management piece of it. And that I think is this is where AI does require change management. You know, I think more traditional selling orgs that are heavily wedded to older forms of forecasting processes, there's a lot of other things that go with that. Right. They'll be very much like we've, we've mapped out a buying process for every buyer and we treat every buyer the same way. So at stage discovery, they get this collateral. At stage proposal, they get this collateral. Here's the script that you have to read. Here's the playbook. The thing that AI changes that does take a little bit longer culturally to change is we're actually moving in a much smarter direction for selling. So I, I Think you would really, based on our conversations, you would vehemently agree with me on this, which is if we can come to buyers as trusted advisors as a smarter version, you know, people who are coming to help them solve problems, not read scripts, not force them into a journey that maybe is not the journey they want to be on. We're going to find a lot more sales happen and a lot more satisfied buyers on the other side of that.

No doubt about that. No doubt.

AI should enable that. And that requires shifting all that time you're saving, getting that massive productivity in less than a month to higher value management activities. Like things like can I convert my pipeline review session into a strategy session which where the AI has told me the three deals I need to worry about. We spend our entire hour talking about those three deals. Can I convert my forecast Fridays into training sessions where I help people get elevated sales skills? You know, strategy training, coaching, converting an organization to that and then getting the rest of the organization on board to say we're all accountable, like, hey you lawyer, you can affect the odds too. So like how can we enlist you in the process that's a little bit longer of a journey that I would say is more like 2/4 to 3/4.

Yeah, I get that. I can see that humans adjusting to this new world will be interesting, right? So a couple things come up for me when you just said those things. So interestingly, I think what a lot of managers don't know is how many of their salespeople hide behind process because they don't know how to sell well, but they're really good at process, so they satisfy these needs and they get a pat on the head all the time for being the most buttoned up, you know, or robust, you know, person. But if they didn't have to do that anymore, they wouldn't know what the hell to do with themselves. You know, I mean, I, I, I actually, it's weirdly, but I usually tell and I, I, as you know, I do this a lot. I'm like, you know, the, the people in your organization whom ESU or avoid or are sort of almost allergic to, you know, CRM systems are probably the best people you have. They're likely the ones that have the skills that you really need. They're the ones that want to break free. They're that the ones and go do the job the way they're born to do it, not the way you're constraining them. And you know, if you look at people in the way that they enter data and you're rewarding people for doing that. You're going to end up with a bunch of lousy salespeople but really great data scientists, which you don't really didn't hire them for that, you know. And so I think that what this system is going to do, and I think it's good is like to your point, it's going to start to surface. We need people, we don't need that anymore. You know, we now need people who can go have conversations and be persuasive and like you said, like ask good questions and inquire people's needs and listen to them.

Relationships.

Yeah, I mean one of the reasons that this, I started this company, it's really interesting story. It's co related to this. So I was a buyer, I was, I, I ran a P and L at an agency and so a lot of technology companies were coming to our offices on a weekly basis selling me stuff, you know, I was the decision maker, you know. And so what I, what I felt it was palpable. Palpable how? These salespeople were trained to ask me questions for the purposes of getting my answers so they can put them into some bucket that they needed to satisfy. When they went back to their office. It had very little to do about the conversation we were having. It had more to do with the conversation that they had to have when they went back to the office. I, it was clear to me and I was thinking like are you, what are you doing? And I knew it wasn't their fault. It was the way they were being dispatched and they were, they were being managed, was come back with the right answers for me so I can measure that meeting appropriately. That's horrible. And that's what's going on right now. And I do see the future where if those sort of things. This is the training thing as well. But more it is and we're in agreement here. It's a constraint thing. It's because the businesses have become data collection centers. They haven't become, they're not sales problem solvers, you know. So that leads me to the second question, which is, is, is a platform like yours, what's the difference between using AI in a, in a company that's more transactional, like a company that has lots of sales, short sales cycles as opposed to longer term, more complex sales. Where do you see AI being used differently within those two different types of cultures?

Yeah, well, so I think there's some commonalities which is much of sales is about focus. So imagine if you're a Seller. And by the way, I love the inspiration for your business because it's true. I mean, any of us who've been on the other side have been buyers who've trained salespeople you can reverse engineer. Oh yeah, training by the questions you're getting. You know, when you want, you have a specific question you want answered. And well, can you walk me through your problem statement? Like, can you?

Like crazy.

It's crazy. So I think when you talk about highly transactional versus complex selling, you know, someone who's in a highly transactional sale is likely to have a very large pipeline of opportunities that are meant to move through the pipeline quickly, but more than they can work on in any given period of time. And so AI is going to provide you with odds, machine generated odds, to say, like, here are the ones, here are the people who are popping their heads up and showing or exhibiting interest in a way that you should be paying attention to at this moment. In a more complex sale, I think it's very important that you couple AI with transparency, meaning you're going to have the opportunity odds which are much more difficult to calculate. So there's the volume on the transactional side. That's what makes it difficult to calculate the odds. Just because people don't have time to do it. In a complex sale, there's a lot of people. So I'll often ask sales managers, I'll say, all right, here's a deal you have in your pipeline. There's 10 buyers on one side and eight people on the selling team. They've got their procurement people, you've got your security people, blah, blah, blah. I'm like, okay, now tell me what stage this is. Well, you know, this person has given me a verbal yes, but they've told me they need to bring in these three other people to agree with them. Or the business person has said, yes, but now it needs to go through procurement. Is that discovery? Is that proposal? Is that closing? Is. So what AI can do is AI can tell you as these people enter the sale, are they positively or negatively impacting your likelihood of closing? It can then help you figure out who think, I think of the seller as the conductor and the orchestra. Like, who do you need to bring in on your side to help you at any given moment? Like at some point you need to bring in the lawyers. At some point you need to bring in sometimes senior management to say, like, I need somebody senior to help me close this deal and get it over the finish line. So the AI will let you know what are the output of your interactions, is it furthering the outcome you want or is it deterring the outcome you want from happening?

Got it. It's great. Interesting. So what's the right size of a company that could use this? I mean, is there a point at which it's just too small and there's not enough data? Like where's your sweet spot in terms of when a company should start thinking about implementing something like this?

So I, we, typically we've, we've worked with larger companies but now we're expanding into mid market and moving, you know, sort of to smaller organizations.

How do you, how do you define mid market in your world?

Anyone who has 50 sellers or more.

So it's number of sellers is really ultimately the number.

Yeah, the number of salespeople that they're managing. But anyone can use AI. I mean it's just like chat GPT. You know, you could be a small organization and need to produce content blogs and don't want to spend five hours of human time producing a content blog. Same thing with a smaller organization that just wants to know the odds and where to focus and forecast on their business. Like these are sort of common needs across every size of business.

Is there anything that we haven't covered in this conversation that you think would be left out if we ended it right now?

I mean, I would say that the biggest thing is something that you asked me before, like when is the right time to start? And, and it's now. You know, there may be fits and starts where there's some applications that don't live up to their potential. But I would say anyone who is a CRO today, given the productivity gains that you can get within a matter of weeks, given the transparency into your business, it should start today. And then the second thing is to really think, think about how do you get people on the bus like CROs. I think CROs are going to be the next farm team for CEOs because there's no doubt. I, I do. Because if you can understand deeply and scientifically how revenue is generated, which requires you then understanding your customers and how market shifts are happening, you have the majority of skills that are great ce. And by the way, you, you can lead people through disruptive times, which is what AI is about to usher in, you have the skill, the makings of an amazing CEO. So I think the people who are early adopters in this space are going to end up sitting really in the C suite and on the boards at a very, very senior level.

That's great. So let's Tap into that for a second. So the CROs who are listening to this right now, Even the aspiring CROs who are listening to this right now, what can they actually do today to start? Like, what's the. Right. Because it's overwhelming. I mean, they're getting all these inbound emails, and they probably have a ChatGPT or, you know, or OpenAI account, and they're looking, staring at it, wondering what the hell they're doing. What's the way that they can practically take steps? Should they be reading certain things? Is there a place that they should go? What are the things they can actually do that maybe, let's say in a month they'd feel more advanced in terms of their ability to understand this and be able to use it in some way?

Yeah. Well, if you don't mind me saying, we do have weekly event on innovation that I would love for your audience to. To participate.

Sure. What is it? Let me know what it is. Why don't you just talk about it right now?

It's. It's called Collective I Forecast and Collective Eye Forecast. Collective Forecast. If you go to ciforecast.com you'll see we routinely have speakers across different sectors of innovation. We always have AI people coming to speak. We have the CEO of DeepMind coming. We have the CEO of Stability AI who spoke. And it's. It's Q and A community Q and A. So you can come and listen to other people's questions, or you can submit one of your own to. And I'd love to have CROs represented there because they ask tremendous questions. We also have an upscale section in our application. And one of the reasons I believe that's so important is any great AI company is going to be very interested in you understanding where the world is headed. So, you know, how do you spot a fake? It's the people who tell you that, you know, process is king, that you should. Here's a piece of technology that's using AI to help you improve your process. AI destroys each process for lunch. The idea behind AI is to eliminate process so that you can actually get straight to AI action. So you should be looking for companies like Collective Eye who are willing to take the time to educate you on the technology itself, who are transparent about their mission, and who are open about the fact that you're going to have to make some changes in the way people operate in order for it to be successful. So I would say that TRO should definitely be looking and listening to podcasts like yours. They should be, you know, joining sessions like CAI, forecast.com and then they should be looking for partners who have positioned themselves as thought leaders because they are, because they're really out there to help their clients get to the next level.

Got it. So in the ecosystem, for someone who is dabbling right now, they have the wherewithal to recognize that this is something that they need to learn. It's a skill set that I need to upscale on now to be completely competitive. And they have a chat GPT or an OpenAI account. What are some things that you suggest they do to start just making small applications that they can start to develop an acuity around how to use these things in a more practical way instead of it being some mystery, which I'm having. I'm actually asking this for myself because I'm playing with this a lot. But, you know, it's just like an overwhelming amount of things and I don't know if I'm doing it properly, you know.

Yeah, great question. So, you know, start small. Start with like, what are things that take me along time to produce? So, you know, maybe it's, maybe it's emails, right? Like running. Run a couple emails through Chat GPT and see how it helps your editing help. Maybe it makes the first draft of a blog post that would better describe your technology. You know, I have, one of my co founders is using it to produce a board book. Literally. He's, you know, figured out a way to have it produced by Python code that can analyze data, that can put things together for him in, you know, minutes versus the days it would take him to do things. Just start to play around with it for smaller applications. A lot of it is for ChatGPT is in the prompts that you use. What do you ask the application? There's lots of resources that you can find that will teach you how to do better prompts.

Are there particular prompt applications or resources that you prefer that you think are particularly good?

I have to think, I don't know, off the top of my head, but as a follow up to you and anyone in your audience who's interested, they can actually just Tap me on LinkedIn and I'm happy to, to share, you know, what ones I found that I can't think of at this moment that.

I. Yeah, I just think it's just so overwhelming. I mean, I look at Twitter and every third tweet is a thread about new, you know, prompt, you know, tools, and they all look great. I have no way to evaluate whether or not they're, you know, charlatans or they're really brilliant. So what I do, what happens when people get overloaded with information, they just ignore all of it because they don't know what to do.

That's, that's what's tough. But you need to find communities where you feel safe and secure and you can ask questions and you can share notes because, you know, part of the impetus for our name, Collective Intelligence, is that this is moving so fast. No one person can do it alone.

Yeah.

You know, I only know what I know. So I need my community to make sure that I have the most knowledge that I, that I possibly can. The other thing I would say is what's really important about AI is to have a strategy. So, you know, for people who use Collective Eye, we are their AI strategy for sales. Right. Like we are. We're coming to them and saying we're here to help you use AI to optimize every aspect of your sales process. You can take that same concept for every function in an organization and I guarantee you you'll be able to find the collective eye or the open AI of that function. And those companies should help you like they should. They should see it as part of their mission to make it, to enter you into a community. Like we have community community, we have a Discord channel. We've got, you know, forecast, we've got a community called Special Ops. Like all of these are designed to help people share information quickly because the world is changing so much faster than any one person can keep up with.

Great. Well, look, this has been amazing. As I suspected it would be this really fascinating and very pertinent topic. I'm getting a lot of people ask about this, so I have a feeling this is going to be a well listened to and I'm going to get a lot of feedback on this particular conversation. So how do people get a hold of you? What are the ways that people can find out about you? What are some other things that people need to know before we, we sign off?

You can get a hold of me on LinkedIn so you can look up my profile. Heidi Messer on LinkedIn. Very happy to connect there. Sign up for the waitlist for intelligence.com because that's going to be a cool network that I'm not at liberty to speak a lot about right now. But I'm going to ask you to come back on this amazing podcast and talk about it that launches.

Sure.

But in short, it's a community of connectors that we've uncovered through our graph as being very, very influential in helping opportunities happen. And my email. H. Messer collectivei.com Happy to. You know, I'm a little slower on email because of the volume I get every day, but. But always happy to hear from CRO, as always.

That's good.

And Warren, I can't thank you enough for for hosting me. You're somebody I've really admired. The first time we met I was so impressed by how open minded and what a growth mindset you have about learning about technology and new things. And you're just incredibly talented. Gracious. Well, thank you.

It mean means a lot to me. Thank you so much. And this is helpful. I mean this is so pertinent to what the people listening to this need. So I know we'll be talking again. I know that. So well thank you so much for doing this and everyone. We'll see you next time.