Market Dominance Guys

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The Right Skills for the Job - AI vs. Humans

February 17, 2021

Market Dominance Guys Chris Beall and Corey Frank Right Skills for the Job

Chris Beall and Corey Frank, our Market Dominance Guys, explore the subject of artificial intelligence taking over jobs held by humans. It’s an emotional issue, to be sure. But instead of looking at this as an either/or concern, the Market Dominance Guys take a different tack by asking,” What do humans do well? What do machines do well? And what can they do together?” You may be thinking, “Wait a minute! Using AI will help us run our business much more cheaply than keeping all those humans on our payroll.” If so, Chris asks you to take a few steps back and look at the big picture by asking yourself, “What’s my main goal here?” In other words, should you be concentrating on how to operate your company more cheaply, or should you be thinking about what will help you dominate your market? And what skill sets are required for your company to do that?


Using a sales department as an example, Chris and Corey discuss the different cluster of skills needed for each type of job in that division and which ones can be handled by either humans or artificial intelligence — or by a combination of both. As usual, you can trust the Market Dominance Guys to steer you in the right direction when it comes to dominating YOUR market, just as they do on today’s podcast, “The Right Skills for the Job.”

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The complete transcript of this episode is below:


Announcer (00:06):

our Market Dominance Guys, explore the subject of artificial intelligence taking over jobs, held by humans. It's an emotional issue to be sure, but instead of looking at this as an either or concern, the Market Dominance Guys takes a different tack by asking what to humans do well, what do machines do well? And what can they do together?

Announcer (00:58):

You may be thinking, "Wait a minute, using AI will help us run our business much more cheaply than keeping all those humans on our payroll." If so, Chris asks you to take a few steps back and look at the big picture by asking yourself, "What's my main goal here?" In other words, should you be concentrating on how to operate your company more cheaply? Or should you be thinking about what will help you dominate your market and what skill sets are required for your company to do that? Using a sales department, as an example, Chris and Corey discuss the different cluster of skills needed for each type of job in that division, and which ones can be handled by either humans or artificial intelligence or by a combination of both. As usual, you can trust the Market Dominance Guys to steer you in the right direction when it comes to dominating your market just as they do on today's podcast, The Right Skills for the Job.

Corey Frank (01:57):

Hello. Welcome to another episode of the Market Dominance Guys with Corey Frank and the indomitable, [inaudible 00:02:05] of sales, the profit of profit, the rasputin of revenue. How about that? That's a new one, the rasputin of revenue, mad monk rasputin and the controversial Chris Beall. And today, one of our topics we're going to tackle is this little thing called scale, Chris, you and I were talking the last several weeks about this going back and forth about AI and the advent of machine learning and can human scale? A human with all its faults can it compete with a fully automated, fully mandated machine learning algorithm that learns on the fly? And when you consider human scaling at the same rate as maybe software, how does that play into what folks want to do from a market dominance perspective? So I figured we could start there.

Corey Frank (02:54):

This will be a fun one. We have a lot of folks of our colleagues on both sides of the aisle businesses that depend predominantly on humans. And we certainly have our share of folks who are on the tip of the spear so to speak on folks who are really focusing on the AI side particularly when it comes to sales, will we ever be out of a job, so to speak? So let's start there, Chris, what's your opinion on scale using humans versus machines and a profession from top of funnel all the way through discovery, to closing?

Chris Beall (03:27):

Sure. It's a very interesting topic. I've been obsessed with it for I don't know, 40 something years. This question of what do humans do well? What do machines do well? And what can they do together that they can't do separately that's of high value? I think that's actually the interesting question. I think in the engineering world, there's an either or mindset that tends to show up. It's like, I want to make the humans go away. It's almost like a science project. I want to make them go away to show I can make them go away. And as though that last human, that last drop of blood running through the system somewhere is going to carry a fatal disease. And yet when you come right down to it, any system that can do something marvelous without any human involvement can do something better with human involvement.

Chris Beall (04:15):

Even if the only thing it does better is throws out the silly stuff that the machine has come up with because of some limitation and its training set and the algorithms that it's developed so far. A car that has a human in it, who somehow stays alert very hard in it self-driving cars or semi-autonomous cars. But somebody who pays attention all the time, never runs over somebody who kind of looks like a pedestrian, but not quite enough in order to have the self-driving car, having seen one of those before and so boom. And that happened in Tempe a few years ago, as you well know. And it's one that when you look at it and you say, "Would a human have hit that person driving a car?" The answer is, "Not, if they were sober." The machine was perfectly sober, but it just hadn't seen those lighting conditions and that kind of combination of somebody walking in front of it and somebody died.

Chris Beall (05:04):

And I think that problem at the margin is always there, but I don't think it's always at the margin. I think the problem is actually more in the center. So I run a company that has humans in the loop doing something that almost everybody imagines a machine could do. They say, "Well, you have a dialer." Sure. Okay. For legal reasons, my quote unquote dialer, which it's not, when it makes dials in parallel, which is what makes it fast. One of the things that makes it fast. If you're doing five things at a time you're five times faster than somebody who's doing one thing at a time and the same thing, it's all there is to it. It's just pretty simple, right? Somebody once asked me by the way is [inaudible 00:05:40] always faster? I said, "No, it's not always faster. It's only faster on days when five times three equals 15, all the other days, it's not really faster at all."

Chris Beall (05:49):

Because if five things at a time or six at a time times a unit rate of three times faster, because we specialize specialists are always faster. You ever watch a specialist do anything they do it in a way after they're experienced in a way that kind of freaks you out. You ever sat down at a blackjack table? Remember the first time you ever did that in Las Vegas, it's like these dealers are really, really fast. The game is going much, much faster than you're comfortable with. And eventually I used to play blackjack for a living. So I went through that process and eventually it slowed down. So this whole question of a person in a loop is very close to my heart so to speak in my professional life, because we do it. We employ roughly speaking, 500 people. And those people navigate phone calls on behalf of salespeople.

Chris Beall (06:39):

Now, why would you divide the labor between navigating phone calls and dialing or whatever that is that we expect reps to do? I mean, people often say reps should pick up the phone. It's kind of a moral thing. Like pick up the phone you wimp and when you come right down to it, when a rep picks up the phone, 95% of the time, they end up navigating a fun system to nowhere voicemail. There's no point in navigating to voicemail, unless you think leaving voicemails is a great idea. And even if you're going to do that, you may as well then have somebody else navigate to voicemail and leave the voicemail for you unless you think your brilliant personalization of the voicemail is going to make the difference in which case you're living 20 years ago and kind of catch up with the times.

Chris Beall (07:18):

So navigating to voicemail is essentially a wasteful activity for a sales rep, but it's a necessary activity if you want to talk to anybody, because there are people out there to talk to. And if you navigate 25 dials to voicemail, somewhere in there on average, you'll probably talk to one person. It'd be three. It could be 57. It could be 11, whatever it happens to be there's some ratio out there for that moment in the day for that particular or target market that you're going to hit. And the alternative is to leave those conversations to your competitor. This is where all of this really comes down. It's not a theoretical exercise. Like, "Hey, what's cheaper?" That's not the question. The question is what dominates markets. If you want to be cheap, don't start a business. That's the cheapest thing to do, you spend no money whatsoever.

Chris Beall (08:08):

And you've succeeded in the world of cheekiness, right? I didn't spend any money. I sat back and I watched the market go by. Maybe I'd make some passive investments. And that's it. If you have an idea and the idea that you think is helpful to businesses and there's enough of them to sell to, and you think they're going to get enough value and you can provision that solution at a reasonable price that gives you gross margin. That's high enough that it's worth doing then you kind of have only one question, which is, who's going to dominate that market? You or somebody else? That's really the question. That's why we're doing this podcast. That's why we're writing this book. It's all about one thing. Hey, everybody, let's focus on this. Failure to dominate markets equals going out of business. You just don't know when.

Chris Beall (08:54):

So it's basically a question of what can you do that's high enough value that somebody will take you up on it, choose you first before they choose the other guy, and will find value and stick with you? So the cheapest thing is rarely the thing that provides the most value. If you put a human in the loop, even just to handle stupid exceptions. There's just stupid exceptions. So ask a computer to tell the difference between a dog and a cat. It'll tell you some things are cats that clearly aren't cats. If this weren't true, those little captcha things, and you're logging in and says, which pictures have a truck in them? If that was an easy problem for computers to solve that wouldn't be how a captcha works because they're trying to figure out if you're a human and it's trivially easy for most people.

Chris Beall (09:43):

It's not for me because I have to go get my glasses. And I can't see the little tiny things that 22 year olds who programmed this stuff think that you can see really, really small pictures, but that's okay, eventually I get through it. I don't have difficulty recognizing a truck compared to a picture of a truck on a billboard, for instance, or on a car that's clearly not a truck, but it has some boxy characteristics on it. And it has something behind it that looks like a trailer. And maybe the Subaru Forester with the trailer on it might look like a truck, but it ain't a truck, right? AI might get that wrong. And that's what they're trying to weed out. Weeding out silly stuff is just one thing that humans can do exceptionally well.

Chris Beall (10:29):

And in concert with machines, whether the machines a AI, or it's just a sorter or a search mechanism or whatever. We used to do a product back in the late 1990s did it from 1992 to 2001 or something like that two different products and what their job was among other things was taking all the world's product and service information and putting it in a catalog that could be searched by any company, according to its subset of the catalog, the products that they wanted to buy, not everything across all vendors and do it in a way that would globally. So we published it in 14 languages every night.

Chris Beall (11:09):

Now that sounds like you could have a lot of automation in there and you sure can, but getting something wrong is the same as hiding it. So putting the bolt in with the cars, because there's a Chevy bolt is a mistake. The quarter inch hex head of stainless steel bolt is not a Chevy bolt and vice versa. So we put humans in the loop and have the machines do the first level kind of, I'm absolutely sure of this with some human QA sampling downstream, but whatever we had ambiguity, we had an efficient means of putting a human up and say, "Better one, better two, or not any of us." And the human and that about a ton could do that.

Corey Frank (11:52):

I think you gave the example when we were talking earlier about voicemail, how quickly can we recognize a voicemail for human versus some of the statistics that you gave versus having a machine do it so I can either transfer that call or I can move on to the next call, et cetera?

Chris Beall (12:11):

Yeah. Nobody knows what the theoretical limit is, but we have a pretty big training set. I mean, we called 380 million people or something like that. So pretty good big training set to figure out what sounds like a voicemail, what sounds like a person and it includes by the way, a disposition on every single call saying whether it's a voicemail or a person. So our training set is perfect. It's pristine for making this distinction because it's what we do for a living. We navigate dials. And if it's a person we transfer and if it's a voicemail, we don't, and we navigate them. We don't just call direct numbers and hope for the best. It takes care of all this other stuff, dial by name directories, and this, that, and the other thing, human gatekeepers and everything else.

Chris Beall (12:51):

How do you tell the difference? Well, we don't know how our machine tells the difference, but we have a machine that tells the difference and it's great. And we can just about use it, but we want to have a human in the loop because it takes almost a second and the human takes 192 milliseconds. And it's a race. The difference between a second and two tenths of a second is big, eight-tenths of a second it's five times. So going five times slower, I got to have a lot cheaper people. And there are folks who are just attracted to cheap. It's like, Oh, if I can get the cost down low enough, that's the right answer, but that's not.

Corey Frank (13:31):

So you would believe this technological singularity of AI, other folks will say that it's imminent this singularity, the singular moment where machines will be preeminent in sales or discovery, but from your perspective, that will never happen. There will always be a control joining of these two forces. And certainly that's farther down the loop simply because of these nuances that machines can do only a certain part and humans can do others?

Chris Beall (14:04):

Yeah. I am not looking in a practical sense to a day where machines do everything that's required in selling, identify who to sell to maybe they'll do pretty well with that, actually, because that can be based on a lot of data, having a conversation with somebody and causing that person to trust you. Well, we did have ELIZA up back in the fifties, a program that everybody trusted because ELIZA would use what's called Rogerian non-directive therapy approach, and answer your questions with questions that kind of sounded pretty good. And you'd kind of go for it. Most people actually bought ELIZA. Maybe ELIZA would still make a great salesperson for all I know, but ELIZA ain't much of a closer. So knowing when to close, when is that moment and doing it correctly, that might be a little bit tricky for machines to do.

Chris Beall (14:57):

Getting to the edge cases, which is where business is interesting noticing opportunities that were not in the playbook. That's extremely hard. I mean, not in the playbook means machine learning has a tough time dealing with it. The thing is business is vast and multi-dimensional, and it's ill characterized. It's very, very hard to make a training set in which you've captured everything that's relevant. And then you truly know the outcome of it on some crude closed one versus something else kind of outcome. And very little of sales is understandable from what ended up being closed one, the interesting action was before the end. So for instance, we've just learned of a way to extract the gut feel from a rep about the seven key relationships in any deal, along three dimensions, to get that out of a rep by a machine asking the rep the questions is really hard and a human interviewer can do it really easily, super valuable.

Chris Beall (15:58):

The machine can calculate whether this collection of answers equates to 78% chance of close or 23% chance of close. Machines are really, really good at that. It's not great at interacting with the rep in a way that is curious when the rep says something in a funny tone of voice that the machine goes, "Huh? That didn't sound quite right." That causes me to go down this Y path and ask, "Well, why do you think that's true?" And it's the key part of the entire process is knowing when to ask why. So these things are bound throughout all of sales and anyway, being cheap in sales might not be the main thing. So as long as you're defeated by somebody who wins the deal, you don't win in a zero sum game or a winner take all game. We talked about this once in a episode. Sales is a winner-take-all game, but it's not a winner take all game in the classic sense. It's a winner take all game in which the habit of the winner taking all becomes dominance.

Chris Beall (17:01):

It's a runaway, it's like a nuclear explosion and it's not good to be on the wrong side of it. So losing being in a hundred races and losing a hundred races by one second is really bad. Being in a hundred races and losing one by a hundred seconds and winning 99 by one second. It's really good. And so we tend to forget in business and sales as the spear point of business, it's truly about winning or losing. It's not about imagine this I'm going to lose every single deal by 2%, but I'm going to do it at one-tenth the cost.

Announcer (17:45):

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Chris Beall (18:24):

Well, I lose the market. I might as well stay home.

Corey Frank (18:28):

Right? Right. So I think in the classic sense, Chris, where folks will push back and say, that is it on scale on humans versus machine is in my tech stack, right? I have this plethora of tools that I can add to make my folks each have an iron man suit, but at the end of that equation, I still need somebody to pull the trigger. But the advent of intense tools of what time people are picking up their phone, what time they're answering an email, what's the best day of the week, what cadence works, that type of nuanced behavior to help in collection of all these basis points into real close rates. We're seeing that a lot in the face today. It's not necessarily just adding more bodies, it's adding more pieces of technology to each of those bodies.

Chris Beall (19:25):

Yeah. Which makes it harder to be the rep. Because now in addition to learning how to sell, which most reps actually don't know how to do and could stand to be taught. And I'm not saying that cruelly. I'm just saying, the most contained part of selling was the cold call and we teach cold calling now, why do we do that? Because we noticed after 14 years that most reps didn't know how to cold call. And if you give them technology to let them have 30, 40, 50 conversations a day, finally, you may as well teach them how to cold call, right? There's a very simple thing. It can be done in five sentences. There's a certain framework. There's a tonality that means something. There's an emotional journey that's well understood. There's a way to handle it when it goes this way, that way or the other way, none of this is actually theoretically, particularly hard and it be hard for me to even say it's interesting, except it's pretty interesting when you dig into it, right?

Chris Beall (20:15):

So here you have a human, who has a hard time with something that doesn't require any technology, other than a way to talk to he a remote person for a remote cold call and that's hard for them to use. So when the telephone itself, after the connection is made is hard to use because sales is hard and sales is hard because it's essentially a psychological game played against this very troubling backdrop of time. That is even when played perfectly you're only at an 8.3% or 8.6% or whatever win rate, it gets really, really hard to interpret the data, putting more tech out there, just as more variables, the more variables you have, the harder it is for you to understand what's making a contribution and what's getting in the way.

Chris Beall (21:02):

But you know, one thing for sure, each piece of tech has to be learned. Each one has to be wired up to all of the others or some of the others in some way, all of the data has to be consistent. They haven't made any tech in the tech stack yet that can deal with the fact that Mary says busy call back. And Joe says not interested reason given on exactly the same conversation. There is no tech that can make sense of that. I don't care what somebody says on that one. So we have this problem that data is not consistent and can't be. It's naturally variant. It's variant both in form and in meaning, but the syntax and the semantics will vary. Then it varies over time. Folks have different meanings for the same field. Oh, we stopped using this field, but we didn't want to make a new one. So what do we do? Now you actually take this value and you put it in this field.

Chris Beall (21:55):

You don't use these other two anymore. And when you say this it actually means this, and then you look over here to see what the real value is, because we also didn't want to add an object to the Salesforce at that point, because we have a rule against custom objects. You ever heard that before? I mean, I think everybody's for that one. So tech stacks are fundamentally attractive because it seems like there's all these jobs you can make easier, but they don't make the job easier, the job of being a rep. In fact, I would contend and I get feedback on this every day that even a CRM makes your job harder. And if you have to put data in and keep a CRM up to date, it means you have to know all the rules for putting the data in. The rules have to not change out from under you in interesting ways. I mean, I have a hard time in our CRM putting in an opportunity. Why? There are about three required fields that I actually don't understand.

Corey Frank (22:53):


Chris Beall (22:53):

I don't understand what they're for. I run the company, right? And I sell a lot. I sell [inaudible 00:22:57] six million a year, so it's not like I don't have any reason. So I do something simpler. I delegate that to somebody who's a specialist. That's the answer to the tech stack is it turns out there are distinct jobs that have sort of clusters of skills around them. So the sales rep job has a cluster of skills around talking, listening, empathy, and problem solving in the moment and a knowledge base around the resources, internal and external that could be brought to bear to help a customer. Those are the five skill areas that cluster around somebody being a sales rep. And when we think of making a sales rep better, we think of helping them say the right things in the right way. Listen for things where they kind of adjust what they're doing next in a way that's useful, recognize the difference between no and not now, and not me, those kinds of things.

Chris Beall (23:54):

So we need for them to do those things. When they get down to problem solving, we need them to have a problem solving mindset. So that they're thinking what is the customer's problem? And they have a mental model of the customer's problem, and they know how to do the talking and listening to validate. We look at those skills, right? Well, do any of those skills, including... And I'll just go with ConnectAndSell parochially. Do any of those skills include navigating a phone system, being an expert at using a dial by name directory. Is that part of any of those skills? No, it's not. That's part of a different skillset. That's around getting a conversation with somebody. There's another skill set around updating your CRM. We try to keep reps out of the CRM, make the data go in, but the data goes in with the robot doing it.

Chris Beall (24:40):

So our robots take the data in you finish a ConnectAndSell call. You take your notes, you hit your disposition, busy, call back, interested, send information or whatever you put in your followup date if you want to talk to them in the future. And you're little teleprompter so you know what to say, when that pops up on the screen three months from now or whatever, when you decide that's when you want to talk to him and you get ahold of them again. So all of that, should you go over and type that into the CRM after navigating down to the task record, knowing that you set it up like this in order to show a complete telephone test, no robots are really good at that.

Chris Beall (25:14):

So we delegate that to a robot great, but then when it comes time to understanding these conversations took place I wonder if I don't see quite the right close rate of conversations to meetings. I wonder what that means. Well, then I put a human back in the loop to listen to the conversations, but I use the data and the machine to say, but these are the good ones to look at. So machines are really good at looking at data and saying, "This is probably more interesting than that." Humans are really good at actually looking going, no, it's not, but as long as you don't make the human, do that hard work of calculating what might be interesting and let them just take a look. And humans are especially strong when it comes to visual stuff. So we're doing something right now at ConnectAndSell internally that's really fun.

Chris Beall (26:05):

We're taking all of the data about all of our customers and putting it up in a single chart that a human can look at and say that customer is having a problem with this particular thing say their dial to connect is increasing suddenly compared to the last say, 30 days. And they're really important because their bubble is big and they're not getting very much economic value because it's red. I want to go hover over that and see, what's true about them yesterday, last week and last month, and I don't want to click, if it looks interesting, then click. All of their humans, then turn into bubbles and it's the same evaluation. There's the person that is most responsible for most impacted by this. And they're important because in our case, they're using a lot of our product.

Chris Beall (26:58):

And so expectations are high because somebody's spending a bunch on it. I think everybody can do stuff like that and the human visual system, how hard is it to say that's up there, that's over there, that's green, that's red, that's big, that's little? Nothing to it. You see it all at once. That's a hard one for the machine to pick out because at the margin, it doesn't quite know what you know, like yeah, but that's these guys and they are always like that I don't worry about them. So it's those kinds of things.

Corey Frank (27:30):

Well, you think about it. What I hear you saying is that you look at the skills that go into a professional, simple sales person. Forget about complex sales, but just keep it in simple sales for a moment. And that a specialist will always trump a generalist. And when you look at what is asked for, what is expected of, most sales reps today is to do cold calling. Well, that cold calling seems to have two distinct skill sets. One is the process before I actually talk to somebody, navigating phone trees, [inaudible 00:28:11] screeners, et cetera, cadences.

Corey Frank (28:14):

And then the second one is actually once somebody who is on my list, who picks up the phone, that's a completely different skill set. Then after that, I have the discovery skill set, which we'll talk about next time and next call, I know we're anxious to talk about what goes into an ideal discovery call. And then you have kind of the closing and the pursuit pattern. And yet it does seem a bit challenging today to have, or to expect a sales professional, to be a specialist in all of those areas when clearly there can be machines that can help them with certain pieces of that to optimize their success.

Chris Beall (28:58):

Yeah. And it's really easy for a specialist to learn, to use a machine that they use every day or a tool they use every day. It's trivially easy, right? Even difficult to use tools are easy to use one. It's what you do all the time. And anybody's ever worked in a kitchen knows this. I used to work in a kitchen. There are some tools in there that are actually very difficult to use really, really well. But if you use them every day, they get pretty easy. The most challenging of them all is a chef's knife. Most people never master it. And as a result, they're slow, they're clumsy, they're cuts aren't even blah, blah, blah, blah, blah. Right? So here's a machine that as a specialist that I use it every day and it just starts to make sense. You have that feel as it slides down the fingernail of the middle finger of your left hand, that slight touch that tells you it's where I want it to be.

Chris Beall (29:48):

You've got that pullback feel where you're making that next slice with the left hand, pulling back, you know what you're controlling. So even hard to use machines are complex machines. Jet fighters are probably pretty complex and yet smart kids out of school are taught to fly these things in combat situations relatively quickly. And they get used to all the complexity because they train and they train and they train and their specialist and they use it all the time. If you threw them in a helicopter and they hadn't flown one, well, it's hard. It's hard to train for both at the same time, right? So that's why we specialize. There's fighter pilots and there's helicopter pilots. And maybe they start out with similar skills, but some are better at one than the other, in some ways. And in sales, we have a funny situation where a lot of the things that need to be done can be done by very low cost labor that loves doing them.

Chris Beall (30:41):

It fits not only their skills, but their temperament, which is important and navigating phone systems as an example. There are people who love to navigate phone systems, but would never want to have one sales conversation from now until just after the day they die. They really don't want to have sales conversations because it's uncomfortable for them. There are people who can have sales conversations at the top of the funnel very comfortably, but they have an issue talking about money. And so for them to set appointments is easy. They can believe in them. They can get those appointments set, no problem. But if they have to close for money, then their family issues, the way that they were raised, the way money was seen in their family can block them.

Chris Beall (31:22):

So if you want to hire SDRs, one of the things you want to do, if you want them to be with you for a long time as SDRs, top of the funnel, BDRs, whatever you want to call them, hire people who have the skills that are needed there and have an anti skill, which is the ability to talk about money because you're actually hiring from a more specialized cohort. You'll get a better price, so to speak and you'll get better performance and you'll stay in the seat longer, which is a big deal too.

Corey Frank (31:49):

That makes perfect sense. That's great. Yeah. The chef's knife example is also a great analogy, too. It's a simple tool, but in the hands of a Emeril or Gordon Ramsey they are just a maestro with their ability to slice an onion without losing a couple of inches of their left index finger. Sure. I could see that. So in conclusion here, Chris, we're going to wrap this up here. When you look at, and I know you hate this question every episode when we talk about it is first feel the prognosticator, right?

Corey Frank (32:19):

Is where do we see it today? Will machine learning with all the equations, its ability to defeat Kasparov in chess, its ability to navigate the NSA security codes in a matter of minutes, and to map the human genome, but yet where are also the opportunities that you see that didn't quite wide-scale enough yet where machine learning and AI can really help what we're doing in getting maybe top of funnel folks on the hook faster? Or have we reached again that singularity with regards to machines or sorry, humans and machines will really be focused more on other things such as data or dialing technology and things of that nature. Where do you see it?

Chris Beall (33:09):

Well, I don't see the machines anytime soon, taking over the conversation. Trusting the machine with your career, which is what must happen in B2B sales the buyer must trust the machine with the seller, with their career, who would ever trust the sellers machine? Oddly enough, we will trust a human being because it's our nature to do so. But if we knew that somebody had made it a machine whose purpose is to sell it to us at all costs, no matter what, we're not going to trust it, it's just the way it is. It's like here's a machine that is better at manipulating you than any human. It can manipulate your emotions, but I can't hide the fact that it's a machine. So it's very machiness is going to mitigate against it being successful in creating trust and trust is the essence of the B2B equation.

Corey Frank (33:56):


Chris Beall (33:57):

It's hard. That one's a hard one. Data? Man machines are great at data and they can find candidates to talk with like nobody's business. They're really good at keeping up with changes in the data world, which is so hard for a human to do. A machine, could look at everything that happened on LinkedIn yesterday and do it with no issue whatsoever and do it quite quickly and find you out of all the people who've changed jobs in all 27 of them that you should probably talk with, but the machine won't be able to talk with them as well as you can. And I think that's a distinction that's going to be made for quite a while. And machines are better at data than we are. They still do silly things. I actually think that's a psychological issue for sales reps.

Chris Beall (34:45):

Some sales reps hate to have one conversation that day that's with somebody they obviously shouldn't talk with. I can tell you right now, I'm about to close a quarter million dollar a year business with somebody who was introduced to me by somebody who I never should have talked with. [inaudible 00:35:00] somebody turned into a brilliant customer of a very niche kind and fell so in love with our product, that's sitting in first class on a flight at random, told somebody else about connected cell and there's the quarter million dollar deal. I don't think any machine would anticipate that. And I was going a little bit on, I really liked this guy. I want to help him.

Corey Frank (35:21):

It would have been classified as a false positive, sorry for slipping through the cracks. It would have been the data elements somewhere, but another man's trash is another man's treasure.

Chris Beall (35:30):

Exactly. And you have to actually dig through the trash which we do with conversations.

Corey Frank (35:34):

Absolutely. Well, excellent. Well, great, Chris, I'm glad we finally got a chance to talk a little bit more in detail about this. I know how strongly you feel and especially where a lot of the noise in the industry is moving towards this direction of putting folks like me out of work and putting us on the street. I can't handle a chef's knife. So the only thing I do know how to do is pick up the phone and talk to strangers and ask them for time or money. So it's good to know that at least I have a runway of another few years or so, this has been another episode of the Market Dominance Guys. Until next time, this is Corey Frank and Chris Beall. Until then.