The term “AI” is used everywhere and sometimes seems like a buzzword more than anything else. What is truly defined as “Artificial Intelligence” and how do organizations leverage AI to accelerate growth and retain revenue?

In this episode of The Revenue Engine Podcast, Heidi Messer, a serial entrepreneur, board director, advisor, and investor, shares her insights into how organizations that have not embraced AI will be left behind. We also dive into how organizations can embark on true end-to-end digital transformation through automation, augmentation, and acceleration.

Grab your headphones and your notebooks and learn all about AI, data, transformation - and maybe even underground poker - from this powerhouse leader.

🔗 LINKS

Connect with Heidi on LinkedIn or at the Collective[i] website. And you can find that Vogue article here.

Follow Rosalyn on LinkedIn.

The Revenue Engine is powered by Outreach.io.

The opinions expressed in this episode are the speaker's own and do not purport to reflect the opinions or views of Sales IQ or any sponsors.

The term AI is used everywhere and sometimes seems like a buzzword more than anything else. What is truly defined as artificial intelligence and how do organizations leverage AI to accelerate growth and retain revenue? In this episode of the revenue engine podcast, I am joined by Heidi Messer, who is a true power.

She is a serial entrepreneur, a board director and investor, and an advisor for a number of different organizations. Currently she's the chairman and co-founder of collective eye. The world's first B2B network devoted to helping enterprises manage and grow revenue.

Today's podcast is sponsored by Outreach.io. Outreach is the first and only engagement and intelligence platform built by revenue innovators for revenue innovators. Outreach allows you to commit to an accurate sales forecast, replace manual process with real-time guidance and unlock actionable customer intelligence that guides you and your team to win more often.

Traditional tools don't work in a hybrid sales world. Find out why Outreach is the right solution at click.outreach.io/RevEngine.

In this episode of the Revenue Engine podcast, Heidi shares her insights about what AI really means, why we need to take humans out of the data input equation and how we are all really in sales.

We also dive into how organizations can embark on true end to end digital transformation. Oh, and yes, we also talk about the power of community and a little bit about underground poker. So please take a listen and grab your notebook. You'll definitely want to take some notes.

So super excited to be here today with Heidi Messer, the co-founder and chairman of collective eye collective. I is the world's first B2B network devoted to helping enterprises manage and grow. And they are on a mission to use technology and science, to transform the most valuable data and information into knowledge, decisions, and actions, and to make it accessible to business users.

So welcome Heidi. And thank you so much for joining me.

So can you tell us a little bit more about you, you know, your. Story. And maybe your journey that sort of led you to where you are today? Sure.

Very good about that. If you know, you know, the lawyers and entrepreneurs sometimes are, have very different perspectives on the world. And actually went to Harvard law school and none of my family came to my graduation because why are you doing this with your life? And, oh my goodness. And of course, you know, for anyone who's ever rebelled against their parents, you, you find very quickly that your parents sometimes know you better than you know yourself.

So actually my. He had worked in a family business, growing up, doing everything in that, in that business. Including sales ended up coming up with a concept right at the Dawn of the internet. So I had worked in the cable industry, which was pretty forward thinking at the time. And really saw that, that this thing called the internet was, was approaching.

I would call it, it was, you know, an enormous digital sales network of websites that were recommending merchants who were selling goods and services online. And it became known as affiliate marketing, which is a separate category of, of marketing. We created the first one of the first ad networks, the first affiliate net.

Grew it over a period of over a decade which, you know, the greatest businesses are overnight successes or for an overnight success. And and ended up selling it for about a half, a billion dollars to. Who has since gone to do amazing things, both with Rakuten LinkShare and also Rakuten as a, as a business.

But I would say the biggest learning that we took away from that was one, I saw the transformation of marketing that happened in that period from the mid nineties to the mid two thousands. And it really went from a group of marketers who did focus groups and spent millions of dollars on a once a year, Superbowl ad.

And kinda said, okay, whatever happens, happens. And, and, and there's nothing we can do to optimize. There's nothing we can do to, to change the outcome of that. And they really worked on averages. Like, what is the average person going to like, what's the average person going to watch? And that transformation went from.

You know, sort of gut-based decision-making to let me guess what is going to be a successful advertisement to let me daily, let me optimize daily on different digital mediums you know, Google and LinkShare being two of them and it really changed the whole, the whole nature of how marketing was done.

And so. Having kind of a front row seat to that and seeing first of all, how much better the results were, how much more scientific the technology became and how much more sophisticated the people who were able to leverage the technology were by virtue of having access to these great tools. My brother and I looked around and actually joined up with a third co-founder who actually is my husband.

He was formerly the CEO of overstock.com and he'd seen the same transformation happen in e-commerce. And we said, okay, well, you know, we have experienced with a digital reseller network. You've got experience with an online sales company, you know, a business to consumer. So. What if we looked at B2B sales and said, you know, this has been dominated by a CRM framework, a CRM mindset.

And what if we disrupt that area and move some of the gut-based decision-making that was happening into scientific decision-making. So that's, that's what led us to collective eye, which is short for collective intelligence, the name and and the one big addition that I would say are the one. Sort of, you know, web 2.0 now moving into web 3.0, was that data was such a prominent role in, in bringing transformation to companies and then data combined with AI was beyond transformative.

And so it was all of those things that led us to to, to develop collectivize as a platform, to help companies better forecast, manage and grow revenue.

Exponential change. And and I think that vision was from the very beginning of collective. I would say the thing that's most changed or evolved over the years is the market around us. So I think, you know, take for example, and, and I don't want this to come across as anti CRM, cause I'm not, I think CRM is an incredible piece of technology.

I think what Salesforce has done to move people into the cloud is, is astonishing. But really the actual functionality of. You know, that whole framework is, is dumb functionality. And what I mean by that is whatever you put in is what you get out. And so when you think about, you know, collective, I, the idea was to create an intelligent application in the, in the same vision as say, you know, a Google.

Netflix you know, Facebook now, Matta I guess Google now alphabet created. So this idea that, you know, when you join this collective, when you access this technology, you're not just getting your own knowledge captured and stored. You're actually getting a level of intelligence that is. The only thing I would say that's evolved from our point of view is initially we thought we'd start more on the marketing side of the fence.

And then when we realized, when we took a look over, you know, to, you know, marketing's relation sibling or whatever you want to call it, sales, we saw how starved sales was for intelligence. We immediately said, this is the area that we need to triple down on. I think that sales. You've seen such a dramatic transformation.

You've seen it in your career. I mean, I'm astonished at what you've done, Rosalyn, you know, across these multiple companies that you've impacted. It's, it's incredible just to see how, how fast it's changing and how much technology is ushering in that change and accelerating it.

I mean, I think AI is just used everywhere right. These days. And sometimes it seems like it's almost being used as more of a buzz word more than anything else. And I think intelligence, you know, also has a place in all areas. Right of go to market as well. You hear revenue intelligence and conversational intelligence and sales intelligence, and even change intelligence.

And I know it's the name of the network really behind your technology. So when you think about AI, you know, what does AI mean to you and how do you approach AI at collective?

Unfortunately I think it's also used as a buzzword. And it's often thrown around to describe a lot of things that I would consider less transformative or maybe more antiquated versions of the technology itself. So I used to joke, you know, with my co-founders that the surest way to see that AI is fake is someone puts it in their, you know, their names.

If you see.ai, I'm sure a company that's probably the only AI you're you're getting, but You know, the thing that's most different about artificial. And so artificial intelligence has been around for, for decades. It's the, the concept of it is not new. It's gone under different names. At one point, people were calling it big data and, and there's various forms of it.

So, you know, there's machine learning, there's deep learning. There's, you know, neural networks, there's all sorts of different flavors of this technology. So I would say that the most. Important thing to think about when you think about artificial intelligence, and as you said, the creation of intelligence you know, you can call different versions of it is that AI needs data in a way that other technologies didn't.

So you could get, you know, technologies that, that made you more efficient. Like cloud is a great example. You know, it was a way of delivering. Functionality in a way that was much less expensive and you could get much more sophisticated advancements access to them without having to build it yourself in a custom way that that was pure technology.

So, you know, you can have a conversation of whose cloud is better based on how you know, expensive. They are not expensive. They are et cetera. When you get in the land of artificial intelligence. It's almost like, think about the more modern versions of AI as replicating how the human brain works. And so if I gave you, you know, an infant today and said, okay I want you to turn this infant into Albert Einstein.

You said, okay. That's, I've got my marching orders. The first thing you would do is say, what, what materials do I have to train this child to learn new information? If I said, well, here's one book you can read to it for the next 18 years. And you know, and that's going to turn it into a genius. You would look at me and say, you're absolutely crazy.

That's right. And AI is the same way. So you have to think about what are the training sets of data. That you provide to the artificial intelligence to help it do things like forecast revenue, for instance, or or surface risks within opportunities or predict how an individual buyer will behave. And if that data is in some way, biased is in some way limited the AI and the insights and the intelligence that you will produce will actually be the opposite of that.

And we'll we'll be institutionalized. Hmm. So I say that for example, where, you know, there are companies that I know that will allow, for example, people to edit what information goes into an algorithm for to spot patterns. As soon as you start down that path, you've introduced bias into AI and you've created problems.

You see this in the hiring space. Eat a bias set of information into an algorithm that says who are my most successful employees. And guess what? The people that I've judged to be the most successful fall into a particular category by race or gender, the AI is going to predict that that race and gender will be more successful in my company.

So the only way to get around that, and this is core to the model of collective eye is to have. Network model of data. That's so large and so diverse and is across spans across companies, across buyers, across sellers, across the economy. Because by that training set of data being so large, you can spot patterns that an individual company or person could not.

And you also mitigate the impact that bias could potentially have on the outcomes and the intelligence that's produced.

And what I saw is, you know, you're sharing sort of the three key components, right? Automation. Augmentation and acceleration. Can you share more about this approach and maybe help our listeners understand why, you know, taking this comprehensive approach is really key to what we all at the end of the day, what we all want right.

Is to grow more revenue faster. Yeah.

And at some point it becomes overwhelming to say, okay, You know, you, you do one function here, one function there, none of these things talk to each other. So if you think about what you're doing is like you're building the fastest car you can create. It's almost like saying, okay, I'm going to go and put the rear view mirror down the street and put the windshield over here.

And the steering wheel. You know, you can't see out of the windshield because it's in the back of the car because it's a different piece that you're trying to build. Something that really is the responsibility of a platform builder like myself. So our job at collectivize is to think about what are any possible way that we can give human beings an advantage through technology.

So one example is, you know, in the, in this modern age, there's no reason why people should be doing manual data entry like that. That's it. My co-founder says there should be like a Geneva convention against torture and, you know, you take very social beings, you know, amazing salespeople and you say, okay, I'm going to pay you a majority of your salary on performance.

Oh. And by the way, I want you to do this tasks that can take up to a day a week to do and fill in, you know, data into CRM like that. That's just a basic example of where automation can give human beings back, you know, not only. More time in their day, but actually more dignity associated with the jobs that respects the talents that they bring to.

Augmentation goes back to the thing we were discussing before about, you know, how do I produce intelligence? The, the purpose of intelligence should be, how do I give you a piece of information that you, you might not already know? So my favorite example is, you know, the ways app, like it's not that I don't know how to drive to work every day, but if I live in a.

Like, you know, California Southern or Northern California traffic, I'm saying California as if it doesn't exist in other places like in New York where you're in your car and, you know, I know how to get to work every day. It's not the knowledge of what's the right directions. What I don't know is where's that traffic jam in the road where where's that accident going to slow everyone down.

And so if a piece of technology can help me read that. And give me guidance. It'll actually make me a better driver. And it'll give the outcome that I want. It'll optimize the outcome that I want. So that's what we mean by augmenting. The last piece is acceleration, which is what are the advantages I can give to a sales professional at sales manager or revenue operations leader that helped get to a better outcome faster.

How can I shorten sales cycles? How can I Get someone to pay attention to an opportunity that has very high odds, but maybe they've forgotten that they haven't followed up with someone. Maybe there's a person in their network who they don't realize has a very close relationship with the buyer.

Can I surface that information and help accelerate an outcome? That is a positive outcome for everyone?

It's not easy to have accurate, comprehensive data. That's actually always on, you know, it's real time and it's available to them. People, I mean, you work with so much, you know, so many different organizations you've obviously, you know, have a lot of expertise around data. Do you have any advice for organizations that are maybe struggling to have, you know, that accurate data and insights?

Like, I, I, I almost, it's almost to the point where someone tells me they have perfect CRM I'm immediately. Like, I don't, I don't believe anything that comes out of your mouth. So I think it's a universal challenge. The key thing for me is to have a strategy around that and to understand. You can on one side of your mouth, say, I want to be data-driven.

And then on the other side, say, you know, I don't have any data. That's trustworthy to do that from, I think you have to sort of come up with a way to solve for that. So, you know, I'll get a little more specific and tactical if it helps. But I think, for example, on the CRM data front, you know, when you rely on human beings to input data, In themselves, you know, manually you're going to have errors, errors on them.

Like I forget the, the percentage of errors that are in Excel spreadsheets. It's, it's sort of astronomical. When you ask human beings to predict the future and say, what do you commit to in a forecast? The it's limited by human perception. I mean, forget all the incentives that might be there to not, you know, produce what, what they really believe versus what they're saying.

Even if, you know, you had a complete, if I hooked every sales person up to a lie detector and said, okay, you can't put anything into your commit field, unless you pass the lie detector test, you still would have flaws with that because we can only interpret our world through our own eyes and our own perception.

So the first thing is how do I get the human beings out of the equation of data capture? That, that is something that we're not meant to do. We're we're we're analysis engines. We're, you know, I think Jeff Bezos said we're human beings are incredibly efficient and analyzing data. But we're not the people that are, should be.

You know, sort of debunking that and putting it into CRM. The second piece is if I want to understand my buyers better, if I want to be truly data-driven. If I want to personalize the output, I need to plug into data sources that are not just my own, because my own perception is skewed by the, the single.

Interactions I have with individual buyers. And I say this because this is, this is why we built a model that candidly is much harder to build than traditional SAS you know, cooling data into a massive network. And I would argue our network of datas is one of, if not the largest networks of business to business data, that's been assessed.

Was it a Herculean task? I mean, you mentioned I've been doing this for over a decade. It took that much time to, to amass that kind of a dataset. And you know, the benefit of it is we want people to plug in because when you plug into a system like collective, I, when you use a system like ways, when you use search like Google you get the benefit of getting much better in insights because these engines and these algorithms are learning from.

The holistic picture. So I would say, you know, to get back to your original question, data is, is incredibly imperative. If you want to use AI in any way, if you want to make data-driven decisions, you've got to start and get the right data. It's just a question of how do I get the data into my CRM that I can then use to.

Optimize my internal processes. And then how do I plug into a system that will give me insights about buyers that incorporates external data that I wouldn't have access to?

As you said from their perspective, right? From their experience especially when it comes to, you know, forecast forecasting or any type of predictive you know, analysis.

And then you said, you know what? I'll decide whether or not you're a good stock broker, but ho how accurately you predict. That's a great analogy. Yeah. Who would, who's gonna, who's gonna make me the most, you know, the biggest return off of my portfolio. It's in some ways I think this quest for data from humans, for things that they're not capable of seeing, like what is the future has actually distracted us from getting to the result that we want and focusing more on outcomes.

So I don't know if that. Kind of answers the question that you have or takes us.

So let's talk a little bit about the community. What is your philosophy I guess, around community and how has that helped? If at all, and really establishing you as a thought leader, but also as a partner to your customers.

You know, I, I think the connections that we make with other people, the knowledge sharing that happens and this incredible podcast, you know, this is instead of saying the wisdom of the crowd. To me, it's the wisdom of the community. You, you, you find a group of people who each has a note of intelligence or knowledge or wisdom to contribute, and when you pull it together and again, this comes back to our name, collective collective, I, when you pull it together, the power of that is so astonishing.

So, so for example, we do something called forecast. Is is a weekly session. I call it 90 minutes of Davos, you know, where people come to sit and learn from innovators. And you might ask, why does, why does a company that's providing. You know, forecasting technology, AI insights around sales. Why would you do something where you, you, you feature, you know, people like Eric Schmidt and Goldie Hawn and Danny Meyer and all these incredible innovators.

And for me, it's, it's, the community helps each other get smarter. And we, we live in an age where we're so connected and those connections are so powerful and sales, you know, I, I think. Look, fundamentally everyone is in sales, whether or not they realize it, or don't, you know, whether it's getting yourself a job, whether it's getting tickets to a game that you want to see, whether it's, you know, acquiring knowledge about, you know, someone that you're trying to do business with or a subject matter area, we're all in sales.

We all have to have that curiosity. And what salespeople do so well is learn from each other. And I think that learning, we take and we've made it part of the DNA of our company, because from our perspective, you know, you should be able to come to collective eye and get smarter in every aspect of things you do, because you're plugging into something that's larger than yourself.

So don't hold me to it. When there's like a job description for a woman to apply, let's say there's 10 criteria. She has to meet eight of them. And for a man to apply, he, he has to meet, he, he decides he'll, he'll apply meeting four of those criteria. I forget. It's, it's something that basically says, like men will apply for jobs that they're grossly underqualified for women.

Like we'll wait until they're like overqualified. Yes, they put their hat in the ring. And so there's one question, which is okay, like, should that be the case? Which I think it would be nice if everyone actually applied for jobs that they were qualified for. So I'm not so sure that you want to go the route of saying, okay then on this, but, but you say, well, how did they get all these jobs and how do they get ahead?

When literally most of them are half as qualified as the women who were applying for them. And if you peel back the onion, I would say it's because of. You know, there's, there's a natural networking that happens on, they used to call it the old boys club. And one of the things I recommend to women is spend time nurturing your network, spend time making those connections.

I mean, you're, you're a natural at it, you know, you and I have LinkedIn and. You know, I probably did a cold outreach to you cause I'm just going to pass by all the things you've done. And, and, you know, you were brave enough to answer me and to actually engage and, and look at the great things that come from that.

So I would say it's the, it's the silent help that you get from the people that, you know, a collective, I, we call them connectors, like the connectors who. You know, find out about the job opportunity that maybe wasn't announced yet or vouch for you when you say, you know, I really want that role and I've got to stretch to get it, but, but this person has seen me and knows that even though my resume doesn't speak to it, that I have the skills and I'm the dedication.

So I would say it's, you know, get in the right communities, be active in them, build your network. And devote a percentage of your time as a professional to doing that, even if it's not directly related to the knowledge that you use in your work.

I think it's just, you know, getting out there and not being afraid to make those connections as well, and to, you know, lean on each other and learn from each other and also help others as well. I mean, I think that's a big part of it.

Right. And I think for me, it's been a lot of that. Right. And a lot of just that giving back and sharing what I've learned and hoping that, you know, in some small way it may help somebody else.

So I couldn't agree with you.

There is a winner and a hundred losers. So I would say if you don't have a strategy to bring AI into your sales stuff, If you, if you don't have a point of view on it it can't be a sideshow. It has to be, I I'm committed to transformation. I'm committed to leveraging artificial intelligence to make us efficient in every possible way we can.

And then from there, starting with the question that you asked me before, what's the data set that I need access to, or the applications that are built off of a data set that is. Bar expansive from the one that I have and how can I partner with those people? So I would, and I would say by the way, that framework and thinking has to extend to every portion of your organization.

So, you know, if, if your CEO isn't committed to that kind of transformation and they see it as a side show, if your legal department doesn't understand the difference between how a technology that leveraged AI works versus. What I would call, you know, dumb technology that, that works off of isolated datasets.

You've got to get everyone on, on the bus for that. So that's number one. The second thing I would recommend is sales has been in what I would say an attainment mindset or, you know, there's, there's something called a fixed mindset and a growth mindset. I would say sales has been in a fixed mindset, which is.

There's a certain revenue that I can hit every quarter. And my goal is to make sure that I predict that revenue and I hit it to the penny. I think that the focus is shifting very rapidly way. And you saw this happen in marketing by the way, from, you know, okay, half my revenue dollars are wasted. I don't know which half.

So as long as I'm at the 50% mark, I'm fine. That's changing to a growth mindset, which is, if you look at, for example, the stock market and for any company, Thinking about going public. The market rewards growth and it rewards growth with such a premium over consistency. And so how do I get my whole sales organization to be thinking with an optimization mindset?

Like what, where can we get better? I'm the same way an athlete would say, you know, what's the perfect routine for me to be working in working out in or the same way that, you know, the military would go about picking out where to have a mission. Using intelligence and dynamically adapting. That's where we have to be, which is sort of the third area I would say, which is we live in a world where agility is essential.

And we've seen this during the pandemic of how companies had to create massive shifts in the way that they service their customers the way they did sales. Right. We went from in-person events to. Each of us sitting in our living rooms, you know, with kids and dogs and whatever in the background. I think that is an extreme.

Orientation into the new world, but I think that's the new normal, which is we can't late in people with so much process and so many playbooks in so many rules. What we should be doing instead in revenue operations is, is promoting agility. How do I give everyone the intelligence and tools they need at the moment?

They need it to make the decision that's right for that moment, that right buyer and that right time.

And I was like, okay, now what do you do? Like now that you've spent two days. You know of everyone's time producing the Mona Lisa, I was like, how are you going to monetize that? And so, you know, my point of view on a forecast is let's, let's get, create that insight as quickly as possible and as reliably as possible, but it should be the bridge to how do I help salespeople adapt?

How do we help us hit a bigger number? And so changing some of the mindset around, we have to put more process in place to, we have to get more adaptations. Being available to the people that work at our companies. You

know, I love that. I love that analogy, you know, as a, I guess, as a founder and having, you know, done so many different things in your career, is there anything that you wish you knew earlier?

Or maybe that you might do differently if you could go back and, you know, kind of hit that reset button and do it all over again. I mean, every, I think everybody wishes, they didn't make the mistakes they made in life, but I'm, I'm the kind of person that I think you can't drive looking in the rear view mirror.

And, and so, you know, even when I look back on mistakes I've made in the past all of them have led me to. To an outcome. You know, my mother-in-law used to say everything happens for a reason. And and I, I think she's, she's right. And, and if you can't learn from your mistakes, you never grow as a person.

The one thing I would say if I had to pick it might be to be a little less constrained by the status quo. I think sometimes, you know, we, we, we limit ourselves and we limit our imagination to, to what is, and not what could. Hmm. And I think that this pandemic was, if you want to look for a silver lining out of a very difficult time it was that all of a sudden these changes that we're seeing in the workplace.

And even some of the challenges, you know, we talked about women and, and, you know, balancing work and family. It's not like women. Weren't doing that before this pandemic. What shred of. You know, when the tide was, was ripped away when like schools closed down. So all of a sudden it was, everyone was like, wait a minute, this is hard.

Like, you know, any woman they'd be like, are you kidding me? Like 15, you know, dishes in the air. And it was only when like someone blew out the air that they all fell down. Yeah. I, I think, you know, the, the thing that's exciting to me about times like this is it, it allows you the latitude to think about the possibilities and re-imagine things.

So one of the things we're doing at collective eye is we're, we're thinking about how do we, how do we create a talent first platform for our people? That says, you know, you need flexibility. We'll give you flexibility. Like I don't, I don't need to tell you when, you know, to pick up your kid at school versus when to hit your KPIs.

Like you can manage that on your own. To dictate how much vacation you have a year, if you're burnt out, take the time, like, you know, just sort of saying these things that were just there because they were there, but didn't necessarily have to be. So I would say, you know, being able to really maintain this, this sort of creative freedom that some in some ways was enabled by a pandemic out of necessity.

Beyond when you're in sort of this crisis mode of, you know, everyone in the world is locked in their houses, you know, masking up and afraid to leave.

One what is the one thing about you that others might be surprised? To learn and to what is that one thing that you really want everyone to know about you?

And one of the things that I observed was the men that helped me and the men that I saw were most successful would, would be networking in these social environments. Whether it was a golf game going to the super bowl or whatever. And I lived in New York and I had to find, you know, the equivalent of urban golf and it turned out to be poker.

And the idea behind it was to make meaningful connections between a group of incredible women who could help each other get board seats and investments and opportunities from each others and sharing knowledge with each other. So it's, it was written up in Vogue. If you want to see an article about it, then all the winnings go to charity that's for the benefit of women and girls.

And we allow female entrepreneurs to pitch their company. So it's, it's a really, it's one of the things that I'm, I'm really proud of and

So I would say just more generally speaking that when you see very successful women like yourself very successful people who come from underrepresented backgrounds I would say, you know, if you look at all those statistics. So people talk a lot about diversity and inclusion initiatives. And one of the things that I think is unfortunate about that lens of looking at it makes it look like almost people who got into positions were given favors to get there and what I've seen, it's actually quite the opposite.

So if you look and say 2% of funding goes to women, Well, I think like half a percent goes to women of color. Whenever I see a woman business raise a lot of money or a woman of color business, you know, go public and and get billion, become unicorn status to me, I look and say, there's gotta be an exceptional, extraordinary person to have broken through all of the barriers that were in that person's way.

So I would say when you see someone who's surprising in their background in a highly successful role to, to dig deeper beneath the surface, because chances are they're twice as extraordinary as the person who got that opportunity because it was part of the status quo.

Thank you

This episode was digitally transcribed.