Why 1+ 1 = 3 : From Kitchen table Math to Fortune 500 Strategy ft. Jason L Zimmerman | Ep.21

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This is the Startup Still Safe podcast. Thank you for
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joining ken you as a favor like, subscribe on YouTube
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or LinkedIn, and be sure to give us repeat them
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Hope you enjoy this episode. Hello everyone, thanks for joining
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us on another brand new episode of Startups. They'll say,
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we're joining you here after a few weeks break, And
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I'm delighted to bring to you this episode because this
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is a pretty special and unique episode because we've not
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had someone who's coming from this particular background as our
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guest on this call. So I'm excited for all of
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you to know our guest here. Jason Zimmerman is joining us.
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And I got to know Jason a few weeks ago
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through a mutual friend, and I've been super impressed at
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what Jason's been up to. Don't worry about it, Jason.
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Dogs are welcome. So Jason and his doggies on our
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podcast today. And Jason, why didn't you kick us off
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by telling us A oh, I should say?
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I mean I know Jason as a few things.
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I mean in my two or three weeks of knowing him.
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Jason is an economist. That's what he studied when he
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did his bachelor's program. He's a strategist. You know, he's
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focused on a lot of strategy and I mean, and
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I must also say, given his new adventure, Jason will
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also call himself a founder, right, so he is going
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to tell us about Threefold Collective. I know three things
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about Jason already, So how about that for a start, Jason,
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So give us a little bit about a background and
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we'll go from there. Yeah.
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No, I appreciate you having me on first off, So
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thank you. Yeah, the time that I got to know you,
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I told you this before. I love your style. It's organic,
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use your quick wit intelligence to sort of prompt up
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the right questions. And so this is a real treat
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for me, and so I genuinely appreciate you having me
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as a guest, as well as my dog, who will
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make an appearance at some point. So yeah, So, as
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you said, I founded Threefold Collective, which is a brand
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new consulting organization that leverages my background in three specific areas.
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As you mentioned economics, where I actually studied behavioral economics
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and applied that to certain areas. And so there's the
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research component in the research element that a lot of
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my work is grounded. But at the same time I
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used that, and I pushed it into sort of an
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applied understanding of strategy. And so I moved into consulting
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and from there what we were looking at leveraging a
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lot of the sort of psychological elements of the economics
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background and applied it into organizational sociology and from there
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and we'll get into this, I'm sure, but I applied
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that directly inside of an organization of Fortune five hundred firm,
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where I got to experiment with those ideas and really
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inform all the way back into how strategy, how recent
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can approach a problem in a new way, in a
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very holistic way. So I used that to found Threefold Collective.
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And yeah, so far I've had some early successes and
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I'm excited to share the journey with you.
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Perfect.
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So let's I think rolled back a few years, right,
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I mean I've always maybe it's just me, economics was
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always a boring subject, right, I mean, you know, not
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a lot of people will say, you know what I
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want to do economics, you know, and in your case,
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behavioral economics.
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I'm like, my goodness, I would never pick that.
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So what what what made you even pick that as
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something that you wanted to start your you know, college
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journey with and of course, I mean, you know, based off.
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Of that, Yeah, you're going to build your career as well.
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I mean, was there a pull of any sort that
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kind of drew you to the adjacent.
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Yeah, it's a great question. There is a pool behind there.
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So if we go back to me as a child, right,
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I was growing up with my mother and she was
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a very creative type, and she, for whatever reason, could
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not figure out how to balance a checkbook. That always
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became finance has always became a little bit of a
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struggle for her and for me. I looked at that
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as a young child, and it only took me years
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later to actually articulate what that was. But it was
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simple math. It was simple addition and subtraction, and for
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whatever reason she couldn't put those two things together. It
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took me some time to realize that after injecting the
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sort of psychological component of that. When you start to
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think about how money plays a role in society and
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you start to think about sort of the representation that
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money can have in terms of status and even the
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reflection of who you are, this very simple math injected
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with psychology became this almost non Euclidean geometry. For her,
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it became so complex that she couldn't necessarily interact and
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engage with it. And so for me, money, I think
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became a really fascinating topic very early on, which is economics,
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or at least it can be market traits, and those
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sort of things can really be part of what economics
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is founded on. It's trade, it's bartering back and forth.
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Right when I got out of school, you know, twenty
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years ago now, behavioral economics coming out of University of
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Chicago and Richard Taylor's work was just coming to the forefront.
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And so when I came across that, it became sort
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of the voice, the articulation that I was always looking
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for to sort of understand at a very micro level
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at this point what it was that was blocking my
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mother and at this point a lot of people that
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I saw around me when it came to money and
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sort of its role not just in society, but in
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terms of who we are as individuals.
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Fearful, I mean, I always love the backstory to these things.
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I mean, you know, yeah, more often than not, it
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starts somewhere in our childhood, right right, And I mean
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I've had twenty episodes so far. I mean, then you're
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not the first person who said it started with my mother.
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But I always love those stories where you know what,
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I saw my mother doing this and it was so
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great that she did it, or you know that triggered
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me to do something like that. I mean, you know,
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we all thank our mothers for what we are right now,
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and I guess in your case it's no different.
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It's exactly yeah, exactly.
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Yeah, so so very cool. So, so you did your economics.
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I mean I believe that's in the Indiana University.
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Yeah, university to study economics. Yeah. I walked through degrees
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in finance and accounting as well, so you can see
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the quantitative underpinions of sort of where I began.
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Yeah. So, and then I'm trying to build up to
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what you're trying to do.
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Row.
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So you went on to do your MBA as well.
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I mean, I'm assuming Kellogg's was a great experience.
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Yeah, Northwestern calog it was a great experience. So there
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is a little bit of of story between there. So,
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coming out of undergrad I had intentions of going to
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get my PhD in economics and really pursue the theoretical
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component I always you know, even as a kid, I
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just really loved thinking about a problem and that was
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my intention. But at some point when I was applying
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to PhD program, someone told me, They're like, you know,
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you might have a better experience if you get a
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little bit of real world experience underneath your belt.
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That would help you.
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Understand not just economics and the theory, but it would
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help you understand the economics in the applied sense. And
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so that's where I found myself. At a small research
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consulting firm where we were I got very fortunate to
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get connected in with a researcher at the time who
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was looking into the shift of our government clients from
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defined benefit plans to define contribution plans. And what his
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major thesis was is that benefit plans being driven by
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professional money managers, and you know that shipped into defined
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contribution plans run by individuals like my mother for instance,
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had significantly lower outcomes. And what we were trying to
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use or think about was how to apply economic incentives
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mixed in with psychology to drive better outcomes. And so yeah,
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that intervening period between getting my master's where I really
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focus on organizational sociology in my MBA started out by
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looking at the individual and its role, and that sort
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of propelled me into consulting, where I worked at Mercer
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on the mergers and acquisitions team, looking at both pre
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and post acquisitions. And if you think about that thread
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that kind of weaves through there, that thread relies on
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the individual. If you take two organizations is coming together,
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the success of that organization is going to be based
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off of whether those people can connect. Right, people are
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a very sasset of any firm, and tapping into that
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is what we were trying. Compatibility basically, precisely, precisely, yeah, yeah,
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And so that's that thread of sort of how behavioral
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economics can be extended not from the individual but more
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to a macro view like an organization and a sociologist.
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So fascinating, Jason.
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I mean, I will admit I have absolutely no idea
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into this field, but every time I speak to you,
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I learn a lot, you know. I mean, of course
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I understand the whole change management aspect of it, which
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comes along with it, but I mean that's just a
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miniscule aspect of what goes into this. Right to even
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see to your point, is there a good fit if
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it's an M and A. You know, if it's just
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an a part of the M and A, is that
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even a good fit for them to come in Because
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the end of the day, it's not.
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About what you acquire or merge with. I mean, it's
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about whether those people can work together.
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Right, Yeah, no, is this is what I said at
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the start of this, Like your raw intelligence and curiosity
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allow you to pick up on an idea and articulate
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it in a beautiful way, and that's what you just did.
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It really is. I like to think of it sort
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of like any organization coming together or any change event
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coming together, is how do you make it greater than
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some of its parts. One of the things you hear
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me say over and over again is I care about
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one equation, and that's making one plus one equal three.
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And that is you know, any change of any evolution,
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whether it's us as individuals or an organization, that change
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allows us to get to a better place. But there
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is I believe a fundamental underlying conceit within that that
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change is hard. Right, we don't know necessarily what we're
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getting into, or maybe there's certain hurdles or barriers or
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certain friction points that we have to get it through
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and that is a purely psychological problem. And I do believe,
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especially in the arena that I work in, you think
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about it from an organizational standpoint, and now you're starting
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to talk about how individuals interact with each other, and
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so the sociological component of how that operates gets in
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obviously economics. You're working within a capitalistic society and at
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the end of the day, we are trying to make
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money for our organization, and so it's it really is
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this interdisciplinary approach to what I like to say is
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just to accelerate that change, to make that change less frustrating,
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to make that change, you know, less full of friction,
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to allow people to sort of regain clarity and control
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as they move into that new change. And by doing that,
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I think you can benefit both the employees of an organization.
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You can benefit yourself as an individual, but you can
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also benefit the goals of the organization.
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Well, let's move on in a little bit in your journey, right,
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So you did your MBA. You obviously you told me
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about the real world experience. I think whoever gave you
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that advice was very wise. I mean, I wish everybody
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does that right, because I mean, especially before doing an MBA,
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I think the real world experience really helps you connect
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to a lot of the things that they teach you
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in the NBA.
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I found that out myself. That's why I can speak
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to that.
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And then you mentioned earlier Jason, you you were in
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a fortune one hundred, fortune five hundred company for a while.
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You did a bunch of roles. There a lot of
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that to do with economics. You also did a lot
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of strategy right in terms of you know what the
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company should be doing or the different departments.
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How did that experience?
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Tell us a little bit about that experience and how
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that has helped you to get to where.
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You are right now. Yeah.
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The one thing that maybe think about sort of the
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shape this part of the story is there's research out
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there by John Cotter coming out of Harvard, and a
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lot of people listening to this may know this work,
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but it's John Cotter's similar work that says seventy percent
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of strategies fail and when you look at the eight
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reasons that he came up with, they fail at implementation,
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summarized by me as employee resistance, by certain psychological factors
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that individuals that companies struggle with when it comes to execution,
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and so for me, because theory was always my end game,
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because I just wanted to develop sort of the next
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horizon of thinking, especially within behavioral economics, I shifted into
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strategy thinking that was the next level down. And while
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I was there, I brought this quote this research to
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one of my global directors and I said, how do
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we know that our strategy isn't part of that seventy percent?
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And paraphrasing a very long conversation, I was basically told,
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why do we care? We've already been paid, And that
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propelled me to understand and think about that at a
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deeper level. If I could understand how execution works, then
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I could make better strategy, which that better strategy would
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then go into a better theory. And so, having spent
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six seven years in this research in theoretical and strategic place,
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I had an opportunity to move in with this financial
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services firm and they allowed me to really begin to
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think about how to execute these things. I worked inside
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the executive office for most of my time there, and
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what I was doing is taking the strategy that that
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executive office was coming up with and connecting it back
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into the organization, and so I was directly close using
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that gap that John Cotter identified, that seventy percent gap.
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And so I spent thirteen years inside the organization doing this,
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and I think had a lot of success doing it.
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You know, I there are some failures there, which of
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course we all learn from our failures, but by continuing
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to learn and evolve from that, I ended up getting
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to a place, given my research background, where I would
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actually be much closer to a not a seventy percent
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failure rate, but much closer to a thirty or forty
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percent failure rate. So I would really be successful maybe
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sixty to seventy percent of the time. And that is
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not exactly reassuring, But when you think about the complexity
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of a fifteen thousand percent organization and moving a firm
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through that change, you know, the thirty to forty percent
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failure rate is astronomical. It's it's really a very pusial number.
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That yeah, I think I attribute a lot to sort
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of my experimentation and learning back in theory and research,
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but also just obviously the people surrounded with me, and
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you know, the organization itself to really allow that experimentation
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to take shape.
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Yeah.
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So I'm glad you said successes and failures because that
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is going to be my next question, and I'm going
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to make it a two part question. Right, So obviously,
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I mean, like anything else, you have some wins and losses.
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You know, you might come up with a certain strategy.
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You said, you know that's seventy percent, I mean the
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thirty percent all that good stuffs.
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And to your point, you.
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Know, if you can't make that one plus one equal
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to three, I mean, you know, at least in your words,
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I mean, it's it's a failure, you know. So you
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also mentioned research, I mean, and I believe that's a
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big part of you know, the success as well, if
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you I mean, I've known cases where people just go
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by gutfield versus research, which is such a bad.
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Thing to do. Yeah.
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So I mean, have you done any sort of correlation Jason,
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to see, oh my research was wrong or I studied
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the wrong inputs in this case are the wrong parameters?
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Have you seen any sort of correlation between research and
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actual success of whatever program you went through or initiative
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you went through.
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Yeah, I mean it's always a complex answer, right, the
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answer is yes and no. So as a a researcher,
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I want to have this in a lab where I
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can control all the variables and say this is exactly
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what's pointing to it. And unfortunately that research, not only
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through myself or even in the industry right now, is
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still very nascent. It's still growing, and it's just not there.
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I am engaged with a few consulting firms that I
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hope to announce pretty soon where we are actually going
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to try to run those controlled experiments in a much
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greater way and hopefully produce some white paper so that
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we can identify those So from the traditional research background,
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it's just that's not there yet. But from a practiced
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researcher who understands correlation, causation and the scientific method, I've
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done my best to really try to isolate a single
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variable and make that change. And by understanding that one
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variable within a larger system, I can start to get
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a more intuitive or experienced or even anecdotal evidence that says, yeah,
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this is what is driving that. And so I do
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have a bunch of that that I was able to
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explore with and sort of understand at a deeper level.
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And a lot of those cases, including the early success
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of threefold collective is on our website for people to explore,
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and so a lot of that comes down to just
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looking at sort of communication patterns and how individuals communicate
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and how silos may be for and what interjections you
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can play in order to drive that communication forward in
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order to close that communication silo. Right At the same time,
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some of the greater success that we've had is thinking
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about and learning about the adoption curve and what it
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is that allows people to embrace change as opposed to
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reject change or to resist change. A lot of those
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things come down to more of the psychological factors that
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are heavily researched and we can point back to in
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terms of just loss of clarity, loss of control, and
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just the general nature of influence that we can tap into.
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And so there are some interesting tools and techniques that
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allow us to measure and quantify how an organization might look,
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and by doing that, we're able to retest those measures
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and see how these interventions can sort of drive a
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certain outcome that we're looking to have. So yeah, we
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we we've definitely approached this as scientifically as we can.
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But yeah, yeah, I mean again, you know, frameworks, I
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love frameworks because at least they put some discipline in
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your thought process, you know, I mean, some of them work,
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some of them don't work, but at least there's some
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sort of organization to it.
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And to your point, I mean, you know, based on
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what you said, I.
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Mean, that is the way to go, right, I mean,
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and even even with Threefold, maybe that's a good segue
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for us to get in the research strategize and execution
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of that is super important. So why did we jump
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into your pet project then?
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Jason? What? What?
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What made you move on from what seems to be
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like a pretty exciting role at the Fortune five hundred
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company to do your own thing? I mean, what what
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was the trigger?
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Yeah?
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So Threefold Collective exactly as you said, it's a whole
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stick strategy consulting firm in which we focus on organizational transformation.
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We focus on bringing a company from point A to
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point B. And our skill set, I think our trait
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is really focused on some of the tools within sociological research,
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things like social network analysis that will allow us to
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quantify an organization and sort of their fingerprint, if you will,
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the informal network behind the formal network, in order to
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accelerate change, and as I mentioned earlier, by accelerating change,
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there a certain benefits that both the employee and the
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organization will reap from accelerating through that change. So in
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my time at this Fortune five hundred firm, I was
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fortunate enough to stand up three different organizations to guide
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this organization to set up these divisions and drive that
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change from the ground up. So there was one failure,
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as I mentioned that we stood it up been it
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survived for about twelve to eighteen months, but it actually
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came out just before the COVID nineteen pandemic, and so
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it didn't have enough time to really take route. But
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we did learn from those things, and so the next
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two iterations of this we were able to get better, smarter,
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be more thoughtful about how we went through that, and
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by applying those lessons on this third launch, we were
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actually working with one of the big bulge bracket consulting firms,
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and they told us that by going through this change,
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which affected fifteen thousand people across the organization, it would
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take anywhere from five to ten years, which anybody who's
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worked with the consulting firm knows they're going to be
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more on the optimistic side of those timelines, But having
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worked in those firms myself, I said, hey, listen, we
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need your expertise. That knowledge sharing of what other organizations
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have done and sort of the friction points that they've
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come up with is important. We also sort of need
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the traditional change management techniques that these firms are are
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really known for. But let's interject this social network analysis.
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Let's interject the psychology of the organization, the sociology of
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the organization to identify where those friction points are and
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really who are the influencers in that organization to accelerate it.
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And so if you take even just the more optimistic
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timeline five years, we were able to drive this change
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in two and a half years, and so that is
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doubling of the adoption rate by using the traditional change
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management methods, but just complementing it with these concepts from
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sociology social network analysis to identify influencers, to leverage those influencers,
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and to really propel the organization not from the top down,
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but from the bottom up, to use the people, to
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use the core asset of any organization to propel it forward.
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And so by having this success, do you answer your
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question directly. I thought, well, what's my next move? And
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I've always gone back to, you know, the organization. They've
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always been been very favorable and saying, cool, here's sort
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of the next thing that we want you to do.
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But I thought, why not do this on my own,
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you know, why not go out with Yeah, exactly. I
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enjoy the theory, I enjoy the strategy, I enjoy the
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application of it. But you know, there's a selfish component
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of it of you know, why not benefits myself. But
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at the same time, why not be able to really
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focus on what I love and what I think is
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that real driver of success. So, as we said, what
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are sort of the techniques that I've learned over the
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past twenty years, and that really is social network analysis,
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and our approach to that data collection and to processing
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and interpreting that information is something that we get threefold
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find to be our value, our skill, our proprietary sort
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of you know, proper position to the world. And so yeah,
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I wanted to take that out and see if we
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could apply that in a much greater scale. And so
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that's that's the journey run.
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Yeah, So Mishily all the best first of all, and
436
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I've heard this from many a founder, right, I mean
437
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early days they say, Man, it's very lonely out here.
438
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It's a it's a it's a scary world. So how
439
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has the early days of Threefold been?
440
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I mean, and do you feel validated that, Hey, Jason,
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this is the right move for me to do. And
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I mean, in other words, I mean, where are you
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in your journey as a founder as threefold and what's
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what's coming up with Threefold?
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I think both can be true.
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I do feel validated in the early days, not just
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with the results that we have been able to accomplish
448
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for our clients, but just the feedback that I am
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capturing from from different folks, including yourself when you and
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I first met. And there is some white space in
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the market for this. John Cotter's research, for instance, came
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out forty five years ago, and if you look at
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some of the research coming out by McKenzie and PwC,
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we are still at seventy percent failure rates. And so
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nearly half a century later, we're not getting any better
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at implementation. And obviously that has to do with a
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lot of complexity in our current environment and our current
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ecosystem and all those other factors. That for a drive
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that but the idea is there is a way to
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get better at that and by having some of these
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early results that I've been able to replicate at least
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you know, four times now and be able to share
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that with clients and say here is something that is
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developing and I think brand new that can that can
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really accelerate your ROI yeah, we've had a lot of
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really good success, but at the same time, it is
467
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a lonely space and I wish I knew more about
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that feeling coming into it, because it can be frustrated
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at times, which I know a lot of your guests
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have shared as well, and you know, and a lot
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of our cases. We're developing a new market and we're
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developing something that people haven't necessarily seen or heard about.
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And again we can apply the same notion that I
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work in is change is hard, and so adoption of
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something new is.
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Going to be hard.
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And so there is a lot of work that I
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think any founder has to do in order to establish
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that market and to really build the following. And that is,
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as you know better than any of us. That is
481
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partly you know the results, the quantitative, the things that
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you can point to, but it's also partly the qualitative.
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You know, who you are as that founder and the
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people you surround yourself with, but also the people that
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you connect with the clients and what their challenges are
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and how you can connect with them. And it really
487
00:27:57.960 --> 00:28:02.799
is a You've got to form that deep, interconnected relationship
488
00:28:03.160 --> 00:28:06.240
for all of those elements, and at least for me,
489
00:28:06.640 --> 00:28:09.319
never having done that, I'll say this, I'm I'm glad
490
00:28:09.359 --> 00:28:13.440
I have an experimental, scientific mind, and you know, there
491
00:28:13.480 --> 00:28:15.839
have definitely been some failures along the way, but I
492
00:28:15.880 --> 00:28:18.640
am learning from those failures and getting more successful. And
493
00:28:18.920 --> 00:28:20.279
like I said, I think we're getting a lot of
494
00:28:20.279 --> 00:28:23.839
success with our clients as well, and you know, really
495
00:28:23.839 --> 00:28:25.640
developing this and moving in the right direction.
496
00:28:26.720 --> 00:28:27.640
Beautiful, beautiful.
497
00:28:27.680 --> 00:28:32.039
I mean, obviously there's no reward without risk, right, and
498
00:28:32.839 --> 00:28:35.359
a lot of us regret saying, oh, man, if only
499
00:28:35.400 --> 00:28:39.640
I had done that. Yeah, that's always the thing with
500
00:28:39.720 --> 00:28:43.559
the founders, right, people who never became founders, including I
501
00:28:43.559 --> 00:28:46.440
mean myself, even this podcast. I mean, I've been thinking
502
00:28:46.440 --> 00:28:49.640
about it for a long time and I was like, man,
503
00:28:49.680 --> 00:28:52.759
I mean if I don't start, I would not ever
504
00:28:52.839 --> 00:28:56.079
do it. So I just start, you know, so I'm
505
00:28:56.119 --> 00:28:59.720
so glad I did so. I mean I can echo
506
00:28:59.799 --> 00:29:01.000
that feeling.
507
00:29:01.480 --> 00:29:01.680
Yeah.
508
00:29:02.119 --> 00:29:04.279
One of one of the things that I picked on
509
00:29:04.359 --> 00:29:07.720
what you said is you said social.
510
00:29:07.359 --> 00:29:09.839
Network analysis Jason, right.
511
00:29:10.839 --> 00:29:12.599
And in the world we live in today, I mean,
512
00:29:12.680 --> 00:29:16.960
especially in the US, I mean, it is so complex.
513
00:29:17.480 --> 00:29:20.559
You know, you have you have people from so many
514
00:29:20.559 --> 00:29:21.480
different cultures.
515
00:29:21.559 --> 00:29:22.359
I mean so many.
516
00:29:23.160 --> 00:29:25.799
I mean that's who we are as a country as well,
517
00:29:25.839 --> 00:29:28.440
you know. I mean and in today's world, I mean
518
00:29:28.759 --> 00:29:34.039
at the well even more complex factor of you know,
519
00:29:34.240 --> 00:29:39.400
people of all ages and different dynamics coming together. I
520
00:29:39.400 --> 00:29:42.680
mean have you have you seen in it from through
521
00:29:42.759 --> 00:29:44.799
your lens? I mean, that's a big part of what
522
00:29:44.960 --> 00:29:48.160
you need to analyze. I mean, what are the things
523
00:29:48.200 --> 00:29:52.480
that have struck you as like an aha moment? I mean,
524
00:29:52.480 --> 00:29:54.440
you know, just in terms of you know, how that
525
00:29:54.519 --> 00:29:57.839
impacts a yes or no decision as part of the
526
00:29:57.880 --> 00:30:02.720
strategy or you know, to say, you know what, I mean,
527
00:30:02.759 --> 00:30:05.640
this is great because of that. I mean, I'm just
528
00:30:05.680 --> 00:30:08.799
trying to understand how that factor has evolved over the
529
00:30:08.880 --> 00:30:10.680
last two decades that you've been doing this.
530
00:30:11.640 --> 00:30:16.200
Yeah, No, it's a great question because social network analysis
531
00:30:16.279 --> 00:30:18.400
is one of the tools that I will say, is
532
00:30:18.480 --> 00:30:21.680
the game changer in the industry right now. And social
533
00:30:21.680 --> 00:30:24.279
network analysis has been a lot around for a long time.
534
00:30:24.720 --> 00:30:27.640
It's an easy concept to understand. Right We're all on
535
00:30:27.960 --> 00:30:32.240
social networks. For instance, we're all on Twitter, I guess X.
536
00:30:32.279 --> 00:30:36.039
Now we're all on LinkedIn, and that is our social network.
537
00:30:36.640 --> 00:30:39.240
And there is a term out there that we also
538
00:30:39.440 --> 00:30:44.720
use as sociologist called influencers. And what influencers are, if
539
00:30:44.799 --> 00:30:48.559
you get into the nerdiness of the math that I
540
00:30:48.680 --> 00:30:54.359
really enjoy, is called eigenvector centrality, and eigenvector centrality is status,
541
00:30:54.680 --> 00:30:59.400
is influence. And so if you can identify who has
542
00:30:59.440 --> 00:31:03.839
status or who has influence based on that social network,
543
00:31:03.839 --> 00:31:08.319
because influence will change and evolve based on the environment
544
00:31:08.359 --> 00:31:13.519
that you're in, and so it's it's it's it's critical
545
00:31:13.559 --> 00:31:17.359
to understand the psychology of how influence begins. But once
546
00:31:17.400 --> 00:31:21.920
you can look at and quantify mathematically that igenvector centrality,
547
00:31:22.559 --> 00:31:26.400
that status, you can start to identify who holds that
548
00:31:26.599 --> 00:31:30.720
power in an organization. And when you start to translate
549
00:31:30.759 --> 00:31:34.240
it in that way, that's what I think really connects
550
00:31:34.640 --> 00:31:39.200
in with people qualitatively. If we all assess our own networks,
551
00:31:39.759 --> 00:31:43.319
we know the people that are well connected within there.
552
00:31:44.079 --> 00:31:46.559
And so if we want to get something done, if
553
00:31:46.559 --> 00:31:48.519
we want to connect in with somebody at a different
554
00:31:48.599 --> 00:31:50.880
organization that we may not know, Say we're looking for
555
00:31:51.160 --> 00:31:54.359
a new job and we know somebody works to that organization,
556
00:31:54.519 --> 00:31:58.599
we will qualitatively ask our friends if they know somebody there.
557
00:31:59.599 --> 00:32:03.559
What a quantitative approach allows is it really allows for
558
00:32:04.200 --> 00:32:07.599
not just the scale of that at a much larger
559
00:32:08.960 --> 00:32:11.599
sort of footprint, but it also allows us to be
560
00:32:11.640 --> 00:32:16.359
really precise. And so social network analysis in this way
561
00:32:16.599 --> 00:32:19.359
eigenvector centrality and all the mathic sort of goes behind
562
00:32:19.359 --> 00:32:25.599
it is a descriptive sorry, a descriptive statistic and that
563
00:32:25.720 --> 00:32:29.079
just allows us to describe how the world looks today.
564
00:32:29.640 --> 00:32:32.680
But by understanding that, it allows us to intervene. And
565
00:32:32.720 --> 00:32:35.799
so if we get back to sort of the original question,
566
00:32:36.000 --> 00:32:38.759
and what that context leads up to is if you
567
00:32:38.960 --> 00:32:42.559
know who holds the power in your organization, then you
568
00:32:42.599 --> 00:32:46.920
can leverage their skill set for several different things. If
569
00:32:46.920 --> 00:32:49.680
you're going through a change, you can invite that person
570
00:32:49.720 --> 00:32:52.759
into the conference room and say, hey, here's the approach
571
00:32:52.799 --> 00:32:53.839
that we want to go with.
572
00:32:54.680 --> 00:32:55.400
How would you.
573
00:32:55.400 --> 00:32:59.240
Think this might look or operate within your environment? Is
574
00:32:59.240 --> 00:33:02.799
there anything we need to do to change that. Assuming
575
00:33:02.839 --> 00:33:04.880
that they might buy into that, then maybe they want
576
00:33:04.920 --> 00:33:07.519
to become a person that becomes a champion of that.
577
00:33:07.920 --> 00:33:11.200
Then they can go back and they can help to
578
00:33:11.279 --> 00:33:16.559
spread that word to say, you know, hey, I've experimentmented
579
00:33:16.599 --> 00:33:19.160
with this and this is a great tool for instance
580
00:33:19.160 --> 00:33:21.400
that we're using. I think we should all adopt it.
581
00:33:21.759 --> 00:33:24.799
And if you're a person hearing that from somebody that
582
00:33:24.839 --> 00:33:28.440
you consider influential, right, because influence is always dependent on
583
00:33:29.480 --> 00:33:32.440
you the person receiving that. So beauty is in the
584
00:33:32.480 --> 00:33:35.720
eye of the beholder, and if you can tap into
585
00:33:35.759 --> 00:33:40.440
that and you can influence people, you can scale that
586
00:33:40.559 --> 00:33:43.359
learning curve much more quickly. And so if you think
587
00:33:43.359 --> 00:33:45.480
about it from a perspective, we've all been involved in
588
00:33:45.599 --> 00:33:48.480
change in some way, shape or form, and maybe it's
589
00:33:48.519 --> 00:33:51.440
the CEO of your company and you've never sat down
590
00:33:51.480 --> 00:33:52.200
with that CEO.
591
00:33:52.680 --> 00:33:53.960
You know they.
592
00:33:53.599 --> 00:33:56.920
Certainly have authority, and that's a certain type of influence.
593
00:33:57.559 --> 00:34:00.200
But if you can hear that same message from the
594
00:34:00.200 --> 00:34:02.720
colleague that you've worked with for the past fifteen years
595
00:34:03.599 --> 00:34:07.039
and they have embraced that change, and that's somebody that
596
00:34:07.119 --> 00:34:09.840
you trust and you know has your best interests at heart,
597
00:34:10.119 --> 00:34:12.719
then you're going to be a little bit more open
598
00:34:13.039 --> 00:34:16.400
to accepting what that might look like. And it's that
599
00:34:16.599 --> 00:34:20.039
nature of the psychology mixed in with the mathematics of
600
00:34:20.320 --> 00:34:25.079
sociology applied to change that allows us to really begin
601
00:34:25.199 --> 00:34:30.679
to accelerate that. And yeah, so it's influence is sort
602
00:34:30.679 --> 00:34:32.840
of the name of the game, and that's exactly where
603
00:34:33.920 --> 00:34:36.239
social networking house this allows us to identify, or at
604
00:34:36.280 --> 00:34:39.239
least that is the primary application that we're experimenting with
605
00:34:39.280 --> 00:34:39.920
at this point.
606
00:34:41.000 --> 00:34:45.519
Fascinating again, Jason, And there's so many layers to this, right,
607
00:34:45.599 --> 00:34:50.519
and as humans, we take we understand all this, but
608
00:34:50.559 --> 00:34:52.760
I mean, you know, I mean not to the level
609
00:34:52.800 --> 00:34:56.760
that you just explained. So I'm glad somebody like you
610
00:34:57.000 --> 00:34:59.440
is looking into all these things, you know, checking all
611
00:34:59.440 --> 00:35:02.559
those books is and making sure the math adds up
612
00:35:02.599 --> 00:35:07.360
before those big decisions where a lot of lives are impacted.
613
00:35:07.400 --> 00:35:11.280
I mean literally, yeah, right, so so so cool that
614
00:35:11.360 --> 00:35:11.840
you're doing that.
615
00:35:12.639 --> 00:35:15.760
So I want to pivot to something on the research side.
616
00:35:15.800 --> 00:35:19.000
And I mean, this is something that everybody talks about
617
00:35:19.000 --> 00:35:21.639
these days, so I might want to get your perspective
618
00:35:21.719 --> 00:35:25.320
on that too. So obviously a lot of research is
619
00:35:25.400 --> 00:35:28.840
data driven, right yep. And in today's world, I mean,
620
00:35:29.239 --> 00:35:32.559
everybody wants to use AI for a lot of these things,
621
00:35:32.800 --> 00:35:35.639
right yeah. So what what's your I mean in the
622
00:35:35.719 --> 00:35:41.599
in the three fold approach to what you do with threefold?
623
00:35:41.880 --> 00:35:42.000
Right?
624
00:35:42.119 --> 00:35:42.320
Yeah?
625
00:35:43.800 --> 00:35:46.800
Where where do you see AI kind of helping you?
626
00:35:46.880 --> 00:35:49.639
I mean, I'm assuming there as a play, especially on
627
00:35:49.679 --> 00:35:52.519
the research and data analysis aspect of it.
628
00:35:52.559 --> 00:35:55.360
Have you given it any thought, Jason?
629
00:35:55.920 --> 00:36:01.679
Yeah, so, especially from running the calculations, and I think
630
00:36:01.679 --> 00:36:06.360
when you take any large data set, they're in order
631
00:36:06.400 --> 00:36:08.679
to just run those calculations much more quickly and look
632
00:36:08.719 --> 00:36:12.360
at that from various different perspectives and different angles to us,
633
00:36:12.719 --> 00:36:15.519
that just gives us more opportunity to see something that
634
00:36:15.559 --> 00:36:19.199
maybe we haven't seen. And so there we have worked
635
00:36:19.199 --> 00:36:22.400
with AI in order to apply that, and there are
636
00:36:22.480 --> 00:36:26.079
some applications that will allow us to accelerate that math.
637
00:36:26.679 --> 00:36:29.400
And I think that's great because that gives us more perspective,
638
00:36:29.400 --> 00:36:32.320
but it also cuts down the time in order to
639
00:36:32.360 --> 00:36:36.320
start interpreting it. There are some challenges I think right
640
00:36:36.400 --> 00:36:39.280
now with the data collection side of it. And so
641
00:36:39.559 --> 00:36:46.039
right now, intuitively, as I mentioned, influence is you know
642
00:36:46.159 --> 00:36:49.119
within the holder, right beauty is in the eye of
643
00:36:49.119 --> 00:36:51.920
the beholder, and so influence is defined by the person
644
00:36:51.920 --> 00:36:55.519
that you're talking to. And so right now, collecting that
645
00:36:55.639 --> 00:37:00.079
data is still a very human based effort. But there
646
00:37:00.119 --> 00:37:02.960
are like Microsoft Copilot is out there and they will
647
00:37:03.440 --> 00:37:06.639
do a certain level of social organalysis that a lot
648
00:37:06.639 --> 00:37:09.760
of us maybe are familiar with. Right and so in
649
00:37:09.920 --> 00:37:14.280
Microsoft Copilot, within teams, they have this clout calculation, and
650
00:37:14.320 --> 00:37:17.679
that clout calculation is driven by AI, and what it
651
00:37:17.719 --> 00:37:20.679
does is it looks across the ecosystem and it says,
652
00:37:21.000 --> 00:37:23.280
who is this person emailing with, who is this person
653
00:37:23.400 --> 00:37:26.679
sitting in the conference room with? Who is this person calling?
654
00:37:27.599 --> 00:37:31.880
And there are limitations within that that we should be
655
00:37:31.920 --> 00:37:34.800
aware of. Is, even though you and I may sit
656
00:37:34.880 --> 00:37:41.000
in a conference room together, it may not identify the
657
00:37:41.119 --> 00:37:43.719
nature of the relationship that you and I have. We
658
00:37:43.800 --> 00:37:46.480
have all been in a conference room where somebody has
659
00:37:46.519 --> 00:37:50.679
derailed a conversation, for instance, and so Microsoft Copilot or
660
00:37:50.840 --> 00:37:54.079
AI as it looks today, may look at that and
661
00:37:54.880 --> 00:37:56.880
sort of say, oh, they're in the same room, so
662
00:37:56.920 --> 00:37:59.199
they have influence, they have connection to each other. But
663
00:37:59.239 --> 00:38:01.840
if you actually try to tap into that influence of
664
00:38:01.920 --> 00:38:05.119
that person, who's derailed it. They may not get you
665
00:38:05.159 --> 00:38:08.519
to where you want to be. So it's still i
666
00:38:08.519 --> 00:38:11.039
think comparative at this point in terms of AI to
667
00:38:12.000 --> 00:38:15.199
do the data collection in the traditional sense, but we
668
00:38:15.280 --> 00:38:18.599
can process it through AI and run those calculations much
669
00:38:18.639 --> 00:38:20.480
more efficiently than we've been able to in the past.
670
00:38:21.400 --> 00:38:24.800
That makes sense, Yeah, because I mean, you know, whatever
671
00:38:24.880 --> 00:38:27.639
AI model you might have, I mean, your output is
672
00:38:27.760 --> 00:38:31.239
as good as the data input and then your I
673
00:38:31.239 --> 00:38:33.519
mean to your answer, I mean, if you don't have
674
00:38:33.599 --> 00:38:36.880
that initial good data for the model to take in,
675
00:38:37.320 --> 00:38:38.760
I mean, what you're going to get is I mean
676
00:38:38.880 --> 00:38:40.039
as good as not using it.
677
00:38:40.599 --> 00:38:42.400
So that's right, that makes total sense.
678
00:38:42.440 --> 00:38:44.960
I mean I'm assuming you know, much like everything else,
679
00:38:45.000 --> 00:38:47.119
I mean, this ill the wall as well, and you
680
00:38:47.159 --> 00:38:49.320
can get a lot more out of it. But I
681
00:38:49.320 --> 00:38:52.119
mean I would say we're still in such an early
682
00:38:52.199 --> 00:38:55.960
stages of what is possible and not. I'll be fascinated
683
00:38:56.000 --> 00:38:59.280
to see how you apply to befold as well.
684
00:38:59.679 --> 00:39:02.440
Yeah, I'll be interested to see how it unfold.
685
00:39:03.559 --> 00:39:06.079
Yeah. Perfect, So thanks so much, Jason.
686
00:39:06.119 --> 00:39:08.800
I mean, I as always, I mean, with any conversation
687
00:39:08.880 --> 00:39:11.679
with you, I learned a ton. Uh. It looks like
688
00:39:12.800 --> 00:39:18.360
threefold is something that a lot of companies not just
689
00:39:18.760 --> 00:39:20.559
trying to do M and A, but I mean even
690
00:39:20.599 --> 00:39:25.039
with strategy and especially in the ever changing world that
691
00:39:25.119 --> 00:39:29.280
we live in, UH could use. So I wish you
692
00:39:29.320 --> 00:39:32.320
and the team the very best and I would love
693
00:39:32.360 --> 00:39:35.079
to have you on sometime in the future to tell
694
00:39:35.159 --> 00:39:37.840
us how you're doing both as a founder as well
695
00:39:37.880 --> 00:39:40.800
as you know everything that you love to do, so
696
00:39:41.159 --> 00:39:44.280
on behalf of our podcast. All the best of you, Jason.
697
00:39:44.639 --> 00:39:45.320
Thank you very much.
698
00:39:45.320 --> 00:39:47.840
I appreciate it, and like I said, every conversation with you,
699
00:39:47.920 --> 00:39:50.599
I think I learned something as well. So I appreciate
700
00:39:50.599 --> 00:39:53.280
everything you're doing and just the platform you're creating, not
701
00:39:53.440 --> 00:39:56.039
just for myself like for other founders. So in the
702
00:39:56.079 --> 00:39:57.920
same way, you know, keep up the good work that
703
00:39:57.920 --> 00:39:59.960
you're doing and just know we appreciate you.
704
00:40:00.039 --> 00:40:01.840
Thanks so much, Take care all right. Thank you,