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Why Private Equity Operating Partners Can't Trust Their Own KPIs · Private Equity Data Guy

• 0:00
  You can't put AI on top of three part forms and expect magic to occur.
• 0:03
  People always talk about this concept of common sense.
• 0:06
  And why is common sense so uncommon?
• 0:08
  There's 8.3 billion definitions of what common sense means.
• 0:11
  Therein lies the problem.
• 0:12
  How can I know inside of that first week that things are off the rails as opposed to finding out one hour after I present the board deck that, oh, what my chief revenue officer just painted a picture of
• 0:23
  actually doesn't support what we'd promised them?
• 0:26
  Now there's credibility loss.
• 0:28
  I think there's a systemic risk of credibility loss in the organization up and down the chain, the value chain.
• 0:35
  Behind every value creation plan, there's a data problem nobody wants to talk about.
• 0:39
  Fragmented systems, metrics nobody trusts.
• 0:42
  And decisions made on gut feel dressed up as analysis.
• 0:45
  Welcome to the PE Data Guy.
• 0:47
  Each week, host Graham Crawford talks to the operating partners, advisors and practitioners who are doing the work inside portfolio companies.
• 0:54
  If you care about what actually drives returns in the market, then you're in the right place.
• 0:58
  The PE data guy starts now.
• 1:02
  Vinay Rahman has spent 13 years measuring something most companies never look at directly.
• 1:07
  The gap between what leaders believe is happening and what their organization is actually doing.
• 1:13
  His firm, Carmo, maps execution friction inside growing services businesses.
• 1:18
  He hosts the Business Blind Spots Exposed podcast, and his LinkedIn tagline is one of the best in the game.
• 1:24
  I help operating partners prove what they already suspect.
• 1:27
  Today, we find out what they suspect.
• 1:29
  Welcome to the show, Vinay.
• 1:30
  Great to have you on.
• 1:31
  Yeah, thank you, Graham.
• 1:32
  Very nice to see you again.
• 1:34
  Yeah, no, wonderful.
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  And, you know, we were introduced by Body.
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  Right.
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  And then got to enjoy a wonderful dinner together because we happen to be in the same geography.
• 1:42
  So, yeah, very much enjoyed that.
• 1:44
  Yeah.
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  I look forward to the next time we get together in person.
• 1:46
  Yeah, we had to make sure we get dessert at that place as well.
• 1:49
  Oh, yeah, the.
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  The Korean dessert place.
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  How did those cakes go down when you got them home?
• 1:55
  Well, I.
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  I think I.
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  I probably bought more, but I don't necessarily know that all of them arrived intact when I got home, but everyone else enjoyed what.
• 2:02
  What.
• 2:03
  What.
• 2:03
  What arrived.
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  Yeah.
• 2:04
  Well, that's a great.
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  That's a great review.
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  So Breeze Bakery in Annandale.
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  For any.
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  Annandale, Virginia, for anyone wondering, amazing selection of cakes and pastries.
• 2:13
  So you spent 13 years, like we talked about, measuring the gap between what people think is happening and what the organization's actually doing, and you say that operating partners have suspicions.
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  Your tagline says you'll help them prove what they suspect.
• 2:26
  So let's start with there.
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  Like, what did the operating partners suspect?
• 2:30
  Yeah, it's a great question and it's so nuanced too.
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  They suspect what?
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  The execution that's happening on the floor, the shop floor, is not actually what had been communicated as this strategy that meets the mandate.
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  Now how do you prove that?
• 2:48
  Well, gosh, that's, I think hopefully what we'll be discussing here, but absolutely, yeah, they can't prove it.
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  They've got a stinking suspicion, a gut feel that something's just not quite right.
• 2:59
  That's what it is.
• 3:00
  Interesting.
• 3:01
  Well, I should zoom out a little bit and give you a chance to talk about your exposure to this, this type of work and companies like this and situations like this.
• 3:10
  Like what?
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  Tell us a bit more about your career path and what led you to this wonderful world of private equity.
• 3:15
  So serial entrepreneur run a number of companies.
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  In all those companies where I've raised money before, where I've exited before, having worked for Fortune 100, Fortune 1, which is the US federal government Fortune 1.
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  Yeah, that's what they like to call themselves, who have worked in systems with 285,000 users on it.
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  So massive, massive systems.
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  And running the data analytics insight on, on those systems, I think one thing was very clear is that the set of shared assumptions between people is, is not shared.
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  Yes, unshared assumptions.
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  That's right.
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  I mean people always talk about this concept of common sense.
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  And why is common sense so uncommon?
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  Well, that's because they have a different.
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  There's 8 billion people on Earth or 8.3 billion now, and there's 8.3 billion definitions of what common sense means.
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  Therein lies the problem.
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  So background has been around data analytics, artificial intelligence, machine learning.
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  Before it was cool.
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  So 20 plus years ago, who invite invited this propeller head to the meeting 20 years ago.
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  Now everyone's got questions.
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  They're like, oh, you did this a long time ago.
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  I was like, yeah, I've been waiting a long time for this data.
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  Oh, I know that data is cool.
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  Who would have thought?
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  Yeah, that's right.
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  Revenge of the nerds.
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  Right.
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  So it all started with this idea of I call myself a data empath.
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  I used to always take a look at the data and try to start to understand what is the narrative that is coming through.
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  Which always led to more questions that it actually did to answers.
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  Yeah, seen that.
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  And what usually ended up happening to try to confirm the narrative that I'm seeing in early stage companies all the way to having worked with Companies that have been held as within portfolio portfolio firms was that what is
• 4:56
  coming across to me might be completely different than the person on the other side of the table.
• 5:00
  So I need to start asking more questions.
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  We started to realize we can ask four people at that same exact table what that narrative is.
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  And there might be some 60, 70% of it.
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  That's very similar.
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  But the other 20 to 30% is where all the gap in the world is.
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  Yeah, that's a huge divergence.
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  And that's where the.
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  That's where efficiency falls off the table.
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  Right.
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  When.
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  When that happens.
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  Go ahead.
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  Sorry.
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  No, no, I was going to say I might challenge you with one other word instead of efficiency.
• 5:27
  I feel people use the word efficiency a lot because efficiency is doing something that you believe is right again and again and again for less cost.
• 5:34
  Right.
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  I've been trying to champion this idea of using the word effectiveness doing the right thing first.
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  Because a lot of people have been talking about efficiency plays, but never talked about it's having fantastic customer service in your restaurant during COVID You may be incredibly efficient, but nobody's coming to the restaurant anymore, so it's no
• 5:52
  longer effective.
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  I get it.
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  And I've got another example, actually, that I often like to refer to, and that's folks that are out there job seeking at the minute, they measure themselves on.
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  And I've mentored a few people coming out of college.
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  Right.
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  To this end, they measure themselves on how many job applications they submitted.
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  And right now it's possible to submit 5,100 job applications in one day.
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  But if you never hear anything back and they all disappear into a black hole, then, you know, hooray for your efficiency.
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  Right.
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  Yeah, that's.
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  That's exactly.
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  That's a perfect example.
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  Yeah, that's very much the point.
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  I actually read a book last week.
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  Tell me if you've come across it.
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  It's called the algorithm by John McNeil.
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  I think he was the president of Tesla when they went through the sort of big growth stage, the late 19s and the early 2000s.
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  And he spoke about a practice which resonated to me in what you're saying of.
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  At the start of every meeting in Tesla, what they do is they ask everyone individually what is your objective from this meeting?
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  And often they would get the same thing that you just described of like, yeah, there might be some overlap, but there's in some cases a big difference.
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  So what they would do is align on here is actually our objective that we can all agree on.
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  And if this isn't your objective and doesn't matter to you, then you can leave and that's okay.
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  Go and be effective somewhere else.
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  So it was a really nice practice that I saw that I think allows people to kill it in the moment or at least identify it in the moment, even if they can't solve it in the moment.
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  Yeah, I'm writing that.
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  Then that sounds like a kind of nice codified way of doing it that anybody can adopt.
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  That's a nice, nice system there.
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  Yeah.
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  I think you'd very much enjoy the book.
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  I've seen this scenario as well.
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  Company kind of stagnant, not going anywhere, not being very effective.
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  I'm going to keep using that word today.
• 7:31
  Now it's my word of the day.
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  Not being very effective.
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  And yet all the KPIs against the value creation plan are green.
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  So what is it about that standardized reporting that keeps the suspicions that people might have that all is not well, that keeps them unprovable?
• 7:49
  Yeah, this is a good question.
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  There's a nuance here that I want to really kind of shine a light on.
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  Systems like eos or scaling up business operating systems are all about the cadence under which and the hierarchy under which you meet.
• 8:02
  Right.
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  And they're incredibly valuable.
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  We can come back and use a sports analogy here.
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  But you come to practice every day and you know what you're going to do in practice and they schedule different exercises that you do in that practice.
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  And it makes a high performing team, but everybody does that.
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  The problem is what happens when that player is out on the field.
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  When that coo, cto, whatever member, whatever level team is not in that boardroom, will they make that same decision coherent with what everyone else said?
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  I think alignment is something that you reach in a conversation, in a room.
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  Five people walk in, all five people agree to what they're going to do.
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  But the five people quietly walk off and execute something completely separate.
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  And I think that's really the issue here.
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  Right.
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  So your instrument that you have, and you can remind me of the trademarked, wonderful name for the instrument which seeks to identify this misalignment.
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  It asked every leader in the company one question along the lines of can you tell me a bit more about how that question's asked and what you do when the answers come back?
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  Yeah.
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  So there are 15 questions that we ask and it's using a Likert scale, essentially where we're asking them on a scale of 0 to 10, 10 being strongly agree and 0 being strongly disagree.
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  An example could be we've had Strong revenue growth in the last 12 months.
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  Chief Revenue Officer might say, yeah, that's a nine out of 10.
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  Yeah, we've had fantastic revenue growth.
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  CEO might say, yeah, that's A7.
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  And the CFO might respond to two.
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  Now who's right?
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  That's actually the wrong question.
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  It's the.
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  They could all be right in their own lens, right?
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  Yeah.
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  They're all looking at different signals, but the fact is that the narrative amongst them is not shared.
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  And that's where the problem exists.
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  Because when the CRO, for example, has put a nine out of 10, they think things are strong.
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  And they believe adding another program into the mix or another campaign or another ideal customer profile in their go to market, they can say, hey, let's go ahead and do this.
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  And the CFO says, what are you smoking?
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  We're having problems right now.
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  We can't, we can't deliver this one.
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  Yeah, yeah, I'm not seeing anything delivered to the bottom line.
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  Our profitability.
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  Gone for.
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  Gone to the birds.
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  You want to roll out a new program, are you Again, what are you smoking?
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  Right.
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  The problem is not that the CFO is right.
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  It's not that the CRO, COO, or anybody else in that case is right.
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  It's the fact that when we start to see people that start to diverge by one standard deviation in the statistical data set that we have, that's a pretty significant difference of understanding.
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  That means you're going to have latency of decisions like that.
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  CFO saying, whoa, whoa, whoa, whoa, whoa, whoa, hold on a second.
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  You're making us.
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  You're basing this based on something that I don't believe is actually happening.
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  The lights are green on the dashboard.
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  I don't trust what you're telling me, though.
• 10:53
  Absolutely.
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  Yeah.
• 10:55
  The misalignment and you know that spread that you Talked about, like 2 out of 10 to 8 out of 10, like, did that really, did spread really happen to that extent?
• 11:03
  Yeah.
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  So we have two things that we really gauge.
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  One is sort of the distribute or the variance in understanding across each one of these 15 dimensions.
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  Right.
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  So you can kind of see a visual map that shows you the 10, the 9, the 7 and the 2.
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  That visually is striking.
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  And then we build something called a cohesion map, and that shows you how cohesive they are and which topics they're really tightly aligned.
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  Because I would be willing to submit to you that every organization where they're tightly unaligned on a topic that's actually not where they need help as a matter of Fact, their data, their dashboards is probably high integrity in
• 11:39
  those places because they are all seeing the same signal.
• 11:41
  Right.
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  The places where there's a wide divergence, they have different narratives as to how they're interpreting it.
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  And that's the problem.
• 11:49
  Yeah, indeed.
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  And, you know, for regular listeners to the podcast, you know, we're ensconced in the world of private equity.
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  And I actually had a guest on a couple of weeks ago, he was talking about misalignment, and he said that misalignment after a private equity acquisition between the P firm and the leadership team is.
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  He estimates that misalignment exists in over 80% of the cases.
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  So talk to me about that specific scenario.
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  Like a P firm has just come in.
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  They've replaced the leadership team as part of the 100 day plan.
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  Is misalignment more or less likely in that situation?
• 12:25
  Yeah, and I want to shift a little bit away from misalignment because I think misalignment is a point in time.
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  I'd like to talk about this idea of coherence.
• 12:31
  Right.
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  And is.
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  Are.
• 12:33
  Are they all making decisions on.
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  In.
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  In a coherent way?
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  And what that means is under pressure in different rooms, they're all saying, I see the same exact problem and the same target.
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  And everything that I'm doing is in support of that.
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  Right.
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  Alignment.
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  Is everybody nodding their head in the room.
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  And I believe that that's actually part of that 80% number.
• 12:50
  Is.
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  Everybody in that room is nodding, saying yes, because they kind of are somewhat.
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  There's pressure for them to.
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  It rewards agreement, not disagreement.
• 13:00
  So kind of coming back to this question, let's give you a company.
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  I worked in home services for a long time, had a number of companies that were fast growing.
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  Some of them had been bought by PE before, and they were just standing up on another one.
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  This one that I'm thinking about is in the Southwest, started at two and a half million.
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  Four years later, after having worked with them, they exited around 14 and a half million.
• 13:22
  So pretty significant growth, right?
• 13:24
  Solid growth.
• 13:25
  They went from about 23 technicians in the field when we started.
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  They ended, I think, around 139 by the time we left.
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  Something to that effect.
• 13:33
  Yeah, it makes sense.
• 13:33
  Yeah.
• 13:34
  7X the business.
• 13:35
  Yeah.
• 13:36
  So good growth.
• 13:38
  The foundational assumptions on which they built that organization that had 23 technicians was based on how they were doing customer service, how they were sending out invoices, what billing and payment processing systems they were using.
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  Who is reaching out?
• 13:55
  Is that technician supervisor actually reaching out a week Afterwards saying, hey, how was your visit?
• 14:00
  And was it a automated system?
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  Or was an email that they sent up saying, hey, just wanted to check in on Jack who came by?
• 14:06
  Right.
• 14:06
  That system doesn't scale when you have 139 employees now.
• 14:09
  No, I'm sure it just does not work.
• 14:12
  As a matter of fact, when they started being pushed a lot more by a mandate that came from overhead, they were trying.
• 14:19
  So the way I think of it is a.
• 14:21
  I'm kind of hopping analogies here, but I think of a basketball phenom in high school.
• 14:26
  They're playing at a higher level than what basketball really needs.
• 14:30
  They skip college and go directly to the NBA.
• 14:33
  But now all of a sudden, the plays that they use in high school don't scale into professional NBA play because those people have seen it before.
• 14:46
  Yeah, they're bigger faster, they've got bigger wingspans than what?
• 14:49
  The wingspans are huge.
• 14:51
  That's right.
• 14:52
  The Average height is 6.6foot 6, not 6 foot 1, which makes a huge difference in terms of how high they can get up.
• 15:00
  The system didn't scale.
• 15:01
  And that's where I think the fundamental mismatches occur, is that there's an investment thesis and there's never sort of that marriage between what the investment thesis and the operating assumptions that got the business to where they are.
• 15:14
  But hey, let's, let's, let's go to 3X, right?
• 15:17
  Is that.
• 15:18
  How does that resonate with you?
• 15:19
  I mean, you study data and data systems all the time.
• 15:22
  People set KPIs, and five years later, they're still using the same set of KPIs and metrics, expecting different results to occur.
• 15:28
  Absolutely.
• 15:28
  And it's one of the, it's one of the value propositions that I talk about.
• 15:32
  Right.
• 15:32
  The.
• 15:33
  And this is a helpful narrative for preventing kind of misalignment and creating coherence in the businesses that we work in as well.
• 15:41
  Like often, you know, with data and AI consultants, people show up.
• 15:44
  People's first concern is like, oh, no, this is it.
• 15:46
  Our job's being automated.
• 15:48
  When actually, that's very rarely the case.
• 15:49
  And the case is actually the value case is actually we intend to double this business in size, and we don't want to have to double the team when we do that.
• 15:57
  Right.
• 15:57
  So that's the, that's what then drives the operational value creation plan.
• 16:01
  Like how do we build scale into the business before we scale it, not after we scale it.
• 16:05
  So definitely, you know, definitely see a, definitely see a bunch of that happening.
• 16:09
  And I'm Curious about this acquisition line from your perspective, because in the example that I talked about, we replace the.
• 16:18
  We buy the company, we give wonderful exits to everyone.
• 16:21
  We replace the leadership team.
• 16:23
  On one hand, it's an amazing opportunity to create coherence because you've got a brand new leadership team, brand new ownership of the company.
• 16:30
  But on the other hand, you've got a ton of existing employees who are used to doing something a different way, and you've just changed their day to day significantly.
• 16:37
  And all the leadership that they used to look up to and work on does.
• 16:39
  So it's interesting to me, like, is that an opportunity or is that a risk?
• 16:44
  It's probably both.
• 16:45
  An equal measure, right?
• 16:46
  Yeah, it could be.
• 16:47
  It could be either.
• 16:48
  Right.
• 16:48
  I will tell you, when I've worked companies, I've always loved to give.
• 16:52
  I have a friend named Wendy.
• 16:54
  She always says, I love to release people to their destiny.
• 16:57
  Which is a kind of way of saying, hey, I don't think this is a good fit for you anymore.
• 17:01
  But they come to that realization by themselves.
• 17:03
  Because I say, here's the bar I need to meet.
• 17:06
  Now I'd like you to do it because you already have all the domain knowledge.
• 17:09
  You understand those assumptions that we've been going.
• 17:12
  But I need you to upgrade those assumptions now.
• 17:13
  Right.
• 17:14
  Because this is what we're trying to get.
• 17:15
  Here's where the rest of the team is.
• 17:17
  I need you to come over here.
• 17:18
  It actually makes for a very, very nice and clean pathway to make that occur.
• 17:24
  Right.
• 17:24
  Because they're the outlier in a belief system that no longer holds.
• 17:28
  Right.
• 17:28
  We, we got to 12 and a half million in revenue.
• 17:31
  We're now looking at 50.
• 17:33
  It's a different company.
• 17:34
  Yeah, yeah.
• 17:36
  And they get a chance to reinvent themselves.
• 17:38
  Or they say, look, I've heard about P.
• 17:41
  I'm not interested in working those hours.
• 17:44
  I don't know that that's what I'm necessarily asking you.
• 17:46
  But look, this is the target I'm trying to get to.
• 17:48
  I don't want to do that.
• 17:50
  I respect that.
• 17:51
  Right, right.
• 17:52
  And that person could go to another business that's doing 1 million and take that business back up to 12.
• 17:56
  Right.
• 17:56
  And that may be the place that they love to live.
• 17:59
  That's right.
• 18:01
  Yeah.
• 18:01
  So it is an equal measure on both sides.
• 18:04
  It gives you a lens into where somebody is holding on to beliefs, operating assumptions that are creating friction and gives an opportunity to shine that spotlight or flashlight and saying, so here's where I think we need to upgrade
• 18:20
  systemically.
• 18:20
  Would you like to be part of that upgrade.
• 18:22
  Yeah, makes sense.
• 18:24
  Clarity and coherence.
• 18:25
  As you know, you have to deliver it yourself as well as I expect it of others.
• 18:29
  So that seems, that seems absolutely fair.
• 18:31
  Yeah.
• 18:31
  Diligence work.
• 18:32
  I think this is true for both of us, but I'm asking your perspective here.
• 18:34
  Like you've done diligence work that's changed purchase prices.
• 18:37
  The layer of investigation that you do show that that doesn't show up on the P and L.
• 18:42
  So I think.
• 18:42
  The fundamental, if I were to name it, the biggest challenge is leaders, specifically portfolio companies, CEOs and, and then, and to some degree as well, the operating partners.
• 18:53
  The last thing anybody wants is to be surprised by something that they had no idea.
• 18:58
  They call, they come and walk into a board meeting, someone says, hey, dashboard looks so fantastic in your.
• 19:05
  On sales.
• 19:06
  Yeah, they do.
• 19:08
  Why is your P and L in the red?
• 19:09
  They're finding out in real time.
• 19:11
  And that's a terrible place to be considering the diligence process, being able to do the same thing.
• 19:17
  We did work on a project I had.
• 19:20
  I worked with the guy who ran the integrated management Office for a 300 million telecom company that was buying a $200 million telecom company.
• 19:30
  Okay, yeah, two big, two big organizations there, right?
• 19:34
  One played enterprise only, another one was small and medium sized enterprises.
• 19:37
  So kind of a nice natural market expansion type play.
• 19:42
  He spent two years on the ground and I said, hey, let me run this on this, run this on the organization and let me tell you what I can see.
• 19:50
  I ran it and he said, gosh, you wrapped up in one hour.
• 19:55
  What?
• 19:56
  It took me two years of experience trying to deconstruct and understand myself because I'm listening to these hallways and why the hell are they talking about this again?
• 20:06
  That's sort of the experiential narrative he had to go through in order to be able to say, gosh.
• 20:10
  That was never made clear to both sides because one had a quarterly deployment cycle for all of their products, the other one was annual.
• 20:17
  Two completely different deployment and development cycles.
• 20:21
  He said, if I had known that beforehand, in terms of how they see these things, I could have created a microfund, an innovation micro fund.
• 20:27
  I could have set $5 million aside on such a large acquisition and said, hey, we're going to go through and build a training regimen to get all of them to start thinking the same way, whether it was quarterly
• 20:39
  or annually.
• 20:41
  Gosh, could that make a difference in your pricing?
• 20:43
  Because you don't find out 19 months or 14 months into the hold, you find that out before you buy.
• 20:49
  Yeah.
• 20:50
  How much?
• 20:50
  Yeah.
• 20:51
  And you've got that 19 months of opportunity where you could have been doing something about it, Right?
• 20:54
  Yeah.
• 20:55
  Think about the loss of.
• 20:56
  I mean I like to think of either you're compounding or you're not.
• 20:59
  Right.
• 21:00
  Growing or you're dying.
• 21:02
  So if it waits 19 months for that to appear, those 19 months, your multiples and invested capital has been sitting stagnant until you find the issue.
• 21:13
  Yeah.
• 21:13
  And none of the investors like that.
• 21:16
  No.
• 21:16
  And, and consider the longer hold periods right now.
• 21:19
  What, what is causing those longer holding hold periods is that you got to wait 19 months to find.
• 21:24
  Oh yeah, yeah.
• 21:27
  Ex.
• 21:27
  Yeah.
• 21:28
  Exactly.
• 21:28
  Exactly.
• 21:29
  What's the most.
• 21:30
  When you find, when you do find something like this in a diligence piece, like what.
• 21:35
  What's usually the next go to move?
• 21:36
  Is it a let's reprice or walk away?
• 21:40
  Is it a hey, let's build that into the 100 day plan like.
• 21:44
  Or does it depend?
• 21:46
  Yeah, I think it depends.
• 21:47
  Depends on a lot of factors, right?
• 21:48
  I mean, how it depends.
• 21:51
  Depends why they're buying the company, right.
• 21:52
  I mean if I was just talking to one that does a lot of buys was buying a lot of installation companies that do, you know, garage doors and, and, and, and generators and things.
• 22:02
  And they wanted to buy one company in each state in the mid Atlantic.
• 22:06
  And in order to be able to buy those companies, they, they, you know, they went down the road and they said if we not buy something that's reasonably close to our investment thesis and service services, that investment thesis,
• 22:17
  because we need a geographic footprint.
• 22:18
  There was a timeline on which they were trying to do that.
• 22:20
  And that timeline had pressure.
• 22:22
  This company met, you know, probably, you know, 60% of their high level requirements.
• 22:28
  Okay, we can start to fudge some of this stuff, right.
• 22:30
  This is an opportunity in that case because there's that timeline pressure.
• 22:34
  Hey, we can now set this up so that the first hundred days is about creating not just an integration plan in name, but how do we actually build those operating assumptions so that by the end of the 100
• 22:44
  days everybody's operating off the same set of assumptions and that translation is cascading between each.
• 22:49
  Stage and we're rolling.
• 22:50
  And then the compounding starts a lot sooner.
• 22:52
  Yeah.
• 22:53
  And to what you do, imagine if we started to understand what the thesis KPIs are.
• 22:57
  Right.
• 22:58
  And we understand what the senior leadership team's KPIs are and what the delivery team's KPIs are.
• 23:03
  We can start to look at those and start to say, all right, over the course of the next 100 days these are going to, these are going to stay static.
• 23:08
  Here is the thesis KPIs, the Senior Leadership thing team now to start to build an affinity towards servicing these KPIs and the deliveries start to move.
• 23:16
  So now we start moving towards maintenance contracts versus net new as becoming, you know, 40% of revenue as opposed to let's acquire 40 new customers.
• 23:25
  Exactly, because that's what the thesis is as well.
• 23:26
  And it draws forward the important conversation in private equity that's too often hidden.
• 23:31
  Like it creates the data driven ability to question the thesis.
• 23:35
  Right.
• 23:35
  So that's the.
• 23:36
  You know, I've been reading a lot about this week.
• 23:38
  Like we will investigate every possible issue before we get to.
• 23:43
  Perhaps our value creation thesis was wrong.
• 23:45
  And if you align the KPIs across the company to those and make them make the data pipeline good enough that it's actually a true measure, then you can't avoid that conversation.
• 23:55
  Yeah, that's right.
• 23:56
  I mean it becomes uncomfortable when you ask the question because they don't have a clear answer.
• 24:00
  And then you realize that your KPIs are wrong.
• 24:02
  Let's go back to the drawing board.
• 24:04
  And it becomes a litmus test for forward progress in translation downstream.
• 24:09
  Yes, it must be the KPIs.
• 24:10
  Or maybe something's not quite right.
• 24:12
  Or maybe something like.
• 24:13
  Anything but the fact that our value creation thesis might be wrong.
• 24:15
  Yeah, but like we talked about with clarity and coherence, it would be unfair for private equity owner to demand that and then not offer it in return.
• 24:25
  It would be the same with solid KPIs and holding themselves accountable for.
• 24:29
  Like is this actually working?
• 24:33
  Yeah, that's right.
• 24:34
  There's a term I've been using a lot of late.
• 24:37
  Translation tax.
• 24:39
  Right.
• 24:39
  There's a translation tax between the thesis to the investment thesis to the strategy of the company.
• 24:44
  And there's a translation tax between that strategy to execution.
• 24:50
  Execution to ebitda.
• 24:52
  Right.
• 24:53
  And if we can start continually closing that translation.
• 24:57
  I mean, nobody pays taxes.
• 24:58
  At least I don't think anybody likes to pay taxes without knowing what their total income was and what that tax rate is.
• 25:02
  No, of, no, but that translation tax, I think it may have been Bain that quoted that strategy.
• 25:09
  Decay is somewhere on the order of 30 to 40% of the message actually makes it through.
• 25:14
  Gosh.
• 25:15
  And there's no P and L line for that, is there?
• 25:18
  No, that's right.
• 25:19
  Absolutely.
• 25:20
  I'll talk about a scenario that we've both seen this is probably translated from the historical days of private equity when we could leverage cheap debt and then make a purchase, probably do some add ons or tokens and then
• 25:32
  sit back and wait for multiple expansion to make us all super rich.
• 25:36
  The same thing is happening with AI.
• 25:38
  Companies are enabling copilot for every employee and then sitting back and waiting for the AI efficiency gains to roll in.
• 25:45
  And yet surprisingly, nothing changes if you don't put some structure and coherence around that.
• 25:51
  So you've seen the aftermath of that pattern as well as I have.
• 25:55
  Like what?
• 25:55
  Why do you think people keep thinking that's going to work?
• 25:57
  I tell my kids all the time and that may not be the best professional analogy, but I tell my kids all the time there's no such thing as a hack.
• 26:05
  Yes there is.
• 26:08
  When people purport that I'm going to show you how to get 485,000 LinkedIn followers tomorrow.
• 26:15
  You'll end up paying a lot, but you won't get 485 LinkedIn followers organically and sustainably.
• 26:21
  So they may be able to financially engineer it.
• 26:25
  Yeah, absolutely.
• 26:25
  I see this, I get the same emails of like, oh, we'll give you four qualified leads by this Friday.
• 26:30
  And I was like oh yeah, of course you will.
• 26:32
  Yeah.
• 26:32
  So back to your point is, I mean look, any system is only as good as the assumptions on which it's built, right?
• 26:40
  AI is a accelerator and if not a magnifying glass as to what those assumptions are.
• 26:47
  Hey, we're using three part forms to bill our customers and we built our go to market motion before email was invented and we want to put AI on top of that.
• 26:58
  Interesting.
• 26:58
  So what are you going to do?
• 26:59
  You're going to just start scanning in, you know, taking pictures of everything and you have someone, virtual assistant who's just scanning in images and wondering why it's not coming out.
• 27:08
  Because U's are read as O's and because of people's handwriting.
• 27:13
  It's only as good as the process underneath it and assumptions underneath it.
• 27:16
  Right.
• 27:16
  You can't put AI on top of three part forms and expect magic to occur.
• 27:20
  Yeah, no, absolutely.
• 27:21
  And I think there's two failure modes and I think we represent both of them between us.
• 27:24
  Like I often see, AI can fail on data quality, so the underlying quality of the data is not good enough.
• 27:30
  And from your perspective, AI can fail on people and lack of coherence about sure, I've got copilot, but no one told me what I'm supposed to do.
• 27:40
  With it.
• 27:40
  Yeah.
• 27:41
  And I think it's a really interesting point you bring up here.
• 27:43
  Right.
• 27:44
  Because I think the next question is, well, am I at a fork in the road?
• 27:47
  Actually, I don't think you're at a fork in the road.
• 27:49
  I think they actually operate in a sequence.
• 27:51
  Right.
• 27:52
  I think the data.
• 27:54
  Well, I'm saying not.
• 27:55
  I think what I have seen is that the data informs.
• 27:59
  That there is a structural narrative that does not exist anymore.
• 28:04
  Right.
• 28:04
  And that starts with people asking questions with data as opposed to patting themselves on the back.
• 28:10
  The data, it's all green, everything's great.
• 28:13
  Is it?
• 28:13
  Do you trust it?
• 28:14
  Because if you don't trust it, then you kind of get to the people and the behavioral components and you start investigating there.
• 28:19
  I think you got to start with the data first because that's the grounding point of truth.
• 28:24
  After that you can get to the perceptions and operating assumptions.
• 28:27
  Yeah, no, I couldn't agree more on that one for sure.
• 28:30
  And I've come across multiple misalignments and lack of coherence from even in terms of business systems where the leadership team is convinced that certain things, that data is being treated a certain way and it isn't.
• 28:44
  And I've had that debate of like, well, you might think that's what's happening or you might want that to happen, but that's not what's happening.
• 28:50
  Right.
• 28:50
  In terms of, in terms of how data is moving through your, through your system.
• 28:54
  So I've certainly seen that contradiction.
• 28:57
  Quick question for you.
• 28:58
  I mean, in what you see.
• 28:59
  I mean, I think it's very germane to.
• 29:01
  The topic here is so many people say they're data driven.
• 29:05
  I'll stop there.
• 29:06
  What do you want?
• 29:07
  Yes, many people do say they're data driven.
• 29:10
  It's a very fashionable phrase.
• 29:12
  And I think data became cool just before AI became cool.
• 29:15
  So, you know, I've certainly heard it a lot on my data driven strategy.
• 29:19
  But if that data underneath it is not carefully curated and aligned in service of the right coherent goals, then that data is of no use.
• 29:31
  It can drive you in any direction that you want.
• 29:33
  And I think there's a phrase which predates data driven, which I've heard again many times.
• 29:39
  Lies, damn lies and statistics.
• 29:42
  And that in the right hands or the wrong hands, depending on you want to see it.
• 29:46
  Any marketing person or headline writer for a newspaper can take any data point and basically make it adjust it to tell whatever story that they want to tell.
• 29:55
  And I've seen that play out in board meetings and steering committees and Whatever else, like throughout my whole career.
• 30:03
  So, yeah, people can claim to be data driven and use data as a crutch to support whatever hypothesis they're trying to push.
• 30:09
  That doesn't necessarily make it correct.
• 30:11
  And you know, to that point back to the lies, damn lies and statistics.
• 30:15
  I don't know if most people recognize that that's all AI is.
• 30:18
  It's statistical inference based on an existing data set so that it undergirds everything within AI.
• 30:25
  Right.
• 30:26
  So if you don't actually understand the significance and the gravity of that statement lies damn wise in statistics.
• 30:32
  That's why AI is failing you.
• 30:34
  Yes, my favorite, and this is a very simple AI example, but applies at portfolio company level as well.
• 30:41
  One of the best examples of the holes in AI why you need to curate data are the hallucinations, which still happen.
• 30:47
  And you can say, give me a quote from Winston Churchill about this and it will produce a quote that very much sounds like Winston Churchill could have said it because it's got all of his other quotes and writing
• 30:59
  that he's done and everything else.
• 31:01
  And you've set the give me a quote that Winston Churchill would say about this.
• 31:05
  You've opened the gates to hallucination first with your question.
• 31:11
  And secondly, yeah, as you're saying, it's a statistical inference of what Churchill might have said about this thing.
• 31:16
  And that's what you've asked for and that's what you've got back.
• 31:18
  It doesn't mean that he said it though.
• 31:20
  It doesn't mean that he said it.
• 31:21
  And the biggest mistake I see people make with AI is assuming that it's some database in an evil volcano layer somewhere that's got all the answers to.
• 31:30
  It's got the entire database of humanity on the Internet on it and every answer to every question that you have that you've ever got.
• 31:35
  It's never claimed to be that, as you say, it's just a very good predictive model at predicting.
• 31:39
  Like, hey, given the question and the context and my understanding of the data in the background of this, what do I think?
• 31:44
  What do I guess is the best answer?
• 31:48
  It can be incredibly powerful.
• 31:49
  Like I, you know, have a native AI back office, as you do as well.
• 31:53
  And you know, it's incredibly powerful if you give it those things.
• 31:56
  But yeah, without that well structured, high quality data underneath, it's always going to let you down.
• 32:02
  Yeah, garbage in, garbage out.
• 32:03
  Right.
• 32:04
  Or give you some opportunity to let yourself down because you haven't supplied, you know, the relevant context in order to get it to predict the Right thing for you.
• 32:13
  It's the.
• 32:14
  I hear the same thing when, you know, there was a time when we were talking about how AI was, you know, kind of racist and held stereotypes about people and whatever else.
• 32:23
  And like humans are amazing creatures.
• 32:25
  Like it's a reflection, it is a mirror.
• 32:27
  Right.
• 32:28
  It amplifies what we have put out into the world.
• 32:30
  Like no one's sitting in anthropic saying, oh please be a little bit racist here.
• 32:35
  It's like everyone is a little bit racist with what they are and that's why AI is showing up and doing that.
• 32:42
  No one's coding it.
• 32:43
  So a couple of good examples there of how it actually works versus doesn't work.
• 32:48
  I want to give you an opportunity to talk about, if you're willing to talk about it, because I know you're piloting it now and I'm one of the lucky recipients of the pilot.
• 32:55
  A private equity news by role engine that you've got where it collects private equity news and then asks, poses relevant questions to people with the context that you've baked in about what would be important to different people in
• 33:07
  different roles.
• 33:08
  Can you, can you talk a bit more about that if you're willing?
• 33:11
  Yeah, sure.
• 33:12
  And I will tell you, it is a.
• 33:14
  There's a lot of statistical inference, everything baked in the system.
• 33:18
  So a lot of the output is based on AI, but the goal is.
• 33:23
  So look, AI is fantastic at one thing and it can absorb enormous amounts of information.
• 33:27
  It does have recency bias, but it's, it's container recency bias is much bigger than most humans.
• 33:33
  What we've done is every day we go out and cull all of the articles that are out there on the Internet from that the 24 hours proceeding.
• 33:41
  For that given day.
• 33:42
  Yeah, for that given day.
• 33:44
  Right.
• 33:44
  So Today, you know, August 28th from today's Pulse went out from All Azar August 27th.
• 33:50
  We can have anywhere between 23, 27 to 83 articles is what I've seen.
• 33:54
  And what it does is it goes through and scores each one of them and it scores it to kind of show what is that signal to noise ratio.
• 34:01
  I mean how many things are we seeing that is signal the same thing that is actually showing something is moving forward in noise.
• 34:06
  It's just confounding what we're actually hearing.
• 34:10
  And it's just a repeat of, hey, Bain has just told us something that we've all known for six weeks or six months already.
• 34:16
  Yeah.
• 34:16
  Although they put in the same report last quarter.
• 34:18
  Yeah, that's right.
• 34:20
  And what we do is then we contextualize all of that and we say, so what is sort of the set of lenses with a general partner, a sponsor, an operating partner or head of portfolio operations, a portfolio company
• 34:32
  CEO, or what we call an executive guide, coach, consultant, advisor, what are they seeing out of that?
• 34:39
  That gives them a question that they can start to challenge a bit of their own assumptions in terms of how they're believing something occurs in the market.
• 34:46
  That's the first piece of it, and it tells you where that came from.
• 34:50
  But second, it actually also gives you an opportunity to see as an executive guide, as this coach, consultant, adviser, fractional, what is that sponsor thinking about today?
• 34:58
  Because they've got some pressure on them today.
• 35:00
  Their operating partners got some pressure.
• 35:02
  What is causing that pressure?
• 35:03
  What is this the signal and the noise that is creating that pressure for that portfolio company CEO, for example?
• 35:09
  Portfolio company sees that people are now getting sued, firms are getting sued by limited partners.
• 35:15
  That probably makes sponsors a little nervous.
• 35:17
  Yeah, they'll move towards the edge of their seat on that news for sure.
• 35:20
  That's right.
• 35:21
  Operating partners are also getting nervous because now they're stuck to a mandate that they didn't write, but they're responsible for on five portcos that they're managing.
• 35:29
  And that CEO is going to hear very shortly from their board and their sponsor, hey, where the hell are my returns?
• 35:36
  So that executive died walking in the door, you know, whistling their little tune, walks in and says, hey, what's happening?
• 35:42
  That CEO is about to crack under the pressure because they've got to answer the question.
• 35:46
  So what if instead you said general partners are a little worried about limited partners coming after them and calling them to the carpet?
• 35:53
  What can you do to support that sponsor today and give them visibility into what's happening in operations that you can report up as a number that the LPs can believe.
• 36:01
  Yeah.
• 36:02
  To help relieve that pressure that you see is prevalent or portfolio company guess.
• 36:08
  Yeah, I, I don't know.
• 36:10
  Yeah.
• 36:10
  How do we, how do we find that?
• 36:13
  That's.
• 36:14
  That's the goal.
• 36:15
  Yeah, that's.
• 36:15
  It's a great question to answer.
• 36:17
  I wish we had the answer to that question as well as just the question.
• 36:19
  And we could write a book and then just head to a beach if we had an easily reliable kind of way to, to do that.
• 36:26
  So do you, do you mind if I share a little bit more about kind of where.
• 36:29
  Go ahead, go ahead.
• 36:31
  So right now it's a topical level question to spark conversation to what I call an inconvenient question.
• 36:36
  Right.
• 36:37
  That is just beyond the limits of what they have visibility into today.
• 36:41
  What we're working on, what we're testing out, is this idea of what is that talk track that gets to where the heart of the issue exists.
• 36:50
  Right.
• 36:50
  Which obviously, in my estimation of the world, it comes down to fundamental translation between those layers.
• 36:58
  Right.
• 36:58
  What is that portfolio company, CEO believing that operating partner, the sponsor, is not seeing the same exact way?
• 37:06
  Because we can get to the heart of that now.
• 37:08
  We're starting to make some music together.
• 37:09
  Yeah.
• 37:10
  You help people understand the different pressures of different roles that they're not in.
• 37:13
  Right.
• 37:14
  To help them better prepare to support people adjacent to them.
• 37:17
  Yeah, that's right.
• 37:18
  Yeah.
• 37:19
  It's magic.
• 37:20
  I definitely recommend it.
• 37:21
  I don't know how open your pilot is, but I can definitely recommend it to folks.
• 37:25
  Anybody who listens to your podcast, I'd love to send it out to them as well, just because they are.
• 37:29
  They're subscribing to your podcast.
• 37:31
  Cool.
• 37:31
  Yeah.
• 37:32
  So anyone listening, please let me know and, you know, I'll make sure to include your contact details underneath as well as how to.
• 37:38
  How to find the platform.
• 37:39
  It's really nicely formatted.
• 37:40
  I love the data in there about signal versus noise, so it gives me a kind of indicator as well.
• 37:44
  You know, I love a good visual indicator backed by high quality data as well as like posing the questions.
• 37:48
  It's.
• 37:49
  And it's.
• 37:49
  The other thing I love about it is brief, digestible, sits on one page.
• 37:53
  And if you have the option to dive in further if you want to in the email, but it keeps it and short and respects the kind of dopamine attention spans that social media has forced us all into.
• 38:05
  Like, oh, I can't read more than a page.
• 38:07
  We've got this promise that we're trying to deliver in anything that you use.
• 38:11
  I call it the 3:33 promise.
• 38:14
  So we earn 3 seconds of your time with that first question, which earns us 30 seconds more time to give you more bullet points.
• 38:21
  And if that's interesting, then you've got three minutes to go find the article that actually lays it out.
• 38:26
  And everything is laid out in that format.
• 38:27
  So it's.
• 38:28
  Hey, I can just look at.
• 38:29
  Just before I walk into a meeting with somebody, what question do I need to do to start Spark a conversation.
• 38:35
  That's kind of the idea.
• 38:36
  Yeah, I love that.
• 38:36
  No, it's a great product and I'm excited to be around to watch it, watch it grow.
• 38:42
  I want to finish off by asking about the word PE in General you think it's heading.
• 38:47
  I talked over dinner about this concept that has been in all The Bain and McKinsey and KKR and all the reports are saying like, oh, the game is no value creation, right.
• 38:56
  We can't do fun financial engineering anymore.
• 38:58
  Debt is no longer cheap.
• 38:59
  All the low hanging fruit's been picked off the tree in terms of returns.
• 39:03
  So value creation is where it's at.
• 39:05
  And yet PE firms are not stacked with a ton of value creation resources on their books.
• 39:10
  So that plus AI making software really difficult to price as well as another is another like very prevailing headwind in the market.
• 39:19
  Not that software isn't selling.
• 39:20
  Good software with a defensible AI moat and great growth numbers is still selling and still selling for high multiples.
• 39:26
  But people are struggling with how to price AI defensibility coupled with this big push on value creation.
• 39:32
  How do you think that shapes private equity over the next one year, three years, five years?
• 39:37
  I think it brings us back to the top of where we started, right?
• 39:40
  This shift from efficiency back to effectiveness.
• 39:43
  Right.
• 39:43
  I mean the way I think of things and part of why we built the company the way we did is that people have got EOs, they've got scaling up, they've got lots of tools.
• 39:53
  But the question I always ask them is how confident are you that you will get the signal before anybody else does?
• 40:01
  Because there's a psychological component to that, right?
• 40:03
  There's a defensibility component to what you're talking about, AI.
• 40:07
  So how do you start to get that signal earlier?
• 40:10
  Right?
• 40:10
  And I think that's where things are shifting toward is how do we get that signal earlier that things are heading off the tracks so we can course correct sooner.
• 40:17
  I want to know inside of that first week of a quarter that things are off the rails.
• 40:22
  That's now possible.
• 40:23
  How can I know inside of that first week that things are off the rails as opposed to finding out one hour after I present the board deck that oh, what my chief revenue officer just painted a picture of
• 40:34
  actually doesn't support what we'd promised them.
• 40:37
  Now there's credibility loss.
• 40:39
  I think there's a systemic risk of credibility loss in the organization, in a credit up and down the chain, the value chain.
• 40:47
  Yeah.
• 40:48
  And that becomes a big deal very fast, doesn't it?
• 40:49
  And you know, back to your point about efficiency and effectiveness, I know a ton of entrepreneurs in the local area and many of them use EOs, but you have the risk in that and I'm sure it can be
• 41:01
  very effective.
• 41:01
  And the fact that it shows up in PE backed companies, shows that it is effective, I'm sure when used properly.
• 41:06
  But it's very easy to fall into the trap of like, oh, this meeting was supposed to happen once a month.
• 41:11
  And we did this meeting check mark.
• 41:12
  And so our KPI is, we did the meeting that EOS told us to do.
• 41:15
  Like that.
• 41:16
  That can't be your indicator, especially not if you're pulling a bunch of company leadership in.
• 41:20
  Because to your earlier point about compounding, if you're taking your whole leadership team and spending an hour a week basically reiterating the same high level principles that they're still taking different execution inferences away from, then you're just costing
• 41:35
  yourself that you're on the death spiral, not the growth spiral.
• 41:38
  Yeah, I mean, so to maybe wrap that up in with a little bow tie.
• 41:42
  I don't actually even see us as competing with EOS or scaling up any management operating systems.
• 41:48
  Actually I think we're signal that informs those as to, you know, to exactly what you do at some point in time.
• 41:55
  It's time to refresh the KPIs.
• 41:56
  One of the challenges with EOS and scaling up is that we don't know what the underlying assumptions are that are creating the KPIs to be out of date.
• 42:04
  We don't know when.
• 42:04
  So what is the signal that's telling us it's time to update our KPIs because as you well know, KPIs end up being 3, 4/4, sometimes out of date, maybe if not even longer.
• 42:16
  It's no longer answering the narrative that we're trying to deliver.
• 42:19
  So what is the signal?
• 42:21
  Getting that spark, that signal upstream is I think where everything's headed.
• 42:26
  Yeah, absolutely.
• 42:28
  Couldn't agree more.
• 42:29
  And you know, people get normalized to KPIs as well and they sit green for so long that people stop doing anything.
• 42:36
  And I think an important question to ask of any KPI is like, okay, what are the thresholds for this?
• 42:40
  And if it goes above this number, what are we going to do?
• 42:43
  And if it goes below this number, what are we going to do?
• 42:45
  Right.
• 42:45
  There has to be action.
• 42:46
  We can't just like admire it and like look at the number and think, oh that's nice.
• 42:51
  Oh, revenue is 7 this month and it was supposed to be 9, but that's okay, it's still green so we'll just carry on rather than pay attention to the fact that it's moved down to for night.
• 43:02
  For people who want to learn more about what you're doing, find out about where this PE News Roll up newsletter is.
• 43:09
  Look at working with you and examining using their system within your business to identify the drift and the misalignment and the lack of coherence.
• 43:17
  Like what's the best place to find you?
• 43:20
  LinkedIn is a great place to find me.
• 43:22
  I'm quite prolific, or at least I try to be on LinkedIn.
• 43:26
  Yeah, I can attest you are as a fellow prolific LinkedIn.
• 43:29
  Yes, I agree.
• 43:30
  Welcome to the club.
• 43:32
  Yeah, it's a moving though.
• 43:34
  Moving goalpost or website?
• 43:37
  Carmo.com, c a r m o.com Feel free to reach out to myself.
• 43:43
  My team will reach out to you and get you access to what we call the PE Pulse.
• 43:50
  It's just a email that's dropped into your inbox every day and we'll just give you a question to ask that hopefully informs your it drops into your inbox every day at 6am so hopefully before your first meeting, but
• 44:03
  hopefully makes you more informed than anybody else at the table.
• 44:06
  Indeed.
• 44:07
  Wonderful.
• 44:08
  Well, thanks so much for coming on.
• 44:09
  It was great to have the virtual conversation with you after the real life one, and I look forward to getting cake again sometime soon.
• 44:15
  Yeah, that sounds good.
• 44:17
  Thank you for that.
• 44:17
  Take care.
• 44:18
  Bye.
• 44:19
  Thanks for listening to the PE Data Guy.
• 44:21
  The place where Private Equity meets Data.
• 44:24
  Please forward this episode to your favorite private equity friend.
• 44:27
  Thanks for listening.
• 44:28
  See you next time.