🥳 Finally!

The long awaiting Are You Human episode with Sol Rashidi is here! Most of you have probably heard of seen Sol and some of her brilliant talks but if for some reason you still don’t know her – she is Chief Data, Analytics, AI Officer @ Fortune 500s, ex-Chief Analytics Offices at Estee Lauder and the author of a book “Your AI Survival Guide: Scraped Knees, Bruised Elbows, and Lessons Learned from Real-World AI Deployments” (coming out end of this month!).

In our pod episode, we talked about the complexities of leadership, focusing on the nuanced dynamics of organisational change, talent management, and political manoeuvring within workplaces. Sol explained the importance of understanding cultural considerations when leading teams, emphasising the need to challenge the pace of change while respecting the natural rhythm of the company. In other words The-Getting-Sh*t-Done attitude 😉.

Sol gave some brilliant examples of the challenges faced by leaders who navigate political gamesmanship.

We also touched upon my favourite Lawrence Peter Principle phenomenon – highlighting the tendency to promote individuals to positions of incompetence (to put it nicely 😉).

This episode is one of my favourites and I hope you’ll enjoy it too.

Transcript

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so the SE Suite was actually where I

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made a lot of my mistakes especially my

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first

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role because I realize that I can’t

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convince them because I know the space

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and I can’t convince them because we’re

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going to build really cool stuff I

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actually what I learned first and

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foremost in my first EET gig was you

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have to build the relationships with

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them and ask for nothing in return and

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then once you build the relationships

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and you ask for something in return

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they’re willing to give you a chance

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because they bought into

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[Music]

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you no idea that’s what this game was

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about um and I made that mistake I

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didn’t I thought just being smart was

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good enough and building cool things

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were good enough and that was not the

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case but it wasn’t until I got to the

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Sea Suite that I realized how much the

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business did not appreciate data and

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analytics and and Tech I’m going to use

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like an umbrella term um I know we use

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words like Tech as an Ena

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uh but part of that also means that it’s

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an afterthought here’s what I want to do

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go figure it out but they don’t want to

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be involved in the sausage making and

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they have to be involved in the sausage

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making because sometimes they may ask

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for something it’s not really what they

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want and then you go off building

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something and you realize and they’re

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like that’s not what I wanted you’re

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like well this is what you said and put

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in all that effort and time and you kill

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yourself because you want to deliver a

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great product only to find out that no

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they meant something else

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are you

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[Music]

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human hi so it’s been a pleasure to have

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you here I’ve been uh following all your

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things you’re uh posting and and doing

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and and on LinkedIn you are very active

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I I saw that you just uh launched or you

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are you launched a book or it’s going to

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be launched end of April right yeah so

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that’s the interesting thing like I am

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active because for so long I’ve been

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doing the work not talking about the

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work then I was asked to start writing

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about pieces and stuff and then I was

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approached by a publisher to write the

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book um because I think there’s very few

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folks in the market that have hands-on

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experience and it’s not theoretical or

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practical or based on things they read

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but based on mistakes they’ve made

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because they’ve tried they’ve read it

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they’ve tried it or they didn’t know

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because they any articles or newsletters

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or blogs and they’re like okay we’re

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going to set up a Coe for AI or we’re

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going to develop these capabilities how

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do we embed it into the existing

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operating model um and so it was just

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roll after rooll right the the trends

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would the buzzwords would change and so

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I’ve been very much focused on doing the

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work and then this year sort of a year

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of yes where I’m writing more content

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I’m writing newsletters and I wrote and

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published the book so the Kindle is live

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now um the hard copy pre- owners are on

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now but they officially get sent April

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30th and I’m super excited because we

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just found out last week that we got or

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that I got uh bestseller on Amazon Bens

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& Noble work in the book review so it’s

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doing well um it’s crazy because I

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didn’t I don’t know this process at all

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I’m still learning everything about

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books and content and things but

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apparently it’s resonating in a few

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different categories AI is one of them

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but starting a business leadership and

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management because there’s a lot of

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personal stories in there um so it’s

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been it’s been a fun ride this year is

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all about writing what I’ve learned the

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past 20 plus years and some of the

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things that I discovered while I was in

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the sea Suite position so very different

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very fun definitely it’s it’s resonating

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so so much because there is so much

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material still it it feels like it’s a

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new field but it’s not because AI has

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been um around for a while and you’ve

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been involved in even in Watson project

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right in um IBM so you know at thinger

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too um and right now from what I am uh

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seeing around in terms of books or

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materials there is a lot of theory but

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not so much experience and and um so

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that I guess that’s why people are

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looking for for

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somewhere learning advisor and I’m like

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well if you take a look at their entire

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history they just got into the space a

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year ago so I’m like okay they’re leing

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the way but um that’s why was important

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I think when I titled the book it was

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like scrape knees Bru yes exactly learn

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it wasn’t based off of I’m the best here

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my successes I’m like here are all the

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mistakes I made don’t make them it

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doesn’t work so I trying to like turn it

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around based on like practical exper

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experience so far so good it’s

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resonating well um but yeah not an easy

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process I so much credit to all the

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writers and authors out there I didn’t

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realize how difficult it was because

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it’s in your head but the process of

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articulating things that you’ve been

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doing um become second nature to you but

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putting that down on a piece of paper

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and in a flow and in a language that

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anyone can

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understand it’s it’s it’s definitely a

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process so I have great appreciation for

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all authors and writers yeah I I can

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only imagine how many revisions or how

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many changes you would have like you had

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to do uh for the publisher to feel okay

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this will resonate with all like people

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people will understand it like did you

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did you struggle with it like how how

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does it how did it look like my process

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was every morning from 4:30 a.m to 7:30

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a.m. if and I had multiple chapters I

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had themes of the chap so just titles I

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didn’t write sequential order I would

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just write as much as I could that day

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until I was depleted then the next day

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I’d pick the same chapter finish it

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until I was depleted so even though I

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didn’t write the chapters in order I

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picked a chapter and I just everything

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went on paper from like 4:30 to 7:30 and

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then um once it was all done then I

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picked chapter by chapter to go through

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editing and fine-tuning but when I

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finally got to a point where I said okay

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this is progress over Perfection I gave

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it to about five different people um a

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dear friend of mine Joe Reese who also

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wrote a book right a bestseller

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fundamentals of data engineering his

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book was really successful um Cindy HST

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I had one of my clients actually read it

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they own a digital agency firm they are

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not technical but they hired me to do

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some work for them and I was like Jason

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you’re like the prime target for this

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book tell me what doesn’t make sense I

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had another dear friend of mine from

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tribe AI his name was Noah he went

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through it um and then my husband who’s

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not technical whatsoever he went so I

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had five editors all from different

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walks of life go through and go does my

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language make sense does my thought

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process sense do the graphics make sense

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which stories do you like which stories

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do I need to shorten and through their

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critique then I re revised everything

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and then it was ready to go but I did

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the whole thing in like less than four

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months oh wow even more impressed yeah

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November was start February was drop

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dead turnning and I it that was just

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what I was focusing well it just had to

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it had to be done yeah that was that was

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it that was my main objective like and

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very very I can be very manically

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focused on things when I want to get it

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done done um so that was it four months

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it all edits reviews chapters everything

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it’s great that you went um specifically

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to people who are not technical but I

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think one of the biggest struggles

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in any kind of use cases and

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implementing technology is Tech Team not

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understanding business and vice versa I

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think that’s the hardest part that we

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have like book aside I think I was a PR

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practioner my first job and I don’t know

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if everyone knows a story or not but

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like I thought I was going to play

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professional sports my entire life I

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thought I was going to play rugby my

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entire life and then in my 20s I was

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like okay maybe I should pick a

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profession and not be in professional

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sports I just wasn’t recovering as fast

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and the first job I took was a data

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engineer um I could write code but I was

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horrible at it I didn’t enjoy it and my

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teammates even at some point time

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they’re like so you’re not allowed to

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touch a lick a coat again and I was like

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okay so what am I supposed to do so I

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was accept in the tribe but I wasn’t

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nearly as good as the developers that I

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had worked with and interestingly

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enough we were always getting requests

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from the business but no one from our

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team was communicating our work back to

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the business we had individuals who

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weren’t connected to our team doing that

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work and sometimes they got it sometimes

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they didn’t get it and so my career

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actually started being that translator

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because I wasn’t a great developer I

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didn’t know necessarily all the business

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jargon I didn’t know how to run a p&l I

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didn’t know any of that stuff so but I

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was better than the folks in the team

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and I found sort of my calling and then

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I got to know the business and it was

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around understanding what are their

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strategic priorities what are they

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aiming to achieve is it efficiency

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margins and evida or is it around growth

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strategies and markets and channels and

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I would just sit in some calls I took

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some courses to like how does one even

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run a financial Street and I just got a

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hold of the business language um and

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that was important to me and so what

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ended up happening was is I was

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connecting the dots with the great work

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that my teammates were doing with the

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dots of what the business needed and I

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just brought it together so imagine that

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like V diagram the two circles that

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don’t meet and then I just found that

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area in between and that was for a

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decade decade and a half you didn’t know

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what to call it it was a business

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analyst and it was a data analyst

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because you’re not called translator

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translator exactly and then I got into

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MDM then I became a tech lead then I was

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the primary lead on these Erp deployment

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and things just kind of Christmas treed

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its way up but Bridging the Gap between

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the worlds was my advantage I

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accidentally stepped into it but I

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immersed myself into it and I studied

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the business like no others then I

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started getting invited to the business

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meetings when I started getting invited

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invited that’s when the the pivot really

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happened because then I knew how they

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were talking then I knew what was

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important to them I already knew the

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work we were doing and then everything

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just converting that technical work into

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what was relatable for them and then mhm

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Game Changer yeah but I guess this is

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only already half half success was it

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difficult for you to convince um those

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uh decision makers to to try to risk um

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you know because there are lots of

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different factors at play right it’s not

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only uh if it’s efficient if it’s going

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to and potentially generate some money

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so when I was in management consulting

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and I was doing these big delivery

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projects the client had already

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subscribed or Chosen and I was with IBM

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and N to work with us so there wasn’t

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much

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selling um because they knew they needed

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it and they would bring us in I was

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purely in deployment and delivery so it

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00:11:14,040 –> 00:11:17,320
was a matter of just getting the job

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done and getting it done well and then

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making sure that I let them know where

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some the people calls were and making

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different decisions but it wasn’t until

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I got to the Sea Suite that I realized

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how much the business did not appreciate

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00:11:27,399 –> 00:11:32,920
data and analytics and and TX I’m gonna

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00:11:29,760 –> 00:11:34,760
use like an umbrella term um I know we

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00:11:32,920 –> 00:11:39,079
use words like Tech as an

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enabler um but part of that also means

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that it’s an afterthought here’s what I

305
00:11:40,920 –> 00:11:44,880
want to do go figure it out but they

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don’t want to be involved in the sausage

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making and they have to be involved in

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the sausage making because sometimes

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they may ask for something it’s not

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really what they want and then you go

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00:11:51,959 –> 00:11:55,040
off building something and you realize

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and they’re like that’s not what I

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wanted you’re like well this is what you

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said and put in all that effort and time

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and you kill yourself because you want

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to deliver a great product only to find

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00:12:02,639 –> 00:12:07,680
out that no they meant something else

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okay or the second is they may be asking

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00:12:07,680 –> 00:12:10,800
for something but if you have any

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00:12:09,360 –> 00:12:13,160
business sense you realize it’s actually

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00:12:10,800 –> 00:12:14,880
not what they need what they need is

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this and so you have to have that

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00:12:14,880 –> 00:12:19,040
dialogue with them or they may say that

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00:12:17,399 –> 00:12:20,839
we want to be a data driven company or

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we want to be data Centric AI Centric

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and use all the buzzword that they use

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with the executive leadership team or

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with the board of directors but what

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00:12:26,000 –> 00:12:29,360
does that really mean and is the

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workforce ready for that more than

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likely not because you have a lot of

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institutional workers um and so the SE

333
00:12:35,959 –> 00:12:41,199
Suite was actually where I made a lot of

334
00:12:38,320 –> 00:12:43,600
my mistakes especially my first role

335
00:12:41,199 –> 00:12:45,600
because I realize I can’t convince them

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because I know the space and I can’t

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00:12:45,600 –> 00:12:49,480
convince them because we’re going to

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00:12:46,800 –> 00:12:51,240
build really cool stuff I actually what

339
00:12:49,480 –> 00:12:53,440
I learned first and foremost in my first

340
00:12:51,240 –> 00:12:55,000
e gig was you have to build the

341
00:12:53,440 –> 00:12:57,360
relationships with them and ask for

342
00:12:55,000 –> 00:12:58,920
nothing in return and then once you

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build the relationships and you ask for

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something in return they’re willing to

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00:13:00,440 –> 00:13:04,959
give you a chance because they bought

346
00:13:01,720 –> 00:13:07,199
into you I had no idea that’s what this

347
00:13:04,959 –> 00:13:08,519
game was about um and I made that

348
00:13:07,199 –> 00:13:09,959
mistake I didn’t I thought just being

349
00:13:08,519 –> 00:13:11,600
smart was good enough and building full

350
00:13:09,959 –> 00:13:15,240
things were good enough and that was not

351
00:13:11,600 –> 00:13:18,040
the case I’m fortunate I’m an Ambi overt

352
00:13:15,240 –> 00:13:20,000
meaning I’m an extrovert I could work a

353
00:13:18,040 –> 00:13:21,800
room I can go into any social setting I

354
00:13:20,000 –> 00:13:24,199
don’t get nervous I enjoy talking to

355
00:13:21,800 –> 00:13:26,160
people but I do need to retreat to

356
00:13:24,199 –> 00:13:27,959
re-energize so building the

357
00:13:26,160 –> 00:13:30,720
relationships and making friends isn’t

358
00:13:27,959 –> 00:13:32,880
difficult for me so so thereafter I was

359
00:13:30,720 –> 00:13:35,079
able to do it but I learned from the

360
00:13:32,880 –> 00:13:37,079
first seite gig that I had that without

361
00:13:35,079 –> 00:13:39,199
the relationships doesn’t matter how

362
00:13:37,079 –> 00:13:40,800
good you are it’s just not part of their

363
00:13:39,199 –> 00:13:42,079
operating model or the business it’s not

364
00:13:40,800 –> 00:13:44,279
at their core and they’re not going to

365
00:13:42,079 –> 00:13:47,680
get it and if you do find that one

366
00:13:44,279 –> 00:13:51,120
unicorn steak holder it’s very very rare

367
00:13:47,680 –> 00:13:54,959
but going back to your book are there

368
00:13:51,120 –> 00:13:57,240
those kind of lessons and advice and is

369
00:13:54,959 –> 00:14:00,240
it included in your book as well for

370
00:13:57,240 –> 00:14:03,079
example are you providing any Frameworks

371
00:14:00,240 –> 00:14:05,759
uh for non-technical or maybe less

372
00:14:03,079 –> 00:14:08,920
extrovert uh people

373
00:14:05,759 –> 00:14:11,839
to you know who want to embrace Ai and

374
00:14:08,920 –> 00:14:14,759
who want to uh do something um but they

375
00:14:11,839 –> 00:14:18,040
are aware that they are not being heard

376
00:14:14,759 –> 00:14:21,079
yes that for sure

377
00:14:18,040 –> 00:14:25,079
um I think the challenge that we have is

378
00:14:21,079 –> 00:14:27,759
when you grow up in a very technical

379
00:14:25,079 –> 00:14:29,639
world you’re really good at coding and

380
00:14:27,759 –> 00:14:32,720
developing and

381
00:14:29,639 –> 00:14:34,759
and standards and and processes and

382
00:14:32,720 –> 00:14:38,560
procedures and protocols and things like

383
00:14:34,759 –> 00:14:40,839
that however um that’s when you want to

384
00:14:38,560 –> 00:14:42,959
scale something when need to evangelize

385
00:14:40,839 –> 00:14:44,959
something when you need to grow that

386
00:14:42,959 –> 00:14:47,199
capability those skill sets that you

387
00:14:44,959 –> 00:14:48,320
have in actually developing the product

388
00:14:47,199 –> 00:14:51,560
go by the

389
00:14:48,320 –> 00:14:55,240
wayside and so like even which product

390
00:14:51,560 –> 00:14:57,839
to build or which use case to pursue um

391
00:14:55,240 –> 00:14:59,440
I’ve had a lot of AI use cases come my

392
00:14:57,839 –> 00:15:00,800
way where they’re like we’re going to do

393
00:14:59,440 –> 00:15:02,440
this because it has the highest business

394
00:15:00,800 –> 00:15:03,920
value and I was like that’s not the one

395
00:15:02,440 –> 00:15:07,040
we should choose at all and they’re like

396
00:15:03,920 –> 00:15:09,079
well why not if we choose to do this we

397
00:15:07,040 –> 00:15:10,880
can manage it at a POC level but we’re

398
00:15:09,079 –> 00:15:12,600
going to get stuck in Perpetual POC

399
00:15:10,880 –> 00:15:14,600
Purgatory because we don’t have the

400
00:15:12,600 –> 00:15:16,480
infrastructure we don’t have the people

401
00:15:14,600 –> 00:15:18,040
we don’t have the talent and we can’t

402
00:15:16,480 –> 00:15:19,240
embed it in our existing operating

403
00:15:18,040 –> 00:15:21,240
models when we want to push it to

404
00:15:19,240 –> 00:15:23,880
production so it’s going to be forever

405
00:15:21,240 –> 00:15:25,560
stuck here so we should actually focus

406
00:15:23,880 –> 00:15:27,759
on these business cases so like one

407
00:15:25,560 –> 00:15:29,920
framework I teach is if you have

408
00:15:27,759 –> 00:15:32,680
influencer decision making on use Cas

409
00:15:29,920 –> 00:15:35,800
excuse me use cases to pursue never pick

410
00:15:32,680 –> 00:15:37,399
it based on business value because every

411
00:15:35,800 –> 00:15:39,680
use case has business value it depends

412
00:15:37,399 –> 00:15:42,000
on who’s presenting it there could be

413
00:15:39,680 –> 00:15:46,000
manufacturing team supply chain

414
00:15:42,000 –> 00:15:48,240
procurement finance a brand a lab if

415
00:15:46,000 –> 00:15:50,759
they have a problem and they want you to

416
00:15:48,240 –> 00:15:54,040
solve it there’s business value in that

417
00:15:50,759 –> 00:15:56,120
so no how are you going to mediate or be

418
00:15:54,040 –> 00:15:58,480
the neutralizer and determine which one

419
00:15:56,120 –> 00:16:00,920
takes priority and which one doesn’t you

420
00:15:58,480 –> 00:16:00,920
don’t want to

421
00:16:01,000 –> 00:16:03,720
hurt any of the potential relationships

422
00:16:02,440 –> 00:16:05,680
or hurt people’s feelings by not

423
00:16:03,720 –> 00:16:07,480
prioritizing it but at the same time

424
00:16:05,680 –> 00:16:09,279
they all have Merit and you can’t go

425
00:16:07,480 –> 00:16:11,000
well this one has a potential to grow

426
00:16:09,279 –> 00:16:12,920
but this one has the potential to

427
00:16:11,000 –> 00:16:14,800
increase our productivity you get in

428
00:16:12,920 –> 00:16:16,920
this weird

429
00:16:14,800 –> 00:16:19,160
subjective qu

430
00:16:16,920 –> 00:16:21,199
qualitative argument and it’s a bad

431
00:16:19,160 –> 00:16:23,560
place to be so what I learned along the

432
00:16:21,199 –> 00:16:26,319
way was you base a use case based on

433
00:16:23,560 –> 00:16:28,079
complexity and criticality criticality

434
00:16:26,319 –> 00:16:30,560
of the organizations there’s Financial

435
00:16:28,079 –> 00:16:33,000
consequences and finds there’s PR

436
00:16:30,560 –> 00:16:35,079
threats there’s competitive threats

437
00:16:33,000 –> 00:16:36,920
market share is shrinking right there’s

438
00:16:35,079 –> 00:16:38,880
there’s things to look at and that’s in

439
00:16:36,920 –> 00:16:41,040
the toolkit but then there’s also the

440
00:16:38,880 –> 00:16:42,759
complexity to deploy and there’s five or

441
00:16:41,040 –> 00:16:43,600
six markers to take a look at of do you

442
00:16:42,759 –> 00:16:44,880
have the following in your

443
00:16:43,600 –> 00:16:46,959
infrastructure do you have the following

444
00:16:44,880 –> 00:16:49,839
in your data not all data is created

445
00:16:46,959 –> 00:16:52,040
equal there’s different tiers and don’t

446
00:16:49,839 –> 00:16:53,880
expect your data to be perfect it never

447
00:16:52,040 –> 00:16:55,079
is but how do you pick the tiers that

448
00:16:53,880 –> 00:16:57,319
are actually going to give you the

449
00:16:55,079 –> 00:16:59,800
ability to be as successful as possible

450
00:16:57,319 –> 00:17:02,079
with the state of the current hygiene

451
00:16:59,800 –> 00:17:03,759
um and then the talent so and then

452
00:17:02,079 –> 00:17:05,520
there’s a way to plot it against a

453
00:17:03,759 –> 00:17:08,319
quadrant and then how to pick which ones

454
00:17:05,520 –> 00:17:11,000
to prioritize so how to select a use

455
00:17:08,319 –> 00:17:13,480
case what even goes into a data strategy

456
00:17:11,000 –> 00:17:16,120
what goes into an AI strategy or if

457
00:17:13,480 –> 00:17:20,199
you’re ready what process is part of

458
00:17:16,120 –> 00:17:23,520
planning and design and solutioning and

459
00:17:20,199 –> 00:17:25,039
deploying um I have a funny chapter just

460
00:17:23,520 –> 00:17:26,760
focused on what I call the project

461
00:17:25,039 –> 00:17:29,280
killer and that’s your

462
00:17:26,760 –> 00:17:31,240
team you have the luxury of building a

463
00:17:29,280 –> 00:17:33,120
team what should you look for key

464
00:17:31,240 –> 00:17:35,600
characteristics and then how do you map

465
00:17:33,120 –> 00:17:37,080
them out on a will and skill Matrix they

466
00:17:35,600 –> 00:17:39,120
have the skill set and they’re willing

467
00:17:37,080 –> 00:17:40,640
to do the work if you have someone who

468
00:17:39,120 –> 00:17:42,240
has a skill set but not willing to do

469
00:17:40,640 –> 00:17:44,320
the work you got to make organizational

470
00:17:42,240 –> 00:17:45,960
changes but if you have someone willing

471
00:17:44,320 –> 00:17:48,000
to do the work doesn’t have the skill

472
00:17:45,960 –> 00:17:49,720
set but yet it’s coachable coach them

473
00:17:48,000 –> 00:17:52,360
they’re still an asset that’s if you’re

474
00:17:49,720 –> 00:17:54,160
building but if you’re inheriting a team

475
00:17:52,360 –> 00:17:56,080
there are these classic archetypes that

476
00:17:54,160 –> 00:17:59,240
keep showing up the know-it-all the

477
00:17:56,080 –> 00:18:01,400
naysayer the cheerleader the evangelist

478
00:17:59,240 –> 00:18:03,799
every um and there’s about 10 that I’ve

479
00:18:01,400 –> 00:18:05,240
outlined saying everyone can serve a

480
00:18:03,799 –> 00:18:07,320
purpose especially in large

481
00:18:05,240 –> 00:18:09,360
organizations where you may not be able

482
00:18:07,320 –> 00:18:11,480
to make changes and you’re forced to

483
00:18:09,360 –> 00:18:13,080
work with people where do you embed them

484
00:18:11,480 –> 00:18:15,000
into the process so you can work with

485
00:18:13,080 –> 00:18:17,760
them so you can reduce the

486
00:18:15,000 –> 00:18:19,000
frustrations um so just stuff like that

487
00:18:17,760 –> 00:18:20,760
and then there’s a lot of technical

488
00:18:19,000 –> 00:18:23,919
stuff in there too but in business

489
00:18:20,760 –> 00:18:26,000
language like I outline 10 Industries

490
00:18:23,919 –> 00:18:28,360
and 10 functions and give three use case

491
00:18:26,000 –> 00:18:29,520
in each industry and three use case in

492
00:18:28,360 –> 00:18:32,360
each function

493
00:18:29,520 –> 00:18:34,760
so you get 30 and 30 so there’s 60

494
00:18:32,360 –> 00:18:37,400
actual AI applications that are in

495
00:18:34,760 –> 00:18:39,080
market today um and that’s just to

496
00:18:37,400 –> 00:18:41,320
inspire and get us to think outside of

497
00:18:39,080 –> 00:18:43,919
our box around content

498
00:18:41,320 –> 00:18:47,360
creation right just generative AI stuff

499
00:18:43,919 –> 00:18:49,600
out there so it’s all over the stuff um

500
00:18:47,360 –> 00:18:52,320
but I’d like to think that non technical

501
00:18:49,600 –> 00:18:55,240
people could actually start so it helps

502
00:18:52,320 –> 00:18:59,200
to select the theme it helps um it can

503
00:18:55,240 –> 00:19:02,960
help people to find the um the owner of

504
00:18:59,200 –> 00:19:05,360
the project as well and the enabler

505
00:19:02,960 –> 00:19:08,559
enablers yeah and there there’s just a

506
00:19:05,360 –> 00:19:11,559
lot of howtos like if you’re going board

507
00:19:08,559 –> 00:19:13,799
or an executive team or

508
00:19:11,559 –> 00:19:17,360
investors how do you prepare what do you

509
00:19:13,799 –> 00:19:19,080
need to do like Bic stuff um but even if

510
00:19:17,360 –> 00:19:20,679
you’re a business owner like one of the

511
00:19:19,080 –> 00:19:24,039
examples I had was I worked with a

512
00:19:20,679 –> 00:19:25,559
business owner he was a CEO of a $250

513
00:19:24,039 –> 00:19:29,000
million

514
00:19:25,559 –> 00:19:31,640
um Revenue producing company they don’t

515
00:19:29,000 –> 00:19:34,480
have technology in their backbone

516
00:19:31,640 –> 00:19:36,120
whatsoever but they want to do AI so

517
00:19:34,480 –> 00:19:37,799
it’s also like not everything’s about

518
00:19:36,120 –> 00:19:39,400
building foundational models and not

519
00:19:37,799 –> 00:19:41,360
everything’s about models and algorithms

520
00:19:39,400 –> 00:19:43,120
there’s so many off-the-shelf solutions

521
00:19:41,360 –> 00:19:44,720
out there that are either free

522
00:19:43,120 –> 00:19:47,960
extensions that can either provide you

523
00:19:44,720 –> 00:19:49,320
immediate value um or you can try trial

524
00:19:47,960 –> 00:19:51,840
basis and see if it’s something you can

525
00:19:49,320 –> 00:19:54,760
integrate I make recommendations I

526
00:19:51,840 –> 00:19:56,480
wasn’t sponsored or anything so I picked

527
00:19:54,760 –> 00:19:58,039
10 different categories and I made off

528
00:19:56,480 –> 00:20:00,880
the-shelf recommendations of just get

529
00:19:58,039 –> 00:20:04,159
started and or some easy to use ones the

530
00:20:00,880 –> 00:20:05,559
whole point is start you can’t perfect

531
00:20:04,159 –> 00:20:07,480
the first time around so whether you’re

532
00:20:05,559 –> 00:20:09,080
a business owner that doesn’t have a

533
00:20:07,480 –> 00:20:11,799
technical bone in your body and you want

534
00:20:09,080 –> 00:20:13,080
people to absorb AI here’s what to do or

535
00:20:11,799 –> 00:20:15,400
if you’re a technical person and you

536
00:20:13,080 –> 00:20:17,360
want to evangelize and scale all the AI

537
00:20:15,400 –> 00:20:19,120
capabilities you’ve built here’s how you

538
00:20:17,360 –> 00:20:21,000
follow the strategy and the processes

539
00:20:19,120 –> 00:20:23,520
and how to present how do you build a

540
00:20:21,000 –> 00:20:26,440
team so it’s i’ like to think it covers

541
00:20:23,520 –> 00:20:30,960
the Spectrum but by no means a

542
00:20:26,440 –> 00:20:35,919
theoretical futuris Tech deep dive

543
00:20:30,960 –> 00:20:39,440
what there is out there yeah do you also

544
00:20:35,919 –> 00:20:43,120
help this I guess the the almost

545
00:20:39,440 –> 00:20:44,640
impossible tasks but do you help to um

546
00:20:43,120 –> 00:20:48,320
spot

547
00:20:44,640 –> 00:20:50,360
early just hyped uh products which have

548
00:20:48,320 –> 00:20:54,400
little in common

549
00:20:50,360 –> 00:20:57,000
with a on you

550
00:20:54,400 –> 00:20:58,799
know I have this running joke I have

551
00:20:57,000 –> 00:21:00,520
about seven different music news

552
00:20:58,799 –> 00:21:03,840
letterss both public and private that

553
00:21:00,520 –> 00:21:05,760
get sent to me on a daily basis of the

554
00:21:03,840 –> 00:21:08,200
top AI tools or AI companies that were

555
00:21:05,760 –> 00:21:09,400
born that day and also the top AI

556
00:21:08,200 –> 00:21:11,480
companies that

557
00:21:09,400 –> 00:21:14,559
unfortunately

558
00:21:11,480 –> 00:21:17,200
um are not alive today because they ran

559
00:21:14,559 –> 00:21:21,240
out of funding and so keeping track of

560
00:21:17,200 –> 00:21:22,799
that fluctuation is intense um but

561
00:21:21,240 –> 00:21:25,000
there’s some stuff in the book as well

562
00:21:22,799 –> 00:21:26,919
that addresses that um and and how do

563
00:21:25,000 –> 00:21:28,880
you avoid the risk of investing in a

564
00:21:26,919 –> 00:21:32,279
company without being over really

565
00:21:28,880 –> 00:21:34,159
invested because the longevity of of

566
00:21:32,279 –> 00:21:35,360
these companies there’s just so many

567
00:21:34,159 –> 00:21:38,279
born right now in the

568
00:21:35,360 –> 00:21:41,919
marketplace yes

569
00:21:38,279 –> 00:21:46,640
yeah we will see how everything unfolds

570
00:21:41,919 –> 00:21:48,600
so do you also um help to understand

571
00:21:46,640 –> 00:21:51,679
what are the common like

572
00:21:48,600 –> 00:21:55,159
misconceptions um Executives um have

573
00:21:51,679 –> 00:21:57,240
about AI capabilities and

574
00:21:55,159 –> 00:21:59,679
limitations yeah one of the things that

575
00:21:57,240 –> 00:22:03,480
I’ve had the advantage of doing since

576
00:21:59,679 –> 00:22:05,720
2011 um I owe a lot to IBM and giving me

577
00:22:03,480 –> 00:22:06,880
this opportunity and an old old old boss

578
00:22:05,720 –> 00:22:11,039
of mine named

579
00:22:06,880 –> 00:22:13,600
jao um I’ve always had the opportunity

580
00:22:11,039 –> 00:22:15,840
to present to either the board or SE

581
00:22:13,600 –> 00:22:18,600
level Executives of what AI is and what

582
00:22:15,840 –> 00:22:20,559
AI is not and like we all determine it

583
00:22:18,600 –> 00:22:22,440
as artificial intelligence but with a

584
00:22:20,559 –> 00:22:26,600
lot of the capabilities that existed in

585
00:22:22,440 –> 00:22:29,120
2011 12 up until now my humble opinion

586
00:22:26,600 –> 00:22:32,039
is anyone who’s deploying any sort of

587
00:22:29,120 –> 00:22:33,559
AI it’s not artificial intelligence it’s

588
00:22:32,039 –> 00:22:35,520
not artificial at this point in time

589
00:22:33,559 –> 00:22:38,360
there’s automated intelligence there’s

590
00:22:35,520 –> 00:22:40,440
augmented intelligence there’s

591
00:22:38,360 –> 00:22:42,840
anticipatory intelligence right so all

592
00:22:40,440 –> 00:22:45,760
the predictive models there’s automation

593
00:22:42,840 –> 00:22:47,679
to reduce you know remedial repetitive

594
00:22:45,760 –> 00:22:50,039
easy tasks and there’s augmentation to

595
00:22:47,679 –> 00:22:52,159
be able to help knowledge workers or

596
00:22:50,039 –> 00:22:54,360
consumers chat Bots and things like that

597
00:22:52,159 –> 00:22:58,000
but I haven’t come across a single

598
00:22:54,360 –> 00:23:00,400
application that’s artificial in nature

599
00:22:58,000 –> 00:23:03,840
um everything’s been focused on

600
00:23:00,400 –> 00:23:05,799
augmented automated or anticipatory and

601
00:23:03,840 –> 00:23:07,600
so part of it is also explaining how do

602
00:23:05,799 –> 00:23:09,919
you even Define something that’s AI

603
00:23:07,600 –> 00:23:11,760
versus it’s not and a lot of compies are

604
00:23:09,919 –> 00:23:13,000
putting the AI brand and logo and

605
00:23:11,760 –> 00:23:15,320
they’re going to charge you a premium

606
00:23:13,000 –> 00:23:17,440
for it there’s also section on what it

607
00:23:15,320 –> 00:23:19,880
is and what it isn’t and then how not to

608
00:23:17,440 –> 00:23:21,880
fall victim to just someone slapping an

609
00:23:19,880 –> 00:23:24,799
AI logo and saying it’s AI based

610
00:23:21,880 –> 00:23:26,440
actuality it’s not um but because I’ve

611
00:23:24,799 –> 00:23:29,000
had the opportunity of presenting to so

612
00:23:26,440 –> 00:23:30,600
many different boards and executives um

613
00:23:29,000 –> 00:23:32,400
I’ve got that language down now and I’ve

614
00:23:30,600 –> 00:23:35,000
got some fun little cartoons and

615
00:23:32,400 –> 00:23:36,760
examples and scenarios that I just put

616
00:23:35,000 –> 00:23:38,720
those into the conversation and it’s and

617
00:23:36,760 –> 00:23:40,400
it’s an easy and fun sometimes a

618
00:23:38,720 –> 00:23:43,120
whiteboarding session sometimes just a

619
00:23:40,400 –> 00:23:45,720
discussion in your background you had a

620
00:23:43,120 –> 00:23:48,720
chance to work for large organizations

621
00:23:45,720 –> 00:23:53,159
and and also um you worked with

622
00:23:48,720 –> 00:23:56,039
disruptive uh startups and what would

623
00:23:53,159 –> 00:23:59,039
you say are the key uh similarities and

624
00:23:56,039 –> 00:24:03,120
differences uh you have seen in

625
00:23:59,039 –> 00:24:05,880
those two let’s say different uh worlds

626
00:24:03,120 –> 00:24:05,880
and deploying

627
00:24:06,320 –> 00:24:11,440
AI similarities both environments are

628
00:24:11,640 –> 00:24:16,679
chaotic being in a larger Enterprise

629
00:24:14,840 –> 00:24:18,880
that things are more streamlined they’re

630
00:24:16,679 –> 00:24:21,279
more process driven and and they’re not

631
00:24:18,880 –> 00:24:23,520
if anything they’re larger more

632
00:24:21,279 –> 00:24:27,440
complicated Matrix and there’s more

633
00:24:23,520 –> 00:24:29,039
human challenges to deal with so chaotic

634
00:24:27,440 –> 00:24:30,600
but when you’re in a startup and I had

635
00:24:29,039 –> 00:24:32,559
the pleasure of starting my own company

636
00:24:30,600 –> 00:24:35,080
a long time ago working at a startup and

637
00:24:32,559 –> 00:24:37,679
now advising startups it’s also chaotic

638
00:24:35,080 –> 00:24:39,640
you have no processes it’s lean and mean

639
00:24:37,679 –> 00:24:41,480
you’re the plumber the janitor the

640
00:24:39,640 –> 00:24:43,039
developer and the CEO and you’re

641
00:24:41,480 –> 00:24:44,760
creating a pitch deck to investor so you

642
00:24:43,039 –> 00:24:48,919
can get your seed R funding all the same

643
00:24:44,760 –> 00:24:50,960
time it’s also chaotic um so both

644
00:24:48,919 –> 00:24:54,559
environments are chaotic there’s there’s

645
00:24:50,960 –> 00:24:59,399
no green grass on one side versus the

646
00:24:54,559 –> 00:25:02,559
other I think with Enterprises you get

647
00:24:59,399 –> 00:25:04,559
the security of a paycheck you get the

648
00:25:02,559 –> 00:25:05,640
wonderful support of a brand and a logo

649
00:25:04,559 –> 00:25:09,120
especially if you believe in their

650
00:25:05,640 –> 00:25:10,919
missions and values um you get to work

651
00:25:09,120 –> 00:25:13,039
with some smart peers and you get to

652
00:25:10,919 –> 00:25:14,360
work with some really not smart peers

653
00:25:13,039 –> 00:25:17,440
and you wonder how they even got the job

654
00:25:14,360 –> 00:25:19,880
to begin with you really get a great

655
00:25:17,440 –> 00:25:22,399
sense of the workforce because not

656
00:25:19,880 –> 00:25:25,960
everyone is there to make an impact have

657
00:25:22,399 –> 00:25:27,840
a purpose and do great things it is so

658
00:25:25,960 –> 00:25:30,279
clearly obvious who shows up to get a

659
00:25:27,840 –> 00:25:32,120
paycheck it’s so clearly obvious and

660
00:25:30,279 –> 00:25:35,360
when you’re not that person it

661
00:25:32,120 –> 00:25:37,600
fundamentally goes against your DNA um

662
00:25:35,360 –> 00:25:40,960
and that’s the hard part

663
00:25:37,600 –> 00:25:42,760
because showing up is good enough in the

664
00:25:40,960 –> 00:25:44,440
Enterprise world and when you’re leading

665
00:25:42,760 –> 00:25:46,919
a transformation you’re trying to do

666
00:25:44,440 –> 00:25:48,399
something different or you’re trying to

667
00:25:46,919 –> 00:25:50,600
have an impact in an area that they’ve

668
00:25:48,399 –> 00:25:52,600
been struggling in the past it’s really

669
00:25:50,600 –> 00:25:55,399
really hard to work with those

670
00:25:52,600 –> 00:25:57,960
individuals who are just not motivated

671
00:25:55,399 –> 00:25:58,960
and just don’t have the and you have no

672
00:25:57,960 –> 00:26:00,440
choice

673
00:25:58,960 –> 00:26:02,039
so that’s where a lot of the frustration

674
00:26:00,440 –> 00:26:03,720
is I think the other aspect of it is

675
00:26:02,039 –> 00:26:05,760
because it is that mixed bag of nuts of

676
00:26:03,720 –> 00:26:07,720
high performers and study State

677
00:26:05,760 –> 00:26:10,039
performers and low performers things

678
00:26:07,720 –> 00:26:12,720
move very slow decision- making takes

679
00:26:10,039 –> 00:26:14,880
time so I naturally work at a very

680
00:26:12,720 –> 00:26:16,480
different speed than Enterprise um so I

681
00:26:14,880 –> 00:26:18,640
always have to slow down to meet their

682
00:26:16,480 –> 00:26:21,480
speed sometimes I’m successful and

683
00:26:18,640 –> 00:26:23,159
sometimes I’m not in the startup World

684
00:26:21,480 –> 00:26:25,320
however it’s very different in the sense

685
00:26:23,159 –> 00:26:26,120
that you have purpose you have impact

686
00:26:25,320 –> 00:26:28,520
you don’t know if you’re going to be

687
00:26:26,120 –> 00:26:31,600
around in a year or two and who you

688
00:26:28,520 –> 00:26:34,120
bring on board your circle of five are

689
00:26:31,600 –> 00:26:37,320
the most critical hires the most

690
00:26:34,120 –> 00:26:41,840
critical critical hires so you may Bond

691
00:26:37,320 –> 00:26:44,120
and you may um build that tribe but

692
00:26:41,840 –> 00:26:46,600
every person has such heavy weight and

693
00:26:44,120 –> 00:26:49,200
how well this company moves forward that

694
00:26:46,600 –> 00:26:53,840

there is an inherent risk on the outside

695
00:26:49,200 –> 00:26:56,000
so and the part that I’m least Comm of

696
00:26:53,840 –> 00:26:57,760
is you’re always in a selling mode

697
00:26:56,000 –> 00:27:00,320
because you’re competing every other

698
00:26:57,760 –> 00:27:01,679
startup every other scale of out there

699
00:27:00,320 –> 00:27:03,720
and so you really have to rely on your

700
00:27:01,679 –> 00:27:07,200
network and your investor Community to

701
00:27:03,720 –> 00:27:09,480
be able to give you a leg um to stand on

702
00:27:07,200 –> 00:27:13,240
so both have its good and bads if you

703
00:27:09,480 –> 00:27:16,000
will I I love both environments um for

704
00:27:13,240 –> 00:27:19,080
my future I I don’t I never say no to

705
00:27:16,000 –> 00:27:21,240
anything but for me it’s more it’s not

706
00:27:19,080 –> 00:27:24,200
about the work anymore been there done

707
00:27:21,240 –> 00:27:25,919
that I know it about who I work with and

708
00:27:24,200 –> 00:27:26,720
so my peers and my leadership team are

709
00:27:25,919 –> 00:27:28,840
probably going to be the most

710
00:27:26,720 –> 00:27:32,000
influential Factor on where I work

711
00:27:28,840 –> 00:27:33,159
because I can create anywhere I go um

712
00:27:32,000 –> 00:27:34,440
matter of who gives me the runway and

713
00:27:33,159 –> 00:27:36,679
clears the runway for me to be able to

714
00:27:34,440 –> 00:27:38,320
create an evangelist so it’s who I work

715
00:27:36,679 –> 00:27:41,840
with not what I’m

716
00:27:38,320 –> 00:27:46,440
doing it’s the perfect um perfect um

717
00:27:41,840 –> 00:27:49,559
reason to for for working in general and

718
00:27:46,440 –> 00:27:53,760
uh in everything you do and you said and

719
00:27:49,559 –> 00:27:57,080
your ethos um emphasizes uh pragmatism

720
00:27:53,760 –> 00:27:59,159
right so your philosophy as I read uh

721
00:27:57,080 –> 00:28:00,159
states that not everything needs to be

722
00:27:59,159 –> 00:28:04,440
bleeding

723
00:28:00,159 –> 00:28:08,240
edge so how can companies balanc desire

724
00:28:04,440 –> 00:28:11,200
to be a Cutting Edge and with achieving

725
00:28:08,240 –> 00:28:13,679
solid business uh outcomes and

726
00:28:11,200 –> 00:28:15,919
Foundations there is so much loow

727
00:28:13,679 –> 00:28:19,799
hanging

728
00:28:15,919 –> 00:28:21,679
fruit that like below Leading Edge and

729
00:28:19,799 –> 00:28:23,919
being Innovative sorry below being

730
00:28:21,679 –> 00:28:26,200
Cutting Edge and being Innovative

731
00:28:23,919 –> 00:28:29,120
Innovation is great I love discovering

732
00:28:26,200 –> 00:28:31,480
new things I love building new things

733
00:28:29,120 –> 00:28:34,240
but a lot of the work that still needs

734
00:28:31,480 –> 00:28:36,559
to be done is foundational is

735
00:28:34,240 –> 00:28:38,519
fundamental it’s streamlining certain

736
00:28:36,559 –> 00:28:40,559
things it’s taking that group of six

737
00:28:38,519 –> 00:28:43,640
people that have all the knowledge in

738
00:28:40,559 –> 00:28:45,840
their heads that’s never been documented

739
00:28:43,640 –> 00:28:47,640
that follow a process based on what I

740
00:28:45,840 –> 00:28:49,360
call sneaker net and not the internet

741
00:28:47,640 –> 00:28:52,279
they don’t use workflows they just use

742
00:28:49,360 –> 00:28:54,760
email to talk to each other um it’s

743
00:28:52,279 –> 00:28:56,919
inefficient it’s unproductive it’s not

744
00:28:54,760 –> 00:28:59,039
streamlined and you would be amazed at

745
00:28:56,919 –> 00:29:02,279
how many companies have made it to 1

746
00:28:59,039 –> 00:29:04,760
billion 2 billion 15 billion 40 billion

747
00:29:02,279 –> 00:29:06,480
based off of sneaker net not internet

748
00:29:04,760 –> 00:29:09,039
some of it because they have a product

749
00:29:06,480 –> 00:29:10,240
that you can’t live without some of it’s

750
00:29:09,039 –> 00:29:11,760
because they have a product that’s

751
00:29:10,240 –> 00:29:13,840
gotten great traction because there’s

752
00:29:11,760 –> 00:29:15,200
heavy verality on social media

753
00:29:13,840 –> 00:29:18,000
regardless of the

754
00:29:15,200 –> 00:29:19,960
reason there’s so much improvement to be

755
00:29:18,000 –> 00:29:21,679
made to streamline processes that I’m

756
00:29:19,960 –> 00:29:23,600
like if you don’t have the rigor you

757
00:29:21,679 –> 00:29:25,000
don’t have the maturity technolog is not

758
00:29:23,600 –> 00:29:26,480
at the core but sometimes an

759
00:29:25,000 –> 00:29:30,159
afterthought or you considered an

760
00:29:26,480 –> 00:29:32,720
enabler but not a major facilitator in

761
00:29:30,159 –> 00:29:36,159
making your business

762
00:29:32,720 –> 00:29:38,600
happen being bleeding edge sounds great

763
00:29:36,159 –> 00:29:41,159
thinking of the art of the possible is

764
00:29:38,600 –> 00:29:43,080
fantastic but your deployment needs to

765
00:29:41,159 –> 00:29:45,399
be based on the art of the Practical you

766
00:29:43,080 –> 00:29:47,480
have to take into consideration the

767
00:29:45,399 –> 00:29:48,440
workforce that you have at hand here’s

768
00:29:47,480 –> 00:29:51,600
here’s my

769
00:29:48,440 –> 00:29:54,200
example um moving forward like in my

770
00:29:51,600 –> 00:29:56,320
interviews my first question is what’s

771
00:29:54,200 –> 00:30:00,039
the average 10e ship of an employee and

772
00:29:56,320 –> 00:30:00,039
do you offer a pension fund

773
00:30:00,279 –> 00:30:05,200
why would I ask those two questions

774
00:30:02,640 –> 00:30:07,399
because if they have a pension fund I

775
00:30:05,200 –> 00:30:09,039
automatically know people don’t leave

776
00:30:07,399 –> 00:30:11,200
they’re sticking around want to get

777
00:30:09,039 –> 00:30:16,720
their pension those that have been there

778
00:30:11,200 –> 00:30:19,200
15 20 25 30 years how up todate and

779
00:30:16,720 –> 00:30:21,159
Innovative can they honestly like being

780
00:30:19,200 –> 00:30:23,480
realistic can they really be they know

781
00:30:21,159 –> 00:30:24,840
one environment and one environment only

782
00:30:23,480 –> 00:30:27,240
there are no benchmarks there’s no

783
00:30:24,840 –> 00:30:29,880
compare and contrast um and the

784
00:30:27,240 –> 00:30:31,600
organization values that which naturally

785
00:30:29,880 –> 00:30:35,039
means that the way that company can

786
00:30:31,600 –> 00:30:38,519
Excel improve margins eida be put up for

787
00:30:35,039 –> 00:30:41,320
sale be acquired or continue to grow

788
00:30:38,519 –> 00:30:45,080
Topline revenue is great but there’s so

789
00:30:41,320 –> 00:30:46,919
much margin to be made and the processes

790
00:30:45,080 –> 00:30:48,799
because you’ve got a Workforce that

791
00:30:46,919 –> 00:30:50,679
hasn’t necessarily been exposed to what

792
00:30:48,799 –> 00:30:53,679
streamline processes look like only the

793
00:30:50,679 –> 00:30:56,159
processes they know of so I can tell an

794
00:30:53,679 –> 00:30:58,200
organization’s maturity and how much

795
00:30:56,159 –> 00:31:00,039
quotequote bleeding edge capability are

796
00:30:58,200 –> 00:31:01,200
able to do and absorb just based on

797
00:31:00,039 –> 00:31:03,720
asking the question do you have a

798
00:31:01,200 –> 00:31:06,240
pension fund plan and the average 10

799
00:31:03,720 –> 00:31:08,080
years of an employee I now know that

800
00:31:06,240 –> 00:31:09,960
that organization is going to be mostly

801
00:31:08,080 –> 00:31:12,559
process optimization and that low

802
00:31:09,960 –> 00:31:15,320
hanging fruit assuming there’s a mandate

803
00:31:12,559 –> 00:31:16,799
top down otherwise you’re threatening

804
00:31:15,320 –> 00:31:18,760
the uh knowledge workers that have been

805
00:31:16,799 –> 00:31:20,000
doing the same thing the same way for a

806
00:31:18,760 –> 00:31:22,399
really long

807
00:31:20,000 –> 00:31:23,799
time Innovation doesn’t necessarily come

808
00:31:22,399 –> 00:31:24,960
from being bleeding edge I think

809
00:31:23,799 –> 00:31:26,440
sometimes just cutting through the noise

810
00:31:24,960 –> 00:31:28,760
and being a lot more efficient in doing

811
00:31:26,440 –> 00:31:31,880
things so you can do more of it

812
00:31:28,760 –> 00:31:33,600
true true have you have you ever had a

813
00:31:31,880 –> 00:31:38,600
chance to work with Japanese

814
00:31:33,600 –> 00:31:41,960
organizations they they operate in a

815
00:31:38,600 –> 00:31:44,559
similar in similar way in terms of

816
00:31:41,960 –> 00:31:49,760
preserving what they do best and there

817
00:31:44,559 –> 00:31:52,159
are some of the longest uh uh like yes

818
00:31:49,760 –> 00:31:54,919
low longest standing companies so

819
00:31:52,159 –> 00:31:56,639
interestingly Sony Music owned by Sony

820
00:31:54,919 –> 00:31:58,799
is a japanese-based

821
00:31:56,639 –> 00:32:00,399
company um

822
00:31:58,799 –> 00:32:02,320
but of course it’s a music industry so

823
00:32:00,399 –> 00:32:03,960
you get your own spin off of it and it

824
00:32:02,320 –> 00:32:06,679
it definitely operates as a very

825
00:32:03,960 –> 00:32:09,279
separate entity it was weird because I

826
00:32:06,679 –> 00:32:12,799
worked at the music label there were

827
00:32:09,279 –> 00:32:15,200
elements of the Japanese culture that

828
00:32:12,799 –> 00:32:16,559
spilled through that we had to respect

829
00:32:15,200 –> 00:32:19,679
and then there were elements where we

830
00:32:16,559 –> 00:32:22,279
could just be a a Rowdy bunch and be a

831
00:32:19,679 –> 00:32:23,480
music label I worked for someone who

832
00:32:22,279 –> 00:32:27,000
believed that you had to be in the

833
00:32:23,480 –> 00:32:29,200
office Monday through Friday 9 to 6 and

834
00:32:27,000 –> 00:32:32,320
I was I was living in Miami commuting to

835
00:32:29,200 –> 00:32:35,480
New York um when I first took the job

836
00:32:32,320 –> 00:32:38,000
and I said is that mandatory because the

837
00:32:35,480 –> 00:32:40,480
best coders the best developers the best

838
00:32:38,000 –> 00:32:44,039
data scientists that I know don’t work

839
00:32:40,480 –> 00:32:46,200
nine to six they kind of have their own

840
00:32:44,039 –> 00:32:47,600
Rhythm they have their own Cadence now

841
00:32:46,200 –> 00:32:49,960
of course they would be a part of Team

842
00:32:47,600 –> 00:32:52,360
meetings and outings and celebrations

843
00:32:49,960 –> 00:32:53,960
and status updates but they do their

844
00:32:52,360 –> 00:32:56,639
best work at night and they sleep in in

845
00:32:53,960 –> 00:32:58,559
the morning so I don’t necessarily want

846
00:32:56,639 –> 00:32:59,840
the people that are 9 to6 I want them to

847
00:32:58,559 –> 00:33:02,200
be able to have their own schedule

848
00:32:59,840 –> 00:33:03,960
produce the quality work they’re like no

849
00:33:02,200 –> 00:33:05,279
and I remember my boss was like no no no

850
00:33:03,960 –> 00:33:07,399
absolutely and you have to be in the

851
00:33:05,279 –> 00:33:08,880
office too you have to set the example

852
00:33:07,399 –> 00:33:10,080
and you have to be here before they come

853
00:33:08,880 –> 00:33:13,320
and you have to stay here after they

854
00:33:10,080 –> 00:33:16,039
come that’s the Japanese way the other

855
00:33:13,320 –> 00:33:18,880
aspect that was filling in was um when I

856
00:33:16,039 –> 00:33:21,080
worked for that company um I could not

857
00:33:18,880 –> 00:33:23,080
do any conferences I could not do any

858
00:33:21,080 –> 00:33:26,240
public speaking I could not do any

859
00:33:23,080 –> 00:33:28,480
articles newsletters blogs nothing so

860
00:33:26,240 –> 00:33:30,679
when I talk about I was doing the work

861
00:33:28,480 –> 00:33:32,919
not writing the book A lot of it started

862
00:33:30,679 –> 00:33:35,360
from here um because they had a rule

863
00:33:32,919 –> 00:33:37,320
that says we are a very modest culture

864
00:33:35,360 –> 00:33:39,480
we don’t brag about our subject matter

865
00:33:37,320 –> 00:33:41,960
expertise and our knowledge and so it

866
00:33:39,480 –> 00:33:44,559
was we were not allowed to broadcast

867
00:33:41,960 –> 00:33:46,960
anything our personal brand our personal

868
00:33:44,559 –> 00:33:48,720
thought leadership the ironic thing is

869
00:33:46,960 –> 00:33:50,000
they discovered me and found me because

870
00:33:48,720 –> 00:33:53,159
someone recommended me because they

871
00:33:50,000 –> 00:33:54,919
heard me speak at a conference so they

872
00:33:53,159 –> 00:33:57,840
heard me speak but then they weren’t

873
00:33:54,919 –> 00:34:01,480
allowing me to speak so it’s a very

874
00:33:57,840 –> 00:34:03,679
modest be humble Hush Hush culture and

875
00:34:01,480 –> 00:34:06,600
we have a legacy there’s a way to be

876
00:34:03,679 –> 00:34:10,280
seen and we have to respect that so some

877
00:34:06,600 –> 00:34:12,240
of that stuff the methodologies the uh

878
00:34:10,280 –> 00:34:15,040
the very strict way of being and showing

879
00:34:12,240 –> 00:34:16,639
up those things definitely spilled over

880
00:34:15,040 –> 00:34:19,200
um but the labels themselves they could

881
00:34:16,639 –> 00:34:21,960
do whatever they wanted it was the rest

882
00:34:19,200 –> 00:34:23,760
of us to follow those rules so yeah they

883
00:34:21,960 –> 00:34:27,520
are very process oriented they’re very

884
00:34:23,760 –> 00:34:29,599
diligent they’re very proper um but I

885
00:34:27,520 –> 00:34:32,720
feel feel it also sometimes lacks

886
00:34:29,599 –> 00:34:34,240
transparency and trust and directness

887
00:34:32,720 –> 00:34:36,599
because they are more about not being

888
00:34:34,240 –> 00:34:39,679
rude which I understand you could not be

889
00:34:36,599 –> 00:34:43,000
rude and still have an open dialogue

890
00:34:39,679 –> 00:34:44,639
about what you disagree with and they’re

891
00:34:43,000 –> 00:34:45,839
not willing to do that so sometimes it’s

892
00:34:44,639 –> 00:34:48,000
very difficult and I always say I’m not

893
00:34:45,839 –> 00:34:49,359
a mind reader so I find it very

894
00:34:48,000 –> 00:34:53,359
difficult to navigate in japanese-based

895
00:34:49,359 –> 00:34:57,320
companies yeah yeah me too but there are

896
00:34:53,359 –> 00:34:59,240
still so they um their approach to

897
00:34:57,320 –> 00:35:03,240
making decision is completely different

898
00:34:59,240 –> 00:35:06,280
from European or American uh but they

899
00:35:03,240 –> 00:35:10,200
had some success in in in creating

900
00:35:06,280 –> 00:35:11,880
Innovative products and so it is

901
00:35:10,200 –> 00:35:16,640
possible

902
00:35:11,880 –> 00:35:19,280
um like doing it their way yeah um yeah

903
00:35:16,640 –> 00:35:22,440
but I guess the the interesting thing is

904
00:35:19,280 –> 00:35:25,880
that um there are lots of

905
00:35:22,440 –> 00:35:29,760
organizations um or like family larger

906
00:35:25,880 –> 00:35:31,320
but family run businesses uh where they

907
00:35:29,760 –> 00:35:34,359
don’t have because they don’t want to

908
00:35:31,320 –> 00:35:37,560
share the knowledge uh they

909
00:35:34,359 –> 00:35:39,599
don’t like and and for example the sons

910
00:35:37,560 –> 00:35:42,000
or the family members don’t want to take

911
00:35:39,599 –> 00:35:44,880
over they are struggling they they have

912
00:35:42,000 –> 00:35:48,680
to sell for peanuts or they the

913
00:35:44,880 –> 00:35:51,040
businesses um are destined to die yeah

914
00:35:48,680 –> 00:35:53,040
no I agree with you wholeheartedly I

915
00:35:51,040 –> 00:35:55,480
agree with

916
00:35:53,040 –> 00:35:58,599
you it’s very challenging and very

917
00:35:55,480 –> 00:36:03,040
difficult yeah yeah

918
00:35:58,599 –> 00:36:05,760
so in you’ve outlined how AI or like

919
00:36:03,040 –> 00:36:08,640
data teams can struggle uh with

920
00:36:05,760 –> 00:36:10,960
credibility uh and bureaucracy and

921
00:36:08,640 –> 00:36:14,760
Leadership uh bu

922
00:36:10,960 –> 00:36:16,960
in what are your tops strategies for

923
00:36:14,760 –> 00:36:20,480
overcoming these um

924
00:36:16,960 –> 00:36:23,079
hardless I have a repeatable approach

925
00:36:20,480 –> 00:36:24,920
that I take when I first join it took me

926
00:36:23,079 –> 00:36:26,880
some time and it took me a few positions

927
00:36:24,920 –> 00:36:28,359
to figure it out but I think I finally

928
00:36:26,880 –> 00:36:30,599
have it

929
00:36:28,359 –> 00:36:33,680
because data and analytics and even in

930
00:36:30,599 –> 00:36:36,160
many cases the AI teams that I’ve built

931
00:36:33,680 –> 00:36:38,920
we are a service center we are a

932
00:36:36,160 –> 00:36:41,960
function we don’t own the p&l we’re not

933
00:36:38,920 –> 00:36:43,280
driving the p&l so naturally being a

934
00:36:41,960 –> 00:36:46,050
service based function you have to

935
00:36:43,280 –> 00:36:49,149
approach things a very different way

936
00:36:46,050 –> 00:36:49,149
[Music]

937
00:36:49,680 –> 00:36:56,839
um the first three to four

938
00:36:53,160 –> 00:36:58,040
months I hold back from any sort of

939
00:36:56,839 –> 00:37:00,520
opinions

940
00:36:58,040 –> 00:37:03,599
and it’s very hard for me I

941
00:37:00,520 –> 00:37:05,800
have point of view I will see the

942
00:37:03,599 –> 00:37:08,119
obvious things and I will refrain and I

943
00:37:05,800 –> 00:37:10,240
will refrain because you can never go in

944
00:37:08,119 –> 00:37:12,480
as a new leader and call someone else’s

945
00:37:10,240 –> 00:37:14,240
baby ugly even though they hired you

946
00:37:12,480 –> 00:37:16,920
because they know it’s ugly and they

947
00:37:14,240 –> 00:37:19,880
want you to transform it you can’t say

948
00:37:16,920 –> 00:37:21,359
it out loud that’s first second in those

949
00:37:19,880 –> 00:37:22,520
three to four months I meet with

950
00:37:21,359 –> 00:37:26,359
everyone

951
00:37:22,520 –> 00:37:28,480
possible and I ask a few basic questions

952
00:37:26,359 –> 00:37:31,160
what do you think my role is is my job

953
00:37:28,480 –> 00:37:32,800
is what am I here to do what are the

954
00:37:31,160 –> 00:37:34,440
start stop and continues what should we

955
00:37:32,800 –> 00:37:36,119
start doing that we haven’t what should

956
00:37:34,440 –> 00:37:37,359
we stop doing it for some reason we keep

957
00:37:36,119 –> 00:37:40,359
doing and what should we continue doing

958
00:37:37,359 –> 00:37:43,640
that really enjoy and then what are your

959
00:37:40,359 –> 00:37:46,599
top three priorities and do you feel my

960
00:37:43,640 –> 00:37:49,400
group can impact or help you like that’s

961
00:37:46,599 –> 00:37:53,119
it and I just let them talk that’s from

962
00:37:49,400 –> 00:37:55,839
CEO to CFO to any any sea Suite that’s

963
00:37:53,119 –> 00:37:58,880
all I ask for brand presidents Regional

964
00:37:55,839 –> 00:38:00,480
presidents label Pres res and I just get

965
00:37:58,880 –> 00:38:02,800
to know the lay of land of how people

966
00:38:00,480 –> 00:38:04,880
think what they have to say then I meet

967
00:38:02,800 –> 00:38:06,400
with their LTS so I go one layer down

968
00:38:04,880 –> 00:38:08,720
and then I meet with their LTS when

969
00:38:06,400 –> 00:38:11,640
layer down so the first three to four

970
00:38:08,720 –> 00:38:16,280
months I easily meet anywhere from two

971
00:38:11,640 –> 00:38:18,920
to 400 people all I’m doing is hi

972
00:38:16,280 –> 00:38:21,200
listening taking notes and then comes

973
00:38:18,920 –> 00:38:22,680
that moment in time after you the three

974
00:38:21,200 –> 00:38:24,520
four months where I’m synthesizing

975
00:38:22,680 –> 00:38:26,480
everything and it’s like that scene from

976
00:38:24,520 –> 00:38:27,839
A Beautiful Mind where I take everything

977
00:38:26,480 –> 00:38:29,079
that’s been written down and I start

978
00:38:27,839 –> 00:38:31,880
creating

979
00:38:29,079 –> 00:38:34,520
themes what ends up happening

980
00:38:31,880 –> 00:38:37,480
is I when I present my

981
00:38:34,520 –> 00:38:40,079
strategy here’s what I heard here are

982
00:38:37,480 –> 00:38:43,079
symptoms but these symptoms are because

983
00:38:40,079 –> 00:38:46,000
of these core issues so here’s what we

984
00:38:43,079 –> 00:38:47,880
need to fix here’s what we need to build

985
00:38:46,000 –> 00:38:50,920
and here’s what we need to

986
00:38:47,880 –> 00:38:53,280
serve um and then I have this thing

987
00:38:50,920 –> 00:38:56,240
called the wall of quotes where I take

988
00:38:53,280 –> 00:38:58,440
quotes from all the leaders and everyone

989
00:38:56,240 –> 00:39:00,280
and I group them into bundles

990
00:38:58,440 –> 00:39:01,720
and the reason why this is important is

991
00:39:00,280 –> 00:39:03,839
because I didn’t make up this I didn’t

992
00:39:01,720 –> 00:39:05,280
make the stuff up these are literally

993
00:39:03,839 –> 00:39:08,079
the day-to-day

994
00:39:05,280 –> 00:39:10,040
complaints and I always reference that

995
00:39:08,079 –> 00:39:12,480
now the strategy set based on what was

996
00:39:10,040 –> 00:39:15,079
shared we agree on what needs to be done

997
00:39:12,480 –> 00:39:17,119
I then go ask for funding the funding

998
00:39:15,079 –> 00:39:18,920
part is a tricky part because part of

999
00:39:17,119 –> 00:39:21,400
what I come in for is to fix some of the

1000
00:39:18,920 –> 00:39:22,920
things that are creating the issues and

1001
00:39:21,400 –> 00:39:24,920
so sometimes I have to give them an

1002
00:39:22,920 –> 00:39:26,920
analogy one that I use often I said you

1003
00:39:24,920 –> 00:39:28,040
know we’ve got a leaky faucet in this

1004
00:39:26,920 –> 00:39:30,720
case of buiness business process is

1005
00:39:28,040 –> 00:39:32,119
creating bad data I can fix this Le

1006
00:39:30,720 –> 00:39:34,000
leaky faucet but we have two ways of

1007
00:39:32,119 –> 00:39:36,079
doing this either you can give me the

1008
00:39:34,000 –> 00:39:37,680
time and the money so I can go

1009
00:39:36,079 –> 00:39:39,920
troubleshoot and figure out where the

1010
00:39:37,680 –> 00:39:42,680
leak is happening and you’ve empowered

1011
00:39:39,920 –> 00:39:44,839
me to fix that leak so that we no longer

1012
00:39:42,680 –> 00:39:46,520
have a leaky faucet or you’re going to

1013
00:39:44,839 –> 00:39:49,119
have to give me enough money to just buy

1014
00:39:46,520 –> 00:39:51,640
a bunch of pales and I’m going to put a

1015
00:39:49,119 –> 00:39:53,319
temporary fix collect the water and then

1016
00:39:51,640 –> 00:39:55,560
bring the next pail collect the water

1017
00:39:53,319 –> 00:39:56,800
I’m never fixing the issue but I just

1018
00:39:55,560 –> 00:39:59,200
need a bunch of pales to be able to

1019
00:39:56,800 –> 00:40:00,920
collect the leaky faucet so either give

1020
00:39:59,200 –> 00:40:02,359
me the time and the budget to go fix and

1021
00:40:00,920 –> 00:40:04,800
more importantly the empowerment to

1022
00:40:02,359 –> 00:40:07,000
actually fix the root cause or enough

1023
00:40:04,800 –> 00:40:08,680
money to just buy enough pales so I

1024
00:40:07,000 –> 00:40:10,240
continue to catch the Leaky poset like

1025
00:40:08,680 –> 00:40:13,000
the choice is yours I can do either or

1026
00:40:10,240 –> 00:40:14,440
of course I prefer Ro cause you have to

1027
00:40:13,000 –> 00:40:16,760
get creative like that even in the

1028
00:40:14,440 –> 00:40:19,400
financial discussions because then

1029
00:40:16,760 –> 00:40:21,160
they’re signing up for what ends up

1030
00:40:19,400 –> 00:40:23,319
happening is is you get the support you

1031
00:40:21,160 –> 00:40:25,079
need you start in inevitably year year

1032
00:40:23,319 –> 00:40:27,400
and a half people start resisting they

1033
00:40:25,079 –> 00:40:29,400
hit fatigue they start pushing back I I

1034
00:40:27,400 –> 00:40:33,640
will always revert back to the wall of

1035
00:40:29,400 –> 00:40:35,680
quotes the leaky faucet analogy and the

1036
00:40:33,640 –> 00:40:37,079
choices that we made and the reasons why

1037
00:40:35,680 –> 00:40:39,960
we made them knowing what the

1038
00:40:37,079 –> 00:40:42,400
consequences were were I have those

1039
00:40:39,960 –> 00:40:45,560
three to five slides in the deck always

1040
00:40:42,400 –> 00:40:47,160
handy and ready saying I understand that

1041
00:40:45,560 –> 00:40:48,839
what we’re experiencing now is a

1042
00:40:47,160 –> 00:40:51,280
consequence of decisions we all

1043
00:40:48,839 –> 00:40:53,680
collectively made a year ago and we

1044
00:40:51,280 –> 00:40:57,319
highlighted what the consequences were

1045
00:40:53,680 –> 00:40:59,359
so we either have to be okay with it or

1046
00:40:57,319 –> 00:41:01,480
we can revisit another approach I had

1047
00:40:59,359 –> 00:41:03,040
suggested and preferred but at the time

1048
00:41:01,480 –> 00:41:05,920
it wasn’t right for us which is totally

1049
00:41:03,040 –> 00:41:06,800
fine and we won’t have these anymore and

1050
00:41:05,920 –> 00:41:08,599
then you have to pull in the right

1051
00:41:06,800 –> 00:41:10,480
people to make the Judgment call so you

1052
00:41:08,599 –> 00:41:12,440
have to be very political you have to be

1053
00:41:10,480 –> 00:41:14,359
very diplomatic you have to be very

1054
00:41:12,440 –> 00:41:15,680
document oriented because people have

1055
00:41:14,359 –> 00:41:18,200
amnesia so you have to remind them of

1056
00:41:15,680 –> 00:41:21,160
the decisions they made um because you

1057
00:41:18,200 –> 00:41:24,240
can only do so much what what’s that one

1058
00:41:21,160 –> 00:41:25,920
saying you can fill the trough with

1059
00:41:24,240 –> 00:41:27,520
water and lead the horse to the water

1060
00:41:25,920 –> 00:41:29,000
but you can’t force the horse to drink

1061
00:41:27,520 –> 00:41:31,680
the water like

1062
00:41:29,000 –> 00:41:33,800
it’s you you just get to that point so

1063
00:41:31,680 –> 00:41:35,680
you you have to story tell you have to

1064
00:41:33,800 –> 00:41:38,680
document and you have to remind people

1065
00:41:35,680 –> 00:41:39,839
of the decisions they made and why um

1066
00:41:38,680 –> 00:41:42,040
because everyone’s always looking at

1067
00:41:39,839 –> 00:41:44,800
Point figures and you want to not you

1068
00:41:42,040 –> 00:41:47,440
want to avoid that at all costs yeah

1069
00:41:44,800 –> 00:41:50,800
yeah and when something goes wrong they

1070
00:41:47,440 –> 00:41:53,720
um they end up blaming someone who was

1071
00:41:50,800 –> 00:41:56,240
before you or something it’s just it’s

1072
00:41:53,720 –> 00:42:00,079
always easy to do that and and that’s

1073
00:41:56,240 –> 00:42:03,040
not yeah yeah yeah it’s I guess I find

1074
00:42:00,079 –> 00:42:04,960
that the worst the worst bit is navig

1075
00:42:03,040 –> 00:42:08,079
navigating the politics and finding

1076
00:42:04,960 –> 00:42:12,960
people who actually care uh versus those

1077
00:42:08,079 –> 00:42:16,480
who um yeah just talk yep do you want to

1078
00:42:12,960 –> 00:42:18,960
do something like do you have some um

1079
00:42:16,480 –> 00:42:22,839
thing to respond to

1080
00:42:18,960 –> 00:42:27,680
or uh okay let’s talk about I know that

1081
00:42:22,839 –> 00:42:30,040
you’ve also um focused a lot of your uh

1082
00:42:27,680 –> 00:42:32,800
like sharing lessons is

1083
00:42:30,040 –> 00:42:36,839
about security data

1084
00:42:32,800 –> 00:42:39,680
privacy uh fixing the leak and

1085
00:42:36,839 –> 00:42:44,119
and connecting the data in a way that

1086
00:42:39,680 –> 00:42:45,920
you can uh G gain insights from it I saw

1087
00:42:44,119 –> 00:42:49,760
the other day you posted really really

1088
00:42:45,920 –> 00:42:53,520
cool uh post um on LinkedIn about the

1089
00:42:49,760 –> 00:42:56,319
data hoarding and lack of

1090
00:42:53,520 –> 00:42:59,079
integration yeah collecting the data

1091
00:42:56,319 –> 00:43:03,440
we’re horrible connecting with yeah yes

1092
00:42:59,079 –> 00:43:06,920
exactly so how how can you

1093
00:43:03,440 –> 00:43:10,520
make how can you I guess you answered

1094
00:43:06,920 –> 00:43:13,480
that that partly before saying um you

1095
00:43:10,520 –> 00:43:18,440
have to paint the story but how how can

1096
00:43:13,480 –> 00:43:20,280
you uh help people understand how much

1097
00:43:18,440 –> 00:43:22,520
they are missing how much they are not

1098
00:43:20,280 –> 00:43:26,079
seeing because they are not working

1099
00:43:22,520 –> 00:43:27,680
towards um connecting they not helping

1100
00:43:26,079 –> 00:43:32,200
themselves to to

1101
00:43:27,680 –> 00:43:35,480
Fe solution from a true core data and

1102
00:43:32,200 –> 00:43:37,559
analytics competency this is where the

1103
00:43:35,480 –> 00:43:39,359
sem the data architecture is so

1104
00:43:37,559 –> 00:43:42,079
important and this is where the conform

1105
00:43:39,359 –> 00:43:45,960
layer and semantic layer and the layers

1106
00:43:42,079 –> 00:43:48,440
in between really really matter um the

1107
00:43:45,960 –> 00:43:51,160
semantic layer is almost the first

1108
00:43:48,440 –> 00:43:53,880
application layer an opportunity that

1109
00:43:51,160 –> 00:43:56,280
gives users who are not familiar with

1110
00:43:53,880 –> 00:43:58,599
the raw data and the metadata an

1111
00:43:56,280 –> 00:44:01,040
opportunity to actually work with the

1112
00:43:58,599 –> 00:44:04,000
data and

1113
00:44:01,040 –> 00:44:06,520
so you have non-technical folks who just

1114
00:44:04,000 –> 00:44:09,720
want to drag and drop and create filters

1115
00:44:06,520 –> 00:44:13,079
and create dashboards but in order to do

1116
00:44:09,720 –> 00:44:15,440
that you need to connect the information

1117
00:44:13,079 –> 00:44:18,359
that’s coming from multiple sources like

1118
00:44:15,440 –> 00:44:21,599
even here’s an example right not all the

1119
00:44:18,359 –> 00:44:25,200
data is coming from crms and cdps and

1120
00:44:21,599 –> 00:44:28,000
erps and every other database that

1121
00:44:25,200 –> 00:44:29,880
we we we we know r a lot of it’s coming

1122
00:44:28,000 –> 00:44:33,559
from social information or Commerce

1123
00:44:29,880 –> 00:44:36,800
information click rates Impressions Etc

1124
00:44:33,559 –> 00:44:39,200
but you can collect click rates and

1125
00:44:36,800 –> 00:44:41,960
Impressions you can collect an IP

1126
00:44:39,200 –> 00:44:43,880
address for a consumer and it could be

1127
00:44:41,960 –> 00:44:45,559
from a consumer that has bought with

1128
00:44:43,880 –> 00:44:47,720
that has bought from us

1129
00:44:45,559 –> 00:44:49,160
before but you’re throwing at that

1130
00:44:47,720 –> 00:44:50,960
information at them you’re like oh we

1131
00:44:49,160 –> 00:44:54,440
have all this data but none of it’s

1132
00:44:50,960 –> 00:44:57,079
usable until you find a way to integrate

1133
00:44:54,440 –> 00:44:59,440
this consumer ID with this IP address

1134
00:44:57,079 –> 00:45:03,480
address has repeatedly visited our

1135
00:44:59,440 –> 00:45:05,400
website um great at opening horrible at

1136
00:45:03,480 –> 00:45:07,359
closing and so there isn’t a conversion

1137
00:45:05,400 –> 00:45:09,400
to sales well why isn’t there a

1138
00:45:07,359 –> 00:45:11,599
conversion to sales and then being able

1139
00:45:09,400 –> 00:45:13,160
to connect that to which page pages are

1140
00:45:11,599 –> 00:45:15,720
dropped which products are we

1141
00:45:13,160 –> 00:45:19,200
recommending that are not catching that

1142
00:45:15,720 –> 00:45:20,720
endend storyline we just often miss what

1143
00:45:19,200 –> 00:45:22,400
we do is we give them a bunch of website

1144
00:45:20,720 –> 00:45:24,720
data we give them a bunch of client data

1145
00:45:22,400 –> 00:45:27,480
with transactions and they’re left to

1146
00:45:24,720 –> 00:45:28,520
interpreting and unfortunately

1147
00:45:27,480 –> 00:45:31,680
they don’t have the skill sets to

1148
00:45:28,520 –> 00:45:35,119
interpret and because our dates or data

1149
00:45:31,680 –> 00:45:36,760
warehouses or data links or data marks

1150
00:45:35,119 –> 00:45:40,079
whatever you want to call it we don’t do

1151
00:45:36,760 –> 00:45:42,480
a good job of labeling the columns

1152
00:45:40,079 –> 00:45:45,880
labeling the data we have a bunch of

1153
00:45:42,480 –> 00:45:48,480
like symbols and null values you can’t

1154
00:45:45,880 –> 00:45:49,720
let them guess um how the information

1155
00:45:48,480 –> 00:45:52,119
should be connected because then you’re

1156
00:45:49,720 –> 00:45:53,480
just your kpis your metrics anything you

1157
00:45:52,119 –> 00:45:55,440
present in the dashboard is going to go

1158
00:45:53,480 –> 00:45:57,720
Haywire so I do fundamentally believe

1159
00:45:55,440 –> 00:46:00,200
that while we are collecting the data at

1160
00:45:57,720 –> 00:46:01,599
a rapid Pace really the effort should be

1161
00:46:00,200 –> 00:46:04,800
on connecting the data so that

1162
00:46:01,599 –> 00:46:06,599
information is usable um the semantic

1163
00:46:04,800 –> 00:46:09,119
layer is

1164
00:46:06,599 –> 00:46:11,119
probably the most exhausting thing I’ve

1165
00:46:09,119 –> 00:46:12,760
ever had to build in every organization

1166
00:46:11,119 –> 00:46:15,480
because getting everyone to align on a

1167
00:46:12,760 –> 00:46:17,880
single definition is very difficult but

1168
00:46:15,480 –> 00:46:20,480
without it it’s really hard to make that

1169
00:46:17,880 –> 00:46:23,640
data usable

1170
00:46:20,480 –> 00:46:26,119
so I just think it’s it’s key because

1171
00:46:23,640 –> 00:46:27,720
when people say we need more data it’s

1172
00:46:26,119 –> 00:46:30,880
never that they actually need more

1173
00:46:27,720 –> 00:46:33,400
volumes of data they need more usable

1174
00:46:30,880 –> 00:46:36,040
data outside of the existing Dimensions

1175
00:46:33,400 –> 00:46:36,040
that you’ve given

1176
00:46:36,200 –> 00:46:44,079
them and do you think with with the

1177
00:46:39,200 –> 00:46:46,359
promise of AI and you know all llms

1178
00:46:44,079 –> 00:46:50,119
understanding the context of the

1179
00:46:46,359 –> 00:46:53,960
information uh and data not

1180
00:46:50,119 –> 00:46:58,160
information uh do you think it it has a

1181
00:46:53,960 –> 00:47:02,400
chance to help people maybe not automate

1182
00:46:58,160 –> 00:47:07,480
but yeah like speed things up in finding

1183
00:47:02,400 –> 00:47:09,839
those golden nuggets in in the whole um

1184
00:47:07,480 –> 00:47:12,440
leges of

1185
00:47:09,839 –> 00:47:14,359
data I think there’s a lot of great AI

1186
00:47:12,440 –> 00:47:18,599
capabilities that can give us inference

1187
00:47:14,359 –> 00:47:21,280
like here’s an example um I needed to

1188
00:47:18,599 –> 00:47:23,839
build I built My First Data Lake

1189
00:47:21,280 –> 00:47:27,400
environment in 2015 and I got into

1190
00:47:23,839 –> 00:47:29,640
zoning around 2016 17 Zone for data

1191
00:47:27,400 –> 00:47:31,359
scientists zones for data analysts and

1192
00:47:29,640 –> 00:47:35,800
zones for those that were building

1193
00:47:31,359 –> 00:47:38,359
digital products Etc

1194
00:47:35,800 –> 00:47:40,800
um so I’ve built those environments

1195
00:47:38,359 –> 00:47:42,040
across multiple years and what I’ve

1196
00:47:40,800 –> 00:47:44,040
noticed in all of them is you know

1197
00:47:42,040 –> 00:47:46,359
things that should be so easily

1198
00:47:44,040 –> 00:47:48,960
identifiable like something as easy as

1199
00:47:46,359 –> 00:47:50,400
gender well for some reason when you

1200
00:47:48,960 –> 00:47:52,800
pull in the column header from the

1201
00:47:50,400 –> 00:47:57,800
database that tracks gender it’s like 1

1202
00:47:52,800 –> 00:47:59,160
12core xy0 Capital CA versus gender we

1203
00:47:57,800 –> 00:48:01,920
don’t even have the titles for our

1204
00:47:59,160 –> 00:48:03,280
columns right in in our schematics so

1205
00:48:01,920 –> 00:48:04,599
how is someone supposed to know what

1206
00:48:03,280 –> 00:48:06,839
table they’re supposed to go into to

1207
00:48:04,599 –> 00:48:08,359
pull gender and then what’s worse is

1208
00:48:06,839 –> 00:48:10,280
you’ll actually take a look at the data

1209
00:48:08,359 –> 00:48:12,720
in there and sometimes it’s F sometimes

1210
00:48:10,280 –> 00:48:14,559
it’s m sometimes female is spelled out

1211
00:48:12,720 –> 00:48:16,119
sometimes gender is spelled out

1212
00:48:14,559 –> 00:48:19,920
sometimes you get acronyms because we

1213
00:48:16,119 –> 00:48:22,040
haven’t enforced a specific format so

1214
00:48:19,920 –> 00:48:24,119
some capabilities that I’ve built for my

1215
00:48:22,040 –> 00:48:25,400
team because um I used to stand the data

1216
00:48:24,119 –> 00:48:27,359
science team is and I call data science

1217
00:48:25,400 –> 00:48:30,520
as a service we would do some of the

1218
00:48:27,359 –> 00:48:34,319
more complicated um quering and Analysis

1219
00:48:30,520 –> 00:48:35,880
and very bespoke um stuff for very

1220
00:48:34,319 –> 00:48:38,319
specific questions that the Business

1221
00:48:35,880 –> 00:48:39,920
Leaders had and instead of having to

1222
00:48:38,319 –> 00:48:41,880
understand the schematics behind every

1223
00:48:39,920 –> 00:48:44,079
single database we would actually

1224
00:48:41,880 –> 00:48:46,839
leverage AI in a few cases where it

1225
00:48:44,079 –> 00:48:49,240
would help us infer what that table what

1226
00:48:46,839 –> 00:48:51,119
that schematic what that column was so

1227
00:48:49,240 –> 00:48:55,720
instead of understanding what this 1

1228
00:48:51,119 –> 00:48:57,319
12core 03 Capital CA is um we would use

1229
00:48:55,720 –> 00:48:59,079
it for inference

1230
00:48:57,319 –> 00:49:03,400
and saying that based on the fact that

1231
00:48:59,079 –> 00:49:06,359
I’ve read female F FML this is a gender

1232
00:49:03,400 –> 00:49:08,200
column and have them do the translations

1233
00:49:06,359 –> 00:49:10,319
and then we would actually query off of

1234
00:49:08,200 –> 00:49:12,839
that translation that our AI model

1235
00:49:10,319 –> 00:49:14,839
inferred I think that is super useful

1236
00:49:12,839 –> 00:49:18,000
because now we don’t have to learn every

1237
00:49:14,839 –> 00:49:19,760
single schematic or database name and

1238
00:49:18,000 –> 00:49:21,920
understand what’s where and we always

1239
00:49:19,760 –> 00:49:23,880
talk about the the swamp right for the

1240
00:49:21,920 –> 00:49:25,119
data L and things have gotten really

1241
00:49:23,880 –> 00:49:26,480
messy well yeah when you don’t have

1242
00:49:25,119 –> 00:49:28,359
catalog and lineage in place it’s going

1243
00:49:26,480 –> 00:49:30,000
to happen happen and many organizations

1244
00:49:28,359 –> 00:49:32,000
don’t because data cataloging lineage to

1245
00:49:30,000 –> 00:49:35,000
me is amazing but to others not so sexy

1246
00:49:32,000 –> 00:49:36,880
so it the funding always gets cut so you

1247
00:49:35,000 –> 00:49:38,760
could use some of these AI models to do

1248
00:49:36,880 –> 00:49:40,680
um inference detection so you actually

1249
00:49:38,760 –> 00:49:44,160
understand where the information resides

1250
00:49:40,680 –> 00:49:47,200
in this massive SE of

1251
00:49:44,160 –> 00:49:50,960
data yeah it’s yeah definitely use very

1252
00:49:47,200 –> 00:49:54,160
useful application and you know human

1253
00:49:50,960 –> 00:49:58,040
language is so flexible and uh

1254
00:49:54,160 –> 00:50:01,240
especially when companies are growing by

1255
00:49:58,040 –> 00:50:04,160
acquiring other companies it’s it’s

1256
00:50:01,240 –> 00:50:05,599
impossible to to keep everything in line

1257
00:50:04,160 –> 00:50:07,559
and

1258
00:50:05,599 –> 00:50:11,000
sync

1259
00:50:07,559 –> 00:50:14,920
agre I’m very curious about this um term

1260
00:50:11,000 –> 00:50:20,200
you are using uh stain glass

1261
00:50:14,920 –> 00:50:23,240
Transformations um and you I refer to it

1262
00:50:20,200 –> 00:50:26,000
uh a lot and is um I read that it allows

1263
00:50:23,240 –> 00:50:27,880
training AI models without exposing

1264
00:50:26,000 –> 00:50:31,880
sensitive data

1265
00:50:27,880 –> 00:50:34,040
um so can you explain what kind of you

1266
00:50:31,880 –> 00:50:35,359
and I also read sorry I read read that

1267
00:50:34,040 –> 00:50:38,440
you patented

1268
00:50:35,359 –> 00:50:40,359
it um so I didn’t personally patent it

1269
00:50:38,440 –> 00:50:43,040
so there’s a company that I am

1270
00:50:40,359 –> 00:50:44,440
partnering with and working with because

1271
00:50:43,040 –> 00:50:45,880
what’s happened right now in device

1272
00:50:44,440 –> 00:50:48,240
space is like whether you’re talking to

1273
00:50:45,880 –> 00:50:51,200
a CIO or

1274
00:50:48,240 –> 00:50:53,400
ceso they’re having challenges with

1275
00:50:51,200 –> 00:50:54,880
training their own foundational models

1276
00:50:53,400 –> 00:50:57,760
large language models machine learning

1277
00:50:54,880 –> 00:51:00,960
models because if you’re using an offthe

1278
00:50:57,760 –> 00:51:03,599
shelf a model or if you’re using open

1279
00:51:00,960 –> 00:51:05,359
source or closed source and API to be

1280
00:51:03,599 –> 00:51:08,240
able to teach this foundational models

1281
00:51:05,359 –> 00:51:09,640
data leakage is just a concern but if

1282
00:51:08,240 –> 00:51:11,599
you think about the number of instances

1283
00:51:09,640 –> 00:51:14,119
where data leakage has happened it’s

1284
00:51:11,599 –> 00:51:16,799
very very small but everyone’s concerned

1285
00:51:14,119 –> 00:51:18,319
about the what if scenario so what if it

1286
00:51:16,799 –> 00:51:19,640
what if the data gets out there and I’m

1287
00:51:18,319 –> 00:51:21,400
like well what if I cross the street and

1288
00:51:19,640 –> 00:51:25,200
I get hit right by a car that runs a red

1289
00:51:21,400 –> 00:51:27,760
light it’s possible but

1290
00:51:25,200 –> 00:51:29,559
unlikely so because I believe in the

1291
00:51:27,760 –> 00:51:30,799
space and I’ve seen what it could do one

1292
00:51:29,559 –> 00:51:32,760
of the things that I’m starting to

1293
00:51:30,799 –> 00:51:35,079
evangelize this year is don’t let the

1294
00:51:32,760 –> 00:51:38,160
what if situations stop you from

1295
00:51:35,079 –> 00:51:42,760
actually doing good work and so I’m

1296
00:51:38,160 –> 00:51:45,920
tackling a very unsexy topic which is um

1297
00:51:42,760 –> 00:51:48,440
data modeling sorry data privacy and and

1298
00:51:45,920 –> 00:51:50,960
how to fix leakage and so while there

1299
00:51:48,440 –> 00:51:53,200
are a lot of security measures out there

1300
00:51:50,960 –> 00:51:55,359
to deal with information not coming out

1301
00:51:53,200 –> 00:51:57,599
the Stained Glass Technology provided by

1302
00:51:55,359 –> 00:51:59,280
a company called protopia

1303
00:51:57,599 –> 00:52:02,280
I think is the most promising one at

1304
00:51:59,280 –> 00:52:04,920
this point in time um and it solves for

1305
00:52:02,280 –> 00:52:07,480
that very issue so that people aren’t

1306
00:52:04,920 –> 00:52:11,200
focused on the problem but they are now

1307
00:52:07,480 –> 00:52:14,200
focused on the solution you are known as

1308
00:52:11,200 –> 00:52:17,359
a bit of a rogue

1309
00:52:14,200 –> 00:52:20,079
executive who deserves who drives change

1310
00:52:17,359 –> 00:52:23,520
by strategically challenging the status

1311
00:52:20,079 –> 00:52:27,000
quo what does it mean to

1312
00:52:23,520 –> 00:52:29,200
you um

1313
00:52:27,000 –> 00:52:32,559
I’ve never really fit in completely in

1314
00:52:29,200 –> 00:52:35,040
the corporate culture I am

1315
00:52:32,559 –> 00:52:36,559
not what’s a good way of fitting it

1316
00:52:35,040 –> 00:52:38,799
because I’ve always been brought in to

1317
00:52:36,559 –> 00:52:43,200
change to transform to disrupt the

1318
00:52:38,799 –> 00:52:45,680
status quo um my job isn’t 100% to mesh

1319
00:52:43,200 –> 00:52:49,240
and continue to do things the way

1320
00:52:45,680 –> 00:52:51,760
they’ve always been done so they used my

1321
00:52:49,240 –> 00:52:54,680
skill sets and my IQ you know to get the

1322
00:52:51,760 –> 00:52:58,599
work done I think part of my extroverted

1323
00:52:54,680 –> 00:53:01,160
personality and uh you my my social

1324
00:52:58,599 –> 00:53:05,400
quotient uh to build the relationships

1325
00:53:01,160 –> 00:53:08,079
and scale it um but along the way and

1326
00:53:05,400 –> 00:53:10,680
inevitably because I don’t do things the

1327
00:53:08,079 –> 00:53:14,480
way they’ve always been done I naturally

1328
00:53:10,680 –> 00:53:17,400
ruffle some feathers and I clearly know

1329
00:53:14,480 –> 00:53:19,520
where there are pockets of issues and

1330
00:53:17,400 –> 00:53:20,720
challenges and talent that just aren’t

1331
00:53:19,520 –> 00:53:22,680
cutting it for the capabilities that

1332
00:53:20,720 –> 00:53:25,880
we’re trying to build for the future and

1333
00:53:22,680 –> 00:53:29,000
I don’t call them out per se but I’m

1334
00:53:25,880 –> 00:53:31,480
okay challenging and I’m okay pushing

1335
00:53:29,000 –> 00:53:33,200
and I’m okay saying that if we want

1336
00:53:31,480 –> 00:53:35,000
different results we need to do things

1337
00:53:33,200 –> 00:53:37,119
differently and if you’re expecting

1338
00:53:35,000 –> 00:53:38,520
change with the same exact Talent base

1339
00:53:37,119 –> 00:53:39,720
and the same exact way we’re doing it

1340
00:53:38,520 –> 00:53:42,119
that’s just not going to happen and I’m

1341
00:53:39,720 –> 00:53:44,280
not the right person to do it so I do

1342
00:53:42,119 –> 00:53:46,559
push people I do get them past their

1343
00:53:44,280 –> 00:53:48,240
comfort zone and going back to our

1344
00:53:46,559 –> 00:53:51,280
earlier conversation if you’re working

1345
00:53:48,240 –> 00:53:53,920
with someone who’s very

1346
00:53:51,280 –> 00:53:56,160
comfortable showing up at 9:30 and

1347
00:53:53,920 –> 00:53:57,599
leaving every day at 4:30 they’re not

1348
00:53:56,160 –> 00:54:00,280
the ones that are going to go out of

1349
00:53:57,599 –> 00:54:02,880
their way and be totally interested in

1350
00:54:00,280 –> 00:54:06,319
evangelizing and and making an impact

1351
00:54:02,880 –> 00:54:07,839
and creating change um but I push them I

1352
00:54:06,319 –> 00:54:09,480
don’t care how they’re operating I only

1353
00:54:07,839 –> 00:54:11,040
care how I’m operating and my team is

1354
00:54:09,480 –> 00:54:12,319
operating and we’re setting a very

1355
00:54:11,040 –> 00:54:15,040
different bar for the

1356
00:54:12,319 –> 00:54:17,400
organization so I’ve gone Rogue in a few

1357
00:54:15,040 –> 00:54:19,280
areas where I push people harder than

1358
00:54:17,400 –> 00:54:21,280
they want to be pushed or I’m

1359
00:54:19,280 –> 00:54:23,839
introducing new things that haven’t been

1360
00:54:21,280 –> 00:54:25,640
introduced before I think differently I

1361
00:54:23,839 –> 00:54:26,880
speak differently I have a different

1362
00:54:25,640 –> 00:54:29,799
standard

1363
00:54:26,880 –> 00:54:32,359
and so sometimes I could be considered

1364
00:54:29,799 –> 00:54:33,839
um being Rogue because I don’t the

1365
00:54:32,359 –> 00:54:37,440
standard mold and and I’m very

1366
00:54:33,839 –> 00:54:39,760
comfortable with being very graceful but

1367
00:54:37,440 –> 00:54:44,799
calling out the obvious as

1368
00:54:39,760 –> 00:54:47,920
well so I I indirectly been tagged

1369
00:54:44,799 –> 00:54:51,000
that yeah it’s I it looks like it serves

1370
00:54:47,920 –> 00:54:55,280
you well you are definitely different

1371
00:54:51,000 –> 00:54:57,839
than most of the exacts and and leaders

1372
00:54:55,280 –> 00:55:01,520
yeah in the in the tech especially

1373
00:54:57,839 –> 00:55:02,680
industry so how can you challenge for

1374
00:55:01,520 –> 00:55:06,400
someone

1375
00:55:02,680 –> 00:55:09,160
who yeah I guess who is maybe less uh

1376
00:55:06,400 –> 00:55:11,960
extrovert than you how they how how can

1377
00:55:09,160 –> 00:55:15,520
you challenge the status quo and and

1378
00:55:11,960 –> 00:55:19,520
lead by principle of a fear that’s a

1379
00:55:15,520 –> 00:55:21,480
hard question because I think naturally

1380
00:55:19,520 –> 00:55:24,039
well I don’t know how to answer that I

1381
00:55:21,480 –> 00:55:25,839
don’t know how to challenge a status quo

1382
00:55:24,039 –> 00:55:27,400
I think if you’re in a position where

1383
00:55:25,839 –> 00:55:28,760
they’re high ing you because they need

1384
00:55:27,400 –> 00:55:31,079
something done because it hasn’t been

1385
00:55:28,760 –> 00:55:31,079
done

1386
00:55:31,240 –> 00:55:37,160
before um you’re put in that position

1387
00:55:34,480 –> 00:55:39,480
you’re meant to challenge I think the

1388
00:55:37,160 –> 00:55:42,119
way you do it though you have to take

1389
00:55:39,480 –> 00:55:44,000
into cultural considerations you have to

1390
00:55:42,119 –> 00:55:46,640
take into the natural pace of the

1391
00:55:44,000 –> 00:55:48,000
company you can’t have them Drive 120

1392
00:55:46,640 –> 00:55:51,039
miles an hour if the fastest they’ve

1393
00:55:48,000 –> 00:55:52,760
ever gone is 40 um you have to know the

1394
00:55:51,039 –> 00:55:55,280
talent at hand do they have a pension

1395
00:55:52,760 –> 00:55:56,520
fund in place like all different

1396
00:55:55,280 –> 00:55:58,440
components that you have to take into

1397
00:55:56,520 –> 00:56:00,240
consideration to understand you are

1398
00:55:58,440 –> 00:56:01,559
brought on board to change calibrating

1399
00:56:00,240 –> 00:56:04,079
your expectation of how fast you’re

1400
00:56:01,559 –> 00:56:04,079
going to be able to

1401
00:56:04,160 –> 00:56:11,079
change yeah but sometimes um you know

1402
00:56:07,079 –> 00:56:13,240
people are brought on those positions

1403
00:56:11,079 –> 00:56:16,559
May and as

1404
00:56:13,240 –> 00:56:19,599
a they they become the victim of

1405
00:56:16,559 –> 00:56:22,400
a political

1406
00:56:19,599 –> 00:56:25,079
game very much so and there are

1407
00:56:22,400 –> 00:56:27,599
individuals who are phenomenal at that

1408
00:56:25,079 –> 00:56:29,680
game phenomenal

1409
00:56:27,599 –> 00:56:33,160
and that’s how they’ve made their career

1410
00:56:29,680 –> 00:56:35,520
they’re what I call like professional

1411
00:56:33,160 –> 00:56:38,079
politicians they didn’t make it because

1412
00:56:35,520 –> 00:56:41,079
they know an area overly well they

1413
00:56:38,079 –> 00:56:43,480
didn’t make it because um they’re very

1414
00:56:41,079 –> 00:56:45,359
well respected in their area they made

1415
00:56:43,480 –> 00:56:47,440
it they never got into trouble and they

1416
00:56:45,359 –> 00:56:50,640
tiptoed around what they needed to and

1417
00:56:47,440 –> 00:56:52,400
they got far enough um I can spot those

1418
00:56:50,640 –> 00:56:54,039
people a mile away because you get into

1419
00:56:52,400 –> 00:56:55,480
a room with them and they’re

1420
00:56:54,039 –> 00:56:57,760
pontificating on something and you’re

1421
00:56:55,480 –> 00:56:59,520
like you just read an article and you’re

1422
00:56:57,760 –> 00:57:01,240
regurgitating it to me and I know that’s

1423
00:56:59,520 –> 00:57:03,440
what you’re doing because I know this is

1424
00:57:01,240 –> 00:57:04,960
an area that you know well

1425
00:57:03,440 –> 00:57:07,359
whatsoever

1426
00:57:04,960 –> 00:57:09,359
um but there are individuals that

1427
00:57:07,359 –> 00:57:11,039
fundamentally built their careers that

1428
00:57:09,359 –> 00:57:14,200
way and you have no choice but to work

1429
00:57:11,039 –> 00:57:18,000
with them that’s just the sad reality

1430
00:57:14,200 –> 00:57:21,119
true and it reminds me of the um Lauren

1431
00:57:18,000 –> 00:57:23,480
Peter principal yeah you’ve heard of

1432
00:57:21,119 –> 00:57:24,400
yeah but it’s good for everyone because

1433
00:57:23,480 –> 00:57:26,920
I don’t think everyone’s heard the

1434
00:57:24,400 –> 00:57:29,760
Lawrence Peter principal I read yes yeah

1435
00:57:26,920 –> 00:57:32,359
so it was just in in the book um Peter

1436
00:57:29,760 –> 00:57:35,280
argues that promoting uh incomp

1437
00:57:32,359 –> 00:57:38,880
incompetent people is the norm not the

1438
00:57:35,280 –> 00:57:41,160
exception and it’s uh he’s like he says

1439
00:57:38,880 –> 00:57:43,280
in time every po post tends to be

1440
00:57:41,160 –> 00:57:46,000
occupied by an employee who is

1441
00:57:43,280 –> 00:57:48,559
incompetent to carry out their duties he

1442
00:57:46,000 –> 00:57:51,280
writes work is accomplished by those

1443
00:57:48,559 –> 00:57:53,760
employees who do not who have not

1444
00:57:51,280 –> 00:57:56,640
reached their level of

1445
00:57:53,760 –> 00:57:59,079
incompetence um so and

1446
00:57:56,640 –> 00:58:02,359
yes then the more the higher those

1447
00:57:59,079 –> 00:58:05,119
people are the more they are forgiven in

1448
00:58:02,359 –> 00:58:07,119
a way they are yeah and that’s the

1449
00:58:05,119 –> 00:58:09,160
funniest thing I think the and the

1450
00:58:07,119 –> 00:58:11,440
reason why that resonated the first

1451
00:58:09,160 –> 00:58:13,599
thing I learned

1452
00:58:11,440 –> 00:58:17,640
is being an

1453
00:58:13,599 –> 00:58:22,200
executive is less about your IQ and more

1454
00:58:17,640 –> 00:58:24,119
about your EQ SQ and BQ you don’t

1455
00:58:22,200 –> 00:58:25,400
actually there are some brilliant

1456
00:58:24,119 –> 00:58:26,680
Executives please don’t get me wrong and

1457
00:58:25,400 –> 00:58:29,039
I have to put that out there

1458
00:58:26,680 –> 00:58:30,760
brilliant Executives who

1459
00:58:29,039 –> 00:58:32,640
fundamentally understand and have their

1460
00:58:30,760 –> 00:58:35,119
finger on the pulse with how to move

1461
00:58:32,640 –> 00:58:38,359
things maneuver things um and I learn

1462
00:58:35,119 –> 00:58:40,799
from them every single day but in a

1463
00:58:38,359 –> 00:58:43,000
small case I don’t know if it’s majority

1464
00:58:40,799 –> 00:58:45,559
but I would say my experience and this

1465
00:58:43,000 –> 00:58:46,799
is personal it’s been 5050 you question

1466
00:58:45,559 –> 00:58:49,640
you’re like why are you in the position

1467
00:58:46,799 –> 00:58:51,599
you’re in it just doesn’t make any sense

1468
00:58:49,640 –> 00:58:53,119
um realize the higher up you go it is

1469
00:58:51,599 –> 00:58:55,880
less about how smart you are it’s more

1470
00:58:53,119 –> 00:58:59,480
about how well you get along with others

1471
00:58:55,880 –> 00:59:02,280
yeah and what what can you do for them

1472
00:58:59,480 –> 00:59:03,880
or what can they do for you yes yeah

1473
00:59:02,280 –> 00:59:07,079
exactly

1474
00:59:03,880 –> 00:59:09,960
exactly uh something close to close to

1475
00:59:07,079 –> 00:59:11,760
it so you filter for those who simply

1476
00:59:09,960 –> 00:59:14,920
get [ __ ]

1477
00:59:11,760 –> 00:59:19,400
down what are the signs you look for in

1478
00:59:14,920 –> 00:59:22,400
vetting uh those um transformation

1479
00:59:19,400 –> 00:59:24,760
soldiers I’m a GS deer I like getting

1480
00:59:22,400 –> 00:59:27,720
[ __ ] done I am very biased towards

1481
00:59:24,760 –> 00:59:29,839
action I but but not everyone again is

1482
00:59:27,720 –> 00:59:33,200
built that way but I don’t do things

1483
00:59:29,839 –> 00:59:35,280
unless I’m 100% in it love it um or want

1484
00:59:33,200 –> 00:59:36,480
to pursue it manically and so I’ve

1485
00:59:35,280 –> 00:59:38,520
always taken that approach with my

1486
00:59:36,480 –> 00:59:41,480
position but again not everyone is built

1487
00:59:38,520 –> 00:59:44,960
that way so there is a group of other

1488
00:59:41,480 –> 00:59:47,319
gsds that I have known and got to know

1489
00:59:44,960 –> 00:59:50,200
and and I adore them because we just

1490
00:59:47,319 –> 00:59:51,799
want to get good stuff done um so I

1491
00:59:50,200 –> 00:59:54,480
think I always say it’s a mentality and

1492
00:59:51,799 –> 00:59:57,000
a mindset I really really appreciate it

1493
00:59:54,480 –> 01:00:00,760
this was this was fun this is great

1494
00:59:57,000 –> 01:00:02,960
thank you likewise and all the best with

1495
01:00:00,760 –> 01:00:05,880
with your book uh it’s it’s been doing

1496
01:00:02,960 –> 01:00:10,119
great so far but I’m really looking for

1497
01:00:05,880 –> 01:00:15,920
hard copy end of and April thank you so

1498
01:00:10,119 –> 01:00:15,920
much and all the best thanks so byebye