December 10, 2025 • 56 mins
In this episode of The Hope Table, Erin Brinker welcomes trailblazer Manal Iskander, founder of PC Tronics. Manal shares her inspiring journey from Egypt to Austria to the United States, rising as a leader in IT and innovation. The conversation explores how Manal leveraged global perspectives, embraced the tech revolution, and now helps businesses and nonprofits harness AI for growth and efficiency. She discusses the vital role of women’s voices in shaping the future of technology, the importance of digital readiness, and how AI can empower organizations of all sizes. The episode closes with a call to support children’s literacy and community engagement.
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Erin Brinker (00:00):
Erin. I'm Erin Brinker, and welcome to the hope
(00:10):
table. I'm so excited about ournext guest, and you're going to
love her as much as I do. Aftera successful career in Austria,
where she worked in finance andthen later assisted her family
business, she became one of theleading IT consultants and
Hewlett Packard distributors inVienna. She returned to the
United States in 1997 andfounded PC tronics. She
surrounded herself withincredible people, engineers
(00:33):
from MIT and other places, andshe became one of the first
engineers to obtain a formalMicrosoft certification. Well,
one of her engineers was, that'sManfred schmutzer, great
Austrian name there, one of thefounding members of PC tronics,
who's the CTO of the company,but all led by Manal. If you've
been in the Inland Empire, youknow who she is. Manal Iskander,
(00:54):
welcome to the show.
Manal Iskander (00:56):
Thank you for having me. Erin, what a great
introduction. Really appreciatethat.
Erin Brinker (01:01):
So tell me about who you are, and were you born
in Vienna, or did you move fromsomewhere else to Vienna?
Manal Iskander (01:08):
So basically, I'll give you a little recap, a
little bio. I consider myself arenaissance woman. There you go.
So I was born in Egypt,Alexandria, Egypt. My dad was in
the UN my biological father, andbasically we moved a lot due to
his work assignment. So I hadsome experience of living in
(01:30):
different countries growing up,and eventually landed here in
the US, but wanted to further myeducation back in Europe, so I
once again, left the US and wentto Austria Vienna to intern and
start studying, also in Austria.
So I had an aunt and uncle wholived there, which helped the
journey, and then basically, Iwent over there, did some
(01:54):
education, and then startedworking in the IT field in
Austria in the mid 90s, whenWindows and Microsoft was just
starting to make everything userfriendly, and computers weren't
really a major or it didn'treally exist back then, yet, it
was all kind of brand new anddeveloping, and so I kind of
jumped up on that, And mybackground wasn't in
(02:19):
engineering. So what I thinkmakes my story a bit fascinating
is my background was in finance.
So I studied finance and Iworked in the markets. I
actually got a job when I livedin Austria at the DAX. So I was
noticing, like millions,billions of dollars flowing into
(02:45):
just technology, the computers,computer chips, building
computers, internet, internetstartups. So I just saw lots of
money moving into thatdirection. So I kind of know
from my background, if youfollow the money, you can follow
the industry. So I knew thatthere was going to be a big
opportunity in tech, so Istarted working with my family's
(03:10):
company, and they owned acompany called the medis, and
they were the first HewlettPackard kind of partnership in
Austria, and they kind of got areally good deal, because
basically, it's not a monopoly,but it works like that. You get
these contracts and they'relocked in. So they were pretty
much like the sole supplier forHewlett Packard products in
(03:33):
Austria. So they grew reallyquickly in the tech industry,
and I got to see how it wasbuilt. And then, like mid to
late 90s, around mid 96 or so, Istarted realizing, like, hey,
maybe the US would be a betteropportunity. Since, if you've
ever lived in Europe, you know,it's not like the best for
business owners. It's a lot morebureaucratic, a lot more tax
(03:56):
heavy. So I thought maybe Icould try my hand here in the
US, where building a business isa little bit, let's say, less
restrictive. So I came here anddecided I was going to try to
duplicate that business here inSoCal, in which I did, and it
(04:17):
was like instantly successful,and I was right. And money and
industry all went into tech. Andeverybody was needing, you know,
an infrastructure, emailservers. Everybody was needing
everything they needed in orderto keep them relevant. And
that's kind of how I startedlong story. But that's it.
Erin Brinker (04:36):
Oh, that's incredible. I'm, I'm struck not
only with your ability to adaptto a changing market, and then
to seek out what you need. Somoving from Austria to the
United States, recognizing thatit's, it's more business
friendly here and it is, that'sa huge move. And I wonder, you
know you're, you're moving fromdifferent countries with your
(04:58):
family as you were growing up.
I'm sure, played a role in yourability to then adapt to a new
culture, because Egypt isnothing like United States,
which is nothing like Austria,and, you know, kind of, can you
talk about those culturaldifferences and what that was
like for you?
Manal Iskander (05:13):
Yeah, basically, I think living abroad does make
it's a it's a two fold, right?
Like one it, I don't get to sayI have these childhood friends.
I have a couple that I met whenwe moved to the United States
that are still my friends today.
But overall, you know, I can'tsay I went to kindergarten and
lived in the same neighborhoodlike everybody else, and can
(05:35):
talk about those stories. Sothat's the negative side of it.
The positive side of it is, isthat you are exposed to adapting
into a new culture. And I wouldgo to schools that were
basically English speaking. Soyou got to choose when you're in
the UN either like English orFrench, and so everybody's
speaking the same language, nomatter where they're from. So
(05:56):
that's kind of helpful, at leastfor me there, but I got to live
in, like, some fun places andsome scary places. I lived in
Costa Rica, I've lived inLondon, I've lived in Marrakech,
I've lived in Vienna, I've livedin Paris, I've lived in accent
Provence, I've lived inHonolulu. I've lived in
(06:17):
Montreal, Canada. So, yeah, wemoved around quite a bit. So
yeah, it does make it like, notas scary. I guess maybe the
biggest takeaway, I could tellyou is maybe fear isn't an
option, so you just don't evenget to think about it.
Erin Brinker (06:33):
It probably didn't occur to you to be afraid,
because you didn't have muchchoice,
Manal Iskander (06:37):
exactly, exactly. So it was kind of a
natural thing for me to bumparound. And it's actually
interesting, because I've livedin the same house now for like,
10 years, and I it's the longestI've ever lived in one place.
And so I always think maybe thisis a sign of age now
Erin Brinker (06:55):
well, and I wonder, you know, as you're
moving around, how you groundedyourself. Everybody has to be
grounded somewhere, right? Andsometimes it's with people, with
the family around you, andsometimes it's with place where
you're you're grounded. Myhusband, I joke that he's like
an oak tree. He He loves wherehe grew up. His father was in
the Air Force. When they finallylanded at Norton Air Force Base.
(07:18):
They stuck in this area. And,you know, there's, you know,
taking him out of our house in apine box. He'll, we will, he
will always live here. And so,so I wonder, Where did you find
your grounding growing up?
Manal Iskander (07:31):
So, you know, it's really interesting. I have
answered that question before,and what I've noticed about me
is that, because I'm new goingsomeplace, you know, I'm the new
person in town all the time thatit's easier to help people. So I
know I have a set of skills thatI take with me wherever I go,
because I've had exposure tothings that most people don't.
So I can go into a new town ornew area and kind of recognize
(07:55):
what value I could add. So Iusually ground myself by helping
people. So it helps, because nowI get to teach something, which
in turn creates the reciprocity,you know, kind of behavior, and
then people didn't want to guideme through the new town, new
system, new work environment,whatever it is. So I ground
(08:15):
myself really by helping people.
And it makes me feel morehumble. It makes me realize
that, you know, we're all inthis together, and centers me.
So that's usually how I getgrounded, is, so
Erin Brinker (08:25):
you meet the people and then and then learn
about them, and they learn aboutyou, and that then connects you
to where you are exactly. Ithink that's beautiful. Now,
when I think about both Egyptand Austria and but also many of
the other places that you'velived, they're old cultures with
(08:45):
traditions. I mean, Austria waswas, you know, 100 and a little
over 100 years ago, was thebreakup of an Austro Hungarian
empire. And there's echoes ofthat regal culture still there.
At least there was 30 years ago,or 40 years ago, when I was
there. And, you know, Egypt is,I mean, you can't, I don't know
(09:08):
that you can get a more famousancient culture than Egypt, as
as a as you move from differentcultures and and connected with
their history, because that'spart of their their present.
What was that like.
Manal Iskander (09:21):
So I love a lot of tradition. I love old
culture. I love countries thatgrow from their history. You
know, unfortunately, I can saywith Egypt, not as much. I was
really small. I didn't return alot as an adult, but I learned
it through family, right? Sobasically, in the home, culture
(09:43):
is that way. In Austria, I gotto visit it firsthand and meet
several other families thatdidn't belong to me, and watch
how they incorporate theirhistory and tradition. Is, I
think, helps connect people andit helps give. People a
commonality right away thatmakes them feel that they're
(10:04):
friends. And I think that'sreally important. And I think
when I come here to the States,or even when I'm traveling and
I'm in the States again, I donotice that we have a much more
individualistic society wheretheirs is more collective. And
so I find that a little bitdifficult when I come here, that
(10:24):
it's kind of each out for theirown. And so you have to, you
have to kind of articulate that.
You have to build it yourselfhere. So it's possible, but you
have to be the olive branch. Youhave to be the person that
offers first. So I can createthat same culture here, but I
have to go out and serve, andthen what I'm serving, then
people see my intention is,like, maybe pure, or I want to
(10:47):
elevate them, or I want to helpthem, and then they create me to
be inclusive. So they theninvite me into their world, and
then we have, like, a shared ashared goal or shared
friendship, and it works muchbetter for me that way.
Erin Brinker (11:03):
So, you know, Americans are like that because
we don't share a common history,because we come from everywhere,
as, you know, as families, asancestors, some hundreds of
years ago, some five minutesago, and and so we don't have
that. We have to create that.
And we tend to connect and makefriends in the United States by
doing things together, whetherit's playing baseball or
(11:23):
volunteering at a cleanup orwhatever we we connect by by
activities, which I don't thinkis in every culture. I don't
think that's the case.
Manal Iskander (11:34):
Correct, correct? Yeah. I mean, a lot of
cultures, it's already justbuilt in. I mean, collectivism
is really common, and, you know,we're just a very individualist
society. And I know churches andthings like that try to, like be
the healing source for thatdilemma that we have, but
overall, I think it's built intoour upbringing. So, you know,
(11:55):
the nuclear family, not verymany people spend time with
their extended family andcousins and things like that.
So, you know, I just noticedthat it's different. But I
accept all cultures, and I tryto like understand them to the
best of my ability. And, youknow, this country has given me
so many opportunities that Ilike, you know, I can't, I can't
(12:15):
stop but be grateful, right?
Erin Brinker (12:17):
So, so let's talk about your American experience.
Where in the United States haveyou lived?
Manal Iskander (12:23):
So I've lived in like the SoCal area, so like
Huntington Beach, ManhattanBeach area and Riverside San
Bernardino areas, and I've livedin Honolulu, and I spend quite a
bit of time back east, like DCarea. So that's pretty much it.
I don't have a lot of usexperience, but I mean,
(12:46):
California, I think is, in anutshell, the best experience
you can have if you're going tolive in the US, right? Indeed,
we
Erin Brinker (12:53):
certainly have the best weather by far. Yeah. So
let's talk about your your PCtronics business. And you know,
the 30 years in in the techindustry is like a millennia.
Things move so fast. So talkabout how things have changed.
(13:13):
What you've learned. Talk aboutthat experience.
Manal Iskander (13:18):
When I came here, I kind of knew very early
that this was booming, and I'mgoing to just bring it up to
today, because I definitely,honestly believe that, you know,
we can see how industries moveby watching where investments
take place. So like as today,everyone hears the big buzz
(13:40):
word, artificial intelligence.
And billions, if not trillions,of dollars are moving into
artificial intelligence. Ai, thesame thing was happening to me
in the 90s. I was watching thesame type of money roll into
tech startups.com, internet was,you know, just booming, and
everybody was entering themarket? Well, when everybody
enters the market, there's not,like, really a set of rules or
(14:03):
culture that's really developed,and everybody's just trying to
sell you something, and peopleare buying it, but they really
don't even understand whatthey're buying. So what happened
was, I was noticing when I camehere to the United States, that
the humanity of it was alreadylost. So I thought, you know, I
watched, I worked for anothercompany, first, to try to learn
(14:26):
it here, how it worked in theUS. And I was noticing that they
would go in, sell to a company,set them all up, give them the
servers, emails, everything thatthey needed. And then once they
were kind of up and running,they would chase the next new
customer. So the maintenance andthe kind of customer care was
being neglected, so thesecustomers would like put in
(14:48):
requests, but not really getgood service, which was a really
big common thing that happenedin the early 2000s late 90s. And
so I kind of thought I want tochange that. I want. To be the
person that takes care of thepeople that were kind of left
behind and make sure that theirenvironments are working and
they're staying up to date andmaking sure that the software or
(15:08):
SaaS they add is valuable tothem. Because I understood the
concept of business, so when Istarted doing that, I decided I
was going to go after companiesthat were not needing a new
setup, but that were feelingmore neglected by their IT
companies. So that's how Istarted. So I started by taking
(15:30):
companies that didn't feelheard, and then I was giving
them a voice, and I was guidingthem through the new technology
change, how to use the the newtools how to operate, windows,
Calendar, Outlook, and I endedup kind of becoming like a
strategic partner instead ofjust an IT firm. And why I like
(15:50):
that story is because here I am,30 years later, and like half of
my customers are from that timestill, oh, wow, yeah. So if they
stayed in business, they grewwith me, you know. So basically
they grew if they needed newlocations, if they needed to
upgrade, if they wanted to go tothe cloud. Now a lot of them
(16:12):
are, like, asking me about AI,and now I can still, like, grow
with them. So I tell myself, Iknow I have to say I'm an IT
company, but I'd like to say I'ma strategic partner, because it
means more to me that I am thepartner that tells you how to
use technology to meet youroutcome, instead of saying you
(16:36):
need to store your data, so youneed a you need storage, data,
storage, or you need a newbackup system. Instead of saying
that, I want to be able to sayhow much data do you need to
store, and why are we using it?
Like, what can we do with thisinformation? So now I'm more
like with what you have now, howcan we make that make sense for
(17:00):
you, like, with this informationthat you have, with this
technology, do you have? What doyou hope to get from it? And
then we try to work backwards,so we're like outcome based.
Erin Brinker (17:10):
I love that. And I worked in tech. During that tech
revolution as well. I wasworking for an application
service provider doing projectmanagement, and our sales people
would sell what I lovinglycalled a vaporware Sure, Mr.
Customer, this project productcan do whatever you want. It'll
mow your lawn, it'll make yourbeds, it'll do all the things.
(17:32):
And then the customer would cometo me and say, you know, to help
me build it my I was a liaisonwith with the actual engineers.
And I'm like, well, it won'tquite do that, but it will do.
People were just saying whateverthey wanted, and they, you know,
and that was, it was the wild,wild west.
Manal Iskander (17:49):
So, so, you know, I'm, like, really
determined to be a voice in thisfield. I'm determined to have
women sit at this table andshape the conversation. I
believe so adamantly that thishas not been written yet, and as
it's being written, if we take aseat at the table, we have a
(18:12):
opportunity to change what itlooks like. We have an
opportunity to change thenarrative of what AI means in
the workforce for us today. Sofor me, it's really important
that we talk about it.
Erin Brinker (18:25):
I agree. And you're, you're like a Sherpa on
Everest, because althoughEverest, I know, is a well a
well traveled path now, okay,you're a Sherpa on k2 you know,
trying to tend to help peoplenavigate the unnavigable. And I,
I'm hearing from differentsources, oh AI, it's, it's going
to take over the world. It'sgoing to create terminators, and
(18:46):
the other side is, oh AI, it'sgoing to be this great tool. And
there's a lot of confusion. Solet's, let's just what is AI and
how is it being used today?
Manal Iskander (18:57):
Okay, so AI is a big buzzword, so we have to be
careful. So Artificialintelligence has been around
around for a very, very longtime. It's just been the last
few years, people have beentalking about it openly because
llms came to the to the surface,which is just a large language
model that is a predictivemodel. So all it is is like an
(19:17):
algorithm that predicts the nextword by likelihood from all the
information that's ever been onthe internet. That, to me, is
not AI, but it is what hasallowed, like a layer of
intelligence, intelligentthinking, to be interjected into
certain software tools. So rightnow, people are going into chat.
(19:40):
GPT, Gemini, Claude, anthropic,whatever your tool may be, or
some of the image generators midjourney, nano banana, there's so
many out there, and those aretools. And those tools people
are just pulling through themarkets and just using them.
Yes, they're great. Yes,they're. But they're also very
(20:01):
dangerous. So all of that istrue. Whatever you put out
there, you don't know what'shappening with that information.
It's being used to train peopleare taking their customer data,
important information, and goinginto the chat, depositing it to
ask a question. What they don'trealize is they're putting their
their customer or their employerat risk. They're allowing
(20:23):
sensitive information to bereleased to they have no idea
where, because once it's beenput into the chat, you don't
know where exactly thatinformation is going, and nobody
can answer that question. Sobasically, there's some risks
involved with all of that, andthat's what people are talking
(20:43):
about.
Erin Brinker (20:43):
Okay, I'm processing what you're saying.
So if I'm I have a problem, abusiness problem, that I need to
solve, and I use people's realnames, you know, such and such.
Joe Smith, customer, this iswhat's happening. I need help.
And how do I solve this problem?
Then that I have then made thatsomehow public 100% 100%
Manal Iskander (21:04):
that information can is now variable, so they can
actually query your exact promptin that answer
Erin Brinker (21:12):
and oh my gosh. So I'm certain nobody knows that.
Manal Iskander (21:16):
That's what I'm saying. It's just like because
it's it got introduced to thepublic massively, without any
governance, governance or ethicsapplied to it, we have now some
danger spots for environmentslike specifically work
environments where you're you'rehaving your secretary or your
sales person put in a data sheetthat they've just dropped into a
(21:39):
chat on the way to a customer toget information that is
dangerous because yourenvironment isn't secure. Okay?
So to me, that's one side of AIthat we need to be talking
about, and we need to be able tocreate safe environments. So you
can create safe environments.
There are safe ways to downloadllms or to use llms in an
environment they're not trained,and they stay safe within
(22:02):
something called your tenant,your work environment. Okay, so
to me, like people in myprofession, should be talking to
everybody about that number one.
Okay, so that's the safetygovernance side of it. Now
there's another side of it thatpeople just aren't really
understanding. Ai having a chatbot that's just like a fun
(22:24):
little tool, or creating apicture that's a fun little
tool. But what makes artificialintelligence useful as a
business is that it can putintelligence in your data. So we
have all this data. Why do wehave all this data? It must be
good for something, right,right? How do we want to use it?
That is where AI should come in.
So I'm going to say, I have thislittle saying that. I say we're
(22:46):
customer zero. So I'm just goingto give you for my own company,
to give you an example, and thenwe'll think about it for any
other company you want. So in myown company, what would be
helpful to my customers with thedata that I have that I could
use AI, I decided, well, we do asecurity analysis on all
customers, so like, if we'vegone out to fix their systems
(23:10):
manually, remotely, what attackswere attempted, which ones were
resolved, which ones, what? Whatemployees had difficulty this
month. Just a general executivesummary of everything that
happened in their environment.
Well, we have software thatgives each different report for
(23:35):
each of these things. And ifyou've ever gotten a security
report, it's very blah, it'svery dry. It's like reading,
like, I don't know, like athesis, you know, you just don't
necessarily want to do right?
Erin Brinker (23:46):
You're not going to sit down, you know, with a
cup of tea and enjoy thatreading
Manal Iskander (23:51):
Exactly, exactly. So what we did was we
created an intelligence layerthat told about the customer, so
it's the customer profile, so ittells how does the customer like
to receive their information.
What language do they want tohave it in? What items are the
most important to them, and whatdo they not care about? Right?
And then we plug thatintelligence layer. So we used,
we used a Microsoft productcalled Azure AI foundry, and I
(24:14):
think they've just relabeled itto Microsoft foundry, but
basically there's all differenttypes of intelligence layers.
That's just the one we used,okay? And so basically we
created the profile all theinstructions in that. And then
what we did was we created anautomation from another tool. I
know these are a lot, but it'scalled N 8n. Okay. And basically
(24:35):
what we did was we told theautomation go into each one of
our SAS tools, pull out thereport, apply this customer
profile to it, and now create auser friendly fun infographic, a
fun email that lets them know,maybe not fun, depending how
they want it read, right, andautomatically send that report
(24:57):
directly to the. Customer aswell as deposit that into our
SharePoint as a record. So now,at the end of every month, we
don't do anything thisautomation we created with this
intelligence layer goes into allour reporting systems, pulls out
all the reports, deposits itinto a database. Then it takes a
intelligence layer with thecustomer profile, pulls out the
(25:20):
necessary information from eachreport, puts it in the format
the customer wants to have, putsit into a report and an email,
and automatically sends it tothat customer, as well as sends
it to our security analysisanalyst, as well as deposits it
into SharePoint. Now that is anAI automation that I've used to
now make my business moresuccessful. So now customers are
(25:44):
getting like a report that tellsthem, you know, you have this
one employee that called in moreHelp Desk than anyone else, or
you have this one aging system,you better be careful the next
month it's coming up, so youshould be budgeting for it, or
this software renewal is goingto be up in the next quarter, so
now they have this, like, way toread their data that's like,
(26:07):
understandable to them. Wow.
Erin Brinker (26:12):
Okay, so I'm processing all of this. It. This
is people companies would spendmillions of dollars on
consultants, you know, from BainCapital and McKinsey and all of
those to do this work, and nowyou've created a system that
will do it for you.
Manal Iskander (26:27):
Yes, anybody can, which is the greatest
thing. So this tool is going tolevel up small and medium sized
businesses to be able to work atan enterprise level, because if
you can automate. So basically,what you need to know in order
for any of this to work is youhave to have clean data, and you
have to know your workprocesses, right? So I have a
(26:48):
little thing that I call it'scalled the double diamond
theory, and it's discover,define, that's the first
diamond, and then design,deliver, that's the second
diamond. So the first diamond isdiscover, define. So we got to
go in, and we have to discoveryour workflows, your friction
points, what work is repetitive,that can be automated. What? How
(27:11):
do you use your data? What doyou want your data to do? What
are we trying? What is what arewe trying to create here? And
then, once we do that, we candefine it. We can say, okay,
these work processes can beprocesses can be automated.
These work processes can beassisted. You know, this is the
map or the map flow, or workflowthat we can create and make it
(27:34):
more efficient. So now we're ina definition process where we
can now decide what are all theproblems you want to solve? What
do the workflows look like? Nowwe can actually decide design
them, and once we design them,then we can deliver them. So
basically, anything can becreated. Now if you have clean
data, everything's AI readable.
So is it in a safe environment?
(27:56):
Is the data protected? Can theAI intelligence layer easily
access it, read it, identify it,and use it. So for us now, all
these small businesses are goingto be able to automate things
that used to be very, veryexpensive to do. They would need
employees to be able to sortdata, send emails, send like I'm
(28:22):
going to give you another one weautomated. So we automated this
blog profile. So basically itwas a blog profile for a
customer that's in blockchain,and basically they wanted all
the latest blockchain news, sowe connected it to coin desk. It
takes all of the latest newsfrom blockchain, it turns it
into a current blog. It goes onto WordPress, it it posts it for
(28:44):
them, and then it sends them acopy, and then it gives them
analytics at the end of themonth. So the whole thing is
automated. The whole thing isautomated, but it's staying
current, so it's exactly whatthe person would have done. So
it's not giving like fake AIinformation. It's not like fake
AI news. It's doing what a humanwould have done anyway. It would
have gone to coin desk, pulledtoday's latest news and then put
(29:07):
it into their blog with today'slatest news.
Erin Brinker (29:10):
So for those of you all who are listening and
they don't, you don't know whatblockchain is. It's Bitcoin,
right?
Manal Iskander (29:16):
Yeah, well, Bitcoin was created on
blockchain, right? Bitcoin isjust a use case scenario. So
blockchain is any any data thatis you want to put on something
that's immutable, and you can't,you can't, you can't delete it,
and you can always trace it. Sowhat they did was they created
Bitcoin as a proof of conceptthat blockchain works and
(29:38):
blockchain works. So Ethereum ison blockchain. Solana's on the
ledger. There's lots ofblockchains, and basically it's
like a block where you can putdata in it, but nobody can
access it unless they have keys,and nobody can change the data.
So this is on a tangent, butI'll give you like a little way
I used to help childrenunderstand it. You know, when
(30:00):
you were a kid, and you wouldget, like, those paper mache
little things, and you wouldmake like a like a ring. You
would take a piece of strip ofpaper, and you would glue it
together, and you would make itinto like an oval shape, and
then you'd add another one toit, and another one to it, and
another one to it. Okay, sothat's kind of like blockchain.
If you want to change somethingin the middle, you can't without
(30:21):
destroying the entire chain. Soyou can't go backwards. You can
only go forward. So anythingthat's ever been created is
always discoverable. That's whyit's like being used in like the
financial worlds, because peoplecan't go back and alter their
accounting. So that's a wholenother story. But in any case,
(30:42):
my point is, is that you canautomate anything, so like the
security reporting the you couldautomate. You know, in my
company, we automate, likeonboarding new customers.
Instead of having a tech go outthere, enter all their
information into a CRM thenenter it into the PSA, all
that's just automated. So if youthink about how many hours a
(31:02):
week do you spend doing tasksthat don't need your judgment or
discernment, then should youalleviate those tasks so that
your time can be used on thingsthat need your judgment and
discernment? Right?
Erin Brinker (31:19):
Exactly, well, and it's something that you would
you would have hired somebodybefore. Somebody before, and you
may not be able to that may notbe in your budget. This is
something that's a lot morestreamlined. It takes time
upfront, because you have to puttogether the framework for to
teach the AI, what you want itto do. And then once you've done
that, then it's automated. Isthat correct?
Manal Iskander (31:39):
Well, you don't have to teach, which is great.
It does learn over time. Butlike, let's say, I'm just trying
to think of what you have. Like,let's say, because you you used
to, or, I don't know if youstill have hope foundation, but
do you still have the thatfoundation?
Erin Brinker (31:55):
So So I, I am not no longer with the making hope
happen foundation. I am doingsome independent consulting and
then focusing on the InlandEmpire children's book project.
Manal Iskander (32:05):
Okay, okay, well, let's I'm just going to
use the make it Hope Foundationas an example. But let's say you
had a list of investors anddonors there, and they get
reports on how their money isbeing utilized, and also maybe
reminders to donate or investagain. Okay, so maybe you have
all this is where your data hasto be clean. So let's say you
(32:27):
put all of your monthly updatesonto your foundation, into a
SharePoint folder, and you alsohave all your donor information
and all of your highlights,whatever it is that you use to
give people information, you canthen create a workflow, and
that's what it would be. Itwould be like a workflow that
says, take the donorinformation, investment
(32:50):
information, etc, etc, create amonthly newsletter letter that
has a donate Link button there,and have it sent out to this
email address that lets donorsknow where their money is going,
and then it updatesautomatically every month for
you. And basically, you writethe intelligence layer telling
it what to do with the data.
That's all you're really doing.
(33:12):
It's not learning from it.
You're telling it what to do. Soinstead of you doing it, you're
saying, I want this data to beextracted and given to donors as
a recap of where their money isbeing spent, something like
that. And then it would takethat data, it would read it, it
would recap it, and then itwould send it automatically to
(33:32):
those donors with maybe a donateLink button. So every month,
they get an email that tellsthem where their money is going
and if they want to donate moremoney, fantastic, a workflow
program or workflow process. Soyou're not necessarily having to
teach the intelligence. Whatyou're really having to do is
understand your workflows, andthat's what people are missing.
It's not about the intelligencelayer. The intelligence layer is
(33:55):
the bonus. What it is is, do youunderstand your workflow like,
do you understand what it takesto get your work done on a daily
basis, weekly basis and monthlybasis, and what do those
workflows look like? And if youunderstand that, then you can
make sure those workflows nowhave readable data. So is it
something you're handwriting?
(34:17):
Well, then that won't work. Sohow do you now make that
digital. Is it something like,let's say it's these podcasts
you're doing. What if thesepodcasts were the transcriptions
were automatically taken putinto like, these transcriptions
are turned into five LinkedInposts and five blog articles
(34:39):
from everything you and I talkedabout for every guest,
fantastic, right? Then that's aworkflow. So then you could
you're then it would take thistranscription, it would download
it in a database, and then itwould take the intelligence
layer of us telling it what wewant it to do with these
transcriptions. And then itwould create your LinkedIn
profiles. It would search. Youryour photo gallery for
(35:02):
applicable pictures, it wouldyou could give it the log on to
your WordPress, and it would goinside and log on to your
WordPress and and deposit blogs.
It would go into your LinkedInand update your in post LinkedIn
articles so it could do all ofthat if you understood the
workflow and what you want fromit. And that's what I noticed.
(35:23):
People don't have. They eitherdon't have everything digitized,
so they don't have clean data.
So they're like, Oh, we wrote itdown. It's on these posted
notes. And somebody
Erin Brinker (35:33):
that doesn't work, no, it doesn't,
Manal Iskander (35:35):
it doesn't work, or they don't know what they
want, like, they're like, well,they have this great data tool,
but they don't know what theywant to do with the data. Like,
like, what are you going to dowith these great podcasts and
all these tidbits ofinformation? What's your goal
with it? And then once you canunderstand that, you can be
like, Oh, wow. Like, you know,maybe I could sit take the top
(35:56):
10 founder sayings out of thesepodcasts, and it'll pull it out,
and I'll do a series on the top10 founder sayings, or whatever
it could be. But you now get tobe creative right now. Now you
get to say, what do I want outof my data? If I could do
anything I wanted with it?
Erin Brinker (36:15):
Oh my gosh. It's fantastic. And become a much
better producer than I wouldhave been otherwise. Exactly.
Manal Iskander (36:20):
So for me, AI is a buzzword that needs to be
grounded, and people need tounderstand there's like, ethics
and governance that we have totake very seriously. And it is
true that we are in a verydangerous time of society right
now with AI, and there is goingto be a lot of data linked. And
(36:42):
we do need governance andethics, and we need security all
around people who use AI. Andthen we also need to be able to
lead AI into having it help uslike it should be our hands and
feet. It should be the busywork. It should be the noise
Canceler, and that's what I wantit to do, for me, is just cancel
(37:03):
out all the noise so I can forgethe actually, what I want to do
is kind of forge the path to letpeople understand it, especially
women, because I think it's agreat opportunity that these new
workflows are created with awoman's voice in mind.
Erin Brinker (37:19):
So I definitely want to talk about that, but
before, before we do, I want totalk about some of the risks,
because the potential for AI tobe used for hacking and phishing
and otherwise scamming is alsovery high, and so how do you
protect yourselves from that?
Because, and I'll use this verybasic example, you get a spoof
email. You know, this is yourinvoice for whatever, and it
(37:41):
comes from an email address youdon't recognize, or maybe you
do, but the language isn'tright, so you know that your
colleague didn't actually sendit to you because it was written
by somebody who doesn't speakEnglish. All of that goes away
with AI. And it could very muchlook like, like, you know, an
attachment and email. This is abasic example. Could look like
something that is completelylegitimate, but it's not you
(38:03):
your AI, could you know, onceit's infiltrated your system, it
can do whatever it wants, orwhatever it was designed to do.
Kind of talk about the riskthere.
Manal Iskander (38:18):
Human risk is always going to be a risk. It's
something you can't even 100%eliminate. So that's the second
part of the story, but there areways we are working on it. So as
well as AI is getting developed.
So is it in a hacker's world? Soour security platforms now all
are having built in AI. So howthat works is there are lots of
security platforms we use, likeHuntress Sentinel one guards.
(38:40):
And the ones that we use have anAI intelligence layer built into
the security so the one we useis called like, it has purple AI
built in. But basically what itdoes is it has, like a training,
a security awareness trainingthat goes out to the
organization. So like, if youhired me, you would have a
(39:01):
training that goes out to all ofyour employees that they have to
take, and it then sends me backresults, and it's the latest AI
hacking phishing attempts. Sothe security training recognizes
the latest ones that are outthere. It then purposely pushes
them through to your employeesin a safe garden environment.
(39:22):
Then it allows you to see whichemployees are opening up those
unsafe emails them to havefurther training before they're
allowed to be on yourenvironment. Then they pass a
training every month that letsus know that they're now being
aware as well as us having thisAI intelligence layer that's
(39:43):
pulling the emails ahead oftime, so any of these suspicious
emails already being pulled sothey never even get to the end
user, they're going into aquarantine folder where they're
getting analyzed by our team,which is also now art of.
Artificial Intelligence. Soartificial intelligence now
analyzes this batch of emails,decides that they were unsafe.
(40:05):
If they are, they're eliminatingthem. If they were safe, they're
re pushing them through to theend user. So now companies like
mine are even more important,because you really do need this
layer between us and youremployees, because your
employees are your greatest riskto your company indeed.
Erin Brinker (40:25):
I mean, you know, from opening that email to
finding a finding a flash drivein the parking lot and thinking
it's a good idea to plug it in,things have happened. Yes, so
you
Manal Iskander (40:38):
we've had a customer do that one time we
were let go, and because theythought we were a little
expensive. And to be honest, I'mnot expensive, so it was quite
an insult, but I was graciousenough to just back out kindly.
And they used a company that didnot have the software built in,
and they got ransomware for oneBitcoin, which was like just
(41:02):
under $100,000 at the time, andthey had to pay it to get their
data back. And I realized that,you know, you can't. There is no
cost to security,
Erin Brinker (41:11):
really, no. Oh, my goodness. Oh my goodness. So you
started talking about makingsure that women, that female
entrepreneurs, are at theforefront of this and that they
understand what's going on.
Let's explore that a little bit,because, and I will tell you
that I reached out to you and wehave, we have both been in this
area for and worked alongsideeach other for a long time, but
(41:32):
I had never interviewed you,despite being on the air for a
while and and you wrote anarticle posted on LinkedIn that
just moved me so much aboutabout women being relevant in
and staying relevant, especiallyolder women in in the workforce,
and how things are changing andthat that things are flattening,
(41:52):
as far as opportunity isconcerned, talk about that a
little
Manal Iskander (41:59):
bit like women older than 50 have a really
interesting opportunity intoday's world. Not only did we
forge a place for ourselves in aworld that had no place for us,
and I find that reallyfascinating, but now we have a
way to basically influence whatwe look like in this world of
(42:23):
AI, how AI is utilized andspoken to. It's very important
for us to take a seat at thetable, because the industry
technology in itself, has beenshaped mostly by men, language
by men, outcomes by men. Andunfortunately, we are a world
full of diversity, not justgender, but all types of
(42:46):
diversity that is not reallybeing accounted for. And little
things I noticed, like I gaveyou an example earlier about the
security reporting, I always sayto my team, because I'm a female
leader, and I work with a lot ofmen, and there's often not very
many women to hire, which isalso very disappointing for me,
(43:07):
and indeed, more and more womento enter this field, as well as
do I speak to a woman'scybersecurity club, because I
really want them to be in thisfield. To shape the language
that security reporting I toldyou about changed, because the
reporting that I was gettingfrom my team prior was very
(43:27):
technical reporting. You've gotmostly men in that work in tech,
and they think like engineers.
They develop with a very clear,concise outcome, which is great,
but the world is filled withcolor and emotion and connection
that gets dropped, you know, andwith the security reporting in
the intelligence layer, I wasable to write a profile that
(43:51):
talked about the women leaders.
I have two or three companieswith women leaders. I'm speaking
to women, not men. I know howthey want their material. I know
how they would like to be spokento. I know how they would enjoy
seeing their highlights, ortheir team highlighted, not just
(44:11):
the things that were fixed. So Iwas able to use my voice to
create these security reportsthat now have such a different
feel than the ones before, andthat's because we as women now
have are touching these fieldslike we are going to be able to
shape how information is goingto be transferred from company
(44:33):
to company, human to human, howthe language models when you go
into chat, GPT or Gemini, howit's going to deliver
information to you. So for me,we have a very large opportunity
now to take our seats at thetable. We shouldn't be waiting
for permission. We need to speakup, we need to write, we need to
(44:57):
post. We need to figure out. Ourworkflows are, and make sure we
write the intelligence layersthat show how that workflow gets
automated, how that workflowgets actualized, right? So for
me, it's really important thatwe let our 50 years or more of
judgment and discernment, ourwisdom, our ability to nurture
(45:21):
our ability to recognize humanconnection, allow all those
things to take part in whatwe're building right now. It's
really important to me to seewomen do that.
Erin Brinker (45:31):
You know, it's interesting because, you know,
because it's a computer andbecause it's, you know, it's
based on logic models. It's,there is no human element in AI.
So if you want a human element,you have to put it there, and
you have to recognize that it'smissing. And I, and I think that
that's more than likely thatthat it would seem that that
(45:53):
would come from women more thanfrom men.
Manal Iskander (45:56):
Yes, exactly, you know. And you know, we are
in a position where we are nowkind of redefining what work
looks like, right? So right now,in this time, like no other time
before, there's going to be anew definition of work. I've
just told you about all theseautomations. So think about a
year from now, how many jobswill be replaced by these
(46:19):
automations. Okay? So if thesejobs are replaced, what happens
to those work? That workforce?
It doesn't disappear. It getsredefined right like now, humans
are going to be used to do othertypes of work. What does that
look like now we sit at theforefront where we get to define
that. To me, it's reallyimportant to decide, you know,
(46:40):
we're still relevant. How do wefit in? How do we use the skills
we've crafted to create new waysfor us to contribute to this new
definition of work? Right? Like,I definitely think you're going
to see the whole definition ofwork be redefined within the
next one year to 18 months, that
Erin Brinker (47:05):
wow and it, you know, think about, we talked
about the the the internet boom,and in the late 90s and early
2000s we thought that movedquickly. This dwarfs that
exactly.
Manal Iskander (47:18):
It's going to happen so fast. I have been
speaking at these panels acrossthe world. I went to Amsterdam
and spoke last October, and Iwas in Colorado and spoke a
couple months ago, and I've beenjust traveling and speaking on
this topic. And interestingenough, I'm going to speak next
summer at a big AI conference inUtah called Beyond 2026 and I
(47:46):
was trying to like, put togetherwhat my breakout room is going
to look like, like what I'mgoing to be teaching people. And
I realized that whatever I puttogether today may be obsolete
by June or maybe so widely knownalready that it will be not
obsolete, but be too familiar.
So I'm realizing that, like whatI'm talking to about now, like
(48:10):
taking a work workflow,automating it with an
intelligence layer, you know,using these in SAS tools, it
will probably be commonknowledge by March already. So
information is going so fastthat even trying to find things
to talk about, I have to get tothe podium quickly, like I'm
like, I can give you this, thisspeech on AI, but we have to do
(48:34):
this like now, because it ismoving so quickly that I can't
even tell you that some of thethings we're talking about now
will just be very commonknowledge six months from
Erin Brinker (48:47):
now, wow, it'll be common knowledge. However, it
won't be, because I think forthose people who are not living
in a tech world, they're thesmall business, they're running
a bakery, they're running apreschool, they're doing
whatever it is that they do,they're being told, AI is great,
Ai, you need AI, you need AI.
And they're like, I can't evenget my brain around it, and I
read something and it'scompletely contradictory to
(49:09):
something else. And what do Iuse? Which system do I use?
Gemini? Do you use chat? Youknow, does it have the capacity
to do all the things I don'teven know? You know where this
fits, and I'm overwhelmed, as Ithink that this is moving so
fast that, you know, if the techpeople are feeling exhausted,
everybody else is lost,
Manal Iskander (49:32):
same time, you're not going to be able to
function without it. AI is nowavailable to the small and
medium business, and becauseit's not going to have a price
tag attached to it like it didtwo years ago or a year ago,
it's it's going to make a lot ofsense I'm going to give you
because you know this person, soKim Calvin, for example, owns a
(49:56):
coma unity organization LearningCenter and. She has been
operating in this NGO space withyou know how everyone else has
been doing their best, trying tosurvive, trying to get the data
organized, etc. I went and spokewith her, and I'm like, Listen,
you are super primed to be ableto get an AI ready, readiness
(50:20):
grant where you are able toorganize your data to be aI
retrievable, so AI systems cansit on top of any NGO and be
able to scale it. And we wentin, and she's eligible for like
95% of the grants out there. Andoh my gosh, that's great. $1
billion worth of grants outthere to help companies become
(50:44):
AI ready. So what I want you tohear, it may be overwhelming, I
hear that, but you've gotcompanies like open AI Gemini,
who have spent billions, if nottrillions, of dollars,
developing this, and now theyneed people to use it safely.
How are they going to do that?
If it's not working the way theywant it to, they're putting
grants and money out there soorganizations can adapt, adapt
(51:05):
it correctly, so thatorganizations will start to use
and be aI ready, AI first. Sobasically, these people, every
massive enterprise organizationthat's dealing with AI, needs
your data to be aI ready so theycan access it, so they can build
even further. So a company likeGoogle needs every small and
(51:28):
medium business to be aI ready,so that their data is AI
readable, so that they can buildit better. So they're going to
encourage companies like yours,mine, anybody, to be able to to
have this learning curve be notso difficult. So I'm telling you
it's going to go out there bygetting companies, foundations,
(51:50):
their digital readiness first.
And that's what you're going tosee, a big, massive push towards
digital readiness. AI readydata, and there'll be grants and
money out there to help thishappen, and there'll be
companies like me that are goingto be hired to help companies do
that. And that's what I'm that'swhat I'm doing. I'm hoping to
(52:13):
get grants for companies whowant to hire me so that I can
help them organize theirworkflow and then automate it
and put intelligence layers sothat they can be outcome based.
Erin Brinker (52:24):
Wow, wow. It's, that's, that's fantastic. And
and the next question, thenthis, all of this, begs the
question, do we have the energyto power all of this as a global
community?
Manal Iskander (52:39):
So obviously, that is the big buzz word and
environmental harm andeverything else. So I'm an
optimist, so I believe that AI,the usage of AI, is going to
help solve a lot of our AIissues. So I feel like it's a
tool that helps us connect areasthat we don't see connected yet,
(53:03):
information that we don't seethat we're not able to see,
because we broadly can't seeeverything at once. So I am a
huge optimist, optimist thatbelieve that this issue will be
solved through AI, and that AIwill eventually help us become
carbon negative, not just carbonzero. So I am a huge optimist. I
(53:27):
am not a typical voice outthere, but I have watched
industries grow and seen doomand gloom, and you're as old as
I am, so I'm sure you remember y2k where we thought at the year
2000 everything was going to goand then nothing. Then nothing
happened, and everything wasfine. I kind of feel like as
humans, we catastrophize thingsthat we don't know. And I do not
(53:52):
believe that this is somethingthat is going to be a war of AI,
robots, destruction. I don'tbelieve that. I think this is
going to be our our way, toallow humans to be redefined,
for us to have more creativity,for us to undo the damage we've
done, for us to be able to bemore self regulated. So I see
(54:15):
this is a big upgrade tohumanity, and we're just going
to go through chaos for the nextfive years until the upgrade is
completed, if that can makesense.
Erin Brinker (54:24):
But well, Manel Iskander, we are completely out
of time. How do people find findyou on social media, if you're
on social media, and moreimportantly, how do they connect
with you at PC tronics
Manal Iskander (54:36):
appreciate that.
So I'm mostly on LinkedIn. Idon't do a whole lot of social
media. So LinkedIn, ManelIskander, president and founder
of PC tronics and Fugazi, whichis a marketing company, and I am
at PC tronics.us, so you can doI have like a reach out to me on
that. So you can reach out to meeither by LinkedIn or www.
(54:57):
Seatronics.us on my website.
There is a way to contact methere.
Erin Brinker (55:04):
Well, what a treat this has been. Manaliskander,
thank you so much for joining metoday.
Manal Iskander (55:09):
Thank you so much, Erin. I appreciate having
you having me on today.
Erin Brinker (55:13):
Well, that is all we have time for today. Thank
you so much for spending thistime with me. And I hope you
learned a lot. I know I did. I'mErin Brinker, and you've been
listening to the hope table.
I'll see you next week.
(55:34):
This holiday season, while manychildren unwrap toys and treats,
some will unwrap something farmore powerful their very first
book at the Inland Empirechildren's book project, we
believe every child deserves thejoy of reading the kind that
sparks imagination, buildsconfidence and opens the door to
a brighter future. But rightnow, more than 75% of low income
(55:55):
students aren't reading at gradelevel, and many have no books at
home at all. You can change thatwith your support, we're putting
books directly into the hands ofchildren, one gift, one child,
one story at a time. Join us ingiving the gift of literacy.
Visit Inland Empire children'sbook project.org. To donate or
get involved. That's InlandEmpire children's book
(56:18):
project.org. We're a 501, c3,non profit organization and 100%
volunteer. Give a book. Givehope, give the gift of literacy.
At Inland Empire children's bookproject.org, you.
Erin. I'm Erin Brinker, and welcome to the hope
(00:10):
table. I'm so excited about ournext guest, and you're going to
love her as much as I do. Aftera successful career in Austria,
where she worked in finance andthen later assisted her family
business, she became one of theleading IT consultants and
Hewlett Packard distributors inVienna. She returned to the
United States in 1997 andfounded PC tronics. She
surrounded herself withincredible people, engineers
(00:33):
from MIT and other places, andshe became one of the first
engineers to obtain a formalMicrosoft certification. Well,
one of her engineers was, that'sManfred schmutzer, great
Austrian name there, one of thefounding members of PC tronics,
who's the CTO of the company,but all led by Manal. If you've
been in the Inland Empire, youknow who she is. Manal Iskander,
(00:54):
welcome to the show.
Manal Iskander (00:56):
Thank you for having me. Erin, what a great
introduction. Really appreciatethat.
Erin Brinker (01:01):
So tell me about who you are, and were you born
in Vienna, or did you move fromsomewhere else to Vienna?
Manal Iskander (01:08):
So basically, I'll give you a little recap, a
little bio. I consider myself arenaissance woman. There you go.
So I was born in Egypt,Alexandria, Egypt. My dad was in
the UN my biological father, andbasically we moved a lot due to
his work assignment. So I hadsome experience of living in
(01:30):
different countries growing up,and eventually landed here in
the US, but wanted to further myeducation back in Europe, so I
once again, left the US and wentto Austria Vienna to intern and
start studying, also in Austria.
So I had an aunt and uncle wholived there, which helped the
journey, and then basically, Iwent over there, did some
(01:54):
education, and then startedworking in the IT field in
Austria in the mid 90s, whenWindows and Microsoft was just
starting to make everything userfriendly, and computers weren't
really a major or it didn'treally exist back then, yet, it
was all kind of brand new anddeveloping, and so I kind of
jumped up on that, And mybackground wasn't in
(02:19):
engineering. So what I thinkmakes my story a bit fascinating
is my background was in finance.
So I studied finance and Iworked in the markets. I
actually got a job when I livedin Austria at the DAX. So I was
noticing, like millions,billions of dollars flowing into
(02:45):
just technology, the computers,computer chips, building
computers, internet, internetstartups. So I just saw lots of
money moving into thatdirection. So I kind of know
from my background, if youfollow the money, you can follow
the industry. So I knew thatthere was going to be a big
opportunity in tech, so Istarted working with my family's
(03:10):
company, and they owned acompany called the medis, and
they were the first HewlettPackard kind of partnership in
Austria, and they kind of got areally good deal, because
basically, it's not a monopoly,but it works like that. You get
these contracts and they'relocked in. So they were pretty
much like the sole supplier forHewlett Packard products in
(03:33):
Austria. So they grew reallyquickly in the tech industry,
and I got to see how it wasbuilt. And then, like mid to
late 90s, around mid 96 or so, Istarted realizing, like, hey,
maybe the US would be a betteropportunity. Since, if you've
ever lived in Europe, you know,it's not like the best for
business owners. It's a lot morebureaucratic, a lot more tax
(03:56):
heavy. So I thought maybe Icould try my hand here in the
US, where building a business isa little bit, let's say, less
restrictive. So I came here anddecided I was going to try to
duplicate that business here inSoCal, in which I did, and it
(04:17):
was like instantly successful,and I was right. And money and
industry all went into tech. Andeverybody was needing, you know,
an infrastructure, emailservers. Everybody was needing
everything they needed in orderto keep them relevant. And
that's kind of how I startedlong story. But that's it.
Erin Brinker (04:36):
Oh, that's incredible. I'm, I'm struck not
only with your ability to adaptto a changing market, and then
to seek out what you need. Somoving from Austria to the
United States, recognizing thatit's, it's more business
friendly here and it is, that'sa huge move. And I wonder, you
know you're, you're moving fromdifferent countries with your
(04:58):
family as you were growing up.
I'm sure, played a role in yourability to then adapt to a new
culture, because Egypt isnothing like United States,
which is nothing like Austria,and, you know, kind of, can you
talk about those culturaldifferences and what that was
like for you?
Manal Iskander (05:13):
Yeah, basically, I think living abroad does make
it's a it's a two fold, right?
Like one it, I don't get to sayI have these childhood friends.
I have a couple that I met whenwe moved to the United States
that are still my friends today.
But overall, you know, I can'tsay I went to kindergarten and
lived in the same neighborhoodlike everybody else, and can
(05:35):
talk about those stories. Sothat's the negative side of it.
The positive side of it is, isthat you are exposed to adapting
into a new culture. And I wouldgo to schools that were
basically English speaking. Soyou got to choose when you're in
the UN either like English orFrench, and so everybody's
speaking the same language, nomatter where they're from. So
(05:56):
that's kind of helpful, at leastfor me there, but I got to live
in, like, some fun places andsome scary places. I lived in
Costa Rica, I've lived inLondon, I've lived in Marrakech,
I've lived in Vienna, I've livedin Paris, I've lived in accent
Provence, I've lived inHonolulu. I've lived in
(06:17):
Montreal, Canada. So, yeah, wemoved around quite a bit. So
yeah, it does make it like, notas scary. I guess maybe the
biggest takeaway, I could tellyou is maybe fear isn't an
option, so you just don't evenget to think about it.
Erin Brinker (06:33):
It probably didn't occur to you to be afraid,
because you didn't have muchchoice,
Manal Iskander (06:37):
exactly, exactly. So it was kind of a
natural thing for me to bumparound. And it's actually
interesting, because I've livedin the same house now for like,
10 years, and I it's the longestI've ever lived in one place.
And so I always think maybe thisis a sign of age now
Erin Brinker (06:55):
well, and I wonder, you know, as you're
moving around, how you groundedyourself. Everybody has to be
grounded somewhere, right? Andsometimes it's with people, with
the family around you, andsometimes it's with place where
you're you're grounded. Myhusband, I joke that he's like
an oak tree. He He loves wherehe grew up. His father was in
the Air Force. When they finallylanded at Norton Air Force Base.
(07:18):
They stuck in this area. And,you know, there's, you know,
taking him out of our house in apine box. He'll, we will, he
will always live here. And so,so I wonder, Where did you find
your grounding growing up?
Manal Iskander (07:31):
So, you know, it's really interesting. I have
answered that question before,and what I've noticed about me
is that, because I'm new goingsomeplace, you know, I'm the new
person in town all the time thatit's easier to help people. So I
know I have a set of skills thatI take with me wherever I go,
because I've had exposure tothings that most people don't.
So I can go into a new town ornew area and kind of recognize
(07:55):
what value I could add. So Iusually ground myself by helping
people. So it helps, because nowI get to teach something, which
in turn creates the reciprocity,you know, kind of behavior, and
then people didn't want to guideme through the new town, new
system, new work environment,whatever it is. So I ground
(08:15):
myself really by helping people.
And it makes me feel morehumble. It makes me realize
that, you know, we're all inthis together, and centers me.
So that's usually how I getgrounded, is, so
Erin Brinker (08:25):
you meet the people and then and then learn
about them, and they learn aboutyou, and that then connects you
to where you are exactly. Ithink that's beautiful. Now,
when I think about both Egyptand Austria and but also many of
the other places that you'velived, they're old cultures with
(08:45):
traditions. I mean, Austria waswas, you know, 100 and a little
over 100 years ago, was thebreakup of an Austro Hungarian
empire. And there's echoes ofthat regal culture still there.
At least there was 30 years ago,or 40 years ago, when I was
there. And, you know, Egypt is,I mean, you can't, I don't know
(09:08):
that you can get a more famousancient culture than Egypt, as
as a as you move from differentcultures and and connected with
their history, because that'spart of their their present.
What was that like.
Manal Iskander (09:21):
So I love a lot of tradition. I love old
culture. I love countries thatgrow from their history. You
know, unfortunately, I can saywith Egypt, not as much. I was
really small. I didn't return alot as an adult, but I learned
it through family, right? Sobasically, in the home, culture
(09:43):
is that way. In Austria, I gotto visit it firsthand and meet
several other families thatdidn't belong to me, and watch
how they incorporate theirhistory and tradition. Is, I
think, helps connect people andit helps give. People a
commonality right away thatmakes them feel that they're
(10:04):
friends. And I think that'sreally important. And I think
when I come here to the States,or even when I'm traveling and
I'm in the States again, I donotice that we have a much more
individualistic society wheretheirs is more collective. And
so I find that a little bitdifficult when I come here, that
(10:24):
it's kind of each out for theirown. And so you have to, you
have to kind of articulate that.
You have to build it yourselfhere. So it's possible, but you
have to be the olive branch. Youhave to be the person that
offers first. So I can createthat same culture here, but I
have to go out and serve, andthen what I'm serving, then
people see my intention is,like, maybe pure, or I want to
(10:47):
elevate them, or I want to helpthem, and then they create me to
be inclusive. So they theninvite me into their world, and
then we have, like, a shared ashared goal or shared
friendship, and it works muchbetter for me that way.
Erin Brinker (11:03):
So, you know, Americans are like that because
we don't share a common history,because we come from everywhere,
as, you know, as families, asancestors, some hundreds of
years ago, some five minutesago, and and so we don't have
that. We have to create that.
And we tend to connect and makefriends in the United States by
doing things together, whetherit's playing baseball or
(11:23):
volunteering at a cleanup orwhatever we we connect by by
activities, which I don't thinkis in every culture. I don't
think that's the case.
Manal Iskander (11:34):
Correct, correct? Yeah. I mean, a lot of
cultures, it's already justbuilt in. I mean, collectivism
is really common, and, you know,we're just a very individualist
society. And I know churches andthings like that try to, like be
the healing source for thatdilemma that we have, but
overall, I think it's built intoour upbringing. So, you know,
(11:55):
the nuclear family, not verymany people spend time with
their extended family andcousins and things like that.
So, you know, I just noticedthat it's different. But I
accept all cultures, and I tryto like understand them to the
best of my ability. And, youknow, this country has given me
so many opportunities that Ilike, you know, I can't, I can't
(12:15):
stop but be grateful, right?
Erin Brinker (12:17):
So, so let's talk about your American experience.
Where in the United States haveyou lived?
Manal Iskander (12:23):
So I've lived in like the SoCal area, so like
Huntington Beach, ManhattanBeach area and Riverside San
Bernardino areas, and I've livedin Honolulu, and I spend quite a
bit of time back east, like DCarea. So that's pretty much it.
I don't have a lot of usexperience, but I mean,
(12:46):
California, I think is, in anutshell, the best experience
you can have if you're going tolive in the US, right? Indeed,
we
Erin Brinker (12:53):
certainly have the best weather by far. Yeah. So
let's talk about your your PCtronics business. And you know,
the 30 years in in the techindustry is like a millennia.
Things move so fast. So talkabout how things have changed.
(13:13):
What you've learned. Talk aboutthat experience.
Manal Iskander (13:18):
When I came here, I kind of knew very early
that this was booming, and I'mgoing to just bring it up to
today, because I definitely,honestly believe that, you know,
we can see how industries moveby watching where investments
take place. So like as today,everyone hears the big buzz
(13:40):
word, artificial intelligence.
And billions, if not trillions,of dollars are moving into
artificial intelligence. Ai, thesame thing was happening to me
in the 90s. I was watching thesame type of money roll into
tech startups.com, internet was,you know, just booming, and
everybody was entering themarket? Well, when everybody
enters the market, there's not,like, really a set of rules or
(14:03):
culture that's really developed,and everybody's just trying to
sell you something, and peopleare buying it, but they really
don't even understand whatthey're buying. So what happened
was, I was noticing when I camehere to the United States, that
the humanity of it was alreadylost. So I thought, you know, I
watched, I worked for anothercompany, first, to try to learn
(14:26):
it here, how it worked in theUS. And I was noticing that they
would go in, sell to a company,set them all up, give them the
servers, emails, everything thatthey needed. And then once they
were kind of up and running,they would chase the next new
customer. So the maintenance andthe kind of customer care was
being neglected, so thesecustomers would like put in
(14:48):
requests, but not really getgood service, which was a really
big common thing that happenedin the early 2000s late 90s. And
so I kind of thought I want tochange that. I want. To be the
person that takes care of thepeople that were kind of left
behind and make sure that theirenvironments are working and
they're staying up to date andmaking sure that the software or
(15:08):
SaaS they add is valuable tothem. Because I understood the
concept of business, so when Istarted doing that, I decided I
was going to go after companiesthat were not needing a new
setup, but that were feelingmore neglected by their IT
companies. So that's how Istarted. So I started by taking
(15:30):
companies that didn't feelheard, and then I was giving
them a voice, and I was guidingthem through the new technology
change, how to use the the newtools how to operate, windows,
Calendar, Outlook, and I endedup kind of becoming like a
strategic partner instead ofjust an IT firm. And why I like
(15:50):
that story is because here I am,30 years later, and like half of
my customers are from that timestill, oh, wow, yeah. So if they
stayed in business, they grewwith me, you know. So basically
they grew if they needed newlocations, if they needed to
upgrade, if they wanted to go tothe cloud. Now a lot of them
(16:12):
are, like, asking me about AI,and now I can still, like, grow
with them. So I tell myself, Iknow I have to say I'm an IT
company, but I'd like to say I'ma strategic partner, because it
means more to me that I am thepartner that tells you how to
use technology to meet youroutcome, instead of saying you
(16:36):
need to store your data, so youneed a you need storage, data,
storage, or you need a newbackup system. Instead of saying
that, I want to be able to sayhow much data do you need to
store, and why are we using it?
Like, what can we do with thisinformation? So now I'm more
like with what you have now, howcan we make that make sense for
(17:00):
you, like, with this informationthat you have, with this
technology, do you have? What doyou hope to get from it? And
then we try to work backwards,so we're like outcome based.
Erin Brinker (17:10):
I love that. And I worked in tech. During that tech
revolution as well. I wasworking for an application
service provider doing projectmanagement, and our sales people
would sell what I lovinglycalled a vaporware Sure, Mr.
Customer, this project productcan do whatever you want. It'll
mow your lawn, it'll make yourbeds, it'll do all the things.
(17:32):
And then the customer would cometo me and say, you know, to help
me build it my I was a liaisonwith with the actual engineers.
And I'm like, well, it won'tquite do that, but it will do.
People were just saying whateverthey wanted, and they, you know,
and that was, it was the wild,wild west.
Manal Iskander (17:49):
So, so, you know, I'm, like, really
determined to be a voice in thisfield. I'm determined to have
women sit at this table andshape the conversation. I
believe so adamantly that thishas not been written yet, and as
it's being written, if we take aseat at the table, we have a
(18:12):
opportunity to change what itlooks like. We have an
opportunity to change thenarrative of what AI means in
the workforce for us today. Sofor me, it's really important
that we talk about it.
Erin Brinker (18:25):
I agree. And you're, you're like a Sherpa on
Everest, because althoughEverest, I know, is a well a
well traveled path now, okay,you're a Sherpa on k2 you know,
trying to tend to help peoplenavigate the unnavigable. And I,
I'm hearing from differentsources, oh AI, it's, it's going
to take over the world. It'sgoing to create terminators, and
(18:46):
the other side is, oh AI, it'sgoing to be this great tool. And
there's a lot of confusion. Solet's, let's just what is AI and
how is it being used today?
Manal Iskander (18:57):
Okay, so AI is a big buzzword, so we have to be
careful. So Artificialintelligence has been around
around for a very, very longtime. It's just been the last
few years, people have beentalking about it openly because
llms came to the to the surface,which is just a large language
model that is a predictivemodel. So all it is is like an
(19:17):
algorithm that predicts the nextword by likelihood from all the
information that's ever been onthe internet. That, to me, is
not AI, but it is what hasallowed, like a layer of
intelligence, intelligentthinking, to be interjected into
certain software tools. So rightnow, people are going into chat.
(19:40):
GPT, Gemini, Claude, anthropic,whatever your tool may be, or
some of the image generators midjourney, nano banana, there's so
many out there, and those aretools. And those tools people
are just pulling through themarkets and just using them.
Yes, they're great. Yes,they're. But they're also very
(20:01):
dangerous. So all of that istrue. Whatever you put out
there, you don't know what'shappening with that information.
It's being used to train peopleare taking their customer data,
important information, and goinginto the chat, depositing it to
ask a question. What they don'trealize is they're putting their
their customer or their employerat risk. They're allowing
(20:23):
sensitive information to bereleased to they have no idea
where, because once it's beenput into the chat, you don't
know where exactly thatinformation is going, and nobody
can answer that question. Sobasically, there's some risks
involved with all of that, andthat's what people are talking
(20:43):
about.
Erin Brinker (20:43):
Okay, I'm processing what you're saying.
So if I'm I have a problem, abusiness problem, that I need to
solve, and I use people's realnames, you know, such and such.
Joe Smith, customer, this iswhat's happening. I need help.
And how do I solve this problem?
Then that I have then made thatsomehow public 100% 100%
Manal Iskander (21:04):
that information can is now variable, so they can
actually query your exact promptin that answer
Erin Brinker (21:12):
and oh my gosh. So I'm certain nobody knows that.
Manal Iskander (21:16):
That's what I'm saying. It's just like because
it's it got introduced to thepublic massively, without any
governance, governance or ethicsapplied to it, we have now some
danger spots for environmentslike specifically work
environments where you're you'rehaving your secretary or your
sales person put in a data sheetthat they've just dropped into a
(21:39):
chat on the way to a customer toget information that is
dangerous because yourenvironment isn't secure. Okay?
So to me, that's one side of AIthat we need to be talking
about, and we need to be able tocreate safe environments. So you
can create safe environments.
There are safe ways to downloadllms or to use llms in an
environment they're not trained,and they stay safe within
(22:02):
something called your tenant,your work environment. Okay, so
to me, like people in myprofession, should be talking to
everybody about that number one.
Okay, so that's the safetygovernance side of it. Now
there's another side of it thatpeople just aren't really
understanding. Ai having a chatbot that's just like a fun
(22:24):
little tool, or creating apicture that's a fun little
tool. But what makes artificialintelligence useful as a
business is that it can putintelligence in your data. So we
have all this data. Why do wehave all this data? It must be
good for something, right,right? How do we want to use it?
That is where AI should come in.
So I'm going to say, I have thislittle saying that. I say we're
(22:46):
customer zero. So I'm just goingto give you for my own company,
to give you an example, and thenwe'll think about it for any
other company you want. So in myown company, what would be
helpful to my customers with thedata that I have that I could
use AI, I decided, well, we do asecurity analysis on all
customers, so like, if we'vegone out to fix their systems
(23:10):
manually, remotely, what attackswere attempted, which ones were
resolved, which ones, what? Whatemployees had difficulty this
month. Just a general executivesummary of everything that
happened in their environment.
Well, we have software thatgives each different report for
(23:35):
each of these things. And ifyou've ever gotten a security
report, it's very blah, it'svery dry. It's like reading,
like, I don't know, like athesis, you know, you just don't
necessarily want to do right?
Erin Brinker (23:46):
You're not going to sit down, you know, with a
cup of tea and enjoy thatreading
Manal Iskander (23:51):
Exactly, exactly. So what we did was we
created an intelligence layerthat told about the customer, so
it's the customer profile, so ittells how does the customer like
to receive their information.
What language do they want tohave it in? What items are the
most important to them, and whatdo they not care about? Right?
And then we plug thatintelligence layer. So we used,
we used a Microsoft productcalled Azure AI foundry, and I
(24:14):
think they've just relabeled itto Microsoft foundry, but
basically there's all differenttypes of intelligence layers.
That's just the one we used,okay? And so basically we
created the profile all theinstructions in that. And then
what we did was we created anautomation from another tool. I
know these are a lot, but it'scalled N 8n. Okay. And basically
(24:35):
what we did was we told theautomation go into each one of
our SAS tools, pull out thereport, apply this customer
profile to it, and now create auser friendly fun infographic, a
fun email that lets them know,maybe not fun, depending how
they want it read, right, andautomatically send that report
(24:57):
directly to the. Customer aswell as deposit that into our
SharePoint as a record. So now,at the end of every month, we
don't do anything thisautomation we created with this
intelligence layer goes into allour reporting systems, pulls out
all the reports, deposits itinto a database. Then it takes a
intelligence layer with thecustomer profile, pulls out the
(25:20):
necessary information from eachreport, puts it in the format
the customer wants to have, putsit into a report and an email,
and automatically sends it tothat customer, as well as sends
it to our security analysisanalyst, as well as deposits it
into SharePoint. Now that is anAI automation that I've used to
now make my business moresuccessful. So now customers are
(25:44):
getting like a report that tellsthem, you know, you have this
one employee that called in moreHelp Desk than anyone else, or
you have this one aging system,you better be careful the next
month it's coming up, so youshould be budgeting for it, or
this software renewal is goingto be up in the next quarter, so
now they have this, like, way toread their data that's like,
(26:07):
understandable to them. Wow.
Erin Brinker (26:12):
Okay, so I'm processing all of this. It. This
is people companies would spendmillions of dollars on
consultants, you know, from BainCapital and McKinsey and all of
those to do this work, and nowyou've created a system that
will do it for you.
Manal Iskander (26:27):
Yes, anybody can, which is the greatest
thing. So this tool is going tolevel up small and medium sized
businesses to be able to work atan enterprise level, because if
you can automate. So basically,what you need to know in order
for any of this to work is youhave to have clean data, and you
have to know your workprocesses, right? So I have a
(26:48):
little thing that I call it'scalled the double diamond
theory, and it's discover,define, that's the first
diamond, and then design,deliver, that's the second
diamond. So the first diamond isdiscover, define. So we got to
go in, and we have to discoveryour workflows, your friction
points, what work is repetitive,that can be automated. What? How
(27:11):
do you use your data? What doyou want your data to do? What
are we trying? What is what arewe trying to create here? And
then, once we do that, we candefine it. We can say, okay,
these work processes can beprocesses can be automated.
These work processes can beassisted. You know, this is the
map or the map flow, or workflowthat we can create and make it
(27:34):
more efficient. So now we're ina definition process where we
can now decide what are all theproblems you want to solve? What
do the workflows look like? Nowwe can actually decide design
them, and once we design them,then we can deliver them. So
basically, anything can becreated. Now if you have clean
data, everything's AI readable.
So is it in a safe environment?
(27:56):
Is the data protected? Can theAI intelligence layer easily
access it, read it, identify it,and use it. So for us now, all
these small businesses are goingto be able to automate things
that used to be very, veryexpensive to do. They would need
employees to be able to sortdata, send emails, send like I'm
(28:22):
going to give you another one weautomated. So we automated this
blog profile. So basically itwas a blog profile for a
customer that's in blockchain,and basically they wanted all
the latest blockchain news, sowe connected it to coin desk. It
takes all of the latest newsfrom blockchain, it turns it
into a current blog. It goes onto WordPress, it it posts it for
(28:44):
them, and then it sends them acopy, and then it gives them
analytics at the end of themonth. So the whole thing is
automated. The whole thing isautomated, but it's staying
current, so it's exactly whatthe person would have done. So
it's not giving like fake AIinformation. It's not like fake
AI news. It's doing what a humanwould have done anyway. It would
have gone to coin desk, pulledtoday's latest news and then put
(29:07):
it into their blog with today'slatest news.
Erin Brinker (29:10):
So for those of you all who are listening and
they don't, you don't know whatblockchain is. It's Bitcoin,
right?
Manal Iskander (29:16):
Yeah, well, Bitcoin was created on
blockchain, right? Bitcoin isjust a use case scenario. So
blockchain is any any data thatis you want to put on something
that's immutable, and you can't,you can't, you can't delete it,
and you can always trace it. Sowhat they did was they created
Bitcoin as a proof of conceptthat blockchain works and
(29:38):
blockchain works. So Ethereum ison blockchain. Solana's on the
ledger. There's lots ofblockchains, and basically it's
like a block where you can putdata in it, but nobody can
access it unless they have keys,and nobody can change the data.
So this is on a tangent, butI'll give you like a little way
I used to help childrenunderstand it. You know, when
(30:00):
you were a kid, and you wouldget, like, those paper mache
little things, and you wouldmake like a like a ring. You
would take a piece of strip ofpaper, and you would glue it
together, and you would make itinto like an oval shape, and
then you'd add another one toit, and another one to it, and
another one to it. Okay, sothat's kind of like blockchain.
If you want to change somethingin the middle, you can't without
(30:21):
destroying the entire chain. Soyou can't go backwards. You can
only go forward. So anythingthat's ever been created is
always discoverable. That's whyit's like being used in like the
financial worlds, because peoplecan't go back and alter their
accounting. So that's a wholenother story. But in any case,
(30:42):
my point is, is that you canautomate anything, so like the
security reporting the you couldautomate. You know, in my
company, we automate, likeonboarding new customers.
Instead of having a tech go outthere, enter all their
information into a CRM thenenter it into the PSA, all
that's just automated. So if youthink about how many hours a
(31:02):
week do you spend doing tasksthat don't need your judgment or
discernment, then should youalleviate those tasks so that
your time can be used on thingsthat need your judgment and
discernment? Right?
Erin Brinker (31:19):
Exactly, well, and it's something that you would
you would have hired somebodybefore. Somebody before, and you
may not be able to that may notbe in your budget. This is
something that's a lot morestreamlined. It takes time
upfront, because you have to puttogether the framework for to
teach the AI, what you want itto do. And then once you've done
that, then it's automated. Isthat correct?
Manal Iskander (31:39):
Well, you don't have to teach, which is great.
It does learn over time. Butlike, let's say, I'm just trying
to think of what you have. Like,let's say, because you you used
to, or, I don't know if youstill have hope foundation, but
do you still have the thatfoundation?
Erin Brinker (31:55):
So So I, I am not no longer with the making hope
happen foundation. I am doingsome independent consulting and
then focusing on the InlandEmpire children's book project.
Manal Iskander (32:05):
Okay, okay, well, let's I'm just going to
use the make it Hope Foundationas an example. But let's say you
had a list of investors anddonors there, and they get
reports on how their money isbeing utilized, and also maybe
reminders to donate or investagain. Okay, so maybe you have
all this is where your data hasto be clean. So let's say you
(32:27):
put all of your monthly updatesonto your foundation, into a
SharePoint folder, and you alsohave all your donor information
and all of your highlights,whatever it is that you use to
give people information, you canthen create a workflow, and
that's what it would be. Itwould be like a workflow that
says, take the donorinformation, investment
(32:50):
information, etc, etc, create amonthly newsletter letter that
has a donate Link button there,and have it sent out to this
email address that lets donorsknow where their money is going,
and then it updatesautomatically every month for
you. And basically, you writethe intelligence layer telling
it what to do with the data.
That's all you're really doing.
(33:12):
It's not learning from it.
You're telling it what to do. Soinstead of you doing it, you're
saying, I want this data to beextracted and given to donors as
a recap of where their money isbeing spent, something like
that. And then it would takethat data, it would read it, it
would recap it, and then itwould send it automatically to
(33:32):
those donors with maybe a donateLink button. So every month,
they get an email that tellsthem where their money is going
and if they want to donate moremoney, fantastic, a workflow
program or workflow process. Soyou're not necessarily having to
teach the intelligence. Whatyou're really having to do is
understand your workflows, andthat's what people are missing.
It's not about the intelligencelayer. The intelligence layer is
(33:55):
the bonus. What it is is, do youunderstand your workflow like,
do you understand what it takesto get your work done on a daily
basis, weekly basis and monthlybasis, and what do those
workflows look like? And if youunderstand that, then you can
make sure those workflows nowhave readable data. So is it
something you're handwriting?
(34:17):
Well, then that won't work. Sohow do you now make that
digital. Is it something like,let's say it's these podcasts
you're doing. What if thesepodcasts were the transcriptions
were automatically taken putinto like, these transcriptions
are turned into five LinkedInposts and five blog articles
(34:39):
from everything you and I talkedabout for every guest,
fantastic, right? Then that's aworkflow. So then you could
you're then it would take thistranscription, it would download
it in a database, and then itwould take the intelligence
layer of us telling it what wewant it to do with these
transcriptions. And then itwould create your LinkedIn
profiles. It would search. Youryour photo gallery for
(35:02):
applicable pictures, it wouldyou could give it the log on to
your WordPress, and it would goinside and log on to your
WordPress and and deposit blogs.
It would go into your LinkedInand update your in post LinkedIn
articles so it could do all ofthat if you understood the
workflow and what you want fromit. And that's what I noticed.
(35:23):
People don't have. They eitherdon't have everything digitized,
so they don't have clean data.
So they're like, Oh, we wrote itdown. It's on these posted
notes. And somebody
Erin Brinker (35:33):
that doesn't work, no, it doesn't,
Manal Iskander (35:35):
it doesn't work, or they don't know what they
want, like, they're like, well,they have this great data tool,
but they don't know what theywant to do with the data. Like,
like, what are you going to dowith these great podcasts and
all these tidbits ofinformation? What's your goal
with it? And then once you canunderstand that, you can be
like, Oh, wow. Like, you know,maybe I could sit take the top
(35:56):
10 founder sayings out of thesepodcasts, and it'll pull it out,
and I'll do a series on the top10 founder sayings, or whatever
it could be. But you now get tobe creative right now. Now you
get to say, what do I want outof my data? If I could do
anything I wanted with it?
Erin Brinker (36:15):
Oh my gosh. It's fantastic. And become a much
better producer than I wouldhave been otherwise. Exactly.
Manal Iskander (36:20):
So for me, AI is a buzzword that needs to be
grounded, and people need tounderstand there's like, ethics
and governance that we have totake very seriously. And it is
true that we are in a verydangerous time of society right
now with AI, and there is goingto be a lot of data linked. And
(36:42):
we do need governance andethics, and we need security all
around people who use AI. Andthen we also need to be able to
lead AI into having it help uslike it should be our hands and
feet. It should be the busywork. It should be the noise
Canceler, and that's what I wantit to do, for me, is just cancel
(37:03):
out all the noise so I can forgethe actually, what I want to do
is kind of forge the path to letpeople understand it, especially
women, because I think it's agreat opportunity that these new
workflows are created with awoman's voice in mind.
Erin Brinker (37:19):
So I definitely want to talk about that, but
before, before we do, I want totalk about some of the risks,
because the potential for AI tobe used for hacking and phishing
and otherwise scamming is alsovery high, and so how do you
protect yourselves from that?
Because, and I'll use this verybasic example, you get a spoof
email. You know, this is yourinvoice for whatever, and it
(37:41):
comes from an email address youdon't recognize, or maybe you
do, but the language isn'tright, so you know that your
colleague didn't actually sendit to you because it was written
by somebody who doesn't speakEnglish. All of that goes away
with AI. And it could very muchlook like, like, you know, an
attachment and email. This is abasic example. Could look like
something that is completelylegitimate, but it's not you
(38:03):
your AI, could you know, onceit's infiltrated your system, it
can do whatever it wants, orwhatever it was designed to do.
Kind of talk about the riskthere.
Manal Iskander (38:18):
Human risk is always going to be a risk. It's
something you can't even 100%eliminate. So that's the second
part of the story, but there areways we are working on it. So as
well as AI is getting developed.
So is it in a hacker's world? Soour security platforms now all
are having built in AI. So howthat works is there are lots of
security platforms we use, likeHuntress Sentinel one guards.
(38:40):
And the ones that we use have anAI intelligence layer built into
the security so the one we useis called like, it has purple AI
built in. But basically what itdoes is it has, like a training,
a security awareness trainingthat goes out to the
organization. So like, if youhired me, you would have a
(39:01):
training that goes out to all ofyour employees that they have to
take, and it then sends me backresults, and it's the latest AI
hacking phishing attempts. Sothe security training recognizes
the latest ones that are outthere. It then purposely pushes
them through to your employeesin a safe garden environment.
(39:22):
Then it allows you to see whichemployees are opening up those
unsafe emails them to havefurther training before they're
allowed to be on yourenvironment. Then they pass a
training every month that letsus know that they're now being
aware as well as us having thisAI intelligence layer that's
(39:43):
pulling the emails ahead oftime, so any of these suspicious
emails already being pulled sothey never even get to the end
user, they're going into aquarantine folder where they're
getting analyzed by our team,which is also now art of.
Artificial Intelligence. Soartificial intelligence now
analyzes this batch of emails,decides that they were unsafe.
(40:05):
If they are, they're eliminatingthem. If they were safe, they're
re pushing them through to theend user. So now companies like
mine are even more important,because you really do need this
layer between us and youremployees, because your
employees are your greatest riskto your company indeed.
Erin Brinker (40:25):
I mean, you know, from opening that email to
finding a finding a flash drivein the parking lot and thinking
it's a good idea to plug it in,things have happened. Yes, so
you
Manal Iskander (40:38):
we've had a customer do that one time we
were let go, and because theythought we were a little
expensive. And to be honest, I'mnot expensive, so it was quite
an insult, but I was graciousenough to just back out kindly.
And they used a company that didnot have the software built in,
and they got ransomware for oneBitcoin, which was like just
(41:02):
under $100,000 at the time, andthey had to pay it to get their
data back. And I realized that,you know, you can't. There is no
cost to security,
Erin Brinker (41:11):
really, no. Oh, my goodness. Oh my goodness. So you
started talking about makingsure that women, that female
entrepreneurs, are at theforefront of this and that they
understand what's going on.
Let's explore that a little bit,because, and I will tell you
that I reached out to you and wehave, we have both been in this
area for and worked alongsideeach other for a long time, but
(41:32):
I had never interviewed you,despite being on the air for a
while and and you wrote anarticle posted on LinkedIn that
just moved me so much aboutabout women being relevant in
and staying relevant, especiallyolder women in in the workforce,
and how things are changing andthat that things are flattening,
(41:52):
as far as opportunity isconcerned, talk about that a
little
Manal Iskander (41:59):
bit like women older than 50 have a really
interesting opportunity intoday's world. Not only did we
forge a place for ourselves in aworld that had no place for us,
and I find that reallyfascinating, but now we have a
way to basically influence whatwe look like in this world of
(42:23):
AI, how AI is utilized andspoken to. It's very important
for us to take a seat at thetable, because the industry
technology in itself, has beenshaped mostly by men, language
by men, outcomes by men. Andunfortunately, we are a world
full of diversity, not justgender, but all types of
(42:46):
diversity that is not reallybeing accounted for. And little
things I noticed, like I gaveyou an example earlier about the
security reporting, I always sayto my team, because I'm a female
leader, and I work with a lot ofmen, and there's often not very
many women to hire, which isalso very disappointing for me,
(43:07):
and indeed, more and more womento enter this field, as well as
do I speak to a woman'scybersecurity club, because I
really want them to be in thisfield. To shape the language
that security reporting I toldyou about changed, because the
reporting that I was gettingfrom my team prior was very
(43:27):
technical reporting. You've gotmostly men in that work in tech,
and they think like engineers.
They develop with a very clear,concise outcome, which is great,
but the world is filled withcolor and emotion and connection
that gets dropped, you know, andwith the security reporting in
the intelligence layer, I wasable to write a profile that
(43:51):
talked about the women leaders.
I have two or three companieswith women leaders. I'm speaking
to women, not men. I know howthey want their material. I know
how they would like to be spokento. I know how they would enjoy
seeing their highlights, ortheir team highlighted, not just
(44:11):
the things that were fixed. So Iwas able to use my voice to
create these security reportsthat now have such a different
feel than the ones before, andthat's because we as women now
have are touching these fieldslike we are going to be able to
shape how information is goingto be transferred from company
(44:33):
to company, human to human, howthe language models when you go
into chat, GPT or Gemini, howit's going to deliver
information to you. So for me,we have a very large opportunity
now to take our seats at thetable. We shouldn't be waiting
for permission. We need to speakup, we need to write, we need to
(44:57):
post. We need to figure out. Ourworkflows are, and make sure we
write the intelligence layersthat show how that workflow gets
automated, how that workflowgets actualized, right? So for
me, it's really important thatwe let our 50 years or more of
judgment and discernment, ourwisdom, our ability to nurture
(45:21):
our ability to recognize humanconnection, allow all those
things to take part in whatwe're building right now. It's
really important to me to seewomen do that.
Erin Brinker (45:31):
You know, it's interesting because, you know,
because it's a computer andbecause it's, you know, it's
based on logic models. It's,there is no human element in AI.
So if you want a human element,you have to put it there, and
you have to recognize that it'smissing. And I, and I think that
that's more than likely thatthat it would seem that that
(45:53):
would come from women more thanfrom men.
Manal Iskander (45:56):
Yes, exactly, you know. And you know, we are
in a position where we are nowkind of redefining what work
looks like, right? So right now,in this time, like no other time
before, there's going to be anew definition of work. I've
just told you about all theseautomations. So think about a
year from now, how many jobswill be replaced by these
(46:19):
automations. Okay? So if thesejobs are replaced, what happens
to those work? That workforce?
It doesn't disappear. It getsredefined right like now, humans
are going to be used to do othertypes of work. What does that
look like now we sit at theforefront where we get to define
that. To me, it's reallyimportant to decide, you know,
(46:40):
we're still relevant. How do wefit in? How do we use the skills
we've crafted to create new waysfor us to contribute to this new
definition of work? Right? Like,I definitely think you're going
to see the whole definition ofwork be redefined within the
next one year to 18 months, that
Erin Brinker (47:05):
wow and it, you know, think about, we talked
about the the the internet boom,and in the late 90s and early
2000s we thought that movedquickly. This dwarfs that
exactly.
Manal Iskander (47:18):
It's going to happen so fast. I have been
speaking at these panels acrossthe world. I went to Amsterdam
and spoke last October, and Iwas in Colorado and spoke a
couple months ago, and I've beenjust traveling and speaking on
this topic. And interestingenough, I'm going to speak next
summer at a big AI conference inUtah called Beyond 2026 and I
(47:46):
was trying to like, put togetherwhat my breakout room is going
to look like, like what I'mgoing to be teaching people. And
I realized that whatever I puttogether today may be obsolete
by June or maybe so widely knownalready that it will be not
obsolete, but be too familiar.
So I'm realizing that, like whatI'm talking to about now, like
(48:10):
taking a work workflow,automating it with an
intelligence layer, you know,using these in SAS tools, it
will probably be commonknowledge by March already. So
information is going so fastthat even trying to find things
to talk about, I have to get tothe podium quickly, like I'm
like, I can give you this, thisspeech on AI, but we have to do
(48:34):
this like now, because it ismoving so quickly that I can't
even tell you that some of thethings we're talking about now
will just be very commonknowledge six months from
Erin Brinker (48:47):
now, wow, it'll be common knowledge. However, it
won't be, because I think forthose people who are not living
in a tech world, they're thesmall business, they're running
a bakery, they're running apreschool, they're doing
whatever it is that they do,they're being told, AI is great,
Ai, you need AI, you need AI.
And they're like, I can't evenget my brain around it, and I
read something and it'scompletely contradictory to
(49:09):
something else. And what do Iuse? Which system do I use?
Gemini? Do you use chat? Youknow, does it have the capacity
to do all the things I don'teven know? You know where this
fits, and I'm overwhelmed, as Ithink that this is moving so
fast that, you know, if the techpeople are feeling exhausted,
everybody else is lost,
Manal Iskander (49:32):
same time, you're not going to be able to
function without it. AI is nowavailable to the small and
medium business, and becauseit's not going to have a price
tag attached to it like it didtwo years ago or a year ago,
it's it's going to make a lot ofsense I'm going to give you
because you know this person, soKim Calvin, for example, owns a
(49:56):
coma unity organization LearningCenter and. She has been
operating in this NGO space withyou know how everyone else has
been doing their best, trying tosurvive, trying to get the data
organized, etc. I went and spokewith her, and I'm like, Listen,
you are super primed to be ableto get an AI ready, readiness
(50:20):
grant where you are able toorganize your data to be aI
retrievable, so AI systems cansit on top of any NGO and be
able to scale it. And we wentin, and she's eligible for like
95% of the grants out there. Andoh my gosh, that's great. $1
billion worth of grants outthere to help companies become
(50:44):
AI ready. So what I want you tohear, it may be overwhelming, I
hear that, but you've gotcompanies like open AI Gemini,
who have spent billions, if nottrillions, of dollars,
developing this, and now theyneed people to use it safely.
How are they going to do that?
If it's not working the way theywant it to, they're putting
grants and money out there soorganizations can adapt, adapt
(51:05):
it correctly, so thatorganizations will start to use
and be aI ready, AI first. Sobasically, these people, every
massive enterprise organizationthat's dealing with AI, needs
your data to be aI ready so theycan access it, so they can build
even further. So a company likeGoogle needs every small and
(51:28):
medium business to be aI ready,so that their data is AI
readable, so that they can buildit better. So they're going to
encourage companies like yours,mine, anybody, to be able to to
have this learning curve be notso difficult. So I'm telling you
it's going to go out there bygetting companies, foundations,
(51:50):
their digital readiness first.
And that's what you're going tosee, a big, massive push towards
digital readiness. AI readydata, and there'll be grants and
money out there to help thishappen, and there'll be
companies like me that are goingto be hired to help companies do
that. And that's what I'm that'swhat I'm doing. I'm hoping to
(52:13):
get grants for companies whowant to hire me so that I can
help them organize theirworkflow and then automate it
and put intelligence layers sothat they can be outcome based.
Erin Brinker (52:24):
Wow, wow. It's, that's, that's fantastic. And
and the next question, thenthis, all of this, begs the
question, do we have the energyto power all of this as a global
community?
Manal Iskander (52:39):
So obviously, that is the big buzz word and
environmental harm andeverything else. So I'm an
optimist, so I believe that AI,the usage of AI, is going to
help solve a lot of our AIissues. So I feel like it's a
tool that helps us connect areasthat we don't see connected yet,
(53:03):
information that we don't seethat we're not able to see,
because we broadly can't seeeverything at once. So I am a
huge optimist, optimist thatbelieve that this issue will be
solved through AI, and that AIwill eventually help us become
carbon negative, not just carbonzero. So I am a huge optimist. I
(53:27):
am not a typical voice outthere, but I have watched
industries grow and seen doomand gloom, and you're as old as
I am, so I'm sure you remember y2k where we thought at the year
2000 everything was going to goand then nothing. Then nothing
happened, and everything wasfine. I kind of feel like as
humans, we catastrophize thingsthat we don't know. And I do not
(53:52):
believe that this is somethingthat is going to be a war of AI,
robots, destruction. I don'tbelieve that. I think this is
going to be our our way, toallow humans to be redefined,
for us to have more creativity,for us to undo the damage we've
done, for us to be able to bemore self regulated. So I see
(54:15):
this is a big upgrade tohumanity, and we're just going
to go through chaos for the nextfive years until the upgrade is
completed, if that can makesense.
Erin Brinker (54:24):
But well, Manel Iskander, we are completely out
of time. How do people find findyou on social media, if you're
on social media, and moreimportantly, how do they connect
with you at PC tronics
Manal Iskander (54:36):
appreciate that.
So I'm mostly on LinkedIn. Idon't do a whole lot of social
media. So LinkedIn, ManelIskander, president and founder
of PC tronics and Fugazi, whichis a marketing company, and I am
at PC tronics.us, so you can doI have like a reach out to me on
that. So you can reach out to meeither by LinkedIn or www.
(54:57):
Seatronics.us on my website.
There is a way to contact methere.
Erin Brinker (55:04):
Well, what a treat this has been. Manaliskander,
thank you so much for joining metoday.
Manal Iskander (55:09):
Thank you so much, Erin. I appreciate having
you having me on today.
Erin Brinker (55:13):
Well, that is all we have time for today. Thank
you so much for spending thistime with me. And I hope you
learned a lot. I know I did. I'mErin Brinker, and you've been
listening to the hope table.
I'll see you next week.
(55:34):
This holiday season, while manychildren unwrap toys and treats,
some will unwrap something farmore powerful their very first
book at the Inland Empirechildren's book project, we
believe every child deserves thejoy of reading the kind that
sparks imagination, buildsconfidence and opens the door to
a brighter future. But rightnow, more than 75% of low income
(55:55):
students aren't reading at gradelevel, and many have no books at
home at all. You can change thatwith your support, we're putting
books directly into the hands ofchildren, one gift, one child,
one story at a time. Join us ingiving the gift of literacy.
Visit Inland Empire children'sbook project.org. To donate or
get involved. That's InlandEmpire children's book
(56:18):
project.org. We're a 501, c3,non profit organization and 100%
volunteer. Give a book. Givehope, give the gift of literacy.
At Inland Empire children's bookproject.org, you.
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