- Welcome back to Adobe Think Tank
and make sure to use #AdobeTT
if you're tweeting along.
With me is Virginia Backaitis,
reporter, CMSWire and Digital Polaris.
Virginia, thanks for joining us.
- Thank you for having me.
- First question I want to get with you is
why is it so difficult, and to me
this is kind of a loaded question,
but why is it so difficult to recruit
and retain top tech talent?
- Well one is the unemployment rate is
basically extremely low.
- Yeah, 4%.
- So almost everybody
who wants to have a job
already has one.
And so basically, the
only thing you can do
with tech talent is play musical chairs.
So if you pull somebody out of one job,
you have to put them into another one
and they could pretty
much name their project
and name their price.
- And when we're getting
into higher tech fields
where it's a limited amount of people
that can actually do that position,
I mean, that becomes even more difficult.
Are you seeing initiatives being done
to try and persuade people to get
into these specific fields, especially AI?
- Well, I mean obviously
colleges have programs now
and people are graduating at crazy rates.
But nobody wants to hire people
that are recent college graduates
so it will take time before--
- It's a four, five, six year cycle.
- Six year, yeah, before the
field begins to populate.
And then the other thing is
is that the data scientists
and the AI people
who are already out
there have decided that
"this is what I want to specialize."
And so they might be in biotech
and so for them to come over
and work for a marketing
company or something like that
is not what they want to do.
So the fields of people who are interested
in a particular area in data science
or machine learning or AI,
you know, they only wanna do, let's say,
look at molecular slides
or something like that.
They don't want to go help marketers
create great campaigns.
- They don't, they don't
want to work with (laughing)
- Well no!
- No but I...
- The other way around too.
I think that a lot of people
who are used to producing really great ads
and really great content
really don't wanna go figure out
what kind of disease
you know, a particular, they're seeing
under a microscope slide.
- That's the interesting thing
yeah, they're kind of
two polarized fields.
When it comes to enterprise
content management,
where can machine learning
kind of help with that,
or can it?
- I guess the same way as with
images and things like that,
it can tag things, it can
help people search for things.
Let's say if you're making a lot of
enterprise content management,
some of it is about
getting drug submissions through the FDA.
And those pages tend to be,
or the submissions tend to be
more than 5000 pages long,
and so for people to be able to
replicate content in
specific places, you know,
fill in the blank a certain
way, AI can help with that.
It can help summarize what
any particular page says,
any particular section
says, so it can basically
take the boring part
and the repetitive part
out of people's jobs and
then let them go do the
higher-level work that really
uses their brain power.
- Absolutely.
Do you have two or three
good, like your best
pieces of advice for companies
to remain competitive?
And maybe two good pieces of advice
and one bad piece of advice.
- In hiring people or in general?
- No, just in general for companies
to kind of access and utilize AI best.
- Yeah, so I think that the
big thing to do right now
is to maybe start an experiment with AI.
Start an experiment with machine learning.
Because despite all the hype, you know,
if you read enough of the press you think
everybody's doing it, they have
600 data scientists--
- They're still in adoption
right now.
- And things like that,
and while that might be true at Facebook
or a place like that, they might have
a thousand data scientists,
but if you look at
a bank like JPMorgan Chase,
they're actually still
gonna have about 50 of them
and they'll have all these
different departments
that wanna use them.
And what else would be really good is
if there were a group of data scientists
or a group of AI people
who all knew each other.
Because in companies what happens is that
one department hires some people,
one department hires some people,
another department hires some people,
and those people don't know each other
- Common company Silo Effect, but yeah,
if they've worked
together before in teams,
collaboration obviously helps.
What, I said bad piece
of advise just to be fun,
but what is something
that you see being done
in practice in AI company level
that maybe could be improved the most?
- Well one of them is more collaboration.
- Sure, kind of all comes
back to that, right?
- Yeah, I think, and the
other thing is best practices,
so that people aren't relearning
the same stuff over and over again.
So I think that that would help a lot.
Probably leaders who understood what AI is
and what it can and can't do.
Because you see a commercial on TV
or you listen to something on Bloomberg
and these executives come into the office
and say "Hey, let's do that,"
and this thing is not yet possible.
- Where does that degree
in machine learning AI
kind of rank with tech
recruiters these days?
- I'm sorry you caught--
- Oh I was just saying where does
a AI machine learning degree rank
with tech recruiters right now?
- Well, so one is people
are difficult to recruit,
they're not hard to find,
so we can identify them.
- Well because they have options, right?
It's kind of almost supply and demand.
- That, partly, and
the other thing is that
companies still grade
these jobs not according to
"this is the hottest commodity
and I'm gonna have to pay $30,000 more,"
they're not willing to do that.
- So the wage isn't catching up
with the demand yet, basically.
- Right, so they understand
that they have to do that
but they basically have a level 8 position
and a level 10 position
and they can't go above
the mid-point of that position
to bring in a new person.
- And see, that might be
the most important point
mentioned today because we
talk about these in silos,
all people in this room or in this box
get excited about this,
but at the corporate level
if we haven't caught up on that,
that's what's making things difficult
and it's just hard to move
a giant ship versus a speedboat, right?
- And I think that the
name-your-price thing is just,
especially that's the place where
the economy still is where it is,
it's somewhat stuck there,
and so they're not ready to let
people name their price yet.
And people are basically,
companies are doing everything they can
to keep you comfortable.
If you're an AI person working there--
- They'll throw benefits,
they'll throw all kinds
of sway and other stuff.
- And a lot of these
people like to do research.
And so it means that they
get to take a month off
to go publish a paper,
or take a research paper--
- It's a different
functionality inside of a company,
they might not be used to that, right?
- The companies aren't used to that, yeah,
and the people are,
they spent all this time
at Stanford getting their PhD.
- Right, so they might wonder
why after a couple months
that person's already gone
even though they're paying--
- Well no, literally what these companies
and some advertising
agencies do is they say
"go write a paper" and obviously
what the company does
is loses productivity
of a person for that amount of time.
- And they can maybe
win back that with cache
or being able to be thought leaders
in the business but yeah it's
hard in the here-and-now.
- And what I'm missing, I guess,
the point I'm missing
getting through is that
those people don't
actually leave those jobs
to write papers, what they do is--
- It's just that's their
focus rather than--
- They get some paid
time off to go do that.
And companies have to provide that,
and if they don't then
those people won't do that
because what they wanna know is
"I'm interested in advancing your career
from an academic perspective and from
doing experiments that may not ever
materialize to anything that's
any good for my company."
- That's another good point.
Well Virginia, thanks so much
for joining me in the tank.
- Great, nice to meet you.
- Very nice to meet you.
- Nice talking with you.
- Thank you.
And again follow us at #AdobeTT,
and that's today's Adobe Think Tank.
