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SAM RANSBOTHAM: Engineers
are not stereotypically known

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for their dancing prowess, but
increasingly, organizations

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need to choreograph how humans
and machines work together.

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In this episode, we chat
with Sidney Madison Prescott,

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global head of
intelligent automation

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at Spotify, about the dance
between humans and machines

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that improves
business processes.

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Welcome to Me, Myself,
and AI, a podcast

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on artificial
intelligence in business.

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Each episode, we introduce you
to someone innovating with AI.

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I'm Sam Ransbotham, professor
of information systems

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at Boston College.

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I'm also the guest editor for
the AI and Business Strategy

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Big Ideas program at MIT
Sloan Management Review.

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SHERVIN KHODABANDEH: And
I'm Shervin Khodabandeh,

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senior partner with BCG, and
I colead BCG's AI practice

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in North America.

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Together, MIT SMR
and BCG have been

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researching AI for five
years, interviewing hundreds

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of practitioners and
surveying thousands

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of companies on what it takes
to build and to deploy and scale

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AI capabilities and
really transform

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the way organizations operate.

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SAM RANSBOTHAM: Today we're
talking with Sidney Madison

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Prescott.

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She's the global head of
intelligent automation

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at Spotify.

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Sidney, great to have you.

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Thanks for joining us.

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SIDNEY MADISON PRESCOTT:
Great to be here.

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SAM RANSBOTHAM: Well,
the title of our podcast

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is Me, Myself, and
AI, and we tend

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to focus on you
as an individual,

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so maybe let's start there.

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Can you tell us a little
bit about your current role

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at Spotify?

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SIDNEY MADISON
PRESCOTT: I currently

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head up a global team.

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We are primarily within the U.S.
and the U.K./European areas,

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and we are working towards
modernizing and really

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improving the efficiency of
the workforce within Spotify.

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So this is looking
specifically at all

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of the processes across
many cross-functional teams

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to ensure that,
whether we're working

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with the UI of the application,
whether we are talking

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about the finance
function, we are

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being as efficient as
possible in the ways

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that we go about our daily
routines as Spotifiers.

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And really, we're taking
that on by looking

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very closely at ways to
leverage intelligent automation.

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This is, in essence,
a tool stack

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where we combine robotic
process automation,

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artificial intelligence, and
machine learning in order

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to really facilitate that
digital transformation.

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And our team is working very
closely primarily right now

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within the finance function.

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However, we have branched out to
our ads business operations as

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well, really looking at, again,
that digital transformation

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and how we can really unify the
front-office and back-office

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processes that our
Spotifiers work on today.

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SAM RANSBOTHAM: So
your degree is ...

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you have a major in
philosophy with a double minor

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in political science and ethics
from Georgia State University

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-- go Panthers!

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That's certainly not
a typical connection

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to artificial intelligence.

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Can you make a connection
between your background

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and your path to get from
there to your current role?

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SIDNEY MADISON
PRESCOTT: I agree;

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it's not the traditional
trajectory of someone

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who would work specifically
within these really

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kind of emerging and
innovative technologies.

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I would say my degree
really prepared

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me to think critically
about various tasks,

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various responsibilities,
and to identify

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a business problem [and]
potentially pitch a solution

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to that business problem
that was technical in nature.

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And I really do rest very
firmly on the critical thinking

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and the logic skills
that I received out

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of my philosophy degree.

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Those really helped
facilitate my growth

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and really amplified my
strengths in terms of process

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reengineering and really
being able to think outside

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of the box when it comes to
how we are translating business

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problems into
business efficiencies

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through technology.

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My career path really
began with an internship,

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and I was specifically working
with configuration and asset

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management.

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And so I was actually a
pre-law concentration,

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and I wanted to become a lawyer.

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I began working on
software contracts,

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and that really was my
first foray into technology.

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I began to understand business
law and business contracts;

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however, I really started
to see the amazing contrast

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between how we think about
what technologists do

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and the reality of the work
that is done around the world.

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Once I started diving
into the software contract

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and the true-up aspect
of the business --

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and that's really where I
moved very firmly into becoming

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a technologist -- once
I made that transition,

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from there I was really
diving very heavily

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into the connections between
the disparate systems

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and the challenges that we
have with those configurations

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and the outputs or the quality
of the data that comes from

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those various configurations
and integrations with different

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systems.

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And so through that, I
really started to home

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in on a passion for data
quality and governance,

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and really looking
at data as an asset.

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And this is where I began
to firmly understand

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the correlation between process
efficiency, process automation,

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and [how] moving towards a more
automated business provides you

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with greater data
insights and better ...

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executive decisions that you
can make based off of those data

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attributes.

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I started that journey within
Fiserv, and then from there,

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I had an opportunity to
dive into a proof of concept

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on robotic process automation.

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And this was
relatively early on,

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so I would call myself
basically an early evangelist

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of that technology.

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This was back in maybe 2015.

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And so through that
proof of concept,

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I began to understand: What
is robotic process automation?

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Why is it something
that can be used

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to facilitate a digital
transformation at an enterprise

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level?

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I also started
diving at that point

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into intelligent
automation, which

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is that combination
of robotic process

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automation, artificial
intelligence and machine

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learning, and sometimes optical
character recognition as well.

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So it was very much a transition
from software contract

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legalese, to configuration
and asset management,

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to data quality and
governance, and then that

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segued into more of the
emerging technologies,

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such as artificial intelligence
and robotic process automation.

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SAM RANSBOTHAM: That's
particularly interesting,

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because most of
the people, I think

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they come to this awareness
of the importance of data

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governance and quality
after that foray

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into machine learning and
artificial intelligence,

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when they find how
much depends on data.

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And then they investigate why
everything's falling apart,

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because of ugly
data, and then they

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get interested in governance.

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And you've swapped
that around to come

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from the other direction, then.

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I don't want to take this too
much in a governance angle,

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but I find it difficult
to talk about.

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So one of the things
I talk about in class

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is the importance of data
quality and governance.

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I just feel so
boring as I say it.

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How do you get over that
sort of, away from --

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SHERVIN KHODABANDEH: How
do you make it interesting?

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SAM RANSBOTHAM: Yeah, how
do I make that interesting?

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How do you get people fired
up about those aspects?

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SIDNEY MADISON PRESCOTT:
Data quality and governance

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is often seen as
an afterthought,

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meaning it's just not
something to focus on, right?

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You're more focused on
accelerating business growth

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or solving different
business challenges.

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But from my standpoint and
how I speak to my stakeholders

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about this is, the
acceleration of your processes

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and the automation of your
processes, that is only one

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piece of the bigger puzzle.

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And so if you are
automating processes,

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but the outputs or the
outcomes of those processes

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are data elements that are
not reliable, in essence

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you are defeating the
purpose of that automation.

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I mean, automation is only
as good as the outputs

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that it provides
back to us, right?

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Whether it's the
engineering team,

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whether it's business
stakeholders in finance, etc.

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And particularly when
you look at the decisions

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that we have to make
based off of data outputs,

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that is where it really
becomes critical.

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Because when we look
at risk and control,

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when we look at SOX
[Sarbanes-Oxley Act] processes

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specifically -- so, those that
impact the financial health

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of a company -- this is where
data is absolutely essential.

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It's essential that
we get it right,

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that we can rely on that data.

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And I think really
what I bring attention

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to is a deep desire to
mitigate the risk that

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is created by bad data in your
environment and the decisions

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that you make based off
of that incorrect data.

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SAM RANSBOTHAM: As
part of your role,

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your team goes in and
increases automation in areas.

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How do people react to that?

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Are they thinking, "Oh my
gosh, there goes my job?

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I used to do these tasks,
and now they're going away?"

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Or are people happier?

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Do they feel threatened?

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What's the reaction to
the sorts of processes

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that your team brings in?

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SIDNEY MADISON PRESCOTT: It
has been very interesting.

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Initially, there is
a thought that, "Oh,

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it's going to be scary."

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But the great
thing about Spotify

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is it's a very dynamic and
agile environment, and a very

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creative environment.

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And so, because of
that, it was very

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conducive to more excitement and
curiosity about the technology.

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It's been primarily
really excited.

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People are happier.

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They enjoy the bots.

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They nickname their bots.

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So it's been primarily, I
would say, very positive,

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for the most part.

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And one thing I'll
say as well --

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I think that is a result of
both the culture within Spotify

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and also the fact that
we are very much focused

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on making the Spotifiers'
engagement within the company

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more efficient and hopefully
facilitating less attrition

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as a result because of
that increase in employee

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satisfaction with their role.

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SHERVIN KHODABANDEH:
Sidney, I'd like

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to pivot on something you said.

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You've been talking about
automation and various ways

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of automation,
intelligent automation.

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What are your thoughts on other
ways that AI can create value?

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Because automation is sort of
at the far end of the spectrum,

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right, where AI decides and
executes, and completely

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replaces [a] human?

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And we're seeing a lot
of interest and attention

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recently in where
AI and human work

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together to do something
that neither one of them

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could do alone as effectively.

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What are some examples
of that, and what

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are your thoughts on
more, I would say,

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integrated human-AI
ways of working?

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Does that make sense?

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SIDNEY MADISON
PRESCOTT: It does.

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And this is actually a subject
I'm very passionate about,

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because I think this is the
future of what we are going

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to see, not only as
technologists, but really

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as a society in terms of
incorporating machines that

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are capable of,
I'll say, a version

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of cognitive functionality, with
that intuitive cognitive nature

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that humans have.

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The merging of those two?

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I think that is an extremely
propelling value proposition,

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specifically in
relation to the ways

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that we can continue
to evolve technology.

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And I would agree: I think
what we are seeing now is less

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of a desire to say that humans
step away from a particular

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process and the machine takes
over, and more of a, I'll say,

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almost a dance -- a
collaborative dance,

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between the machine
and the human.

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And I think this is wonderful,
because what it enables us

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to do is look at the ways that
humans flourish in relation

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to their cognitive capabilities,
and then the ways that machines

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can basically help enable humans
in the areas where we are not

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as strong, with large
populations of data being able

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to quickly assess
deltas in that data --

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being able to see patterns
in millions of rows of data.

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These are the areas where AI
can really flourish for us,

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AI/machine learning.

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The more cognitive functions
where maybe a decision

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needs to be made
off of that data,

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that's where the humans can come
back in and pick up that work.

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I call it human, really,
augmentation of humans,

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at the end of the day.

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And the goal would be maximizing
our potential as humans

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to really --

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SHERVIN KHODABANDEH:
Digital dancing.

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SIDNEY MADISON PRESCOTT: Yes.

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Exactly, exactly.

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To really facilitate
better outputs,

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and to also allow
us to really ...

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it's almost as if we amplify
our own abilities with these

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machines, to the point that
we can really say that we are

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operating at an optimal
level, whether we're business,

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whether we are engineers.

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And then of course those
outputs would naturally

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benefit the enterprise
at the end of the day.

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SHERVIN KHODABANDEH: So this
notion of the digital tango --

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and, Sam, I have to give
you credit it for that,

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because this is something
you've been talking about --

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it's actually quite
interesting and it feels quite

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underappreciated: that it's
either human solo or AI solo,

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and the middle ground, where
[a] human has to do things

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differently because of AI, but
AI sort of morphs around what

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humans [are] good at and adapts,
and [the] human does a bit

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of the same thing.

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It feels completely
underexplored,

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partly because it feels
a little scary, right?

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You have to rethink processes
and existing ways of working,

295
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and existing norms of job
descriptions and things

296
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like that, and as you said,
this is sort of the future.

297
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What are your thoughts or ideas
on how this could become more

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00:14:27.120 --> 00:14:32.150
accepted -- that evolution,
that opening of people's eyes

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to, it's not all or nothing,
but it's actually in the middle

300
00:14:35.740 --> 00:14:38.500
ground, that there's so
much opportunity for us.

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SAM RANSBOTHAM: Any
analogy for dancing

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00:14:40.490 --> 00:14:42.216
is going to terrify
me right off the bat.

303
00:14:42.216 --> 00:14:43.010


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SHERVIN KHODABANDEH:
Well, you came up with it.

305
00:14:45.382 --> 00:14:47.840
SIDNEY MADISON PRESCOTT: It is
a very interesting question.

306
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And I think that the hesitancy
to really embrace this kind

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of human-in-the-loop machine
and human partnership comes from

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the human resistance to change.

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And we all know that this
is something that we really

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struggle with --
change -- as humans.

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We believe we are
quick to adapt,

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but in reality, we don't adapt
quite as quickly as we'd like.

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And I think the fact that
we have not yet embraced

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the digital-human workforce,
and really combining those two

315
00:15:23.540 --> 00:15:27.080
together in almost a
seamless workforce,

316
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is because we are so reliant on
our prior understanding of what

317
00:15:32.520 --> 00:15:35.800
work -- and I'll put that in
air quotes -- what "work" is,

318
00:15:35.800 --> 00:15:36.300
right?

319
00:15:36.300 --> 00:15:40.130
The fundamental element
that is going to change this

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00:15:40.130 --> 00:15:44.610
is redefining what we mean
as humans when we say work.

321
00:15:44.610 --> 00:15:45.940
What does work mean?

322
00:15:45.940 --> 00:15:49.500
Whether it is an accountant,
whether it is an engineer,

323
00:15:49.500 --> 00:15:52.330
whether it is an
engineering manager,

324
00:15:52.330 --> 00:15:56.040
an executive in the C-suite,
what do we mean by work?

325
00:15:56.040 --> 00:15:58.430
And we've started to see
a bit of this transition

326
00:15:58.430 --> 00:15:59.920
within the pandemic, right?

327
00:15:59.920 --> 00:16:01.880
We've gone into
the virtual world.

328
00:16:01.880 --> 00:16:04.170
We've heard pros
and cons about this.

329
00:16:04.170 --> 00:16:07.900
And we started to see pushback
on some of the companies that

330
00:16:07.900 --> 00:16:13.850
are attempting to embrace that
more virtual workforce concept.

331
00:16:13.850 --> 00:16:16.620
And this, I believe,
is the playing ground

332
00:16:16.620 --> 00:16:19.400
for the future, which
is, can we embrace

333
00:16:19.400 --> 00:16:24.290
a virtual workforce and a
reality in which workers

334
00:16:24.290 --> 00:16:29.000
around the world are able
to work in a virtual manner

335
00:16:29.000 --> 00:16:31.780
to facilitate the
growth of the business?

336
00:16:31.780 --> 00:16:35.750
And the next piece of that
is, in that virtual work,

337
00:16:35.750 --> 00:16:41.160
can we move away from thinking
of these silos of human

338
00:16:41.160 --> 00:16:44.340
versus machine-relegated tasks?

339
00:16:44.340 --> 00:16:46.860
And I believe if we can
merge those together --

340
00:16:46.860 --> 00:16:51.710
the virtual workforce combined
with a redefining of what it

341
00:16:51.710 --> 00:16:54.770
means to work as a human,
and almost think of ourselves

342
00:16:54.770 --> 00:16:58.280
as "What skills do the
future accountants need?

343
00:16:58.280 --> 00:17:01.460
What skills do the future
backend engineers need?

344
00:17:01.460 --> 00:17:06.040
And how are those skills going
to play seamlessly with these

345
00:17:06.040 --> 00:17:08.859
emerging technologies?"
-- that, I believe,

346
00:17:08.859 --> 00:17:12.380
is where we're going to hit the
sweet spot where we no longer

347
00:17:12.380 --> 00:17:15.140
have this almost
tension between, "OK,

348
00:17:15.140 --> 00:17:17.280
well only humans can do this."

349
00:17:17.280 --> 00:17:19.119
I hear and see this
a lot in my work,

350
00:17:19.119 --> 00:17:21.020
which is, "Oh, a
human has to do this."

351
00:17:21.020 --> 00:17:22.990
I'm like, "Well, humans
are prone to error,

352
00:17:22.990 --> 00:17:27.240
so maybe we don't always need
to rely solely on ourselves,"

353
00:17:27.240 --> 00:17:31.200
and thinking less of the
machine as an adversary

354
00:17:31.200 --> 00:17:35.250
and more as a partner and
enabler in that business

355
00:17:35.250 --> 00:17:35.750
process.

356
00:17:35.750 --> 00:17:39.750
SAM RANSBOTHAM: It sounds like I
know you have this citizen data

357
00:17:39.750 --> 00:17:41.140
scientist program.

358
00:17:41.140 --> 00:17:43.740
That sounds like exactly what
that program is all about,

359
00:17:43.740 --> 00:17:45.428
teaching those
basic dance steps.

360
00:17:45.428 --> 00:17:47.220
SIDNEY MADISON PRESCOTT:
Yes, very much so.

361
00:17:47.220 --> 00:17:51.140
So within Spotify, we're
very focused on our citizen

362
00:17:51.140 --> 00:17:53.430
developer community,
which is what we call it.

363
00:17:53.430 --> 00:17:56.710
And this is where we are
enabling our business

364
00:17:56.710 --> 00:18:00.330
stakeholders, through
upskilling, various boot camps

365
00:18:00.330 --> 00:18:03.570
and trainings internally,
to really enhance

366
00:18:03.570 --> 00:18:07.070
their understanding of
these emerging technologies,

367
00:18:07.070 --> 00:18:10.640
and we are really encouraging
our business stakeholders,

368
00:18:10.640 --> 00:18:15.700
specifically, to embrace the
technology in a way that really

369
00:18:15.700 --> 00:18:19.510
resonates with their job
responsibilities, currently

370
00:18:19.510 --> 00:18:21.760
and in the future as we
continue to grow the business.

371
00:18:21.760 --> 00:18:24.850
SAM RANSBOTHAM: Sidney, we're
recording this episode right

372
00:18:24.850 --> 00:18:27.740
around the holiday season,
when Wrapped has just come out.

373
00:18:27.740 --> 00:18:31.090
So in this Wrapped product,
Spotify members get a deep dive

374
00:18:31.090 --> 00:18:33.480
into their most memorable
listening moments of the year,

375
00:18:33.480 --> 00:18:36.370
including podcasts, and we
hope this podcast, I guess.

376
00:18:36.370 --> 00:18:38.810
Can you perhaps use that
to illustrate how you're

377
00:18:38.810 --> 00:18:40.680
doing some of these things?

378
00:18:40.680 --> 00:18:42.180
SIDNEY MADISON
PRESCOTT: Absolutely.

379
00:18:42.180 --> 00:18:45.500
So there is a heavy amount of
data mining/machine learning

380
00:18:45.500 --> 00:18:48.180
that's being used in
order to facilitate

381
00:18:48.180 --> 00:18:51.310
kind of the presentation
of, "Here's your top artist.

382
00:18:51.310 --> 00:18:53.170
Here's how many times
you listened to them.

383
00:18:53.170 --> 00:18:56.260
Here are the different
genres that you've visited."

384
00:18:56.260 --> 00:19:00.410
It's a great example of,
again, leveraging data mining

385
00:19:00.410 --> 00:19:04.460
in such a way to create almost
this visualization, if you

386
00:19:04.460 --> 00:19:08.020
will, of what your
listening landscape has

387
00:19:08.020 --> 00:19:10.610
been as a customer of Spotify.

388
00:19:10.610 --> 00:19:13.590
And we even see this in a
lot of our back-end processes

389
00:19:13.590 --> 00:19:17.500
as well, where we are really
looking at ways to leverage

390
00:19:17.500 --> 00:19:20.190
we have massive amounts of
data, and we're really looking

391
00:19:20.190 --> 00:19:21.600
at, "OK, what are the insights?

392
00:19:21.600 --> 00:19:25.040
How do we actually use this to
do some predictive analytics?"

393
00:19:25.040 --> 00:19:27.552
whether it's the ways our
systems are interacting,

394
00:19:27.552 --> 00:19:29.010
whether it's actually
understanding

395
00:19:29.010 --> 00:19:30.610
process inefficiencies.

396
00:19:30.610 --> 00:19:34.980
So we're looking very closely
at monitoring deviations

397
00:19:34.980 --> 00:19:38.080
in our various
process flows in order

398
00:19:38.080 --> 00:19:41.930
to better identify where we
have areas of inefficiency

399
00:19:41.930 --> 00:19:45.380
that we can zero in on
and tackle those to make

400
00:19:45.380 --> 00:19:48.900
the overall workflows
of our employees faster

401
00:19:48.900 --> 00:19:50.030
and more efficient.

402
00:19:50.030 --> 00:19:52.780
So it really is about
mining that data,

403
00:19:52.780 --> 00:19:55.790
but we're mining it for
critical information that

404
00:19:55.790 --> 00:20:00.010
can help us to be more proactive
about inefficiencies, more

405
00:20:00.010 --> 00:20:03.340
proactive about making better
decisions, predictive analytics

406
00:20:03.340 --> 00:20:06.600
on maybe challenges that we
might foresee coming down

407
00:20:06.600 --> 00:20:09.130
the road for a particular
business process,

408
00:20:09.130 --> 00:20:11.490
even predicting
increases in volume

409
00:20:11.490 --> 00:20:14.630
and how that will translate
throughout the environment.

410
00:20:14.630 --> 00:20:17.090
So it's a really
great use of, I think,

411
00:20:17.090 --> 00:20:22.010
data that otherwise would ...
if you don't use that data,

412
00:20:22.010 --> 00:20:25.950
it's almost a waste, because
it's like you can just glean

413
00:20:25.950 --> 00:20:27.440
so many insights from it.

414
00:20:27.440 --> 00:20:29.450
SHERVIN KHODABANDEH:
Sidney, there's

415
00:20:29.450 --> 00:20:35.200
a war for talent in this
space, and many companies,

416
00:20:35.200 --> 00:20:37.240
digital natives
and otherwise, are

417
00:20:37.240 --> 00:20:41.490
struggling to attract,
and retain, and cultivate

418
00:20:41.490 --> 00:20:43.050
the top talent here.

419
00:20:43.050 --> 00:20:45.612
What are your thoughts
on that, and is there

420
00:20:45.612 --> 00:20:47.070
something you could
share about how

421
00:20:47.070 --> 00:20:49.225
Spotify goes about doing that?

422
00:20:49.225 --> 00:20:50.032


423
00:20:50.032 --> 00:20:51.240
SIDNEY MADISON PRESCOTT: Yes.

424
00:20:51.240 --> 00:20:53.350
So this is a really
interesting topic,

425
00:20:53.350 --> 00:20:56.320
because it really comes
down to the pipeline --

426
00:20:56.320 --> 00:21:00.030
the pipeline from all the
way up to elementary school,

427
00:21:00.030 --> 00:21:03.420
down to whether or not
it's technical college,

428
00:21:03.420 --> 00:21:06.320
four-year college, etc.,
and into the professional

429
00:21:06.320 --> 00:21:07.330
workforce.

430
00:21:07.330 --> 00:21:11.490
And the question becomes
kind of almost back

431
00:21:11.490 --> 00:21:15.500
to what we initially spoke
about: Are we facilitating

432
00:21:15.500 --> 00:21:18.700
an understanding of
what the future roles

433
00:21:18.700 --> 00:21:20.740
within the enterprise
will look like

434
00:21:20.740 --> 00:21:22.970
and the skill sets
that will be needed?

435
00:21:22.970 --> 00:21:27.600
And are we enabling individuals
throughout the course

436
00:21:27.600 --> 00:21:31.310
of their academic or
technical lifetimes

437
00:21:31.310 --> 00:21:34.510
to better engage with these
different technologies

438
00:21:34.510 --> 00:21:35.820
at an early age?

439
00:21:35.820 --> 00:21:37.620
And I think what
we're seeing is,

440
00:21:37.620 --> 00:21:41.310
there's a lack of
understanding of what

441
00:21:41.310 --> 00:21:43.070
it means to be a technologist.

442
00:21:43.070 --> 00:21:46.240
I think there's a lot of
misconceptions floating out

443
00:21:46.240 --> 00:21:49.030
there, specifically
within, I would say,

444
00:21:49.030 --> 00:21:50.270
the younger generation.

445
00:21:50.270 --> 00:21:55.470
And I think we need to do a
better job of really teaching

446
00:21:55.470 --> 00:21:58.680
younger students --
and even preteens --

447
00:21:58.680 --> 00:22:02.270
what a career in technology
looks like and, more

448
00:22:02.270 --> 00:22:04.540
importantly, what it looks
like 10 years from now,

449
00:22:04.540 --> 00:22:07.680
15 years from now, when they'll
be entering the workforce.

450
00:22:07.680 --> 00:22:11.370
And then you back in from
the enterprise perspective,

451
00:22:11.370 --> 00:22:14.330
and you start reaching out
to those college students,

452
00:22:14.330 --> 00:22:16.760
you start reaching out
to those high schoolers,

453
00:22:16.760 --> 00:22:20.120
and you help them to
see a day in the life.

454
00:22:20.120 --> 00:22:22.550
We do a lot of "a day in
the life of an engineer,"

455
00:22:22.550 --> 00:22:25.080
"a day in the life of
an engineering leader,"

456
00:22:25.080 --> 00:22:28.360
and kind of showing it's
not someone in a hoodie

457
00:22:28.360 --> 00:22:32.800
that's hacking on a computer,
which is the movie element,

458
00:22:32.800 --> 00:22:33.300
right?

459
00:22:33.300 --> 00:22:35.030
It's a lot more than that.

460
00:22:35.030 --> 00:22:37.760
And even myself, when I
moved into technology,

461
00:22:37.760 --> 00:22:41.870
I found it quite fascinating
to see the day to day,

462
00:22:41.870 --> 00:22:44.670
because I myself had no idea.

463
00:22:44.670 --> 00:22:47.760
I had no foundation
to understand what

464
00:22:47.760 --> 00:22:49.700
it meant to be an engineer.

465
00:22:49.700 --> 00:22:52.970
And I think that piece
is where we can really

466
00:22:52.970 --> 00:22:57.330
tap into a future workforce,
helping them to understand

467
00:22:57.330 --> 00:23:02.080
the nuances of today's engineer
but also tomorrow's engineer,

468
00:23:02.080 --> 00:23:04.410
and what it takes
to actually succeed

469
00:23:04.410 --> 00:23:06.340
in a career in technology.

470
00:23:06.340 --> 00:23:07.840
SHERVIN KHODABANDEH:
The distinction

471
00:23:07.840 --> 00:23:10.320
between today and tomorrow
that you rightly point out

472
00:23:10.320 --> 00:23:13.290
is actually quite
important, because I

473
00:23:13.290 --> 00:23:16.270
could imagine if
my son asked me,

474
00:23:16.270 --> 00:23:19.450
but for the fact that I
spent time in this area

475
00:23:19.450 --> 00:23:22.210
professionally, I
would be quite biased

476
00:23:22.210 --> 00:23:25.180
about what engineering
was like in my era.

477
00:23:25.180 --> 00:23:27.720
And it feels like
the rate of change

478
00:23:27.720 --> 00:23:32.560
is so fast that prior
experience and advice becomes

479
00:23:32.560 --> 00:23:34.100
very quickly irrelevant.

480
00:23:34.100 --> 00:23:37.610
And so this notion of
... if you go back 20,

481
00:23:37.610 --> 00:23:42.080
30 years ago and you looked up
at somebody in a STEM program,

482
00:23:42.080 --> 00:23:47.410
or would be looking up to folks,
trying to understand what it

483
00:23:47.410 --> 00:23:49.550
would be like to
step in their shoes,

484
00:23:49.550 --> 00:23:52.130
it feels like those steps
are going to be completely

485
00:23:52.130 --> 00:23:53.820
different in the future.

486
00:23:53.820 --> 00:23:55.950
And so this
willingness to really

487
00:23:55.950 --> 00:24:00.220
be open-minded, and also the
exemplars of, what [does]

488
00:24:00.220 --> 00:24:01.680
tomorrow's engineer look like?

489
00:24:01.680 --> 00:24:05.050
Because it feels like
the old approaches

490
00:24:05.050 --> 00:24:06.390
are not going to work anymore.

491
00:24:06.390 --> 00:24:09.000
I mean, folks have
to be much more

492
00:24:09.000 --> 00:24:12.150
exposed to what's
happening now versus what

493
00:24:12.150 --> 00:24:13.760
happened 10 years ago.

494
00:24:13.760 --> 00:24:15.710
SIDNEY MADISON PRESCOTT:
Agreed, and this also

495
00:24:15.710 --> 00:24:22.370
leads back to that need to
really ensure that as engineers

496
00:24:22.370 --> 00:24:23.880
not only move into
the workforce,

497
00:24:23.880 --> 00:24:27.120
but as they mature out their
career footprint, that they are

498
00:24:27.120 --> 00:24:31.370
consistently remaining
abreast of the changes

499
00:24:31.370 --> 00:24:35.060
and the ever evolving
ways that technology

500
00:24:35.060 --> 00:24:37.110
is manifesting in their field.

501
00:24:37.110 --> 00:24:40.050
Because even the engineers
today will tell you,

502
00:24:40.050 --> 00:24:43.680
getting into engineering is very
different, even from sometimes

503
00:24:43.680 --> 00:24:45.160
what they are taught in school.

504
00:24:45.160 --> 00:24:47.970
And so that really is
the disconnect: Are we

505
00:24:47.970 --> 00:24:49.750
teaching the right things?

506
00:24:49.750 --> 00:24:53.240
And then also, are we continuing
the upskilling, right?

507
00:24:53.240 --> 00:24:56.160
As technology evolves,
are we still moving

508
00:24:56.160 --> 00:24:59.450
midcareer engineers
into a position where

509
00:24:59.450 --> 00:25:03.950
they feel empowered to continue
learning about new technologies

510
00:25:03.950 --> 00:25:06.370
and being open to
new opportunities

511
00:25:06.370 --> 00:25:08.490
to actually leverage
those innovative tools?

512
00:25:08.490 --> 00:25:09.310


513
00:25:09.310 --> 00:25:10.810
SHERVIN KHODABANDEH:
Very well said.

514
00:25:10.810 --> 00:25:12.810
SAM RANSBOTHAM: What are
you excited about next?

515
00:25:12.810 --> 00:25:14.230
What's coming next for Spotify?

516
00:25:14.230 --> 00:25:15.750
What's fun for you?

517
00:25:15.750 --> 00:25:17.630
What's big on the
horizon that we

518
00:25:17.630 --> 00:25:19.202
should be looking forward to?

519
00:25:19.202 --> 00:25:21.410
SIDNEY MADISON PRESCOTT:
Oh, I'm really excited about

520
00:25:21.410 --> 00:25:25.620
the, again, that human
augmentation, looking for ways,

521
00:25:25.620 --> 00:25:28.500
whether it is through
artificial intelligence,

522
00:25:28.500 --> 00:25:31.750
whether it's through chatbots,
whether it's through optical

523
00:25:31.750 --> 00:25:37.120
character recognition, of really
creating a seamless experience

524
00:25:37.120 --> 00:25:37.620
--

525
00:25:37.620 --> 00:25:39.740
I'll say a seamless
work experience --

526
00:25:39.740 --> 00:25:42.580
between the human
and the machine.

527
00:25:42.580 --> 00:25:46.940
I fundamentally believe
that that next frontier

528
00:25:46.940 --> 00:25:50.440
is a frontier where
humans almost seamlessly

529
00:25:50.440 --> 00:25:51.870
interact with the
different tools

530
00:25:51.870 --> 00:25:54.760
in their environment in a
way to better facilitate

531
00:25:54.760 --> 00:25:56.150
faster outcomes.

532
00:25:56.150 --> 00:25:58.230
And we're really
looking, specifically

533
00:25:58.230 --> 00:26:00.920
at Spotify within my team,
we're looking at ways

534
00:26:00.920 --> 00:26:02.930
to leverage both
machine learning

535
00:26:02.930 --> 00:26:05.280
and artificial
intelligence in order

536
00:26:05.280 --> 00:26:09.200
to really enable the
business to move faster

537
00:26:09.200 --> 00:26:12.350
and to focus on more
value-added tasks.

538
00:26:12.350 --> 00:26:16.950
And so we're looking at the
amplification of our existing

539
00:26:16.950 --> 00:26:19.270
machine learning
footprint -- specifically,

540
00:26:19.270 --> 00:26:23.530
looking at ways that we can
take data that is currently not

541
00:26:23.530 --> 00:26:27.430
machine readable, translate
that into machine readable data,

542
00:26:27.430 --> 00:26:30.680
pass that off to a bot, and
then potentially pass that off

543
00:26:30.680 --> 00:26:31.460
to a human.

544
00:26:31.460 --> 00:26:34.470
So again, building that
tool stack, and really

545
00:26:34.470 --> 00:26:38.590
a very almost intuitive
workflow between the machine,

546
00:26:38.590 --> 00:26:40.940
between the human,
and the outputs

547
00:26:40.940 --> 00:26:42.930
that are received out of that.

548
00:26:42.930 --> 00:26:45.270
And we're also looking
at ways that we

549
00:26:45.270 --> 00:26:48.920
can start to continue to
mature out the tool stack,

550
00:26:48.920 --> 00:26:51.970
whether we're looking
at workflow automation.

551
00:26:51.970 --> 00:26:55.610
We're starting to look at
the dynamic between having

552
00:26:55.610 --> 00:26:58.180
a front end, which might
be some sort of user

553
00:26:58.180 --> 00:27:01.510
interface with a chatbot, and
then have a back end where it's

554
00:27:01.510 --> 00:27:04.690
actually a robotic process
automation workflow built

555
00:27:04.690 --> 00:27:07.590
into it, to then trigger
some different workflows.

556
00:27:07.590 --> 00:27:12.550
So creating more, I'll say,
complex and nuanced ways

557
00:27:12.550 --> 00:27:15.230
to leverage these tools
rather than siloing them

558
00:27:15.230 --> 00:27:18.510
off and saying, "Oh, this
process is only robotic process

559
00:27:18.510 --> 00:27:21.310
automation," or, "This
process is only a chatbot."

560
00:27:21.310 --> 00:27:24.640
So, breaking down those
barriers, and then, of course,

561
00:27:24.640 --> 00:27:29.530
continuing to break down the
two barriers that I feel truly

562
00:27:29.530 --> 00:27:33.220
exist within most
enterprise environments

563
00:27:33.220 --> 00:27:36.170
and also lead to the
most blockers, which

564
00:27:36.170 --> 00:27:40.910
are the silo between the
business and the technology

565
00:27:40.910 --> 00:27:43.560
side, and then also
the silos that exist

566
00:27:43.560 --> 00:27:47.420
between the front-office and
the back-office functions.

567
00:27:47.420 --> 00:27:50.600
SAM RANSBOTHAM: Sidney,
all quite fascinating.

568
00:27:50.600 --> 00:27:52.893
I think I particularly
enjoyed discussing

569
00:27:52.893 --> 00:27:54.310
breaking these
important barriers,

570
00:27:54.310 --> 00:27:56.350
like the barriers between
business and technology,

571
00:27:56.350 --> 00:27:58.230
and between the front
office and back office.

572
00:27:58.230 --> 00:28:00.900
And I think that
analogy to digital dance

573
00:28:00.900 --> 00:28:02.258
has a lot of potential.

574
00:28:02.258 --> 00:28:04.300
Thanks for taking the time
to talk with us today.

575
00:28:04.300 --> 00:28:05.360
I've really enjoyed it.

576
00:28:05.360 --> 00:28:06.860
SIDNEY MADISON
PRESCOTT: Absolutely.

577
00:28:06.860 --> 00:28:09.230
This has been a
great conversation.

578
00:28:09.230 --> 00:28:13.750
And again, this is ever
evolving, this entire industry,

579
00:28:13.750 --> 00:28:16.310
and it's very exciting to be a
part of, so I would definitely

580
00:28:16.310 --> 00:28:20.110
encourage listeners to
dive in and start really

581
00:28:20.110 --> 00:28:22.940
learning about all of this,
because it's the future,

582
00:28:22.940 --> 00:28:26.250
but it's also now, so it's
really important for all of us

583
00:28:26.250 --> 00:28:28.550
to get engaged and
get excited about what

584
00:28:28.550 --> 00:28:31.255
this means for each and every
single one of us in society.

585
00:28:31.255 --> 00:28:32.880
SHERVIN KHODABANDEH:
Thank you, Sidney.

586
00:28:32.880 --> 00:28:34.050
You've been very inspiring.

587
00:28:34.050 --> 00:28:34.550


588
00:28:34.550 --> 00:28:36.120
SAM RANSBOTHAM:
Thanks for joining us

589
00:28:36.120 --> 00:28:38.470
for Season 3 of
Me, Myself, and AI.

590
00:28:38.470 --> 00:28:42.030
We'll be back in two weeks to
kick off Season 4 with a lively

591
00:28:42.030 --> 00:28:44.700
conversation about innovation
-- and Star Wars --

592
00:28:44.700 --> 00:28:47.700
with Mark Maybury, chief
technology officer of Stanley

593
00:28:47.700 --> 00:28:48.660
Black & Decker.

594
00:28:48.660 --> 00:28:51.880
In the meantime, please
remember to subscribe and rate

595
00:28:51.880 --> 00:28:54.570
and review our show on
Apple podcasts and Spotify.

596
00:28:54.570 --> 00:28:55.070


597
00:28:55.070 --> 00:28:57.100
ALLISON RYDER:
Thanks for listening

598
00:28:57.100 --> 00:28:58.570
to Me, Myself, and AI.

599
00:28:58.570 --> 00:29:01.050
We believe, like you,
that the conversation

600
00:29:01.050 --> 00:29:03.270
about AI implementation
doesn't start and stop

601
00:29:03.270 --> 00:29:04.380
with this podcast.

602
00:29:04.380 --> 00:29:07.000
That's why we've created a
group on LinkedIn, specifically

603
00:29:07.000 --> 00:29:08.070
for leaders like you.

604
00:29:08.070 --> 00:29:10.810
It's called AI for Leaders,
and if you join us,

605
00:29:10.810 --> 00:29:12.780
you can chat with show
creators and hosts,

606
00:29:12.780 --> 00:29:16.450
ask your own questions, share
insights, and gain access

607
00:29:16.450 --> 00:29:18.940
to valuable resources
about AI implementation

608
00:29:18.940 --> 00:29:21.030
from MIT SMR and BCG.

609
00:29:21.030 --> 00:29:27.287
You can access it by visiting
mitsmr.com/AIforLeaders.

610
00:29:27.287 --> 00:29:28.870
We'll put that link
in the show notes,

611
00:29:28.870 --> 00:29:31.320
and we hope to see you there.

612
00:29:31.320 --> 00:29:37.000