Sam: What role did artificial intelligence have
in helping combat the coronavirus pandemic?
Find out today when we talk with an innovative company
that used artificial intelligence
to help solve the critical problems
society faced in the last year.
Welcome to "Me, Myself, and AI,"
a podcast on artificial intelligence in business.
Each episode,
we introduce you to someone innovating with AI.
I'm Sam Ransbotham, Professor of Information Systems
at Boston College.
I'm also the guest editor
for the AI and Business Strategy Big Idea program
at MIT Sloan Management Review
Shervin: And I'm Shervin Khodabandeh
Senior Partner with BCG,
and I co-lead BCG's AI practice in North America.
And together MIT SMR and BCG have been researching AI
for five years,
interviewing hundreds of practitioners
and surveying thousands of companies
on what it takes to build
and to deploy and scale AI capabilities
across the organization,
and really transform the way organizations operate.
(upbeat music)
Sam: Today we're talking with Dave Johnson,
Chief Data and Artificial Intelligence Officer at Moderna.
Dave, thanks for joining us.
Welcome.
Dave: Thanks guys for having me.
Sam: Can you describe your current role at Moderna?
Dave: I'm Chief Data and AI Officer at Moderna.
In my role,
I'm responsible for all of our enterprise data functions
from data engineering to data science integration.
And I also manage a software engineering team building,
you know,
unique custom applications
to curate and create new data sets,
but also to then take those AI models that are created
and build them into processes.
So it's kind of end-to-end
everything to actually deploy an AI model
to build, deploy, and put an AI model into production.
Sam: How did you end up in that role?
I know you have physics in your background.
That's not... I didn't hear any physics
in what you just said.
Dave: Yeah, no, it's a good point.
So I have my PhD in what's called Information Physics,
which is a field closely related to data science actually.
It's about the foundations of Bayesian statistics
and information theory,
a lot of what is involved in data science.
My particular research was in applying that
to a framework that derives quantum mechanics
from the rules of information theory.
So that part, you're right is not particularly relevant
to my day-to-day job,
but the information theory part and the Bayesian stats is
completely on target for what I do.
In addition to that,
I spent many years doing independent consulting
in a kind of a software engineering data science capacity.
And when I finished my PhD, you know,
I realized academia wasn't really for me,
I wanted to do applications.
And I ended up with a consulting firm
doing work for large pharmaceutical companies.
So I spent a number of years doing that
and it turned out to be a real great marriage
of my skill sets, you know,
understanding of science, understanding of data,
understanding of, you know, software engineering.
And so I did one project in particular for a number of years
in research at a pharmaceutical company
around capturing data, you know,
structured, useful way in the preclinical space
in order to feed into kind of advanced data
and advanced models.
So very much what I'm doing today.
And about seven years ago, I moved over to Moderna.
And at that time we were, you know,
a preclinical stage company.
And the big challenge we had was producing
enough small-scale mRNA to run our experiments.
And what we're really trying to do
is accelerate the pace of research,
so that we can get as many drugs in the clinic
as quickly as possible.
And one of the big bottlenecks was having
this mRNA for the scientists to run tests in.
And so what we did is we put in place a ton
of robotic automation,
put in place a lot of digital systems
and process automation and AI algorithms as well.
And what went from maybe, you know,
about 30 mRNAs manually produced in a given month,
to a capacity of about a thousand in a month period.
So without
you know,
significantly more resources
and much better consistency in quality and so on.
And so then I just kind of from there grew with the company
and grew into this role that we have now
where I'm applying those same ideas
to the broader enterprise.
Shervin: That's great Dave,
can you comment a bit on the spectrum of use cases
that AI is being applied to here
and is it really making a difference?
Dave: For us what we've seen
a lot of is in the research space
and particularly in Moderna,
that's been because that's where we digitized early.
We see that putting in digital systems and processes
to actually capture, you know, homogenous good data
that can feed into that
is obviously a really important first step,
but it also lays the foundation of processes
that are then amenable to these greater degrees
of automation.
So that's where we've seen a lot of that value is, you know,
in this preclinical production,
we have kind of high throughput.
We have lots of data.
We're able to start automating those steps
and judgments that were previously done by humans.
So one example is our mRNA sequence design.
We're coding for some protein,
which is an amino acid sequence,
but there's a huge degeneracy
of potential nucleotide sequences that could code for that.
And so starting from an amino acid sequence,
you have to figure out what's the ideal way to get there,
right?
And so what we have is algorithms
that can do that translation
in an optimal way.
And then we have algorithms that can take one
and then optimize it even further
to make it better for production
or to avoid things that we know
are bad for this mRNA in production or for expression.
And so we can integrate those into these live systems
that we have
so that scientists just press a button
and the work is done for them,
and they don't know what's going on behind the scenes,
but then poof,
the outcome is this better sequence for them.
And then we've seen it with quality control steps as well.
We're also doing some work right now
with our clinical partners in the clinical operations space,
in terms of like optimal trial planning.
We're doing some work right now
around our call center planning
now that we're rolling our vaccine out
across the whole world.
More and more phone calls are coming in.
And as we look to launching in new countries,
we have to start planning our resources for that.
So we're looking at machine learning models
to help predict the forecast of these calls,
so we can then staff up appropriately.
So we just see it across, you know,
a variety of different areas.
Sam: You mentioned pressing the button,
your scientists press the button and some trials happen.
So what are these scientists thinking?
I mean, you've suddenly taken away something that used to be
something that they did
and you're having AI do it.
What's the reaction? Do they, are they thrilled?
Are they despondent? You know, or somewhere in between?
Dave: I would say closer to the thrilled side,
usually how it works.
You know we're a company
that believes in giving people a lot of responsibility
and, you know, people work really hard
and what that leads to
is people doing a lot of work, right?
And so what often happens is folks will come to us and say,
"Look, I'm doing this activity over and over.
I would really love some help to automate this process."
And so in that case they're thrilled, right?
They don't want to be,
you know, looking at some screen of data
over and over and over again.
They want to be doing something insightful and creative.
And so that's where we really partner with them
and take off that component of what they do.
Shervin: Dave, I want to build on that,
because I think you're putting your finger
on something quite interesting.
And in addition to the financial impact
that many get from AI
productivity efficiency and all of that,
you talked about some of those, Dave.
There is an impact in overall organizational culture
and you know teams being more collaborative,
higher morale, happier,
more confident, et cetera.
Are those some of the things
that you guys are seeing as well?
Dave: Yeah, for sure.
I think one of the surest signs of that
is we get a lot of repeat customers.
You know, if we do some particular algorithm for somebody,
that person comes back with the next one or,
you know, that their team comes back time and time again,
we don't think about AI in the context of replacing humans.
Like we always think about it in terms of
this human-machine collaboration,
because they're good at different things.
You know,
humans are really good at creativity and flexibility
and insight,
whereas machines are really good at, you know,
precision and giving you the exact same result
every single time and doing it at scale and speed.
What we find the most successful projects
are where we kind of put the two together,
have the machine do the parts of the job that it's good at.
Let the humans take over for the rest of that.
Sam: So with this freedom, what have people done?
You know you've opened up this time,
(Shervin chuckles)
What kind of new, you know...
Shervin: I got two shots of that.
(all laugh)
Shervin: What people have done...
Sam: Yeah actually there's at least one product
that's in the market now isn't there?
I think I've heard something
in the news.
Dave: Just there's one. Yeah. You know,
I always like to joke that work is like a gas
that always expands to fill the container.
So if you take something off somebody's plate,
there's all this mountain of work
that they didn't even realize just wasn't being done.
And so people are always relieved to then go on and find,
you know, the next mountain to climb
and the next thing to do.
Sam: But what are these kinds of things
like, you know how are people choosing how to, you know,
expand to fill that space?
Dave: Well, if you think of the examples, you know,
like the preclinical quality control steps
that we've automated,
the reality is, you know,
one operator stretched over a huge amount of work is,
it's really hard for them
to really do really in-depth inspection of these samples.
And so by taking off, you know,
a bulk of that work,
80-90% for the algorithm to do that,
we're able to do just do a better more thorough job
of inspecting the samples that are left.
It also means that we're not hiring
a whole bunch of other people
just to go look at, you know, screens of data.
It's a bit of an immediate gain for the people who are there
and then kind of this longer-term gain
on our headcount plans.
Some folks, you know, talk about AI
in the pharma space being like,
"I just want an algorithm that can predict from
the you know, the structure of a small molecule,
the efficacy in humans" right?
Like that's the entire drug discovery process.
That's just not going to happen.
That's completely unrealistic.
So, you know, we just think about the fact that, you know,
there are countless processes, it's a
very complicated process to bring something to market.
And there are just numerous opportunities along the way.
Even within a specific use case,
you're rarely using one AI algorithm.
And so often, you know, for this part of the problem,
I need to use this algorithm,
for this I need to use another.
Shervin: Dave I want to ask you something about
the talent base and people.
You commented that Moderna is a kind of company
that likes to give people a lot of freedom,
you know, highly motivated,
smart, ambitious team working to do the best they can.
How do you bring in and cultivate that talent?
And what are you finding to be some of the lessons learned
in terms of how to build a high performing team?
Dave: It's a good question.
I don't know that, you know,
if we look across the company as a whole,
there is one particular place where we hire people.
We get people from biotechs, you know,
five people, to pharmas of a hundred thousand people,
and everywhere in between
inside the industry and outside the industry.
I think for us it's always about finding
the right person for the job
regardless of where they come from and their background.
I think the important thing for us
is to make sure that we set expectations appropriately
as we bring them in.
And we say, look this is a digital company.
We're really bold. We're really ambitious.
We have really high quality standards.
And if we set those expectations really high,
you know, it does start to self-select a lot of the people
who want to come through that process.
Sam: Do you want to flip over and talk you know,
you mentioned some of
the infrastructure I would call it that you put in place
that suddenly the world benefited from a few months ago.
How do people know to get those things set up
in the first place?
You mentioned being able to scale from,
I think you know, 30 to a thousand difference.
How did you know that was the direction
or that was the vision to get those things set up?
Dave: Yeah, that's a great point.
The whole COVID vaccine development,
you know, we're immensely proud of the work
that we've done there.
And we're immensely proud of the superhuman effort
that our people went through
to bring it to market so quickly.
But a lot of it was built on just what you said,
this infrastructure that we had put in place
where we didn't build algorithms specifically for COVID.
We just put them through the same pipeline of activity
that we've been doing.
We just turned it as fast as we could.
When we think about everything we do at Moderna,
we think about this platform capability.
You know,
we were never going to make one drug,
that was never the plan.
The plan was always to make a whole platform around mRNA.
Because since it's an information-based product,
all you do is change the information encoded in the molecule
and you have a completely different drug.
We knew that if you get one in the market,
you can get any number of them to the market.
And so all the decisions we made around
how we designed the company
and how we designed the digital infrastructure
was all around this platform notion
that we're not going to build this for one thing,
we're going to build a solution
that services this whole platform.
And so that's exactly why we built, you know,
this early preclinical stuff
where, you know, we can just crank
through quite a few of these.
That's why we built these algorithms to automate activities
anytime where we see something where we know that,
you know,
scale and making it parallel was going to improve things,
we put in place this process.
Sam: The proof is certainly in the pudding.
One thing that I'm kind of finding fascinating is how
like normal this all...
like I guess I'm just surprised
at how much that seems to be part of your,
can I use the word DNA here in this,
(Sam chuckles)
all right RNA, Shervin: Is part of mRNA
Dave: Yeah, no, it's true.
You know,
we were founded as a digital biotech
and like a lot of companies say things
and put taglines on stuff,
but we really meant it.
And we have pushed on this for many years
and we've built out this for many years.
Shervin: Yeah it's the platform we built
and now, now he's running.
Dave: Yeah.
And it's the platform approach we take to our data science
and AI projects as well.
I hear a lot of struggles from folks around
like, great I built a model in a Jupyter Notebook
now what do I do with it? Right?
Because there's all this data cleansing and data curation
to even get it to be in a useful state.
And then they don't know where to go from it to deploy it,
right?
And we took the same platform approach
to our data science activities.
We spent a lot of time on the data curation,
the data ingestion to make sure the data
is good to be used right away.
And then we put a lot of tooling and infrastructure in place
to get those models into production and integrated.
This platform mentality
is just so ingrained in how we think.
Sam: Take us back to, you know,
early in the COVID race for a vaccine,
you know,
what was it like being part of that team
and a part of that process?
I mean, what were the emotions like when the algorithms
when the people find something that seems to work
or it seems promising.
Does that lead to a massive appetite
for more artificial intelligence and more algorithms?
I guess to tell us a little bit about that story.
Dave: I think if you look at how people felt
in general at the time,
I mean it was a real sense of honor and pride right?
We felt very uniquely positioned.
We even spent a decade getting to this point
and putting all of this infrastructure in place
and putting things in the clinic before this,
to get to this moment.
And so we just felt truly honored to be in that position.
And for those of us on the digital side,
who have kind of contributed to this and built it.
You know, this is why we did it.
This is why we're here
is to help bring as many patients
as fast lanes as quickly and safely as possible
to the world.
But there was always the question of
would this thing work in the real world?
You know, and that's where the proof came
in the clinical data
and we're all anxiously waiting
like everybody else to see that readout.
Shervin: Was AI always front and center at Moderna
or has it become more critical
as a pillar of growth and innovation over time?
Dave: Yeah I think it's always been there
that we probably didn't call it that in the early days
it's become obviously much more of, you know,
a hot marketing term than it used to be.
But the notion of algorithms taking over decision making,
and you know, data science capability
was absolutely always there.
We were very thoughtful about how we
built this digital landscape,
such that we're collecting structured data
across all these steps,
knowing full well that what we want to do
is then turn those into algorithms to do things.
So it was very purposeful for that.
But I do think it's also come into a greater focus, right?
Because we've seen the power of it
very recently obviously we've seen
how this digital infrastructure
and how these algorithms
can really help push things forward.
And so it's got that kind of a renewed focus
and importance in the company.
We tend not to be a company of half measures.
So when we decided we're going to do something,
we're going to do it.
And so it's been a very strong message
from our senior leadership
about this is the future of the company
is in injecting digital and AI into everything we do.
And under no uncertain terms,
this is happening to the point that, you know,
as we think about the fact that
we are growing really fast as a company,
you know, we just doubled,
we're probably going to double again.
We're bringing in a lot of new folks
from outside the company to grow,
who are not necessarily familiar with this digital culture
that we've had.
And so what we're working on right now
is actually developing what we're calling an AI academy,
which we intend to be a very thorough in-depth training
for our company.
So from people who would use and interact with AI models
in their daily basis
to senior leaders who would be responsible,
kind of a portfolio of potential projects in their areas.
And that just shows the level of serious commitment
we have about this.
We were built in this concept of having a smaller company
that's very agile and can move fast.
So we see digital as a key enabler for that
and AI as a key enabler for that.
And so the hope is that that helps us to compete
in ways that other companies can't.
And that is certainly the intention here.
Sam: Well Dave thanks so much for talking with us today.
We really enjoyed,
I mean, you mentioned Moderna hires smart people
and we know that from the sample size of one
that's clearly true.
Thanks for taking the time to talk with us today.
Shervin: Thank you so much.
Dave: Absolutely guys, I really appreciate it.
(upbeat music)
Shervin: Sam that was an awesome conversation with Dave.
What do you think?
Sam: Yep. Impressive.
I'm glad he's around.
I'm glad Moderna is around.
Shervin: That's right.
He's not...
lip service to buzzwords and this that
the other he's just like, yeah, we started this way.
That's why we're doing it.
We would not have existed without digital
and AI and data and analytics.
Of course, it's real.
Like, that's what we are.
I mean, he said Moderna is a digital company,
that's what he said.
Sam: It's just part of their process.
I mean, some of the questions,
it didn't even occur to him
that it was artificial intelligence
and that's just the way they do things.
I wonder if that's like, the new industry after industry,
we're going to see the Moderna-type approach
has come into industries
and just be the dominant, you know,
the vestiges of historical
we've been around for a hundred years
are almost a liability versus a plus.
Shervin: I think this sort of contrast, you know, Sam,
that you were sort of trying to get at, which is like,
how come it's so easy for you guys?
And what about the pre-POST and the transformation?
I mean, these guys, you know, he's like, well,
we actually started this way.
You know, we said, we want to be...
Sam: They started POST.
Shervin: Yeah we started POST.
We wanted to be agile.
We wanted to be small.
We wanted to do a lot more with everything that we had.
And so that had to be platform centric, data centric,
AI centric, and that's how we built.
And so AI is everywhere.
Why are you surprised Sam?
That AI is everywhere? Of course, it's everywhere.
We do it for planning and trials and sequencing
and it's quite energizing
and intriguing how it's just like
a very different mind towards AI.
Sam: Right and I know that
we don't want to make everything AI.
There's a lot that is going on there.
That's not artificial intelligence you know,
I don't want to paint it as entirely,
but that certainly was a big chunk of this,
of the speed story here.
And it's pretty fascinating.
(upbeat music)
Narrator: Thanks for joining us
for this bonus episode of "Me, Myself, and AI."
We'll be back in the Fall
with new episodes for season three.
In the meantime, stay in touch with us on LinkedIn.
We've created a group called AI for Leaders,
specifically for audience members like you.
You can catch up on back episodes of the show,
meet show creators and hosts,
tell us what you want to hear about in season three,
and discuss key issues about AI implementation
with other like-minded people.
Find the group at www.mitsmr.com/AIforLeaders
which will redirect you to LinkedIn,
where you can request to join.
We'll put that link in the show notes as well,
and we hope to see you there.
(upbeat music)