-: Flowers are not digital products at all
but digital technologies like artificial intelligence
can still offer considerable value to companies
that sell non-digital products.
In today's episode, Amit Shah, president
of 1-800-Flowers describes how AI
and machine learning offered
through a platform like 1-800-Flowers can empower
small companies to compete with much larger organizations.
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.
-: 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.
-: Today, we're joined by Amit Shah.
Amit's the president of 1-800-Flowers.
Amit, welcome.
We're excited to learn more about what you're doing.
-: Thank you so much, Sam and Shervin, great to be here.
-: Amit, we talked earlier, and discovered that we all,
had some connection to the state of Georgia,
and you and Shervin and I.
But now you're in Long Island.
You're the president of 1-800-Flowers.
Tell us a little bit about your career path to get there.
-: So I started out as an analyst at McKinsey,
which is very well known for helping teach problem solvings
both individually and collectively at scale,
and then worked at a range of startups in the Northeast area
and ended up being part of Liberty Media ProFlowers Group
at an early stage of my career, and got very much involved,
I would say, in the front seat of growth hacking.
And that's what led me to sort of the current career
as I've seen growth hacking and sort of the mindset
of the hacker, looking for the continuous change
continuous upliftment, and a continuous desire
to provide the best customer experience,
actually both prepaid,
and ultimately become the most critical element
of any C-suite or boardrooms at large.
-: Amit, starting in a marketing role
and then transitioning to president,
that marketing background must've made a difference.
How has being
from marketing affected your role as president?
-: That's a great question Sam.
Evolving into the role of leading, you know,
multiple functions, departments
and colleagues outside of marketing,
what really stands out is two key elements.
The first thing is that I think marketing traditionally
was seen as one of the functional competencies in empowering
and accelerating problem solving.
Marketing is becoming a growth function,
and growth is becoming the key differentiator of companies.
So I expect to continue to see an acceleration
of marketing leaders actually taking on much more leadership
responsibilities and ultimately starting to steer the ship
because growth has become the essential currency
and essential differentiator.
So that is one vector.
And then I would say the second vector that informs me
and prescient to our AI conversation is that I feel
like people in the marketing sphere are actually much
more contextually aware and contextually practicing
advanced problem solving, using machine learning
than a lot of their peers.
So I think there's a unique ability and a unique mindset
that I bring to this leadership role as president
of 1-800-Flowers, having been exposed to that quality
and quantum of problem solving,
which is surrounding a lot of growth
and marketing leaders around us.
-: Let's address the obvious question, flowers themselves
aren't digital at all.
How is 1-800-Flowers using digital data,
and AI in particular?
-: Currently, we are a platform of 15 brands.
So if you think about it, you know,
we are a platform that empowers engagement.
We play along the full spectrum
of human expression and engagement, starting from birthdays
all the way to sympathy and everything in between.
So that is who we are.
You know, we think that we have built an all-star range
of brands to really power up an engagement platform.
So if you think
about what differentiates modern organizations,
it is not just the ability to adopt technologies
which has become a table stake, but the ability
to out-solve their competitors in facing deep problems.
So when I think about AI, I think about our competitiveness
on that frontier.
Are we better problem solvers?
And I'll give you a terrific example of that.
You know, when I started my career 20 years ago
as a young analyst at McKinsey,
there was a clear differentiator
between people who are masters
of Excel and who are not, right?
It was a tool that empowered decision making
at scale and communication of the decision making.
When I think about AI
and its power five years down the road,
I think every new employee that starts
out will actually have an AI tool kit
like we used to get the Excel toolkit
to both solve problems better and communicate
that better to clients, to colleagues, to any stakeholder.
So AI to me is not a skill-set issue,
it is a mindset issue and over the longterm,
companies that adopt and understand that this is a mindset
and a skill-set game, actually will be the ones
that are more competitive than their peer reference group.
-: Yeah. That's super insightful and right on.
And it's almost exactly the conversation
that we were having with Will from Google.
It is all about mindset.
Yet, it's quite daunting, I would say,
that for so many companies,
they, they're still viewing it
as a technology issue, as a technology black box.
As you know, we need a lot of data.
We need a lot of data scientists, which is,
of course you do need those, but you need to also focus
on the right problems and that problem definition
and how you go about solving those problems.
And then how do you change the mindset of the actual users?
Because I could imagine you have been able to shift
the mindset of two kinds of groups, like the mindset
of the consumers, where 20 years ago, they wouldn't,
they wouldn't share information on birthdays
and things like that with any digital platform.
But of course you've changed that mindset, including myself.
But you've also, must've changed the mindset
and the ways of working of so many,
like mom and pop local florists that are actually engaging
with a platform to do business.
Can you comment on that a little bit
and how hard was to do that? -: I think Shervin, you have,
you have brought a very important crux of this issue.
You know, when we, when we talk about a mindset shift,
a metamorphosis of acceptance of, you know,
mindset over even skill-set,
it really requires a multi-stakeholder approach.
And certainly we are very proud
that we support almost a community of 4,000
plus florists who are powering Main Street businesses.
And I would say one of the last remaining outposts
of successful Main Street businesses
in the US is the florist,
and it plays a very important part in the community
not just as sort of a trusted provider
to all the important occasions
for everyone in the community.
But also I would say as a light post
of how the context with AI is evolving.
And let me give you a few examples of it.
The question comes down to, is the context
around me and getting more competitive and evolving?
And I would say for the small florist
and a company like ours, being surrounded
by platforms like Facebook and Google,
which are auction-rich machine learning environments
set up to extract the highest yield per click
means that any business owner that is seeking growth,
that is seeking to get in front of customers,
already is being mediated
by machine learning and artificial intelligence.
So when I think about this multi-stakeholder empowerment,
I think about how do we empower the smallest florist
in heartland of America compete
with this evolution of context?
You know, how do we empower that, you know,
small business entity to get to that strength?
And I think that's where the mindset comes in,
'cause what this requires is, first of all,
understanding that the context is already rich in AI and ML.
The second point is that unless you can,
you can assemble a response to it,
you are always on the losing side.
So our thinking is that by providing those suite
of services, by providing and working very closely
with our florist community, our supplier community,
we are actually providing them relevance
in a rapidly evolving context, where getting in the front
of their customers itself is a machine learning problem.
-: How do you go about doing that?
How much of that is technologically driven
through the platform and how much of that is good
old fashioned human grease and relationship management
and working closely with these little places?
How much of that was technology solving the problem
versus people and processes
and change management and those kinds of things?
-: A very strong starting point is realizing, how
can you basically collect data and make inferences at scale?
So I'll give you a simple example.
To set up a reminder program
on our platform is actually a perpetual cold start problem.
And let me explain what that means.
It means that for example, if you come
to our website or you come to any florist website
and you, let's say you have come
in to express happy birthday to your sister
whose birthday's a week away, and you might come
and pick an arrangement, let's say she loves, you know,
white calla lilies, and you come and, you know,
do some clicking on white flowers, white arrangements,
and then pick a calla lily arrangement and send it to her.
Most companies will take a record of that data
and say that the next time Shervin comes to our site,
let's show him white, for example.
But it could be that the,
your next visit is actually right before Valentine's.
You're here to buy flowers, which are predominantly
generally red or pink for Valentine's.
And you're trying to express that.
So your, your entire click history, your entire corpus
of digital breadcrumbs that you have given us
to solve a machine learning problem is actually irrelevant
because you're starting again as a cold start outcome.
And this fact of personalization, you know,
the enormity of data, the enormity
of decisions required to resolve this outcome
so that you can create a better customer experience is,
is what we are empowering our stakeholders
to really realize, right?
So that is one dimension of it.
The second dimension of it is what we talked
about that currently customers are intermediated
by extremely expensive, I would say,
auction rich environments, controlled
by a few major platforms and to play in those platforms
you need to have a baseline competency.
So we employ a lot
of advanced algorithmic trading and algorithmic models
for example, to understand what should be your bid
at any given time of the day, day of the week,
and the month of a year in order to maximize your,
your yield and minimize your CAC.
And those data sets, that sophistication,
that investment is, is almost outside the realm,
I would say, of a lot of localized businesses and outcomes.
So this question of building alliances,
this question of trusting larger entities,
is going to become also more important over time.
So when we think about our mission and our vision,
we are inspired by what part can we play
in catalyzing those outcomes
and empowering in accelerating those outcomes?
Because whether we are talking
about florists on the Main Street, as one
of the last remaining independent important businesses
in America, or we think about someone who is trying to get
to a funeral home to express something very personal
to them, those moments define us
and define the communities that we live in.
And we think that, you know,
we have a strong part to play in,
in helping realize that vision.
And we look at that vision
not just as a financial or a transactional outcome,
but we look at that as an outcome of, for the whole society.
So for example, you know,
we have free e-cards that you can come
to our site right now and send.
We really want you to just literally express
to someone that, hey, you are thinking of them,
because we think that it's very more important
for us to appreciate and empower that expression.
That over time
hopefully leads you to have a deeper connection
with us as a brand, deeper connection with us as a platform
and then use us to express that emotion.
But the empowerment of emotion in and of itself
is a very important part of our mission and our vision.
And going back to AI, and the reason I talked
about solving fundamental personalized problems
at scale is that all
of our expressions are ultimately personalized expressions.
So unless you are employing and deploying those technologies
and the mindset that customers are here to express
and connect, you are not going to be looking at the problem
or the solution in the way that empowers
that end customer first.
-: Was there something
about your background that shaped how you think
about customers or maybe that affects how
you get people working in that customer-first mindset?
-: I think it was a mix of my liberal arts education
and a desire to push problem solving
as a key characteristic and an attribute of my skill-set
as I moved through the various
leadership challenges and ranks.
One of the key lessons that I took away
from my liberal arts education at Bowdoin
was around the importance of this learning quotient
and having an LQ-first mindset
because what liberal arts really forces you to do
is adopt a continuous learning and asking questions
which are deeper than the functional competency.
And this, I think over time, actually, you know,
when machines start doing repetitive tasks,
decisioning will become actually a very
important ethical choice as well.
When I mentor college students and I give talks,
I always point out the primacy
of take your non-technical classes very seriously
and consider a liberal arts education.
Because I think the seminal questions faced
by a leader 10 years, hence 15 years hence,
are not going to be just around how competent they are,
but how thoughtful they are, how good they are at learning.
-: Well as a professor
at a university that focuses on liberal arts education,
I can wholeheartedly agree with that.
But I also want to think about,
is there an example of a place?
So you mentioned you were trying to learn
about individual customers and how difficult that is,
because in your context, it's not just
here's what they did last time,
and we predict that they do more of the same.
In fact, last time we told them exactly the opposite
of what they were going to do this time.
So can you give us some examples
of how are you using AI to learn about your customer's needs
and what kinds of things you've learned and how have you set
up your organization to learn those things?
-: It's exceedingly hard, no matter what AI leaders
and the ecosystem likes to talk about it,
chiefly because of three reasons.
I think all business leaders face a trifecta of issues.
When they think about AI adoption,
the first starts
with having cross-functional and competent teams.
You know, generally what you find within organizations is
that the teams are spoken for and especially data science
and machine learning competencies are extremely
hard to find and fund, I would say.
The second issue is the data sets
are noisy and incomplete.
So when we talk about essential ingredients of AI,
in most companies, actually that data is extremely siloed,
extremely difficult to join and often incomplete.
And the third, which is a much more evolving vector is
that it has to be explainable in its end state, right?
It has to be trustworthy as a stack.
So what we actually found is, is rapidly evolving.
And I think this is going to be very true
of most organizations is to adopt AI as a service.
Most companies I think can get very quickly started
to your question, Sam, by adopting AI as a service
and then asking a very simple question,
what two or three problems can I solve better tomorrow
employing the stack that I'm not doing currently?
And there is very interesting outcomes when
you start looking under the layer.
So one of the problems, I said,
is a cold start problem for us.
So we are working on a recommendation system
which has been very successful, that utilizes a lot
of neural learning and sort of learning
with very thin data sets, to make inferences, right?
The other place that we found is forecasting for example.
You know, forecasting is a very difficult exercise,
especially if you can imagine that, you know, for example
Valentine's Day actually moves by day of the week, right?
So last year it was a Friday, this year, it was a Sunday.
And, you know, compared to Mother's Day,
which is always on a Sunday, right?
And that has very deep business implications
as an outcome, right?
So forecasting is a perfect candidate to put towards this.
But the mindset again is are you testing
and learning along the way?
You know, in some cases, the early attempts
at machine learning will be no better
than your decision based engines.
But what we have seen is that actually persistence
over, even the medium term, has very asymmetric payoffs
and extremely important to evangelize
and understand those payoffs.
Because as I said,
the context that most modern companies find themselves
in is already awash in machine learning.
-: So two of the three things you mentioned involve
cross-platform, it's the idea of,
of people working together.
You mentioned it from several, from the data perspective
and also from the team perspective, the tension is
if everyone can't work on everything all the time.
Otherwise that's not a team, that's the whole organization.
So how do you set up that within your organization?
So that you've got that nice blend of cross-functional
but not everybody involved in everything?
-: I would say, you know, to be brutally honest,
it's a field of aspirational tensions.
You know,
when you are trying to shift mindsets over skill-sets,
it's not about sort of how do you assemble teams
and how do you get to a solution,
but how do you ultimately sell your vision and how do you,
how do you get people enthusiastically believing
in that vision?
So I would say our early attempts
at sort of organizing what a lot more command
and control we are, we were sort of saying that,
"Hey, if you have data science background
or you have analytics background
maybe you are primed for this."
I think over time
what we have realized is actually learning systems
our self-organizing principle at their core.
So now we are thinking more about, as I was saying,
the early days of just rolling out Excel to everyone.
What if we rolled out AI as a service to everyone?
You know, is if someone is just making a schedule
of meetings, do they get more empowered by AI as a service?
You know, will they themselves find out some novel solutions
to something that was completely not thought of as,
as an important-enough problem, right?
And the reason I say that Sam is, is not to suggest
that there is not a cohesive sort of listing
of problems that can be solved by AI
and assembling cross-functional teams and doing that.
I think that's the easier part, but what I'm suggesting,
and you know, egging on my peer reference group
to really think about is that the real empowerment
and the real transformation in the mindset will come
when you roll out AI to every end point, right?
Like we don't think twice about rolling out email
to every new employee.
Why do we constrain and, and sort of self limit ourselves
to think about AI as only the domain of specialists, right?
It's a problem solving methodology.
It's a problem solving mindset.
-: It's an operating system, we build apps on it.
-: Exactly.
-: And I think that's quite insightful
because whatever you make available
to smart and inquisitive people
ends up becoming better.
And that's a,
that's a very good challenge to any organization.
You know, why not have the suite of AI products self-service
for the layman user to be able to do things with?
-: To your point.
One other thing that comes
to mind is the importance of appreciating failures
as essential input to better learning.
I think what I find
in adopting an AI-first mindset is a deep respect
and celebration of failure as an organizational currency.
If you think about the history
of employees within an organization, all the origin stories
and the stories thereafter are around successes
but an AI-first mindset
in my mind is how do you actually collectively embrace,
you know, not, not by putting up posters that, you know,
run fast and fail fast.
You know, don't,
those don't really change people's activities,
their behaviors, and their acceptance
of their career trajectory as much as celebrating failures.
And the reason I say that is
that all machine learning, all learning
in the future actually has a very healthy equilibrium
between outcomes that are successful and outcomes
that failed because outcomes that fail actually
teach the system equally as much as outcomes that succeeded.
-: And I think it's a very important point on failure.
How do you operationalize that?
-: I pray a lot and I tossed the coin a lot
but, you know, it's a very important question.
I think it has to start from the leadership.
I think it has to start from a very,
very human manifestation
of how decisions are extremely difficult.
And even for leaders to be very open about when
their decisions did not lead to successful outcomes.
So I think one of the key learnings
in my life and which I've tried to follow very deeply
is around radical transparency,
around making sure that people appreciate,
that these were the reasons I took a certain decision
and that I'm open enough at the end of it
for any inputs, right?
Whether it went successfully or it didn't go successfully.
So that is one way of operationalizing it when
the leadership starts living out that outcome.
The second, I think very important part
of it is how do you incentivize that outcome?
So for example, you know,
we have a constant red team that we call internally,
that runs up against the main growth team, for example.
So if the main growth team has a $100 million
to spend on marketing, I give 10%
to a red team that is actually going
against the conventional wisdom.
And the reason it is going
against the conventional wisdom is actually to build
up a corpus of failures that then can act
as a foil to what did we learn
from spending that $100 million.
And this is a very important part again
of increasing the collective LQ of the team, right?
Because if everything is done by consensus,
we know from behavioral economics and a lot of studies done,
it is not the best decision making outcome as well, right?
So that is one example of it.
So how do you set up team structures and incentives?
And then the last thing I would say,
which has been a learning mode of late to me,
is how do you actually translate
that into ESG or ethical goals?
Because what I have seen with the newer cohort
of employees of stakeholders that we have had
is that it is not so much just about learning,
but learning within a context that I believe in.
So my newer understanding more and more
has to be around like, hey, if he ingests AI models,
are they explainable, are they de-biased?
Can I make sure
that the team appreciates that sudden choices
that we may make may not have the immediate business payoff
but are actually much more better aligned
with our vision and our mission?
-: Well, we started this discussion talking about mom
and pop flower shops, and that resonates with me actually
I didn't mention it, but my mom owned a flower shop.
So mom and pop is actually literal in this point.
Amit, we really appreciate you taking the time
to talk with us today.
Thanks for spending some time with us.
-: Yeah. Thank you so much.
This has been very insightful.
Thank you.
-: I love this conversation.
Thank you guys.
Appreciate it.
(relaxed technical music)
-: Sure enough.
It was was quite interesting.
One thing that struck me was how, you know,
we talk about, "Oh yeah
the machines can learn from past, et cetera, et cetera."
But how at, every scenario for them is a bit
of a cold start problem
because, you know, every holiday is different.
Every time someone comes to them, they're getting something
for a different reason and it wouldn't be a cold start
if they knew the underlying reasons, but they don't always.
When we go to, you know,
any of the normal collaborative filtering platforms
like a Netflix or other places, or even transportation
like Uber and Lyft, those people have a much
better ability to build off our history
than 1-800-Flowers does.
It, it is once, you know, cold start,
and tying that to how emotionally aware they need to be.
Because by definition,
these are very human experiences that they're involved in.
If they screw that up, that's not good.
-: Yeah, also it's a cold start, which by definition
means it's a learning opportunity
'cause a cold start problem is the same
as a learning problem.
And if you have many cold star problems,
then isn't that another way
of saying you've basically have to be comfortable
with a very accelerated rate of learning
and that's your success, 'cause otherwise, yes
everything is a sympathy or everything is
that one demographic that I really really know.
And rather than being adaptive to all those situations.
-: The other thing that is emerging theme
over research that he talked
about a lot was the notion of learning quotient, right?
You know, we asked him about teams and he said,
what they care about a lot is an individual's desire
and willingness and ability to want to learn.
And that fits so well with what AI itself is
which is it's all about learning and the notion
of human and machine learning from each other
which is also the theme of our work.
I found it quite insightful that he picked up on that.
And in many ways it sort of also fits
into his point around mindset
and culture change because he also talked about, you know,
it's not so much about the skill-set or the tech,
it's much more about changing the ways of working,
and changing the operating model and changing the mindset
of what you can and should do with AI, with this tool
and capability that he thought would just be as commonplace
as an Excel spreadsheet, that is not pretty commonplace.
-: Exactly.
-: And so the importance of learning and ongoing learning
and adaptability, I thought was quite elegant
in what he said.
-: Well, you're not gonna get an argument from me.
I mean, I'm, I'm a professor, I'm an academic.
So I think that.
-: Yeah, you love learning, right.
-: I'm biased to think that learning is kind of a big thing
but even more than that, he also mentioned a little bit
the importance of liberal arts thinking in that learning.
You and I, we make fun of our engineering backgrounds a lot
but as we're seeing these technologies get easier
and easier to use,
it's really highlighting the importance of the human,
and importance of the human working with the machine.
I think, you know, if we go back 20 or 30 years ago,
there was so much talk about the death of IT,
And IT doesn't matter, you remember that phase.
-: Yeah.
-: But, nope, that didn't happen a bit.
I mean, as IT became easier to use,
companies just wanted more and more of it.
And this is the natural extension of that.
-: Yeah, and I think there's this notion
of technology raising the playing field
so that humans can operate at a higher level,
and then humans inventing better technology
so that, that level again keeps, you know, getting raised.
I think that's sort of a common theme
that's happened with technology.
You know, actually chess is a great example of that,
because if you look at how 20 years ago,
you talked about Sam 20 years ago, the death of IT.
Like 20 years ago, 25, 30 years ago,
it was almost like the death of the computer in chess
because it was like argued that there's no way.
Yeah. Right.
Like no way a human could be beaten by a computer.
And then the game change when yeah.
With, with Kasparov lost to Deep Blue,
but then what happened is chess players got smarter.
So the chess Elo ranking, the highest ranking
of highest chess players
has been, you know, steadily increasing.
-: Right.
-: Because of how, you know,
AI has helped humans get smarter.
-: Thanks for listening.
Next time, we'll talk with JoAnn Stonier
Chief Data Officer at MasterCard
about how MasterCard uses design thinking to ensure its use
of AI supports its overall business strategy.
-: Thanks for listening to Me, Myself, and AI.
If you're enjoying the show,
take a minute to write us a review.
If you send us a screenshot,
we'll send you a collection of MIT SMRs best articles
on artificial intelligence free for a limited time.
Send your review screenshot to smrfeedback@mit.edu.