SAM RANSBOTHAM: AI applications involve many different levels
of risk.
Learn how Stanley Black & Decker considers its AI risk portfolio
across its business when we talk with the company's
first chief technology officer, Mark Maybury.
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 Ideas program at MIT Sloan Management Review.
SHERVIN KHODABANDEH: And I'm Shervin Khodabandeh,
senior partner with BCG, and I colead BCG's AI practice
in North America.
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.
SAM RANSBOTHAM: Today we're talking
with Mark Maybury, Stanley Black & Decker's
first chief technology officer.
Mark, thanks for joining us.
Welcome.
MARK MAYBURY: Thank you very much for having me, Sam.
SAM RANSBOTHAM: Why don't we start with your current role.
You're the first chief technology officer
at Stanley Black & Decker.
What does that mean?
MARK MAYBURY: Well, back in 2017,
I was really delighted to be invited
by our chief executive officer, Jim Loree,
to really lead the extreme innovation enterprise
across Stanley Black & Decker.
So I get involved in everything from new ventures
to accelerating new companies, to fostering innovation
within our businesses and just in general
being the champion of extreme innovation across the company.
SAM RANSBOTHAM: You didn't start off as a CTO of Black & Decker.
Tell us a bit about how you ended up there.
MARK MAYBURY: If you look at my history -- you know,
"How did you get interested in AI?" --
I mean, AI started when literally, I was 13 years old.
I vividly remember this; it's one of those poignant memories:
[In] 1977, I saw Star Wars, and I remember walking out of that
movie being inspired by the conversational robots --
R2-D2, C-3PO -- and the artificial intelligence between
the human and the machine.
And I didn't know it at the time,
but I was fascinated by augmented intelligence
and by ambient intelligence.
They had these machines that were smart
and these robots that were smart.
And then that transitioned into a love
of actually understanding the human mind.
In college, I studied with a number of neuropsychologists
as a Fenwick scholar at Holy Cross,
working also with some Boston University faculty,
and we built a system to diagnose brain disorders
in 1986.
It's a long time ago, but that introduced me
into Bayesian reasoning and so on.
And then, when I initiated my career,
I was trained really globally, so I studied in Venezuela
as a high school student; as an undergraduate,
I spent eight months in Italy learning Italian;
and then I went to England and Cambridge
and I learned English.
SAM RANSBOTHAM: The real English.
MARK MAYBURY: The real English.
[Laughs.]
SAM RANSBOTHAM: C-3PO would be proud.
MARK MAYBURY: C-3PO, exactly!
...
Indeed, my master's was in speech and language processing.
Sorry, you can't make this up.
I worked with Karen Spark Jones, a professor there
who was one of the great godmothers
of computational linguistics.
But then I transitioned back to becoming an Air Force officer,
and right away, I got interested in security:
national security, computer security, AI security.
I didn't know it at the time, but we
were developing knowledge-based software development,
and we'd think about "How do we make
sure the software is secure?"
Fast-forward to 20 years later.
I was asked to lead a federal laboratory, the National
Cybersecurity Federally Funded Laboratory at Mitre,
supporting NIST [the National Institute of Standards
and Technology].
I had come up the ranks as an AI person applying AI to a whole
bunch of domains, including computer security --
building insider threat detection modules,
building penetration testing, automated agents,
doing a lot of machine learning of malware --
working with some really great scientists at Mitre,
in the federal government, and beyond,
[in] agencies and in commercial companies.
And so that really transformed my mind
in terms of how do we for example,
I'll never forget working together with some scientists
on the first ability to secure medicine pumps that
are the most frequently used device in a hospital.
And so that's the kind of foundation
of security thinking and risk management that comes through.
I got to work with the great Donna Dodson
at NIST and other great leaders.
And so those really were foundational theoretical and
practical underpinnings that shaped my thinking in security.
SAM RANSBOTHAM: But doesn't it drive you crazy, then,
that so much the world has this "build it and then secure it
later" approach?
I feel like that's pervasive in software
in general, but certainly around artificial intelligence
applications.
It's always the features first and secure it later.
Doesn't it drive you insane?
How can we change that?
MARK MAYBURY: There are methods and good practices, best
practices, for building resilience into systems,
and it turns out that resilience can be achieved
in a whole variety of ways.
For example, we mentioned diversity.
That's just one strategy.
Another strategy is loose coupling.
The reason pagodas famously last for hundreds and hundreds
of years in Japan is because they're
built with structures like, for example,
central structures that are really strong,
but also hanging structures that loosely couple and that
can absorb, for example, energy from the earth
when you get earthquakes.
So these design principles, if you think about a loosely
coupled cyber or a piece of software system,
and even of course decoupling things so that you disaggregate
capabilities, so that if a power system or a software system
goes down locally, it doesn't affect everyone globally --
some of these principles need to be applied.
They're systems security principles,
but they can absolutely be applied in AI.
I mean, it's amazing how effective people can
be when they're in an accident.
They've got broken bones, they've
got maybe damaged organs, and yet they're still alive.
They're still functioning.
How does that happen?
And so nature's a good inspiration for us.
We can't forget, in the end, our company
has a purpose for those who make the world.
And that means that we have to be empathetic and understanding
of the environments in which these technologies are going
to go into and make sure that they're intuitive,
they're transparent, they're learnable,
they're adaptable to those various environments,
so that we serve those makers of the world effectively.
SHERVIN KHODABANDEH: Mark, as you're describing innovation,
I think your brand is very well recognized and a lot
of our audience would know [it], but could you just maybe
quickly cover -- what does Stanley Black & Decker do,
and how have some of these innovations maybe changed
the company for the better?
MARK MAYBURY: Well, it's a great question.
One of the delights of coming to this company
was learning what it does.
So I knew Stanley Black & Decker,
like many of your listeners will know,
as a company that makes DeWalt tools --
hand tools or power tools or storage devices.
Those are the things that you're very familiar with.
But it turns out that we also have
a several-billion-dollar industrial business.
We robotically insert fasteners into cars,
and it turns out that 9 out of every 10 cars
or light trucks on the road today
are held together by Stanley fasteners.
Similarly, I didn't know beforehand,
but in 1930 we invented the electronic door --
the sliding door.
So next time you walk into a Home Goods or Home Depot
or a Lowe's, or even a hospital or a bank,
if you look up and you look to the left, you'll notice --
there's a 1-in-2 chance there'll be a Stanley logo,
because we manufacture one of every two electronic doors
in North America.
And there are other examples, but those are innovations,
whether it be protecting 2 million babies
with real-time location services in our health care business
or producing eco-friendly rivets that lightweight electric
vehicles [use].
These are some examples of the kind of innovations that we're
continuously developing.
Because basically, every second, 10 Stanley tools are sold
around the world -- every second.
And so whether it's Black & Decker, whether it's
DeWalt, whether it's Craftsman, these are household brands
that we have the privilege to influence
the inventive future of.
SHERVIN KHODABANDEH: You're really everywhere.
And every time I sit in my car now,
I'm going to remember that, like the strong force that
keeps the nucleus together, you are keeping my car together.
That's fantastic.
Can you give us an example of extreme innovation
versus nonextreme?
MARK MAYBURY: Sure.
By extreme, we really mean innovation
of everything, innovation everywhere, innovation
by everyone.
We actually, interestingly, within the company delineate
between six different levels of innovation.
We're just in the past six months
becoming much more disciplined across the entire corporation,
with a common rubric for how we characterize things.
So it's a great question.
Levels one and two, those are incremental improvements,
let's say to product or a service.
Once we get to level three, we're
talking about something where we're
beginning to make some significant change
to a product.
When we get to level four, we're talking about maybe three
major or more new features.
It's something that, really, you're
going to significantly notice.
When we talk about a level five, this
is first of a kind, at least for us.
It's something that we may have never experienced
in our marketplace.
Those we oftentimes call breakthrough innovations.
And finally, on level six, which are radical innovations,
those are things that are world firsts.
And to give you a concrete example,
we just introduced to the marketplace the first
utilization of pouch battery technologies,
a successor to the Flexvolt batteries,
which are essentially an ability,
using pouch technology, to double the power --
a [100%] increase in power in batteries for tools,
two times the life cycle, reductions in the weight
and the size of those.
So that's an example of an extreme innovation that's going
to revolutionize power tools.
That's called Powerstack.
Another example we brought forward in Black & Decker [is]
Pria.
Pria is the first conversational home health care companion.
It's an example of using speech and language
and conversational technology to support medicine distribution
to those who want to age in place, for example,
in the home, but also using AI to detect anomalies and alert
caretakers to individuals.
So those are examples that can be really transformative.
SHERVIN KHODABANDEH: Levels one through six
implies there is a portfolio and that there
is an intention about how to build and manage and evolve
that portfolio.
Can you comment a bit [on] how you think about that
and how much, like in level one versus level six,
and what are some of the trade-offs that you consider?
MARK MAYBURY: That's an excellent question.
Basically, it is really market-driven,
and it's even going to be further product
and segment-driven.
If you're selling a software service,
you're going to want to have that modified almost [in]
real time.
But certainly within months, you're
going to want to be evolving that service so
that incremental modification might occur.
We have an ability to just upload a new version
of our cyber-physical end effector, if you will --
whatever it happens to be.
But to answer your question, oftentimes companies will,
over time, if they don't pay attention to their level one
through six -- so from incremental all the way up
to radical -- they'll end up with a portfolio that drifts
to the incrementalism, that's only focused on minor
modifications.
Those are easy to do.
You get an immediate benefit in the marketplace,
but you don't get a long-term, a medium- or long-term, shift.
And so what we intentionally do is measure
in empirical fashion, how much growth and how much margin
and how much consumer satisfaction am
I getting out of those level ones all the way up
to level sixes?
Because any organization is going to naturally be resource
constrained in terms of money, in terms of time,
in terms of talent.
What you need to do is you need to ideally optimize.
And if the marketplace is rewarding you for, let's say,
having new products and services in class four,
which have major improvements, but they penalize you
for having radical improvements because they just can't ...
it's this cognitive dissonance: "What do you mean home health
companion?
I don't know what that is.
I just want a better tongue depressor."
And so in that case, you really need
to appreciate what the marketplace is
willing to adopt, and we have to think about,
if you do have a radical innovation, how are you
going to get into the channel?
And one final thing I'll say, because your question's
an excellent one about portfolio,
is, we actually go one step further,
which is not only do we look at what
the distribution of the classes are
and what the response to those investments are over time,
but we further, for any particular individual
investment, we actually put it into a portfolio
that characterizes the technical risk and the business risk.
We actually use technical readiness levels,
which come out of NASA and the Air Force --
my previous life -- and are used now in the business community,
and then we invent it.
Actually, previously, when I was working
for the federal government, we created commercial readiness
levels, and I've imported those into Stanley Black & Decker.
And now we actually have a portfolio
for every single business and the company as a whole,
for the first time ever.
And that's something that we're really delighted to finally
bring to the company -- an ability to look
at our investments as a portfolio --
because only then can we see, are we trying to achieve
"unobtainium," because it's technically unachievable,
or, equally bad, is there no market for this?
You may invent something that's really great,
and if the customer doesn't care for it,
it's not going to be commercially viable.
And so those are important dimensions
to look at in portfolio analysis.
SHERVIN KHODABANDEH: I'm really happy that you covered risk,
because that was going to be my follow-on question.
Even that must be a spectrum of risk and a decision:
How much risk is the right level of risk,
and how much do you wait to know whether the market's really
liking something or not?
I'm not going to put words in your mouth,
but I was just going to infer from that, that you were saying
that's a lever and a decision that you guys manage based
on how the economics of the market and the company are,
and when you want to be more risky versus less risky.
MARK MAYBURY: Absolutely.
And there are many voices that get an opportunity to influence
the market dynamics.
If you think of the Five Forces model of Porter,
classically you've got your competitors, your suppliers,
your customers, and yourself, and all
of these competitive forces are active.
And so one of the things we try to do is measure, is listen.
Our leadership model within our operating model at the company
is listen, learn, and lead.
That listening and learning part is really, really critical;
if you're not listening to the right signals --
if you don't have a customer signal,
you don't have a technological disruption signal,
if you don't have an economic signal,
a manufacturing and supply signal,
you need all those signals.
And then, importantly, you need lessons learned;
you need good practices.
Early in the idea generation side,
are you using design thinking?
Are you using diverse teams?
Are you gathering insights in an effective way?
And then, as you go through to generating opportunities,
are you beginning to do competitive analysis,
like I just talked about?
As you begin to look into these specific business cases,
are you trying things out with concept cars or proof
of concepts and then getting to ...
"Maybe we don't have the solution.
Maybe we ought to have some open innovation
outside the company."
And then, ultimately, in our commercial execution,
do they have the right sales teams, the right channels,
the right partnerships to go to scale?
And so the challenge is, we can oftentimes --
whether it be manufacturing or products --
we can get into pilot purgatory.
We can create something that looks really, really exciting
and promising to the marketplace,
but it's unmanufacturable, or it's unsustainable,
or it's uninteresting or uneconomical.
And that's really not good.
You really have to have a holistic intent in mind
throughout the process, and then, importantly,
a discipline to test and to measure
and to fail fast and eventually be
ready to scale quickly when something does actually hit,
if you will, the sweet spot in the market.
SAM RANSBOTHAM: So there's lots of different things
in these levels.
Can you tie them to artificial intelligence?
Like, is artificial intelligence more risky in market risk?
Is it more risky in technical risk?
How is that affecting each of your different levels,
and what's the intersection of that matrix
with artificial intelligence?
MARK MAYBURY: Great question.
Our AI really applies across the entire company.
We have robotic process automation [RPA],,
which is off the shelf, low risk, provable,
and we automate IT elements in finance, elements in HR.
We have actually almost 160 digital employees today
that just do automated processes,
and it makes our own we call it not only AI,
but sometimes augmented intelligence, not
artificial intelligence.
How do we augment the human to get them more effective?
So to your question, what's risky --
SAM RANSBOTHAM: That seems [like] less risk.
MARK MAYBURY: That's very low risk.
RPAs are very, very low risk.
However, if I'm going to introduce Pria
into the marketplace, or Insight,
which is a capability in our Stanley industrial business
for IoT measurement, for predictive analytics,
for shears indoor extensions, to very large-scale excavation
equipment, and so on -- in that case,
there could be a very high risk, because there might be user
adoption risk, there's sensor relevance risk,
there's making sure your predictions are going to work
well.
There could be a safety risk as well as an economic risk.
So you want to be really, really careful to make sure
that those technologies in those higher-risk areas
will work really, really well, because they might be life
critical if you're giving advice to a patient
or you're giving guidance to an operator of a very
big piece of machinery.
And so we really have AI across our whole business,
including, by the way, in our factories on automation.
One of the ways we mitigate risk there is we partner.
We work with others so that they actually
have de-risked a lot of the technologies.
So you'll see mobile robots from third parties,
you'll see collaborative robots from parties
that we're customizing and putting
to scale in our factories and de-risking them.
So on that matrix, they're much more
distributed across the spectrum of risk.
SAM RANSBOTHAM: One of the things
that Shervin and I've talked about a few times
is this idea how artificial intelligence maybe
even steers people toward these incremental improvements.
Maybe it's [that] the ability for these algorithms to improve
an existing process may somehow steer people toward the level
one versus the level six.
Are you seeing that?
Are you able to get to apply artificial intelligence
to these level five, level six types of projects?
MARK MAYBURY: We absolutely have AI across the spectrum.
When it comes to AI, the stuff lower
in the technical-commercial list tends
to be commercially proven.
So it tends to have multiple use cases:
Others have deployed the technology;
it's been battle hardened.
But the reality is, there's whole a series of risks.
And we actually have just recently
published our responsible AI set of policies at the company
and made them publicly available.
So any other diversified industrial or tech company
or other consulting small-to-medium enterprise
can take a look at what we do.
And I'll give you a very simple example,
and it gets a bit to your point of "Well,
will they gravitate to the easier problems?"
Well, not necessarily.
One of the areas of risk is making sure
that your AI sensors or classifiers
are in fact not biased and/or they're resilient.
And one of the ways you make sure they're
resilient and unbiased is you make sure
that you have much more diversified data.
That means if you have more users or more situations that
are using your AI systems and there's active learning going
on -- perhaps reinforcement learning while that
machine's operating, most likely human supervised,
because you want to make sure that you're not releasing
anything that could adversely affect an operator or end user
-- actually, the more data you get,
the better and more effective the more risk you can reduce,
but actually the higher performance you can get.
So it's a bit counterintuitive.
So you can actually become a bit more innovative in some sense,
or just smarter in the AI case, because you have more exposure,
in the same way that people who go through high school
to university to graduate school,
because their challenge is increased
along those levels, their capacity to learn,
to communicate, becomes much more effective as they
go through that training.
Same thing with a machine: You can give it easier examples,
so they might be more incremental,
simple challenges to that system,
and as I get more difficult -- so I go from the consumer,
to the prosumer, to the pro -- my intelligence of that system
because the pro knows a lot more.
She's been out working, constructing, for 20 years
or building things in a factory for a long time
and knows what kinds of learning that machine
can leverage and can expose that machine
to more sophisticated learning.
For example, for predictive analytics,
if I want to predict an outage, if I've only
seen one kind of outage, I will only
be able to deal with that outage.
If I've seen 30 different kinds of outages,
I'm much better and much more resilient,
because I know both what I know, but equally important --
perhaps more important --
I know what I don't know.
And if I see something for the first time,
and I've seen 30 different things,
and this is a brand-new one, I can say,
"This doesn't fit with anything I've seen.
I'm ignorant.
Hold up -- let's call a human.
Tell them it's an anomaly.
Let's get the machine to retrain."
SAM RANSBOTHAM: Where did these responsible principles
come from?
Is that something you developed internally,
or is that something you've adopted from somewhere else?
MARK MAYBURY: So first, the motivation.
Why do we care about responsible AI?
It starts from some of my 31 years
working in the public sector, understanding
some of the risks of AI, having been on the government
side funding a lot of startups, a lot of large companies
and small companies, building defense applications
and/or health care applications, national applications for AI.
We recognized the fact that there are lots of failures.
The way I think about the failures,
which motivate responsible AI, is they can be your failure
to if you think of the OODA loop -- observe, orient, decide,
and act.
Observation: You can have bad day data, failed perception,
bias, like I was suggesting.
And so machines literally can be convinced
that they're seeing a yield sign when they see a stop sign.
So there actually have been studies done
that have demonstrated this.
SAM RANSBOTHAM: Right, the adversarial.
MARK MAYBURY: Exactly: adversarial AI.
You can also confuse an AI by biasing a selection,
by mislabeling or misattributing things
so they can be oriented in the wrong way.
See, the classifications I talked about before:
You could force them to see a different thing or misclassify.
Similarly, they can decide poorly.
They could have misinformation; there could
be false cues or confusion.
And we've seen this actually in the flash crash, where
AIs were trained to actually do trading
and then their model didn't actually
recognize when things were going bad,
and poor decisions were made.
And then finally, there can be physical world actions.
We've had a couple of automated vehicles
fail because of either failed human oversight of the AI,
over-trusting the AI, or under-trusting the AI,
and then poor decisions happen.
And so that's the motivation.
And then we studied work in Europe and Singapore
and the World Economic Forum.
In the U.S., there's a whole bunch of work in AI principles,
in algorithmic accountability, and White House
guidance on regulation of AI.
We've been connected into all of these things
as well as connected to the Microsofts and the IBMs
and the Googles of the world in terms of what they're doing
in terms of responsible AI.
And we, as a diversified industrial,
said, "We have these very complicated domain applications
in manufacturing, in aviation, in transportation and tools,
in health care products or just home products,
and so how do we make sure that when we are building AI
into those system, we're doing it in a responsible fashion?"
So that means making sure that we're
transparent in what the AI knows or doesn't know;
making sure that we protect the privacy of the information
we're collecting about the environment,
perhaps, or the people; making sure
that we're equitable in our decisions and unbiased;
making sure that the systems are more resilient, that they're
more symbiotic so we get at that augmented intelligence
piece we talked about before.
All of these are motivations for why,
because we're a company who really firmly believes
in corporate social responsibility,
and in order to achieve that, we have to actually build it
into the products that we're producing
and the methods and the approaches we're taking,
which means making sure that we're stress-testing those,
that we're designing them appropriately.
So that's the motivation for responsible AI.
SAM RANSBOTHAM: What are you excited about at Stanley Black
& Decker?
What's new?
You mentioned projects you've worked on in the past.
Anything exciting you can share that's on the horizon?
MARK MAYBURY: I can't go into great detail,
but what I can say right now for your listeners is,
we have some extreme innovation going on
in the ESG [environmental, social, and governance]
area, specifically when we're talking about net zero.
We've made public these statements that our factories
will be carbon neutral by 2030.
We have 120 factories and distribution centers
around the world.
...
No government has told us to do that.
That's self-imposed.
And by the way, if you think, "Oh, that's a future thing;
they'll never do it," we're already ahead of target
to get to 2030.
But we're also, by 2025, pulling in a little bit closer,
we're going to be plastic free in our packaging.
So we're getting rid of those blister
packs that we all have gotten so accustomed to.
Why?
Because we want to get rid of microplastics in our water,
in our oceans, and we feel that it's our responsibility
to take the initiative.
No government has asked us to do this.
It's just that we think it's the right thing to do.
We're very, very actively learning right now
about how we get materials that are carbon-free,
how we operate our plants and design products that
will be carbon-free, how we distribute
things in a carbon-neutral way.
This requires a complete rethinking,
and it requires a lot of AI, actually,
because you've got to think about smart design:
Which components can I make to be reusable?
Which can be recyclable?
Which have to be compostable?
The thing here is really to think outside the box.
We're a 179-year-old company, so we've been around for a while.
And, as an officer of the company,
my responsibility is as a steward, really,
to make sure that we progress along the same values that
Frederick Stanley, who was a social entrepreneur,
the first mayor of New Britain [in Connecticut],
[who] turned his factories to building toys when there were
no toys during the war for children I mean,
just a very community-minded individual.
And that legacy, that purpose, continues on in what we do.
And so, yes, we want high-power tools,
and, yes, we want lightweight cars,
and we want all those innovations,
but we want them in a sustainable way.
SAM RANSBOTHAM: Thank you.
I think that many of your things about, for example,
the different levels that you think about innovation
will resonate with listeners.
It's been a great conversation.
Thanks for joining us.
SHERVIN KHODABANDEH: Mark, thanks.
This has been really a great conversation.
MARK MAYBURY: Thank you very much.
SAM RANSBOTHAM: We hope you enjoyed today's episode.
Next time, Shervin and I talk with Sanjay Nichani,
vice president of artificial intelligence and computer
vision at Peloton Interactive.
Please join us.
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