What is conversational AI anyway?
It's a big topic, an important topic and, today, on CxOTalk, we are exploring this subject
with somebody who is running a large program at a major financial institution.
I'm Michael Krigsman.
I'm an industry analyst and the host of CxOTalk.
I want to say a grateful thank you to IPsoft.
We are in New York City in their AI experience lab.
It's companies like IPsoft that make CxOTalk possible.
We're speaking right now with Ken Dodelin, who is the vice president of conversational
AI products at Capital One.
Hey, Ken, how are you?
Great.
How are you, Michael?
I'm good.
Tell us about Capital One.
I think we know the name but tell us what you guys do and your size.
Sure.
Sure, so Capital One is a large bank in the U.S.--credit cards, banks, auto loans, all
those sorts of things--and has been leaning heavy into the digital space and, in particular,
into the intelligent assistant space, which is what we're here to talk about today.
Now, you're the VP of conversational AI products.
That's right.
Tell us what that means and what you do.
In practice, what does that involve?
Sure.
We build digital products.
In my case, they're in the conversational AI space, which means that they have artificial
intelligence behind them and that they interact with customers in a conversation.
Very often, customers can speak or text in natural language and interface with Capital
One through an AI.
Sometimes it's our own, which we call Eno.
We'll talk about that in a bit.
Sometimes it's through a parent AI, like an Alexa, Cortona, or something like that.
Now, Ken, as the VP of conversational AI products, what's the scope of your responsibilities?
Sure.
The products I just mentioned are all live today.
We were the first bank to release a skill on the Amazon Alexa platform that enabled
customers to ask Alexa about their accounts and get information, even pay their bill all
through voice.
We extended that skill over to the Cortona platform run by Microsoft.
Last year, we turned out attention a little bit towards the text space, and so we were
the first bank to release a natural language chatbot or intelligent assistant through the
SMS channel.
Customers can text Eno, our intelligent assistant, and ask questions about their accounts, when
is their bill due, pay their bill, and so forth, all through the familiarity of what
we all do every day, which is texting.
Why is conversational AI so important to Capital One?
Yeah, it's a few things.
First of all, Capital One is a very tech-forward company.
We're the first bank to go to the cloud.
We're the first, or one of the first, banks to have an API-based infrastructure that powers
all of our digital experiences.
We're always looking for ways to use technology to improve the customer experience.
In conversational AI, it used to be that you could either come to our graphical user interface,
which would be an app or a website, where we, in some ways, try to guess what it is
you're looking for, right?
You have a certain finite number of buttons and links to click on or tap on.
In the conversational interface, it's whatever is on your mind.
You can just text in natural language, and then we're able to start with that rather
than starting with all the guesswork.
It's a great complement to GUI experiences, and it's also a great way to interact with
customers without having to pull them into our uber website or app.
Is it mostly a function of user experience?
Is that your goal, or are there other goals as well?
Well, there are fantastic business and customer benefits to conversational AI.
There are three things going on here.
The first one is, we have a recent emergence in natural language processing technology.
Not too many years ago, we just couldn't understand customers, what the intent of their spoken
or typed utterances were in the way that we can today.
That advancement has enabled these experiences to reach a threshold where they're actually
useful.
The second thing is the availability of data.
Our ability to get answers and connect into our API infrastructure, the same one that
powers our websites and apps, is at a place now where we can use real-time context to
adapt the conversation, so it is more conversational and not just a predetermined conversation
that wouldn't feel very human.
Then the third thing, in addition to the NLP and the data, is the proliferation of the
Internet of Things devices.
Whereas in the not too distant past it was pretty much a website or an app that you were
going to interact with Capital One through, or else you were going to call the call center.
Now we have things like Alexa that use voice-enabled touchpoints with the customer within their
car, in their living room, or wherever they put those connected devices that enable interaction.
Also, since we don't have to have a GUI come with us wherever we want to interact with
customers, since we can do it simply through natural language, we can go to places like
text messaging, messaging apps, and other similar things where the interaction is all
just natural language.
Even emojis, right?
Who would have thought you'd be paying your bill with a thumbs-up emoji, but that's what
we've enabled, and customers have gravitated to?
Ken, I certainly can see very quickly the user experience value here, but can you elaborate
on the business value as well?
Sure.
Every company that has a large customer service need is looking for ways to get customers
to A) not have problems and B) be able to self-service in digital.
There are business benefits to that.
The other one is, there are lots of micro examples where this is helpful.
You can imagine people coming to our websites and, instead of just having to look around
and not finding what they need, they can actually ask and get to what they need to do, which
leads to a lot of desired business behaviors.
They're actually paying their bill; they're actually enrolling in paperless statements;
whatever it is that is a desired customer behavior.
I'll give you another one that's kind of interesting.
Like every bank, we have a fraud alert that goes out.
That fraud alert is very rigid just because of when it was created where it will say,
"Hey, we noticed a suspicious charge on your account.
Text us back "confirm" or "deny" whether or not it was yours.
If you text back "confirm" or "deny," everything is great.
If you fat finger it, you make a typo, or you try to say something like "yes" or, as
we've seen in our logs, "Yes, that's the pair of shoes I bought when I was visiting my sister
in Philadelphia last weekend," we don't understand any of it."
You can imagine the business benefit of being able to understand these things that the customer
is saying so that, if there is a fraud event, we can turn off the card and take corrective
actions more quickly so that the fraud stops.
Now, maybe this is a very basic question, but what's the difference between the type
of AI that you're talking about that has access to data and has built-in intelligence?
How is that different from the simplistic chatbot that every website has got these days?
Well, a few things.
One is, we're very niche, right?
We are building an intelligent financial assistant.
We found when we were building Eno, we used some off-the-shelf natural language processing
solutions that were okay.
We gravitated to, "Hey, if we train it on banking, we'll find that it's better at banking
than the off-the-shelf ones."
It's terrible at everything else.
It doesn't know sports and things like that.
But, we really home in on the types of things that our customers are looking to do, and
we have very short conversations about prioritization because we know exactly what customers want
to do with Eno by what they text in, right?
You're always guessing on a website and such, but we have great data on that.
The other side of how it's different is that Eno will reach out to you.
We like to say the call center rarely calls you as a customer, but Eno will reach out
to you in the moments that matter in the channel that works for you because Eno is always minding
your money and thinking about it even when you're not.
The same things you would want from a human personal financial assistant will be accomplished
through our virtual one.
Your development goal then was to create a very narrowly focused AI that had a great
deal of depth inside that narrow focus about which you care.
That's right.
That's right because, look, I have great empathy for the folks who are at the big five tech
companies working on Siri, Alexa, Google Assistant and some others where they're trying to answer
any question about anything, which is a really hard problem to solve.
We have a more narrow focus and can achieve, I think, some higher levels of success in
our niche because we're so focused.
Now, you mentioned that there's a combination of products that you've developed in-house,
as well as external products.
Is that correct?
We've worked with various vendors along the way and come up with a mix of homegrown technology
and some other things.
Look, we generally build things ourselves at Capital One.
We leverage the fact that we're in the cloud, we have this API-based infrastructure, and
sort of a passion for open source software.
We tend to leverage the great talent we have on the team to build things and let the customer
experience guide us.
Your enterprise, your underlying enterprise architecture facilitates what you're doing
with AI.
Absolutely.
The same technology that powers our websites and apps and authentication is leveraged in
the conversational AI products that we release.
The part that's new is that the inbound activity by the customer comes in natural language
and the response comes in either voice or text or a mix of maybe text and GUI based
upon where you're interacting.
We have our Cortana skill, our Alexa skill, and our Eno SMS chatbot all running on the
same platform right now, which is great.
Did you hire people to do things like natural language processing?
We haven't gone public with how we've built each component of the Eno experience.
But, again, we went out and looked at what we were trying to do from a vision standpoint
and said, "Hey, there are some places where we feel like we have the expertise and other
places we want to bring it in."
I'll give you a couple of examples.
To work on natural language processing, we brought in one of the lead engineers who helped
build the first AI to win on Jeopardy.
When we were thinking about the character of Eno, we kind of looked around and said,
"Well, we're a bank.
We don't really build characters," and so we went out and hired a woman with a lot of
experience in working with characters from Pixar to come in and give us a backstory for
Eno, some character traits, and some consistent personality so, as you interact with Eno,
you don't have one personality in one place and one personality in another.
That's really interesting.
Yeah, it's been a fun journey.
We certainly had to stretch our skillsets to succeed in this space.
It sounds, as well, like this is a strategic mandate of the organization to develop these
types of technology products.
What are the kind of metrics related to AI that you track?
There are a lot of similarities between what you would track on mobile apps, for example,
but one of them we look at is a customer success rate.
We want to know, customers who start interacting with Eno, whether it's through a proactive
outreach by Eno or a customer's inbound outreach, did they achieve their goal?
We look at--whether it's a direct answer from Eno or Eno steering them to a certain place--how
can we continue to improve the ability for Eno to provide the customer with what they're
looking for?
That's a function both of the technology as well as the data and the way that you're using
the data?
Yeah.
Everything is data driven.
There is some subjectivity sometimes around the edges but, like I said, we have short
conversations about what the customer is looking for now because there's just such a great
insight into what the customer is thinking at any given time.
Now, what about your call centers?
You mentioned that this is a complement to your existing contact centers.
The contact centers remain unchanged?
These are just additional channels that customers can talk with you?
That's right.
I think the call center folks are actually excited about the deployment of machine learning
to enhance what they're doing.
Certainly, anything that can predict what types of issues a customer might be calling
about to help route them more efficiently to the agent who can give them their answer
to cue up the right systems that get them to the information they need all is value-add
and allows them to spend more of their time doing the higher value human side of servicing
customers versus trying to do that simple navigation stuff.
The level of investment that you've placed here obviously is very significant, which
means that you must have ways of evaluating your progress and evaluating how do we know
if we're being successful here or not.
Mm-hmm.
Could you share with us some of those mechanisms for evaluating what you're doing?
Sure.
Not unlike most of our products, our digital products, we want to know, do people use it;
do they love it?
Adoption being one?
Sure.
What's interesting is that, when we launched Eno as an SMS intelligent assistant, it's
different than launching an app in that, when you launch an app, it comes with all the infrastructure
of, well, you're in an app store now and you have ratings and reviews and places where
people can leave those ratings and reviews.
We didn't have that, but we wanted it, so we actually stood up our own.
We solicit customer feedback, and we post it up on a webpage.
The feedback so far has been surprisingly positive for a product that's in a nascent
stage.
I think it has north of 4.5 out of 5 stars, and we see people enjoying the fact that it's
another way for them to get access to information very quickly, get their questions answered,
and they can ask, particularly if it's something like, "Well, this might be on the website.
I'm not sure.
Let me just ask, and I can find out if it's there or not before I invest the time it takes
to make a phone call or what have you."
Are there specific technologies that you see coming down the pike that are important?
Sure.
I think there's the natural language processing, which is very good at deriving a customer's
intent, where you can then go and give them a response.
There are a lot of advances starting to take place along dialog management and natural
language generation.
These types of things are still in the formative stage, but they allow conversations to not
just be natural from a question and answer standpoint, but more of a conversation that
can go in many directions, which is more of how human conversations go.
We're excited about those.
We're monitoring them.
We do some of our own research on them.
I think they'll be a driving force in the years to come.
We've been talking with Ken Dodelin, who is the vice president of conversational AI products
at Capital One.
Ken, thanks so much for taking time with us today.
Thanks for having me, Michael.
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