August 14, 2025 • 32 mins
Is your business rushing into AI without really understanding how it fits into your strategy—or worse, letting “FOMO” drive your next big tech investment?
On this episode of the Delighted Customers podcast, I challenge leaders to think beyond the hype and get real about the transformational (and sometimes risky) power of AI in delivering great customer experiences. With renowned customer experience innovator Bill Staikos as my guest, we dig into the strategic decisions today’s C-suite faces: not just adopting new technology, but making sure it’s purpose-built to enhance both customer and employee outcomes. Bill’s candid, practical approach exposes common missteps—like dropping AI tools into teams with no training or clarity—and offers an essential reset for anyone who wants lasting, measurable results.
Bill Staikos brings rare insight as a former Senior Vice President at Medallia and Bank of New York (the world’s largest custodian bank), and as founder of the boutique consultancy B Customer Led. Tune in to learn directly from someone who has led next-gen customer experience transformations at major financial institutions, built teams to scale, and now pushes the field forward with his own new show, The Multimodal Experience.
Here are three core questions Bill answers in our episode:
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What are the biggest mistakes companies make when incorporating AI into customer experience—and how can you avoid them?
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How should leaders think about the boundaries between automation and the human touch, especially in sensitive customer journeys?
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What’s changing about who your “customer” truly is in an age of agentic AI, and how does that reframe loyalty and relationship-building?
Don’t miss this episode if you want actionable, clear-eyed guidance on making AI work for the people who matter most—your customers and employees. Listen now and subscribe via Apple Podcasts or Spotify, or find us on all your favorite podcast platforms!
Meet Bill Staikos
Bill Staikos is a visionary leader in customer experience, AI strategy, and organizational change, with a career spanning top roles at industry giants such as Medallia, Bank of New York, JPMorgan Chase, Credit Suisse, and American Express. He’s known for driving and scaling business outcomes in some of the most data-rich, complex environments in the world, particularly in financial services. As founder of B Customer Led, Bill helps organizations rethink how technology, data, and talent intersect to build customer-centric growth.
Bill’s approach is rooted in challenging the status quo—pushing companies to break old habits, design with both the “front-of-house” and “back-of-house” in mind, and always put human goals before shiny tech. He is also the creator and host of the new podcast, Multimodal Experience, where he explores how advanced technologies are fundamentally reshaping the way brands and people connect.
You can connect with Bill on LinkedIn: Bill Staikos LinkedIn
References from the Show
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Strawberry Agentic AI Browser (referenced as an example in the discussion)
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Well, thank you for joining us today on the Delighted Customers
podcast. I am so excited to have a returning guest, Bill
Stakos. Bill and I met several years ago
at a conference at Michigan State University. And at the time,
Bill was a senior vice president at Medallia and
impressed me. He took a role at one of the
(00:21):
largest banks in the world, actually bank of New York,
which is the largest custodian bank with about
52.1 trillion in assets under management
and is the 13th largest bank in the United States. Before that,
Bill held Leadership positions at JPMorgan Chase, Credit
Suisse and American Express. He is one of my
(00:43):
favorite visionaries and his company, a boutique consultancy,
b customer led, helps companies drive and scale
business outcomes. And he's also launching a new podcast called the
Multimodal Experience, a weekly show on how. How advanced
tech is shaping relationships with the brands we
buy from every day. And so today we're going to talk a little
(01:06):
bit about the everyday experiences we have as consumers
and also how AI interconnects with that. Bill, thank you
so much for coming back to the show. It is my privilege and
honor to be back here speaking with you and it's just, it's a wonderful
show and platform you've got. So thanks for having me back on. Thank you, Bill.
And I meant it when I said you're a visionary, always pushing the edges, always
(01:28):
pushing us to think harder outside the box, always willing to
challenge the status quo. I love that about you.
Thank you. Thank you. I try, I try and ruffle some feathers now and again.
It's always fun to break some glass every once in a while. Well, it is,
because it keeps us thinking. And today I wanted
to bring you back because. What? I don't know, it's been a year
(01:49):
or a year and a half since you were last on the show. So much
has changed, especially in the world of AI. And you were
leading a change at one of the largest banks, bank of
New York, with a big team. And I want to talk today about the
direct impact of AI on customer experience, management and
the consumer. So kind of looking at both sides of the coin, if
(02:10):
that's okay. Yeah. So, first of all, let's do some level
setting for the audience when we talk about direct impact
of AI on customer experience, management
and the consumer. Help us understand
what exactly we're expressing when we say that. Well,
I think that, you know, one is what are
(02:33):
the capabilities, not just technology, but
also the data, the architecture,
the talent on the company side that we need to
deliver an enhanced consumer or client experience
to the individuals that are interacting with that brand every day. And
that's very different than designing the experience
(02:55):
that is consumer facing and leveraging capabilities
like artificial intelligence or even simply the
app on your phone and the interconnectedness between
the two. Some people call that service design. What is the front of the
house and the back of the house look like? Others are just like, hey, I
need to build this. Here's the goal and the objective and the outcome
(03:17):
that I want to drive. I want to drive 30% more
digital sales. Okay? That's a real outcome that we can design
around. Right. And build the capabilities internally
and then measure that from a customer perspective in achieving
that goal. So it's really taking a front of the house and back of the
house view on how the capability operates,
(03:39):
both behind the scenes and what's consumer facing as well.
And then really painting that picture and that vision clearly for the firm and then
building towards that vision and having the metrics behind it to then
measure its success overall. Okay, well, thanks for clarifying that. Now,
hopefully, in terms of further clarifying, as we continue
our conversation, maybe I might ask you to paint a picture with a couple of
(04:01):
actual illustrations that are happening inside of companies. But
before we do that, one of the next questions I always like to ask is,
if I'm a C suite leader, why should I care about this? Well,
let me start with why you shouldn't care about it. Actually, that's also an
important point. One, you shouldn't care about it because it's all over
the place and it's a flavor du jour. I think that companies are
(04:23):
jumping into AI in the wrong way. They're not.
And they're saying, what's our AI strategy? What they should be
asking is, how does AI fit into my strategy?
How does AI fit into the experience that we want
to deliver for our consumers? How does AI fit into the talent
and the organizational construct? And do I even have the right model,
(04:46):
business model or organizational structure
to be able to deliver on, you
know, through these new capabilities? And is that even meaningful to my
clients or my customers? So like we're rushing into
hiring the chief AI officer and other this stuff and like, yeah, okay, that's fine.
It's, it's nice in an HBR article and we should do that. But like, we're
(05:08):
not asking what the, we're not starting with the why first now,
why it's important and why CEOs should, should pay attention.
One, the technology is transformational on a number
of different levels. And your customers are using it and playing with it every
day on their phones or on their laptops or
PCs, what have you. So they are
(05:30):
experiencing something that they are going to
ultimately relate that experience back to
what you are delivering from an experiential perspective. Here's a great
example and you might have seen this in a past life too when, when
Apple just kind of delivered like these, you know, agree from their
like terms and conditions. No, like everyone's like, oh, I want terms and conditions as
(05:53):
easy as Apple or I want an Uber experience like
a large bank. Saying we want an Uber experience is absolutely
irrelevant. 1 For starters, you are not Uber, you are a bank.
Now how do you take the context of banking and think about the
ease of an Uber experience? I think is the way to think about it.
So similarly for AI, so how are your consumers or clients or
(06:15):
customers using this capability? Where can it be meaningful to
enhance that experience for your customers? By
augmenting service interactions or sales interactions
or digital interactions. And then like, like how do we
implement that internally and what does that need to look like for our people to
be using it in a smart way to deliver on our
(06:37):
strategic objectives? So what I heard you say, Bill, was that one of
the mistakes that leaders can is this
FOMO worry. Yeah.
And they jump in with both heels and spend a lot of money
on hiring someone or dedicating resources or bringing someone
from the outside in to develop this AI strategy. And
(06:59):
what you're saying is really the, the strategy for the organization, the
customers it serves, the way the, the market it wants to
deliver to, needs to be what drives the
AI, not the other way around. Correct? Right. The concept
of human in the loop, which you may have heard or your listeners may have
heard of, is a little bit strange to me. It means that we are part
(07:21):
of this AI construct. A different way to be thinking about this
is no AI is part of our business construct. And
I think that we've put too much into the capability without
really understanding first why are we even using it. Like
why is that important to our company to use it and for our customers,
for us to be using it, but also to put it in front of them
(07:44):
in some customer facing capacity, whether that's a service interaction or
otherwise. And when we say human in the loop, it takes the
human element out of that. And that's a really dangerous
proposition, frankly for a lot of organizations to jump in because of the
FOMO piece, because you make mistakes
through that process. You jump in too quickly without really answering the tough
(08:07):
questions that your employees, frankly need you to answer to. Bill, I want
to dig a little bit for some additional mistakes that
you already see happening. One of the. Just kind of going back to
what we just talked about. You wrote a post very recently
about the role of AI and the organization,
the hiring of CAIOs. And this may
(08:29):
be consistent with what we just talked about is kind of bringing in resources,
spending money, elevating this thing. And I
responded back and said the craftsman is using the
tool, not the other way around. That's right. But, but I want, I want to
ask you if you are aware of other significant
mistakes that you see leaders making when
(08:52):
it comes to their perception and application of AI
in their business. Yeah, I think one of the biggest mistakes is
giving people tools without giving them the resources,
knowledge, understanding on how to use it. And
you've got, you know, all these studies around percentages of
AI use in business contexts. Frankly, like,
(09:14):
I think those numbers are BS1, because you've said we're
now giving everybody access to ChatGPT. Well, congratulations. I've been
using it for the last year on my phone personally. Okay, fantastic. Rah,
rah, rah. Great. How am I supposed to use this in a business
context? Is it safe for me to use it if I, if I help
it, if I have it, Help me develop a presentation that I deliver
(09:36):
to the CEO or an executive at the company.
Will you look at me differently? Will you actually even believe the
result? Will I potentially be fired because I've
now saved a week's worth of time and boiled it down into 15 minutes and
delivered a result maybe that you were super happy with? Now I'm petrified.
So I think, like in all things, like we saw this in digital
(09:58):
transformation 15, 20 years ago, like, this is about
change leadership and about change management. And it comes down
to people, help them understand the why behind the
technology, help them understand and train them on how to use it effectively
in their roles so they can produce more? Right. That's a
huge part of it. And right now too many companies are just
(10:20):
dropping the capability on someone's desktop and saying, go forth and use it.
And I think that's a really scary proposition for employees
because of the fear of, gosh, 50% of white collar
jobs will be gone in six months or year kind of storyline out there.
So I think people are not using it or not using it effectively because companies
have just dropped it on them. Bill, tell me if you think this line of
(10:42):
thinking is right, because when you're talking, I can't help but think of
the ad car model, which is one of the most common
popular models for change management, which is awareness,
desire, knowledge, capability and
reinforcement of it. And so that means the employees need to be fully
on board and have the tools, have the knowledge and the
(11:05):
skills to deliver and they have to know what's in it for
them. All these things are part of any. If you believe in the
ad car model, which I think most change leaders do, acknowledge
it at least, at least one of the best ones for many applications.
It's true for AI. AI is really sort of an ongoing
change. Management isn't really is. I mean one, it's
(11:27):
evolving so quickly, right? I mean, six months ago we had
different models and we were using different models in different ways. Now we're
creating almost long form video on some level
with language that matches the video. I mean, it's just insane how quickly this stuff
is evolving. And look at the end, like people don't create
good narratives in their heads when they have fear. Like they're worried about their
(11:49):
job, they've got to pay their bills, they have families to support, right? They've
got their own issues at home. So like you really need to
spend the time up front helping people understand
why is the company rolling this out? Why is it important for the firm at
this time? Why is it important for their customers to be using these
capabilities with that brand and then kind of going through, and I love
(12:11):
the Adkar model, then kind of following those steps to make sure that
every step of the way people are clear on how they should be
using the capability, they are trained on it and they understand how
it impacts their role day to day and what your expectation is of
them. With those tools and capabilities too, I think that you can still take that
ADCOM model, by the way, and turn the screen around and show it to your
(12:34):
customers. Like, here's how we want you to be using this capability with us and
here's why it's important to us as a firm, right? It not only
improves the experience, but it actually makes us more efficient as a business
so we can continue to deliver those great experiences and products to you every day.
So I, you know, going back to the original question, around sort of the two
sides of the same coin, like there's a lot of work that
(12:55):
is not necessarily happening today around artificial intelligence from a
corporate perspective that really needs to come to the fore.
So that chief AI officer, I do hope that they are
as equally focused on the change aspect of this and not just change
management, but also change leadership. Creating that vision
and that rallying cry as anybody else in
(13:18):
the organization might be. Yeah. And spot on with
change leadership and knowing that we need to bring
everyone along, give them the tools and they need to be clear as
to what it's used for. And speaking of which, you and I had a previous
conversation about efficiency versus effectiveness and
another potential pitfall. Share your thoughts on that. So I
(13:40):
think one of the myths around AI is that it guarantees cost
savings. And I think, I think, look, the upfront investment in
infrastructure talent change can
significantly outweigh any immediate savings, right?
And also over automation can really backfire
if customer satisfaction, customer delight
(14:02):
dips too. I think that there is a little bit of a misnomer and
I think that's a pitfall that we all need to be mindful of.
Most companies, frankly, I'm going to go out there on a limb and just say
most are not making money on AI right now, given the
investment required to roll us out and deliver.
And I think that's something that people really need to and going
(14:24):
back to the why and the change management, you really need to think about what
is the investment today and then when will it actually help us.
I also think that more data doesn't necessarily mean
better AI, right? Or better AI results. Volume without
relevance is just more noise, right? I hear executives
all the time say I have so much data and not enough
(14:46):
insight. As CX pros ourselves, we heard that practically most of our careers
in some way like quality annotated data
aligned to clear goals. That's what drives precision.
The old adage garbage in, garbage out. The same is true with AI.
It's just going to scale bad processes, bad data and
provide bad results. So let's let me push you a little bit
(15:09):
on helping us understand
the limitations sort of where does AI's
capability end? Where does the human interaction need
to continue to take precedence? So I think in the
like, in the super transactional, like
password change, you know, account set up,
(15:32):
like super transactional experiences in your organization
are really great use cases for AI. And
I actually don't believe that the capability has
limitations, frankly. Like there should be people in your organization
that just think about crazy use cases all day long and
how to maybe apply AI to them. And I think that's a really
(15:55):
potentially important role to play. Where the limitation is,
is where that human element really is
required for your brand. And like, you know, we've got a, you know,
we've got a lot of financial services folks, you know, who listen to this podcast.
So let's think about mortgage foreclosure like think about
the severity of that from a customer perspective. And
(16:16):
nobody wants to go through that. But what people don't want in that
situation is to have to go through it by engaging a
bot. So I think it's really important for
organizations to think about what are the journeys that we
absolutely can transform and automate,
because our customers are going through a journey where they just want to get through
(16:38):
it quickly and they can care less if it's a human or a bot. They
just need to execute quickly. Could be a service inquiry. Where is
the handoff? Maybe from a bot to an agent as well?
I mean, an agentic AI. And then what are the handoffs then to bring a
human in? But then what are those really deep
emotional journeys and experiences that our customers go through
(17:00):
that we don't as a brand believe it should be
automated? And, you know, homeowner, like, what are those big happy
or those deeply sad experiences that as consumers we
can go through? And I think that's where you need to look
at, like there needs to be an individual involved here
to either handhold someone through a very sort of delicate
(17:23):
or sensitive or sad experience, like a mortgage
foreclosure or something that makes you super happy, like buying
even a home. So that's where I think that more thought needs
to. Needs to come into play in terms of, you know, truly what are the
journeys that we want to automate or enhance through AI
and which ones do. We absolutely just must have people in the loop. Excellent.
(17:44):
Thanks, Bill. And let's drill down another layer and get into a little bit more
of strategic guidance conversations to organizations. One of
the things you mentioned in the last comment was agentic
AI. And I had someone recently on a show
who misspoke about agentic AI. He
flipped generative AI and agentic AI
(18:06):
definitions around. And I got a call out from my friend shout out to Eric
Ail, who wrote me about this, and I missed it
because I probably just assumed that he was talking about
generative AI. But I'd love for you to talk about agentic
AI, what it is, and specifically how it relates
to who is my customer, who is the customer
(18:29):
going forward. This is, by the way, a
topic that we can probably spend three hours talking about. And
frankly, I think one that so many companies are missing the mark
on. So agentic AI works on someone's behalf using
tools, working towards an objective and goal autonomously.
So if I am using an agentic AI browser as
(18:52):
an example, and I use a browser called Strawberry,
I say go and book tickets for my next vacation, find
hotels that are four stars and up. You know, it should be,
you know, a high end beach vacation with kid friendly
amenities, but also nice places for my wife and I to go to dinner off
property. Right. All right, great. Now that agent is off going and looking
(19:14):
at places, it comes back and says, okay, here are your three options.
Which one do you want to pick? And I say, this is the one at
this price point, thanks very much. Go and book it. At that point, I am
no longer the customer. And if you have agents
now that you've got to deal with as a brand who are very transactional,
how do I build loyalty? How do I think about re engaging that
(19:36):
agent to come back and book again? So I think
that marketing, certainly customer experience,
Certainly roles like CIOs and COOs
really need to be rethinking who is the customer because
now it's two of them. How do I maintain a relationship with
Bill but also be able to engage their agent
(19:59):
to make sure that they keep on coming back and buying from me? Because what
an agent is going to do is it's going to expose me to brands that
I never even thought of buying from in the past. It's going to take
my prompt and say, here are your four or five options based on what you
wanted and it may not be a Marriott or a Hilton
that I stay at next time. So I think that there needs to be
(20:19):
a lot of just deeper conversation internally at brands to
say, who is our customer? Is it now two of them? And
how do we ensure that if Bill is using
an agent, that agent keeps on using Marriott. If Bill hasn't put in,
I want it to be a Marriott property. So I think that there
is and then there's legal implications associated with that. Like what if the
(20:42):
agent makes a mistake and the company gets sued? What if it's the
company's agent? So I think there's a lot of really complex big
questions that we still have not answered across industry
that we need to and hopefully we can get there pretty soon. Yeah,
it's going to be fascinating, it's going to be an interesting ride. And you bring
up a great point about who is the customer and how do we
(21:04):
impact. And there's no, I guess those answers are still evolving.
But it's a really good question that I think we need to ask.
How might AI prove actionable insights directly to
customers or empower employees who serve them? So I think that,
look, it's happening today. You have organizations out there,
a large credit card company With a blue logo. I won't mention the name. Right.
(21:27):
Like, they are creating synthetic NPS based on
the sentiment of every contact center call. They're no longer serving
customers, or they do every once in a while to train the model that they've
built and that also delivers coaching
for the next call. And that's a great use case to be able
to enhance the experience for the employee so they continue to improve
(21:49):
and deliver better experiences to your customers. And they've been
doing this for several years now and continue to refine and build on
that capability. So, like, I think it's really interesting
ways like that, helping you understand your ideal customer profile,
resegmenting your customers, creating Personas based
on customer activity. There's countless ways that
(22:12):
you can leverage AI to get smarter around who your customer
is, what's important to them, and then what should I
be building next to continue to enhance that experience and keep them
coming back? So many organizations are working towards that and
through that capability today in that way. And I'm super
excited about sort of like where this and how this evolves as well. You know,
(22:34):
when we talk about the customer journey in that regard,
where the journey is no longer
monolithic, it is, we've got ways that customers
interact with us. Correct. And they are
walking information beings. For example,
they may have a wearable iWatch. They've got
(22:57):
their phone on them, they've got data there. They could
have through Zoom. They're working on Zoom or on
Facebook, on social media. And Ray Ban is coming out
with some glasses that do amazing things. So we've got the whole
wearable piece. How. How can AI help
organizations deliver a better customer experience
(23:19):
and help ultimately with business outcomes that C
Suite is really interested in. Yeah. So great question.
I think that, you know, CX is no longer, I hope,
although in many cases still is. Unfortunately, CX no longer is like, do the
survey and then go, you know, develop the report and say, here are your five
pain points, go fix them. I think Voice of the Customer surveys have
(23:41):
a spot, but like Voice of the Customer feedback and
insights and signals, generally operational data, financial data,
behavioral data, digital surveys, certainly still
AI can help bring all that data together for us,
analyze that data and produce results that are
aligned to what we want to deliver and drive from a strategic
(24:02):
perspective. So years ago, I mean, really a
long time ago, at Wallet Credit Suisse, we flipped the question on its
head and saying, if the firm wants to drive revenue by 10% this
year, how do we design experiences that drive revenue by 10%?
You can design experiences in a customer friendly way that also help you deliver
a strategic objective. I think AI is going to help us accelerate that
(24:26):
plus be able to identify new and novel ways to deliver
those experiences for us. One,
certainly around aggregation and delivery of
insights. Two, being able to automate closed loop
feedback based on those insights in real time with customers
to maintain that loyalty, to fix those issues quickly and then
(24:48):
three, being able to deliver real time information to people
that interact with customers every day so that they can engage the
customer in a way they want to be engaged with and develop ideas,
solutions, commentary, dialogue in a way
that makes that bond richer and stronger with your end consumer. Yeah,
and also affirming that and also adding on to that. Another
(25:11):
cut is I had Jason Barro, who's the founder of
NPS Prism at Bain on the show a while back and he talked
about the ability to know which levers
drive loyalty and which ones to pull on
and how far to pull on them. In other words, you can overspend or
over overvalue a particular one to the point
(25:34):
where as long as your X percentage better than your next
closest competitor, that's all you need to do. How, how
does AI, how do you think AI can help with
that sort of intelligence? Well, I think that it's going to be able to look
at all this data in a different way
and not necessarily from the perspective of what someone
(25:57):
might do. I think that you're going to have much
clearer cause and effect analytics
embedded into your workflows. In fact, even workflows
can be treated like a product now because of AI that that's
going to deliver insights to us much faster than an MPS
model might. I think still asking the question is valid and I love
(26:19):
the metric in certain contexts. But I also think that
AI will accelerate sort of time to insight
or time to understanding. It will certainly accelerate time to
action based on those insights, but it's also going to be able to look
at larger sort of swaths of data so we can
truly understand what those levels levers are.
(26:41):
But then with precision to your point, pull and
push on those levers in a most economical way for the
company to push and pull on them. Right. Like we in
the past we did a survey, we pulled or pushed a lever, we did
another survey to see how much that that moved right
after the fact. Right now it's going to be much more real time and
(27:03):
we see that with journey analytics capabilities today, other technology
that's able to at real time what's happening and why
and then how is that impacting your business objectives. Yeah. Which is a big
deal because that lag can create all sorts of inefficiencies,
customer churn, and ultimately impact the numbers
100%. And your competitors are sitting there, you know, picking up the pieces
(27:25):
while you're trying to figure out where they fell. So, you know, like, that
is, that is a really big deal for an organization to be able to figure
out. And, you know, again, like, with all things, it comes down to what
am I trying to achieve? Do I have the talent to achieve those objectives?
Do they have the tools to be able to achieve those objectives? And how quickly
can we put them in front of people to start achieving those goals?
(27:47):
So, yeah, it's an exciting time. Scary as all get out too, but certainly an
exciting time. Yeah, I appreciate you acknowledging that. I think there are a lot of
people listening who appreciate you saying that. And it is scary.
It is. And I think part of it is just admitting, look, we don't
have all the answers, but we're going to start small and we're going to build
from there and we're going to do this together. And
(28:09):
that message, I think, resonates and I think, you know,
takes sort of the fear out of this stuff, at least a little bit, so
people are more motivated and driven to use these tools in an effective way. Excellent,
Bill. Great conversation. I want to. I want to. So many great
tools, so many great insights here as we navigate this
uncertain future. I want to end with one question that I ask
(28:31):
all my guests, which is, what does it mean to you?
Or better said, what delights you as a customer? I think for
me, I'm pretty easy on brands given the work that I've done in
my career. I think for me it's just like, you know, is.
And maybe it's easier said than done, but like, what my expectation. Did my
experience match the expectation? Is there any daylight between those things?
(28:54):
And I give brands a lot of grace because I understand how complex and hard
that is to actually do and close that gap. But
I think what makes me happy is like, you know, are you
actually making my life better? Like, are you. Are you
helping me be a better person? Like, really quick story. I
love my Traeger Grill. I'm sorry to mention a brand on the show, but like,
(29:18):
I smoked a 15 pound bone in, you know, pork butt
the other day. That made me super happy. It created joy for my
family because they love right tacos with, you know, with, with
smoked pork. Like, you know, are. Is the, is the product
or is the brand or the service is it actually helping me create
joy or happiness for the things that are meaningful to me? And I
(29:40):
think that is a really tough thing to do I think because it's so
personal like I'm a sample of one. But that's what I really, you know,
that's what I'm looking for in brands. Excellent Bill, great question. If our
listen our listeners would like to get ahold of you to chat more, what's the
best way? Hey Look I'm on LinkedIn regularly. Feel free to DM
you know my my contact details are on LinkedIn as well. I put them out
(30:03):
there and happy to have a conversation. If you've got any questions and
if you need any help just let me know. I'm here to help. Bill, thanks
so much for coming back on the Delighted Customers podcast. It's been great
to be with you again and really appreciate you having me on.
Well, thank you for joining us today on the Delighted Customers
podcast. I am so excited to have a returning guest, Bill
Stakos. Bill and I met several years ago
at a conference at Michigan State University. And at the time,
Bill was a senior vice president at Medallia and
impressed me. He took a role at one of the
(00:21):
largest banks in the world, actually bank of New York,
which is the largest custodian bank with about
52.1 trillion in assets under management
and is the 13th largest bank in the United States. Before that,
Bill held Leadership positions at JPMorgan Chase, Credit
Suisse and American Express. He is one of my
(00:43):
favorite visionaries and his company, a boutique consultancy,
b customer led, helps companies drive and scale
business outcomes. And he's also launching a new podcast called the
Multimodal Experience, a weekly show on how. How advanced
tech is shaping relationships with the brands we
buy from every day. And so today we're going to talk a little
(01:06):
bit about the everyday experiences we have as consumers
and also how AI interconnects with that. Bill, thank you
so much for coming back to the show. It is my privilege and
honor to be back here speaking with you and it's just, it's a wonderful
show and platform you've got. So thanks for having me back on. Thank you, Bill.
And I meant it when I said you're a visionary, always pushing the edges, always
(01:28):
pushing us to think harder outside the box, always willing to
challenge the status quo. I love that about you.
Thank you. Thank you. I try, I try and ruffle some feathers now and again.
It's always fun to break some glass every once in a while. Well, it is,
because it keeps us thinking. And today I wanted
to bring you back because. What? I don't know, it's been a year
(01:49):
or a year and a half since you were last on the show. So much
has changed, especially in the world of AI. And you were
leading a change at one of the largest banks, bank of
New York, with a big team. And I want to talk today about the
direct impact of AI on customer experience, management and
the consumer. So kind of looking at both sides of the coin, if
(02:10):
that's okay. Yeah. So, first of all, let's do some level
setting for the audience when we talk about direct impact
of AI on customer experience, management
and the consumer. Help us understand
what exactly we're expressing when we say that. Well,
I think that, you know, one is what are
(02:33):
the capabilities, not just technology, but
also the data, the architecture,
the talent on the company side that we need to
deliver an enhanced consumer or client experience
to the individuals that are interacting with that brand every day. And
that's very different than designing the experience
(02:55):
that is consumer facing and leveraging capabilities
like artificial intelligence or even simply the
app on your phone and the interconnectedness between
the two. Some people call that service design. What is the front of the
house and the back of the house look like? Others are just like, hey, I
need to build this. Here's the goal and the objective and the outcome
(03:17):
that I want to drive. I want to drive 30% more
digital sales. Okay? That's a real outcome that we can design
around. Right. And build the capabilities internally
and then measure that from a customer perspective in achieving
that goal. So it's really taking a front of the house and back of the
house view on how the capability operates,
(03:39):
both behind the scenes and what's consumer facing as well.
And then really painting that picture and that vision clearly for the firm and then
building towards that vision and having the metrics behind it to then
measure its success overall. Okay, well, thanks for clarifying that. Now,
hopefully, in terms of further clarifying, as we continue
our conversation, maybe I might ask you to paint a picture with a couple of
(04:01):
actual illustrations that are happening inside of companies. But
before we do that, one of the next questions I always like to ask is,
if I'm a C suite leader, why should I care about this? Well,
let me start with why you shouldn't care about it. Actually, that's also an
important point. One, you shouldn't care about it because it's all over
the place and it's a flavor du jour. I think that companies are
(04:23):
jumping into AI in the wrong way. They're not.
And they're saying, what's our AI strategy? What they should be
asking is, how does AI fit into my strategy?
How does AI fit into the experience that we want
to deliver for our consumers? How does AI fit into the talent
and the organizational construct? And do I even have the right model,
(04:46):
business model or organizational structure
to be able to deliver on, you
know, through these new capabilities? And is that even meaningful to my
clients or my customers? So like we're rushing into
hiring the chief AI officer and other this stuff and like, yeah, okay, that's fine.
It's, it's nice in an HBR article and we should do that. But like, we're
(05:08):
not asking what the, we're not starting with the why first now,
why it's important and why CEOs should, should pay attention.
One, the technology is transformational on a number
of different levels. And your customers are using it and playing with it every
day on their phones or on their laptops or
PCs, what have you. So they are
(05:30):
experiencing something that they are going to
ultimately relate that experience back to
what you are delivering from an experiential perspective. Here's a great
example and you might have seen this in a past life too when, when
Apple just kind of delivered like these, you know, agree from their
like terms and conditions. No, like everyone's like, oh, I want terms and conditions as
(05:53):
easy as Apple or I want an Uber experience like
a large bank. Saying we want an Uber experience is absolutely
irrelevant. 1 For starters, you are not Uber, you are a bank.
Now how do you take the context of banking and think about the
ease of an Uber experience? I think is the way to think about it.
So similarly for AI, so how are your consumers or clients or
(06:15):
customers using this capability? Where can it be meaningful to
enhance that experience for your customers? By
augmenting service interactions or sales interactions
or digital interactions. And then like, like how do we
implement that internally and what does that need to look like for our people to
be using it in a smart way to deliver on our
(06:37):
strategic objectives? So what I heard you say, Bill, was that one of
the mistakes that leaders can is this
FOMO worry. Yeah.
And they jump in with both heels and spend a lot of money
on hiring someone or dedicating resources or bringing someone
from the outside in to develop this AI strategy. And
(06:59):
what you're saying is really the, the strategy for the organization, the
customers it serves, the way the, the market it wants to
deliver to, needs to be what drives the
AI, not the other way around. Correct? Right. The concept
of human in the loop, which you may have heard or your listeners may have
heard of, is a little bit strange to me. It means that we are part
(07:21):
of this AI construct. A different way to be thinking about this
is no AI is part of our business construct. And
I think that we've put too much into the capability without
really understanding first why are we even using it. Like
why is that important to our company to use it and for our customers,
for us to be using it, but also to put it in front of them
(07:44):
in some customer facing capacity, whether that's a service interaction or
otherwise. And when we say human in the loop, it takes the
human element out of that. And that's a really dangerous
proposition, frankly for a lot of organizations to jump in because of the
FOMO piece, because you make mistakes
through that process. You jump in too quickly without really answering the tough
(08:07):
questions that your employees, frankly need you to answer to. Bill, I want
to dig a little bit for some additional mistakes that
you already see happening. One of the. Just kind of going back to
what we just talked about. You wrote a post very recently
about the role of AI and the organization,
the hiring of CAIOs. And this may
(08:29):
be consistent with what we just talked about is kind of bringing in resources,
spending money, elevating this thing. And I
responded back and said the craftsman is using the
tool, not the other way around. That's right. But, but I want, I want to
ask you if you are aware of other significant
mistakes that you see leaders making when
(08:52):
it comes to their perception and application of AI
in their business. Yeah, I think one of the biggest mistakes is
giving people tools without giving them the resources,
knowledge, understanding on how to use it. And
you've got, you know, all these studies around percentages of
AI use in business contexts. Frankly, like,
(09:14):
I think those numbers are BS1, because you've said we're
now giving everybody access to ChatGPT. Well, congratulations. I've been
using it for the last year on my phone personally. Okay, fantastic. Rah,
rah, rah. Great. How am I supposed to use this in a business
context? Is it safe for me to use it if I, if I help
it, if I have it, Help me develop a presentation that I deliver
(09:36):
to the CEO or an executive at the company.
Will you look at me differently? Will you actually even believe the
result? Will I potentially be fired because I've
now saved a week's worth of time and boiled it down into 15 minutes and
delivered a result maybe that you were super happy with? Now I'm petrified.
So I think, like in all things, like we saw this in digital
(09:58):
transformation 15, 20 years ago, like, this is about
change leadership and about change management. And it comes down
to people, help them understand the why behind the
technology, help them understand and train them on how to use it effectively
in their roles so they can produce more? Right. That's a
huge part of it. And right now too many companies are just
(10:20):
dropping the capability on someone's desktop and saying, go forth and use it.
And I think that's a really scary proposition for employees
because of the fear of, gosh, 50% of white collar
jobs will be gone in six months or year kind of storyline out there.
So I think people are not using it or not using it effectively because companies
have just dropped it on them. Bill, tell me if you think this line of
(10:42):
thinking is right, because when you're talking, I can't help but think of
the ad car model, which is one of the most common
popular models for change management, which is awareness,
desire, knowledge, capability and
reinforcement of it. And so that means the employees need to be fully
on board and have the tools, have the knowledge and the
(11:05):
skills to deliver and they have to know what's in it for
them. All these things are part of any. If you believe in the
ad car model, which I think most change leaders do, acknowledge
it at least, at least one of the best ones for many applications.
It's true for AI. AI is really sort of an ongoing
change. Management isn't really is. I mean one, it's
(11:27):
evolving so quickly, right? I mean, six months ago we had
different models and we were using different models in different ways. Now we're
creating almost long form video on some level
with language that matches the video. I mean, it's just insane how quickly this stuff
is evolving. And look at the end, like people don't create
good narratives in their heads when they have fear. Like they're worried about their
(11:49):
job, they've got to pay their bills, they have families to support, right? They've
got their own issues at home. So like you really need to
spend the time up front helping people understand
why is the company rolling this out? Why is it important for the firm at
this time? Why is it important for their customers to be using these
capabilities with that brand and then kind of going through, and I love
(12:11):
the Adkar model, then kind of following those steps to make sure that
every step of the way people are clear on how they should be
using the capability, they are trained on it and they understand how
it impacts their role day to day and what your expectation is of
them. With those tools and capabilities too, I think that you can still take that
ADCOM model, by the way, and turn the screen around and show it to your
(12:34):
customers. Like, here's how we want you to be using this capability with us and
here's why it's important to us as a firm, right? It not only
improves the experience, but it actually makes us more efficient as a business
so we can continue to deliver those great experiences and products to you every day.
So I, you know, going back to the original question, around sort of the two
sides of the same coin, like there's a lot of work that
(12:55):
is not necessarily happening today around artificial intelligence from a
corporate perspective that really needs to come to the fore.
So that chief AI officer, I do hope that they are
as equally focused on the change aspect of this and not just change
management, but also change leadership. Creating that vision
and that rallying cry as anybody else in
(13:18):
the organization might be. Yeah. And spot on with
change leadership and knowing that we need to bring
everyone along, give them the tools and they need to be clear as
to what it's used for. And speaking of which, you and I had a previous
conversation about efficiency versus effectiveness and
another potential pitfall. Share your thoughts on that. So I
(13:40):
think one of the myths around AI is that it guarantees cost
savings. And I think, I think, look, the upfront investment in
infrastructure talent change can
significantly outweigh any immediate savings, right?
And also over automation can really backfire
if customer satisfaction, customer delight
(14:02):
dips too. I think that there is a little bit of a misnomer and
I think that's a pitfall that we all need to be mindful of.
Most companies, frankly, I'm going to go out there on a limb and just say
most are not making money on AI right now, given the
investment required to roll us out and deliver.
And I think that's something that people really need to and going
(14:24):
back to the why and the change management, you really need to think about what
is the investment today and then when will it actually help us.
I also think that more data doesn't necessarily mean
better AI, right? Or better AI results. Volume without
relevance is just more noise, right? I hear executives
all the time say I have so much data and not enough
(14:46):
insight. As CX pros ourselves, we heard that practically most of our careers
in some way like quality annotated data
aligned to clear goals. That's what drives precision.
The old adage garbage in, garbage out. The same is true with AI.
It's just going to scale bad processes, bad data and
provide bad results. So let's let me push you a little bit
(15:09):
on helping us understand
the limitations sort of where does AI's
capability end? Where does the human interaction need
to continue to take precedence? So I think in the
like, in the super transactional, like
password change, you know, account set up,
(15:32):
like super transactional experiences in your organization
are really great use cases for AI. And
I actually don't believe that the capability has
limitations, frankly. Like there should be people in your organization
that just think about crazy use cases all day long and
how to maybe apply AI to them. And I think that's a really
(15:55):
potentially important role to play. Where the limitation is,
is where that human element really is
required for your brand. And like, you know, we've got a, you know,
we've got a lot of financial services folks, you know, who listen to this podcast.
So let's think about mortgage foreclosure like think about
the severity of that from a customer perspective. And
(16:16):
nobody wants to go through that. But what people don't want in that
situation is to have to go through it by engaging a
bot. So I think it's really important for
organizations to think about what are the journeys that we
absolutely can transform and automate,
because our customers are going through a journey where they just want to get through
(16:38):
it quickly and they can care less if it's a human or a bot. They
just need to execute quickly. Could be a service inquiry. Where is
the handoff? Maybe from a bot to an agent as well?
I mean, an agentic AI. And then what are the handoffs then to bring a
human in? But then what are those really deep
emotional journeys and experiences that our customers go through
(17:00):
that we don't as a brand believe it should be
automated? And, you know, homeowner, like, what are those big happy
or those deeply sad experiences that as consumers we
can go through? And I think that's where you need to look
at, like there needs to be an individual involved here
to either handhold someone through a very sort of delicate
(17:23):
or sensitive or sad experience, like a mortgage
foreclosure or something that makes you super happy, like buying
even a home. So that's where I think that more thought needs
to. Needs to come into play in terms of, you know, truly what are the
journeys that we want to automate or enhance through AI
and which ones do. We absolutely just must have people in the loop. Excellent.
(17:44):
Thanks, Bill. And let's drill down another layer and get into a little bit more
of strategic guidance conversations to organizations. One of
the things you mentioned in the last comment was agentic
AI. And I had someone recently on a show
who misspoke about agentic AI. He
flipped generative AI and agentic AI
(18:06):
definitions around. And I got a call out from my friend shout out to Eric
Ail, who wrote me about this, and I missed it
because I probably just assumed that he was talking about
generative AI. But I'd love for you to talk about agentic
AI, what it is, and specifically how it relates
to who is my customer, who is the customer
(18:29):
going forward. This is, by the way, a
topic that we can probably spend three hours talking about. And
frankly, I think one that so many companies are missing the mark
on. So agentic AI works on someone's behalf using
tools, working towards an objective and goal autonomously.
So if I am using an agentic AI browser as
(18:52):
an example, and I use a browser called Strawberry,
I say go and book tickets for my next vacation, find
hotels that are four stars and up. You know, it should be,
you know, a high end beach vacation with kid friendly
amenities, but also nice places for my wife and I to go to dinner off
property. Right. All right, great. Now that agent is off going and looking
(19:14):
at places, it comes back and says, okay, here are your three options.
Which one do you want to pick? And I say, this is the one at
this price point, thanks very much. Go and book it. At that point, I am
no longer the customer. And if you have agents
now that you've got to deal with as a brand who are very transactional,
how do I build loyalty? How do I think about re engaging that
(19:36):
agent to come back and book again? So I think
that marketing, certainly customer experience,
Certainly roles like CIOs and COOs
really need to be rethinking who is the customer because
now it's two of them. How do I maintain a relationship with
Bill but also be able to engage their agent
(19:59):
to make sure that they keep on coming back and buying from me? Because what
an agent is going to do is it's going to expose me to brands that
I never even thought of buying from in the past. It's going to take
my prompt and say, here are your four or five options based on what you
wanted and it may not be a Marriott or a Hilton
that I stay at next time. So I think that there needs to be
(20:19):
a lot of just deeper conversation internally at brands to
say, who is our customer? Is it now two of them? And
how do we ensure that if Bill is using
an agent, that agent keeps on using Marriott. If Bill hasn't put in,
I want it to be a Marriott property. So I think that there
is and then there's legal implications associated with that. Like what if the
(20:42):
agent makes a mistake and the company gets sued? What if it's the
company's agent? So I think there's a lot of really complex big
questions that we still have not answered across industry
that we need to and hopefully we can get there pretty soon. Yeah,
it's going to be fascinating, it's going to be an interesting ride. And you bring
up a great point about who is the customer and how do we
(21:04):
impact. And there's no, I guess those answers are still evolving.
But it's a really good question that I think we need to ask.
How might AI prove actionable insights directly to
customers or empower employees who serve them? So I think that,
look, it's happening today. You have organizations out there,
a large credit card company With a blue logo. I won't mention the name. Right.
(21:27):
Like, they are creating synthetic NPS based on
the sentiment of every contact center call. They're no longer serving
customers, or they do every once in a while to train the model that they've
built and that also delivers coaching
for the next call. And that's a great use case to be able
to enhance the experience for the employee so they continue to improve
(21:49):
and deliver better experiences to your customers. And they've been
doing this for several years now and continue to refine and build on
that capability. So, like, I think it's really interesting
ways like that, helping you understand your ideal customer profile,
resegmenting your customers, creating Personas based
on customer activity. There's countless ways that
(22:12):
you can leverage AI to get smarter around who your customer
is, what's important to them, and then what should I
be building next to continue to enhance that experience and keep them
coming back? So many organizations are working towards that and
through that capability today in that way. And I'm super
excited about sort of like where this and how this evolves as well. You know,
(22:34):
when we talk about the customer journey in that regard,
where the journey is no longer
monolithic, it is, we've got ways that customers
interact with us. Correct. And they are
walking information beings. For example,
they may have a wearable iWatch. They've got
(22:57):
their phone on them, they've got data there. They could
have through Zoom. They're working on Zoom or on
Facebook, on social media. And Ray Ban is coming out
with some glasses that do amazing things. So we've got the whole
wearable piece. How. How can AI help
organizations deliver a better customer experience
(23:19):
and help ultimately with business outcomes that C
Suite is really interested in. Yeah. So great question.
I think that, you know, CX is no longer, I hope,
although in many cases still is. Unfortunately, CX no longer is like, do the
survey and then go, you know, develop the report and say, here are your five
pain points, go fix them. I think Voice of the Customer surveys have
(23:41):
a spot, but like Voice of the Customer feedback and
insights and signals, generally operational data, financial data,
behavioral data, digital surveys, certainly still
AI can help bring all that data together for us,
analyze that data and produce results that are
aligned to what we want to deliver and drive from a strategic
(24:02):
perspective. So years ago, I mean, really a
long time ago, at Wallet Credit Suisse, we flipped the question on its
head and saying, if the firm wants to drive revenue by 10% this
year, how do we design experiences that drive revenue by 10%?
You can design experiences in a customer friendly way that also help you deliver
a strategic objective. I think AI is going to help us accelerate that
(24:26):
plus be able to identify new and novel ways to deliver
those experiences for us. One,
certainly around aggregation and delivery of
insights. Two, being able to automate closed loop
feedback based on those insights in real time with customers
to maintain that loyalty, to fix those issues quickly and then
(24:48):
three, being able to deliver real time information to people
that interact with customers every day so that they can engage the
customer in a way they want to be engaged with and develop ideas,
solutions, commentary, dialogue in a way
that makes that bond richer and stronger with your end consumer. Yeah,
and also affirming that and also adding on to that. Another
(25:11):
cut is I had Jason Barro, who's the founder of
NPS Prism at Bain on the show a while back and he talked
about the ability to know which levers
drive loyalty and which ones to pull on
and how far to pull on them. In other words, you can overspend or
over overvalue a particular one to the point
(25:34):
where as long as your X percentage better than your next
closest competitor, that's all you need to do. How, how
does AI, how do you think AI can help with
that sort of intelligence? Well, I think that it's going to be able to look
at all this data in a different way
and not necessarily from the perspective of what someone
(25:57):
might do. I think that you're going to have much
clearer cause and effect analytics
embedded into your workflows. In fact, even workflows
can be treated like a product now because of AI that that's
going to deliver insights to us much faster than an MPS
model might. I think still asking the question is valid and I love
(26:19):
the metric in certain contexts. But I also think that
AI will accelerate sort of time to insight
or time to understanding. It will certainly accelerate time to
action based on those insights, but it's also going to be able to look
at larger sort of swaths of data so we can
truly understand what those levels levers are.
(26:41):
But then with precision to your point, pull and
push on those levers in a most economical way for the
company to push and pull on them. Right. Like we in
the past we did a survey, we pulled or pushed a lever, we did
another survey to see how much that that moved right
after the fact. Right now it's going to be much more real time and
(27:03):
we see that with journey analytics capabilities today, other technology
that's able to at real time what's happening and why
and then how is that impacting your business objectives. Yeah. Which is a big
deal because that lag can create all sorts of inefficiencies,
customer churn, and ultimately impact the numbers
100%. And your competitors are sitting there, you know, picking up the pieces
(27:25):
while you're trying to figure out where they fell. So, you know, like, that
is, that is a really big deal for an organization to be able to figure
out. And, you know, again, like, with all things, it comes down to what
am I trying to achieve? Do I have the talent to achieve those objectives?
Do they have the tools to be able to achieve those objectives? And how quickly
can we put them in front of people to start achieving those goals?
(27:47):
So, yeah, it's an exciting time. Scary as all get out too, but certainly an
exciting time. Yeah, I appreciate you acknowledging that. I think there are a lot of
people listening who appreciate you saying that. And it is scary.
It is. And I think part of it is just admitting, look, we don't
have all the answers, but we're going to start small and we're going to build
from there and we're going to do this together. And
(28:09):
that message, I think, resonates and I think, you know,
takes sort of the fear out of this stuff, at least a little bit, so
people are more motivated and driven to use these tools in an effective way. Excellent,
Bill. Great conversation. I want to. I want to. So many great
tools, so many great insights here as we navigate this
uncertain future. I want to end with one question that I ask
(28:31):
all my guests, which is, what does it mean to you?
Or better said, what delights you as a customer? I think for
me, I'm pretty easy on brands given the work that I've done in
my career. I think for me it's just like, you know, is.
And maybe it's easier said than done, but like, what my expectation. Did my
experience match the expectation? Is there any daylight between those things?
(28:54):
And I give brands a lot of grace because I understand how complex and hard
that is to actually do and close that gap. But
I think what makes me happy is like, you know, are you
actually making my life better? Like, are you. Are you
helping me be a better person? Like, really quick story. I
love my Traeger Grill. I'm sorry to mention a brand on the show, but like,
(29:18):
I smoked a 15 pound bone in, you know, pork butt
the other day. That made me super happy. It created joy for my
family because they love right tacos with, you know, with, with
smoked pork. Like, you know, are. Is the, is the product
or is the brand or the service is it actually helping me create
joy or happiness for the things that are meaningful to me? And I
(29:40):
think that is a really tough thing to do I think because it's so
personal like I'm a sample of one. But that's what I really, you know,
that's what I'm looking for in brands. Excellent Bill, great question. If our
listen our listeners would like to get ahold of you to chat more, what's the
best way? Hey Look I'm on LinkedIn regularly. Feel free to DM
you know my my contact details are on LinkedIn as well. I put them out
(30:03):
there and happy to have a conversation. If you've got any questions and
if you need any help just let me know. I'm here to help. Bill, thanks
so much for coming back on the Delighted Customers podcast. It's been great
to be with you again and really appreciate you having me on.
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