And I think
there's going to be a lot of good chatter for this first speaker who comes from there. That comes from an organization that I
really don't think needs any introduction. But I'm American and I like to hear myself
talk, so I'm going to do it anyway. McKinsey
and Company is a global management consulting company that's leading the way
in digital transformation. John has participated in numerous webinars
and research initiatives on how AI is going to be impacting business
and moving forward. So I'm really looking forward to this
talk. So please
welcome John from McKinsey to the stage. Thanks, Clay. What a number two or three on this page,
which I'll flip to. But before I kick off a little bit
about me, Clay already did a great introduction
to McKinsey and a little bit about myself. And then thank you also Jerry and the rest
of the Booth team for inviting me. My training and background is actually
electrical engineering and machine learning applications there,
so you might be wondering why did he go into consulting
to just give advice to people? It turns out actually McKinsey,
half of our more than half of our hours today is spent on machine
learning, data, science and engineering. So while we are traditionally a management
consulting firm, we're actually more
participating in digital these days. Now, myself, I spend my time in customer operations
software and excuse me, I've been in it for about five years,
specifically in conversational and AI and customer operations
more broadly for about a decade. So as much as I love
engineering and technology, what I also like about the work
that I do at McKinsey is it forces me to take a step back from what's the technology itself to what's the market and the demand
and the challenge that it's solving. And that's why I wanted to start here. The numbers
you see come from a survey that we run every two years
and from last year's survey, despite the decades that we've heard of
digital self-serve, is going to go up. There's going to be the data voice,
what you see there on the left hand side, contact rates keep going up year over year
and demand keeps going up. It's. Meanwhile, more recently,
we've seen increased employee attrition and lack of integration
across the technology stack. So while your demand is going
up, the ability to solve the that demand is actually going down and becoming harder
than it was previously. So what are we doing about it? In that same survey, over
half of the people that we polled and these are executives at some of our
clients, over 200 around the world said that their budget
is going to go towards some form of AI or digital application
in the next 2 to 3 years, and you see it across the entire customer
funnel. How are we going to enhance self-serve? How are we going to improve access
to different channels for our customers? How are we going to enable our agents
to solve the problems of our customers better? It's not going to be a single place
in this stack. It's going to be across the stack. And lastly on the bottom there,
how are we actually going to make our management
more effective at solving those problems? I think one of the biggest problems
I see with our clients today is that there's this operational gridlock
for so long. The way that we've been improving customer
operations has been incredibly manual. How are we randomly sampling calls to then go into a manual review of the process
in the way that we're reviewing it to the manually redesign and implemented
in the knowledge base RPA Business Process Management, Next Best action, all these
different technologies out there. So actually one of
the things that we're most excited about are some of the tools that are coming
to market in the applications within generative AI that are helping
reduce some of that gridlock. And we'll talk a little bit
more about that in a moment. But before I get there,
I think one of the most important things in these conversations
that we have about generative AI isn't to educate,
but it's to level sense because there's a lot happening in the last six months
to what Jerry was saying. Chat CBT is the lowest common denominator that the masses around the world
understand. But in fact this technology is well-proven
and is going to exist for much longer. The birth of it is pointed to widely
across the industry with the start of a Google
pay for called attention is all you Need. That came out in 2017. Google then led the way actually with the first large language
model called Bert, then open. I came along now to achieve it. If we fast forward a little bit,
it was not an AI breakthrough. GPT three was the underlying
AI algorithm for chat. CBT It's then what was it? It was a user interface breakthrough. It was the ability to engage
with a large language model in the way that the general public could understand
and experience and experiment with. So it's really important
as we look backwards to understand this is market proven technology. This isn't something new. What's new is the experience that the mass public
is starting to understand and engage with. But then as we look forward, what you see happening right now
with Jeopardy for Llama Alpaca and all these other names Palm
that I really don't know how they come up with, but they do. Performance is increasing
at an exponential rate right now and these large language models
that will not go on forever. So what's happening next? What are some of these highlighted for you see geographical expansion with buy
these are anybody you see open sourcing with Stanford's
alpaca and met Islamists and then you see more consolidation around some of the major players
like Amazon and Google. What does that point to in a market that's changing rapidly
and it's tough to keep track with looking ahead
when performance improvements slow down, we're going to be looking at cost
efficiencies, open sourcing, how do we make this more available
and accessible to everyone? And we're
also going to be looking at distribution to the point on accessibility. How are some of the major players
creating platforms around these tools and distributed out to the masses
and make it available to enterprises? Because right now it's mostly consumer
products in the way that we see chat, CBT So then as we look at what is all the excitement coming from on the right hand side,
I think what you have here is the way that a lot of people understand
generative AI, It's in the title, it's the ability to create across
a number of different modalities large language models, text images with Dolly too, and stability
coding with GitHub, copilot and others you don't see here
audio, video and the like. Okay. But natural language, understanding and processing existed
before generative AI. So why is generative AI interesting? And I think it's this here on the left
hand side, the ability to understand and right at the center of it is that transformer model
which came out of the Google paper. If you think about the first time,
you might have interactive the chatbot. You probably put a question in that
you could answer with a Google keyword search just to kind of test out,
see what happens. Okay. Came back with a good answer that I get to use Google to do. That would have taken me 30 seconds. What if I give you a question
that I would ask Wikipedia and spend maybe 5 to 10 minutes reading Wikipedia,
researching to understand? Okay, well, actually came back with a pretty decent answer. What if I ask you a question
that would take me 2 to 3 hours of internet research to understand I have
these ingredients in my refrigerator. Help me plan a healthy meal. I'm looking to get in a 30 minute workout
because I'm jetlagged. I'm staying in a hotel with, you know,
basic dumbbells and some treadmills. What's good workout? I could do? Oh, wow, That's an impressive answer. Came back with. So it's that ability to understand
that's really capturing the minds of the public right now. Because while the natural
language technology existed, the level at which it understands
the questions we're asking and how to form a response to
it is where the real value is coming from. But we're here to talk about
customer experience, not technology. I told you, do you like my technology? So as McKinsey, how do we see this coming together
in customer experience? Number one, it's going to touch customers,
agents and management. Management is very important
here to the point I made earlier around that gridlock. Some of the technologies
that we've been deploying more recently in the last 5 to 10 years, let's say most of them are technologies
for management. So what does that lead to? We used to talk about this in waves, but how quickly
we're seeing the space evolve. And then as we think about what it takes
to deploy a use case for test for generative A.I. customer
self-service, I'll use the dirty word, but virtual agents, a copilot for your agents, or even the insights hub
for your management. We think it's actually going across
all of these. First, how do you remove that gridlock of operational debt
and start to work through the backlog? You're looking at your productivity boosts
last agent in management time spent on non value add technologies
and manual tasks. Let's get rid of those first. Now that we relieve some of that pressure,
where can we go from there? Service innovation is maybe one of the
most important aspects that we see here. Air itself is different
than traditional software. Put the same thing in twice. You can get two different things out. It's very hard
to train and train and test that. So what if we took our workforce
and we made them part of that, made them the contributors today? Are I training and testing? Give them that little thumbs
up, thumbs down. Now they're innovating the service,
they're providing that feedback and as this becomes more standard,
what we're seeing is behavioral shifts. You'll see a chart later. But at the end of the day, as people really start to realize that
they have these tools available to them, and their nature
of the nature of their role is changing. We're seeing that they are more engaged. They want to drive customers
and participate in their own work towards something that is more engaging
and they become more affluent and they become promoters
of the product themselves. So then what's the roadmap in the sequence for how we do this? We're seeing some early traction
in the use cases you see up here,
but it's a little bit more than that. While each one of these
has value by themselves independently, the capabilities of generative,
I actually actually run across these. So now we're starting to think a little bit more about,
well, PTA is the lowest common denominator. Like I said earlier, people will jump to I want to do a self-serve,
but kind of like churchy PTA. But then they start to think about it. I've heard
about these hallucination things. Sometimes it just makes up data. Maybe
I don't want to start with customers. Maybe I'll give it to my agents first
and let them try it out. But then they start to think like, gosh,
all the data we have that we use to train it,
it's pretty bad. We don't want to replicate
a wasteful process. We want to empower our agents
with a new innovative process. So how is our actual processes designed? And this is where we get into
the sequencing standpoint, starting with continuously improving your knowledge
base, using both the knowledge base itself as well as other sources like your contact
recordings, your chats, your calls, and tying that to agent performance. You can figure out where is this working? Well, how are my best agents doing this? I'm going to extract that trap knowledge
and I'm going to use content generation to actually start authoring
some of these knowledge base articles. Great. Now that I have that real time data,
especially on voice, is incredibly difficult to integrate
with and respond very rapidly. So I'm going to start to build out use cases
they're taking the life data and then from my agents with that
optimized knowledge I designed for them. Now that I've overcome the real time
data hurdle, I'm going to go back to that customer
self-serve case. But I'm still worried about some of this. How do I know that I'm ready to go build the integrations, invest the capital
that's required because a lot of my systems
are actually quite disconnected towards building that self-serve use case. Well,
I mean, ask my experts, agents themselves, they're going to give that thumbs
up, thumbs down. And by providing that feedback, once
I cross that confidence threshold, that's when I know I'm going to be ready
to deploy to customers and really deliver
that self-serve experience. So as we're looking at the roadmap
and how you sequence your technology deployment, we see it
starting with removing that operating debt and innovating on your processing,
empowering your people to be humans in the loop and provide the feedback
and then moving on to actually deploying it to customers. So what's the end result? We looked at this about two months ago
to figure out how the demands would shift in customer
contact rates. June 14th,
you're going to see a larger article come out from us on the overall
macroeconomic impacts of Generative IV. It's part of this research
that was going to have a lot more. So where we see it today, about 25% of contacts are on made in the future. We see 50%. Some of the numbers
you might have heard, it's actually a lot less than that, surprisingly. Why is that? First of all, the contacts,
you don't want to automate fraud and safety, fraud, health and safety
very high touch, sensitive things where you want that human involvement
and that empathy of a bot. Just the simple knowledge of knowing
you're working with a bot could have a very adverse effect. Next limitations. I made the point open I in Chad CBT
as a consumer product in its current form. I'm not sure about sending my PI, my high off to the cloud. We're in Europe. Different countries
have different regulations around data and how it's stored. That's very hard to control
if you're just sending it off to an API. These are known issues, but they're still
working on it for the enterprise. How are you going to deploy on prem? We see companies emerging around that,
what will be the data security. But then lastly, as I mentioned,
demand shifts earlier. You see it's relatively flat. The first three years and then the manual contact rate goes
down, automated goes up. So one interesting thing, automation can
continue to absorb the growth of contact. So we expect to continue. That's actually really good news. A lot of the clients we talk to you,
they're at 95% utilization rates. It's very hard to keep people. We can absorb that. Then it goes down. What's actually happening there though, that we're not seeing here? The length of time people are spending on
the phone is going up. We're in conversation. And that's actually good
news because it means with your workforce today,
you can still continue to maintain them. You can shift them towards
these more complex conversations that actually create better engagement
with your customers and use technology to remove
all of the waste that's happening today. And we're pretty excited about that. So this is my last page as you think about how to get started
and the different ways you will deploy this technology,
we are at a technology conference. We see four ways emerging. On the left hand side, you have
your software as a service application. It is an AI application for a product
much like this. Then you see AI's sorry APIs
like Eyes and others that are coming out and then you see up going to the far
right, getting into managed services, then actually standing up your own
private cloud, depending on where people are in terms of the technology
availability, how you're going to spend your budget, the amount of data scientists
you have on your staff. It helps guide what you might be selecting. But one of the things
we fundamentally believe is this will not be a monolithic technology
stack. Every company will have a blend
of all of these approaches. It'll depend on the use case. And then as you think about the use case, you're going to be thinking about
what's the value I get from the left hand side versus the right hand side versus
the effort it's going to take to put in. As I think about that roadmap
that I showed earlier, can I be stacking
capabilities on themselves As I stack those capabilities, can I get increasingly better value
out of using those capabilities and redeploying them in different use
cases and contact reasons? So what are we doing and what are we
encouraging our clients to go to? Number one, get started. Now, if you wait a year saying, well, the technology is changing fast,
I'm a little nervous, you will 100% be behind your competitors
a year from now. Okay. But John, you just said the technology is changing fast and we don't know where
it might all land in a year. And he just told me to get started. Despite all of that. How do I do that? The second thing we're doing
is we're saying try a number of use cases
and pilots to get going. It's not usually the McKinsey way. For those of you who might have interacted with us,
we like a good strategy diagnostic. We make this nice little chart. It's two axes, impact and feasibility. We go through this whole four week
ish, 4 to 6 weeks process, look at all the different use cases. Say these are the first two
you should go after. Now let's talk
about planning and launching because this technology is evolving so rapidly
and it's so easy and accessible to get to a pilot within
about that same time period, 4 to 6 weeks, we're saying,
you know what, Take a different approach. Think about this
like an investment portfolio. Pick five use cases. Most the conversations I'm in, if I say
pick five, use cases to every executive in that conversation, 80% of them agree
on at least three of those. It's not that hard. You don't need a big McKinsey diagnostic
to tell you that you know your business,
you know where the challenges have been. You see the potential of this technology. Most people agree from there, how do we de-risk this in a way, knowing
the technology is changing while still placing strategic bets
on building out our technology stack? Okay. We're
going to launch three of those pilots. Some of them are going to be strategic
bets. We're on the right hand side. Some of them are going to be quick wins,
obvious value. We're on the left hand side. In two months
time, we're going to check in. We're going to say,
how is our investment going? We've set aside more budget
than we need for two months, and we're not going to pick every single
pilot that we've run to continue on. And that's okay. This is in a learning phase. It's a volatile growth market. You can't get that 100% sorry American analogy. I'm trying to think of a soccer team
even calling it soccer. You can't get a hat trick every time. Not every shot on goal is going
in the net. There we go. That's a good analogy. Not every
shot on goal is going in the net. And that's okay
because you're learning about behaviors and the way that your organization
is going to respond. So in that two month check ins,
take the capital you've set aside, put it towards 1 to 2 of the use cases
that have that strategic importance and are going well. And think about how you scale, because I think the thing that
I want to leave you with is that scaling the piloting is incredibly easy
with this technology. Scaling is incredibly hard because of some of those limitations
I mentioned. This technology is not a silver bullet. In fact,
it's only about 20% of the solution. There's going to be things of how do we integrate
with other technologies in our stack. Some of them are in a platform
that's cloud deployed. Some of them might be in mainframes. Those all exist today. This isn't greenfield generative. A.I. does not solve all of it. But more importantly, there's going to be customer
experience design, employee experience, design,
change management, adoption, all these other things
that sit around the technology and are required to make it successful. One of my clients, where we did this
at scale in North America, they ran departments, one for people answering questions,
What's my least price? Do you allow grills? They're spending
at least 2 hours per person a day responding these sorts of things. Okay, well, we automated that great promise. One for people. How do you get 20% of value out of that? So rather than looking at it
as a local staffing or sorry, an independent site staffing model,
one of four people for site, we said what if we did it locally? What if we made it 5 to 10 apartment
buildings? These people are working across
and got value out that way. Okay, that'll work. But what do we need to do
as a result of it? Well, the way people are working right now, they've been having this conversation back
and forth with this individual and building a relationship but they're going to need to shift to a way
where they can just log in, understand where they are in the day,
pick up the first task on the list, pick up the conversations
if they know the person. That wasn't
just the guy that did that for them, that was redesigning their salesforce
interface, that was redesigning
the work that they were doing and training them on how to do it
and still feel like they were providing the same value because people
want to provide value in their jobs. So as you think about
how do I get started? How do I start to pilot this
knowing it's moving quickly, but then where do I continue to go deep
and really do what it takes to scale? Think about it in steps, not in the near long term,
but how you just put your bets, how you then test it later,
how you measure the performance, and then how you continue
to go deep on those certain use cases. So with that, I'm at the end of my time the so much everyone
thanks again to the Boost team will be around more during the day
if you'd love to talk and than that I look forward to having the next
I think Clay I'm going to hand off to you. Thank you.
