November 28, 2023 • 40 mins
The capabilities of artificial intelligence are increasing at a breakneck speed. Companies must leverage generative AI to transform everyday customer and employee experiences. In this episode of Smart Talks with IBM, Jacob Goldstein sits down with Susan Emerson, Senior Vice President of Product: AI, Analytics & Data at Salesforce & Matthew Candy, Global Managing Partner of Generative AI at IBM Consulting.
They chat about the latest evolution of the two tech innovators’ decades-long relationship, explore the opportunity AI presents for customer
experiences, and break down how better experiences will remove friction for all.
Visit us at: ibm.com/smarttalks
Read more about elevating experiences in the CEO’s Guide to Generative AI from the IBM Institute for Business Value.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:04):
Welcome to Tech Stuff, a production from iHeartRadio. Today, we
are witnessed to one of those rare moments in history,
the rise of an innovative technology with the potential to
radically transform business and society forever. That technology, of course,
(00:24):
is artificial intelligence, and it's the central focus for this
new season of Smart Talks with IBM. Join hosts from
your favorite Pushkin podcasts as they talk with industry experts
and leaders to explore how businesses can integrate AI into
their workflows and help drive real change in this new
era of AI, and of course, host Malcolm Gladwell will
(00:47):
be there to guide you through the season and throw
in his two cents as well. Look out for new
episodes of Smart Talks with IBM every other week on
the iHeartRadio app, Apple Podcasts, wherever you get your podcasts.
Learn more at IBM dot com slash smart Talks.
Speaker 2 (01:06):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glappo. This season,
we're continuing our conversations with new creators visionaries who are
creatively applying technology and business to drive change, but with
a focus on the transformative power of artificial intelligence and
(01:30):
what it means to leverage AI as a game changing
multiplier for your business. Today's episode highlights the power of collaboration.
IBM has long been a supporter of the better Together
mindset and embrace his partnerships. They have been working together
with Salesforce for more than two decades, but have recently
(01:51):
launched a new collaborative effort surrounding generative AI. Pushkin's very
own Jacob Goldstein sat down with Matt Candy and sus Emerson.
Matt is the global managing Partner of Generative AI at
IBM Consulting, helping clients and partners around the world embrace
this new era of technology, and Susan is a senior
(02:14):
vice president for Salesforce dedicated to AI, analytics and data.
They discussed the historic collaboration between the two tech giants,
explored the opportunity AI presents for customer service, and walk
through how businesses can use generative AI to interface with clients. Okay,
(02:35):
let's get to the conversation.
Speaker 3 (02:39):
Thank you guys for coming this morning. So I'm interested
in how you both came to generative AI, or maybe
it sort of came to you in the way it
sort of came to all of us, But how did
you arrive at working on generative AI.
Speaker 4 (02:52):
As part of my remitted Salesforce. Over the years, I've
brought a lot of analytics and data and machine learning
products to life under the Einstein brand at Salesforce. So
as we pivoted Salesforce into taking advantage of the generative
AI moment, it was natural that I became part of
the advanced team leveraging generative AI, and it's become interesting.
(03:19):
But what I see as I speak with customers the
moment that everyone is facing in terms of how they
incorporate genitive AI into their businesses, their workforces, and their
technical stacks. It's actually opening up a lot of doors
to other utility of analytics, data and AI. So it's
(03:40):
been this big pull through in terms of incorporating not
just generative AI, but a larger conversation around how we
become all better using data in our day jobs.
Speaker 3 (03:54):
So that's a great frame for sort of what's going
on at Salesforce with generative AI. Tell us a little
bit about you know, how that fits with the way
IBM is approaching with space.
Speaker 5 (04:05):
Yeah, so I guess through three sides to that question.
And so there's the technology side of it. So IBM
has a technology organization, and so you know, we are
building and have been over many years decades. In fact,
IBM has been working in this space a generative AI
stack that allows organizations to adopt generative AI technology aimed
(04:30):
at enterprise and business use within their organizations. So then
within the consulting business, you know, we have one hundred
and sixty thousand people who work every day with clients
across every industry, regulated industries, government organizations, and so this,
you know, is a really important technology that those companies
(04:50):
are going to be using to drive the next level
of transformation in their enterprises processes and the types of
experiences they build for their customers. And so you know,
we work extensively with partners technology such as Salesforce, AWS, Microsoft,
as well as our own technology. And then finally, I
guess the third angle is the work that we've got
(05:12):
to do to reinvent the business of consulting. And so
if I think about you know, consulting in systems integration,
you know, ultimately we are knowledge workers, right, and so
from an industry perspective, I think, you know, our industry
is same as many others is it's going to go
undergo a level of disruption caused by this technology, but
(05:33):
therefore that will also create a huge opportunity for us
as well.
Speaker 6 (05:37):
So those three aspects, Jacob, great.
Speaker 3 (05:39):
So, so that's the point of view sort of from
your companies in your work. I'm curious to talk for
a moment about AI from the point of view of
consumers and employees kind of out in the world today.
So just to start with consumers, when I'm just out
as a person as a consumer in the world, how
am I AI today?
Speaker 6 (06:02):
I'll give you a great little use case.
Speaker 5 (06:03):
Actually, I was on holiday three weeks ago in Tenerif
in Spain, and I was trying to find somewhere to
park the car with the.
Speaker 6 (06:11):
Family for dinner that evening.
Speaker 5 (06:14):
And I found this area next to this kind of
shopping center and there was this sign there and I
couldn't quite work out if it was saying I could
park there or not, And so I took a photo
of the sign and I uploaded it to an AI
tool and I said, what does this mean? And it
basically explained to me what the sign was saying and
(06:35):
basically told me that I shouldn't be parking there, and
so I drove on and I found some somewhere else
to park. But you know, that allowed me in under
sixty seconds to probably avoid one hundred euro fine by
parking the car there. So just a simple example, but
I think the ability that these tools have to take
(06:56):
friction out of our daily lives, you know, and to
be able to make just things that we do in
our everyday life simple and more frictionless. You know. That's
how I look at how mat the consumer is going
to benefit from some of this type of technology.
Speaker 4 (07:11):
And from my perspective, it's also a travel story. I
spend a lot of time on the road for work,
but recently had to send my sister and her family
to a destination they had never been to for a wedding.
And it was really quick and easy to use some
generitive tools to come up with a whole plan for
(07:33):
them because they love to hike and to be outdoors
and to hike in areas that aren't overly crowded with people,
and so Jenai very quickly gave me an itinerary of
a bunch of terrific hikes for them for a destination.
So things like that.
Speaker 3 (07:48):
Great, And then what about the effect of AI and
of automation more generally on employees on the workforce.
Speaker 4 (07:58):
Well, there's so many dimensions to take that from. Generative
AI really can up level a workforce in all sorts
of ways by providing these consistent ways to engage with technology,
with these natural language experiences. So I think it changes
everything from it finds us content, it generates us content,
It makes it easier to work with our systems of
(08:20):
engagement and operation, and for many organizations it can be
a lifting factor in terms of bringing a more consistent
workforce experience because these tools can just be ever present
in our systems of work.
Speaker 5 (08:36):
I mean, I'll give you a little example here in IBM,
we have something called our Skajar and so that's our
conversational AI interface that we use to interact with HR
services and ninety four percent of every employee interaction now
happens without human intervention through that interface.
Speaker 6 (08:56):
But you would never know that.
Speaker 5 (08:58):
And so if I think about, you know, our HR processes,
You know, we have this amazing conversational based AI that
we use for all of our HR interactions, and we
surface that through SLACK and so Slack becomes the front
door for how we access a lot of these different
enterprise processes and capabilities and how we surface AI. In fact,
(09:19):
I'm taking a flight shortly back to the UK and
our our skar Bos is reminding me that it's raining
in the UK and I should take an umbrella.
Speaker 4 (09:27):
Isn't it always like raining in England?
Speaker 5 (09:32):
Yeah, I don't think there's any AI needed for that.
I think that's just a hard coded If England, then
take umbrella.
Speaker 4 (09:38):
That's right, that's just a rule.
Speaker 2 (09:39):
That's just a.
Speaker 5 (09:40):
Rule, right, and you're able to converse and yeah, I
need to book holiday, I need to move somebody between managers.
I need to figure out the policy on this. And
the AI basically navigates across the different systems to be
able to help get that information, to summarize it back,
to be able to carry out the transactions that I
need carried out, and it just just removes all of
(10:01):
that complexity and makes it easier to get things done.
Speaker 3 (10:06):
When you are working with companies to implement generative AI. Now,
what do you find tends to be their primary focus?
Speaker 4 (10:18):
I mean I speak with a lot of customers each week,
and for the last several months, most organizations have just
been reorienting themselves in terms of where are we in
this moment, what is this technology capable of? What are
the risks and governance and frameworks that I need to
establish in order to engage and talk to everyone. Talk
(10:41):
to my vendors, talk to my cloud providers, talk to
my consultants, talk to academics, and generally get your sea
legs under them. And the sort of the unstructured hand
on keyboards fiddling with technology seems to be moving towards
let's get some points on the board, turn this stuff
on and go. So that's what I've been seeing in
(11:03):
terms of, you know, the work within the salesforce ecosystem. Matt,
you've got a larger aperture as well. What are you seeing?
Speaker 6 (11:11):
Yeah, so I definitely agree.
Speaker 5 (11:14):
I think, you know, there's been lots of getting sea
legs experimentation, just trying to build knowledge, being able to
try and build almost you know, internal organizational point of
view and reference framework. I've seen lots of what I
would have referred to as random acts of AI.
Speaker 6 (11:34):
In terms of in terms of experimentation.
Speaker 5 (11:36):
But I think I think people now looking into twenty
twenty four and this is all about now adoption and scaling.
What's become really clear is organizations have started to realize
this is going to be a very multi model world
that they're going to live in. There is no one
AI that is the answer for their organization, and so
they're going to have lots of different generative AI models
(11:58):
and technologies that they're going to sit in the organization
servicing different use cases, different domain areas, different products and services,
and so therefore having to figure out how they're going
to navigate and manage this kind of open world that
they're going to be sitting in and the decisions that
they're going to have to make around that. I think
the second thing that I've seen that people are now
(12:21):
becoming very clear that this needs to be what I
would refer to as use case lead and outcome focused,
and so really needing to start with thinking about the
business outcome and the problem that you know we're trying
to solve, and therefore, how do I use generative AI
as part of the mechanism to solve that problem? And
(12:42):
I think you know what Susan and the Salesforce team
do is an amazing example of that. You know, they've
got this incredible platform and engine that allows companies to
transform their sales and service processes and to be able
to put data in the hands of users, to be
able to make better decisions, et cetera. And so now
by weaving generative AI into that platform, we're going to
be able to make those processes workflows even more efficient. Right,
(13:05):
So it's generative AI plus all of these other amazing
things that are there, but it will be led through
business outcome, and it will be led through use case
and the business problem or workflow that we're trying to improve.
And then I think the third thing is shifting from
this experimentation to scale. You know, I think everybody's really
early in this journey, but what's become clear is that
(13:26):
you know, everybody now need realizes and is starting to
lay down these these ground rules, the guardrails, the frameworks
to allow them to scale this across the organization. So,
you know, I think we're in for an exciting, exciting
time in twenty twenty four.
Speaker 3 (13:45):
So now that we're getting to this moment, what are
the key things companies have to figure out about scaling
generative AI?
Speaker 4 (13:54):
I would put that in kind of two categories and
following on what Matt was saying in terms of use,
case defined and outcome lead one hundred percent on that
in terms of starting with a hypothesis of value, while
at the same time people are getting closer to the
technology to know what their bounds are. But the biggest
you know, set of conversations is in the enterprise area
(14:19):
in terms of embarking and using with generative AI, how
to do it in ways that is safe for use
of data that is safe around not just the larger
topic of generative AI and hallucinations, which which are fun
to talk about in the media.
Speaker 3 (14:36):
But it's a fun word, right. If it was called
something other than hallucinations, people wouldn't talk about it as much.
Speaker 4 (14:42):
It was just mistakes, Yeah, that's right, just things that
aren't factually true. We've been doing a lot of work
at Salesforce around using you know, dynamic and structured grounding
the data so we can give very strong and non
naive prompt instructions to lllms to get return on that. So,
just to summarize, top of mind for organizations using you know,
(15:04):
large language models is using their data in ways that
are safe, trusted, not exposed, and reducing the opportunity for
hallucinations and maximizing relevant content.
Speaker 5 (15:17):
Great.
Speaker 3 (15:17):
So, so Matt Susan was talking about, you know, both
what organizations are concerned with as they scale generative AI
and how Salesforce is working to sort of address those concerns.
What are you seeing at IBM.
Speaker 5 (15:30):
Here, So I think certainly from a scaling of generative
AI perspective, you know this topic of governance, you know,
and how organizations are going to have to govern all
of these models that sit withinside, how they manage kind
of bias fairness, model drift, you know, if you think
about the data that's gone into a model and the
(15:53):
output it gives to start with, not because the model changes,
but because the context of the world moves on. And
so being able to kind of manage this model drift
is going to be a really important thing. I think
data really matters, and so quality access security around data
within the enterprise is going to be critical to scaling
generative AI. And the other one I think that's going
(16:13):
to be really important, and I think many organizations haven't
even got there yet in their thinking is around the
ESG implications. So carbon you know, the use of this
technology does not come without a cost of carbon.
Speaker 3 (16:25):
Carbon meaning it's very energy intensive.
Speaker 5 (16:28):
Correct, Yeah, the training of the models and so thinking
about carbon disclosures and thinking about where I'm infusing it
into my business and how much I'm using it and
what the carbon cost of that is. As I think
about the you know, you know, my own organizational responsibilities
to reduce carbon I think, you know, there's all of
(16:49):
these things that I think are going to become important
factors as people are thinking about the scaling implications of
this technology.
Speaker 2 (16:56):
AI is already making new experiences possible, but we must
be in mind in how we integrate this new technology
as we continue scaling generative AI. Matt touched on some
crucial aspects from an IBM perspective. Governance, bias, fairness, and
security are all key considerations when organizations aim to expand
their use of generative AI. The environmental aspect is especially important,
(17:21):
and it's refreshing to hear leading thinkers like Matt and
Susan highlight these issues. As this technology continues to evolve,
these factors are becoming increasingly important for organizations to address.
The Historic collaboration between IBM and Salesforce is helping to
remedy issues companies face when scaling AI.
Speaker 3 (17:45):
So IBM and Salesforce recently announced a new collaborative project
around generative AI. Tell me more about that.
Speaker 5 (17:54):
We've been partners for over two decades now IBM and Salesforce,
and so within our consulting business, you know, we work
with Salesforce technology to help our clients implement that technology
to transform their businesses. We've got a huge practice, over
twelve thousand people with certifications around Salesforce platforms, and so
(18:14):
you know, as Susan and her team and the broader
team in Salesforce are infusing more capability into the platform
around generative AI, then our mission is really simple. It's
to help clients who are using the Salesforce platform adopt
those capabilities to help.
Speaker 6 (18:29):
Them get more benefit within their organization.
Speaker 5 (18:31):
You know, we're also a significant user of Salesforce technology
within IBM. We're one of Salesforce's largest customers globally, and
so you know, as we continue to transform our own
sales and service processes within IBM, then you know, our
use of the generative AI capabilities that they're infusing into
sales cloud, service, cloud slack, et cetera will be something
(18:52):
that will become really important to us driving productivity within
the company. And then the other thing that I would
say is, you know, as I think about the work
that we do with clients, you know, as they're implementing
and on their generative AI journeys, you know, they're going
to utilize and leverage the salesforce capabilities within the platform
and their generative AI technologies. But then you start thinking
(19:13):
about processes and workflows that run beyond the walls of CRM,
right that run into supply chain and into the finance
area of the organization. And so there is work that
we're doing with clients where we're using ibms. What's the
next platform to be able to help get access to
to generate insights from data sources that sit in all
(19:33):
of these kind of back office areas of the enterprise,
and to be able to get that data across the
salesforce into these customer interaction points and into the employees
who are servicing those customers using salesforces AI and generative
AI technologies.
Speaker 6 (19:48):
So there's a.
Speaker 5 (19:49):
Kind of one plus one equals three kind of you know,
better together, you know, and being able to bring our
technologies together in service of these clients. Problems as you
think about these processes that run across their enterprise. So, yeah,
it's so huge hutunity and what we're doing together in
the market to help clients.
Speaker 4 (20:08):
Yeah, and just building it on that. It is a
huge moment for organizations and for technology companies like Salesforce,
and we couldn't be happier to have partnerships like we
have with IBM. Like the range of thought leadership that
is appropriate at the moment is everything from what is
that hypothesis of value and what are those use cases?
(20:30):
And what is the order of operation in terms of
approaching it just in terms of focus, but then things
that would help organizations assess their AI readiness and then
their approach like you know, we talked earlier about frameworks
and guardrails. You know, what are use cases that we're
comfortable with given the state of the technology that face
(20:50):
employees or face customers. So creating these much larger roadmaps
in terms of how to approach this over a series
of initiatives, it can fundamentally change the way we engage
with technology and what that means for the you know,
training and change management and use cases that fundamentally shift
(21:13):
how you engage with systems like salesforces. There's just a
massive opportunity for us together.
Speaker 3 (21:19):
So you're talking in sort of general terms, I'm interested in,
you know, thinking in particular about the way generitive AI
can essentially lead to better business outcomes, right Like, what
does that look like? How do you measure it? You know,
there's a certain bottom line question there, right like, how
does AI make businesses work better? And in what ways?
Speaker 4 (21:40):
You know, as consumers of products and services, we all
love and respect great service, you know, in terms of
getting timely, quick answers, resolving issues quickly, all those those
types of things. And from the perspective of using generative
and predictive capabilities for agents who are interacting with customers,
there is just a whole ton of opportunity to take
(22:03):
friction out of the process in terms of finding answers,
resolving issues, in terms of using these generative capabilities that
will bring you know, answers and content to the fingertips
more easily to the human agents that are working with customers. Now,
taking that to the next step for organizations when they're
ready to move into more customer facing automation, that's yet
(22:26):
another channel. As a consumer, we'll all enjoy with the
brands and the products and the services that we want
in terms of fast answers and resolutions to customers. And
as we all know, great customer experience yields return business.
Now on the sales side, you know, maybe a different example,
and these are areas where I think the capability of
(22:46):
predictive and generative go very well together in terms of
focusing on business outcomes. And a classic example would be,
you know, predictions that help us understand customer health. You know,
is this customer engaged, is this customer at risk? Predictions
that help us understand next best product or next best conversation.
(23:07):
These all help focus sales team's time on a customer
or a territory, and so that deep focus puts all
the wood behind an arrow, so to speak, in terms
of where we should be engaging. And those types of
driven sales organizations that have these capabilities just lead to
(23:29):
better performance and outcomes and customer experience too. Now, let's
also layer in generitive capabilities where we're using the generative
capabilities to assist and augment a sales team, where we're
using the power de generitive for everything like generating personalized
and relevant customer interaction content, for example, leveraging our customer
(23:51):
data like engagement history, product purchases, service history to create
an email or a campaign. And this scale a lout
of has just never been possible before. And you know,
maybe even taking this one step further re genitive, where
we take all the administrative friction out of the day
job and doing things for sales teams like summarizing their
calls or creating a meeting plan for them, and you know,
(24:15):
very broadly speaking, using generative AI to change the interaction
mode with systems like Salesforce from clicks and training where
people have to focus on the process to more conversational
user experiences which are much more engaging and easier to use.
So all of this together is just incredible and transformational
(24:36):
and makes all businesses and people work better.
Speaker 3 (24:40):
So I just want to spend one more moment on
the partnership between IBM and Salesforce and genitive AI. And
there's this phrase that's interesting to me. It's ecosystem partnership
that I think is relevant here. So what is an
ecosystem partnership and why is it you know, helpful in
(25:00):
scalable AI solutions.
Speaker 5 (25:02):
This idea of being open I think is probably one
of the most important premises for US as technology companies,
for US as consultancies and system integrators, and for our
clients to think about that the sources of value that
can be created through taking an open approach is hugely important.
(25:23):
So if I think about for US, ecosystem means making
sure that we have all of the different partnerships that
we need with technology providers, with service providers that we
can bring to our clients the right set of capabilities
to solve the problem that they've got and not thinking
(25:43):
that just you know, what we have in house, or
what we have with just one other partner that we
work with, you know, is the right thing. And so
you know, I think every problem that our clients have
is solved through a range of technologies that come together
in service of creating that business outcome.
Speaker 3 (26:01):
I want to touch briefly on ethics and governance. Something
like eighty percent of CEOs see explainability, ethics, bias, trust
as major concerns on the road to AI adoption, and
so I'm curious how business leaders navigate these things, and
(26:21):
in particular, how Salesforce and IBM are building these concerns
into how they work with customers.
Speaker 4 (26:29):
You know, we've been incorporating predictive machine learning into our
products since mid last decade, and at that time we
started with all of our ethics and governance work at
that time in terms of frameworks for engaging with AI
in ethical and safe ways and have a lot of
guidance for customers in terms of those programs. The machine
(26:52):
learning focus that we've had at Salesforce has always been
deeply focused on explainability. So if we're making you know,
predictive recommendations to explain how we got to that, you know,
whether that's something that a user sees, is they're engaging
with it so they have full trust in terms of
interacting with it, but also for the practitioners who are
(27:15):
building it. So we have this like long standing vibe
and capability with our predictive side of the house and
on the generative side of the house. You know, the
state of the marketplace right now is llms for most
people are are largely black boxes in terms of not
fully interpretable in terms of how they come up with
(27:35):
their content. Now that said, there is a lot that
you can do in terms of audit, in terms of
you know, transparency in terms of what are the prompts
that are being submitted to these llms, what do these
llms provide back in terms of return? And then what
did the human do to change it, use it, or
(27:56):
adjust it. So we've been updating all of our ethics
and government it's frameworks now, I guess I would call
it with safety components as well in terms of how
to work with data in safe ways and with these
trened parents governance models. Yeah.
Speaker 5 (28:10):
So, I mean this is an area that IBM has
been kind of working on for many years. And so
you know, our AI Ethics Board that we have internally
kind of governs and provides frameworks and guidance for everything
that we do in the company. There's a lot of
work that we do to help our clients and organizations
establish their strategies for AI governance as well as their
(28:32):
own internal policies, models, approaches, ethics boards, et cetera. And so,
you know, helping them put in place these ground rules
and guardrails, organizational process changes, et cetera. I think is
a really important part of this scaling discussion that we
were having earlier, as people are going to be kind
(28:52):
of rolling out more of this technology internally, and then
I think there's a lot that organizations are going to
have to do to think about, especially in the generative world,
around all of the different types of models that they're using,
models that they're training and tuning and building, and how
they manage all of those for explainability and bias drift
(29:13):
and actually regulatory requirements, Like if you think about what's
happening around the world, there's different countries, the EUAI Act,
you know, there's lots of different regulatory requirements that are
going to be coming in and so for multinational companies
operating across multiple countries, how they're going to have to
(29:33):
make sure that they're complying with all of not only
their own internal policies, but the requirements of the country
as well as potentially industry regulatory requirements as well.
Speaker 6 (29:47):
And so there's a lot.
Speaker 5 (29:48):
That we are doing and going to be doing in
helping them manage complexity. But IBM has a very firm
view that we believe that this is all about regulating
AI risk, not ail rhythms, and so focusing on precision regulation,
so you know, use the bodies and regulatory bodies that
are out there to provide the control as opposed to
(30:11):
trying to regulate the technology.
Speaker 3 (30:14):
So genitive AI is changing kind of absurdly quickly. Right,
a year and a half ago, we wouldn't have been
having this conversation. We're here today. Everything's happening now. I'm
curious what you both think about about the near term
future of genitive A. Right, if we came back in
a year, or let's say two years from now. If
we came back two years from now to talk about
the work you're doing in genitive AI, what would we
(30:35):
be talking about.
Speaker 4 (30:38):
I use this example sometimes I have three kids, and
I don't think any of them have ever gone into
a bank to deposit a check. Right, They pull out
their mobile phone and they scan the check with the
camera and they're done.
Speaker 3 (30:53):
I'm surprised that they even know what a check is.
Speaker 4 (30:56):
For the record, but yeah, right, well, yeah, sometimes their
parents give them one, like they get direct deposit. But anyway,
like this experience of like, what do you mean I
go into a branch in cash a check. I just
do this with my mobile phone. And I think a
little bit of it that way, in terms of the
systems that we use at work. I can imagine explaining
(31:17):
to my kids like, oh yeah, at Salesforce. You know,
back when someone had their first day on the job,
you know, as a service agent or as a salesperson,
they would have tabs on the screen and they would
be trained where to click, and they'd have documented processes
in manuals and that showed them where to get from
point A to point B. And as the clock turns forward,
(31:40):
they're just interacting with the natural language prompt. But it
just kind of fundamentally changes the way we'll be able
to interact with our systems a record at work.
Speaker 3 (31:51):
It'll be just much more conversational. Instead of clicking through something,
you'll just basically have a conversation.
Speaker 4 (31:57):
Much more conversational.
Speaker 5 (31:58):
Yeah, this is the biggest paradigm shift in how we
interact with technology, I think since the invention of the
graphical user interface, and it's going to enable us to
almost put aside all of that complexity within organizations around
system silos, process silos, flows, because you're just going to
layer this just simple natural language interface over all of
(32:20):
that complexity.
Speaker 6 (32:22):
Yeah, it's going to.
Speaker 5 (32:22):
Amplify, i think the potential of every person on every
team in a way that we've never been able to
see before. And the other thing that I think as
you project forward in a couple of years, and Susan
just picking up on the point that you talked about
about blanking, you know.
Speaker 6 (32:37):
I think there's a wonderful little example.
Speaker 5 (32:40):
Look, if you think back to the seventies and the
eighties when the ATM kind of cash machines were rolling out,
and at that time, it wasn't really a reaction that
was one of awe or appreciation for convenience, but people
were concerned that we were automating away the bank teller jobs.
Speaker 6 (32:56):
Right. But now, when.
Speaker 5 (32:58):
You think about it, what actually had and was this
technology allowed the banks to scale their branch networks, more
branches never before, more bank tellers than ever before. Bank
teller employment and salaries increased, even though we automated them
out of work, because when they weren't having to spend
their time counting cash out for people, they were able
(33:18):
to do more valuable things, right, and new types of
financial products and services and mortgages and so like. If
I think back to that in the seventies and eighties
and then I project to where we are today, we're
just going to unleash this creativity and potential for employees
and enterprises by freeing up the time that they're spending
on things that you know, they can do far more
value added tasks. And so I think we're going to
(33:40):
be amazed I think around what happens and what companies
and people are going to be able to do as
we give them the time and space to be able
to do that great.
Speaker 3 (33:49):
So, just to close, I want to talk about how
both of you use creativity in your own work. Just
to start with you, Matt, I know that you love
to combine create and technology through design. Do you use
generative AI in your own creative process?
Speaker 6 (34:07):
Yeah?
Speaker 5 (34:07):
So I'm a firm believer that this combination of experience
in AI is going to be the thing that makes
a difference. Like these large language models, and this technology
has been around actually for a number of years, and
it's only at the point late twenty twenty two where
open AI wrapped a digital experience around this and put
it in the hands of people that suddenly the transformative
(34:30):
power of this technology was realized. And so I think
the way that we surface these capabilities and put them
in the hands of people to be able to adopt
it in a really frictionless way is the thing that's
going to be hugely important to the adoption and.
Speaker 6 (34:45):
Scaling of this.
Speaker 5 (34:46):
So I think the most important thing for companies to
do is to make people, not technology central to their strategy.
Speaker 3 (34:53):
Just to go more broadly into your works as a
I mean, I know that you have launched sales for
versus AI products into the market, and that you know
a lot of those have been built obviously given Salesforce
business around helping people build stronger customer relationships, right, and
so I'm curious what creativity did you bring to that work.
Speaker 4 (35:14):
Some of the products that I've worked with Salesforce, they're
they're deeply visually focused. And my personal perspective is is
that the world can be really noisy. We're just inundated
with all sorts of demands on our time through so
many channels, right, Like the phone is firing off, you're
getting instant messages, you're getting slack messages, you're getting you know, DMS,
(35:38):
you're getting emails, your phone is ringing. There's processes that
are bearing down on you. And if we can use
really good design to filter out and essentially weed the garden,
because you know, we have this this phrase at Salesforces everything,
if everything's important, nothing's important. So using really good design
to create the user experience in salesforce, that just brings
(36:02):
stuff to life in the most powerful way. So I
always think of it from that perspective, like, if I'm
going to put this on a screen and salesforce, what
did I not put on? Is this the most important thing?
And is this the thing that's going to align everyone
to the larger initiative of the firm. So it's that
kind of design thinking that I use probably every moment
(36:24):
of the day, whether I'm building a demo or talking
to an executive as a company in terms of as
I see a vision for how they might deploy our
products to actual product development.
Speaker 3 (36:35):
Just to kind of bring together these two themes we've
been talking about, on the one hand, the sort of
ecosystem partnerships and on the other hand, creativity. I mean,
can you talk a little bit about how working with
working with partners can foster a different kind of creativity.
Speaker 4 (36:53):
More perspectives are always better than few perspectives.
Speaker 6 (36:56):
I completely agree.
Speaker 5 (36:57):
I think the mole minds, the more perspective, the more experiences.
You know, if I think about some of the best sessions,
best workshops, best work we do with clients. It's when
you've got people not just from one industry, but from
many industries, because actually the adjacencies and the things that
are happening in these other spaces trigger new thoughts and
(37:20):
new ideas. And so, you know, I think the richness
that we get when we partner with Salesforce together around
helping clients transform their front office, their sales service marketing processes.
Speaker 6 (37:32):
We all bring these unique.
Speaker 5 (37:33):
Experiences, and I think that just opens the aperture to
better outcomes and better perspectives for our clients.
Speaker 4 (37:41):
Well, you know, you've been asking these questions about like
the use of tech and AI and creativity are sort
of in the same sentence. And one of the things
that I also think of is in terms of remaining
deeply creative is the actual process of unplugging from all
that stuff. So taking a trail run with no earphones
(38:02):
in your head, for me, is always a really good
way of unleashing and unbridening a lot of you know,
creative spirit. Just that downtime and the unstructured time where
your brain can just run free, actually not assisted by
any kind of device in my head or in my face.
Speaker 3 (38:20):
So I think with that praise of unplugged time. We
should say goodbye and let's unplug it. It's lovely to
talk with you guys. It was really interesting to learn
about your work and the relationship between the company. So
thank you for your time.
Speaker 6 (38:33):
Thank you, Jacob, thank you.
Speaker 2 (38:36):
A huge thanks is due to Jacob, Matt and Susan
for illuminating the possibilities of generative AI. This technology has
great promise for creating new experiences in the future, but
requires the scaling capabilities made possible by partnerships like IBM
and Salesforce. As our conversation with Susan and Matt illustrated,
(38:59):
we're at an exiting phase of adoption. Most companies have
moved beyond experimentation and are now prioritizing scaling. The key
areas of focus for organizations now include managing multiple AI models,
as well as thinking about specific use cases and desired outcomes. However,
this scale is difficult for companies to do on their own.
(39:22):
To unlock the real potential of generative AI in transforming experiences,
they'll require the scaling capabilities made possible by partnerships like
IBM and Salesforce. This conversation showed the promise of teamwork.
When massive companies combine their brain power to push forward technology,
(39:42):
their collaborative efforts have the potential to revolutionize industries. One
quick programming note, we will be taking a little time
off and will be returning in just a few weeks
with a new episode. Smart Talks with IBM is produced
by mattro Joey Fishground, David Jaw and Jacob Goldstein. We're
(40:04):
edited by Lydia Jane Kott. Our engineers are Jason Gambrel,
Sarah Bruguier and Ben Holliday. Theme song by Gramoscope. Special
thanks to Andy Kelly, Kathy Callahan and the eight Bar
and IBM teams, as well as the Pushkin marketing team.
Smart Talks with IBM is a production of Pushkin Industries
(40:25):
and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,
listen on the iHeartRadio app, Apple Podcasts, or wherever you
listen to podcasts. I'm Malcolm Gladwell. This is a paid
advertisement from IBM.
Welcome to Tech Stuff, a production from iHeartRadio. Today, we
are witnessed to one of those rare moments in history,
the rise of an innovative technology with the potential to
radically transform business and society forever. That technology, of course,
(00:24):
is artificial intelligence, and it's the central focus for this
new season of Smart Talks with IBM. Join hosts from
your favorite Pushkin podcasts as they talk with industry experts
and leaders to explore how businesses can integrate AI into
their workflows and help drive real change in this new
era of AI, and of course, host Malcolm Gladwell will
(00:47):
be there to guide you through the season and throw
in his two cents as well. Look out for new
episodes of Smart Talks with IBM every other week on
the iHeartRadio app, Apple Podcasts, wherever you get your podcasts.
Learn more at IBM dot com slash smart Talks.
Speaker 2 (01:06):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glappo. This season,
we're continuing our conversations with new creators visionaries who are
creatively applying technology and business to drive change, but with
a focus on the transformative power of artificial intelligence and
(01:30):
what it means to leverage AI as a game changing
multiplier for your business. Today's episode highlights the power of collaboration.
IBM has long been a supporter of the better Together
mindset and embrace his partnerships. They have been working together
with Salesforce for more than two decades, but have recently
(01:51):
launched a new collaborative effort surrounding generative AI. Pushkin's very
own Jacob Goldstein sat down with Matt Candy and sus Emerson.
Matt is the global managing Partner of Generative AI at
IBM Consulting, helping clients and partners around the world embrace
this new era of technology, and Susan is a senior
(02:14):
vice president for Salesforce dedicated to AI, analytics and data.
They discussed the historic collaboration between the two tech giants,
explored the opportunity AI presents for customer service, and walk
through how businesses can use generative AI to interface with clients. Okay,
(02:35):
let's get to the conversation.
Speaker 3 (02:39):
Thank you guys for coming this morning. So I'm interested
in how you both came to generative AI, or maybe
it sort of came to you in the way it
sort of came to all of us, But how did
you arrive at working on generative AI.
Speaker 4 (02:52):
As part of my remitted Salesforce. Over the years, I've
brought a lot of analytics and data and machine learning
products to life under the Einstein brand at Salesforce. So
as we pivoted Salesforce into taking advantage of the generative
AI moment, it was natural that I became part of
the advanced team leveraging generative AI, and it's become interesting.
(03:19):
But what I see as I speak with customers the
moment that everyone is facing in terms of how they
incorporate genitive AI into their businesses, their workforces, and their
technical stacks. It's actually opening up a lot of doors
to other utility of analytics, data and AI. So it's
(03:40):
been this big pull through in terms of incorporating not
just generative AI, but a larger conversation around how we
become all better using data in our day jobs.
Speaker 3 (03:54):
So that's a great frame for sort of what's going
on at Salesforce with generative AI. Tell us a little
bit about you know, how that fits with the way
IBM is approaching with space.
Speaker 5 (04:05):
Yeah, so I guess through three sides to that question.
And so there's the technology side of it. So IBM
has a technology organization, and so you know, we are
building and have been over many years decades. In fact,
IBM has been working in this space a generative AI
stack that allows organizations to adopt generative AI technology aimed
(04:30):
at enterprise and business use within their organizations. So then
within the consulting business, you know, we have one hundred
and sixty thousand people who work every day with clients
across every industry, regulated industries, government organizations, and so this,
you know, is a really important technology that those companies
(04:50):
are going to be using to drive the next level
of transformation in their enterprises processes and the types of
experiences they build for their customers. And so you know,
we work extensively with partners technology such as Salesforce, AWS, Microsoft,
as well as our own technology. And then finally, I
guess the third angle is the work that we've got
(05:12):
to do to reinvent the business of consulting. And so
if I think about you know, consulting in systems integration,
you know, ultimately we are knowledge workers, right, and so
from an industry perspective, I think, you know, our industry
is same as many others is it's going to go
undergo a level of disruption caused by this technology, but
(05:33):
therefore that will also create a huge opportunity for us
as well.
Speaker 6 (05:37):
So those three aspects, Jacob, great.
Speaker 3 (05:39):
So, so that's the point of view sort of from
your companies in your work. I'm curious to talk for
a moment about AI from the point of view of
consumers and employees kind of out in the world today.
So just to start with consumers, when I'm just out
as a person as a consumer in the world, how
am I AI today?
Speaker 6 (06:02):
I'll give you a great little use case.
Speaker 5 (06:03):
Actually, I was on holiday three weeks ago in Tenerif
in Spain, and I was trying to find somewhere to
park the car with the.
Speaker 6 (06:11):
Family for dinner that evening.
Speaker 5 (06:14):
And I found this area next to this kind of
shopping center and there was this sign there and I
couldn't quite work out if it was saying I could
park there or not, And so I took a photo
of the sign and I uploaded it to an AI
tool and I said, what does this mean? And it
basically explained to me what the sign was saying and
(06:35):
basically told me that I shouldn't be parking there, and
so I drove on and I found some somewhere else
to park. But you know, that allowed me in under
sixty seconds to probably avoid one hundred euro fine by
parking the car there. So just a simple example, but
I think the ability that these tools have to take
(06:56):
friction out of our daily lives, you know, and to
be able to make just things that we do in
our everyday life simple and more frictionless. You know. That's
how I look at how mat the consumer is going
to benefit from some of this type of technology.
Speaker 4 (07:11):
And from my perspective, it's also a travel story. I
spend a lot of time on the road for work,
but recently had to send my sister and her family
to a destination they had never been to for a wedding.
And it was really quick and easy to use some
generitive tools to come up with a whole plan for
(07:33):
them because they love to hike and to be outdoors
and to hike in areas that aren't overly crowded with people,
and so Jenai very quickly gave me an itinerary of
a bunch of terrific hikes for them for a destination.
So things like that.
Speaker 3 (07:48):
Great, And then what about the effect of AI and
of automation more generally on employees on the workforce.
Speaker 4 (07:58):
Well, there's so many dimensions to take that from. Generative
AI really can up level a workforce in all sorts
of ways by providing these consistent ways to engage with technology,
with these natural language experiences. So I think it changes
everything from it finds us content, it generates us content,
It makes it easier to work with our systems of
(08:20):
engagement and operation, and for many organizations it can be
a lifting factor in terms of bringing a more consistent
workforce experience because these tools can just be ever present
in our systems of work.
Speaker 5 (08:36):
I mean, I'll give you a little example here in IBM,
we have something called our Skajar and so that's our
conversational AI interface that we use to interact with HR
services and ninety four percent of every employee interaction now
happens without human intervention through that interface.
Speaker 6 (08:56):
But you would never know that.
Speaker 5 (08:58):
And so if I think about, you know, our HR processes,
You know, we have this amazing conversational based AI that
we use for all of our HR interactions, and we
surface that through SLACK and so Slack becomes the front
door for how we access a lot of these different
enterprise processes and capabilities and how we surface AI. In fact,
(09:19):
I'm taking a flight shortly back to the UK and
our our skar Bos is reminding me that it's raining
in the UK and I should take an umbrella.
Speaker 4 (09:27):
Isn't it always like raining in England?
Speaker 5 (09:32):
Yeah, I don't think there's any AI needed for that.
I think that's just a hard coded If England, then
take umbrella.
Speaker 4 (09:38):
That's right, that's just a rule.
Speaker 2 (09:39):
That's just a.
Speaker 5 (09:40):
Rule, right, and you're able to converse and yeah, I
need to book holiday, I need to move somebody between managers.
I need to figure out the policy on this. And
the AI basically navigates across the different systems to be
able to help get that information, to summarize it back,
to be able to carry out the transactions that I
need carried out, and it just just removes all of
(10:01):
that complexity and makes it easier to get things done.
Speaker 3 (10:06):
When you are working with companies to implement generative AI. Now,
what do you find tends to be their primary focus?
Speaker 4 (10:18):
I mean I speak with a lot of customers each week,
and for the last several months, most organizations have just
been reorienting themselves in terms of where are we in
this moment, what is this technology capable of? What are
the risks and governance and frameworks that I need to
establish in order to engage and talk to everyone. Talk
(10:41):
to my vendors, talk to my cloud providers, talk to
my consultants, talk to academics, and generally get your sea
legs under them. And the sort of the unstructured hand
on keyboards fiddling with technology seems to be moving towards
let's get some points on the board, turn this stuff
on and go. So that's what I've been seeing in
(11:03):
terms of, you know, the work within the salesforce ecosystem. Matt,
you've got a larger aperture as well. What are you seeing?
Speaker 6 (11:11):
Yeah, so I definitely agree.
Speaker 5 (11:14):
I think, you know, there's been lots of getting sea
legs experimentation, just trying to build knowledge, being able to
try and build almost you know, internal organizational point of
view and reference framework. I've seen lots of what I
would have referred to as random acts of AI.
Speaker 6 (11:34):
In terms of in terms of experimentation.
Speaker 5 (11:36):
But I think I think people now looking into twenty
twenty four and this is all about now adoption and scaling.
What's become really clear is organizations have started to realize
this is going to be a very multi model world
that they're going to live in. There is no one
AI that is the answer for their organization, and so
they're going to have lots of different generative AI models
(11:58):
and technologies that they're going to sit in the organization
servicing different use cases, different domain areas, different products and services,
and so therefore having to figure out how they're going
to navigate and manage this kind of open world that
they're going to be sitting in and the decisions that
they're going to have to make around that. I think
the second thing that I've seen that people are now
(12:21):
becoming very clear that this needs to be what I
would refer to as use case lead and outcome focused,
and so really needing to start with thinking about the
business outcome and the problem that you know we're trying
to solve, and therefore, how do I use generative AI
as part of the mechanism to solve that problem? And
(12:42):
I think you know what Susan and the Salesforce team
do is an amazing example of that. You know, they've
got this incredible platform and engine that allows companies to
transform their sales and service processes and to be able
to put data in the hands of users, to be
able to make better decisions, et cetera. And so now
by weaving generative AI into that platform, we're going to
be able to make those processes workflows even more efficient. Right,
(13:05):
So it's generative AI plus all of these other amazing
things that are there, but it will be led through
business outcome, and it will be led through use case
and the business problem or workflow that we're trying to improve.
And then I think the third thing is shifting from
this experimentation to scale. You know, I think everybody's really
early in this journey, but what's become clear is that
(13:26):
you know, everybody now need realizes and is starting to
lay down these these ground rules, the guardrails, the frameworks
to allow them to scale this across the organization. So,
you know, I think we're in for an exciting, exciting
time in twenty twenty four.
Speaker 3 (13:45):
So now that we're getting to this moment, what are
the key things companies have to figure out about scaling
generative AI?
Speaker 4 (13:54):
I would put that in kind of two categories and
following on what Matt was saying in terms of use,
case defined and outcome lead one hundred percent on that
in terms of starting with a hypothesis of value, while
at the same time people are getting closer to the
technology to know what their bounds are. But the biggest
you know, set of conversations is in the enterprise area
(14:19):
in terms of embarking and using with generative AI, how
to do it in ways that is safe for use
of data that is safe around not just the larger
topic of generative AI and hallucinations, which which are fun
to talk about in the media.
Speaker 3 (14:36):
But it's a fun word, right. If it was called
something other than hallucinations, people wouldn't talk about it as much.
Speaker 4 (14:42):
It was just mistakes, Yeah, that's right, just things that
aren't factually true. We've been doing a lot of work
at Salesforce around using you know, dynamic and structured grounding
the data so we can give very strong and non
naive prompt instructions to lllms to get return on that. So,
just to summarize, top of mind for organizations using you know,
(15:04):
large language models is using their data in ways that
are safe, trusted, not exposed, and reducing the opportunity for
hallucinations and maximizing relevant content.
Speaker 5 (15:17):
Great.
Speaker 3 (15:17):
So, so Matt Susan was talking about, you know, both
what organizations are concerned with as they scale generative AI
and how Salesforce is working to sort of address those concerns.
What are you seeing at IBM.
Speaker 5 (15:30):
Here, So I think certainly from a scaling of generative
AI perspective, you know this topic of governance, you know,
and how organizations are going to have to govern all
of these models that sit withinside, how they manage kind
of bias fairness, model drift, you know, if you think
about the data that's gone into a model and the
(15:53):
output it gives to start with, not because the model changes,
but because the context of the world moves on. And
so being able to kind of manage this model drift
is going to be a really important thing. I think
data really matters, and so quality access security around data
within the enterprise is going to be critical to scaling
generative AI. And the other one I think that's going
(16:13):
to be really important, and I think many organizations haven't
even got there yet in their thinking is around the
ESG implications. So carbon you know, the use of this
technology does not come without a cost of carbon.
Speaker 3 (16:25):
Carbon meaning it's very energy intensive.
Speaker 5 (16:28):
Correct, Yeah, the training of the models and so thinking
about carbon disclosures and thinking about where I'm infusing it
into my business and how much I'm using it and
what the carbon cost of that is. As I think
about the you know, you know, my own organizational responsibilities
to reduce carbon I think, you know, there's all of
(16:49):
these things that I think are going to become important
factors as people are thinking about the scaling implications of
this technology.
Speaker 2 (16:56):
AI is already making new experiences possible, but we must
be in mind in how we integrate this new technology
as we continue scaling generative AI. Matt touched on some
crucial aspects from an IBM perspective. Governance, bias, fairness, and
security are all key considerations when organizations aim to expand
their use of generative AI. The environmental aspect is especially important,
(17:21):
and it's refreshing to hear leading thinkers like Matt and
Susan highlight these issues. As this technology continues to evolve,
these factors are becoming increasingly important for organizations to address.
The Historic collaboration between IBM and Salesforce is helping to
remedy issues companies face when scaling AI.
Speaker 3 (17:45):
So IBM and Salesforce recently announced a new collaborative project
around generative AI. Tell me more about that.
Speaker 5 (17:54):
We've been partners for over two decades now IBM and Salesforce,
and so within our consulting business, you know, we work
with Salesforce technology to help our clients implement that technology
to transform their businesses. We've got a huge practice, over
twelve thousand people with certifications around Salesforce platforms, and so
(18:14):
you know, as Susan and her team and the broader
team in Salesforce are infusing more capability into the platform
around generative AI, then our mission is really simple. It's
to help clients who are using the Salesforce platform adopt
those capabilities to help.
Speaker 6 (18:29):
Them get more benefit within their organization.
Speaker 5 (18:31):
You know, we're also a significant user of Salesforce technology
within IBM. We're one of Salesforce's largest customers globally, and
so you know, as we continue to transform our own
sales and service processes within IBM, then you know, our
use of the generative AI capabilities that they're infusing into
sales cloud, service, cloud slack, et cetera will be something
(18:52):
that will become really important to us driving productivity within
the company. And then the other thing that I would
say is, you know, as I think about the work
that we do with clients, you know, as they're implementing
and on their generative AI journeys, you know, they're going
to utilize and leverage the salesforce capabilities within the platform
and their generative AI technologies. But then you start thinking
(19:13):
about processes and workflows that run beyond the walls of CRM,
right that run into supply chain and into the finance
area of the organization. And so there is work that
we're doing with clients where we're using ibms. What's the
next platform to be able to help get access to
to generate insights from data sources that sit in all
(19:33):
of these kind of back office areas of the enterprise,
and to be able to get that data across the
salesforce into these customer interaction points and into the employees
who are servicing those customers using salesforces AI and generative
AI technologies.
Speaker 6 (19:48):
So there's a.
Speaker 5 (19:49):
Kind of one plus one equals three kind of you know,
better together, you know, and being able to bring our
technologies together in service of these clients. Problems as you
think about these processes that run across their enterprise. So, yeah,
it's so huge hutunity and what we're doing together in
the market to help clients.
Speaker 4 (20:08):
Yeah, and just building it on that. It is a
huge moment for organizations and for technology companies like Salesforce,
and we couldn't be happier to have partnerships like we
have with IBM. Like the range of thought leadership that
is appropriate at the moment is everything from what is
that hypothesis of value and what are those use cases?
(20:30):
And what is the order of operation in terms of
approaching it just in terms of focus, but then things
that would help organizations assess their AI readiness and then
their approach like you know, we talked earlier about frameworks
and guardrails. You know, what are use cases that we're
comfortable with given the state of the technology that face
(20:50):
employees or face customers. So creating these much larger roadmaps
in terms of how to approach this over a series
of initiatives, it can fundamentally change the way we engage
with technology and what that means for the you know,
training and change management and use cases that fundamentally shift
(21:13):
how you engage with systems like salesforces. There's just a
massive opportunity for us together.
Speaker 3 (21:19):
So you're talking in sort of general terms, I'm interested in,
you know, thinking in particular about the way generitive AI
can essentially lead to better business outcomes, right Like, what
does that look like? How do you measure it? You know,
there's a certain bottom line question there, right like, how
does AI make businesses work better? And in what ways?
Speaker 4 (21:40):
You know, as consumers of products and services, we all
love and respect great service, you know, in terms of
getting timely, quick answers, resolving issues quickly, all those those
types of things. And from the perspective of using generative
and predictive capabilities for agents who are interacting with customers,
there is just a whole ton of opportunity to take
(22:03):
friction out of the process in terms of finding answers,
resolving issues, in terms of using these generative capabilities that
will bring you know, answers and content to the fingertips
more easily to the human agents that are working with customers. Now,
taking that to the next step for organizations when they're
ready to move into more customer facing automation, that's yet
(22:26):
another channel. As a consumer, we'll all enjoy with the
brands and the products and the services that we want
in terms of fast answers and resolutions to customers. And
as we all know, great customer experience yields return business.
Now on the sales side, you know, maybe a different example,
and these are areas where I think the capability of
(22:46):
predictive and generative go very well together in terms of
focusing on business outcomes. And a classic example would be,
you know, predictions that help us understand customer health. You know,
is this customer engaged, is this customer at risk? Predictions
that help us understand next best product or next best conversation.
(23:07):
These all help focus sales team's time on a customer
or a territory, and so that deep focus puts all
the wood behind an arrow, so to speak, in terms
of where we should be engaging. And those types of
driven sales organizations that have these capabilities just lead to
(23:29):
better performance and outcomes and customer experience too. Now, let's
also layer in generitive capabilities where we're using the generative
capabilities to assist and augment a sales team, where we're
using the power de generitive for everything like generating personalized
and relevant customer interaction content, for example, leveraging our customer
(23:51):
data like engagement history, product purchases, service history to create
an email or a campaign. And this scale a lout
of has just never been possible before. And you know,
maybe even taking this one step further re genitive, where
we take all the administrative friction out of the day
job and doing things for sales teams like summarizing their
calls or creating a meeting plan for them, and you know,
(24:15):
very broadly speaking, using generative AI to change the interaction
mode with systems like Salesforce from clicks and training where
people have to focus on the process to more conversational
user experiences which are much more engaging and easier to use.
So all of this together is just incredible and transformational
(24:36):
and makes all businesses and people work better.
Speaker 3 (24:40):
So I just want to spend one more moment on
the partnership between IBM and Salesforce and genitive AI. And
there's this phrase that's interesting to me. It's ecosystem partnership
that I think is relevant here. So what is an
ecosystem partnership and why is it you know, helpful in
(25:00):
scalable AI solutions.
Speaker 5 (25:02):
This idea of being open I think is probably one
of the most important premises for US as technology companies,
for US as consultancies and system integrators, and for our
clients to think about that the sources of value that
can be created through taking an open approach is hugely important.
(25:23):
So if I think about for US, ecosystem means making
sure that we have all of the different partnerships that
we need with technology providers, with service providers that we
can bring to our clients the right set of capabilities
to solve the problem that they've got and not thinking
(25:43):
that just you know, what we have in house, or
what we have with just one other partner that we
work with, you know, is the right thing. And so
you know, I think every problem that our clients have
is solved through a range of technologies that come together
in service of creating that business outcome.
Speaker 3 (26:01):
I want to touch briefly on ethics and governance. Something
like eighty percent of CEOs see explainability, ethics, bias, trust
as major concerns on the road to AI adoption, and
so I'm curious how business leaders navigate these things, and
(26:21):
in particular, how Salesforce and IBM are building these concerns
into how they work with customers.
Speaker 4 (26:29):
You know, we've been incorporating predictive machine learning into our
products since mid last decade, and at that time we
started with all of our ethics and governance work at
that time in terms of frameworks for engaging with AI
in ethical and safe ways and have a lot of
guidance for customers in terms of those programs. The machine
(26:52):
learning focus that we've had at Salesforce has always been
deeply focused on explainability. So if we're making you know,
predictive recommendations to explain how we got to that, you know,
whether that's something that a user sees, is they're engaging
with it so they have full trust in terms of
interacting with it, but also for the practitioners who are
(27:15):
building it. So we have this like long standing vibe
and capability with our predictive side of the house and
on the generative side of the house. You know, the
state of the marketplace right now is llms for most
people are are largely black boxes in terms of not
fully interpretable in terms of how they come up with
(27:35):
their content. Now that said, there is a lot that
you can do in terms of audit, in terms of
you know, transparency in terms of what are the prompts
that are being submitted to these llms, what do these
llms provide back in terms of return? And then what
did the human do to change it, use it, or
(27:56):
adjust it. So we've been updating all of our ethics
and government it's frameworks now, I guess I would call
it with safety components as well in terms of how
to work with data in safe ways and with these
trened parents governance models. Yeah.
Speaker 5 (28:10):
So, I mean this is an area that IBM has
been kind of working on for many years. And so
you know, our AI Ethics Board that we have internally
kind of governs and provides frameworks and guidance for everything
that we do in the company. There's a lot of
work that we do to help our clients and organizations
establish their strategies for AI governance as well as their
(28:32):
own internal policies, models, approaches, ethics boards, et cetera. And so,
you know, helping them put in place these ground rules
and guardrails, organizational process changes, et cetera. I think is
a really important part of this scaling discussion that we
were having earlier, as people are going to be kind
(28:52):
of rolling out more of this technology internally, and then
I think there's a lot that organizations are going to
have to do to think about, especially in the generative world,
around all of the different types of models that they're using,
models that they're training and tuning and building, and how
they manage all of those for explainability and bias drift
(29:13):
and actually regulatory requirements, Like if you think about what's
happening around the world, there's different countries, the EUAI Act,
you know, there's lots of different regulatory requirements that are
going to be coming in and so for multinational companies
operating across multiple countries, how they're going to have to
(29:33):
make sure that they're complying with all of not only
their own internal policies, but the requirements of the country
as well as potentially industry regulatory requirements as well.
Speaker 6 (29:47):
And so there's a lot.
Speaker 5 (29:48):
That we are doing and going to be doing in
helping them manage complexity. But IBM has a very firm
view that we believe that this is all about regulating
AI risk, not ail rhythms, and so focusing on precision regulation,
so you know, use the bodies and regulatory bodies that
are out there to provide the control as opposed to
(30:11):
trying to regulate the technology.
Speaker 3 (30:14):
So genitive AI is changing kind of absurdly quickly. Right,
a year and a half ago, we wouldn't have been
having this conversation. We're here today. Everything's happening now. I'm
curious what you both think about about the near term
future of genitive A. Right, if we came back in
a year, or let's say two years from now. If
we came back two years from now to talk about
the work you're doing in genitive AI, what would we
(30:35):
be talking about.
Speaker 4 (30:38):
I use this example sometimes I have three kids, and
I don't think any of them have ever gone into
a bank to deposit a check. Right, They pull out
their mobile phone and they scan the check with the
camera and they're done.
Speaker 3 (30:53):
I'm surprised that they even know what a check is.
Speaker 4 (30:56):
For the record, but yeah, right, well, yeah, sometimes their
parents give them one, like they get direct deposit. But anyway,
like this experience of like, what do you mean I
go into a branch in cash a check. I just
do this with my mobile phone. And I think a
little bit of it that way, in terms of the
systems that we use at work. I can imagine explaining
(31:17):
to my kids like, oh yeah, at Salesforce. You know,
back when someone had their first day on the job,
you know, as a service agent or as a salesperson,
they would have tabs on the screen and they would
be trained where to click, and they'd have documented processes
in manuals and that showed them where to get from
point A to point B. And as the clock turns forward,
(31:40):
they're just interacting with the natural language prompt. But it
just kind of fundamentally changes the way we'll be able
to interact with our systems a record at work.
Speaker 3 (31:51):
It'll be just much more conversational. Instead of clicking through something,
you'll just basically have a conversation.
Speaker 4 (31:57):
Much more conversational.
Speaker 5 (31:58):
Yeah, this is the biggest paradigm shift in how we
interact with technology, I think since the invention of the
graphical user interface, and it's going to enable us to
almost put aside all of that complexity within organizations around
system silos, process silos, flows, because you're just going to
layer this just simple natural language interface over all of
(32:20):
that complexity.
Speaker 6 (32:22):
Yeah, it's going to.
Speaker 5 (32:22):
Amplify, i think the potential of every person on every
team in a way that we've never been able to
see before. And the other thing that I think as
you project forward in a couple of years, and Susan
just picking up on the point that you talked about
about blanking, you know.
Speaker 6 (32:37):
I think there's a wonderful little example.
Speaker 5 (32:40):
Look, if you think back to the seventies and the
eighties when the ATM kind of cash machines were rolling out,
and at that time, it wasn't really a reaction that
was one of awe or appreciation for convenience, but people
were concerned that we were automating away the bank teller jobs.
Speaker 6 (32:56):
Right. But now, when.
Speaker 5 (32:58):
You think about it, what actually had and was this
technology allowed the banks to scale their branch networks, more
branches never before, more bank tellers than ever before. Bank
teller employment and salaries increased, even though we automated them
out of work, because when they weren't having to spend
their time counting cash out for people, they were able
(33:18):
to do more valuable things, right, and new types of
financial products and services and mortgages and so like. If
I think back to that in the seventies and eighties
and then I project to where we are today, we're
just going to unleash this creativity and potential for employees
and enterprises by freeing up the time that they're spending
on things that you know, they can do far more
value added tasks. And so I think we're going to
(33:40):
be amazed I think around what happens and what companies
and people are going to be able to do as
we give them the time and space to be able
to do that great.
Speaker 3 (33:49):
So, just to close, I want to talk about how
both of you use creativity in your own work. Just
to start with you, Matt, I know that you love
to combine create and technology through design. Do you use
generative AI in your own creative process?
Speaker 6 (34:07):
Yeah?
Speaker 5 (34:07):
So I'm a firm believer that this combination of experience
in AI is going to be the thing that makes
a difference. Like these large language models, and this technology
has been around actually for a number of years, and
it's only at the point late twenty twenty two where
open AI wrapped a digital experience around this and put
it in the hands of people that suddenly the transformative
(34:30):
power of this technology was realized. And so I think
the way that we surface these capabilities and put them
in the hands of people to be able to adopt
it in a really frictionless way is the thing that's
going to be hugely important to the adoption and.
Speaker 6 (34:45):
Scaling of this.
Speaker 5 (34:46):
So I think the most important thing for companies to
do is to make people, not technology central to their strategy.
Speaker 3 (34:53):
Just to go more broadly into your works as a
I mean, I know that you have launched sales for
versus AI products into the market, and that you know
a lot of those have been built obviously given Salesforce
business around helping people build stronger customer relationships, right, and
so I'm curious what creativity did you bring to that work.
Speaker 4 (35:14):
Some of the products that I've worked with Salesforce, they're
they're deeply visually focused. And my personal perspective is is
that the world can be really noisy. We're just inundated
with all sorts of demands on our time through so
many channels, right, Like the phone is firing off, you're
getting instant messages, you're getting slack messages, you're getting you know, DMS,
(35:38):
you're getting emails, your phone is ringing. There's processes that
are bearing down on you. And if we can use
really good design to filter out and essentially weed the garden,
because you know, we have this this phrase at Salesforces everything,
if everything's important, nothing's important. So using really good design
to create the user experience in salesforce, that just brings
(36:02):
stuff to life in the most powerful way. So I
always think of it from that perspective, like, if I'm
going to put this on a screen and salesforce, what
did I not put on? Is this the most important thing?
And is this the thing that's going to align everyone
to the larger initiative of the firm. So it's that
kind of design thinking that I use probably every moment
(36:24):
of the day, whether I'm building a demo or talking
to an executive as a company in terms of as
I see a vision for how they might deploy our
products to actual product development.
Speaker 3 (36:35):
Just to kind of bring together these two themes we've
been talking about, on the one hand, the sort of
ecosystem partnerships and on the other hand, creativity. I mean,
can you talk a little bit about how working with
working with partners can foster a different kind of creativity.
Speaker 4 (36:53):
More perspectives are always better than few perspectives.
Speaker 6 (36:56):
I completely agree.
Speaker 5 (36:57):
I think the mole minds, the more perspective, the more experiences.
You know, if I think about some of the best sessions,
best workshops, best work we do with clients. It's when
you've got people not just from one industry, but from
many industries, because actually the adjacencies and the things that
are happening in these other spaces trigger new thoughts and
(37:20):
new ideas. And so, you know, I think the richness
that we get when we partner with Salesforce together around
helping clients transform their front office, their sales service marketing processes.
Speaker 6 (37:32):
We all bring these unique.
Speaker 5 (37:33):
Experiences, and I think that just opens the aperture to
better outcomes and better perspectives for our clients.
Speaker 4 (37:41):
Well, you know, you've been asking these questions about like
the use of tech and AI and creativity are sort
of in the same sentence. And one of the things
that I also think of is in terms of remaining
deeply creative is the actual process of unplugging from all
that stuff. So taking a trail run with no earphones
(38:02):
in your head, for me, is always a really good
way of unleashing and unbridening a lot of you know,
creative spirit. Just that downtime and the unstructured time where
your brain can just run free, actually not assisted by
any kind of device in my head or in my face.
Speaker 3 (38:20):
So I think with that praise of unplugged time. We
should say goodbye and let's unplug it. It's lovely to
talk with you guys. It was really interesting to learn
about your work and the relationship between the company. So
thank you for your time.
Speaker 6 (38:33):
Thank you, Jacob, thank you.
Speaker 2 (38:36):
A huge thanks is due to Jacob, Matt and Susan
for illuminating the possibilities of generative AI. This technology has
great promise for creating new experiences in the future, but
requires the scaling capabilities made possible by partnerships like IBM
and Salesforce. As our conversation with Susan and Matt illustrated,
(38:59):
we're at an exiting phase of adoption. Most companies have
moved beyond experimentation and are now prioritizing scaling. The key
areas of focus for organizations now include managing multiple AI models,
as well as thinking about specific use cases and desired outcomes. However,
this scale is difficult for companies to do on their own.
(39:22):
To unlock the real potential of generative AI in transforming experiences,
they'll require the scaling capabilities made possible by partnerships like
IBM and Salesforce. This conversation showed the promise of teamwork.
When massive companies combine their brain power to push forward technology,
(39:42):
their collaborative efforts have the potential to revolutionize industries. One
quick programming note, we will be taking a little time
off and will be returning in just a few weeks
with a new episode. Smart Talks with IBM is produced
by mattro Joey Fishground, David Jaw and Jacob Goldstein. We're
(40:04):
edited by Lydia Jane Kott. Our engineers are Jason Gambrel,
Sarah Bruguier and Ben Holliday. Theme song by Gramoscope. Special
thanks to Andy Kelly, Kathy Callahan and the eight Bar
and IBM teams, as well as the Pushkin marketing team.
Smart Talks with IBM is a production of Pushkin Industries
(40:25):
and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,
listen on the iHeartRadio app, Apple Podcasts, or wherever you
listen to podcasts. I'm Malcolm Gladwell. This is a paid
advertisement from IBM.
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