IBM and Salesforce Unite on Generative AI
Key Points
- Malcolm Gladwell introduces the “Smart Talks with IBM” podcast season, which spotlights visionary “New Creators” using artificial intelligence as a transformative, game‑changing multiplier for business.
- IBM’s long‑standing “better together” partnership with Salesforce has expanded into a new collaborative effort focused on generative AI, highlighting how both giants are combining forces to accelerate AI adoption.
- Susan Emerson explains Salesforce’s evolution from the Einstein analytics platform to a dedicated generative‑AI team, stressing that the technology is unlocking broader opportunities for data‐driven decision‑making across enterprises.
- Matt Candy describes IBM’s multi‑decade investment in a generative‑AI stack built for the enterprise, positioning the company to help clients and partners integrate these capabilities into existing technical ecosystems.
- The discussion emphasizes practical applications of generative AI—particularly in customer service—and outlines how businesses can leverage the combined IBM‑Salesforce expertise to create more intelligent, data‑centric interactions with their clients.
Sections
- AI Collaboration Between IBM & Salesforce - The episode introduces IBM’s long‑standing partnership with Salesforce, highlighting their new joint generative‑AI initiative and how it can transform customer service and business operations.
- AI Simplifies Everyday Decisions - Matt describes using an AI image‑recognition tool on holiday to decipher a parking sign, avoiding a fine and illustrating how AI reduces friction for consumers.
- From Rules to Generative AI - The speakers contrast simple rule‑based automation (e.g., an “umbrella” rule for England) with generative AI that can navigate multiple systems to fulfill requests, and note that companies are currently focused on grasping the technology’s capabilities, risks, and governance frameworks before broader adoption.
- Scaling Generative AI in Enterprises - The speaker explains how embedding generative AI into a sales‑service platform can boost workflow efficiency, emphasizing outcome‑driven use cases and the shift from early experimentation to organization‑wide scaling with proper guardrails and frameworks.
- Scaling Generative AI: ESG and Governance - The speakers highlight that while data quality, access, and security are essential for expanding generative AI, organizations must also embed ESG concerns—particularly carbon emissions—and robust governance on bias, fairness, and security into their scaling strategies.
- AI Integration Across Enterprise Platforms - The speakers describe how IBM watsonx and Salesforce’s AI combine to surface back‑office data (supply chain, finance) within customer‑facing workflows, illustrating a partnership‑driven “one plus one equals three” opportunity and outlining the strategic steps for defining value, use cases, and implementation order.
- Generative AI Transforming Sales Operations - The speaker outlines how generative AI can automate personalized customer outreach, reduce administrative friction for sales teams, and shift from click‑based to conversational interfaces, emphasizing the importance of an IBM‑Salesforce ecosystem partnership for scaling these solutions.
- Balancing Explainability and LLM Transparency - Salesforce stresses explainable AI and robust governance while tackling the opaque nature of generative LLMs through prompt auditing, result tracking, and updated ethical safety frameworks.
- Predicting Generative AI's Near‑Future Shift - Jacob and Susan speculate that in the coming years generative AI will overhaul existing workflows—from banking to salesforce onboarding—replacing manual processes and manuals with intuitive, AI‑driven experiences.
- Creativity, AI, and Human‑Centered Strategy - Matt and Susan discuss how they blend generative AI with creative thinking in their work, emphasizing people‑first adoption and innovative product development at Salesforce.
- Diverse Collaboration Fuels Innovation - They discuss how cross‑industry teamwork and intentional unplugged, unstructured time unleash fresh ideas, enhancing creativity and delivering better outcomes for clients.
Full Transcript
# IBM and Salesforce Unite on Generative AI **Source:** [https://www.youtube.com/watch?v=MaIFF5h2_UM](https://www.youtube.com/watch?v=MaIFF5h2_UM) **Duration:** 00:39:45 ## Summary - Malcolm Gladwell introduces the “Smart Talks with IBM” podcast season, which spotlights visionary “New Creators” using artificial intelligence as a transformative, game‑changing multiplier for business. - IBM’s long‑standing “better together” partnership with Salesforce has expanded into a new collaborative effort focused on generative AI, highlighting how both giants are combining forces to accelerate AI adoption. - Susan Emerson explains Salesforce’s evolution from the Einstein analytics platform to a dedicated generative‑AI team, stressing that the technology is unlocking broader opportunities for data‐driven decision‑making across enterprises. - Matt Candy describes IBM’s multi‑decade investment in a generative‑AI stack built for the enterprise, positioning the company to help clients and partners integrate these capabilities into existing technical ecosystems. - The discussion emphasizes practical applications of generative AI—particularly in customer service—and outlines how businesses can leverage the combined IBM‑Salesforce expertise to create more intelligent, data‑centric interactions with their clients. ## Sections - [00:00:00](https://www.youtube.com/watch?v=MaIFF5h2_UM&t=0s) **AI Collaboration Between IBM & Salesforce** - The episode introduces IBM’s long‑standing partnership with Salesforce, highlighting their new joint generative‑AI initiative and how it can transform customer service and business operations. - [00:04:36](https://www.youtube.com/watch?v=MaIFF5h2_UM&t=276s) **AI Simplifies Everyday Decisions** - Matt describes using an AI image‑recognition tool on holiday to decipher a parking sign, avoiding a fine and illustrating how AI reduces friction for consumers. - [00:08:23](https://www.youtube.com/watch?v=MaIFF5h2_UM&t=503s) **From Rules to Generative AI** - The speakers contrast simple rule‑based automation (e.g., an “umbrella” rule for England) with generative AI that can navigate multiple systems to fulfill requests, and note that companies are currently focused on grasping the technology’s capabilities, risks, and governance frameworks before broader adoption. - [00:11:43](https://www.youtube.com/watch?v=MaIFF5h2_UM&t=703s) **Scaling Generative AI in Enterprises** - The speaker explains how embedding generative AI into a sales‑service platform can boost workflow efficiency, emphasizing outcome‑driven use cases and the shift from early experimentation to organization‑wide scaling with proper guardrails and frameworks. - [00:15:01](https://www.youtube.com/watch?v=MaIFF5h2_UM&t=901s) **Scaling Generative AI: ESG and Governance** - The speakers highlight that while data quality, access, and security are essential for expanding generative AI, organizations must also embed ESG concerns—particularly carbon emissions—and robust governance on bias, fairness, and security into their scaling strategies. - [00:18:08](https://www.youtube.com/watch?v=MaIFF5h2_UM&t=1088s) **AI Integration Across Enterprise Platforms** - The speakers describe how IBM watsonx and Salesforce’s AI combine to surface back‑office data (supply chain, finance) within customer‑facing workflows, illustrating a partnership‑driven “one plus one equals three” opportunity and outlining the strategic steps for defining value, use cases, and implementation order. - [00:22:30](https://www.youtube.com/watch?v=MaIFF5h2_UM&t=1350s) **Generative AI Transforming Sales Operations** - The speaker outlines how generative AI can automate personalized customer outreach, reduce administrative friction for sales teams, and shift from click‑based to conversational interfaces, emphasizing the importance of an IBM‑Salesforce ecosystem partnership for scaling these solutions. - [00:25:49](https://www.youtube.com/watch?v=MaIFF5h2_UM&t=1549s) **Balancing Explainability and LLM Transparency** - Salesforce stresses explainable AI and robust governance while tackling the opaque nature of generative LLMs through prompt auditing, result tracking, and updated ethical safety frameworks. - [00:29:10](https://www.youtube.com/watch?v=MaIFF5h2_UM&t=1750s) **Predicting Generative AI's Near‑Future Shift** - Jacob and Susan speculate that in the coming years generative AI will overhaul existing workflows—from banking to salesforce onboarding—replacing manual processes and manuals with intuitive, AI‑driven experiences. - [00:32:45](https://www.youtube.com/watch?v=MaIFF5h2_UM&t=1965s) **Creativity, AI, and Human‑Centered Strategy** - Matt and Susan discuss how they blend generative AI with creative thinking in their work, emphasizing people‑first adoption and innovative product development at Salesforce. - [00:35:52](https://www.youtube.com/watch?v=MaIFF5h2_UM&t=2152s) **Diverse Collaboration Fuels Innovation** - They discuss how cross‑industry teamwork and intentional unplugged, unstructured time unleash fresh ideas, enhancing creativity and delivering better outcomes for clients. ## Full Transcript
Malcolm Gladwell: Hello, hello. Welcome to Smart Talks with IBM, a podcast from Pushkin Industries,
iHeartRadio and IBM. I’m Malcolm Gladwell. This season, we’re continuing our conversations
with New Creators— visionaries who are creatively applying technology in business to drive change,
but with a focus on the transformative power of artificial intelligence and 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 embraces partnerships. They have been
working together with Salesforce for more than two decades, but have recently launched a new
collaborative effort surrounding generative AI. Pushkin’s very own Jacob Goldstein sat down
with Matt Candy and Susan 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 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 walked through how businesses can use
generative AI to interface with clients. Okay, let’s get to the conversation.
Jacob: 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?
Susan: As part of my remit at 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 advance team leveraging generative AI. And it's—it's
become interesting. What I see as I speak with customers—the moment that everyone is facing
in terms of how they incorporate generative AI into their businesses, their workforces and their
technical stacks—it's actually opening up a lot of doors to the utility of analytics, data and AI.
So it's 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.
Jacob: So that's a great frame for sort of what's going on at Salesforce with generative AI. Matt,
tell us a little bit about, you know, how that fits with the way IBM is approaching the space.
Matt: So I guess—probably three sides to that question. And so there's the technology side to
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, uh, aimed at enterprise and
business use within their organizations. So then within the consulting business,
you know, we have 160,000 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 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—uh, 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 to do to reinvent the business of consulting. And so if I think about,
you know, consulting and 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 gonna, is gonna go—undergo a level of disruption
caused by this technology, but therefore that will also create a huge opportunity
for us as well. So those three aspects, Jacob.
Jacob: Great. 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 experiencing AI today?
Matt: I'll give you a great little use case, actually. I was on holiday, uh, three weeks ago,
in Tenerife in Spain. And, uh, I was trying to find somewhere to park the car with the family
for dinner that evening. And, uh, I found, I found this area, next to this, uh, kind of
shopping center. And there was this sign there. And, uh, 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 basically told me that I shouldn't be parking there. And so I
drove on and I found some—somewhere else to park. But yeah, that, that allowed me, in under sixty
seconds to probably avoid a hundred- euro fine by parking the car there. So—just a simple example,
but I think the ability that these tools have to take 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—um,
you know, that's how I look at, how “Matt the consumer” is going to benefit from
some of this type of technology.
Susan: And from my perspective, it's also a travel story. I spend a lot of time on the road for, for work, but recently had to send
my sister and her family to a destination they'd never been to for a wedding. And, it was really
quick and easy to use some generative tools to come up with a whole plan for them, because they
love to hike and to be outdoors and to hike in areas that aren't overly crowded with, um, people.
And so gen AI very quickly gave me an itinerary of a bunch of terrific hikes for them, uh,
for a destination. So, things like that.
Jacob: Great, and then what about the, the effect of AI and of automation more generally on, on employees, on the workforce?
Susan: 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 engagement and operation.
And for many organizations, uh, it's, uh, it can be a, a lifting factor in
terms of bringing a more consistent workforce experience, because these tools can just be
ever present in our systems of, of work.
Matt: I'll give you a little example. Here in IBM, we have something called Ask HR. And so that's our conversational AI interface
that we use to interact with HR services. And 94 percent of every employee interaction now
happens without human intervention, through that interface. But you would never know that. And so
if I think about, you know, our HR processes, you know, we have this amazing conversational- based,
uh, 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, I'm taking a flight shortly back to the
UK and our Ask HR boss is reminding me that it's raining in the UK and I should take an umbrella.
Susan: Isn’t it always, like, raining in England? Matt: 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.” That's right.
Susan: That’s just a rule. That's just a rule, right?
Matt: Right, and you're able to converse, and, you know, “I need to book a 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 to be carried out.
And it just, just removes all of that complexity and makes it easier to get things done.
Jacob: Uh, when you are working with companies to implement generative AI now,
what do you find tends to be their primary focus? Susan: 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 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,
hands-on-keyboards fiddling which technology seems to be moving towards. “Let's get some points on
the board. Let's turn this stuff on and go.” So that's what I've been seeing in terms of, uh, you
know, the work within the Salesforce ecosystem. Matt, you've got a larger aperture as well.
Uh, what, uh, what are you seeing?
Matt: Yeah, so I, I—I definitely agree. 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 a, uh, you know, internal organizational point
of view and reference framework. You know, I've seen lots of what I would refer to as
“random acts of AI,” you know, in, in, in terms of, uh, in terms of experimentation,
but I think, I think people—now looking into 2024 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 multimodal world that they're going to live in. There is no one AI
that is the answer for their organization. And so there's—they're going to have lots of
different generative-AI models and technologies that are 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 becoming very clear that this needs to be what I would refer to as
“use-case led 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 I think, you know, what, 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, you know, 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 and workflows even more efficient,
right? So it's generative AI plus all of these other amazing things that are there, but it will
be led through business outcomes and it will be led through the 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, you know, everybody now needs—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, I think we're in for an exciting, exciting time in 2024.
Jacob: So now that we're getting to this moment, what are the key things companies have
to figure out about scaling generative AI?
Susan: I would put that in , kind of two categories. And following what—on what Matt was saying in terms of use-case defined and outcome
led—100 percent on that in terms of starting with a hypothesis of value while at the same
time people are getting, uh, you know, closer to the technology, to know what their bounds are.
But the biggest, you know, set of conversations is in the enterprise area in terms of embarking
and using with generative AI—how to do it in ways, that is safe for—uh, 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, but—
Jacob: It’s a fun word, right? If it was called something other than “hallucinations,” people wouldn't talk about it as much. It was just mistakes.
Susan: 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 not naive prompt
instructions to LLMs to get return on that. So, so just to summarize, uh: top of mind for
organizations using, you know, 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.
Jacob: Great. So, so Matt, Susan was talking about, you know, both what organizations are, 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?
Matt: Yeah, so I think—certainly from a, from a “scaling of generative AI” perspective, this topic of governance, and how organizations
are going to have to govern all of these models that sit, sit with—inside, 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 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, uh, around data within the enterprise is going to be critical to
scaling generative AI. And the other one I think that's going 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.
Jacob: Carbon meaning it's very energy intensive.
Matt: 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, my own organizational responsibilities to reduce carbon, I think, you know, there's all of
these things that I think are going to become important factors as people are thinking about
the scaling implications of this technology.
Malcolm Gladwell: AI is already making new experiences possible, but we must be mindful in how we integrate this new technology as we
continue scaling generative AI. Matt touched upon 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,
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.
Jacob: So IBM and Salesforce recently announced a new collaborative project
around generative AI. Tell me more about that.
Matt: We've been partners for over two decades now, IBM and Salesforce. And so within our consulting business, we work with Salesforce
technology to help our clients implement that technology to transform their businesses.
We've got a huge practice—over 12,000 people with certifications—around Salesforce platforms. And
so, you know, with Susan and her team and the broader team in Salesforce,
we're 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 them get more benefit within their organization. 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 our use of the generative-AI capabilities that they're infusing into Sales
Cloud, Service Cloud, Slack, et cetera will be something 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 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 IBM's watsonx platform to be
able to help get access to—to generate insights from data sources that sit in all 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
Salesforce's AI and generative-AI technology. So there's a kind of “one plus one equals three” kind
of, you know, “better together,” and being—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, so, yeah, so, so huge, huge opportunity in what we're doing
together in the market to help clients.
Susan: Yeah, and just building on that, uh, it is a huge moment for, 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?” 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. Uh, you know, “What are use cases that we're comfortable with,
given the state of the technology, that face employees or face customers?” So creating
these much larger roadmaps in terms of how to approach this over a series of initiatives.
The way 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 how you engage
with systems like Salesforce. There's just a massive opportunity for us together.
Jacob: So you're talking in sort of general terms. I'm interested in, you know, thinking in particular about the way generative
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?
Susan: You know, as consumers of products and services, we, 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, of using generative and predictive capabilities for agents who are
interacting with customers, there is just a whole ton of opportunity to take 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, uh, when they're ready to move into more customer-facing automation,
that's yet 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 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 the next best product or
next best conversation—these all help focus a 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 better performance and outcomes and customer experience, too.
Now, let's also layer in generative capabilities, uh, where we're using the generative capabilities
to assist and augment a sales team, where we're using the power of generative for
everything like generating, uh, personalized and relevant customer- interaction content.
For example, leveraging our customer data—like engagement history, product purchases,
service history—to create an email or a campaign. And, uh, the scale of automation has just never
been possible before. And, you know, maybe even taking this one step further with generative,
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, very broadly speaking, using generative AI to change the interaction
mode with systems like Salesforce from clicks and, and training, uh,
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, uh, and makes, uh, all businesses and people work better.
Jacob: I just want to spend one more moment on the partnership between IBM and Salesforce
and generative 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, in creating scalable AI solutions?
Matt: 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 the, the sources of value that can
be created through taking an open approach is hugely important. 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 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, is, 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.
Jacob: I want to touch briefly on ethics and governance. Something like 80 percent of CEOs see explainability, ethics, bias,
trust as major concerns on the road to AI adoption. And so I'm, I'm curious how
business leaders navigate these things, and in particular how Salesforce and IBM are building
these concerns into how they work with customers.
Susan: You know, we've been incorporating predictive machine learning into our, 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-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 as
they're engaging with it, so they have full trust, uh, in terms of interacting with it, but
also for the practitioners who are 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,
uh, in terms of not fully interpretable in terms of how they come up with 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 adjust it?” So we've been updating all of our ethics and,
and governance 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 transparent governance models.
Matt: Yeah. 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, 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 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, uh, changes,
et cetera, I think is a really important part of this scaling discussion that we were having
earlier as, as, as people are going to be kind 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 and, actually, regulatory requirements.
Like if you—if you think about what's, what's happening around the world as different
countries—uh, the EU AI 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 be—have to make sure that they're, 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.
And so there's a lot 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 AI algorithms, and so focusing on, precision regulation. So, you know,
use the, use the bodies and—regulatory bodies that are out there to provide the, the control,
as opposed to trying to regulate the technology.
Jacob: So generative 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 generative AI, 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 generative AI,
what would we be talking about?
Susan: 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.
Jacob: I'm surprised that they even know what a check is, for the record, but yes.
Susan: Right. Well, yeah, uh, 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 and cash a check? I just do this with my mobile
phone.” And I, I think a little bit of, of it that way in terms of the systems that we use at work. I
can imagine explaining to my, my kids like, “Oh, yeah, at Salesforce, you know, back when someone
had their first day on the job, you know, as a, 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 it showed them where to get from point A to point B.”
And as the clock turns forward, they're just interacting with a natural-language prompt. But,
it just kind of fundamentally changes the way we'll be able
to interact with our systems of record at work.
Jacob: It’ll be just much more conversational. Instead of clicking through something, you'll just basically have a conversation.
Susan: Much more conversational.
Matt: Yeah, this is the biggest paradigm shift in how we interact with technology, I think, since 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 that complexity. Yeah. It's going to 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 banking, you know,
I think there's a wonderful little example. If you think back to the ’70s and the ’80s,
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,
right? But now when you think about it, what actually happened was, this technology allowed
the banks to scale their branch networks, more branches than ever before, more bank tellers than
ever before. Bank-teller employment and salaries increased, even though we automated a lot of work,
because when they weren't having to spend their time counting cash out for people,
they were able to do more-valuable things, right? And new types of financial products and services
and mortgages. And so if I think back to that, in the ’70s and ’80s, 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 be amazed, I think,
around what, 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.
Jacob: Great. 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 creativity and technology through design. Do you use
generative AI in your own creative process?
Matt: Yeah, so I, I, I'm a firm believer that this combination of experience and 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 2022, where open AI wrapped a digital experience
around this and put it in the hands of people, that suddenly the transformative
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, is the thing that's going to be, hugely important to the adoption and scaling
of this. So I, I think the most important thing for companies to do is to make people,
not technology, central to their strategy.
Jacob: Just to go more broadly into your work, Susan—I mean, I know that you have, have launched Salesforce's AI products into
the market and that, you know, a lot of those have been built—obviously,
given Salesforce’s business—around helping people build stronger customer relationships.
Right? And so I'm curious: what creativity did you bring to that work?
Susan: Some of the products that I work with at Salesforce—they're, they're deeply visually
focused. And my personal perspective is, is that the world can be really noisy. Uh,
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, 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 Salesforce,
is “Everything—if everything's important, nothing's important.” So using really good design
to create the user experience in Salesforce just brings 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,
and, 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, so it's that
kind of design thinking that I use probably every moment of the day, whether I'm building a demo or
talking to an executive at a company in terms of—as I see a vision for how they might deploy
our products, to actual product development.
Jacob: And then just to kind of bring together these two themes we've been talking about: on the one hand, this 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?
Susan: More perspectives are always better than few perspectives.
Matt: I completely agree. I think the more minds, the
more perspectives, the more experiences, 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 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, we all bring these unique experiences.
And I think that just opens the aperture to better, better
outcomes and better perspectives for our clients.
Susan: Well, you know, you've been asking these questions about, like the use of, of tech and AI and creativity all 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 for all—from all that stuff. So taking a trial run with no
earphones in your head for me is always a really good way of unleashing and unbridling a lot of,
you know, creative spirit. Just that, 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. So—
Jacob: I think, with that praise of unplugged time, we should say goodbye and let's unplug. It was lovely to talk with you guys. It was
really interesting to learn about your work and the relationship between the companies.
So thank you for your time.
Matt: Thank you, Jacob.
Thank you. Malcolm Gladwell: 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, we’re at an exciting 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.
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 brainpower to push forward technology, 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 Matt Romano, Joey Fischground, David Zha, and
Jacob Goldstein. We’re edited by Lidia Jean Kott. Our engineers are Jason Gambrell, Sarah Bruguiere,
and Ben Tolliday. Theme song by Gramoscope. Special thanks to Andy Kelly, Kathy Callaghan,
and the EightBar and IBM teams, as well as the Pushkin marketing team. Smart Talks with IBM is a
production of Pushkin Industries 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.