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