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