Salesforce & IBM: Revolutionizing Experiences with Generative AI
39:45 Share

Hello, hello. Welcome to Smart Talks with IBM, a podcast from Pushkin Industries, iHeartRadio, and IBM. I'm Malcolm Glaupo. 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 discuss the historic collaboration between the two tech giants, explore the opportunity AI presents for customer service, and walk through how businesses can use generative AI to interface with clients. Okay, let's get to the conversation. 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? 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 advanced team leveraging generative AI. And it's become interesting, but what I see as I speak with customers is

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 other 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 more

all better using data in our day jobs. 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 how that fits with the way IBM is approaching the space. Yeah, so I guess there are three sides to that question. And so there's the technology side to it. So IBM has a technology organization. And so

We are building and have been over many years, decades in fact, IBM has been working in this space, a generative AI stack that allows organizations to adopt generative AI technology aimed at enterprise and business use within their organizations.

So then within the consulting business, we have 160,000 people who work every day with clients across every industry, regulated industries, government organizations.

And so this 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 we work extensively with partners, technologies such as Salesforce, AWS, Microsoft, as well as our own technology. And then finally, I guess the third angle is

is the work that we've got to do to reinvent the business of consulting. And so if I think about consulting and systems integration, ultimately we are knowledge workers, right? And so from an industry perspective, I think our industry is same as many others is going to 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.

Great. So that's the point of view sort of from your companies and 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? I'll give you a great little use case, actually. I was on holiday three weeks ago in Tenerife in Spain.

And I was trying to find somewhere to park the car with the family for dinner that evening. And I found this area next to this kind of shopping center. And there was this sign there. And I couldn't quite work out if it was saying I could park there or not. And so I took a photo of the sign.

and I uploaded it to an AI tool and I said, what does this mean? And it basically explained to me what the sign was saying and basically told me that I shouldn't be parking there. And so I drove on and I found somewhere else to park. But that allowed me in under 60 seconds to probably avoid a 100 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 is

and to be able to make just things that we do in our everyday life simple and more frictionless. That's how I look at how Matt, the consumer, is going to benefit from some of this type of technology. And from my perspective, it's also a travel story. I spend a lot of time on the road for work, but recently had to send my sister and her family to a destination they had never been to for a wedding.

And, um, 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, for a destination. So things like that. Great. And then

What about the effect of AI and of automation more generally on employees, on the workforce?

Well, there's so many dimensions to take that from. Generative AI really can uplevel 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, it can be a lifting factor in terms of bringing a more consistent workforce experience because these tools can just be ever present in our systems of work. I mean, 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% 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 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.

Isn't it always like raining in England? 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. That's just a rule. That's just a rule. Right. And you're able to converse and, you know, I need to book holiday. I need to move somebody between managers. I need to figure out the policy on this.

And the AI basically navigates across the different systems to be able to help get that information, to summarize it back, to be able to carry out the transactions that I need carried out. And it just removes all of that complexity and makes it easier to get things done. When you are working with companies to implement generative AI now, what do you find tends to be their primary focus?

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 hand-on keyboards fiddling with 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, you know, the work within the Salesforce ecosystem. Matt, you've got a larger aperture as well. What are you seeing? Yeah, so 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 an internal organizational point of view and reference framework. I've seen lots of what I would refer to as random acts of AI in terms of experimentation. But 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 multi-model world that they're going to live in. There is no one AI that is the answer for their organization. And so they're going to have lots of different generative AI models 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 we're trying to solve and

And therefore, how do I use generative AI as part of the mechanism to solve that problem? And I think what Susan and the Salesforce team do is an amazing example of that. You know, they've got this incredible platform and engine that allows companies to transform their sales and service processes and to be able to put data in the hands of users, to be able to make better decisions, etc.,

And so now by weaving generative AI into that platform, we're going to be able to make those processes workflows even more efficient. Right. So it's generative AI plus all of these other amazing things that are there, but it will be led through business outcome and it will be led through use case and the business problem or workflow that we're trying to improve. And then I think the third thing is shifting from this experimentation to scale. I think everybody's really early in this journey.

But what's become clear is that everybody now realizes and is starting to lay down these ground rules, the guardrails, the frameworks to allow them to scale this across the organization. So I think we're in for an exciting time in 2024. So now that we're getting to this moment, what are the key things companies have to figure out about scaling generative AI?

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% on that in terms of starting with a hypothesis of value while at the same time people are getting, 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, right?

in terms of embarking and using regenerative AI, how to do it in ways that is safe for use of data, that is safe around not just the larger topic of generative AI and hallucinations, which are fun to talk about in the media. 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.

That's right. Just things that aren't factually true. We've been doing a lot of work at Salesforce around using dynamic and structured grounding to data so we can give very strong and non-naive prompt instructions to LLMs to get return on that. So just to summarize, top of mind for organizations using large language models is using their data in ways that are safe, trusted, not exposed, and

and reducing the opportunity for hallucinations and maximizing relevant content. - Great, so Matt, Susan was talking about, you know, both what organizations are concerned with as they scale generative AI and how Salesforce is working to sort of address those concerns. What are you seeing at IBM? - Yeah, so I think certainly from a scaling of generative AI perspective, you know, this topic of governance,

and how organizations are going to have to govern all of these models that sit with inside, how they manage 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, a

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.

Carbon, meaning it's very energy intensive. 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 my own organizational responsibilities to reduce carbon, I think 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.

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 on some crucial aspects from an IBM perspective. Governance, bias, fairness, and security are all key considerations when organizations aim to expand their use of generative AI. The environmental aspect is especially important.

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.

So IBM and Salesforce recently announced a new collaborative project around generative AI. Tell me more about that. 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 as Susan and her team and the broader team in Salesforce are infusing more capability into the platform around generative AI, then our mission is really simple. It's to help clients improve.

who are using the Salesforce platform adopt those capabilities to help them get more benefit within their organization. We're also a significant user of Salesforce technology within IBM. We're one of Salesforce's largest customers globally. And so 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, etc.,

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, 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 what's an x platform to be able to help

get access 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 to 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, you know, and being able to bring our technologies together in service of these clients' problems as you think about these processes that run across their enterprise. So, yeah, so huge, huge unity in what we're doing together in the market to help clients.

Yeah.

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

And then their approach, like we talked earlier about frameworks and guardrails. 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 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.

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?

As consumers of products and services, we all love and respect great service in terms of getting timely, quick answers, resolving issues quickly, all those types of things. From the perspective of using generative and predictive capabilities for agents who are interacting with customers, there is just...

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 answers and content to the fingertips more easily to the human agents that are working with customers.

Now, taking that to the next step for organizations when they're ready to move into more customer-facing automation, that's yet 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, maybe a different example is

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.

Is this customer engaged? Is this customer at risk? Predictions that help us understand 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 customers.

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 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 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 the scale of automation has just never been possible before. And, you know, maybe even taking this one step further with JetEditive 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 training where people have to focus on the process to more conversational user experiences, which are much more engaging and easier to use. So all of this together is just incredible and transformational and makes all businesses and people work better.

So I just want to spend one more moment on the partnership between IBM and Salesforce in 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 helpful in creating scalable AI solutions? This idea of being open, right?

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 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

What we have in-house or what we have with just one other partner that we work with is the right thing. And so 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. I want to touch briefly on ethics and governance.

Something like 80% of CEOs see explainability, ethics, bias, trust as major concerns on the road to AI adoption. And so I'm curious how business leaders navigate these things and in particular how Salesforce and IBM are building these concerns into how they work with customers.

You know, we've been incorporating predictive machine learning into our products since mid last decade. And at that time, we started with all of our ethics and governance work at that time in terms of frameworks for engaging with AI in ethical and safe ways and have a lot of guidance for customers in terms of those programs. The machine 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 in terms of interacting with it, but also for the practitioners who are building it. So we have this like longstanding vibe and capability with our predictive side of the house and,

And on the generative side of the house, you know, the state of the marketplace right now is LLMs for most people are largely black boxes 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 governance frameworks. Now, I guess I would call it with safety components as well in terms of how to work with data, um,

in safe ways and with these transparent governance models? 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 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, etc. And so, you know, helping them put in place these ground rules and guardrails, organizational process changes, etc., I think is a really important part of this scaling discussion that we were having earlier as people are going to be kind of rolling out more of this technology internally.

and then i think there's um 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 happening around the world, there's different countries, the EU AI Act, there's lots of different regulatory requirements that are going to be coming in. And so for multinational companies,

operating across multiple countries, how they're going to have to make sure that they're complying with all of not only their own internal policies, but the requirements of the country, as well as potentially industry regulatory requirements as well.

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 bodies and regulatory bodies that are out there to provide the control as opposed to trying to regulate the technology.

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 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? 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. I'm surprised that they even know what a check is for the record, but yes. Well, yeah. Sometimes their parents give

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 think a little bit of it that way in terms of the systems that we use at work. I can imagine explaining to my kids like,

Oh, yeah. At Salesforce, you know, back when someone had their first day on the job, you know, as a service agent or as a salesperson, they would have tabs on the screen and they would be trained where to click and they'd have documented processes in manuals and everything.

that 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.

It'll be just much more conversational. Instead of clicking through something, you'll just basically have a conversation. Much more conversational. Yeah, this is the biggest paradigm shift in how we interact with technology, I think, since the invention of the graphical user interface. And it's going to enable us to almost...

put aside all of that complexity within organizations around system silos, process silos, flows, because you're just going to layer this just simple natural language interface over all of 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 like 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 all 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 the amount of work.

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 happens and what companies and people are going to be able to do as we give them the time and space to be able to do that. Great. 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?

Yeah, so 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 OpenAI 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 the thing that's going to be hugely important to the adoption and scaling of this. So I think the most important thing for companies to do is to make people, not technology, central to their strategy.

Just to go more broadly into your work, Susan, I mean, I know that you have launched Salesforce's AI products into the market and that, you know, a lot of those have been built, obviously, given Salesforce business around helping people build stronger customer relationships, right? And so I'm curious, what creativity did you bring to that work?

Some of the products that I work with at Salesforce, they're deeply visually focused. And my personal perspective is that the world can be really noisy. We're just inundated with all sorts of demands on our time through so many channels, right? Like the phone is firing off. You're getting instant messages. You're getting Slack messages. You're getting 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 phrase at Salesforce is everything, if everything's important, nothing's important. So using really good design to create the user experience in Salesforce that 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 Salesforce, you know,

What did I not put on? Is this the most important thing? And is this the thing that's going to align everyone to the larger initiative of the firm? So it's that kind of design thinking that I use probably every moment of the day, whether I'm building a demo or talking to an executive as a company in terms of as I see a vision for how they might deploy our products to actual product development.

Just to kind of bring together these two themes we've been talking about, on the one hand, the sort of ecosystem partnerships, and on the other hand, creativity. I mean, can you talk a little bit about how working with partners can foster a different kind of creativity? More perspectives are always better than few perspectives. I completely agree. I think the more minds, the more perspectives. Yeah.

the more experiences um you know if i think about some of the best sessions best workshops best work we do with clients it's when you've got people not just from one industry but from many industries because actually the adjacencies and the things that are happening in these other spaces trigger new thoughts and new ideas and so

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 outcomes and better perspectives for our clients.

Well, you know, you've been asking these questions about like the use 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 from all that stuff. So taking a trail run with no earphones in your head for me is always a really good way of

unleashing and unbridling a lot of creative spirit, just that downtime and the unstructured time where your brain can just run free, actually not assisted by any kind of device in my head or in my face. I think with that praise of unplug 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 company. So thank you for your time. Thank you, Jacob. Thank you.

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 we'll be returning in just a few weeks with a new episode. Smart Talks with IBM is produced by Matt Romano, Joey Fishground, David Jha, and Jacob Goldstein. We're edited by Lydia Jean Cott. Our engineers are Jason Gembrell, Sarah Bruguere, and Ben Tolliday. Theme song by Gramascope.

Special thanks to Andy Kelly, Kathy Callahan, and the 8 Bar 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.