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Welcome to textile production from I heart, radio today we are witness to one of those rare moments in history. The right. Is of an innovative technology, with the potential to radically transformed business in society forever. That technology, of course, is artificial intelligence. and it's the central focus for this new season of smart talks with ibm, join hosts from your favorite pushkin bod guests, as they talk with industry experts and leaders to explore how businesses can integrate a I into their workforce and helped drive real change in this new era of a pie and, of course, host malcolm Gladwell, be there to guide you through the season and throw in his to sense as well look out for new episodes of smart talks with ivy every other week on the radioactive apple podcast, wherever you get your pod

tests and learn more at ibm, dot com, slash, smart talks, Hello, hello, welcome to smart talks with I b m, a podcast from Pushkin industries. I heart radio and I b m- I malcolm Gladwell. This season we are continuing our conversation with new creators, visionary as you are creatively applying technology in business to drive change, but with a focus on the transformative power. Artificial intelligence and what it means to leverage ay, I the game, changing multiplier for your business. I guess today's greame youssef, senior vice president of product management and growth for ibm software creams, focus it ibm his on product strategy, thinking about the roadmap for m software products and how they can deliver effective and compelling customer experiences.

With the current boom in generating? I kareem's job is to help businesses figure out how they can apply artificial intelligence at scale to help solve big problems and boost productivity at new orders of magnitude, and so you'll hear karim, explain how a high powered by foundation models can make a adoption by surprise, businesses even easier? How Gunnar did a. I will change the way businesses process and make decisions and how these concern durations influenced the design of watson, axe, ibm next generation, hey, I and data platform cream spoke with Jacob Goldstein, hosted the Pushkin podcast. What's your problem, a veteran business journal Jacob has reported for the wall street journal, the miami Harold and was a lie

on time, most of the npr programme- planet, money. Ok, let's get to the interview, I'm Jacob goldstein, I'm one of the host at Pushkin and of course, funded on the show- and I am delighted to have you here- can you introduce yourself hi, I'm cream use from the senior vice president of product management and growth. For I b m software, you can think of me as the chief product officer for a bm software. Okay sounds like a big job. We're here we're here today to talk about ai, we ve heard really an extraordinary amount in the last few months about chat jpg, and you know particularly in how it's used in the very kind of consumer facing way. But I'm curious. What is the rise chat. You pity and you know a lot more generally. What does it mean for business? Were you know if you can step back and think about what really happens? You know in a business,

you're really talking about a set of processes right in our activities that represent what a business needs to get done, whether its product they produce and then sell or service that they provide an inherent to operate in the business I would say are too Key factors, data and then the decisions you make around that data and then Actually. Lastly, processes are activities you do in accordance with that decision. So if you, and think about a I as apply to business right? In that context, the first place it often starts it. How do you make sense of a lot of the data associated with driving the business and so It has always been in my mind at its foremost about gaining insights then leading to supporting decisions and ultimately ended at helping to automate the activities that then executed,

as a result of those decisions? So that's got my simple way of thinking of a high and we can obviously colouring with examples, but that's my simplest way of thinking about a high when you think about in the business contacts, gain insightful masses of data to support decisions and then drive actions that are really helpful framework. And then, if we think about sort of what's happening in the world now, with with you know, enterprise businesses, any I what are you seeing with enterprise adoption of ai at this moment? So we're really. Talking about almost it he'll of two periods? So let me first war kind of take you back before. The advent of I'll call, generative a high and the whole jackie pity to what been going on in what I would term the realm of more standardized machine learning models, a lot of what has been going on has been very much in the realms of certain things like an anomaly detection, all optimist

Patient right use, a machine learning models to do that kind of work and where might its apply was governor connally detection in security software right, detecting threats based upon different events flowing through all now, enterprise asset management, software monitoring equipment and detained in anomalies within their behaviour? or even an m. I t automation software once again. Check your anomalies, based upon what's going on with various eighty events and then task that should occur. Optimization, often play around in the realm, as you might imagine, to solve problems of resource optimization, whether you think of that in the country, stop application, resource management for eighty or in the context of supply chain. These are being very classical applications of

machine learning, a I to really make sense of the data and provide a basis to drive decisions. Now. What is characterized by all those examples of given and the state of the art of that kind of technology has always been it's very task specific so there Airports if I may kind of limitation in the sense that the taught you ve had be very tusks specific. And so you ve seen a lot of broad based adoption within the enterprise right, but its very, very tusk, specific, as you might imagine now. What has happened recently and has been brought to the fore has been this discussion of generative lie, which is powered by very specific, innovation, this notion of foundation models and, in the simplest way to think about it. It's about time

this large model that can then be refined to various tasks and The easiest one that everybody recognised at the moment is the notion of a large language model, a model Has an understanding. a lot of the elements of a language such that it can be defined to a variety of test right? An essay answer, a question single socks silences some of what I write to light. Could the power, if you like- and this will speak to the, why everybody so excited about it? Why would argue inflection point I like to like any to teach her child the alphabet when it when you teach had shouted alphabet, it's a set of let's right, let's call that our foundation model, but over time Knowledge of the alphabet is tuned to read a book right enough

Did you a composition, greatest songwriter poet right an invalid you you understand what I'm eating right and so from one foundation model. You can support multiple targeted tusks, as opposed to sticking with the analogy to having the model for reading writing thinking of doing a poem during an essay so on and so forth, and so in the enterprise context. That means that we are now talking about being able to unlock even additional value at scale because of the nation of major foundation models and the appeal to generate a few cases generated from this case mean creation of new content So let's talk about watson ex ibm recently announced watson annex just first of all. What is that? What is what an ex, what what's an extra first through our

It is our brand for our platform. That was a platform for really taking advantage of generative ai within the enterprise within business, and so when you begin to think about what does that mean? While it leads you to the components of what's next and to a set of use cases? So let me paint a few quick pictures for you here. What's an ex first of all, it's about enabling our customers to manipulate models against their task, manipulate these from dish models. It gets out there tat. Our belief is that the world is a more time model world right and especially when you think about it. In the context of business, models are gonna come from various sources once we supply the ones out there in open source in serbia. But there are activities

he to do around these models too, as I said, apply them to your use case and we'll talk about use cases in a bit. So, what's next, the ai is that environments that build a tool. If you like for being able to do those manipulation of models and beaches, produce case thinks that people will recognise in the field prompt engineering promptitude in fine tuning those kind of activities which are autumn the pollution of models to meet your used. Tat this, component is dot data, so what's an extra is essentially exploration data stories based upon something for it was an open data like house architecture that helps to bring together the data that's needed to actually do the air this case when you think about many police in a model, a foundation model. Your jill uses some data to prompted junot train it. You you'll use cases I so we provide.

Very open data stored. I lost all manner of data for much to be brought through to do that, and the third component is what's next on governance, and this is all about the framework and a tool kit required to apply the right governance principles across doing this kind of work because deploying a lie within the enterprise governance is actually important. Right is critical to understand why the data coming from what did it You add in how is your model performing? Are you able to keep an appropriate audit trail of activities for your own internal policy and compliance needs, offer regulatory these well. So this platform, this system, that you're describing I'm curious. How is it different from the you know the generative ay. I options that you know we ve all been hearing about sort of in the press were. I think it really.

was down to the the ethos of the principles that first war drive the work that we do it. The first out fixated on is being open. Right. We fundamentally believe that to do this kind of work within the enterprise, you need an open platform that, as I said, is open to all men. The models from all sources is one of the rest, This is why we announced our partnership with ugly face to make sure our clients can gain access to open source innovation within the platform to do their work and hugging face to declare a sort of the open source ay. I kind of hub that's right! That's correct! Yes, it's a marketplace hub for all kinds of ecosystem coordinator for opensource models, and I believe that's a lot of innovation going on out there. So first of all, open becomes important. The second targeted

so our focus is very much on enabling these business use cases right you might say what can I use cases are we talking about? I give you three very quick ones that without customers are focused on a lot of focused around enhancing customer service, use cases. Think of this as czech lots of digital assistance- a further trained in more and more information about what the company has to offer or could be internal policies, external policy and so on and so forth. This means a platform that makes it really easy to bring your own data. To train and june the model, while protecting your own data as extremely important for the enterprise right

another important use case seen a little focused on what I'll call a based orchestration automation of tasks whereby think about like an hr professional as an example going through a job requisition is able to interact with multiple systems via a very simple chat interface and have work dynamically sequenced to support them in doing their task. That once again requires a notion of working with models and technology in a way that in many ways can be unique for how a business wishes to work, and he did it where's cases can embody what, because the does she secret source of differentiated advantage so once again, a platform that recognises that an insane for business? That's not the same scope, a frame of reference for rum,

a consumer platform, and then you know we also see a lot of work a round cogeneration application. What may see shouldn you know and people enhancing their skills, so targeted becomes really import mentioned open, and I mention targeted targeted to the business, to the use cases that they need to do on the opinion. That, then, is trusted, so everything I gave way those targeted use cases talk about handling enterprise, proprietary and specific data, we are trusted in this regard. Let me have been serving the business for many many a year, and we designing our platform and even our principles, and we have operated to recognise and enable that both in terms of the work we do around the governance framework and transparency that you're able to gain and apply even in the way we allow our platform to d be deployed in multiple kind of locations or footprints consumed as a service and a hyper

hey look run your own private footprint on prime or your club footprint. All of these need to be brought together to meet the needs of an actual enterprise business. My last comment is where, where I think with and made differentiated is, were really about empowering our customers to take advantage of a to unleash the intelligence capabilities productivity of their own business. This about. Oh, we established a bunch of a eyes that you can ox questions. This is about how do you craft what you need for your business to deliver differentiated value to your customers, shareholders, employees with all the appropriate protection, as well as

there's a lot of focus and what we ve done with a platform on the true said to enable that to enable what we like to call a value creators, not just consumers of eta. When you were talking about basically enterprise adoption of ay, I use the word trust an end. I'm curious, You know what is what is trust mean in the context of a

I am the enterprise. I would kind of deconstruct trust among these key, often use if a guy is about giving you insights to help you support decisions. How do you trust what insights provide what data didn't use? What did it consider based upon that data that therefore led to the insight provided? Why is this important? What why this notion of trust The one you're about to make a decision, so you want to understand the basis for a decision is no different than me asking you something and then saying: ok, can you explain your work? It right. That would be a notion of trust that we establish a very natural interaction, ass humans right. We do it all. The time is right.

so there is the element. The other reason. Why becomes important if europe flying a I into business processes and therefore how your business works You want to make sure that you know what by asses are built in to any decision or not or if the air I the model in effect, is drifting away from kind of the parameters that you would want to remain with him right. Oh go trust, and so in many ways that's one big aspect of trust in the technology, because you applied it into decisions need to make every day and you need to know in very simple terms how it works and how it is working on an element of trust

I think, is important discussion. Who are you getting your eye from that's very important to us as a company here that idea By giving we serve business, that trust becomes extremely important. What are the elements of that trust? What are the customers trying to understand? Well, first and foremost, what's your ethos around here we are very clear on the customers: is there data when they tune or refined those models to meet their use cases? That is all vets and we actually provide the ability for them to do that in very isolated and protected ways as they choose. We never used their data without explicit. Often I'm permissions right customers by say: oh yeah, you

this so that you can make a generally overall better model, but it's full awareness, full transparency that is input, that's the trust of who you're doing business with. Should that's how I think about trust. How would you build systems, you trust and are you working with people? You trust. I find Kareem's point about trust when it comes to data to be so important because as powerful as ai tools can their helpfulness is dependent on how trustworthy the data is. Humans will have to decide if our data are decisionmaking, and our eye insights live up to the vision we hope to achieve and business his grimaud Jacob continues. The conversation Jacob ask some more practical questions, but how businesses can adopt a I into their own processes? Let's listen. How can businesses move toward integrating a I as part of their core business

at all, instead of you, know, sort of as an add on on the periphery. It's funny. You know my simple answer to that. Is it's actually the simplest thing in the world to do by thinking about your business thinking about your elements of differentiation and then thinking about how ai can help you and expand the right. What what do you want to be? No four, I picture very simply use case of customer service interaction. Almost every business needs to do that wants to do it better answer. It becomes a way to stop what damage begin to work here, through you think about various automation of business processes. You think about this issues that need to be made right or how can individuals beheld? How can they be made more productive? I think always becomes a very important one. So you can apply this in many contexts. A friend

sure analysed. Looking at reams of data and trying to delight insights, have you leverage aid to make them financial analysts. Even more powerful and so that's how I advise you to people to always look at it. Think about you. Just think about your business processes. Think about help is needed or win you value could be unlocked, and then you are applying a eyes tool to achieve that. One of the themes return to on this show. A lot is creativity, the relationship between technology and creativity and I'm curious how you think that a I can help people be more creative at work. I I think I can help people we more creative at work by automated the mundane to unlock your mind me with a focus on higher value. You know: I've used a couple of times have talked about deriving insights from data right to drive, who formed

decisions. If you can use a lie to gather a lot more insights into one place than you could quickly. Do yourself or more manually, you'd, have to like write it down. Look at six spreadsheets copy from here to there that you actually have more time to look at that data, digest those insects and think about what what to do with these visits, which direction to I want to go. I think of it as free in us up to do more of what we actually as humans do extremely well, which is actually that creative thinking for a very simple terms. Why do we use a calculator to do arithmetic snub that we cannot necessarily knocked out

it does, but if you're trying to balance your checkbook to use an old phrase or- dare I say, what's what's a check but the fact that but itself, let us modernize that, if you're trying to check your expenses for the month and your performance against budget, yes, you could print out all your statements, circle, everything and add it all up, or you could begin to use technology to improve that expressly can get more time to think about. What really am I learned from my spending patterns, and what do I want to do about it? It's a very simple personal example? But I think it's fundamentally what we're talking about here and that's always been in my mind: the promise of technology free in us up to actually apply ourselves to higher value for

and higher value problems? So we ve been talking basically about the present so far and I'm curious if you feel, about the future, and you think you know medium to long term? How do you I think is gonna transform business and you know how can people

now business leaders now prepare for? What's coming so to an earlier comments I made, I do really think that we are at an inflection point, would be advancement of the technologies of a guy. I talked about foundation models. We definitely at the cost of being able to address use cases at scale that were more challenging before, and so I do think the future looks like a lot more generative ay I surfacing within the enterprise and within business processes and manifest in in interest and which I think it's almost a given that any

A piece of software right think: where do you think of it in terms of application or you think about it in terms of either the interact with the website, will have conversational enabled interfaces from the analysts saying give me the latest reports from last three months. Typing that was seated versus the right click file, blah blah. I think I'm going to see it, change in interaction to walk, see, shouldn't interaction, I think particularly check based. We forget that the graphical user interface is just ass, a metaphor right. If that, like the way, computers work, it's just an interface, and if my chat is a better interface people use chair, I think we're going to see that really explode and thus powered by a lot of this generative. I work because it becomes for you to feel natural for it to be, as in full,

readily. As I said, link things to get an overstreet, that's a big party. I see that happening and the appropriate or associated productivity unlocks. You begin to see with that, we'll just change what kind of decisions the ease with which we can make more and more informed business decisions, and so for me it's that rolling out at scale touching everything procurement hr, think about the I front of the spreadsheet and how many different roles it just ended up touching and everybody can use or just use a spreadsheet in business in some shape size or form. So I think of this as ai at scale and so what it therefore means from, as you say, getting prepared. Well,

It's all about gaining. First of all, the right understanding of the technologies and part of what law will be talking about necessary ingredients began to be well it. Why? When applied first, what data do I need to bring together to to readily support that? What unlocks, what new value- and I think it's going to be like this rollout right you're going to stop this project and then is never project and very soon it will be so much will be ubiquitous in the weight and supports the work we need to do that it will just be to a new way of us working. That is when you now look back three different from how we work today. You see the seeds today, but I would argue think of that now, like fully bloomed as a forest, not up not a flower bed. You know the idea.

Great one, other one other sort of loose thread. I wanna. I want to return to an that's. That's governance, read you talked about governance, and maybe just just to help, sir. Set the table like you, you mentioned it in a broadway but narrowly Governance means in the context of ibm, work on enterprises. I think, as the word tries to suggest it is about having the way to govern. Once activities in this room, which rarely speaks, policies, rules

works within which to understand all of that now before we died in the direction of regulation, which is where people often go, policies can be all internal so think about it. This way, if I say to you, when I build a I, I do not use my customers. Data is their customs data there from a governess perspective. I need processes that sure I know what data using and I can prove

to myself just festival internally forget about anybody else that actually has earring to the policies of laid out. That, in my mind, is a lot of what governance is about and in the context of ai. It always tends to I think, structure on three key areas: data where did it come from, and what did I do with it, and how did I apply it and where did I use it and then usage? What do I expect this model to do in this model still performing the way? I think it should be performing. What am I perceive to address whether the answers that yes or no and manage that through and then in fortunately, so this is then to bridge to regulation. If you take a look at what's going on in the in the water,

the collation and our points of view on this by the way that you actually regulate the use cases, not the technology that, from a governess perspective, how are you able to clearly understand track, and couch for what use cases you are leveraging ai for and then back to my earlier comments how the ai is performing, and when you talk about governance, how do you make sure that you have the governance? You need With out inhibiting innovation, I think what is ambitious key, a key design point for what we do with what's annex is how you make governance, seamless in situ versus another activity that you do right, and so our goal is to train driver kind of seamless interactions of a value in turn

governance, so that when oh, let's pull through the history right of everything, we ve done here, what prompts you ve created or what data reviews it's kind of already there right I'm sure you can feel free to be innovative. And tat S, not you're different products, and not at all that stuff will bring it in your datasets. Without saying oh before I do, that I need to make sure I run this checker that, but you can kind of bring it in systems, can automatically category you can go in a later, very five validate or explore, say no, we're gonna take this path based upon these facts, I ain't the more we can make it woven natural extension of the activities that need to be done.

The more we can make. It then just a part of what needs to be done and, as you to your point, gain our governance needs or supports the governance needs of our customers without stifling the innovation of the individuals at the glass trying to think through I intuitively think through new friday ways to do work. Excellent. Let me do. Are there things I didn't ask you that I should are there things you want to talk about that we didn't talk about. I think we covered quite a lot. Truth be told: no, I think we we recovered the bases there. earlier gray mentioned, that we are at an inflection point in a technology? Employment in ai in business will get easier and ai platforms like what's next. Can empower even the largest enterprise businesses to reign, and the way they run. A scream said

same way. The spreadsheet took over business operations. The adoption of a I at enterprise scale could be just as ubiquitous: it's not an oversight, don't say that a new era of work, maybe upon us malcolm grab. Well, this is a paid advertisements from serbia. Smart talks with ibm is produced by Matt Romano David jaw, michigan cat and embraced him deserve with chicken coasting were edited by lydia caught our engineers are jason, gambro, cerebral air and then tell the day theme song, but grammar scope, so thanks to call him a glory and kelly Cathy callaghan and eight bar the eight barn ibm teams, as well as the Pushkin marketing team, smart talk, the I is a production of pushkin industries and Ruby. studio. I heart media to find more pushkin podcast, listen,

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