Brendan Buckingham, Business Development Leader, Data and AI at IBM UKI

[Music] hello and welcome to denish Gua YouTube podcast series powered by open bues council.org and cbc.com and today I'm quite excited to continue profiling great personalities and Brands and the ones as well shifting uh the barriers and looking at the opportunities that you can actually understand when we talk about AI when we talk about business when you talk about technology without further delay I want to Welcome to our series of course I think for the first time we're going to have a feature uh about IBM and as well uh personality within the the company that I'm quite interested to profile so uh we have uh for this uh series Brandon Buckingham that is a business development leader in the UK with more than three decades experience working the latest industry Trends and best practice and of course a deep understanding of Technology ethics and regulatory requirements Brandon uh spent larger period of his career more than 27 years at IBM Ireland leading and managing teams involved in data Tech Red at I at IB Cloud information architecture brandom is part of the IBM data science and the AI Elite DSC a team that engag with the organizations across every industry offering data Sciences use cases and overcoming the challenges of AI adoption since it's uh Inception in 2018 the teams have completed engagement with 50 countries across six industry verticals and sectors with the leadership of nearly 100 data scientists Brandon Buckingham combines his expertise and experience across business and industry with technical skills to help cdos data architects and AI Engineers to collaborate with their business colleagues and leaders to develop strategies that address some of the most critical Business and Technology challenges we are all facing Brandon Buckingham uh it's a pleasure to have you here in our series and uh I'm very excited as well to understand about uh IBM uh what you guys are doing because I know that there's a lot of things happening and of course IBM is probably the most econic uh technology company in the world um in the dependent of the 100 years plus of history and it's still a lot of relevant information that is more important than ever so uh Brandon let's start a bit um about your professional background and career so if you can tell us a bit about yourself uh yes certainly and it's a pleasure to be here um so uh my background is kind of unusual in that um I would always argue that I um talked my way into the computer industry on the client side um just because I was F fated and and possibly because I wanted to work in an industry it was constantly changing um and then I found my way um to IBM through a company called Lotus who built collaboration tools um and uh then have done a number of jobs in IBM um moving from Lotus into business Process Management but for the last sort of 10 or 12 years been very focused on emerging technology data and AI in the data and AI space um really the common passion I have is really the interactions of human beings and Technology um I've always been interested in collaboration I've always been interested in data culture and how people mix with data and that's kind of formed a theme um the other the other sort of pattern I also evolved in roles so I started off in technical sales and then technical Sales Management I moved to Services management and then I've then gone back to being anme in this space so so that's what I do in the UK fantastic so from your career and I think this is especially when we're talking about uh technology Ai and data um in terms of your career paths what were the the motivations and the inspiration that more U I would say push to be the leader that you are now well so so obviously variety is key but um I wouldn't say I started off knowing anything about variety um I found myself in a situation where I was working in a European role and and I needed to change I needed to adapt and in IBM there's a a big culture on not just becoming too niched too early and experiencing a v wide variety of roles in a v a wide variety of areas and um it's something you don't actually appreciate until you actually are here for a long time um the advantage of being able to talk about your specialist area as anme but understand the adjacent specialist areas so really it was I wasn't accidental because obviously there was a purpose behind it it's not something I was welcoming at the the original time and once I obviously did my first big change you know moving from collaboration to what I would call business Process Management and Process Management or RPA as you call it or I might call it today um you know I found the exhilaration of being challenged in a new area quite exciting um and then subsequent moves have always taken me out of my comfort zone um and indeed as indeed technology does you know as soon as you be you know start to understand something something else comes along and you're expected to you to understand that so I wouldn't say it was something I started off being comfortable with but it's something I'm now um used to embrace and enjoy and indeed it livens up you know your workday if you have to do something challenging or your role if you have to go go into a new situation or a new challenge you know whether it be uh you know with new technology or a difficult customer situation um or indeed a new role that you find you know appears on your path um I'd like to say my career has being directed um other than the passion I have as I described earlier for people and Innovation um I would say that that I've kind of you know I've lived in the moment and I can't say I've planned anything um but it has given me I think well I think it's given me a lot of experience that I've really bought to every role as I've built that experience like Lego bricks um you know to be able to sort of add value now uh in the role I have amazing and I think Innovation and all the the parts of Technology are critical and especially how we take these and implemented in businesses in particular and of course the um one of the things I always try to talk in this series and with my audience is the importance how we take the theory and the practice and how we actually bring these Innovation to the day-to-day of business so IBM has been organizing a very exceptional event that is the IBM think so the just for people listening to us and of course we'll put more information IBM think comprehends one day dedicated to propelling business forward 11 cities around the world uh a very high profile number of speakers a huge audience and as well a target number of business and a digital component that is quite uh impressive so um and the the event has been traveling around the world and the um and they will be since the 14th of June and you'll be in London on the 10th of October 2023 so can you tell us about first of all the event because not everyone knows about the event and secondly the key topics or innovations that you want to highlight special on data and AI yes so um I the event is a very exciting event as I mentioned it's a global event um but we bring experties you know um to London at this event um and really give um I think customers the advantage to get some insight to the sorts of thinking we're having particularly around um areas in which we believe we're enhancing um the way Enterprises work with AI and our other and indeed our other Solutions um I I think what's really interesting for customers attending is not just the sessions but the ability to talk to each other and the ability to talk to The Experts as well everyone makes them themselves very available um for conversation and indeed there are a variety of topics that you know um really the major topic as a high level topic is you know as you highlighted IBM have been fairly intense uh in their focus on AI over the last few years but not just AI for ai's sake but AI that could be used and and uh in an Enterprise to really achieve a return on investment or to enhance the way an Enterprise works and indeed we'll be focusing on on on that and what we've done um with our generative or AI solution Watson X and how that's focused on really sort of bringing AI to the Enterprise and starting to tackle some of those areas um that we see in the marketplace um that take us Beyond just working with a base model or working with a base piece of AI and really see how you deploy that at scale safely within an Enterprise so that's one area we'll also be talking about our development with Foundation models and the companies we're working with NASA being one of them sap um and indeed other organizations and and the solutions that we're building with those customers indeed also you know other areas like what we're doing with uh to enhance our own products and solutions like Watson assistant and a very clever product called Watson orchestrate so there is a variety of conversations to be had at this uh at this event but I think any organization that that really wants to perhaps you know discuss some thinking or H some thinking about how I take what I'm doing in my sandpit with um models or how do I take what I understand about Chad P PT U to a new level to a level where we can uh they can deliver it within an Enterprise to achieve a return on investment I think the most important thing is what can they do to make it safe both from a reputational risk point of view a regulatory point of view but also in a management and control point of view as well and I think all of those things are are very uh very good discussions that will be had at the think event we're going to have on the 10th of October thank you so uh Brandon one of the things is so you you are a business developing leader and I I wrote some texts of you that you went through the some of the history of AI and th that somehow influen the work you're doing so as a business developing leader in data and theyi at of course I am one of the the the leading organizations and actually one of the organizations that as well was probably one of the fathers of what we mean by AI from the barcode to a lot of things they were created IBM has been on The Cutting Edge of that so at the moment what do you would say for people listening to us and as well people they want to go to the the think event uh the IBM think London what are the most pressing challenges and as well the opportunities that you see in the current landscape and and now you will see your insights and as well your expertise and experience to be incorporated into these Solutions because in the end of the day right now the challenge is about how we bring this solutions to businesses and I think all the business are panic but we need to organize this and I know that event partly is to answer to this so uh for people that want to go to the IBM think but as well I want to know more about this what would be these strategies from an AI and data that you think are more valuable for people listening to us yeah and it's a good point and indeed um often we're shocked our heritage in AI goes back to 1947 which seems a long way I didn't even think AI existed in 1947 but uh there's you know we can trace our lineage back to then and indeed our our work on generative AI goes back some five years and indeed uh involves quite a heavy patent load from our research and development um organization um but when you will look at the work they're doing it's not just around creating models for generative AI but it's really about um creating an environment um that that can deliver AI at scale safely now what do we really mean by that and it you know when I talk to the customers I talk to pretty much on a daily basis the sorts of concerns or the evolution I see lots of them are actually doing quite a lot with models or thinking about doing things with generative AI typically inspired by a demand from the business because everybody's seen what Jack GP T can do and unusually in a market the business are going to the IT department and saying you know I can see what generative AI can do for me they may not call it that you know what are we doing about it how are we actually working uh with it and indeed that usually spawns an evolution of people start um playing it within a sandpit as they might describe it starting playing with models and and indeed what then Dawns on them is is you know uh the risks of playing with with models you know what is the data within them what are the answers I'm getting you know um are they suitable in an Enterprise they might be suitable in a consumer type environment um and that starts to start some concern and I I've met many organizations um taking Extreme Action to then ban access to certain consumer models uh but then drive their it Department to look at other alternatives to using generative AI so really the the sort of concerns that I'm now see is you know are evolving so I I think one of the biggest concerns around is around safety we've got regulations coming along certainly in Europe and many of our customers are multi-jurisdictional in nature so they have to consider European regulations and indeed there are UK regulations and regulations coming industry regulations coming on along for AI and and there should be because it infects the way human beings uh it affects lives of human beings um in their day-to-day roles um and indeed so that's one aspect um we're also seeing a growth in in fear around a reputational risk um which I think is is another big issue and one could argue it's even bigger than regulation it doesn't take very long for a company to completely ruin its reputation um you know I could argue at scale um just by either doing the wrong thing or treating someone in the wrong way in the in the tool that they use so th there's another critical aspect I think there there is also a a growing understanding that if they're going to automate or augment things within their Enterprise they need to have control over them and and that usually means that they have to look at it from a a risk management point of view they need an inventory of the models they using they need to understand whether they're approved for use what they can be using using them for for um you know many of these public models have have legal agreements about what you can use them for you know uh they will have risk profiles as we've discussed you might well use T GPD in your organization but you also might have higher risk use cases where you can't use chat GPT will have to use other models this is all in the in the landscape of having a a vast ecosystem of Mo models probably a number of clouds how are you going to manage this with your Enterprise at scale and indeed that that is a concern that I'm seeing growing and one that IBM really focuses on because actually what we want to do through providing the tooling and the understanding about how you go about managing this growing ecosystem is really placing the Innovation back in the hands of the people who've got the ideas and typically we find the best people um to innovate are the people whove been doing the job within the business for 10 or 15 years the shop floor worker or the nurse for example in the NHS who can quite readily tell you how they can make something more efficient if they understood the capability what we want to do is allow people to innovate with that platform and work collaboratively with the technical people that can develop those solutions to really take those onto a next level but you can't do that if you can't do it safely if you don't understand the risk if you can't actually manage the process of delivering that at PACE because you know the the old days where you were used to wait two years for something to come to fruition and it probably died before it arrived in many cases with AI it is not economically sustainable and it's not also going to encourage the Innovation because people just say well I put a lot of effort into this it never made it to production I never saw the benefit so why should I contribute to the next next one that comes along so we need to keep this Innovation cycle running with the business we need to put them in contact with with that Innovation and feel that they're in control of it and see the results quickly um within the uh within the organization and this is definitely the biggest challenge uh because like you mentioned there's a reputational there's a a Readiness there's a learning curve and there's well how prepared are you to deal with all the disruption that comes out of this so um AI I think right now of course is the the word that is in the mouths of everyone especially any business and any industry or sector are more more um dependent at least of what is going to come out of this because it's still in the early days at least in terms of mainstream adoption so IBM was one of the first um from well the creator of the Watson which was probably in terms of mainstream perception of AI and machines was probably the first company in the world that led that so at the moment uh in terms of the the present um you mention um Watson AI or Watson x uh which serves a lot of different uh Solutions so in terms of the AI for business across Industries where are the some concrete examples of how IBM AI Solutions have been instrumenal or case studies in helping companies enhance their operations Drive Innovation and stay competitive yeah so I mean there there are three that come to mind and two are fairly public and and and they're all different different in nature so I mean the first public one some of the things we've done at Wimbledon and the US Open um and coming up the the Masters um which actually uh in tennis and golf um which I really in illustrate and correlate with things that enterprises are trying to do but show our Innovation so one of the most interesting and exciting ones is we took a at you know um the outside Cults at Wimbledon and produced AI commentary so we took the movement of the the players on the pitch generated uh actually voice commentary with annotated notes for highlights um and that's quite groundbreaking in its nature is is you know in terms of two two models working together to produce output another thing we did at Wimbledon which also reflects something that that many organizations are doing is U we actually predicted uh or produced some uh capability to predict the pathway that players would take through through the draw so you know if one player beat another what would that mean for them in the draw and the other people they would meet and that's very much predictive in nature but um and quite interesting to look look at in the context of many organizations uh another area that I I think is is really um talks to the way IBM Works within the context of the world is the work we've done with NASA recently so we were the first people to ever take um the NASA satellite imagery and um produce a a a model to look at CH the effects of climate change things like flood planes burn scars on the planet um and indeed the model we created um actually proved itself to be % more accurate than any other model in this space and actually use half of the tokens to generate it and we taken that model and we released it to hugging face um as an open model but you'll also see us develop it further um in terms of adding weather sit weather information to it and other areas working with you know with NASA and other groups like Nvidia Etc you know and it's a really great collaborative example of very Advanced Innovation that's coming out of our research team the third example I think that really sort starts to bring this into reality is um we did some work um with uh East and North Hots NHS trust um and if you think think about um that organization has some six six and a half thousand people in it what we built is um an AI interface to their HR System so it enabled you know their their employees 24 by7 to ask questions about training policies registration roles um all the sorts of things that that allow um the organization to run more effectively um dayt day now um there's some interesting figures around that which can't relate but you know beyond just cost savings vast amounts of time saving you can imagine being a nurse you know on on duty in the middle of the night having a query that's worrying you that's perhaps distracting you even being able to get whatever you know Channel you choose to use whether that be chat or or voice to be able to get an answer um to a question that's related to you have that interface understand and understand you uh and be able to answer that is being very effective way to to make the organization more effective and indeed make employ or you know able uh the organization to have happier employees at the end of the day so that's a very transformative use case I think at a very real level um and it it talks to IBM's determination you know beyond all the technology that we've talked about really our focus is is on outcomes outcomes that transform organizations so whenever you talk to someone in IBM they will always ask you what outcome are you trying to achieve it's not about about what technology we can deliver and indeed a lot of our investment and the thing that we really probably should talk about more is investment we've made in our client engineering teams and they're unusual in the way we go to market because what they do is essentially talk to customers about those outcomes and we have very defined ways in which we try and highight those ideas and and crystallize them into um in in in a way that we can actually build MVPs um for for the customer and this is all done on our investment so it's about trying instead of saying here's what's an X or here's a tool it's about say looking at an outcome building something for the customer that is designed for them uh in four to six weeks that proves or Builds on the idea they have that then proves the value of whatever technology we bring to it and in many cases it's not just our technology it's how we augment other vendors technology to really deliver that outcome and I think that's a very powerful thing that we do in front of customers you know that goes beyond the fact that we're just a technology company you know we're prepared to invest in helping customers crystallize their outcomes and deliver it you know and illustrating value to them um and this team is a very big team of very talented people designers uh data scientists engineers Architects that that bring all the together in a very effective way within a variety of Industries and we're working in in lots of companies right now doing just that it's impressive and of course these three cases reflect well three major events or organizations in this case one of the biggest tennis event or the biggest tennis event in the world of course NASA and NHS so in terms of the case studies for business of course not as big as these ones are not as impactful can you share you mentioned the the products and the investment you did in creating tools and process how to take these and I think that's probably one of the things that people are more looking for is because of course even sh GPT at the end of day is is advanced search uh but it doesn't solve the problems for a business that needs to to solve the problem so I know that IBM is very focused on these case studies and of course all the history is about creating products that actually affect entire Society so any story or two that ex exemplify the impact of I M Solutions on I would say smmes or big uh or medium highend segments as well that are looking for this well in indeed we take take the reference I talked to you about in terms of Northeast Hearts NHS trust you know we're work you know we have some of the bigger implementations of of customer service interfaces um you know within banking for example in the UK um so um uh so that West Bank for example use in their core app you use that same technology um to enhance their net net presenter scores and enhance their C customer satisfaction these are all very uh very great use cases but there's also a number of others that we can't talk about that really enhance you know internal applications and the ways they're working what I what I'm finding exciting in our interactions through the the client engineering teams that we're having is is really the new use cases that are coming on the things that are inspiring customers to change the way they work or enhance the way they're they're actually working um you know generative AI rise raises a lot of excitement but also when you start to crystallize what it can do it's not you know the the use cases it generates aren't necessarily destinations in themselves you know they're part of another process and invariably they're about speeding up a process or augmenting something that a human being is already doing so actually it becomes critical to understand the outcome and understand that if you speed up that process what the effect on the rest of the process is and indeed what's the effect on on on on the people who are using that process so I'm seeing a lot of that going on um within customers when I talk about the governance aspect um certainly we're working and have been working with AI with very large organizations um who have for example development environments that don't include IBM products but are using us to bring their data scientists together um bringing their um various development environments and deployment environments so I'm thinking about companies like credit Mutual in France for example classic example of where we've employed that level that helps them productionize AI without necessarily having to go in and and say you have to use our studio to develop your Solutions or you know our capabilities to develop them so there are a lot of different use cases um both Technical and non-technical that we see surfacing and some really exciting new ones that are coming along you know particularly about bringing you know the intelligence of the organization to bear in front of individuals so you know there's a lot of BU in the market around retrieval augmented Generation Um you know I think the commonality between semantic search and generative AI really um but we're seeing lots of customer service people uh lots of customers saying you know I want to bring for example all my engineering documents to bear in front of my Engineers so I can support not just the ones that have been there for 30 years but the ones that we're on boarding with you know the best information I can give them within the context of the role they're doing so we're seeing lots of those sorts of use cases arise uh the other thing we're seeing quite a lot of is generative use cases so you know for example you can think in retail there's a lot of um pin data um that that's you know uh that exists um and indeed there are quite large departments turning that P pin data into marketing data um and if you look at look at that process we can generate that data um automatically um and and augment that work so it's a loss media and you can see that then is a step to hyper person alization which is a really big thing in the marketplace where actually if we can generate really effective marketing information out of the back end of that that product based product information that the suppliers give it means we could also if we know about you as an individual generate that marketing information for you as an individual so we're seeing lots of different use cases start to surface within this Market that are going to be very effective and transformative but as ever each case is different each set of circumstances is different and what you know the components that people already have in place are different and indeed the days when you could go and say well you just need to rip that out replace it have gone so we're very much around you know what do you have already also where where do you plan to go and how can we augment what you're doing to achieve the goal that you have and that's that's why we take the client engineering approach in many cases yeah that I think this is the narrative that we need to open and and I I one of the things I've been finding is how we go from the theory to the practice but how do you take these case studies and apply to the business and as well to each case because dealing um one sector is slightly different from the other but even inside of the same sector there's a lot of case studies that are different and people really I think at the moment there's so much Buzz around this and so much panic at the same time people forget probably the most most important things that is really case studies and really things that can actually make us move forward so it talks to the whole thing between evolution and Revolution I think a lot of people are thinking that this is going to be revolution it's going to fundamentally CH and it will on some levels fundamentally change things but I think in many cases we're talking about improving the situation and augmenting what human beings are doing and and that will be evolution in nature for a variety of reasons and it should be Evolution it should be about basically improving their situation making them more effective um making the company more effective as a as a result of that so um you know I think you know a calm approach looking at the potential outcomes proving those outcomes having kpis around those outcomes measuring them moving them to production and doing the same is going to be critical a colleague of mine is very familiar for saying that AI is like a vegetable it has a shelf life it goes off so you need need to be constantly iterating um what you're doing and indeed that um EAS and North H uh reference I talked about you know very much you know that was developed out of lab services and client engineering approach to developing the solution what's interesting actually when you see some of the feedback for that is part of that process is to take the people who are are using the system feedback to iterate that system constantly so what are the questions it couldn't ask what are the things I'd love it to be able to ask those types of things so for me it's not it's a situation of constant improvement with a AI it's not a application you put in place and then do a revision of it in 18 months and hopefully you know hopefully that'll keep everyone happy we're talking about a constantly istrative process here and the same will be true of generative AI you know has it answered the question I wanted to did it answer with cander did it answer in a way that was positive did I you know was it accurate enough all of these things are going to be things that are going to cause us to want to be on top of the way it works day in day out and be on top of constant innovation in these areas yeah and this is the biggest uh challenge we are all facing because in the end of the day one thing is an interface like a shed GPT the other thing is like you mentioned a kpi Net score case study for for an healthare for for a given organization so when you look ahead to the future of AI of course you mention the evolutionary part there's a lot of right now questions that that happens but as well when you talk about AI is related with emerging Technologies and there's a lot of Trends and developments um so what would be the ones that you anticipate will shape more the present day ey landscape and you tou some of this but I would like more in an open way way here and especially how is IBM position itself to lead in this evolving space because of course you mention NAA of course there's not much more advanced organizations than that but at the same time there's a lot of I think a need from business to come up with solutions that can tackle because of course I think especially open I open a Pandora box of opportunities but actually is not creating Solutions and I think that's probably one of the questions that I'm interested to hear from you and IBM uh as well how you see the trends but as well some of the concrete things that you can actually touch uh and and anticipate well so I mean they happen all all sorts of different levels at the technology level um you know we're about to release some curated models so you know taking the content that models have onto the next level um you know using corpuses of information we know we have um copyright access to um you know D duplicate in it making sure that there are no blocked websites on that making sure that we remove hate and profanity you know so that that actually the The Source data is trusted and there's a lot of effort going on in terms of research and looking at for example how do I take the ethical part of my organization so the body that decides how my or organization wants to represent itself to its customers and its employees and how do I then um allow that to feed its way through to the how the model answers questions and if you think about that very carefully um you know at the moment that's something that's often governed by the organization that produces the model not necessarily the organization that wants to use the model and and and that can cause some big gaps in business in terms of you know if we look back to traditional AI you know we back to bias you want to debias a model but if you're in the insurance industry and you're in car insurance it's built off being biased you know if you're 18y old with a Ferrari you're going to have to pay more money that's the nature of that business therefore you cover that with explainability but I think you're going to have lots of situations with generative AI where it's not just about um you know trying to improve the accuracy it's also going to be about trying to improve the you know the intent and the way um Jones for AI asks another area I'm finding in the near term that's really interesting that is around personas so you know creating assistance with Persona personas in them um you know I can think of situations where even in my own organization where we have a legal person that will want to interrogate a document and improve a document using their legal language um but when I talk to them from a from a business point of view because I'm working on something with a customer um you know the language I want to interpret that document with or ask questions on is is a totally different set of business language and business terms that I then want to pass on to my customer so I can see the use of of assistants and personas becoming more and more powerful within the generative AI space you know and and and adding in the context of the legal document starting to produce some more powerful outcomes as well um and and I think s generally you know I tend to be a bit of a realist and I think you know keeping small steps in mind there's so much we can do to enhance our understanding of how models are working um how we actually govern the foundation models themselves as well not just the models they create or the use cases they create or the AI That's there I think all of those those areas will gradually improve um in terms of the use cases I think there's a way to go for those I think they're only limited by by the imagination of the organizations we deal with you know the the better understanding of what AI generative Ai and existing processes can do when they're bought together um you know as the business understands those better The Innovation will really start to accelerate and I think we're only at the beginning of that you know new business models new ways of working um new uh new talents we're seeing new roles evolved you know who thought that a prompt engineer wouldn't be a data scientist would probably be more likely to be an English expert um or a language expert you know those things are evolving very quickly and you know we're all beginning to understand them this is very good point and I I think this is one of things I'm excited as well to learn more and especially I know that when it comes to um the IBM think uh the one in London on the 10th one of the things you highlight is precisely uh connect learn and engage and this is actually one of the things that is more important because in the end of the day anything that we're talking about uh all of these Technologies there's really a lot of questions but as well a lot of practicalities that we have to consider and as well we can only understand this discussing connecting learning but as well taking it in case studies so um on the IBM think London um what can attendees uh expect uh in terms of insights announcements related to this future AI advancements I know that you have as well a lot of focus on sustainability and a lot of different other areas if you could tell us a bit more so there'll be a big focus on on sustainability obviously uh what's next because everyone's interested in generative AI so we'll go into man of detail there and that includes you know explaining what we're doing but also having round tables and opportunities for customers to get involved in discussions and ask questions around that area um we'll also be talking about our foundation models that we're releasing um you know in terms of you know them being C curated and and transparent um I think we're going to be talking about our code assistant so we talked about an anable code assistance so something that generates anable code um but also um we're developing and have announced a a cobal code system so Cobalt to Java and indeed we've got lots of use cases with customers who just want to document the Cobo that's there because obviously the the workforces or people are leaving the workforce who maybe wrote that original Co cobal and the organization maybe doesn't want to even migrate it they just want to understand it better so that's something we're seeing quite a lot of we'll also be talking about where we're taking our assistant technology which you know is Market leading in nature so you know we see it at the front end of HR applications and indeed we use it in IBM in our H askhr application but also how we're embedding um uh large language capability in that to really take it on to the next level both from a point of view of training it using natural language but also bringing better insight to it um we'll also be talking about things like what's and orchestrate which is really around how someone gets through and orchestrates their day-to-day role um instead of having processes bring skills you know to things like hiring um so you know being able to find the right candidates then assemble the right candidates in the right thing filter them you know be able to you know actually progress the whole procedure for that or it might be any process that anyone does um and what's an orchestrate is a very exciting um uh area uh clearly ASG is a very important thing we've got a market leading product called invis but we're also bringing that whole generative um space in in that area but we have lots of other areas you know process Automation and and other big areas that we cover um you know ac across our portfolio so there's something pretty much for everyone and if if if you're one of the people that doesn't want to just come for generative AI they're talking points in a lot of other areas I think one of the things you mentioned which is the ability to engage uh I'd like to say collaborate um there will be an immense amount of expertise there and people who are just open to talking and and having a conversation and discussing discussing things like we are and getting to the bottom of things and indeed you know I think there's the opportunity to walk up to anyone and then get led to an expert and really perhaps discover something that that might help um all and indeed I think we're very open to getting feedback I don't think anyone's got 100% of the answers so we're constantly evolving with feedback from customers and indeed every day I learn something new as indeed I probably probably you do too so that interaction is really important as you highlight for our market and highlight for I think where we go beyond the need to compete actually doing better things you know in on the planet so U we welcome that interaction um we will Pro we will have someone also explaining the NASA um example I've said and showing that capability which I find fascinating and and indeed a good use case for collaborating outside your organization with other bodies to innovate which is another area that fascinates as well there's a lot of things I have probably much more questions but I I I want to be respectful of your time I think especially for people listening to us and as a wrap up so definitely the case case studies of weblon wish we of course going to put links during the interview and of course you can understand more of course Naza I'm actually very curious to know more of course I read about it but I want to go more on the details and of course even the NHS trust I think it's a very interesting one the AI interface for HRC system and the net West Bank because I think we like you mentioned one of the challenge friend I was in a research recently in a very big group big project for cities and people were panicking because some of the credit scores were putting like an entire region was completely zero and like you said these things have to be taken out of context and adopted because sometimes some AIS don't get these so for people listening to us uh thank you so much uh Brandon for your time I think so so just as a a final so the IBM think is going to be on tour London uh on the 10th of October 2014 from 8:00 a.m. to 1830 and they'll be on the brewery uh in the center of London so everyone can find information thank you say hi oh definitely thank you take care thank [Music] you

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Id: FX28JN2ttMQ

Length: 45min 58sec (2758 seconds)

Published: Thu Oct 05 2023