AWS’s Ishit Vachhrajani on How Leaders Can Navigate Generative AI

foreign speaks we're talking about how Tech leaders can prepare for generative AI including the big challenges posed by gen AI as well as the enormous potential to discuss that I'm joined by ishid vashtrajani global head of Enterprise strategy at AWS they should very good having with us today hey James good to be here with you so I think it's it's been amazing since chat DP debuted in November 2022 we've seen a mini hysteria about generative AI uh obviously it's a lot of potential but certainly some challenges what are some of the challenges you you see posed by by generative AI these days yeah look so generative AI we believe is uh is truly a transformative technology of our time with tremendous potential um AWS and Amazon broadly has been using machine learning and AI in our operation for over two decades now from pretty much everything to how we sort and Route packages to optimizing routes within our fulfillment center uh to billions of transactions and interaction with Alexa devices um as we talk to Enterprises especially there are a few consideration when it comes to generative AI uh the first one is to think about well what problem an opportunity you're going to go after right so it's really working backward from the problem you're trying to solve rather than actually starting with the solution and then applying back to uh a problem that you're looking for right uh the second piece is how far down you are with your foundational Journey whether it is cloud or data because that's really going to differentiate those who succeed with this uh versus those who are maybe successful with one or two pilot but are not able to scale because they haven't actually invested in the foundational capabilities uh when it comes to cloud and data uh the third piece is around uh responsible Ai and governance right what are the consideration uh in terms of security and privacy of your own data what guard rails are you going to put in place within your organ on organization uh to validate the outcomes and outputs of some of these models and what practices you're going to build inside and then really the fourth one which uh I think underpins pretty much all three of them uh is the skill and talent uh this is going to require uh leaders but also uh employees and staff at all levels uh to reskill and learn uh new things uh and so what's the strategy that you have from an organization standpoint to prepare for and skill your staff uh and build a talent pipeline around that I think those are the four uh key considerations that we see when we talk to leaders around the world around generative AI that last one is certainly a big one I think there's it could be a skills Gap involved too I mean I want to I want to take a look at that second one for a moment if I understand you correctly you talked about it was a foundational idea was making sure you have a cloud it's having a good Cloud platform in place a coherent Cloud strategy and a data analytics application in place of data analytics your strategy is that what you mean by that what what are the foundation elements there just technically correct so if if you think about the inflection point with generative AI it is largely enabled by uh just the proliferation and availability of massive amount of data but enabled by Cloud our ability to actually process and make sense of and use that data to train foundational model uh and so as we say well don't try this at home this is something that you don't want to try without cloud because you want to make sure that you are integrating this in rest of your application and workflows so that this is not just some side project but actually truly solving customer experience uh in productivity or the opportunities that you're going to go after within your organization and so integrating that with rest of your infrastructure rest of your application Foundation of data that is unique to your business because that's going to be your differentiation as well because you want to use data that you have and then tune and customize some of these foundational models that power generative AI to serve your customers better and all of these will mean that companies that have been invested in that foundational Cloud strategy uh they've been on the migration and modernization path they know what their data strategy is or are actually working on their data strategy are going to actually stand out when it comes to the usage of children AI okay so you you've talked about this you've talked a bit about how to best address these challenges but is there I think the real challenge is how do companies best address these challenges while also enabling the enormous potential of generative AI we've got to balance those two things absolutely I think this is uh there are just so many opportunities uh from just increasing productivity um from your developer productivity with let's say uh coding companions uh like code Whisperer Amazon code Whisperer which increasing developer productivity by 57 uh now think about pretty much any organization today and someone is writing a code and you can actually start to use that today by enabling those developers and Engineers to spend more time on solving business problems and challenges rather than the undifferentiated work-off uh writing the basics of the code this is where the code companions can actually increase the productivity what that also means is you can now roll out features functionality faster for your customers uh think about opportunity to reimagine a customer interaction uh we have customers who are looking to build uh better conversational agent when they have interaction with their customers using generative AI where generative AI is assisting the agent on the call with the insight to make sure that that experience every time a customer has an interaction with your business and the company is as fruitful and productive for the customer as possible and then you get all the insights back from those interaction which you can then use to fine tune the model further to then sort of build that flywheel of improvement supply chain is another area content generation marketing media and entertainment so there are a number of areas where customers are looking to reinvent their business using generative AI to stand out uh with the competition what about the area of large language models it feels like the of course a generative AI application is gonna is is going to need a large language model as a source seems like some companies have some companies don't have them they're maybe they've been sold in the marketplace I mean how do you see the idea of like oh should a company think to themselves I need a large language model if I'm going to get the best from generative AI so I think if you if you think about large language models um are part of what we broadly describe as Foundation models right they are one type of foundation models that are trained based on massive amount of data uh what we believe is that there isn't going to be one model that is going to rule it all rule it rule out right that means uh based on the use case based on the problem that you're trying to solve you want flexibility and choice of the models and this is why the approach that AWS has taken especially with uh uh Amazon Bedrock which is a service that we have announced in preview it makes a number of these models from third party like anthropic stability ai ai 21 lab as well as our own foundational models from Amazon called Titan available for customers so based on the use case that you're trying to solve for you may want to choose a different model for that particular use case and the challenge now there will be all some companies that actually want to build a foundation model right they may have large amount of data already or they are trying to build a business where they provide others with these large language models like some of our other partners are doing and in that case what is really important is to get a better price performance out of it because it is very expensive and very compute sense intensive to train and build these large language models and this is why we are also investing in a custom silicon at the chip level to our processors like trainium and influentia which is actually improving the price performance so companies have the ability to do this at uh at a price point that they are comfortable with so we're trying to address this at sort of each stack of this whole challenge because we believe that customers are going to come from different angles they are going to have different requirements and we want to help them all okay so you've talked about AWS a bit and let's let's drill down into it so how is the AWS Enterprise strategy team addressing the the complex generative AI needs of its clients so what is the AWS advantage in this sense sure so let me uh let me start with the Enterprise strategy team which is the team that I'm part of um we had a team of former customer cxos uh from large Enterprises okay so in our previous lives uh each one of us said lead major transformation using data AI Cloud uh in large Enterprises so we've been in our customer shoes before okay um and so what we do is we work with customers to help them with mental models strategies Lessons Learned uh in many cases mistakes that we make uh so that they don't have to make them um and generative AI like a lot of other past technological Revolution uh relies on some of the mental models around operating model changes culture reskilling getting buying from your peer Executives how do you actually bring the technology but also make it really solve your business problems and so those are the kind of conversations that Enterprise strategy team has with Executives around the world uh if we think about broadly across us there are a number of things that AWS is doing to help our customers we recently announced uh generative AI Innovation Center because a lot of our customers are coming to us and saying hey Amazon you you've been at this for over two decades you use machine learning in your operation you've been building uh machine learning and AI services and helping over 100 000 customers with machine learning at AWS how can you help me because I already have applications running on AWS I've been using AWS for a lot of my infrastructure and my workloads are already there come and help me be successful with generative AI so what generated AI Innovation Center does is where we bring our expertise of solution Architects strategists data scientists engineers to work with customers in a very tangible and practical way to apply generative AI to their business and we're making 100 million dollar investment uh in general Avi Innovation Center um skill and and training and talent development was the other part that I I mentioned earlier we launched a course uh in partnership with Andrew Yang uh on Coursera on generative AI with large language models which is available and has got tremendous response because we believe that not only large companies but pretty much across the sector companies of all size even individual Builders and developers would want the skill for us largely as an industry to take advantage of this so there are a number of ways beyond our choice of product and services that I talked about that we are helping our customers be successful uh just a quick question about the Innovation Center is there actually a physical place to this Innovation Center is that an online location with strategists so Innovation Center is basically a function or a team think about that as a cross-functional team of experts from AWS that then work with the customers to deeply understand their problem and then apply generative AI to it so it's not necessarily a physical location there are obviously a number of places where we uh also host our customers for example our executive briefing Center where we bring folks from Innovation Center as well to uh Co develop and solve the problems together so there may be physical places involved in the whole process but that is really more about a center of expertise uh across a cross-functional expertise from Amazon that we are bringing to the customers sure sure well I think the the big question the really fascinating question is the future of generative Ai and the Enterprise and it feels like the technology is still so much I mean obviously AI is not an activity ai's been around for many years um is a high level almost unpredictability about it because that is such a power it's a certain potential how do you see it evolving in the Enterprise in the next few years what what sort of changes are Milestones can we expect yeah so we like to uh joke in our team that today was the longest year in generative AI um the pace of change is is remarkable right and right truly uh mind-boggling and it's amazing it's fascinating um but when you think about uh the pace of change with any technology and what's going to change I like to actually flip it and think about what is not going to change and if you think about generative AI uh what is not going to change is that businesses are still going to look for flexibility and choice they are still going to look for convenience and ease of use and they're going to still look for Better Price performance overall and so if you think about those three aspects from a business standpoint from an Enterprise standpoint as a company leader within an Enterprise you want to make sure that when you are building your generative AI strategy you're partnering and thinking about choice of models because if you look at just uh in last 6 to 12 months the size of the models the number of parameters that they are now trained on uh has been exponentially growing and that pace will continue to change there will be better models in the future there will be newer models there will be models that will be very specific to Industries and use cases that you may be trying to solve and so that flexibility and choice is going to be really really important and this is why the approach that we've taken with bedrock is to offer the choice and flexibility the second is the ease of use you want to again I'll go back to integrating this in your workflow and application so that generative AI does not become something that you know people go to to take advantage of General Avi powered application somewhere else and then come back to their workflow where they're still using traditional infrastructure in the tool you want to integrate that into your course systems and workflow this is why end-to-end stack and connectivity throughout different services and products that you use is going to be critical uh and then and then the third piece is price performance because even within a company you may have some use cases for example I talked about coding companion where you don't need to build your own foundational model you just want to call an API or provide a service to your developers where they just use generative AI in their job but there may be other use cases where you may want to take some foundational models tune it with your data and build your own application on top of that and so in each of these cases having Better Price performances is going to be a differentiation as well as companies adopt this so those those are the three things that are sort of uh the things that will remain unchanged and I advise customers to build your generative AI strategy around that what is going to change is obviously and what is going to require is the investment in training and reskilling and so that is something that leaders have to stay on top of for themselves but as well as enabling that people so when there is new innovation they are actually learning and they have mechanism and time set aside to allow for people to actually get trained and Implement some of those learning in their job immediately you know the one of the really interesting pieces of your of your forecaster is that generative AI would be built into every part of the stack it's not like you would go someplace else to use gen Ai and then then go back to your legacy application it would be built into all sorts of things from probably our word processing documents to ulcers of workflow email texting anything it would all and generally I would be built into all of it so I'm understanding it correctly and that that strikes me as something that would move the pace of work and or life in general it would move it exponentially faster than it is right now absolutely and I mean think about um this is why I like to apply the lesson of History to the Future right because history is thought of many things think about data right um uh several years back when companies started to build data strategies uh the data team was independent data tools were independent but really when the power of data was Unleashed when it was actually made available in the context of a workflow or transaction so if you're a salesperson uh talking to a customer about a deal you want the data in the context of that transaction if you are a customer support agent on a call with the customer you want data in the context of that transaction and I think same mental model applies to generative AI whether you're trying to solve for supply chain or if you're in marketing building and using generative AI for your next branded campaign or you are a Storyteller in media and trying to build the script ideas for your next show uh you won't generate AI to be part of what you do every day not just yet another thing and a sideshow that you need to go to hmm it's going to be fascinating I should I think you said it uh it's a lot of interesting stuff and it's going to be a fascinating sector to follow in the years ahead uh thank you so much for sharing your expertise today and I hope you come back and talk with us again sometime thank you for having me James

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