1 00:00:00,240 --> 00:00:07,880 Today on Episode #799 of CXO Talk, we're discussing 2 00:00:07,920 --> 00:00:11,400 enterprise A I the Leadership Lessons. 3 00:00:11,920 --> 00:00:17,320 We're speaking with Sunil Sennen, Head of Data Analytics 4 00:00:17,320 --> 00:00:21,530 and a I for Infosys. I've been with the company for 5 00:00:22,250 --> 00:00:26,610 over 22 years working with customers on their digital and 6 00:00:26,770 --> 00:00:30,330 AI led transformation journeys. For the industry leaders you 7 00:00:30,330 --> 00:00:33,810 know across the globe, Tell us about your role. 8 00:00:33,810 --> 00:00:37,890 You have a really interesting title, Global Head of Data 9 00:00:37,890 --> 00:00:41,650 Analytics and AI. I work very closely with our 10 00:00:41,650 --> 00:00:46,530 clients, CXOS and really, you know, helping them understand. 11 00:00:47,200 --> 00:00:53,880 How they can look at data and AI or their transformation and 12 00:00:53,880 --> 00:00:56,880 really sift through, you know, all the hype, to then convert 13 00:00:56,880 --> 00:01:01,720 this into a meaningful blueprint that delivers value, right? 14 00:01:01,720 --> 00:01:04,840 That delivers on the promise of data and AI, not just for their 15 00:01:04,840 --> 00:01:08,040 enterprise, but also for the society that they touch in. 16 00:01:08,790 --> 00:01:12,270 And also, you know, really creating what we call data 17 00:01:12,310 --> 00:01:15,870 economy around the enterprises, which is a very meaningful way 18 00:01:16,070 --> 00:01:19,590 in which you can create value for all stakeholders and bring 19 00:01:19,590 --> 00:01:23,710 in a network of entities and partners, citizens and consumers 20 00:01:23,710 --> 00:01:26,030 together to then create net new value. 21 00:01:26,390 --> 00:01:29,550 And that's something that data and AI holds for nations, 22 00:01:29,550 --> 00:01:34,430 societies and communities. So Neil, you're speaking with so 23 00:01:34,430 --> 00:01:38,510 many different companies of varying sizes. 24 00:01:39,030 --> 00:01:41,950 What do people tell you about AI? 25 00:01:41,950 --> 00:01:44,870 Everybody is curious. Everybody is interested in AI. 26 00:01:44,870 --> 00:01:46,790 Everybody knows they have to do something. 27 00:01:47,070 --> 00:01:51,030 But can you with a broad brushstroke, describe the 28 00:01:51,030 --> 00:01:56,030 general state of the market as you're seeing it? 29 00:01:56,470 --> 00:01:59,150 This is the conversation and then the. 30 00:02:00,170 --> 00:02:04,810 In the boardrooms that our customers are having, clearly 31 00:02:04,810 --> 00:02:10,930 there is a very, very heightened interest in learning about AI 32 00:02:10,930 --> 00:02:12,930 and what it means for enterprises. 33 00:02:13,970 --> 00:02:18,170 But I think the key questions that our customers have is how 34 00:02:18,170 --> 00:02:21,810 do I translate the potential of AI for my business? 35 00:02:22,930 --> 00:02:28,650 And you know how I can reimagine my business, how I can reimagine 36 00:02:28,650 --> 00:02:32,210 my business models, what it means for my products and 37 00:02:32,210 --> 00:02:35,570 services, what it means for my customers and other stakeholders 38 00:02:36,210 --> 00:02:40,130 whom I serve, and most importantly, how do I go about 39 00:02:40,130 --> 00:02:43,650 it right? You know there isn't a Big Bang 40 00:02:43,690 --> 00:02:48,330 approach to AI. And it's something that touches 41 00:02:48,330 --> 00:02:53,090 the roots of the organization. There are cultural aspects of 42 00:02:53,090 --> 00:02:56,690 things, there are processes and obviously there is impact on 43 00:02:56,690 --> 00:03:00,210 people that needs to be well understood and articulated for 44 00:03:00,210 --> 00:03:03,050 how it will amplify the potential for people. 45 00:03:03,800 --> 00:03:06,080 You know within the organization and outside, but how do you 46 00:03:06,080 --> 00:03:09,800 translate this really into an execution blueprint so that you 47 00:03:09,800 --> 00:03:14,040 can deliver on the value that data and I promised for the 48 00:03:14,040 --> 00:03:15,680 organization. So these are, you know some of 49 00:03:15,680 --> 00:03:19,040 the key questions and I would say there are more questions 50 00:03:19,040 --> 00:03:21,880 than answers in their mind and that's why they are reaching out 51 00:03:21,880 --> 00:03:26,080 to us and we are engaging them on figuring this for them as 52 00:03:26,080 --> 00:03:28,320 well as the industry in which they operate. 53 00:03:28,950 --> 00:03:30,950 You made an interesting comment just now. 54 00:03:30,950 --> 00:03:37,510 You said that there is no Big Bang approach to AI and for 55 00:03:37,630 --> 00:03:42,990 folks who may be younger that have not been through large ERP 56 00:03:43,030 --> 00:03:49,230 projects, in quick summary, Big Bang means do it all at once. 57 00:03:49,230 --> 00:03:54,840 Take the company live is 1 huge expensive long project and 58 00:03:54,840 --> 00:03:57,160 you're saying Sunil that AI is different. 59 00:03:57,160 --> 00:04:01,160 Can you elaborate on that? We live in a world where there 60 00:04:01,160 --> 00:04:05,760 is a continuous delivery of new capabilities that allows. 61 00:04:06,410 --> 00:04:10,130 Not only the enterprise to learn as to how to operate newer 62 00:04:10,130 --> 00:04:13,810 systems such as these, but also the the users, the customers, 63 00:04:13,810 --> 00:04:17,130 the consumers to really embrace that and and there is a 64 00:04:17,130 --> 00:04:21,170 continuous feedback that then makes this system evolve. 65 00:04:21,170 --> 00:04:25,090 But if I if I have to break it down into two or three elements 66 00:04:25,090 --> 00:04:29,010 as to why AI systems are like this compared to, you know, the 67 00:04:29,290 --> 00:04:31,090 Erps and others that you spoke about. 68 00:04:32,170 --> 00:04:36,410 First, there is a clear adoption, you know problem, 69 00:04:36,490 --> 00:04:39,330 right? Which is the trust deficit in 70 00:04:39,330 --> 00:04:42,330 terms of what AI systems would tell you versus what the tribal 71 00:04:42,330 --> 00:04:44,830 knowledge is? I think it takes certain 72 00:04:44,830 --> 00:04:48,230 experience, you know work for the systems as well As for the 73 00:04:48,990 --> 00:04:52,870 humans who are interacting or using such systems to then build 74 00:04:53,910 --> 00:04:59,150 the trust and the way of utilizing such capabilities for 75 00:04:59,350 --> 00:05:02,470 amplifying the potential, getting that productivity that 76 00:05:02,470 --> 00:05:06,670 is needed, making that impact on the business and the customers. 77 00:05:07,530 --> 00:05:11,090 But underlying problems are also that of data quality. 78 00:05:11,730 --> 00:05:15,810 How do you govern such systems? How do you make sure that the 79 00:05:15,810 --> 00:05:20,490 system is operating on ethical considerations that are very 80 00:05:20,490 --> 00:05:25,130 important for the society and also making that larger impact? 81 00:05:25,650 --> 00:05:31,290 Converting this a I effort into something that can deliver good 82 00:05:31,290 --> 00:05:34,730 for the society and for, you know, everyone who's going to 83 00:05:34,730 --> 00:05:37,090 interact with it, I think it takes certain. 84 00:05:37,610 --> 00:05:42,730 Amount of maturity in order for these systems to be tuned and 85 00:05:42,730 --> 00:05:45,130 really looking at you know how this is working with the 86 00:05:45,170 --> 00:05:49,290 ecosystem and then you put this at scale which is very, very 87 00:05:49,290 --> 00:05:52,410 important. You know you can't get the value 88 00:05:52,450 --> 00:05:57,330 out of these systems by only limiting it to small Poc's or 89 00:05:57,330 --> 00:05:59,610 experimentations that are important to get started. 90 00:05:59,850 --> 00:06:03,610 But I think the end goal is to then scale it at the enterprise 91 00:06:03,610 --> 00:06:07,050 level and that is why the journey goes through the quick. 92 00:06:07,250 --> 00:06:11,970 Iterations and what we call digital is to then take this to 93 00:06:11,970 --> 00:06:15,970 business functions, operate it at an ecosystem level and so on 94 00:06:16,490 --> 00:06:20,690 with these large traditional software projects. 95 00:06:20,690 --> 00:06:25,050 They were highly technology based, but still they had impact 96 00:06:25,050 --> 00:06:29,050 across the company. If you were doing an ERP system 97 00:06:29,330 --> 00:06:37,100 for example, with these AI systems, there still is impact 98 00:06:37,100 --> 00:06:41,780 across the company as you were just describing, but it's very 99 00:06:41,780 --> 00:06:45,820 different. So how are these AI systems 100 00:06:45,820 --> 00:06:50,060 different from traditional enterprise software? 101 00:06:50,460 --> 00:06:55,940 The enterprise softwares gave a new way of or an automated way 102 00:06:56,340 --> 00:07:00,660 of executing right, which is how you could run a process at a 103 00:07:00,660 --> 00:07:04,430 global scale. You could standardize a process 104 00:07:04,550 --> 00:07:07,310 even though there were specific customizations for how 105 00:07:07,670 --> 00:07:11,070 individual reasons needed to cater to local compliance laws 106 00:07:11,070 --> 00:07:17,550 and so on, but the idea was to bring an automated system and 107 00:07:18,190 --> 00:07:21,230 industrialization and standardization of that process. 108 00:07:21,850 --> 00:07:26,490 What we are talking about in terms of AI is to bring the 109 00:07:26,530 --> 00:07:29,730 cognitive capabilities into a system that would interact with 110 00:07:29,770 --> 00:07:33,050 humans and the other systems at large. 111 00:07:33,850 --> 00:07:38,490 And this has to learn from the data that exists in the 112 00:07:38,490 --> 00:07:41,570 ecosystem and within the enterprise and as you can 113 00:07:41,570 --> 00:07:45,000 imagine. If you have a bias, let's say 114 00:07:45,000 --> 00:07:49,160 existing in the existing data, AI would amplify that. 115 00:07:49,240 --> 00:07:53,120 And that's something that would completely distort at minimum 116 00:07:53,400 --> 00:07:55,200 and give out inaccurate decisions. 117 00:07:55,200 --> 00:07:57,360 But also it would then not be fair. 118 00:07:57,560 --> 00:08:00,440 It's not going to be free. It's not something that would 119 00:08:00,440 --> 00:08:04,520 drive, you know, or even meet the ethical considerations with 120 00:08:04,520 --> 00:08:09,320 which we all operate. And hence the AI systems need to 121 00:08:09,840 --> 00:08:13,960 be governed and need to be looked at differently from that 122 00:08:13,960 --> 00:08:18,240 full automation journey to then say how am I tuning this? 123 00:08:18,240 --> 00:08:22,400 What business problems am I solving and am I solving it in 124 00:08:22,400 --> 00:08:26,840 the ways that are acceptable to the enterprise standards and 125 00:08:26,840 --> 00:08:30,760 also to the societal standards? Would it be correct to say that 126 00:08:30,760 --> 00:08:37,669 with these AI projects that they have, they retain the elements 127 00:08:38,390 --> 00:08:45,550 of traditional software, but now you have these layers that did 128 00:08:45,550 --> 00:08:49,710 not exist before, such as learning from the data as you 129 00:08:49,710 --> 00:08:52,790 were just describing? At some level, I think it goes 130 00:08:52,790 --> 00:08:55,390 beyond that, Michael, in my view. 131 00:08:56,540 --> 00:09:01,500 When you start to look into the trust deficit aspect of things 132 00:09:01,500 --> 00:09:04,660 and how do you bridge that trust between air systems and the 133 00:09:04,660 --> 00:09:10,780 tribal knowledge, you know the the story starts to diverge from 134 00:09:10,780 --> 00:09:13,620 that of Erp's and other rollouts that we've done. 135 00:09:13,900 --> 00:09:15,700 But there's also an aspect of culture. 136 00:09:16,540 --> 00:09:20,060 You know, the the culture of data, the culture of insights 137 00:09:20,060 --> 00:09:23,260 driven or data-driven decision making. 138 00:09:23,940 --> 00:09:26,620 Is a journey. And you know, as as you would 139 00:09:26,620 --> 00:09:31,140 imagine in large enterprises, it is not only an individual who 140 00:09:31,140 --> 00:09:36,100 has to get on to a system like this and really understand how 141 00:09:36,100 --> 00:09:39,620 to work with the system, but it's also the groups of people, 142 00:09:40,540 --> 00:09:43,540 teams, not necessarily in one department, but also cutting 143 00:09:43,540 --> 00:09:46,300 across other departments and oftentimes even across other 144 00:09:46,300 --> 00:09:48,820 companies. And how do you bring an 145 00:09:48,820 --> 00:09:51,820 ecosystem? To that level of understanding 146 00:09:51,900 --> 00:09:58,420 and having that expertise to say how you leverage data in AI and 147 00:09:58,420 --> 00:10:02,460 solve problems together, this is where this journey starts to 148 00:10:02,580 --> 00:10:07,020 diverge and look at how the adoption and the utility of AI 149 00:10:07,100 --> 00:10:10,020 for different business functions would emerge. 150 00:10:10,820 --> 00:10:15,180 The other thing is most of what we're going to do in AI is to 151 00:10:15,180 --> 00:10:18,100 reimagine the model. You're going to see things that 152 00:10:18,100 --> 00:10:21,600 we have not seen before. And in that sense, it's a great 153 00:10:21,600 --> 00:10:25,440 opportunity for organizations to differentiate, to create that 154 00:10:25,440 --> 00:10:30,560 discontinuous growth potential and also create new models 155 00:10:30,560 --> 00:10:32,840 etcetera. But on the other hand, it's also 156 00:10:32,840 --> 00:10:37,120 something that needs to be imagined, tested, experimented 157 00:10:37,320 --> 00:10:40,920 and then put into place. And hence, you know, pivoting AI 158 00:10:41,040 --> 00:10:44,880 on what it means for business solving problems is where the 159 00:10:44,880 --> 00:10:48,520 starting point is. AI cannot be done for the sake 160 00:10:48,520 --> 00:10:51,540 of AI. It's not a system that you're 161 00:10:51,540 --> 00:10:55,100 looking to put in place because that's the system that's your 162 00:10:55,100 --> 00:10:57,980 end goal. End goal is essentially driving 163 00:10:57,980 --> 00:11:02,060 transformation for business. Getting that outcome that you 164 00:11:02,060 --> 00:11:06,020 know the enterprise is emphasizing not for itself or 165 00:11:06,020 --> 00:11:10,060 not only for itself but also for you know the, the entities that 166 00:11:10,060 --> 00:11:13,340 interacts with and the and the stakeholders that it is serving. 167 00:11:13,860 --> 00:11:17,140 Please subscribe to our YouTube channel. 168 00:11:17,640 --> 00:11:20,720 Hit the subscribe button. It's at the bottom of our 169 00:11:20,720 --> 00:11:23,280 website. Check out CXO talk.com. 170 00:11:23,280 --> 00:11:27,800 We really have great shows coming up and we have a very 171 00:11:27,800 --> 00:11:30,640 interesting question from Arsalan Khan. 172 00:11:30,640 --> 00:11:33,240 Arsalan is a regular listener. He asked wonderful questions and 173 00:11:33,240 --> 00:11:37,320 thank you Arsalan for that. And he says when thinking about 174 00:11:37,320 --> 00:11:41,040 the ethics in a I Are there ethical standards that 175 00:11:41,040 --> 00:11:46,280 organizations can follow? If not, then who decides what is 176 00:11:46,280 --> 00:11:49,470 as ethical? How do you make sure that your 177 00:11:49,470 --> 00:11:54,230 competitor's AI is ethical? That they're not cheating and 178 00:11:54,230 --> 00:11:56,910 putting yourself at a disadvantage? 179 00:11:57,030 --> 00:11:59,190 Any thoughts on this? It's a really thorny it's a 180 00:11:59,190 --> 00:12:01,990 thorny topic. You can see this as an 181 00:12:02,070 --> 00:12:05,820 evolution. Of standards and regulations 182 00:12:05,860 --> 00:12:09,980 that that you have begun to see, but it's also more that are 183 00:12:09,980 --> 00:12:13,620 going to come in. But the, you know, ground level, 184 00:12:13,620 --> 00:12:17,940 if you distillate down to two or three things that you know 185 00:12:18,420 --> 00:12:22,540 companies can look at. One is you know you have 186 00:12:22,780 --> 00:12:24,980 standards around privacy for example. 187 00:12:25,340 --> 00:12:28,460 And it's a very, very important consideration to see how you 188 00:12:28,460 --> 00:12:32,430 build that relationship. With with your consumers and and 189 00:12:32,470 --> 00:12:38,350 partners and employees you know who will have their stakes into 190 00:12:38,350 --> 00:12:42,640 the data that you're processing. Really making sure that you have 191 00:12:42,640 --> 00:12:48,040 the permissions or the consents necessary for you to utilize the 192 00:12:48,040 --> 00:12:51,560 data or store the data or process it for the purpose that 193 00:12:51,560 --> 00:12:53,680 you're stating and for how long you want to do. 194 00:12:53,680 --> 00:12:57,640 That is is known and it's something that has been defined 195 00:12:57,640 --> 00:13:02,160 in many regulations both within the states as well as across 196 00:13:02,360 --> 00:13:06,080 Europe and other geographies even though there is more that's 197 00:13:06,080 --> 00:13:08,800 that's coming in on that you know aspect as well. 198 00:13:09,710 --> 00:13:13,350 Most corporates operate with values and standards that 199 00:13:13,350 --> 00:13:16,630 they're known for, and that's a good guardrail as well. 200 00:13:17,590 --> 00:13:21,110 Most organizations, and successful ones at that, have 201 00:13:21,190 --> 00:13:24,590 looked at the societal values, and you know how they have 202 00:13:24,590 --> 00:13:28,310 created more value for everybody, not only their own 203 00:13:28,310 --> 00:13:31,470 consumers, but also others who operate in the societies in 204 00:13:31,470 --> 00:13:34,750 which they operate. Those guardrails apply to AI as 205 00:13:34,750 --> 00:13:36,740 well. And that's something that's 206 00:13:36,740 --> 00:13:39,060 known. Most importantly, I think it is 207 00:13:39,060 --> 00:13:42,980 also to anticipate and see what kind of you know, regulations 208 00:13:42,980 --> 00:13:47,060 you're going to see in the industry, you know around the 209 00:13:47,060 --> 00:13:51,510 impact of AI on people. In ways that you know would 210 00:13:51,510 --> 00:13:55,430 benefit them if done right, but it would also create negative 211 00:13:55,430 --> 00:13:58,950 impact in the society. Anticipating some of those 212 00:13:58,950 --> 00:14:02,110 preparing for that journey and making sure that you're doing 213 00:14:02,150 --> 00:14:05,030 the right things from that aspect would put you on the 214 00:14:05,030 --> 00:14:08,350 right side of the laws and regulations when they do come 215 00:14:08,350 --> 00:14:09,950 into effect, and we know that they will. 216 00:14:10,720 --> 00:14:13,680 And I think those organizations and enterprises would find 217 00:14:13,680 --> 00:14:17,080 success far more than the ones who don't. 218 00:14:17,800 --> 00:14:22,280 And I think beyond this, there are companies that are working 219 00:14:22,280 --> 00:14:25,800 together to lay down ethical standards that can be referred 220 00:14:25,800 --> 00:14:29,200 to. We are working on some of these 221 00:14:29,200 --> 00:14:32,360 as well. We do help our customers adopt. 222 00:14:32,920 --> 00:14:36,560 Some of these processes and standards as we build those 223 00:14:36,560 --> 00:14:39,800 systems, how do you take care of biases for example, there are 224 00:14:39,800 --> 00:14:43,040 ways to do this and we do incorporate those frameworks 225 00:14:43,640 --> 00:14:47,760 into every project and every AI driven initiative that we take 226 00:14:47,760 --> 00:14:51,100 up for our customers. Marketing, for example, is one 227 00:14:51,100 --> 00:14:56,500 of the most common area where AI has been applied and we have 228 00:14:57,020 --> 00:15:01,780 without exception always held trust, ethics, privacy, 229 00:15:01,780 --> 00:15:03,620 compliance and security standards. 230 00:15:03,620 --> 00:15:07,020 To each one of those are our projects and those customers 231 00:15:07,020 --> 00:15:11,980 have gone about benefiting from the use of AI and share that 232 00:15:11,980 --> 00:15:14,540 value with the consumers that they serve. 233 00:15:14,540 --> 00:15:19,540 So there are frameworks that you could adopt while working. 234 00:15:19,620 --> 00:15:23,940 On the air projects have another great question from Twitter. 235 00:15:23,940 --> 00:15:29,100 This is from Kayla Aragonas and Kayla says what are the biggest 236 00:15:29,100 --> 00:15:34,420 opportunities that you predict AI will yield for enterprises, 237 00:15:34,420 --> 00:15:36,740 Sunil. For the enterprises, it's going 238 00:15:36,740 --> 00:15:41,420 to drive, in our view, 3 theaters of value creation. 239 00:15:42,460 --> 00:15:45,300 You know, AI is going to accelerate growth. 240 00:15:46,310 --> 00:15:51,670 For enterprises, this is by way of identifying newer markets, 241 00:15:51,950 --> 00:15:56,990 newer segments, newer needs that they can serve or serve those 242 00:15:56,990 --> 00:16:00,870 needs in a different way, which is far more valuable for 243 00:16:00,870 --> 00:16:05,510 customers or to even figure their play in the industry or 244 00:16:05,510 --> 00:16:09,070 across industry value chains. You know one of the things that 245 00:16:09,790 --> 00:16:13,710 we always, you know discuss with our customers and and guide them 246 00:16:13,710 --> 00:16:15,830 on is that the physical products. 247 00:16:15,910 --> 00:16:18,590 Don't transcend industry boundary, but when you think 248 00:16:18,590 --> 00:16:24,750 about data, it does and that means tremendous opportunity and 249 00:16:24,750 --> 00:16:29,230 potential for looking at newer ways to create these new 250 00:16:29,230 --> 00:16:31,510 data-driven, air driven products and services. 251 00:16:32,380 --> 00:16:35,700 You could be a medical device manufacturer, for example, one 252 00:16:35,700 --> 00:16:40,460 of our clients and using data that the medical devices in this 253 00:16:40,460 --> 00:16:44,740 particular case for diabetes, they were able to really help 254 00:16:44,860 --> 00:16:48,580 the other parts of the value chain that interacts with those 255 00:16:48,580 --> 00:16:51,760 very patients. It could be you know, hospitals 256 00:16:51,760 --> 00:16:55,440 which are in the same value chain and how do you turn the 257 00:16:55,960 --> 00:16:58,880 the bane of the industry which is post facto that you know 258 00:16:59,000 --> 00:17:03,920 anything that happens is post facto the the sugar event and 259 00:17:03,920 --> 00:17:06,440 using data and AI to predict those events. 260 00:17:06,440 --> 00:17:09,920 You could then turn this into a pre facto, which is, you know, 261 00:17:10,040 --> 00:17:14,240 really working proactively to help the well-being of those 262 00:17:14,240 --> 00:17:18,280 diabetes patients, but also going across other parts of the 263 00:17:18,560 --> 00:17:22,119 industries that touch. Diabetes patients could be 264 00:17:22,400 --> 00:17:27,560 consumer products on one side, the physical lifestyle products 265 00:17:27,560 --> 00:17:31,640 that can increase the activity levels of these very patients 266 00:17:31,760 --> 00:17:34,920 and we all know that has an impact the food industry. 267 00:17:35,560 --> 00:17:38,960 You know, as a big recipient of all this data and how you could 268 00:17:38,960 --> 00:17:42,920 use this for stitching an ecosystem that improves not only 269 00:17:42,920 --> 00:17:45,440 the well-being for these patients, but also in general 270 00:17:45,480 --> 00:17:47,720 for society. So you know, figuring 271 00:17:47,720 --> 00:17:51,080 accelerated growth is 1 big theater of value creation, 272 00:17:51,440 --> 00:17:55,960 analog efficiencies at scale, you know, you could now really 273 00:17:55,960 --> 00:18:00,840 push those economic frontiers to do things at cost that are far 274 00:18:00,840 --> 00:18:05,780 lesser if done right. And drive more efficiencies into 275 00:18:05,780 --> 00:18:12,620 your operation processes in your field operations and how you 276 00:18:13,100 --> 00:18:16,020 operate your business globally. But most importantly also 277 00:18:16,020 --> 00:18:20,180 building connected ecosystems, the kind that I was talking 278 00:18:20,180 --> 00:18:23,780 about both in the medical device industry as well as in general 279 00:18:23,780 --> 00:18:27,060 where you are creating an economy around you through new 280 00:18:27,060 --> 00:18:30,540 data and air driven products and new business models is a 281 00:18:30,580 --> 00:18:32,860 tremendous opportunity and the network effects. 282 00:18:33,290 --> 00:18:38,010 Of such data and AI, you know, products, services can create 283 00:18:38,010 --> 00:18:41,010 immense value, unprecedented value in the industry. 284 00:18:41,010 --> 00:18:45,050 And this is what we have, you know, embraced in our imposis 285 00:18:45,050 --> 00:18:48,840 Topaz. Offering that we launched, it's 286 00:18:48,960 --> 00:18:52,520 a services brand that brings together all of what we have to 287 00:18:52,520 --> 00:18:55,840 offer as in process and the network of partners that we've 288 00:18:55,840 --> 00:18:58,320 switched together. The solution investments that 289 00:18:58,320 --> 00:19:02,560 we're making to help drive on these three objectives for our 290 00:19:02,560 --> 00:19:06,560 customers. Given the differences between AI 291 00:19:06,560 --> 00:19:10,560 projects and traditional enterprise software projects 292 00:19:10,560 --> 00:19:15,390 that you were describing earlier, what are the conditions 293 00:19:15,390 --> 00:19:20,030 that need to be in place in order to get started in the 294 00:19:20,030 --> 00:19:22,670 right way? In other words, what are the 295 00:19:22,670 --> 00:19:27,470 factors at the beginning that will drive downstream success? 296 00:19:27,990 --> 00:19:32,270 AI should not be done for the sake of AI, and what that 297 00:19:32,270 --> 00:19:37,030 essentially means is to envisage and envision what AI means for 298 00:19:37,030 --> 00:19:39,870 the business and the industry in which you know the company 299 00:19:39,870 --> 00:19:42,620 operates. Really thinking about the 300 00:19:42,860 --> 00:19:47,940 fundamentals of what makes AI successful to deliver those 301 00:19:47,940 --> 00:19:51,060 objectives is the very next thing you know data? 302 00:19:51,500 --> 00:19:54,100 Is it in place? Is it accessible? 303 00:19:54,100 --> 00:19:56,820 Is it available? Does it have the quality that 304 00:19:56,820 --> 00:19:58,980 you could trust in? And if there are specific AI 305 00:19:58,980 --> 00:20:02,220 projects on the horizon, you could even start to look into 306 00:20:02,220 --> 00:20:05,260 whether this is the data that you want to base your AI systems 307 00:20:05,260 --> 00:20:07,850 on. The other thing that I would say 308 00:20:07,850 --> 00:20:12,090 is preparing for the journey, you know it's it's oftentimes we 309 00:20:12,090 --> 00:20:17,770 see enterprises seeing a lot of surprises as they start. 310 00:20:17,770 --> 00:20:20,250 You know for example there are many Poc's that don't see the 311 00:20:20,250 --> 00:20:25,130 day, you know the light of the day because the the impact or 312 00:20:25,130 --> 00:20:29,530 the cultural change or the you know, the enablement of people 313 00:20:29,570 --> 00:20:32,850 who will be working on such systems is often not thought 314 00:20:32,850 --> 00:20:37,530 about and the even costs are not, you know, properly 315 00:20:37,530 --> 00:20:41,850 understood or risk mitigations. Contingency planning to see how 316 00:20:41,850 --> 00:20:45,490 you govern such an AI system are not thought through and hence 317 00:20:45,810 --> 00:20:49,130 you know, doing this as a tech first project which is just a 318 00:20:49,130 --> 00:20:54,090 cool shiny technology that has been used often remains in in 319 00:20:54,090 --> 00:20:57,730 that very center as well rather than really bringing it to the 320 00:20:57,730 --> 00:20:59,690 business. So thinking and preparing for 321 00:20:59,690 --> 00:21:04,100 the journey, you know you you should have and should look at, 322 00:21:04,100 --> 00:21:07,580 you know how AI can change the business, but really breaking it 323 00:21:07,580 --> 00:21:11,660 down into smaller blueprints with defined very specific 324 00:21:11,660 --> 00:21:15,300 objectives and bring an ecosystem together to to really 325 00:21:15,300 --> 00:21:18,260 work on, you know, such a thing. The other thing that I would say 326 00:21:18,260 --> 00:21:25,220 is to take a responsible AI design, which is to say that the 327 00:21:25,460 --> 00:21:29,500 ethics, trust, security, compliance, privacy cannot be an 328 00:21:29,500 --> 00:21:32,700 afterthought. It needs to be baked in right at 329 00:21:32,700 --> 00:21:35,860 the front. Even as you communicate, you 330 00:21:35,860 --> 00:21:39,300 know what AI is for your business, that you lay down some 331 00:21:39,300 --> 00:21:42,620 of those principles so all stakeholders know what it is 332 00:21:43,180 --> 00:21:46,620 that AI is seeking to do or for the business, how they can 333 00:21:46,620 --> 00:21:48,340 engage and what are the fundamentals and the 334 00:21:48,340 --> 00:21:51,220 underpinnings of such a system that you would operate. 335 00:21:51,700 --> 00:21:56,820 Arsalan Khan comes back on Twitter and he says. 336 00:21:57,680 --> 00:22:00,720 Organizations want to do something useful with a I but 337 00:22:00,720 --> 00:22:06,320 still struggle with shadow IT. And so he wants to know how 338 00:22:06,320 --> 00:22:11,680 using a I affects the organizational culture and makes 339 00:22:11,800 --> 00:22:17,280 a I more of an enabler rather than an obstructor. 340 00:22:17,600 --> 00:22:21,000 And I think this gets to the dimension of culture and 341 00:22:21,000 --> 00:22:24,360 organizational change. Sunil that you were a leading 342 00:22:24,520 --> 00:22:28,410 alluding to earlier, Absolutely. And I think it's a shift in the 343 00:22:28,410 --> 00:22:35,290 way in which we view AI. AI is not to displace but 344 00:22:35,290 --> 00:22:40,650 essentially to amplify the potential it for example, and 345 00:22:40,650 --> 00:22:43,570 this is something that we have embraced at Infosys as well, 346 00:22:44,490 --> 00:22:48,410 using AI to improve productivity in software engineering, life 347 00:22:48,410 --> 00:22:52,370 cycles in the way in which we test our systems or the systems 348 00:22:52,370 --> 00:22:56,490 that we build for our customers, how we ensure data standards or 349 00:22:56,490 --> 00:22:58,810 data privacy across all our projects and so on. 350 00:22:58,810 --> 00:23:01,930 There are multiple ways in which you would look at AI and what 351 00:23:02,010 --> 00:23:06,970 this does is to shift the work value chain where humans and 352 00:23:06,970 --> 00:23:09,890 software engineers in this case would then shift to more 353 00:23:09,890 --> 00:23:13,730 complex, more value adding activities and you would have 354 00:23:13,770 --> 00:23:17,090 yeah, really amplify the productivity of people by 355 00:23:18,890 --> 00:23:20,850 running many things autonomously. 356 00:23:22,290 --> 00:23:25,450 The same would happen on the business front as well. 357 00:23:26,440 --> 00:23:31,640 And you know we need to look at AI as a way to change or 358 00:23:31,640 --> 00:23:35,200 reimagine the business processes or business functions or 359 00:23:35,200 --> 00:23:40,080 business models and embrace this to design those new systems in 360 00:23:40,080 --> 00:23:42,680 the way in which we need to do. So I think the thing that I was 361 00:23:42,680 --> 00:23:45,920 saying earlier, yeah, for the sake of AI would not envisage 362 00:23:45,920 --> 00:23:48,740 all of this. And I think if we put the right 363 00:23:48,740 --> 00:23:53,020 foundation and envision the future from a business lens 364 00:23:53,020 --> 00:23:57,820 perspective, I think it tends to clearly communicate the purpose 365 00:23:57,860 --> 00:24:02,100 of their AI project and also bring the various teams that 366 00:24:02,100 --> 00:24:05,660 need to come together it business and find those 367 00:24:05,660 --> 00:24:10,180 champions who can then lead the way to create those systems at 368 00:24:10,180 --> 00:24:13,140 scale. As you talk with senior business 369 00:24:13,140 --> 00:24:16,860 leaders and. With boards, to what extent do 370 00:24:16,860 --> 00:24:22,460 you think that there is an understanding of the complex 371 00:24:22,540 --> 00:24:27,660 impact that a I will have on their organization? 372 00:24:27,860 --> 00:24:32,380 Because even when you talk about a I amplifying the benefit 373 00:24:32,380 --> 00:24:36,860 rather than displacing humans, the reality is is that there is 374 00:24:36,860 --> 00:24:40,140 going to be job displacement. As well. 375 00:24:40,300 --> 00:24:44,340 So it's very complex. And so again, to what extent do 376 00:24:44,500 --> 00:24:50,340 boards and senior business leaders recognize the depth of 377 00:24:50,340 --> 00:24:52,820 complexity on their organizations? 378 00:24:53,180 --> 00:24:57,340 I think there's a great appreciation for the complexity 379 00:24:57,340 --> 00:25:01,250 that exists, but I think I would say that understanding that 380 00:25:01,250 --> 00:25:05,810 complexity and in what are the ways in which you could manage 381 00:25:05,810 --> 00:25:10,530 that complexity and turn this into a positive cycle. 382 00:25:11,530 --> 00:25:17,330 It is where the effort and the focus is shifting and that's why 383 00:25:17,370 --> 00:25:20,490 we are helping our customers really understand how do you 384 00:25:20,850 --> 00:25:23,570 bring those aspects into the things. 385 00:25:23,570 --> 00:25:28,690 For example, we take a responsible layer, design by 386 00:25:28,690 --> 00:25:32,130 design for example, that brings that thought process up front in 387 00:25:32,130 --> 00:25:35,650 the process, so that you're putting the right underpinnings 388 00:25:36,140 --> 00:25:39,820 these systems as you build it, rather than letting it be an 389 00:25:39,820 --> 00:25:42,460 afterthought. That can be a nightmare for the 390 00:25:42,460 --> 00:25:46,180 organization and similarly, when you are envisaging your business 391 00:25:46,180 --> 00:25:51,750 blueprints, the thought process on why you're doing it and how 392 00:25:51,750 --> 00:25:54,430 you want to actually do this, how you're going to bring things 393 00:25:54,430 --> 00:25:59,910 together in order to execute on. This is a conversation that we 394 00:25:59,910 --> 00:26:04,390 have upfront that prepares the organization to then run such 395 00:26:04,390 --> 00:26:06,470 systems at scale. And there are several examples 396 00:26:06,470 --> 00:26:11,740 of this where we have changed existing models, they put new 397 00:26:11,740 --> 00:26:16,100 models in place as well bringing new processes and new entities 398 00:26:16,100 --> 00:26:19,180 together to do things that were not done before. 399 00:26:20,020 --> 00:26:23,780 So let me take a few examples of food and beverage company. 400 00:26:24,620 --> 00:26:31,040 We helped them build the AI core that help them pivot to a more 401 00:26:31,560 --> 00:26:36,320 off store model to serve their customers and integrate digital 402 00:26:36,320 --> 00:26:40,920 partners seamlessly while taking care of privacy and compliance 403 00:26:40,920 --> 00:26:46,260 and some of those other aspects. It became the core about the 404 00:26:46,260 --> 00:26:49,980 company whereby they were able to then plug in new partners as 405 00:26:49,980 --> 00:26:54,180 they evolved this model and very successfully, you know continue 406 00:26:54,180 --> 00:26:58,020 to have that consumer loyalty and in fact built new loyalties 407 00:26:58,020 --> 00:27:01,420 on the digital channels which was something that you know they 408 00:27:01,420 --> 00:27:05,060 were able to take advantage of. Similarly for a national 409 00:27:05,380 --> 00:27:09,780 railroad company, they were envisaging a new ecosystem that 410 00:27:09,780 --> 00:27:13,540 you know they they could create that would improve the yield and 411 00:27:13,540 --> 00:27:15,340 the throughput of the value chain. 412 00:27:16,100 --> 00:27:20,220 And this included not only the other partners, the you know the 413 00:27:20,220 --> 00:27:24,980 first mid mile partners, but also their competitors who could 414 00:27:24,980 --> 00:27:28,380 be part of this ecosystem whereby the entire industry is 415 00:27:28,380 --> 00:27:32,180 able to increase the throughput, the economic throughput, but 416 00:27:32,180 --> 00:27:36,260 also shift their position from being a commodity provider which 417 00:27:36,260 --> 00:27:39,740 is capacity in this case to a value added player right, where 418 00:27:39,740 --> 00:27:42,660 you could look at the end to end business outcomes for their 419 00:27:42,660 --> 00:27:45,740 customers and be able to orchestrate it in a very complex 420 00:27:46,100 --> 00:27:50,900 web of partners who can then dynamically come together and so 421 00:27:50,900 --> 00:27:52,060 on. So there are a number of 422 00:27:52,060 --> 00:27:57,860 examples where we have delivered these systems at scale and have 423 00:27:57,860 --> 00:28:02,460 worked through the underpinnings of making sure that we're doing 424 00:28:02,460 --> 00:28:04,700 this right. We're bringing those micro 425 00:28:04,700 --> 00:28:08,940 change management principles which allows the organizations 426 00:28:08,940 --> 00:28:13,420 to scale this and then bring the teams together through those 427 00:28:13,420 --> 00:28:16,460 learnings. We have a really interesting 428 00:28:16,460 --> 00:28:21,460 question from LinkedIn. This is from Mike Prest who is 429 00:28:21,460 --> 00:28:25,660 Chief Information Officer at a private equity investment group, 430 00:28:25,820 --> 00:28:31,100 and he says the following. He says as new adversarial A I 431 00:28:31,100 --> 00:28:36,500 agents are introduced without ethical limitations to penetrate 432 00:28:36,500 --> 00:28:40,080 enterprise systems. Technology leaders often 433 00:28:40,080 --> 00:28:43,640 struggle in balancing optimization and innovation 434 00:28:43,640 --> 00:28:47,080 within their organizations. And here's his question. 435 00:28:47,280 --> 00:28:50,160 What would you say to leaders who are under pressure to 436 00:28:50,160 --> 00:28:56,480 develop a I and the consequences of acting too fast or too slow? 437 00:28:57,040 --> 00:29:01,400 And I would just add to that as I speak with business leaders, 438 00:29:01,400 --> 00:29:04,920 one of the challenges they face which I think is very much along 439 00:29:04,920 --> 00:29:09,110 these these lines is. There's an expectation that they 440 00:29:09,110 --> 00:29:14,390 will make these investments, but yet it's a shifting, it's all a 441 00:29:14,390 --> 00:29:17,270 shifting ground. And so how do you, how do you, 442 00:29:17,430 --> 00:29:20,670 how do you invest in something that you know you need to invest 443 00:29:20,670 --> 00:29:23,750 in, but you don't know exactly what you're investing in because 444 00:29:23,750 --> 00:29:26,870 it's all changing? I'll take this in two parts. 445 00:29:27,030 --> 00:29:32,390 You know, one is how do you balance the need for moving with 446 00:29:32,390 --> 00:29:36,030 speed, but also keeping that purpose and the responsible 447 00:29:36,030 --> 00:29:41,730 design in consideration. I think the first thing is to be 448 00:29:41,730 --> 00:29:45,930 able to clearly articulate what division is and what is it that 449 00:29:46,010 --> 00:29:50,290 you're trying to achieve and having that translate into a 450 00:29:50,290 --> 00:29:54,260 blueprint because that brings the appreciation for what the 451 00:29:54,620 --> 00:29:57,980 what are the design and ethical considerations that need to go 452 00:29:57,980 --> 00:30:00,660 into this. That would also make all the 453 00:30:01,180 --> 00:30:04,500 teams involved in this ready for dealing with that particular 454 00:30:04,500 --> 00:30:09,180 challenge and hence not make decisions that might not meet 455 00:30:09,300 --> 00:30:11,700 those considerations. So I think that articulation is 456 00:30:11,700 --> 00:30:15,620 very important. The 2nd is to look at not only 457 00:30:15,620 --> 00:30:19,380 addressing this on a case to case basis with with each 458 00:30:19,700 --> 00:30:24,480 enterprise with each project, but also to develop not only 459 00:30:24,480 --> 00:30:29,160 standards that your projects can look at but also you're building 460 00:30:29,160 --> 00:30:32,360 your platforms in ways that it has those underpinnings. 461 00:30:32,360 --> 00:30:38,360 In fact for one of the global retailers, we looked at building 462 00:30:38,360 --> 00:30:42,200 a privacy first data platform and what that essentially did 463 00:30:42,200 --> 00:30:45,520 was that in this particular case when they were engaging their 464 00:30:45,520 --> 00:30:48,520 partners, they were engaging various different projects, AI 465 00:30:48,520 --> 00:30:52,380 teams internally. Each team did not have to you 466 00:30:52,380 --> 00:30:56,660 know deal with the complexity as in you know as if they were 467 00:30:56,660 --> 00:30:59,700 doing it for the first time. The lesson learned and the best 468 00:30:59,700 --> 00:31:04,380 practice were best practices were baked into the platform. 469 00:31:04,380 --> 00:31:08,460 So for example, we used AI to discover privacy sensitive 470 00:31:08,460 --> 00:31:11,940 information which was very useful for every AI project as 471 00:31:11,940 --> 00:31:15,140 they were coming out and trying to leverage the data that 472 00:31:15,140 --> 00:31:19,200 existed there. We had automated workflows for 473 00:31:19,240 --> 00:31:22,200 privacy sensitive information that was not properly masked. 474 00:31:22,400 --> 00:31:25,920 So it was not left to the decision of each project to see 475 00:31:25,920 --> 00:31:30,040 what should they be doing. The workflow has baked in rules 476 00:31:30,040 --> 00:31:34,360 and and the actors to whom such an approval should go to and 477 00:31:34,360 --> 00:31:38,560 that allowed the organization to kind of scale this while 478 00:31:38,560 --> 00:31:41,400 protecting the underpinnings that are so very important for 479 00:31:41,400 --> 00:31:43,400 doing this. And I think those things can 480 00:31:43,400 --> 00:31:46,120 meet the need for speed on the business side because they want 481 00:31:46,120 --> 00:31:48,920 to move faster. But you also have a way to 482 00:31:49,160 --> 00:31:54,880 ensure that you're not violating the privacy considerations or 483 00:31:55,120 --> 00:31:57,760 not meeting the compliance standards or the ethical 484 00:31:57,760 --> 00:31:59,400 standards. So those are a few 485 00:31:59,640 --> 00:32:05,720 considerations to keep in mind and work with partners that can 486 00:32:05,800 --> 00:32:10,360 build an ecosystem across people, process and technology. 487 00:32:10,930 --> 00:32:16,130 Now you just spoke about the retention of lessons and 488 00:32:16,130 --> 00:32:21,810 incorporating lessons that are learned into new projects, and 489 00:32:21,810 --> 00:32:25,210 we have a question on exactly that topic from Twitter. 490 00:32:25,810 --> 00:32:30,330 Elizabeth Shaw asks how do you take lessons learned from prior 491 00:32:30,410 --> 00:32:36,010 AI implementation engagements and use them to support support 492 00:32:36,010 --> 00:32:40,660 new client engagements. So this is where this becomes an 493 00:32:40,740 --> 00:32:45,060 evolving, you know, practice. There are there are a few ways 494 00:32:45,060 --> 00:32:49,980 in which we do this. One is we maintain blueprints 495 00:32:50,100 --> 00:32:53,780 that are available to our practitioners globally and this 496 00:32:53,780 --> 00:32:57,900 has all the updated standards best practices. 497 00:32:57,940 --> 00:33:01,380 You know the lesson learned in these standards. 498 00:33:01,700 --> 00:33:04,340 But more importantly, we bring a community of practitioners 499 00:33:04,340 --> 00:33:09,440 together wherein they share the learnings, they share their 500 00:33:09,440 --> 00:33:12,880 experiences and and look at ways in which they've dealt with some 501 00:33:12,880 --> 00:33:16,240 of those challenges. Many of our customers look to 502 00:33:16,240 --> 00:33:19,760 understand, you know, how these things are taken care of. 503 00:33:20,000 --> 00:33:23,560 Obviously the confidentiality of each project is maintained. 504 00:33:24,120 --> 00:33:27,280 It's only the ways in which we are dealing with some of these 505 00:33:27,280 --> 00:33:30,640 challenges that get discussed in the community of, you know, 506 00:33:30,640 --> 00:33:33,790 practitioners. We bake this into our solutions. 507 00:33:34,150 --> 00:33:38,470 So any solution that is used by the practitioners globally have 508 00:33:38,470 --> 00:33:40,430 these standards baked in as well. 509 00:33:41,110 --> 00:33:44,920 And of course for our customers and we are engaging on these 510 00:33:44,920 --> 00:33:48,320 projects, we lead with data strategists who are able to 511 00:33:48,320 --> 00:33:51,040 engage with the business stakeholders, the Cxos and 512 00:33:51,680 --> 00:33:55,200 envision the blueprint or the business potential. 513 00:33:55,200 --> 00:33:58,480 Like we say, the biggest problem to solve in this industry is to 514 00:33:58,520 --> 00:34:01,400 find the right problem to solve. And that's where our data 515 00:34:01,400 --> 00:34:06,200 strategists come in and they are well versed with the standards 516 00:34:06,200 --> 00:34:08,560 and you know the compliance laws. 517 00:34:08,560 --> 00:34:12,320 We guide many of our customers on privacy standards or you 518 00:34:12,320 --> 00:34:14,719 know, looking at remediations that are necessary in their 519 00:34:14,719 --> 00:34:17,679 systems to operate such systems at scale. 520 00:34:18,040 --> 00:34:22,560 So when you are doing this, any kind of such projects building 521 00:34:22,560 --> 00:34:28,040 that ecosystem wherein you can push this into multiple vehicles 522 00:34:28,040 --> 00:34:32,070 that your teams would use for implementing such projects 523 00:34:32,070 --> 00:34:35,070 become important. So best practices industries or 524 00:34:35,070 --> 00:34:38,429 the standards document putting this in their training systems 525 00:34:38,670 --> 00:34:41,909 so that anybody who's getting enabled on this is well aware 526 00:34:41,909 --> 00:34:46,350 of, you know, those standards that need to be invited and then 527 00:34:46,350 --> 00:34:47,949 the projects that they will execute. 528 00:34:48,310 --> 00:34:52,989 And also, you know, making that available through, you know, 529 00:34:53,030 --> 00:34:57,470 data privacy office or the compliance office is also a very 530 00:34:57,470 --> 00:35:01,790 meaningful way so that people know who to go for getting that 531 00:35:01,790 --> 00:35:04,030 guidance. And this office can really take 532 00:35:04,030 --> 00:35:07,830 on the initiative to make everybody aware, enable them, 533 00:35:07,870 --> 00:35:11,750 engage them, become a resource when necessary to to guide those 534 00:35:11,750 --> 00:35:15,830 teams as well. On the topic of teams, what 535 00:35:15,830 --> 00:35:20,590 would you say is the the team composition that an organization 536 00:35:20,950 --> 00:35:25,030 needs to look for? It's clearly a business first 537 00:35:25,310 --> 00:35:29,950 approach to this where you're looking at the business teams 538 00:35:31,000 --> 00:35:34,880 really coming together along with the IT or the technology 539 00:35:34,880 --> 00:35:38,000 teams to deliver this. But they are like we're saying 540 00:35:38,000 --> 00:35:40,960 the considerations of the responsible by design. 541 00:35:41,660 --> 00:35:46,660 So you would definitely have a play of your data privacy or 542 00:35:46,660 --> 00:35:51,460 leads, your compliance leads, you know, can audit the project 543 00:35:51,460 --> 00:35:55,580 or give blueprints upfront for what the projects need to comply 544 00:35:55,580 --> 00:35:59,100 with and similarly looking at the other considerations of data 545 00:35:59,100 --> 00:36:01,980 security etc. To bring those experts. 546 00:36:02,380 --> 00:36:06,540 So it's essentially, you know, our tribe so to speak that 547 00:36:06,540 --> 00:36:11,910 brings together these skills and in an agile passion compose 548 00:36:12,670 --> 00:36:16,830 these teams to to address the skills required for delivering 549 00:36:16,830 --> 00:36:19,310 on that project. Like I was saying earlier, it's 550 00:36:19,310 --> 00:36:23,070 it's a dynamic composition because you would take on, you 551 00:36:23,070 --> 00:36:26,510 know, the business, you know needs in an agile passion and 552 00:36:26,990 --> 00:36:29,710 hence building that tribe wherein you're able to pull 553 00:36:29,710 --> 00:36:33,150 these resources and create the part necessary for for 554 00:36:33,150 --> 00:36:37,170 addressing on this. So it's it's a a more of a cross 555 00:36:37,170 --> 00:36:42,530 functional team that you would have to work on your. 556 00:36:43,090 --> 00:36:46,130 We have a very interesting question again from Arsalan 557 00:36:46,130 --> 00:36:49,890 Khan. And, he says should given the 558 00:36:50,010 --> 00:36:56,890 emphasis on AI systems today, to what extent should organizations 559 00:36:56,890 --> 00:37:02,220 be focused on AI? Tools and projects, as opposed 560 00:37:02,220 --> 00:37:06,980 to traditional business and digital transformation projects 561 00:37:06,980 --> 00:37:09,060 using traditional enterprise software. 562 00:37:09,460 --> 00:37:13,660 Over the past few years, we saw businesses embracing digital 563 00:37:14,300 --> 00:37:17,940 businesses embracing cloud. In fact, the businesses that 564 00:37:17,940 --> 00:37:22,020 embrace cloud were able to respond to events like Pandemic 565 00:37:22,140 --> 00:37:24,900 way better than those who did not. 566 00:37:25,870 --> 00:37:29,190 So they've invested in cloud, they've invested in digital. 567 00:37:29,550 --> 00:37:34,110 And getting to AI is the very next logical step where you're 568 00:37:34,110 --> 00:37:38,230 able to then amplify the outcomes that you can get 569 00:37:38,230 --> 00:37:40,990 through this. So it's kind of a continuum on 570 00:37:40,990 --> 00:37:45,510 that particular chain, but it also leverages the investments 571 00:37:45,630 --> 00:37:50,220 that businesses have made in the digital and data thus far. 572 00:37:50,940 --> 00:37:55,140 It then enables them to get quicker ROI through AI projects, 573 00:37:55,820 --> 00:37:59,060 through AI initiatives and that's you know very important 574 00:37:59,060 --> 00:38:04,460 consideration even as you look at scaling the AI initiatives to 575 00:38:04,460 --> 00:38:07,300 enterprise level, the underpinnings that you have in 576 00:38:07,300 --> 00:38:10,700 your digital and data would allow you to scale it at that 577 00:38:10,700 --> 00:38:13,660 level. Simple example is that if you 578 00:38:13,660 --> 00:38:21,580 are using generative AI for enabling users or consumers to 579 00:38:21,620 --> 00:38:26,100 ask questions and get answers, you would want to have the right 580 00:38:26,100 --> 00:38:27,820 level of authorizations built in. 581 00:38:28,620 --> 00:38:32,620 For example, let's say I'm a non finance person and I shouldn't 582 00:38:32,620 --> 00:38:35,620 be seeing certain numbers. You want to make sure that the 583 00:38:35,620 --> 00:38:38,340 generator via system does not give out the information that 584 00:38:38,340 --> 00:38:42,580 I'm not supposed to see. And those things are well baked 585 00:38:42,580 --> 00:38:46,700 in into the digital and data foundations that most 586 00:38:46,740 --> 00:38:50,660 enterprises have, you know laid and that can be scaled to the 587 00:38:50,660 --> 00:38:52,180 newer systems as you're doing this. 588 00:38:52,180 --> 00:38:54,980 So I think this is a continuum that you build on. 589 00:38:55,540 --> 00:38:58,660 It allows you to get ROI quicker, ROI on the investments 590 00:38:58,660 --> 00:39:01,900 that you've already made, allows you to scale at the enterprise 591 00:39:01,900 --> 00:39:05,530 level and with the right considerations put in place of 592 00:39:05,530 --> 00:39:09,650 responsible by design, you can operate with confidence as well. 593 00:39:09,650 --> 00:39:13,890 So it's kind of an initiative, but there is an aspect of 594 00:39:13,890 --> 00:39:16,690 experimentation that has to take place with AI. 595 00:39:16,690 --> 00:39:23,250 That's a very important aspect of how you will figure or learn 596 00:39:23,250 --> 00:39:26,810 new opportunities that business can really take advantage of and 597 00:39:26,810 --> 00:39:30,880 how ready or what kind of data do you have, what kind of data 598 00:39:30,880 --> 00:39:32,560 quality you have to really solve. 599 00:39:32,800 --> 00:39:35,200 Some of those problems will come through the experimentation 600 00:39:35,200 --> 00:39:39,360 punnel, but when you're scaling it, it's gonna go back to some 601 00:39:39,360 --> 00:39:41,720 of the foundations that have been put in place. 602 00:39:41,720 --> 00:39:46,440 So you're kind of getting from digital cloud to now AI? 603 00:39:46,920 --> 00:39:52,040 That experimentation process or do you find that organizations? 604 00:39:53,240 --> 00:39:56,440 Are having trouble with that, or does it seem to go pretty 605 00:39:56,440 --> 00:39:59,480 smoothly for folks that are very process bound? 606 00:39:59,480 --> 00:40:04,760 I would imagine that this experimentation is just a very 607 00:40:04,760 --> 00:40:07,640 different way of thinking the. Enterprise is to think about 608 00:40:07,640 --> 00:40:09,920 setting up that experimentation ecosystem. 609 00:40:09,920 --> 00:40:13,840 We guide our customers and we do a number of engagements for our 610 00:40:13,840 --> 00:40:16,200 customers. We were thinking through the 611 00:40:16,200 --> 00:40:19,000 experimentation. That is not wasteful but is 612 00:40:19,000 --> 00:40:21,600 productive and there is a way to think about it. 613 00:40:22,220 --> 00:40:25,540 How do you funnel ideas into the experimentation zone? 614 00:40:26,260 --> 00:40:29,580 There are ways to do this, you know, through design thinking on 615 00:40:29,580 --> 00:40:32,500 one side where you're exploring with business what problems can 616 00:40:32,500 --> 00:40:34,700 be solved and in what ways can that be solved. 617 00:40:35,060 --> 00:40:38,500 You could use data to nudge and recommend what areas you could 618 00:40:38,500 --> 00:40:40,220 look at. You know, for example data could 619 00:40:40,220 --> 00:40:43,700 tell you a trade promotion of certain kind, could improve, you 620 00:40:43,700 --> 00:40:47,020 know the sales for other teams and could become the idea for 621 00:40:47,020 --> 00:40:49,640 you to experiment on. Or it could be the business 622 00:40:49,640 --> 00:40:52,960 teams coming out with newer ideas that they would like to 623 00:40:53,040 --> 00:40:56,120 look at because they are hearing those problems in the field or 624 00:40:56,120 --> 00:40:59,600 they are experiencing certain bottlenecks in which you know 625 00:40:59,600 --> 00:41:01,640 the business is experiencing you know problems. 626 00:41:02,520 --> 00:41:06,280 But how do you then run this through the idea funnel to 627 00:41:06,600 --> 00:41:09,560 scenario certain things You could simulate to better 628 00:41:09,560 --> 00:41:14,400 understand those things, convert those into real Poc's and you 629 00:41:14,400 --> 00:41:16,600 know the the small experimentation projects. 630 00:41:16,880 --> 00:41:20,400 But really putting those measure measurements in place whereby 631 00:41:20,400 --> 00:41:23,560 you are able to evaluate what the experimentation is, telling 632 00:41:23,680 --> 00:41:25,520 the business in terms of what it can get. 633 00:41:25,760 --> 00:41:28,320 And then connecting that into, you know, how you can scale 634 00:41:28,320 --> 00:41:31,640 successful ideas when when they need to be pushed to that. 635 00:41:31,680 --> 00:41:34,440 But more importantly, also feeding those experimentations 636 00:41:34,440 --> 00:41:36,960 back into the funnel. So that next time when the 637 00:41:36,960 --> 00:41:39,360 business is looking to do an experiment that has already been 638 00:41:39,360 --> 00:41:42,480 conducted by somebody else, one could discover that and use that 639 00:41:42,480 --> 00:41:44,680 to then see whether there's a need to do this. 640 00:41:44,680 --> 00:41:49,510 So I think there's a a clear experimentation design that one 641 00:41:49,510 --> 00:41:53,430 could adopt, making sure that the whole experimentation cycle 642 00:41:53,750 --> 00:41:58,190 is serving the need to innovate at speed, but also gives you the 643 00:41:58,190 --> 00:42:02,710 basis on which you could scale those ideas and make this a very 644 00:42:02,710 --> 00:42:08,950 productive cycle for yourself. With that, I have to say a huge 645 00:42:08,950 --> 00:42:14,020 thank you to Sunil Sennen. From Infosys for taking the time 646 00:42:14,020 --> 00:42:17,380 to be with us. Sunil, thank you for being here. 647 00:42:17,380 --> 00:42:21,740 I really, really appreciate your time and your expertise. 648 00:42:22,180 --> 00:42:25,340 Thank you so much for having me on your show and it was great 649 00:42:25,900 --> 00:42:29,020 talking with you. And thank you to everybody who 650 00:42:29,020 --> 00:42:32,380 watched, and especially to those folks who asked such great 651 00:42:32,380 --> 00:42:34,860 questions. I always say this, but you guys 652 00:42:34,860 --> 00:42:37,260 are an amazing audience. You're so smart. 653 00:42:37,700 --> 00:42:42,260 And we love your questions and they add so much to Cxotalk. 654 00:42:42,660 --> 00:42:46,740 Now before you go, please subscribe to our YouTube 655 00:42:46,740 --> 00:42:50,460 channel, hit the subscribe button, It's at the bottom of 656 00:42:50,580 --> 00:42:53,420 our website, checkoutcxotalk.com. 657 00:42:53,420 --> 00:42:57,260 We really have great shows coming up and we'll see you 658 00:42:57,260 --> 00:42:58,940 again next time. Thanks so much everybody and 659 00:42:58,940 --> 00:42:59,700 have a great day.