WEBVTT 1 00:00:02.830 --> 00:00:05.220 Flowers are not digital products at all 2 00:00:05.220 --> 00:00:07.920 but digital technologies like artificial intelligence 3 00:00:07.920 --> 00:00:10.130 can still offer considerable value to companies 4 00:00:10.130 --> 00:00:11.853 that sell non-digital products. 5 00:00:13.320 --> 00:00:15.840 In today's episode, Amit Shah, president 6 00:00:15.840 --> 00:00:17.997 of 1-800-Flowers describes how AI 7 00:00:17.997 --> 00:00:19.860 and machine learning offered 8 00:00:19.860 --> 00:00:22.730 through a platform like 1-800-Flowers can empower 9 00:00:22.730 --> 00:00:26.580 small companies to compete with much larger organizations. 10 00:00:26.580 --> 00:00:29.350 Welcome to Me, Myself, and AI, a podcast 11 00:00:29.350 --> 00:00:31.770 on artificial intelligence in business. 12 00:00:31.770 --> 00:00:33.310 Each episode, we introduce you 13 00:00:33.310 --> 00:00:35.640 to someone innovating with AI. 14 00:00:35.640 --> 00:00:36.950 I'm Sam Ransbotham, 15 00:00:36.950 --> 00:00:40.180 Professor of Information Systems at Boston College. 16 00:00:40.180 --> 00:00:42.207 I'm also the Guest Editor for the AI 17 00:00:42.207 --> 00:00:43.250 and Business Strategy 18 00:00:43.250 --> 00:00:47.260 Big Idea program at MIT Sloan Management Review. 19 00:00:47.260 --> 00:00:51.150 And, I'm Shervin Khodabandeh, Senior Partner with BCG, 20 00:00:51.150 --> 00:00:54.960 and I co-lead BCG's AI practice in North America. 21 00:00:54.960 --> 00:00:59.030 And together, MIT SMR and BCG have been researching AI 22 00:00:59.030 --> 00:01:02.640 for five years, interviewing hundreds of practitioners 23 00:01:02.640 --> 00:01:05.370 and surveying thousands of companies on what it takes 24 00:01:05.370 --> 00:01:09.300 to build and to deploy and scale AI capabilities 25 00:01:09.300 --> 00:01:10.670 across the organization 26 00:01:10.670 --> 00:01:13.320 and really transform the way organizations operate. 27 00:01:15.640 --> 00:01:16.980 Today, we're joined by Amit Shah. 28 00:01:16.980 --> 00:01:19.950 Amit's the president of 1-800-Flowers. 29 00:01:19.950 --> 00:01:20.783 Amit, welcome. 30 00:01:20.783 --> 00:01:23.010 We're excited to learn more about what you're doing. 31 00:01:23.010 --> 00:01:26.600 Thank you so much, Sam and Shervin, great to be here. 32 00:01:26.600 --> 00:01:29.110 Amit, we talked earlier, and discovered that we all, 33 00:01:29.110 --> 00:01:30.970 had some connection to the state of Georgia, 34 00:01:30.970 --> 00:01:33.030 and you and Shervin and I. 35 00:01:33.030 --> 00:01:34.470 But now you're in Long Island. 36 00:01:34.470 --> 00:01:36.920 You're the president of 1-800-Flowers. 37 00:01:36.920 --> 00:01:39.960 Tell us a little bit about your career path to get there. 38 00:01:39.960 --> 00:01:42.660 So I started out as an analyst at McKinsey, 39 00:01:42.660 --> 00:01:46.870 which is very well known for helping teach problem solvings 40 00:01:46.870 --> 00:01:49.990 both individually and collectively at scale, 41 00:01:49.990 --> 00:01:54.990 and then worked at a range of startups in the Northeast area 42 00:01:55.350 --> 00:02:00.350 and ended up being part of Liberty Media ProFlowers Group 43 00:02:00.450 --> 00:02:04.370 at an early stage of my career, and got very much involved, 44 00:02:04.370 --> 00:02:08.070 I would say, in the front seat of growth hacking. 45 00:02:08.070 --> 00:02:11.590 And that's what led me to sort of the current career 46 00:02:11.590 --> 00:02:14.440 as I've seen growth hacking and sort of the mindset 47 00:02:14.440 --> 00:02:18.100 of the hacker, looking for the continuous change 48 00:02:18.100 --> 00:02:21.670 continuous upliftment, and a continuous desire 49 00:02:21.670 --> 00:02:24.030 to provide the best customer experience, 50 00:02:24.030 --> 00:02:25.950 actually both prepaid, 51 00:02:25.950 --> 00:02:29.310 and ultimately become the most critical element 52 00:02:29.310 --> 00:02:33.430 of any C-suite or boardrooms at large. 53 00:02:33.430 --> 00:02:35.570 Amit, starting in a marketing role 54 00:02:35.570 --> 00:02:37.300 and then transitioning to president, 55 00:02:37.300 --> 00:02:39.840 that marketing background must've made a difference. 56 00:02:39.840 --> 00:02:40.850 How has being 57 00:02:40.850 --> 00:02:44.150 from marketing affected your role as president? 58 00:02:44.150 --> 00:02:45.590 That's a great question Sam. 59 00:02:45.590 --> 00:02:49.360 Evolving into the role of leading, you know, 60 00:02:49.360 --> 00:02:52.020 multiple functions, departments 61 00:02:52.020 --> 00:02:55.040 and colleagues outside of marketing, 62 00:02:55.040 --> 00:02:58.610 what really stands out is two key elements. 63 00:02:58.610 --> 00:03:01.630 The first thing is that I think marketing traditionally 64 00:03:01.630 --> 00:03:06.630 was seen as one of the functional competencies in empowering 65 00:03:07.200 --> 00:03:09.690 and accelerating problem solving. 66 00:03:09.690 --> 00:03:12.620 Marketing is becoming a growth function, 67 00:03:12.620 --> 00:03:17.330 and growth is becoming the key differentiator of companies. 68 00:03:17.330 --> 00:03:21.110 So I expect to continue to see an acceleration 69 00:03:21.110 --> 00:03:25.330 of marketing leaders actually taking on much more leadership 70 00:03:25.330 --> 00:03:30.330 responsibilities and ultimately starting to steer the ship 71 00:03:30.330 --> 00:03:33.450 because growth has become the essential currency 72 00:03:33.450 --> 00:03:35.470 and essential differentiator. 73 00:03:35.470 --> 00:03:37.170 So that is one vector. 74 00:03:37.170 --> 00:03:39.860 And then I would say the second vector that informs me 75 00:03:39.860 --> 00:03:43.660 and prescient to our AI conversation is that I feel 76 00:03:43.660 --> 00:03:46.480 like people in the marketing sphere are actually much 77 00:03:46.480 --> 00:03:51.200 more contextually aware and contextually practicing 78 00:03:51.200 --> 00:03:54.770 advanced problem solving, using machine learning 79 00:03:54.770 --> 00:03:56.600 than a lot of their peers. 80 00:03:56.600 --> 00:04:00.140 So I think there's a unique ability and a unique mindset 81 00:04:00.140 --> 00:04:02.590 that I bring to this leadership role as president 82 00:04:02.590 --> 00:04:07.130 of 1-800-Flowers, having been exposed to that quality 83 00:04:07.130 --> 00:04:09.350 and quantum of problem solving, 84 00:04:09.350 --> 00:04:10.950 which is surrounding a lot of growth 85 00:04:10.950 --> 00:04:14.240 and marketing leaders around us. 86 00:04:14.240 --> 00:04:16.990 Let's address the obvious question, flowers themselves 87 00:04:16.990 --> 00:04:18.480 aren't digital at all. 88 00:04:18.480 --> 00:04:20.760 How is 1-800-Flowers using digital data, 89 00:04:20.760 --> 00:04:21.913 and AI in particular? 90 00:04:23.090 --> 00:04:27.260 Currently, we are a platform of 15 brands. 91 00:04:27.260 --> 00:04:28.530 So if you think about it, you know, 92 00:04:28.530 --> 00:04:33.130 we are a platform that empowers engagement. 93 00:04:33.130 --> 00:04:35.750 We play along the full spectrum 94 00:04:35.750 --> 00:04:40.180 of human expression and engagement, starting from birthdays 95 00:04:40.180 --> 00:04:43.150 all the way to sympathy and everything in between. 96 00:04:43.150 --> 00:04:44.457 So that is who we are. 97 00:04:44.457 --> 00:04:48.510 You know, we think that we have built an all-star range 98 00:04:48.510 --> 00:04:53.210 of brands to really power up an engagement platform. 99 00:04:53.210 --> 00:04:54.043 So if you think 100 00:04:54.043 --> 00:04:57.450 about what differentiates modern organizations, 101 00:04:57.450 --> 00:05:00.080 it is not just the ability to adopt technologies 102 00:05:00.080 --> 00:05:03.270 which has become a table stake, but the ability 103 00:05:03.270 --> 00:05:08.180 to out-solve their competitors in facing deep problems. 104 00:05:08.180 --> 00:05:12.600 So when I think about AI, I think about our competitiveness 105 00:05:12.600 --> 00:05:15.160 on that frontier. 106 00:05:15.160 --> 00:05:17.400 Are we better problem solvers? 107 00:05:17.400 --> 00:05:19.200 And I'll give you a terrific example of that. 108 00:05:19.200 --> 00:05:22.130 You know, when I started my career 20 years ago 109 00:05:22.130 --> 00:05:24.540 as a young analyst at McKinsey, 110 00:05:24.540 --> 00:05:26.210 there was a clear differentiator 111 00:05:26.210 --> 00:05:28.370 between people who are masters 112 00:05:28.370 --> 00:05:31.440 of Excel and who are not, right? 113 00:05:31.440 --> 00:05:35.500 It was a tool that empowered decision making 114 00:05:35.500 --> 00:05:39.600 at scale and communication of the decision making. 115 00:05:39.600 --> 00:05:41.090 When I think about AI 116 00:05:41.090 --> 00:05:43.270 and its power five years down the road, 117 00:05:43.270 --> 00:05:45.370 I think every new employee that starts 118 00:05:45.370 --> 00:05:49.120 out will actually have an AI tool kit 119 00:05:49.120 --> 00:05:52.000 like we used to get the Excel toolkit 120 00:05:52.000 --> 00:05:54.470 to both solve problems better and communicate 121 00:05:54.470 --> 00:05:59.150 that better to clients, to colleagues, to any stakeholder. 122 00:05:59.150 --> 00:06:02.920 So AI to me is not a skill-set issue, 123 00:06:02.920 --> 00:06:06.990 it is a mindset issue and over the longterm, 124 00:06:06.990 --> 00:06:11.800 companies that adopt and understand that this is a mindset 125 00:06:11.800 --> 00:06:15.390 and a skill-set game, actually will be the ones 126 00:06:15.390 --> 00:06:19.110 that are more competitive than their peer reference group. 127 00:06:19.110 --> 00:06:21.310 Yeah. That's super insightful and right on. 128 00:06:21.310 --> 00:06:24.160 And it's almost exactly the conversation 129 00:06:24.160 --> 00:06:26.310 that we were having with Will from Google. 130 00:06:26.310 --> 00:06:27.560 It is all about mindset. 131 00:06:27.560 --> 00:06:31.150 Yet, it's quite daunting, I would say, 132 00:06:31.150 --> 00:06:34.010 that for so many companies, 133 00:06:34.010 --> 00:06:35.310 they, they're still viewing it 134 00:06:35.310 --> 00:06:38.660 as a technology issue, as a technology black box. 135 00:06:38.660 --> 00:06:40.520 As you know, we need a lot of data. 136 00:06:40.520 --> 00:06:42.180 We need a lot of data scientists, which is, 137 00:06:42.180 --> 00:06:45.970 of course you do need those, but you need to also focus 138 00:06:45.970 --> 00:06:49.040 on the right problems and that problem definition 139 00:06:49.040 --> 00:06:52.060 and how you go about solving those problems. 140 00:06:52.060 --> 00:06:55.560 And then how do you change the mindset of the actual users? 141 00:06:55.560 --> 00:07:00.300 Because I could imagine you have been able to shift 142 00:07:00.300 --> 00:07:03.640 the mindset of two kinds of groups, like the mindset 143 00:07:03.640 --> 00:07:06.960 of the consumers, where 20 years ago, they wouldn't, 144 00:07:06.960 --> 00:07:09.570 they wouldn't share information on birthdays 145 00:07:09.570 --> 00:07:12.410 and things like that with any digital platform. 146 00:07:12.410 --> 00:07:16.430 But of course you've changed that mindset, including myself. 147 00:07:16.430 --> 00:07:19.200 But you've also, must've changed the mindset 148 00:07:19.200 --> 00:07:21.160 and the ways of working of so many, 149 00:07:21.160 --> 00:07:26.070 like mom and pop local florists that are actually engaging 150 00:07:26.070 --> 00:07:28.570 with a platform to do business. 151 00:07:28.570 --> 00:07:30.100 Can you comment on that a little bit 152 00:07:30.100 --> 00:07:34.753 and how hard was to do that? I think Shervin, you have, 153 00:07:34.753 --> 00:07:38.600 you have brought a very important crux of this issue. 154 00:07:38.600 --> 00:07:42.080 You know, when we, when we talk about a mindset shift, 155 00:07:42.080 --> 00:07:45.670 a metamorphosis of acceptance of, you know, 156 00:07:45.670 --> 00:07:48.550 mindset over even skill-set, 157 00:07:48.550 --> 00:07:52.080 it really requires a multi-stakeholder approach. 158 00:07:52.080 --> 00:07:54.200 And certainly we are very proud 159 00:07:54.200 --> 00:07:57.120 that we support almost a community of 4,000 160 00:07:57.120 --> 00:08:00.840 plus florists who are powering Main Street businesses. 161 00:08:00.840 --> 00:08:03.850 And I would say one of the last remaining outposts 162 00:08:03.850 --> 00:08:05.970 of successful Main Street businesses 163 00:08:05.970 --> 00:08:08.180 in the US is the florist, 164 00:08:08.180 --> 00:08:10.770 and it plays a very important part in the community 165 00:08:10.770 --> 00:08:13.880 not just as sort of a trusted provider 166 00:08:13.880 --> 00:08:16.230 to all the important occasions 167 00:08:16.230 --> 00:08:18.240 for everyone in the community. 168 00:08:18.240 --> 00:08:21.880 But also I would say as a light post 169 00:08:21.880 --> 00:08:24.690 of how the context with AI is evolving. 170 00:08:24.690 --> 00:08:27.450 And let me give you a few examples of it. 171 00:08:27.450 --> 00:08:29.930 The question comes down to, is the context 172 00:08:29.930 --> 00:08:32.110 around me and getting more competitive and evolving? 173 00:08:32.110 --> 00:08:33.670 And I would say for the small florist 174 00:08:33.670 --> 00:08:36.650 and a company like ours, being surrounded 175 00:08:36.650 --> 00:08:39.010 by platforms like Facebook and Google, 176 00:08:39.010 --> 00:08:42.720 which are auction-rich machine learning environments 177 00:08:42.720 --> 00:08:46.950 set up to extract the highest yield per click 178 00:08:47.840 --> 00:08:52.130 means that any business owner that is seeking growth, 179 00:08:52.130 --> 00:08:54.830 that is seeking to get in front of customers, 180 00:08:54.830 --> 00:08:56.770 already is being mediated 181 00:08:56.770 --> 00:08:59.960 by machine learning and artificial intelligence. 182 00:08:59.960 --> 00:09:04.640 So when I think about this multi-stakeholder empowerment, 183 00:09:04.640 --> 00:09:09.640 I think about how do we empower the smallest florist 184 00:09:09.730 --> 00:09:13.090 in heartland of America compete 185 00:09:13.090 --> 00:09:15.550 with this evolution of context? 186 00:09:15.550 --> 00:09:18.540 You know, how do we empower that, you know, 187 00:09:18.540 --> 00:09:21.720 small business entity to get to that strength? 188 00:09:21.720 --> 00:09:24.027 And I think that's where the mindset comes in, 189 00:09:24.027 --> 00:09:27.120 'cause what this requires is, first of all, 190 00:09:27.120 --> 00:09:31.093 understanding that the context is already rich in AI and ML. 191 00:09:32.110 --> 00:09:34.700 The second point is that unless you can, 192 00:09:34.700 --> 00:09:37.360 you can assemble a response to it, 193 00:09:37.360 --> 00:09:39.570 you are always on the losing side. 194 00:09:39.570 --> 00:09:42.690 So our thinking is that by providing those suite 195 00:09:42.690 --> 00:09:46.330 of services, by providing and working very closely 196 00:09:46.330 --> 00:09:50.040 with our florist community, our supplier community, 197 00:09:50.040 --> 00:09:52.740 we are actually providing them relevance 198 00:09:52.740 --> 00:09:56.570 in a rapidly evolving context, where getting in the front 199 00:09:56.570 --> 00:09:59.370 of their customers itself is a machine learning problem. 200 00:10:00.230 --> 00:10:02.170 How do you go about doing that? 201 00:10:02.170 --> 00:10:07.040 How much of that is technologically driven 202 00:10:07.040 --> 00:10:11.550 through the platform and how much of that is good 203 00:10:11.550 --> 00:10:15.130 old fashioned human grease and relationship management 204 00:10:15.130 --> 00:10:20.130 and working closely with these little places? 205 00:10:20.440 --> 00:10:22.930 How much of that was technology solving the problem 206 00:10:22.930 --> 00:10:25.160 versus people and processes 207 00:10:25.160 --> 00:10:27.560 and change management and those kinds of things? 208 00:10:28.560 --> 00:10:32.240 A very strong starting point is realizing, how 209 00:10:32.240 --> 00:10:37.240 can you basically collect data and make inferences at scale? 210 00:10:37.700 --> 00:10:39.453 So I'll give you a simple example. 211 00:10:40.340 --> 00:10:42.230 To set up a reminder program 212 00:10:42.230 --> 00:10:47.230 on our platform is actually a perpetual cold start problem. 213 00:10:47.910 --> 00:10:49.830 And let me explain what that means. 214 00:10:49.830 --> 00:10:52.960 It means that for example, if you come 215 00:10:52.960 --> 00:10:56.870 to our website or you come to any florist website 216 00:10:56.870 --> 00:10:58.070 and you, let's say you have come 217 00:10:58.070 --> 00:11:01.740 in to express happy birthday to your sister 218 00:11:01.740 --> 00:11:04.910 whose birthday's a week away, and you might come 219 00:11:04.910 --> 00:11:08.280 and pick an arrangement, let's say she loves, you know, 220 00:11:08.280 --> 00:11:11.190 white calla lilies, and you come and, you know, 221 00:11:11.190 --> 00:11:15.180 do some clicking on white flowers, white arrangements, 222 00:11:15.180 --> 00:11:18.080 and then pick a calla lily arrangement and send it to her. 223 00:11:18.920 --> 00:11:22.760 Most companies will take a record of that data 224 00:11:22.760 --> 00:11:26.070 and say that the next time Shervin comes to our site, 225 00:11:26.070 --> 00:11:28.930 let's show him white, for example. 226 00:11:28.930 --> 00:11:29.870 But it could be that the, 227 00:11:29.870 --> 00:11:33.960 your next visit is actually right before Valentine's. 228 00:11:33.960 --> 00:11:36.850 You're here to buy flowers, which are predominantly 229 00:11:36.850 --> 00:11:40.180 generally red or pink for Valentine's. 230 00:11:40.180 --> 00:11:42.350 And you're trying to express that. 231 00:11:42.350 --> 00:11:45.840 So your, your entire click history, your entire corpus 232 00:11:45.840 --> 00:11:48.930 of digital breadcrumbs that you have given us 233 00:11:48.930 --> 00:11:52.600 to solve a machine learning problem is actually irrelevant 234 00:11:52.600 --> 00:11:55.740 because you're starting again as a cold start outcome. 235 00:11:55.740 --> 00:11:59.300 And this fact of personalization, you know, 236 00:11:59.300 --> 00:12:01.830 the enormity of data, the enormity 237 00:12:01.830 --> 00:12:05.230 of decisions required to resolve this outcome 238 00:12:05.230 --> 00:12:09.470 so that you can create a better customer experience is, 239 00:12:09.470 --> 00:12:13.370 is what we are empowering our stakeholders 240 00:12:13.370 --> 00:12:15.400 to really realize, right? 241 00:12:15.400 --> 00:12:17.320 So that is one dimension of it. 242 00:12:17.320 --> 00:12:19.520 The second dimension of it is what we talked 243 00:12:19.520 --> 00:12:23.770 about that currently customers are intermediated 244 00:12:23.770 --> 00:12:27.390 by extremely expensive, I would say, 245 00:12:27.390 --> 00:12:30.430 auction rich environments, controlled 246 00:12:30.430 --> 00:12:35.430 by a few major platforms and to play in those platforms 247 00:12:35.930 --> 00:12:39.330 you need to have a baseline competency. 248 00:12:39.330 --> 00:12:40.690 So we employ a lot 249 00:12:40.690 --> 00:12:45.690 of advanced algorithmic trading and algorithmic models 250 00:12:46.080 --> 00:12:49.960 for example, to understand what should be your bid 251 00:12:49.960 --> 00:12:53.450 at any given time of the day, day of the week, 252 00:12:53.450 --> 00:12:58.120 and the month of a year in order to maximize your, 253 00:12:58.120 --> 00:13:01.460 your yield and minimize your CAC. 254 00:13:01.460 --> 00:13:04.550 And those data sets, that sophistication, 255 00:13:04.550 --> 00:13:08.990 that investment is, is almost outside the realm, 256 00:13:08.990 --> 00:13:13.990 I would say, of a lot of localized businesses and outcomes. 257 00:13:14.450 --> 00:13:16.510 So this question of building alliances, 258 00:13:16.510 --> 00:13:20.140 this question of trusting larger entities, 259 00:13:20.140 --> 00:13:23.130 is going to become also more important over time. 260 00:13:23.130 --> 00:13:26.340 So when we think about our mission and our vision, 261 00:13:26.340 --> 00:13:31.160 we are inspired by what part can we play 262 00:13:31.160 --> 00:13:33.270 in catalyzing those outcomes 263 00:13:33.270 --> 00:13:36.510 and empowering in accelerating those outcomes? 264 00:13:36.510 --> 00:13:37.700 Because whether we are talking 265 00:13:37.700 --> 00:13:39.990 about florists on the Main Street, as one 266 00:13:39.990 --> 00:13:44.700 of the last remaining independent important businesses 267 00:13:44.700 --> 00:13:49.560 in America, or we think about someone who is trying to get 268 00:13:49.560 --> 00:13:53.760 to a funeral home to express something very personal 269 00:13:53.760 --> 00:13:56.900 to them, those moments define us 270 00:13:56.900 --> 00:13:59.980 and define the communities that we live in. 271 00:13:59.980 --> 00:14:01.560 And we think that, you know, 272 00:14:01.560 --> 00:14:03.580 we have a strong part to play in, 273 00:14:03.580 --> 00:14:05.840 in helping realize that vision. 274 00:14:05.840 --> 00:14:07.120 And we look at that vision 275 00:14:07.120 --> 00:14:10.630 not just as a financial or a transactional outcome, 276 00:14:10.630 --> 00:14:14.160 but we look at that as an outcome of, for the whole society. 277 00:14:14.160 --> 00:14:15.410 So for example, you know, 278 00:14:15.410 --> 00:14:17.320 we have free e-cards that you can come 279 00:14:17.320 --> 00:14:19.033 to our site right now and send. 280 00:14:19.930 --> 00:14:23.500 We really want you to just literally express 281 00:14:23.500 --> 00:14:26.170 to someone that, hey, you are thinking of them, 282 00:14:26.170 --> 00:14:28.580 because we think that it's very more important 283 00:14:28.580 --> 00:14:33.580 for us to appreciate and empower that expression. 284 00:14:33.730 --> 00:14:34.870 That over time 285 00:14:34.870 --> 00:14:38.040 hopefully leads you to have a deeper connection 286 00:14:38.040 --> 00:14:42.350 with us as a brand, deeper connection with us as a platform 287 00:14:42.350 --> 00:14:45.760 and then use us to express that emotion. 288 00:14:45.760 --> 00:14:49.830 But the empowerment of emotion in and of itself 289 00:14:49.830 --> 00:14:53.940 is a very important part of our mission and our vision. 290 00:14:53.940 --> 00:14:56.380 And going back to AI, and the reason I talked 291 00:14:56.380 --> 00:15:00.140 about solving fundamental personalized problems 292 00:15:00.140 --> 00:15:02.350 at scale is that all 293 00:15:02.350 --> 00:15:05.900 of our expressions are ultimately personalized expressions. 294 00:15:05.900 --> 00:15:10.900 So unless you are employing and deploying those technologies 295 00:15:11.130 --> 00:15:15.840 and the mindset that customers are here to express 296 00:15:15.840 --> 00:15:20.800 and connect, you are not going to be looking at the problem 297 00:15:20.800 --> 00:15:23.780 or the solution in the way that empowers 298 00:15:23.780 --> 00:15:25.730 that end customer first. 299 00:15:25.730 --> 00:15:26.580 Was there something 300 00:15:26.580 --> 00:15:28.170 about your background that shaped how you think 301 00:15:28.170 --> 00:15:30.370 about customers or maybe that affects how 302 00:15:30.370 --> 00:15:33.600 you get people working in that customer-first mindset? 303 00:15:33.600 --> 00:15:37.530 I think it was a mix of my liberal arts education 304 00:15:37.530 --> 00:15:40.610 and a desire to push problem solving 305 00:15:40.610 --> 00:15:45.610 as a key characteristic and an attribute of my skill-set 306 00:15:46.240 --> 00:15:47.530 as I moved through the various 307 00:15:47.530 --> 00:15:49.810 leadership challenges and ranks. 308 00:15:49.810 --> 00:15:53.540 One of the key lessons that I took away 309 00:15:53.540 --> 00:15:57.310 from my liberal arts education at Bowdoin 310 00:15:57.310 --> 00:16:01.150 was around the importance of this learning quotient 311 00:16:01.150 --> 00:16:04.100 and having an LQ-first mindset 312 00:16:04.100 --> 00:16:08.440 because what liberal arts really forces you to do 313 00:16:08.440 --> 00:16:13.440 is adopt a continuous learning and asking questions 314 00:16:13.630 --> 00:16:16.870 which are deeper than the functional competency. 315 00:16:16.870 --> 00:16:19.700 And this, I think over time, actually, you know, 316 00:16:19.700 --> 00:16:23.330 when machines start doing repetitive tasks, 317 00:16:23.330 --> 00:16:25.620 decisioning will become actually a very 318 00:16:25.620 --> 00:16:28.600 important ethical choice as well. 319 00:16:28.600 --> 00:16:32.440 When I mentor college students and I give talks, 320 00:16:32.440 --> 00:16:35.180 I always point out the primacy 321 00:16:35.180 --> 00:16:38.650 of take your non-technical classes very seriously 322 00:16:38.650 --> 00:16:41.360 and consider a liberal arts education. 323 00:16:41.360 --> 00:16:44.100 Because I think the seminal questions faced 324 00:16:44.100 --> 00:16:48.150 by a leader 10 years, hence 15 years hence, 325 00:16:48.150 --> 00:16:52.700 are not going to be just around how competent they are, 326 00:16:52.700 --> 00:16:56.700 but how thoughtful they are, how good they are at learning. 327 00:16:56.700 --> 00:16:57.560 Well as a professor 328 00:16:57.560 --> 00:17:00.610 at a university that focuses on liberal arts education, 329 00:17:00.610 --> 00:17:03.200 I can wholeheartedly agree with that. 330 00:17:03.200 --> 00:17:04.710 But I also want to think about, 331 00:17:04.710 --> 00:17:06.050 is there an example of a place? 332 00:17:06.050 --> 00:17:07.840 So you mentioned you were trying to learn 333 00:17:07.840 --> 00:17:11.320 about individual customers and how difficult that is, 334 00:17:11.320 --> 00:17:12.700 because in your context, it's not just 335 00:17:12.700 --> 00:17:14.020 here's what they did last time, 336 00:17:14.020 --> 00:17:16.160 and we predict that they do more of the same. 337 00:17:16.160 --> 00:17:18.470 In fact, last time we told them exactly the opposite 338 00:17:18.470 --> 00:17:20.410 of what they were going to do this time. 339 00:17:20.410 --> 00:17:21.930 So can you give us some examples 340 00:17:21.930 --> 00:17:25.400 of how are you using AI to learn about your customer's needs 341 00:17:25.400 --> 00:17:28.010 and what kinds of things you've learned and how have you set 342 00:17:28.010 --> 00:17:30.440 up your organization to learn those things? 343 00:17:30.440 --> 00:17:34.210 It's exceedingly hard, no matter what AI leaders 344 00:17:34.210 --> 00:17:36.720 and the ecosystem likes to talk about it, 345 00:17:36.720 --> 00:17:38.830 chiefly because of three reasons. 346 00:17:38.830 --> 00:17:43.570 I think all business leaders face a trifecta of issues. 347 00:17:43.570 --> 00:17:45.870 When they think about AI adoption, 348 00:17:45.870 --> 00:17:46.703 the first starts 349 00:17:46.703 --> 00:17:50.170 with having cross-functional and competent teams. 350 00:17:50.170 --> 00:17:52.840 You know, generally what you find within organizations is 351 00:17:52.840 --> 00:17:56.640 that the teams are spoken for and especially data science 352 00:17:56.640 --> 00:17:59.580 and machine learning competencies are extremely 353 00:17:59.580 --> 00:18:02.210 hard to find and fund, I would say. 354 00:18:02.210 --> 00:18:04.630 The second issue is the data sets 355 00:18:04.630 --> 00:18:06.690 are noisy and incomplete. 356 00:18:06.690 --> 00:18:10.980 So when we talk about essential ingredients of AI, 357 00:18:10.980 --> 00:18:15.370 in most companies, actually that data is extremely siloed, 358 00:18:15.370 --> 00:18:20.130 extremely difficult to join and often incomplete. 359 00:18:20.130 --> 00:18:24.980 And the third, which is a much more evolving vector is 360 00:18:24.980 --> 00:18:29.740 that it has to be explainable in its end state, right? 361 00:18:29.740 --> 00:18:33.340 It has to be trustworthy as a stack. 362 00:18:33.340 --> 00:18:36.590 So what we actually found is, is rapidly evolving. 363 00:18:36.590 --> 00:18:38.610 And I think this is going to be very true 364 00:18:38.610 --> 00:18:42.730 of most organizations is to adopt AI as a service. 365 00:18:42.730 --> 00:18:45.730 Most companies I think can get very quickly started 366 00:18:45.730 --> 00:18:50.300 to your question, Sam, by adopting AI as a service 367 00:18:50.300 --> 00:18:53.380 and then asking a very simple question, 368 00:18:53.380 --> 00:18:58.230 what two or three problems can I solve better tomorrow 369 00:18:58.230 --> 00:19:02.100 employing the stack that I'm not doing currently? 370 00:19:02.100 --> 00:19:04.910 And there is very interesting outcomes when 371 00:19:04.910 --> 00:19:06.230 you start looking under the layer. 372 00:19:06.230 --> 00:19:07.460 So one of the problems, I said, 373 00:19:07.460 --> 00:19:09.360 is a cold start problem for us. 374 00:19:09.360 --> 00:19:11.370 So we are working on a recommendation system 375 00:19:11.370 --> 00:19:14.700 which has been very successful, that utilizes a lot 376 00:19:14.700 --> 00:19:17.650 of neural learning and sort of learning 377 00:19:17.650 --> 00:19:21.540 with very thin data sets, to make inferences, right? 378 00:19:21.540 --> 00:19:25.120 The other place that we found is forecasting for example. 379 00:19:25.120 --> 00:19:27.520 You know, forecasting is a very difficult exercise, 380 00:19:27.520 --> 00:19:30.540 especially if you can imagine that, you know, for example 381 00:19:30.540 --> 00:19:34.720 Valentine's Day actually moves by day of the week, right? 382 00:19:34.720 --> 00:19:37.850 So last year it was a Friday, this year, it was a Sunday. 383 00:19:37.850 --> 00:19:40.430 And, you know, compared to Mother's Day, 384 00:19:40.430 --> 00:19:43.300 which is always on a Sunday, right? 385 00:19:43.300 --> 00:19:46.400 And that has very deep business implications 386 00:19:46.400 --> 00:19:47.580 as an outcome, right? 387 00:19:47.580 --> 00:19:50.920 So forecasting is a perfect candidate to put towards this. 388 00:19:50.920 --> 00:19:55.140 But the mindset again is are you testing 389 00:19:55.140 --> 00:19:56.910 and learning along the way? 390 00:19:56.910 --> 00:19:58.780 You know, in some cases, the early attempts 391 00:19:58.780 --> 00:20:00.930 at machine learning will be no better 392 00:20:00.930 --> 00:20:03.150 than your decision based engines. 393 00:20:03.150 --> 00:20:06.110 But what we have seen is that actually persistence 394 00:20:06.110 --> 00:20:11.050 over, even the medium term, has very asymmetric payoffs 395 00:20:11.050 --> 00:20:14.070 and extremely important to evangelize 396 00:20:14.070 --> 00:20:15.930 and understand those payoffs. 397 00:20:15.930 --> 00:20:16.880 Because as I said, 398 00:20:16.880 --> 00:20:19.770 the context that most modern companies find themselves 399 00:20:19.770 --> 00:20:23.550 in is already awash in machine learning. 400 00:20:23.550 --> 00:20:25.840 So two of the three things you mentioned involve 401 00:20:25.840 --> 00:20:28.490 cross-platform, it's the idea of, 402 00:20:28.490 --> 00:20:30.150 of people working together. 403 00:20:30.150 --> 00:20:32.420 You mentioned it from several, from the data perspective 404 00:20:32.420 --> 00:20:34.750 and also from the team perspective, the tension is 405 00:20:34.750 --> 00:20:36.910 if everyone can't work on everything all the time. 406 00:20:36.910 --> 00:20:39.250 Otherwise that's not a team, that's the whole organization. 407 00:20:39.250 --> 00:20:41.680 So how do you set up that within your organization? 408 00:20:41.680 --> 00:20:45.240 So that you've got that nice blend of cross-functional 409 00:20:45.240 --> 00:20:48.430 but not everybody involved in everything? 410 00:20:48.430 --> 00:20:51.370 I would say, you know, to be brutally honest, 411 00:20:51.370 --> 00:20:54.840 it's a field of aspirational tensions. 412 00:20:54.840 --> 00:20:55.719 You know, 413 00:20:55.719 --> 00:20:58.660 when you are trying to shift mindsets over skill-sets, 414 00:20:58.660 --> 00:21:01.870 it's not about sort of how do you assemble teams 415 00:21:01.870 --> 00:21:04.060 and how do you get to a solution, 416 00:21:04.060 --> 00:21:07.740 but how do you ultimately sell your vision and how do you, 417 00:21:07.740 --> 00:21:11.070 how do you get people enthusiastically believing 418 00:21:11.070 --> 00:21:12.180 in that vision? 419 00:21:12.180 --> 00:21:14.470 So I would say our early attempts 420 00:21:14.470 --> 00:21:17.880 at sort of organizing what a lot more command 421 00:21:17.880 --> 00:21:20.527 and control we are, we were sort of saying that, 422 00:21:20.527 --> 00:21:22.580 "Hey, if you have data science background 423 00:21:22.580 --> 00:21:24.840 or you have analytics background 424 00:21:24.840 --> 00:21:26.780 maybe you are primed for this." 425 00:21:26.780 --> 00:21:27.700 I think over time 426 00:21:27.700 --> 00:21:30.600 what we have realized is actually learning systems 427 00:21:30.600 --> 00:21:33.960 our self-organizing principle at their core. 428 00:21:33.960 --> 00:21:36.640 So now we are thinking more about, as I was saying, 429 00:21:36.640 --> 00:21:40.250 the early days of just rolling out Excel to everyone. 430 00:21:40.250 --> 00:21:44.280 What if we rolled out AI as a service to everyone? 431 00:21:44.280 --> 00:21:46.810 You know, is if someone is just making a schedule 432 00:21:46.810 --> 00:21:51.810 of meetings, do they get more empowered by AI as a service? 433 00:21:52.250 --> 00:21:56.510 You know, will they themselves find out some novel solutions 434 00:21:56.510 --> 00:21:59.950 to something that was completely not thought of as, 435 00:21:59.950 --> 00:22:02.670 as an important-enough problem, right? 436 00:22:02.670 --> 00:22:06.360 And the reason I say that Sam is, is not to suggest 437 00:22:06.360 --> 00:22:09.610 that there is not a cohesive sort of listing 438 00:22:09.610 --> 00:22:12.480 of problems that can be solved by AI 439 00:22:12.480 --> 00:22:15.500 and assembling cross-functional teams and doing that. 440 00:22:15.500 --> 00:22:19.610 I think that's the easier part, but what I'm suggesting, 441 00:22:19.610 --> 00:22:22.500 and you know, egging on my peer reference group 442 00:22:22.500 --> 00:22:27.060 to really think about is that the real empowerment 443 00:22:27.060 --> 00:22:30.330 and the real transformation in the mindset will come 444 00:22:30.330 --> 00:22:34.570 when you roll out AI to every end point, right? 445 00:22:34.570 --> 00:22:37.140 Like we don't think twice about rolling out email 446 00:22:37.140 --> 00:22:39.010 to every new employee. 447 00:22:39.010 --> 00:22:42.960 Why do we constrain and, and sort of self limit ourselves 448 00:22:42.960 --> 00:22:47.700 to think about AI as only the domain of specialists, right? 449 00:22:47.700 --> 00:22:50.230 It's a problem solving methodology. 450 00:22:50.230 --> 00:22:52.040 It's a problem solving mindset. 451 00:22:52.040 --> 00:22:54.130 It's an operating system, we build apps on it. 452 00:22:54.130 --> 00:22:54.963 Exactly. 453 00:22:54.963 --> 00:22:55.890 And I think that's quite insightful 454 00:22:55.890 --> 00:22:59.900 because whatever you make available 455 00:22:59.900 --> 00:23:04.540 to smart and inquisitive people 456 00:23:04.540 --> 00:23:06.570 ends up becoming better. 457 00:23:06.570 --> 00:23:07.403 And that's a, 458 00:23:07.403 --> 00:23:09.680 that's a very good challenge to any organization. 459 00:23:09.680 --> 00:23:14.050 You know, why not have the suite of AI products self-service 460 00:23:14.050 --> 00:23:17.550 for the layman user to be able to do things with? 461 00:23:17.550 --> 00:23:18.500 To your point. 462 00:23:18.500 --> 00:23:19.940 One other thing that comes 463 00:23:19.940 --> 00:23:24.940 to mind is the importance of appreciating failures 464 00:23:25.420 --> 00:23:28.170 as essential input to better learning. 465 00:23:28.170 --> 00:23:29.350 I think what I find 466 00:23:29.350 --> 00:23:34.350 in adopting an AI-first mindset is a deep respect 467 00:23:34.720 --> 00:23:39.720 and celebration of failure as an organizational currency. 468 00:23:39.930 --> 00:23:41.610 If you think about the history 469 00:23:41.610 --> 00:23:46.600 of employees within an organization, all the origin stories 470 00:23:46.600 --> 00:23:50.110 and the stories thereafter are around successes 471 00:23:50.110 --> 00:23:51.770 but an AI-first mindset 472 00:23:51.770 --> 00:23:56.640 in my mind is how do you actually collectively embrace, 473 00:23:56.640 --> 00:23:59.500 you know, not, not by putting up posters that, you know, 474 00:23:59.500 --> 00:24:01.720 run fast and fail fast. 475 00:24:01.720 --> 00:24:02.553 You know, don't, 476 00:24:02.553 --> 00:24:06.380 those don't really change people's activities, 477 00:24:06.380 --> 00:24:08.550 their behaviors, and their acceptance 478 00:24:08.550 --> 00:24:13.550 of their career trajectory as much as celebrating failures. 479 00:24:13.630 --> 00:24:14.930 And the reason I say that is 480 00:24:14.930 --> 00:24:17.550 that all machine learning, all learning 481 00:24:17.550 --> 00:24:21.180 in the future actually has a very healthy equilibrium 482 00:24:21.180 --> 00:24:23.530 between outcomes that are successful and outcomes 483 00:24:23.530 --> 00:24:26.900 that failed because outcomes that fail actually 484 00:24:26.900 --> 00:24:30.510 teach the system equally as much as outcomes that succeeded. 485 00:24:30.510 --> 00:24:33.090 And I think it's a very important point on failure. 486 00:24:33.090 --> 00:24:35.140 How do you operationalize that? 487 00:24:35.140 --> 00:24:38.300 I pray a lot and I tossed the coin a lot 488 00:24:38.300 --> 00:24:40.860 but, you know, it's a very important question. 489 00:24:40.860 --> 00:24:43.490 I think it has to start from the leadership. 490 00:24:43.490 --> 00:24:45.440 I think it has to start from a very, 491 00:24:45.440 --> 00:24:49.410 very human manifestation 492 00:24:49.410 --> 00:24:53.600 of how decisions are extremely difficult. 493 00:24:53.600 --> 00:24:57.850 And even for leaders to be very open about when 494 00:24:57.850 --> 00:25:02.410 their decisions did not lead to successful outcomes. 495 00:25:02.410 --> 00:25:04.980 So I think one of the key learnings 496 00:25:04.980 --> 00:25:08.360 in my life and which I've tried to follow very deeply 497 00:25:08.360 --> 00:25:10.630 is around radical transparency, 498 00:25:10.630 --> 00:25:13.500 around making sure that people appreciate, 499 00:25:13.500 --> 00:25:16.810 that these were the reasons I took a certain decision 500 00:25:16.810 --> 00:25:20.390 and that I'm open enough at the end of it 501 00:25:20.390 --> 00:25:22.560 for any inputs, right? 502 00:25:22.560 --> 00:25:25.500 Whether it went successfully or it didn't go successfully. 503 00:25:25.500 --> 00:25:27.610 So that is one way of operationalizing it when 504 00:25:27.610 --> 00:25:30.510 the leadership starts living out that outcome. 505 00:25:30.510 --> 00:25:32.580 The second, I think very important part 506 00:25:32.580 --> 00:25:35.940 of it is how do you incentivize that outcome? 507 00:25:35.940 --> 00:25:37.280 So for example, you know, 508 00:25:37.280 --> 00:25:41.000 we have a constant red team that we call internally, 509 00:25:41.000 --> 00:25:44.000 that runs up against the main growth team, for example. 510 00:25:44.000 --> 00:25:45.980 So if the main growth team has a $100 million 511 00:25:45.980 --> 00:25:48.610 to spend on marketing, I give 10% 512 00:25:48.610 --> 00:25:50.920 to a red team that is actually going 513 00:25:50.920 --> 00:25:53.360 against the conventional wisdom. 514 00:25:53.360 --> 00:25:54.480 And the reason it is going 515 00:25:54.480 --> 00:25:56.760 against the conventional wisdom is actually to build 516 00:25:56.760 --> 00:26:00.240 up a corpus of failures that then can act 517 00:26:00.240 --> 00:26:02.750 as a foil to what did we learn 518 00:26:02.750 --> 00:26:04.350 from spending that $100 million. 519 00:26:05.370 --> 00:26:07.500 And this is a very important part again 520 00:26:07.500 --> 00:26:11.880 of increasing the collective LQ of the team, right? 521 00:26:11.880 --> 00:26:14.450 Because if everything is done by consensus, 522 00:26:14.450 --> 00:26:17.880 we know from behavioral economics and a lot of studies done, 523 00:26:17.880 --> 00:26:21.610 it is not the best decision making outcome as well, right? 524 00:26:21.610 --> 00:26:23.290 So that is one example of it. 525 00:26:23.290 --> 00:26:26.010 So how do you set up team structures and incentives? 526 00:26:26.010 --> 00:26:27.640 And then the last thing I would say, 527 00:26:27.640 --> 00:26:30.890 which has been a learning mode of late to me, 528 00:26:30.890 --> 00:26:33.040 is how do you actually translate 529 00:26:33.040 --> 00:26:37.330 that into ESG or ethical goals? 530 00:26:37.330 --> 00:26:40.980 Because what I have seen with the newer cohort 531 00:26:40.980 --> 00:26:44.490 of employees of stakeholders that we have had 532 00:26:44.490 --> 00:26:48.450 is that it is not so much just about learning, 533 00:26:48.450 --> 00:26:52.170 but learning within a context that I believe in. 534 00:26:52.170 --> 00:26:55.040 So my newer understanding more and more 535 00:26:55.040 --> 00:26:59.640 has to be around like, hey, if he ingests AI models, 536 00:26:59.640 --> 00:27:03.490 are they explainable, are they de-biased? 537 00:27:03.490 --> 00:27:04.550 Can I make sure 538 00:27:04.550 --> 00:27:07.620 that the team appreciates that sudden choices 539 00:27:07.620 --> 00:27:11.910 that we may make may not have the immediate business payoff 540 00:27:11.910 --> 00:27:13.970 but are actually much more better aligned 541 00:27:13.970 --> 00:27:15.653 with our vision and our mission? 542 00:27:16.880 --> 00:27:18.940 Well, we started this discussion talking about mom 543 00:27:18.940 --> 00:27:22.380 and pop flower shops, and that resonates with me actually 544 00:27:22.380 --> 00:27:24.650 I didn't mention it, but my mom owned a flower shop. 545 00:27:24.650 --> 00:27:27.580 So mom and pop is actually literal in this point. 546 00:27:27.580 --> 00:27:29.380 Amit, we really appreciate you taking the time 547 00:27:29.380 --> 00:27:31.000 to talk with us today. 548 00:27:31.000 --> 00:27:32.810 Thanks for spending some time with us. 549 00:27:32.810 --> 00:27:33.840 Yeah. Thank you so much. 550 00:27:33.840 --> 00:27:35.100 This has been very insightful. 551 00:27:35.100 --> 00:27:35.933 Thank you. 552 00:27:35.933 --> 00:27:36.880 I love this conversation. 553 00:27:36.880 --> 00:27:37.730 Thank you guys. 554 00:27:37.730 --> 00:27:38.664 Appreciate it. 555 00:27:38.664 --> 00:27:42.164 (relaxed technical music) 556 00:27:44.240 --> 00:27:45.073 Sure enough. 557 00:27:45.073 --> 00:27:46.810 It was was quite interesting. 558 00:27:46.810 --> 00:27:48.940 One thing that struck me was how, you know, 559 00:27:48.940 --> 00:27:49.773 we talk about, "Oh yeah 560 00:27:49.773 --> 00:27:52.140 the machines can learn from past, et cetera, et cetera." 561 00:27:52.140 --> 00:27:54.100 But how at, every scenario for them is a bit 562 00:27:54.100 --> 00:27:55.690 of a cold start problem 563 00:27:55.690 --> 00:27:58.460 because, you know, every holiday is different. 564 00:27:58.460 --> 00:28:01.060 Every time someone comes to them, they're getting something 565 00:28:01.060 --> 00:28:03.670 for a different reason and it wouldn't be a cold start 566 00:28:03.670 --> 00:28:06.660 if they knew the underlying reasons, but they don't always. 567 00:28:06.660 --> 00:28:08.050 When we go to, you know, 568 00:28:08.050 --> 00:28:10.390 any of the normal collaborative filtering platforms 569 00:28:10.390 --> 00:28:13.660 like a Netflix or other places, or even transportation 570 00:28:13.660 --> 00:28:16.080 like Uber and Lyft, those people have a much 571 00:28:16.080 --> 00:28:18.460 better ability to build off our history 572 00:28:18.460 --> 00:28:20.670 than 1-800-Flowers does. 573 00:28:20.670 --> 00:28:22.500 It, it is once, you know, cold start, 574 00:28:22.500 --> 00:28:26.400 and tying that to how emotionally aware they need to be. 575 00:28:26.400 --> 00:28:28.420 Because by definition, 576 00:28:28.420 --> 00:28:32.010 these are very human experiences that they're involved in. 577 00:28:32.010 --> 00:28:34.430 If they screw that up, that's not good. 578 00:28:34.430 --> 00:28:37.590 Yeah, also it's a cold start, which by definition 579 00:28:37.590 --> 00:28:39.920 means it's a learning opportunity 580 00:28:39.920 --> 00:28:41.760 'cause a cold start problem is the same 581 00:28:41.760 --> 00:28:43.270 as a learning problem. 582 00:28:43.270 --> 00:28:45.390 And if you have many cold star problems, 583 00:28:45.390 --> 00:28:47.050 then isn't that another way 584 00:28:47.050 --> 00:28:50.760 of saying you've basically have to be comfortable 585 00:28:50.760 --> 00:28:54.160 with a very accelerated rate of learning 586 00:28:54.160 --> 00:28:57.090 and that's your success, 'cause otherwise, yes 587 00:28:57.090 --> 00:29:00.900 everything is a sympathy or everything is 588 00:29:00.900 --> 00:29:04.220 that one demographic that I really really know. 589 00:29:04.220 --> 00:29:08.410 And rather than being adaptive to all those situations. 590 00:29:08.410 --> 00:29:10.460 The other thing that is emerging theme 591 00:29:10.460 --> 00:29:11.800 over research that he talked 592 00:29:11.800 --> 00:29:16.140 about a lot was the notion of learning quotient, right? 593 00:29:16.140 --> 00:29:20.590 You know, we asked him about teams and he said, 594 00:29:20.590 --> 00:29:25.590 what they care about a lot is an individual's desire 595 00:29:25.930 --> 00:29:29.240 and willingness and ability to want to learn. 596 00:29:29.240 --> 00:29:33.740 And that fits so well with what AI itself is 597 00:29:33.740 --> 00:29:37.760 which is it's all about learning and the notion 598 00:29:37.760 --> 00:29:41.150 of human and machine learning from each other 599 00:29:41.150 --> 00:29:43.040 which is also the theme of our work. 600 00:29:43.040 --> 00:29:45.610 I found it quite insightful that he picked up on that. 601 00:29:45.610 --> 00:29:48.760 And in many ways it sort of also fits 602 00:29:48.760 --> 00:29:51.590 into his point around mindset 603 00:29:51.590 --> 00:29:55.320 and culture change because he also talked about, you know, 604 00:29:55.320 --> 00:29:58.650 it's not so much about the skill-set or the tech, 605 00:29:58.650 --> 00:30:01.720 it's much more about changing the ways of working, 606 00:30:01.720 --> 00:30:05.130 and changing the operating model and changing the mindset 607 00:30:05.130 --> 00:30:10.130 of what you can and should do with AI, with this tool 608 00:30:10.340 --> 00:30:14.640 and capability that he thought would just be as commonplace 609 00:30:14.640 --> 00:30:17.950 as an Excel spreadsheet, that is not pretty commonplace. 610 00:30:17.950 --> 00:30:18.783 Exactly. 611 00:30:18.783 --> 00:30:21.950 And so the importance of learning and ongoing learning 612 00:30:21.950 --> 00:30:24.633 and adaptability, I thought was quite elegant 613 00:30:24.633 --> 00:30:26.030 in what he said. 614 00:30:26.030 --> 00:30:27.620 Well, you're not gonna get an argument from me. 615 00:30:27.620 --> 00:30:29.830 I mean, I'm, I'm a professor, I'm an academic. 616 00:30:29.830 --> 00:30:30.663 So I think that. 617 00:30:30.663 --> 00:30:32.230 Yeah, you love learning, right. 618 00:30:32.230 --> 00:30:35.130 I'm biased to think that learning is kind of a big thing 619 00:30:36.030 --> 00:30:38.090 but even more than that, he also mentioned a little bit 620 00:30:38.090 --> 00:30:40.810 the importance of liberal arts thinking in that learning. 621 00:30:40.810 --> 00:30:44.490 You and I, we make fun of our engineering backgrounds a lot 622 00:30:44.490 --> 00:30:46.630 but as we're seeing these technologies get easier 623 00:30:46.630 --> 00:30:48.130 and easier to use, 624 00:30:48.130 --> 00:30:50.660 it's really highlighting the importance of the human, 625 00:30:50.660 --> 00:30:53.580 and importance of the human working with the machine. 626 00:30:53.580 --> 00:30:55.640 I think, you know, if we go back 20 or 30 years ago, 627 00:30:55.640 --> 00:30:57.860 there was so much talk about the death of IT, 628 00:30:57.860 --> 00:31:00.720 And IT doesn't matter, you remember that phase. 629 00:31:00.720 --> 00:31:01.553 Yeah. 630 00:31:01.553 --> 00:31:03.310 But, nope, that didn't happen a bit. 631 00:31:03.310 --> 00:31:05.850 I mean, as IT became easier to use, 632 00:31:05.850 --> 00:31:07.800 companies just wanted more and more of it. 633 00:31:07.800 --> 00:31:10.320 And this is the natural extension of that. 634 00:31:10.320 --> 00:31:12.380 Yeah, and I think there's this notion 635 00:31:12.380 --> 00:31:17.380 of technology raising the playing field 636 00:31:17.380 --> 00:31:20.750 so that humans can operate at a higher level, 637 00:31:20.750 --> 00:31:23.410 and then humans inventing better technology 638 00:31:23.410 --> 00:31:26.910 so that, that level again keeps, you know, getting raised. 639 00:31:26.910 --> 00:31:29.860 I think that's sort of a common theme 640 00:31:29.860 --> 00:31:31.930 that's happened with technology. 641 00:31:31.930 --> 00:31:33.773 You know, actually chess is a great example of that, 642 00:31:33.773 --> 00:31:37.120 because if you look at how 20 years ago, 643 00:31:37.120 --> 00:31:39.280 you talked about Sam 20 years ago, the death of IT. 644 00:31:39.280 --> 00:31:42.220 Like 20 years ago, 25, 30 years ago, 645 00:31:42.220 --> 00:31:44.870 it was almost like the death of the computer in chess 646 00:31:44.870 --> 00:31:47.810 because it was like argued that there's no way. 647 00:31:47.810 --> 00:31:48.643 Yeah. Right. 648 00:31:48.643 --> 00:31:51.720 Like no way a human could be beaten by a computer. 649 00:31:51.720 --> 00:31:54.300 And then the game change when yeah. 650 00:31:54.300 --> 00:31:58.170 With, with Kasparov lost to Deep Blue, 651 00:31:58.170 --> 00:32:02.760 but then what happened is chess players got smarter. 652 00:32:02.760 --> 00:32:06.650 So the chess Elo ranking, the highest ranking 653 00:32:06.650 --> 00:32:07.960 of highest chess players 654 00:32:07.960 --> 00:32:10.480 has been, you know, steadily increasing. 655 00:32:10.480 --> 00:32:11.313 Right. 656 00:32:11.313 --> 00:32:12.480 Because of how, you know, 657 00:32:12.480 --> 00:32:15.203 AI has helped humans get smarter. 658 00:32:16.630 --> 00:32:17.620 Thanks for listening. 659 00:32:17.620 --> 00:32:19.310 Next time, we'll talk with JoAnn Stonier 660 00:32:19.310 --> 00:32:20.930 Chief Data Officer at MasterCard 661 00:32:20.930 --> 00:32:23.920 about how MasterCard uses design thinking to ensure its use 662 00:32:23.920 --> 00:32:26.563 of AI supports its overall business strategy. 663 00:32:27.660 --> 00:32:30.460 Thanks for listening to Me, Myself, and AI. 664 00:32:30.460 --> 00:32:32.050 If you're enjoying the show, 665 00:32:32.050 --> 00:32:34.180 take a minute to write us a review. 666 00:32:34.180 --> 00:32:35.800 If you send us a screenshot, 667 00:32:35.800 --> 00:32:39.100 we'll send you a collection of MIT SMRs best articles 668 00:32:39.100 --> 00:32:42.650 on artificial intelligence free for a limited time. 669 00:32:42.650 --> 00:32:47.353 Send your review screenshot to smrfeedback@mit.edu.