WEBVTT d2ef2eec-de77-4ec4-86ac-f4c348e31a93-0 00:00:00.110 --> 00:00:03.652 Rob, welcome. Rob. What should be top of mind for leaders when d2ef2eec-de77-4ec4-86ac-f4c348e31a93-1 00:00:03.652 --> 00:00:07.081 thinking about Jenny I solutions for ecommerce? Yeah, when I d2ef2eec-de77-4ec4-86ac-f4c348e31a93-2 00:00:07.081 --> 00:00:10.455 think about Jenny I solutions and tech I, I really treat it d2ef2eec-de77-4ec4-86ac-f4c348e31a93-3 00:00:10.455 --> 00:00:13.997 very similar to adtech, martech and ecom tech. And so I always d2ef2eec-de77-4ec4-86ac-f4c348e31a93-4 00:00:13.997 --> 00:00:17.652 want to start with the customer experience first. What use cases d2ef2eec-de77-4ec4-86ac-f4c348e31a93-5 00:00:17.652 --> 00:00:19.620 underpin that customer experience, 8dee88fe-56df-4965-a969-290c8cb8c0b7-0 00:00:20.280 --> 00:00:23.242 What technology you have in your existing stack? What's the 8dee88fe-56df-4965-a969-290c8cb8c0b7-1 00:00:23.242 --> 00:00:25.761 interoperability of that technology and what Jenni 8dee88fe-56df-4965-a969-290c8cb8c0b7-2 00:00:25.761 --> 00:00:28.280 solutions are being introduced by those providers? c1f79a20-50c3-4813-a65b-c878388c9b74-0 00:00:28.870 --> 00:00:31.907 And then what's your talent look like? What's your operating c1f79a20-50c3-4813-a65b-c878388c9b74-1 00:00:31.907 --> 00:00:34.894 model? The most important thing is their ability to execute c1f79a20-50c3-4813-a65b-c878388c9b74-2 00:00:34.894 --> 00:00:37.931 against the technology stack that you're introducing. And so c1f79a20-50c3-4813-a65b-c878388c9b74-3 00:00:37.931 --> 00:00:41.118 when I think about build versus buy, let's say I really want to c1f79a20-50c3-4813-a65b-c878388c9b74-4 00:00:41.118 --> 00:00:44.254 look towards my tech providers first, what alphas, betas I can c1f79a20-50c3-4813-a65b-c878388c9b74-5 00:00:44.254 --> 00:00:47.242 get involved in and then what maybe emerging new companies, c1f79a20-50c3-4813-a65b-c878388c9b74-6 00:00:47.242 --> 00:00:50.079 best of breed companies, startups are, are in this space 08da61e3-07f3-4fec-a2fc-1869574259aa-0 00:00:50.750 --> 00:00:53.392 because building in a really rapidly moving technology 08da61e3-07f3-4fec-a2fc-1869574259aa-1 00:00:53.392 --> 00:00:56.466 ecosystem is quite challenging, not just the initial build, but 08da61e3-07f3-4fec-a2fc-1869574259aa-2 00:00:56.466 --> 00:00:57.620 the ongoing maintenance. e1d96367-3654-46ae-96f6-3bd6ffede772-0 00:00:58.500 --> 00:01:02.023 And so if you're just focused on kind of basic Gen AI content, e1d96367-3654-46ae-96f6-3bd6ffede772-1 00:01:02.023 --> 00:01:05.658 supply chain things, I think you can really go with a lot of off e1d96367-3654-46ae-96f6-3bd6ffede772-2 00:01:05.658 --> 00:01:09.069 the shelf solutions and really focus on building your talent e1d96367-3654-46ae-96f6-3bd6ffede772-3 00:01:09.069 --> 00:01:12.256 and your operating model. If you're doing something more e1d96367-3654-46ae-96f6-3bd6ffede772-4 00:01:12.256 --> 00:01:15.780 sophisticated, like you're in a complex category with a lot of e1d96367-3654-46ae-96f6-3bd6ffede772-5 00:01:15.780 --> 00:01:18.688 personalization or customization, or you have a lot e1d96367-3654-46ae-96f6-3bd6ffede772-6 00:01:18.688 --> 00:01:22.211 of first party data that's very proprietary, you might want to e1d96367-3654-46ae-96f6-3bd6ffede772-7 00:01:22.211 --> 00:01:25.622 look towards more building out more sophisticated solutions. e1d96367-3654-46ae-96f6-3bd6ffede772-8 00:01:25.622 --> 00:01:28.362 How is Genai being used effectively in ecommerce e1d96367-3654-46ae-96f6-3bd6ffede772-9 00:01:28.362 --> 00:01:28.810 already? 6ac2ef81-d2a7-4122-a150-2f66ae4a9df9-0 00:01:29.140 --> 00:01:32.973 Yeah, I mean, what I'm seeing is a lot of content supply chain 6ac2ef81-d2a7-4122-a150-2f66ae4a9df9-1 00:01:32.973 --> 00:01:36.686 use cases and so brief writing for editorial, for image, for 6ac2ef81-d2a7-4122-a150-2f66ae4a9df9-2 00:01:36.686 --> 00:01:40.093 copy. Of course, content creation itself, obviously the 6ac2ef81-d2a7-4122-a150-2f66ae4a9df9-3 00:01:40.093 --> 00:01:43.684 generative portion of AI. So doing seeing a lot of written 6ac2ef81-d2a7-4122-a150-2f66ae4a9df9-4 00:01:43.684 --> 00:01:47.639 copy, some image or some image, just to some degree of efficacy, 6ac2ef81-d2a7-4122-a150-2f66ae4a9df9-5 00:01:47.639 --> 00:01:50.682 particularly around product detail pages, writing 6ac2ef81-d2a7-4122-a150-2f66ae4a9df9-6 00:01:50.682 --> 00:01:54.212 descriptions, images, editing, things like that. A lot of 6ac2ef81-d2a7-4122-a150-2f66ae4a9df9-7 00:01:54.212 --> 00:01:57.924 summarization of UGC. So we think about ratings and reviews. 6ac2ef81-d2a7-4122-a150-2f66ae4a9df9-8 00:01:57.924 --> 00:01:59.750 That's really a great use case 3893eb4c-f458-4293-97d6-37e64ade7ca3-0 00:01:59.870 --> 00:02:03.075 and then into loyalty and customer service. And so when we 3893eb4c-f458-4293-97d6-37e64ade7ca3-1 00:02:03.075 --> 00:02:06.335 think about we're seeing a lot obviously with chat bots and 3893eb4c-f458-4293-97d6-37e64ade7ca3-2 00:02:06.335 --> 00:02:09.432 conversational commerce and almost like Jenny, I enabled 3893eb4c-f458-4293-97d6-37e64ade7ca3-3 00:02:09.432 --> 00:02:12.638 client telling. It's a really interesting into the loyalty 3893eb4c-f458-4293-97d6-37e64ade7ca3-4 00:02:12.638 --> 00:02:16.007 side as well. But I'm really interesting interested in seeing 3893eb4c-f458-4293-97d6-37e64ade7ca3-5 00:02:16.007 --> 00:02:18.723 how Jenny eye and content creation can meet media 3893eb4c-f458-4293-97d6-37e64ade7ca3-6 00:02:18.723 --> 00:02:22.146 optimization. So in the last 10 years, obviously we've had a I 3893eb4c-f458-4293-97d6-37e64ade7ca3-7 00:02:22.146 --> 00:02:25.569 and media and now contents kind of or should say media has met 3893eb4c-f458-4293-97d6-37e64ade7ca3-8 00:02:25.569 --> 00:02:28.884 its match with content. So it's really exciting to see hyper 3893eb4c-f458-4293-97d6-37e64ade7ca3-9 00:02:28.884 --> 00:02:30.459 personalization come to play. 0fc7000d-4c6c-4e88-80c2-73c8b85bc80b-0 00:02:30.600 --> 00:02:32.860 Well, thank you so much for your time. Yeah, absolutely. 4e7e39ce-3bf3-49da-b945-ffc61e6c2ce2-0 00:02:33.480 --> 00:02:37.190