WEBVTT 36365404-c939-42de-853d-5f91b7c546f8-0 00:00:00.200 --> 00:00:00.920 Iana, welcome. e866f206-0dfa-481e-a5be-af576227b195-0 00:00:01.640 --> 00:00:02.240 How is GenAI d6adf1ae-25ad-4fb6-a1a9-852446400df1-0 00:00:02.240 --> 00:00:05.214 influencing the customer experience, and where do you d6adf1ae-25ad-4fb6-a1a9-852446400df1-1 00:00:05.214 --> 00:00:06.080 see that headed? 174a1f17-5a15-4faa-9542-01b914017041-0 00:00:06.360 --> 00:00:10.051 Yeah, you know customer experience will be influenced 174a1f17-5a15-4faa-9542-01b914017041-1 00:00:10.051 --> 00:00:11.760 across every single step. 8cd4449c-a9e7-48f3-bc0c-27b6456f94c7-0 00:00:12.040 --> 00:00:16.236 So, imagine a journey and, nowadays, you will infuse 8cd4449c-a9e7-48f3-bc0c-27b6456f94c7-1 00:00:16.236 --> 00:00:19.720 generative AI every single step of the way. 832ed0ae-145e-4231-9988-de0f3c2ed91e-0 00:00:19.960 --> 00:00:23.927 So, the question is, observing many companies and many 832ed0ae-145e-4231-9988-de0f3c2ed91e-1 00:00:23.927 --> 00:00:28.040 organizations, they tend to go for individual use cases. e64c0a0e-ce7e-40b3-8869-a72aa9fc1fd9-0 00:00:28.680 --> 00:00:30.800 However, it's a huge transformation. 01b2d4a2-07fd-431e-a474-832b8ace1bb8-0 00:00:30.800 --> 00:00:34.917 So, what will be super important is: imagine this as a 01b2d4a2-07fd-431e-a474-832b8ace1bb8-1 00:00:34.917 --> 00:00:36.040 transformation. 1f1cbfed-6deb-42f0-92fc-b806e665fb1d-0 00:00:36.040 --> 00:00:40.025 So, come up with a strategy and don't forget that it's not 1f1cbfed-6deb-42f0-92fc-b806e665fb1d-1 00:00:40.025 --> 00:00:43.943 sufficient to just go for individual use cases to get all 1f1cbfed-6deb-42f0-92fc-b806e665fb1d-2 00:00:43.943 --> 00:00:46.240 the impact you could out of this. d8403ac7-3bbf-4926-ad3d-7e8ace1ac0de-0 00:00:46.480 --> 00:00:49.813 Can you outline some of the challenges that organizations d8403ac7-3bbf-4926-ad3d-7e8ace1ac0de-1 00:00:49.813 --> 00:00:51.480 face when implementing GenAI. 4328b2da-5b77-4fd3-9f87-9d3f730c61f5-0 00:00:51.480 --> 00:00:51.880 2bc58d3b-ab53-4cce-917a-ac23d0b3ffb5-0 00:00:52.560 --> 00:00:56.254 Yeah, one of the biggest that we observe right now is around 2bc58d3b-ab53-4cce-917a-ac23d0b3ffb5-1 00:00:56.254 --> 00:00:56.800 adoption. 97a5df0d-2cc0-4cc6-b670-2d7aef511493-0 00:00:57.120 --> 00:01:02.105 So, figuring out all these fantastic things that we can now 97a5df0d-2cc0-4cc6-b670-2d7aef511493-1 00:01:02.105 --> 00:01:07.091 build and improve with, with generative AI, who is going to 97a5df0d-2cc0-4cc6-b670-2d7aef511493-2 00:01:07.091 --> 00:01:08.920 be using this and how? 1f3b073f-b5ea-4a9b-b2a8-dbf062bccaae-0 00:01:09.320 --> 00:01:12.430 And, in my experience, two things are particularly 1f3b073f-b5ea-4a9b-b2a8-dbf062bccaae-1 00:01:12.430 --> 00:01:13.040 important. 63881fc8-b402-4b6e-b4ab-2743e1c52670-0 00:01:13.240 --> 00:01:15.160 One is around trustability. 88330541-a34b-460d-8365-3ab774999dd7-0 00:01:15.160 --> 00:01:20.642 So, the information that I'm getting out of the system that 88330541-a34b-460d-8365-3ab774999dd7-1 00:01:20.642 --> 00:01:24.480 is leveraging AI, how can it be verified? 6d113bb5-cff2-4b1a-ba49-b31e2aa08098-0 00:01:24.480 --> 00:01:29.000 How can we ensure that it's not all some kind of illusion? a56551de-3a19-47c5-82ea-d804d2c2623e-0 00:01:29.400 --> 00:01:32.000 And the second point is around relevance. 14584f1a-d3ff-4e76-a7da-fd3423609e92-0 00:01:32.240 --> 00:01:37.005 So, if you think about the outcomes of these algorithms, 14584f1a-d3ff-4e76-a7da-fd3423609e92-1 00:01:37.005 --> 00:01:40.600 they need to take cognitive load from you. d6f3e2fb-4c9c-4370-bd5a-64ebd7991700-0 00:01:41.080 --> 00:01:44.075 They need to ensure that whatever they deliver is really d6f3e2fb-4c9c-4370-bd5a-64ebd7991700-1 00:01:44.075 --> 00:01:45.600 helpful and relevant for you. 67fde493-a38d-4bad-8178-39728839df5d-0 00:01:46.200 --> 00:01:47.964 How can organizations--individual 67fde493-a38d-4bad-8178-39728839df5d-1 00:01:47.964 --> 00:01:50.560 organizations--overcome some of these challenges? 6bb21ab5-0141-4a9f-974d-645b46ac074a-0 00:01:51.320 --> 00:01:55.244 So, if you think about about this transformation, many 6bb21ab5-0141-4a9f-974d-645b46ac074a-1 00:01:55.244 --> 00:01:59.240 organizations think about generative AI as a technology 6bb21ab5-0141-4a9f-974d-645b46ac074a-2 00:01:59.240 --> 00:02:02.880 and hence this is a technology-led transformation. cea8d42c-247f-4a89-8729-cb576567554a-0 00:02:03.160 --> 00:02:07.080 However, in my experience, a lot of that is about humans. 65dcfcd7-ffa7-4558-81d6-2df6b90e7410-0 00:02:07.320 --> 00:02:08.280 Are they going to use it? 47859e83-f6fd-4582-98aa-f83a7770e4a5-0 00:02:08.280 --> 00:02:09.480 Are they going to adopt it? 4f3c7207-f18a-4bd0-ad40-ce47ff0efded-0 00:02:09.880 --> 00:02:13.880 How is this going to change each and every job in the company? 2980ce42-3e20-41e2-8138-6d50f0da68a0-0 00:02:13.880 --> 00:02:17.446 And so this is where I would start, start thinking about who 2980ce42-3e20-41e2-8138-6d50f0da68a0-1 00:02:17.446 --> 00:02:20.953 is the potential user, who are the stakeholders who will be 2980ce42-3e20-41e2-8138-6d50f0da68a0-2 00:02:20.953 --> 00:02:21.479 affected? 4ade1d17-4824-4f56-aaea-9e1b907103dc-0 00:02:21.760 --> 00:02:25.000 And then, yeah, integrate it into this transformation. 539317fe-1c5d-4af1-80db-d4e3e8fc3c57-0 00:02:25.760 --> 00:02:26.960 Thank you so much for your time. b7a5b951-f327-4cbe-bbd7-e8e36261abf1-0 00:02:26.960 --> 00:02:27.440 Thank you.