WEBVTT NOTE { "characteristics": "public.accessibility.transcribes-spoken-dialog", "language": "en-US", "language-name": "English (US)" } 00:00:06.800 --> 00:00:13.440 What I’m observing, Nitin, is that there seems to be this common theme, and that 00:00:13.440 --> 00:00:20.040 is that we’re seeing fantastic progress in widening access, 00:00:20.040 --> 00:00:26.640 in usage, in adoption. We’re starting to see organizations achieve some pretty 00:00:26.640 --> 00:00:33.559 impressive productivity and efficiency gains. And yet, there still seems to be this question 00:00:33.560 --> 00:00:40.080 around: we don’t feel that we’re realizing the full promise of our 00:00:40.080 --> 00:00:47.080 investments. And even if we may be getting close at the speed that we need to realize 00:00:47.080 --> 00:00:53.720 them to really drive that competitive edge. The question that people are now bringing up is no 00:00:53.720 --> 00:01:00.400 longer about the usage of AI and what we have been articulating. And I’m actually kind of 00:01:00.440 --> 00:01:07.280 stating this very passionately. The R in the return of AI, in the return of investment in 00:01:07.280 --> 00:01:14.280 AI, should perhaps be the third R, not the first R. And what I mean by that is, if you want 00:01:14.280 --> 00:01:20.960 to see the return, the first R needs to be reimagining the business model, the 00:01:20.960 --> 00:01:27.960 processes, the workflow, and how work is even undertaken. You need to reimagine 00:01:27.960 --> 00:01:34.600 that. And as you reimagine it, you then need to focus on the second R. And that second 00:01:34.600 --> 00:01:41.480 R is redistribution of work between a human workforce and an agentic digital 00:01:41.480 --> 00:01:48.400 workforce. Once you kind of take a step back, reimagine the process and the workflow, and think 00:01:48.400 --> 00:01:54.880 through what type of activities should be undertaken by a human workforce versus what type 00:01:54.880 --> 00:02:01.400 of tasks should be executed by a digital workforce and redistributed, you then essentially 00:02:01.400 --> 00:02:07.920 get to the return on investment through the optimal coupling of the tasks that are naturally 00:02:07.920 --> 00:02:12.480 performed by a human workforce versus the activities that are executed by a digital 00:02:12.480 --> 00:02:19.279 workforce. In our report, we use the language “tipping points.” You know, we talk about how 00:02:19.280 --> 00:02:26.199 one year ago we saw these tensions we were trying to navigate, but now we are at that tipping 00:02:26.200 --> 00:02:32.720 point. And I think it really, really reiterates what you’ve highlighted 00:02:32.720 --> 00:02:39.440 there. A year back, a lot of the conversation was around experimenting with AI 00:02:39.600 --> 00:02:45.959 and undertaking proof of concepts. We are at a point of inflection. So what’s happened is the 00:02:45.960 --> 00:02:52.360 dialogue has now moved. And this is kind of what our survey also shows. The dialogue has moved to 00:02:52.840 --> 00:02:59.509 usage, application, scaling, adoption, and return. You know, 00:02:59.550 --> 00:03:06.110 a lot of what we’re discussing in the report is technology alone isn’t going to get us 00:03:06.110 --> 00:03:12.789 there. We need to be very intentional in how we are doing the reimagination, 00:03:12.950 --> 00:03:19.390 if we’re and how we are applying AI and being deliberate in how we design the 00:03:19.390 --> 00:03:25.670 relationships. Reimagine your processes within your business context. 00:03:26.310 --> 00:03:32.429 Take a step back. Think through what are the actual tasks and activities that should be 00:03:32.429 --> 00:03:38.949 undertaken by a human versus a digital workforce. Redistribute those tasks and activities between 00:03:38.949 --> 00:03:44.910 those two bifurcated workforces, and then strive for the optimal coupling of how they work 00:03:44.910 --> 00:03:51.509 together. That ultimately will help you get to the return on investment, and you will then start 00:03:51.509 --> 00:03:58.269 seeing the actual scaling up of AI within an organization, and you will start 00:03:58.270 --> 00:04:04.869 getting past that tipping point wherein you’re going from experimentation to 00:04:04.910 --> 00:04:11.270 actual mass scale adoption, and from proof of concepts to ubiquitous application of AI within 00:04:11.270 --> 00:04:17.870 your organization. To get beyond the tipping point, it’s all about that R at the front 00:04:17.910 --> 00:04:24.669 being reimagination and then bringing in the intentional design of how 00:04:24.670 --> 00:04:31.429 we redesign work for humans and machines to get the reimagination for tomorrow. If 00:04:31.429 --> 00:04:38.429 that’s what everyone remembers, I think we can be excited, Nitin, because the opportunities you 00:04:38.430 --> 00:04:40.469 and I know are endless.