התמלול המלא של Engineering Trustworthy Enterprise AI

00:04:54.879 And then we started designing the resilient systems. 00:04:55.120 --> 00:05:02.160 And then as we got into the boom of the cloud era around the pandemic, you know, everybody was embracing cloud. 00:05:02.399 --> 00:05:08.000 I saw reliability move from what I call as component reliability to service reliability. 00:05:08.079 --> 00:05:13.839 And without sounding too technical, let me kind of elaborate on that with a metaphor and then I'll take it to the AI realm. 00:05:14.000 --> 00:05:15.519 Think about an orchestra. 00:05:15.839 --> 00:05:22.240 You have the band with different instruments, you know, you may have a guitar, you may have a piano, you may have the drums, timbales, what have you. 00:05:22.319 --> 00:05:24.720 And each instrument may be perfectly tuned. 00:05:24.879 --> 00:05:30.639 That is, each component is reliable and it's supposed to do what it's and it's doing what it's supposed to do. 00:05:30.800 --> 00:05:40.240 But in terms of service reliability, when they start playing the orchestra, if they're not in sync, the audience, the hearer, the listener doesn't enjoy the music. 00:05:40.399 --> 00:05:41.759 And it's about that experience. 00:05:41.839 --> 00:05:43.680 So that's the outcome that you're trying to deliver. 00:05:43.759 --> 00:05:50.720 So service reliability does not look at each component, but as a whole, what service is being delivered and is that reliable? 00:05:50.800 --> 00:05:54.720 So that's the morphing that happened with the cloud coming in. 00:05:54.879 --> 00:05:57.120 And then AI took it up another notch. 00:05:57.279 --> 00:06:00.800 A couple other aspects kind of got layered on top of that. 00:06:00.959 --> 00:06:13.120 That is trust, dependability, because now AI is taking the main stage in terms of like you initially described, not just giving suggestions or advice, but also going to the outcomes. 00:06:13.279 --> 00:06:21.199 So if I may put it this way, and then probably I'll make it more real with an example and a business outcome tied to that. 00:06:22.000 --> 00:06:26.319 Back in the day, it was is the system working the way it was designed to work? 00:06:27.040 --> 00:06:37.199 The early days of AI, a couple of years ago, if I go back, and I'm talking more gen AI than the traditional AI, it became can we trust the answer that AI is giving? 00:06:37.439 --> 00:06:50.480 And now the question is if you're making it autonomous agency system, can we trust the outcome that AI is kind of, you know, steering you towards or the actions that AI is taking? 00:06:50.639 --> 00:06:54.639 Let me make it a little bit more real and then I'll pause for your thoughts, your reflections. 00:06:54.879 --> 00:06:59.360 One of our clients, it's a very, very large uh medical distribution company. 00:06:59.519 --> 00:07:02.240 They are like Fortune 10 class company. 00:07:02.879 --> 00:07:09.120 And they have a bunch of systems and different companies, and they've grown through acquisitions over the years. 00:07:09.600 --> 00:07:12.959 Now, in terms of system reliability, one of their firewalls went down. 00:07:13.120 --> 00:07:16.000 This was in the public domain, so I'm okay to talk about it. 00:07:16.160 --> 00:07:22.480 One of their firewalls went down, and they did not have the wiring to be able to find out where exactly things broke. 00:07:22.720 --> 00:07:29.839 But what this led to is the medications that were supposed to reach terminal patients or whatever in the supply chain could not reach on time. 00:07:30.000 --> 00:07:31.439 Huge reputational damage. 00:07:31.680 --> 00:07:39.759 Also, the cost of downtime to a business, something like that, in their own words, is about a million dollars a minute if it's unplanned downtime. 00:07:40.000 --> 00:07:47.680 Now, what AI does, there's a traditional AI, is finds out what's broken where with the observability and the wiring and all of that. 00:07:47.839 --> 00:07:50.560 And then the next level is so can you trust that answer? 00:07:50.639 --> 00:07:52.800 Will you go and take that action with the human in the loop? 00:07:52.959 --> 00:07:59.519 Or the next level with agenc or autonomous operations is will you allow AI to go and fix that? 00:07:59.759 --> 00:08:12.879 So essentially it may be something as pedestrian as a firewall or a system going down, but the implications of the business outcome in terms of the loss damage, million dollars a minute, like I said, or you know, the reputational damage, that could be huge. 00:08:12.959 --> 00:08:20.160 So that's fundamentally how AI is changing, you know, how we look at system reliability and the business outcomes in the AI era. 00:08:20.399 --> 00:08:21.279 Does that make sense? 00:08:21.600 --> 00:08:22.240 Yeah, totally. 00:08:22.319 --> 00:08:24.399 It reminds me of an anecdote early in my career. 00:08:24.480 --> 00:08:32.159 I won't name names, but the customer was 1-800 flowers, you know, you call your for your flowers for your wife or mother, etc. 00:08:32.639 --> 00:08:40.240 And you know, our system, our network went down on Mother's Day, which, as you can imagine, not a good experience. 00:08:40.480 --> 00:08:46.639 Companies like that aren't in the flowers business, they're actually in the IT and phones and phone taking business. 00:08:46.879 --> 00:08:55.600 So, yeah, I understand that sort of reputational damage and how quickly things can get out of pocket, out of hand. 00:08:56.399 --> 00:09:09.279 Now, the exciting news for techies like me is that their research also showed 66% of enterprises are scaling AI, and we love to talk about which models and which AI and where it lives. 00:09:09.360 --> 00:09:11.600 And you know, technologists love this stuff. 00:09:11.919 --> 00:09:16.799 But what's not so exciting is only about a quarter have controls in place. 00:09:17.039 --> 00:09:21.679 So for the CFOs, the board, the CEO, that's kind of concerning. 00:09:21.840 --> 00:09:24.000 Are companies moving too fast? 00:10:09.970

--> 00:10:11.649 I I think so at some level. 00:10:11.730 --> 00:10:14.129 I think there are multiple, let's unpack that a little bit. 00:10:14.289 --> 00:10:16.370 So, first of all, thanks for sharing that story. 00:10:16.529 --> 00:10:17.730 That resonates very well. 00:10:17.889 --> 00:10:27.889 And secondly, the point that you just made the companies are moving too fast because people are embracing, people are adopting AI for the individual productivity. 00:10:28.129 --> 00:10:36.529 And the enterprises are lagging behind and putting the controls, the guardrails, and the HFS study that you alluded to also reflects that. 00:10:36.689 --> 00:10:40.929 In fact, I am reminded of another data point we saw from that study. 00:10:41.330 --> 00:10:45.730 If you, and and sorry, I'll go at a tangent for a moment and then bring it back. 00:10:45.889 --> 00:10:56.450 So the TCS AI framework has five levels of maturity, which is you know, you're using AI just as a tool or an assistant, or you know, something far more advanced, which we'll talk about later. 00:10:56.689 --> 00:11:08.210 But fundamentally, um, in terms of copilot, something that people are using, it was only 8% of the people surveyed said that they would not trust co-pilot results. 00:11:08.370 --> 00:11:11.090 92% are willing to trust co-pilot results. 00:11:11.330 --> 00:11:21.649 But the moment you take it to an autonomous domain, only 20% said that they will trust AI to take the actions, which is 10x. 00:11:22.289 --> 00:11:36.769 So it goes back to the point you're making because the foundational elements, the guardrails, the eval sets, uh, without getting too technical, essentially, and it's not just about the models, because there's been far too much focus on the models. 00:11:37.090 --> 00:11:42.210 You know, you're creating capacity with the models, but capacity does not equate to dependability. 00:11:43.730 --> 00:11:48.129 You have to think about the underlying data, how is the data structure in the enterprise? 00:11:48.210 --> 00:11:58.929 You have to think about the interfaces, you have to think about the integrations, and then you have to think about are the right guardrails put in place for AI to take action? 00:11:59.250 --> 00:12:01.570 And then is it safe? 00:12:01.889 --> 00:12:02.769 Is it secure? 00:12:02.929 --> 00:12:05.330 And all of the yada yada yada, I can go on and on. 00:12:05.490 --> 00:12:12.370 But fundamentally, what I'm trying to say is it also goes into the dimensions of accountability, transparency. 00:12:12.689 --> 00:12:16.850 You know, can you trace the actions down to why did it happen? 00:12:17.090 --> 00:12:30.210 And in a heavily regulated environment, in a lot of industries that we work in, it's very hard to make peace with something like that unless you put the right foundations, the right guardrails, the right enablers, or you need to have a human in the loop. 00:12:30.370 --> 00:12:35.730 That's that's only when people are willing to stand behind the outcomes that AI produces. 00:12:36.769 --> 00:12:37.250 Interesting. 00:12:37.330 --> 00:12:38.370 Yeah, it's funny. 00:12:38.529 --> 00:12:42.610 Life we runs on trust, but so does business and so does technology. 00:12:42.850 --> 00:12:45.730 And, you know, it's such an important question. 00:12:45.970 --> 00:12:57.250 In fact, many companies simply block access to employees when it comes to Chat GPP or Claude and simply block them on the firewall or what have you. 00:12:57.490 --> 00:13:03.250 So we don't necessarily even trust humans or employees all the time to use these tools. 00:13:03.330 --> 00:13:08.129 But when it comes to the agents themselves, which are providing tremendous value. 00:13:08.210 --> 00:13:12.850 I have two agents running for me right now doing work as I'm on this discussion. 00:13:13.009 --> 00:13:15.169 There's no question of the value it provides. 00:13:15.330 --> 00:13:23.090 But how do you decide how much freedom and action and decision making to give these agents? 00:13:24.210 --> 00:13:29.970 I think that's a very interesting question because trust is at the core of it. 00:13:30.289 --> 00:13:40.689 And you know, even when you put human in the loop, does the human have the right understanding, the right context, the right authority, the right data? 00:13:40.850 --> 00:13:45.169 I mean, all of those elements also come into play before you trust the outcome. 00:13:45.330 --> 00:13:49.970 But fundamentally, where I'm going with that even is trust is a human perception. 00:13:51.009 --> 00:14:07.090 I would take it a level beyond, especially from the TCS paradigm, from the engineering paradigm that we come with as a company, I would elevate it to a call of trustworthiness, which I think is an engineering responsibility. 00:14:07.570 --> 00:14:27.090 So what I'm what I'm trying to say with that is if you can engineer the systems, the agents in a way, with all the controls that we talked about, and the whole ecosystem in a way that the evidence and the consistency is always there for humans to stand behind. 00:14:27.409 --> 00:14:29.090 That's when you can trust the system. 00:14:29.409 --> 00:14:35.169 So to me, trust is not just a compliance paradigm, it's a business enabler. 00:14:35.490 --> 00:14:50.610 And to your point, you cannot unlock the power of these agents at the enterprise scale if that trust has inherently not been built in, those foundations have not been built in to unlock that value for the enterprise. 00:14:51.169 --> 00:14:52.289 Yeah, really interesting. 00:14:52.370 --> 00:14:53.970 And things fail, things break. 00:14:54.049 --> 00:14:58.210 We know this from IT, we know this from our personal experiences. 00:14:58.610 --> 00:15:09.169 But while I think the data that you commissioned showed most companies can detect AI failures, like 60% or so, only 23% can routinely stop them. 00:15:09.409 --> 00:15:10.289 You know, why is that? 00:15:10.370 --> 00:15:12.850 Why is stopping a bad AI

decision so difficult? 00:15:13.009 --> 00:15:20.289 There is, I guess there's no break glass in case of emergency button or or you know, kill switch kind of thing. 00:15:20.450 --> 00:15:24.769 How do you stop these AIs uh going bad? 00:15:25.330 --> 00:15:30.450 So I think it's a maturity journey, and we're talking about the AI maturity framework of TCS. 00:15:30.529 --> 00:15:35.730 So let me kind of use that as an anchor and then address a question that you called out. 00:15:37.250 --> 00:15:43.090 At a very basic level, you're using AI either as a tool or as an assistant. 00:15:43.250 --> 00:15:45.649 I think co-pilot, say, for example. 00:15:45.970 --> 00:15:48.689 And there's always a human in the loop in that scenario. 00:15:48.850 --> 00:15:50.370 So that's level one and two. 00:15:50.529 --> 00:15:56.129 And if AI does something that you don't like, you don't act on it, as in essentially if you get an answer. 00:15:56.289 --> 00:16:13.570 But as you move to the next three levels of maturity, which is either it's a supervised agent, which again is significant human oversight, or even the next level could be autonomous workforce or the holy grail, which most enterprises have not gotten to, is the agentic enterprise. 00:16:13.809 --> 00:16:23.889 Now, in those two scenarios, levels four and five, if you've not put the right controls and right guardrails, the agent can be mischievous. 00:16:24.289 --> 00:16:27.809 I mean, the agent can do things to meet its goal. 00:16:27.889 --> 00:16:35.649 There was a salacious article about hugging face and open AI not very long back, say for example, because the agents were trying to meet their goals. 00:16:36.450 --> 00:16:40.289 And the right controls were not given to the agents. 00:16:41.330 --> 00:16:55.409 Again, it was a test environment, but basically, what I'm trying to say is so if you don't put the right checks and balances on when the agent can act, then the agent can run rogue and you can have all of these challenges. 00:16:55.490 --> 00:16:59.809 And that's the 23% who talk about they cannot control what's going to happen. 00:16:59.970 --> 00:17:06.529 So, long story short, and I'll make it real perhaps with uh with an example, with an industry example. 00:17:07.090 --> 00:17:26.210 But it's a maturity journey, and you have to curate that and you have to make sure the agent has the right context, the right guardrails, the right checks and balances, and the kill switch that you talked about should be involved or should happen if it tries to break the mold, essentially, because that requires policy as code. 00:17:26.529 --> 00:17:41.250 That requires, you know, and uh there are multiple technologies to do that, the hardnesses that we've built, say, for example, in these scenarios, or there are scenarios wherein you have an agent checking or an AI checking an AI's job before it's taken into mainstream. 00:17:41.330 --> 00:17:49.330 So, again, the ways to do that checks and balances, but if you don't do the foundations right, if you don't do the basics right, those kind of challenges are likely to happen. 00:17:49.490 --> 00:17:59.250 But one of the large banks we were working with, we were working with, uh, you know, this was about credit card disputes. 00:17:59.650 --> 00:18:02.610 They decided to go down the agentic path. 00:18:02.850 --> 00:18:16.050 Now, if somebody disputes a credit card transaction, it's very easy for the agent to say, okay, you know, what's what's the transaction, where it comes from, what's the data required for it, and so on. 00:18:16.210 --> 00:18:19.330 Now, the agent will tell you this is what went wrong. 00:18:19.490 --> 00:18:33.090 Now, but the question is, have you reached a place in your maturity of the foundation that you'll let the agent take the corrective action and you know pay back that money or do whatever needs to be done to resolve that dispute? 00:18:33.250 --> 00:18:37.890 To get to that level of autonomy, you essentially have to build all the right foundations. 00:18:39.090 --> 00:18:46.130 Yes, and speaking of foundations for AI autonomy and reliability, who who owns that inside a large organization? 00:18:46.290 --> 00:18:47.730 Is it the board? 00:18:47.890 --> 00:18:51.650 Is it customer service, the CIO, all the above? 00:18:51.810 --> 00:18:55.730 It's it must get pretty complicated in the large enterprises that you work with. 00:18:56.290 --> 00:18:57.730 It is a complex question. 00:18:57.810 --> 00:19:13.810 And in fact, uh, you know, I I want to steal a page from our CIO's playbook in TCS, because when we try to do this for ourselves as customer zero, the whole question was that can you stand behind an agentic decision as you're moving to an autonomous enterprise? 00:19:13.970 --> 00:19:21.890 So essentially, to curate these policies and guardrails, you need a small group of people representing the various disciplines that are involved, essentially. 00:19:21.970 --> 00:19:29.010 Like you said, it could be customer service, but you also need legal, you need risk, you need compliance, uh, you need finance. 00:19:29.170 --> 00:19:40.130 So a think tank, a small group of these people is formed, which defines the policies, and the policies should be the gateway before you allow the agents to act on these things. 00:19:40.370 --> 00:19:45.890 That's the way the hardness that I was talking about, or the other uh controls that I was talking about come into play. 00:19:46.050 --> 00:19:49.090 So have you wired it right with these people? 00:19:49.250 --> 00:19:52.450 And essentially the enterprise, the organization has to own that. 00:19:52.530 --> 00:20:04.290 You cannot come back and say, I'm a bank, I don't know why my agent did this, because some decisions are reversible, some decisions are inconsequential, but some nations could have huge repercussions. 00:20:04.530 --> 00:20:14.130 So, where do you give what level of autonomy to agents and have you essentially built those

controls with the right team, with the right accountability, is what it boils down to. 00:20:14.930 --> 00:20:15.730 Fantastic. 00:20:15.890 --> 00:20:27.490 So I'm a huge proponent of agents, I use agents, and I've seen tremendous personal productivity gains from AI, you know, 10x, perhaps even for myself professionally, personally. 00:20:27.730 --> 00:20:39.810 And I think most companies are seeing productivity gains from AI, just if you if you see some of the data from your research and just anecdotally, but that hasn't necessarily turned into revenue growth. 00:20:39.970 --> 00:20:47.330 Uh, how do you close that gap and show the real ROI beyond the productivity benefits? 00:20:47.810 --> 00:20:48.690 Yeah, you're right. 00:20:48.770 --> 00:20:53.010 There's been a lot of focus on activity and individual productivity. 00:20:53.490 --> 00:21:04.210 And part of it is because that behavior was being rewarded, and it was a great way to bring about organizational change, people embracing AI ways of working. 00:21:04.370 --> 00:21:12.290 But when you have to take it to enterprise scale, you have to build the right foundations and you know you have to link it to the right metrics. 00:21:12.530 --> 00:21:21.890 So essentially, again, going back to that three-part framework that we talked about reliability, is the system doing what it's designed to do? 00:21:23.010 --> 00:21:30.610 Can I trust the answer that AI gives me, or can I, you know, trust AI to do that whole business outcome? 00:21:30.930 --> 00:21:33.410 That's essentially where you need to go with it. 00:21:33.490 --> 00:21:35.330 So let me make it real with an example. 00:21:35.490 --> 00:21:42.690 Again, this is a very large bank that we're working with, and one of their lines of business is commercial lending. 00:21:43.650 --> 00:21:57.410 Now, they would receive a lot of checks from the people that they had lent out these loans to, but because of inaccurate information or whatever, these were not applied and they were sitting in the suspense account. 00:21:57.490 --> 00:22:07.970 When they built this whole enterprise grade uh with the end in mind, the AI foundation, and then unlock that use case. 00:22:08.210 --> 00:22:15.490 What they were able to do is get 450 million uptake on the checks that were applied. 00:22:15.730 --> 00:22:20.210 They were able to get 82% reduction in the suspense accounts. 00:22:20.370 --> 00:22:24.290 Likewise, there's another example of revenue that you talked about. 00:22:24.530 --> 00:22:29.810 There's a distributor of electronic parts that's that's a large client of ours. 00:22:30.130 --> 00:22:36.210 And they would get all these components from OEMs, and there would be buyers on their website trying to buy these things. 00:22:36.290 --> 00:22:43.410 And you know, the OEMs keep I mean, thousands and thousands of SKUs, and the OEMs keep coming up with more and more specifications. 00:22:43.570 --> 00:22:47.570 People We're not able to buy, not able to determine what they needed. 00:22:47.890 --> 00:23:03.410 With agentic intervention and autonomous sales, 7% of their sales today, which is north of a billion dollars, and this again in the public domain is being done through agentic autonomous, you know, way in agentic autonomous way. 00:23:03.570 --> 00:23:09.490 So there's a billion dollar plus uptick in revenue for a large enterprise if you do things right. 00:23:09.650 --> 00:23:14.450 So bottom line is what are the KPIs, what are the metrics you're focused on? 00:23:14.610 --> 00:23:20.770 Are those business outcomes or are those, you know, at the enterprise level, or are those individual productivity? 00:23:20.850 --> 00:23:26.850 Can I get my code 10 times faster, or can I, you know, do my meetings quicker, or what have you. 00:23:27.010 --> 00:23:42.610 So you you really have to have the big picture view and the C-suite, the board has to define that this is where we want to go, this is where we want to move the needle on these metrics, could be revenue, could be habitat, could be experience in NPS or whatever quantifiable form you measure, and so on. 00:23:42.690 --> 00:23:45.170 So that's essentially the secret sauce of how to do it. 00:23:45.250 --> 00:23:49.890 But you have to start from the right and then move to the left to how do you go about doing this? 00:23:50.530 --> 00:23:52.130 Yeah, brilliant insight. 00:23:52.290 --> 00:23:56.450 And let's talk about getting to a truly autonomous and AI-driven enterprise. 00:23:56.690 --> 00:24:07.650 You've actually at TCS, I've I've been reading, have been doing a lot of that work internally with the world's biggest AI hackathon and other initiatives to transform yourselves. 00:24:07.810 --> 00:24:09.250 Maybe talk a little bit about that. 00:24:09.330 --> 00:24:18.930 And in the industry, you know, what has to happen before businesses can get to that autonomous enterprise vision that we're so excited about? 00:24:19.650 --> 00:24:32.930 Yeah, so we're very proud of what we have achieved as TCS because uh, you know, we we when we embrace this vision of being the largest AI uh services company, we had to be customer zero for ourselves. 00:24:33.010 --> 00:24:36.610 And that was the first foundational pillar that we put for this. 00:24:36.770 --> 00:24:49.410 And, you know, for a lot of our processes, finance, HR, I mean, everything, uh including the work we deliver for our customers, we have been able to get phenomenal autonomous results essentially using AI. 00:24:49.890 --> 00:24:59.650 Now, while while that's translating into a lot of things, uh, sorry, if you could take me back to your question beyond TCS, you also wanted to know. 00:24:59.810 --> 00:25:02.370 Sorry, if you could repeat the question for me, I won't I lost time. 00:25:02.770 --> 00:25:02.930 Sure. 00:25:03.090 --> 00:25:03.490 No worry. 00:25:03.570 --> 00:25:06.130 Now I'll edit this this part out just for James. 00:25:06.210 --> 00:25:08.210 So I'll start over with

the question again. 00:25:08.290 --> 00:25:10.930 So, and we'll uh three, two. 00:25:11.650 --> 00:25:21.410 So, you know, looking ahead of you know, 12, 24 a month, companies are trying to get to this truly autonomous enterprise, AI-driven enterprise. 00:25:21.490 --> 00:25:22.930 And it's no small feat. 00:25:23.010 --> 00:25:37.250 I know even you at TCS with five, six hundred thousand employees, have managed to really reinvent yourself towards an AI-driven workforce with like the world's biggest hackathon and other initiatives like that. 00:25:37.650 --> 00:25:48.370 So, talk about a little bit about yourself and your transformation, but also you know what needs to happen for your customers for businesses can trust AI to run major operations. 00:25:49.090 --> 00:25:51.410 Yeah, so you're absolutely right. 00:25:51.570 --> 00:25:58.610 Very proud of what we've been able to achieve at TCS and a lot of you know our processes run in an autonomous fashion, and we're getting there. 00:25:58.850 --> 00:26:05.570 I think for large enterprises to be able to do that, very simplistically, there are three sets of things. 00:26:05.890 --> 00:26:09.330 I would say tool set, skill set, and mindset. 00:26:10.610 --> 00:26:12.130 You can provide all the tools. 00:26:12.290 --> 00:26:17.650 Obviously, we talked about the foundation and all of those things, and you can, you know, engineer it right. 00:26:17.730 --> 00:26:20.610 So that is the first foundational block that needs to happen. 00:26:21.090 --> 00:26:25.650 The second is a talent, because it's not just technology talent, i.e., the skill set. 00:26:25.730 --> 00:26:28.930 It's not just a technology talent that is leveraging that. 00:26:29.490 --> 00:26:37.170 You need even the business folks to be equally comfortable with AI to move to that autonomous phase. 00:26:37.330 --> 00:26:48.850 And finally, the mindset, because the biggest change that AI is having is it's not sprinkling the AI pixie dust on your workflows or processes, it's reimagining things and how they can be done all over again. 00:26:49.010 --> 00:26:53.810 And that's where the ways of working, the mindset, the whole operating model needs to be looked at. 00:26:53.970 --> 00:27:01.890 So essentially, organizations are taking a three, uh maybe a couple dimensions, I would say, in which they're moving towards autonomy. 00:27:02.130 --> 00:27:04.050 One is persona-based. 00:27:04.290 --> 00:27:08.210 Which are the personas that would benefit the most from full autonomy? 00:27:08.450 --> 00:27:15.650 And they would start out with that, build it, and they will come kind of a model, or enable them with the right tools and the skill set and the mindset. 00:27:15.810 --> 00:27:17.970 The other could be function-based. 00:27:18.130 --> 00:27:23.330 Okay, AP or AR or whatever could be a function, or it could be even customer service. 00:27:23.490 --> 00:27:25.890 Is that something that I want to move towards autonomy? 00:27:26.050 --> 00:27:36.290 So again, organizations are taking different approaches, but bottom line is you need the tool set, the skill set and the mindset, and it's a journey to embrace full autonomy. 00:27:36.450 --> 00:27:37.970 And there are some journey. 00:27:38.450 --> 00:27:47.410 Sorry, and there are some functions like compliance and regulation, wherein you need to be a little bit more thoughtful about it, a little bit more deliberate about it when you go to that stage. 00:27:47.970 --> 00:27:58.690 So true, and it is a journey, and uh, I'm learning so much from these conversations, and uh, congratulations on the journey so far with a long and interesting road ahead. 00:27:59.010 --> 00:28:02.130 Thanks very much for joining and sharing your insights. 00:28:02.450 --> 00:28:03.890 Thank you very much for having me. 00:28:03.970 --> 00:28:05.090 The pleasure is all ours. 00:28:05.250 --> 00:28:06.210 Thank you, everyone. 00:28:06.530 --> 00:28:08.610 Thank you, and thanks everyone for listening and watching. 00:28:08.690 --> 00:28:14.690 Also, check out our TV show on Bloomberg Television and Fox Business at techmact.tv. 00:28:14.850 --> 00:28:15.730 Thanks, Rushant. 00:28:15.890 --> 00:28:16.450 Thanks, everyone. 00:28:16.770 --> 00:28:17.170 Thank you. 00:28:17.330 --> 00:28:18.290 Bye bye.