What is AI really doing for the enterprise — and what's just hype? Our hosts sit down for an unfiltered conversation about the state of AI in business today. From the difference between prediction and reasoning, to the growing problem of AI sprawl, to why change management might matter more than the technology itself — this is a must-listen for anyone trying to make sense of AI in their organization.
"It's a brilliant piece of marketing to call this technology AI — because it implies a level of reasoning and intelligence behind the tool that's not there." That bold claim from data-scientist-turned-consultant Sahil Panicar sets the tone for a frank, hype-free conversation recorded live at SAFe Summit 2026 in Amsterdam. Joined by Atlassian's Zach Brown and host Raghurani from Accenture, the trio unpacks what today's AI actually is (predictive, not deductive), why companies are bleeding money on "AI sprawl," and what individuals and enterprises should really be doing to get value from these tools. If you've felt the pressure to "go get AI" without knowing why, this episode is your reset button.
Saahil Panikar — CIO of Atlas Revolutions, SAFe SPCT & Fellow, former data scientist. Specializes in lean portfolio management, value-stream management, and cyber-physical systems.
Zack Brown — Partner Solution Strategist at Atlassian. Helps partners understand Atlassian's product strategy and go to market with joint solutions.
Host: Renaud Granier — Accenture Business Agility
| Time | Topic |
|---|---|
| 00:09 | Introductions & guest backgrounds |
| 02:21 | Defining AI: Why LLMs are predictive, not intelligent |
| 04:09 | Practical advice: How individuals should approach AI |
| 06:32 | Business impact: Atlassian's Rovo and enterprise AI use cases |
| 07:33 | AI sprawl: The danger of investing millions without a clear problem |
| 08:22 | "Garbage in, garbage out" — Why data quality matters more than the model |
| 10:28 | Tactical quick wins vs. enterprise-grade AI success |
| 13:44 | Change management: Why training and adoption trump tooling |
| 15:13 | Gated gardens & RAG: Securing your AI's reference data |
| 16:31 | LLM licensing risks — What's happening with your data behind the scenes |
| 17:32 | Building adaptive, AI-native organizations |
| 17:48 | Individual takeaway: Get your hands dirty and experiment |
| 20:34 | Test the AI on something you're an expert in — the "newspaper test" |
| 22:47 | AI hype vs. AI sprawl — Two problems that have meshed together |
| 25:01 | Atlassian's approach: Enterprise-tuned, choice-driven, integration-first |
| 28:39 | Responsible AI operating models — the building blocks |
| 29:27 | Where to find the guests and continue the conversation |
AI is prediction, not reasoning. LLMs don't know when they're wrong — they predict what you want to hear based on billions of parameters.
Don't invent the technology, then find a problem. Understand your workflows and data first; AI is a force multiplier that amplifies what already exists — good and bad.
Beware AI sprawl. Dozens of siloed AI tools create more confusion, not less. Start with specific, high-value use cases.
Data quality is non-negotiable. A "gated garden" (curated, secure data source) combined with RAG is essential for trustworthy AI outputs.
Change management > tooling. Training, communication, and human-in-the-loop governance drive real ROI.
Check your LLM licensing. Know what happens to your data when you use frontier models — many license agreements allow the provider to reuse your inputs.
Start personally. Experiment with AI on something you're an expert in to understand its boundaries — then bring that competence to your organization.
Test an AI on something you're an expert in this week. Ask it questions where you already know the answers, see where it shines and where it breaks — and share what you learn with your team. That single step is where responsible AI adoption starts.
Find more episodes and subscribe to stay ahead of the AI-in-enterprise conversation.
00:09 Hello everyone, my name is Renaud Granier, your host from Accenture and we are here at the Safe Summit in 2026 in Amsterdam. 00:17 I've brought with me two persons that I like to have them introduce themselves very quickly, Sahil on the one side as a safe fellow in It's PCT, that's why you are also with the Safe Summit Sahil. 00:30 And Zach, from Atlassian, working as a solution partner manager. Yeah, I'll go ahead and start. My name's Zach Brown. I'm a partner solution strategist. 00:40 I help our partners understand our products and strategy collection, things that we talk about at the Safe Summit quite often. 00:47 And go to market together with them with joint solutions. Sahil? That's awesome. Thank you so much for inviting me to be here. 00:53 I'm Sahil Panicar. I'm the CIO of Atlas Revolutions, a small digital transformation consultancy based in the U.S. So it's always very exciting for the chance to come to Europe and partner with my European colleagues. 01:05 Like Renault said, I'm a safe SPCT and fellow for my contributions to the scale-dagel framework in lean portfolio management and value-stream management. 01:17 And I spent the last seven years building cyber-physical systems, aircraft and automobiles using SAFE, and we're starting to see a lot of these organizations start to invest in AI, and I thought that would be a really cool thing to come talk about. 01:33 Thank you guys, and say more than that, you also a data scientist. Yeah, I try not to claim that title so much anymore. 01:40 I was a data scientist before I got into the digital transformation space and became a consultant, and so I would say like on the frontier of data science in the early 2010s, but, you know, I did a transformation for a large oil and gas company in their data space about eight years ago, and I said, hey 01:57 , I was a data scientist, too, and they were like, okay, yeah, shut up, grandpa, because data science has evolved a ton since I was, you know, every day doing it, but it's still a passion of mine, and I love seeing the innovations in that space. 02:13 So, yeah, excited to kind of bring some of that insight and perspective to the adaptive, transformative world that we're living in now. 02:21 Could you give us a definition of AI? Because today, I mean, we're seeing it everywhere. What we see in the sci-fi movies are, how would you define AI first at all? 02:31 Yeah, that's a great question. I think it's actually one of the most important questions in this space right now. So absolutely not, the tools that we have today are not like what you see on Hollywood and your movies and TV screens. 02:45 It's a brilliant piece of marketing to call this technology AI because it implies a level of reasoning and intelligence behind the tool that's not there. 02:59 This is a great advancement in machine learning and deep learning. There was an innovation in the late 2010s called Natural Language Processing that led to the creation of large language models. 03:12 That's the actual tool that we're building all this generative AI on. Large language models or LLMs are predictive engines that use millions and billions of references or parameters to predict what the next thing they think you want to hear is. 03:30 So there's no reasoning, there's no intelligence when AI, if you ask your AI something and it gives you a crazy answer, which it is prone to do, it doesn't know that it's wrong. 03:41 Unless you tell it and then it says, oh yeah, you're right, I was wrong. It doesn't even know that, it just thinks that's what you want to hear. 03:47 The AI is predictive, not deductive. There's no reasoning behind it today. There's a lot of really good, in fact really great, technology teams, giving it so much reference material in context that it looks intelligent, it can be packaged and marketed as intelligence, but it's still just prediction, 04:09 really, really advanced prediction, and you have to remember that when you're interacting with the AI tools today. So what would be your recommendation, maybe, for our audience here, in terms of what should I do with these all AI that I see around and outside, first of all? 04:34 Yeah, so I mean, I guess after having made that disclaimer that it's not intelligent, it's still really cool. I think that the effect it has on people, the ability to amplify your skills, open new doors to you that may not have been open before, is just, I mean, I'm gonna say incredible a lot because 04:58 it really, really is. So my recommendation would be, don't think AI is a cure-all, a magic bullet that's gonna solve all the world's problems or make your business super successful. 05:10 But if you do a little bit of research and you figure out how AI can help you personally, it can absolutely have orders of magnitudes of impact on your life. 05:20 I use it for things like planning travel itineraries and checking out, hey, in fact, for my trip to Amsterdam, I asked my own little LLM, hey, I want to find a Marriott branded hotel because I stay in Marriott's, but I don't want to go above this price point and I don't want to be more than a 20 minute 05:41 walk from the conference venue, show me all the Marriott's in that area that that meets this price point and it did a great job. 05:46 And then it knows me because I've interacted it with it a bunch and it said based on your preferences, we think this hotel is the best recommendation for you. 05:54 That kind of stuff, it's really great at. And I think it can really make your life a lot better if you use it for that. 06:01 But just remember, it doesn't reason anything. It's just predicting what it thinks you want it to say. Yeah, I think that's one of the things to drill into a little bit is it's not going to solve all of your problems, but it can solve some of your problems and I think if you give it the right context 06:16 like I was just talking about It's funny I did a similar thing last night with finding our dinner reservation for eight people at the last minute It's super helpful when you give it the right context and when you know how to point it at the right problems How do you think I could help as well in terms 06:32 of business capability and what's the impact in your view? Well, so I think we get this question a lot from the Atlassian side. 06:40 We have a tool called Rovo, which is sort of core to the platform capabilities. And to just sort of continue the conversation on context, for us, the problems we're solving in that space are bringing that capability to what knowledge and what work information you have on a company, feeding that into 07:00 these systems, and then being able to get more our business intelligence and coaching and advice on just what's my day-to-day as a scrum master, an RTE, or a product person, or how do I get some more intelligence on how my portfolio is performing, what types of things should I be investing in, what should 07:17 I be de-investing in, those types of things. But broadly, one of the things we were talking about yesterday was just keeping that data clean, the expectations of how fast you can get started, versus really what you need to to do to get to those cases, I think that's where the disconnect is right now? 07:33 Yes, absolutely. So, I mean, I think what's exhilluting to is this problem of AI sprawl and AI, and massive AI investments all across industry. 07:44 I see it in all of my clients, we're gonna invest $3 million, we're gonna ask $5 million into AI, and I say, great, what problem are you solving? 07:51 And they say, I don't know, we just have to, we have to get AI. And so when it goes back to capabilities, and context, it's a really important question to say, don't invent the technology and then go find a problem to solve. 08:06 You have to understand your own workflows, your own capabilities, your own data, and then understand how AI is going to help you, the AI tools, are going to help you to solve those problems or move faster or make better decisions. 08:22 But the data that's behind those decisions matters way more than you think. We hear people say all the time we are going to get a 10x return on AI or we are going to get 10 times more effective. 08:33 The truth is AI is a force multiplier. It absolutely is, but it's going to multiply what already exists inside your organization. 08:41 If your process is suck, AI is just going to make them suck harder. Yeah, exactly. It identifies. If I go to an agile team working in Scrum and if they have, you know, these burn down charts and if they don't do it correctly, which is like closing the user stories as they are being, you know, absolved 09:01 and then they went the end of the spring to close all at once. I will magnify that, but yeah, they still not be right. 09:08 So I will recommend bad things, essentially. For sure. That's exactly right. I mean, it's about garbage in, garbage out, right? 09:17 The quality that you put in is the reference material, the context that the AI is going to use to make its recommendations. 09:24 And if you have bad data or you have bad data practices, you're not going to get good recommendations. I don't know any other way to put it. 09:33 It's going to be a very painful investment for you. That's why I would recommend to everybody, if you're looking at adopting AI into your workflows at a capability level, you have to understand what your capabilities are and you have to understand what your organization's good at and what it's not good 09:51 at before you do the AI implementation. Hey, what I see behind is data growing. It's like a plan like this, really behind. 10:01 So from a tooling perspective, what do you see in there, if the data is growing, you need to, Yeah. I mean I, oh boy, this is a joke we've been having between ourselves that I misheard something and now it's kind of turning into a fun term. 10:17 There's elements that have been around for years now that are being reframed into new problems or new things that aren't obvious that they're connected. 10:28 So it's really important to still understand the history of you know robotic process automation, machine learning. How do we get to this point? 10:35 And what are the things you need to do to execute quickly. For us, though, when we're talking to customers, there's a real demand and expectation that you get some value quickly out of these massive investments you're talking about, right? 10:48 The same way that we were told, go be agile 10 years ago. Now we're say, go be AI enabled. They're go be 10 times more effective. 10:56 So for us, I would say tactically how that's playing out is we have engagements where we go in and say, okay, let's get to agents started on two very specific use cases, like helping write better user stories, or helping as you automate things to your point, in that workflow, when you drag something 11:14 to ready for PO review, it checks it against their standards of what NFRs need to be there, and what needs to be completed in this template. 11:24 And so there are things you can do to show value, but you still need to think way bigger than that when you're talking about enterprise grade success of these implementations. 11:33 Yeah, well, actually, first I totally agree with everything you just said, but this is a great example of where organizations should actually be starting with their AI implementations. 11:45 I see dozens of companies that I've personally interacted with, and I truly mean dozens, but it's a huge challenge where they're standing up these AI organizations and they're saying, we're going to embed AI into the company, we're going to solve the AI problem, and that's leading to either we're buying 12:04 40 different AI vendor-driven tools or we're investing massively into building our own custom AI workflows and agents. But it's kind of like, I use this analogy a lot, it's kind of like taking a Ferrari to the grocery store. 12:20 There are other cheaper tools that can solve your problems. Maybe AI's not the right one. And when AI is the right one, there's a strong chance that for most of those organizational workflows you're talking about, the major tools that you already have in place are solving that AI problem for you. 12:39 So Atlassian is embedding AI agents into your tools. You don't need to go rebuild the tools that they've already built for you. 12:49 Figure out what they're already doing for you and leverage it. Yeah, and I think there's kind of a challenge and a scary statement to make on this and then we can refine it back to kind of how you can do something with it, but the landscape of keeping that data clean in a way you can use it is only going 13:07 to get more complicated. For us, we're taking multiple LLM models, pulling them into our system and tuning them very heavily to use cases we see people using, and I think organizations themselves that We work with our often saying, let's just build our own LLM and make it specific to this. 13:25 So yeah, there's a bio Ferrari or versus build a Ferrari discussion, too. But I think it'd be interesting from both your perspectives. 13:33 How do we take that growing challenge and make it bite-sized and solvable for each organization as you're going? You see anybody do that well yet? 13:44 So what I say is that it's not the only thing to have the tool. you need to be able to use it because otherwise, I mean, if you upgrade your client with the Ferrari and they still don't know how to drive, it's not going to bring a lot. 13:59 We're doing a lot with this metaphor, I like it. So you're going to do a migration to the cloud or implementing a new tool without having adoption support around that to teach the people how to use the tool. 14:10 And that's change management and that's a good part of the success that you're going to have after using the tool. 14:17 And that's what's really going to enable your new ways of working and your efficiency gains and all of the benefits that you expect from the use case of implementing AI in that case. 14:30 Yeah, the AI change management matters a lot more than you think, the training you offer people, the communications. I mean, even from a perspective of, oh, we're investing massively into AI, that can be really scary for your existing teams and your existing work forces. 14:50 You could invest, you could put the investment on change management and maybe even be more effective somehow. Yes, no, absolutely, but we're seeing it. 14:57 I mean, we are definitely seeing it. I don't know how technical you want to get into like how the AI's work and how some organizations are setting up like local LLMs versus secure LLMs and how many different LLMs you want to bring into your ecosystem. 15:13 one of the big challenges around data is maintaining what we call a gated garden which is not a data garden. 15:21 Not a data garden. A gated garden is a curated, secure, single source of truth for the data that your AI is going to reference to make recommendations to you. 15:33 And so we use a technique or a practice called RAG, retrieval, augmented generation, and we give that RAG process a gated garden in order for it to make better, more effective decisions for your business. 15:49 But businesses, especially at scale, change rapidly. And so curating your gated garden, or what, what, what, what, what, what, what, what our gardening data, gardening, data gardening is my new favorite phrase. 16:02 It's really important. Like you start to have to have questions and conversations about things like who's allowed to modify the gated garden. 16:11 Who can add new inputs into it, give it new reference materials. How do we make sure that all the necessary validations and audit trails are present? 16:21 How do we make sure that data that we put in doesn't get pulled into a public LLM and become available for other organizations to reference? 16:31 And that's where the local versus secure versus cloud based LLM conversations comes in. And then even things like if you're pulling in the large frontier models, that's the ChatGPT brain, not going to ChatGPT.com, but if your LLM uses gpt4.0 or 5.0 as its brain, there is some backend license agreement 16:55 with OpenAI that you need to know, what, what, what, what's being done with your data, information, yeah, exactly, and it's kind of shocking how many organizations have not thought through that question before they start implementing the AI. 17:10 And I guarantee you, it's not just open AI. Every one of them has something in their license agreement that says anything you put in is, you know, we're free to use it. 17:20 This is a totally emerging landscape and technology frontier. We don't know how it's all going to work out yet. And that's why building an AI native organization, building an adaptive organization is so critical. 17:32 You can't just say this is how it's going to work and build all the structure and scaffolding for it You have to build in the idea that it's going to change and it's going to change really fast All right, well, I'll be honest with you guys All of this sounds a little bit scary if I'm just someone learning 17:48 about these different topics If I'm an individual in this space You know like what can I what should I take away from this to try to make it feel a little bit less scary? 17:58 You need to try. You need to get your fingers dirty at some point. And in my view, there is two aspects. 18:05 There is one. You would probably get some help from your company or in the context of your job, you will get some professional helps, a couple of trainings, a couple of people to us to some dedicated people to learn to use new tools, etc. 18:24 The other side is to develop a genuine personal interest into the topic and I think that will take you even further because this is where you will get the guts to try the things that you will maybe not try out or at work or that you are a little bit afraid to ask all these kind of things. 18:45 So you need to develop, you need to get into it and as you do the fear will disappear to a certain extent, in a certain natural way. 18:56 Maybe not all, not all, but the more you go, the more you will know, and the more you will see things differently. 19:03 When I was young, I bought a book and was a math book for doing math exercises. And I remember, visit vividly, the first pages was a sentence, and I said, a problem, and it's solutions. 19:20 are, I need solution, are just two different perspectives of the same situation. And today it's, I mean, wow, it's simple, but, wow, and it's the same with AI, right? 19:33 Right now it looks like a problem is scary, et cetera, but change your perspective and it will not look like a problem anymore. 19:40 Bottom line, educate yourself, get your hands dirty, a little bit, and you will see that it's not that complicated, after all. 19:48 And a lot of the fears will disappear just by getting more acquainted with it. Yeah, that makes sense to me. 19:55 And I think Sahil, there's probably an element, too, to a community of practice that sort of naturally comes together to help understand what your organization can do from that. 20:06 Because if you develop an interest, and then you start talking to your people in your organization who already may have that AI mandate, or already have tools available to you to try out in a work context, you have that individual competence now that you can go start talking to other people and then 20:21 start to understand some of these bigger problems we've talked about. And what I would say to any individual in a community of practice or not is test them out and see each model the same question and see how different the answers you get are. 20:34 And another thing I would recommend is, and maybe this will be dating myself a little bit, But back in the day when I used to read a newspaper, you know, you'd actually get a newspaper and read it You know, there's there's just underlying assumption that they know what they're talking about right like 20:53 They're reporting news to you and then every once in a while there'd be some article on something that I know something about like I'm an expert in data science in in lean agile not that that was in the newspaper back then But I have interest I have hobbies. 21:05 I really like science and astrology astronomy about astrology Very different. Very different things. Yeah, very different things. But I would read that article and I would say they got that wrong. 21:20 Right? And I would say, huh. Well, they got it wrong. Maybe they just didn't know. But then I would immediately forget that. 21:26 And in every other article, I would assume that they got everything right. Right? Because I'm not an expert in those topics. 21:33 So coming back to it, I would say, go talk to an LLM about something you're an expert in. Yeah, right? 21:38 Go interact with it on something that you're truly an expert in and see the boundaries of you know What is it getting right and what is it getting wrong because once you start having those interactions It stops being so scary that you know, this is like, you know a Morpheus AI Boogie man out there that 21:55 is just going to come for everything. It's not I mean the things AI is good at It's really good at but it's not good at everything That's really reassuring to me honestly to hear you both kind of say similar things where it's It's like, you know, personal accountability of understanding these tools is 22:12 something maybe I would be hesitant to do in the past. But if I just start doing that and trying it out, it will gradually become less scary and it will be really clear like what is useful to me, what is not useful to me, what it can do and really what it can so that then I understand that in my professional 22:29 context. And I'll just add, right, because we keep saying it, right, we're referring to AI as this kind of anonymous it. 22:35 The reality is every tool, every model, every LLM is good at something different. So they're not all the same, and you don't necessarily want to treat them all interchangeably. 22:47 Yeah, for sure. When I tried to synthesize all of that, etc., on the one side, I got a little bit the feeling that AI today is a bit like crypto five years ago. 23:01 One or two things are meaningful and are good and are well done, etc. And then you have like me and other things and here may be not. 23:09 So there is the question of how to distinguish the good, the bad, the ugly of that. And at an individual level, but so inside out, But also in terms of companies and cooperates, I mean outside E, I also observe a preparation of multiple AI-enabled tools. 23:34 So here the question remains, okay? What impacts on architecture and these kind of things? What you guys think about? Yeah, if I could start. 23:43 I think that you're really talking about two different things that have kind of meshed together. One is the AI hype cycle, which is all about AI. 23:50 is so cool, we need AI, you must go get AI. And as organizations, we see this mindset of, oh, our competitors are implementing AI. 24:00 I see other people who are embedding AI. We don't do it with our data. We need to. Right, so we have to go get AI. 24:06 Right, exactly. And then what that's actually translating into is what we then call AI sprawl, which is this massive proliferation or spread of different AI tools inside the organization that creates more confusion, more silos, and more dependencies internally because instead of having a simplified workflow 24:28 that's enabled by AI, you have 20 or 30 discrete siloed AI tools that all generate a very specific output that you then have to go figure out how to make useful for the next tool. 24:40 And so the sprawl is causing a lot of problems and what I would say is going back to our earlier conversation, and you have to understand what problem you're solving before you figure out how AI is going to help you solve it. 24:53 And I think Atlassian probably has a pretty solid perspective on that. What do you think, Zach? Yeah, I mean, it's really interesting being on the product side now. 25:01 I've been on the service side as a coach, as an implementer of safe and other frameworks. And I think coming into Atlassian, this is something we're investing in massively, but in a different way, right? 25:14 it's not like we have a mandate to implement this and achieve some outcome, it's an existential investment for us. We have to be AI enabled so that we can meet customers' needs. 25:27 And it's within doing that, though. I think we're approaching it in a way that's very thoughtful in that there's areas where we have expertise that customers expect from us, knowing the context of what you're working on, being able to connect that to, you know, what's the right next thing to work on, 25:45 get more intelligence forward on that, and then pull in other things, because what customers of ours want is choice, whether that's in ways of working, whether that's in how to structure your hierarchy, whether that's like what tools they want to bring that work nicely and integrate nicely with us. 26:02 I think the future is connecting these tools in a thoughtful way, and so I know Atlassian is building in a way which just enables more choice. 26:12 That is enterprise tuned and enterprise ready. So there's so much in everything I just said there, I know. But at a top level, that's kind of how we're approaching it. 26:22 I mean, that's awesome, because what I just heard you say is you're feeling the hype too. It's an existential threat. 26:27 You know you need it. But what's awesome about what you said is you're still thinking about how to integrate it all. 26:33 So you're taking a very thoughtful approach to What is the end user or customer experience going to be? How do we make sure that this isn't 20 silos inside of our tool? 26:43 Yeah, because being someone who helps other people understand our products and use it, I don't feel authentic when I say, oh, our tools can do everything, that's not realistic. 26:54 No tool can do everything. And you guys are independent, so feel free to validate that for me. But we want to be clear on where we play in and how we play with other people because that is more accurate and meaningful for our customers. 27:08 Yeah, and I mean, mostly it's not the tool, right? Yeah, it's the people who is using the tool and all, right? 27:15 So, I mean, yeah, again, it indicates that what the tool can do, know how the tool works, then you'd be able to use it. 27:21 Yeah, it's always the same. Yeah, I mean, I agree 100%. I think there is a real opportunity here to say If you take a step back you look at the problem you're solving and figure out how AI is going to help you do it And then you train your people right you give them that change management and you say 27:44 we're going to show you How this is going to make your life easier right, but it still has to be guided by you It still needs a human in the loop And it still needs Careful consideration of the output it creates and where we're going to use it and implement it you start to get to a really powerful implementation 28:05 of AI. Yeah, and I think if we just sort of scaffold the pieces of this conversation that we have gone through so far, it's individually takes some accountability for understanding these tools and being thoughtful about what your use cases are. 28:19 Take that to your organization and work with people who are being thoughtful about these things with a clear goal on what that is. 28:26 Work with partners who are thoughtful about how to implement this and work with tooling platforms that are being thoughtful about what they can do and how they can work with each other. 28:35 And if you do all of those things, I think you can maybe start to wrangle that sprawl a little bit. 28:39 One hundred percent. And all I will add is we're starting to really see what like responsible AI operating models look like. 28:47 And that's probably a deeper conversation than what we have time for right now. But these are the building blocks, right? 28:55 To do this responsibly, to do it in a way that's cost-effective and creates the actual business outcomes that your organizations want, and the personal outcomes that you as an individual want, you need to think about the full kind of life cycle of how you interact with the AI, and there's a lot of really 29:12 cool stuff happening in this space. Thank you very much, say it absolutely 100% with you guys both on each. I'd love to deepen the discussion on religion, it's databases, and teamwork, and things things like that, but I think for another time. 29:27 For right now, if you would like to know more, then I think you can find us Accenture Site by just googling Accenture Business Agility. 29:38 You'll learn to write people. You have my name out there. I think Renaud Cranier, again, just in case. So we can help on that. 29:46 a companion, the change, and the adoption success of AI tools and things like that. Say, what could I find you if I was outside and I'd like to deepen the discussion or to seek your help? 30:00 Yeah, thanks so much for asking. You can find me at Atlas Revolutions. You can find me on LinkedIn, Sahel Panicar. 30:07 You can find some of the information I've been talking about through AI Native by Scale I am an AI native trainer if you're interested in having one of those certification classes that I talked about Like I did in Munich class week But yeah, please reach out to me. 30:22 I would love to continue this conversation with all of you From an Atlassian standpoint if you want to know more about the platform What we're doing at lacian.com slash trust is a great resource to just Understand our stance on these models and their their capabilities Personally, I linked in it's probably 30:37 the best way Zach Brown, not the country artist if you're in the US and you know their reference. Yeah, I would love to continue the conversation too. 30:45 It's so great to talk to you both.