3116: Persistent Systems' Blueprint for AI-Led Digital Transformation

[00:00:04] Is the enterprise AI landscape at a turning point as we shift towards more integrated and scalable approaches?

[00:00:12] Well, my guest today is the CTO at Persistent Systems.

[00:00:17] Together, we're going to explore the transformative role of AI across multiple industries.

[00:00:23] And my guest brings a wealth of knowledge from his extensive experience in digital engineering, data modernization, and so many other areas at Persistent Systems.

[00:00:31] But today, we'll discuss how businesses are moving away from AI projects to robust platform-driven strategies.

[00:00:40] Strategies that promise to bring about real and lasting change.

[00:00:45] But how is this shift influencing both technical and business aspects of organizations?

[00:00:50] And what does it really mean for the future of enterprise AI?

[00:00:55] Well, enough scene setting for me. Let's get today's guest on now.

[00:00:59] And we will all unpack these questions and much more and shed light on this next wave of AI integration.

[00:01:07] So, a massive warm welcome to the show.

[00:01:10] Can you tell everyone listening a little about who you are and what you do?

[00:01:14] Sure.

[00:01:15] Thank you for having me, Neil.

[00:01:17] My name is Panduram Karmath.

[00:01:19] I am the Chief Technology Officer at Persistent Systems.

[00:01:23] I'm based out of India and lead innovation and R&D for Persistent and our customers globally.

[00:01:31] I've been with the company for 12 years now with a background and a PhD in computer science from Rodriguez University

[00:01:39] and a long history of working for product and research companies in the US before moving back to India and joining Persistent 12 years ago.

[00:01:50] So, that's about me.

[00:01:52] Well, it's a pleasure to have you on the podcast with me today.

[00:01:55] And every day on this show, I try and demystify a lot of things that businesses and people are hearing about

[00:02:01] and put them in a language everyone can understand.

[00:02:03] And, of course, everyone's talking about AI and AI projects and implementing those projects right now.

[00:02:09] So, I'm interested from your perspective, what you're seeing on this shift from isolated AI projects

[00:02:16] to more integrated platform-driven strategies.

[00:02:19] What factors do you see or do you think are driving this evolution?

[00:02:23] And how is it impacting enterprise AI adoption?

[00:02:26] Because that AI adoption is such a big topic right now.

[00:02:30] Yeah, absolutely.

[00:02:31] I see enterprises have been adopting AI for more than a decade now for different predictive analytical purposes and so forth.

[00:02:41] What changed in late 2022 or so is the advent of generative AI in a more accessible and consumable form.

[00:02:54] It's mixing and becoming mainstream first, actually, with the release of ChatGPT in late 2022

[00:03:01] and becoming part of the societal consciousness, then coming with urgency into the enterprise starting 2023.

[00:03:11] That changed a few things.

[00:03:13] One, there were a lot more use cases in terms of intelligence that enterprises could now bring to life within their business.

[00:03:23] Not just predictive, but creating, synthesizing content, code, images, helping understand large, complex, unstructured data,

[00:03:35] changing the way you access and democratize that access to data itself.

[00:03:40] A lot of doors opened up with the advent of generative AI.

[00:03:45] And 2023 was all about, in addition to the consumer-grade AI emerging, how do enterprises adopt it safely and securely and at scale?

[00:04:00] So that required a lot of enterprise-grade software to be released and APIs to be released,

[00:04:06] data protection agreements being in place so that your data is not used to train these models and so forth.

[00:04:13] But 2023 was a lot about experimentation.

[00:04:16] 2024, we have seen the more mature companies go all in on adopting AI and generative AI.

[00:04:24] And they really go together in a lot of respects.

[00:04:28] But we have seen two types of things, right?

[00:04:30] One approach, which is where an organization knows very clearly what they want.

[00:04:35] They are taking a foundational approach to laying a common infrastructure stack, common frameworks, a responsible AI and governance framework to go with it.

[00:04:46] And then letting businesses and engineering teams rapidly experiment and build use cases, which are high impact.

[00:04:54] So they are taking a long-term approach to this innovation.

[00:04:58] While technologies may change, the framework helps them adopt to that technology.

[00:05:02] Models are evolving every month.

[00:05:04] The others are still kind of trying out and figuring out which are the right use cases for them and which are the high impact use cases.

[00:05:14] And that's how the trend is going.

[00:05:16] And just to close out on that, some of the things that are common that have been found finding great option, customer service and contact center transformation for one.

[00:05:26] The entire enterprise search insights, analytics and BI reporting landscape is being transformed.

[00:05:35] That's the other piece, internal facing.

[00:05:38] And then there are domain-specific or industry-specific workflow automation cases that are driving adoption.

[00:05:45] These have been three of the big trends that we have seen.

[00:05:48] Does that help?

[00:05:48] A hundred percent.

[00:05:50] And for listeners of this podcast, enterprise technology is a huge part of that and understanding how the business leaders can leverage technology, how they can implement it, what problems they can avoid.

[00:06:01] And of course, AI is transforming both technical and business domains right now.

[00:06:06] So how do you see its role evolving in areas like digital engineering, data modernization and industry-specific workflow automation?

[00:06:15] Because a lot of people listening will hear about these things, but I'm curious what you're seeing.

[00:06:20] Yeah, absolutely.

[00:06:22] So that was actually one of those things we started internally reflecting on and doing more about in early 23 because our business is digital engineering.

[00:06:34] Our business is helping companies not only build services and products, but to modernize and migrate their application and their data stack.

[00:06:43] And fundamentally, while there has been a lot of talk about individual developer productivity tools and assisted coding and so forth, we have taken a more holistic approach.

[00:06:55] And what we are seeing is this generative AI world or technology rather is transforming everything from ideation and design to creating your backlog, to creating the user stories, to actually building software, to refining it.

[00:07:12] And making it performant all the way into, you know, when software goes into sustenance mode, security patching, sustaining, making incremental changes.

[00:07:23] The entire lifecycle is being disrupted.

[00:07:25] And our approach has been always to build the right tooling for this so that software being a team sport, entire teams are able to move faster, more productively and build more high quality and secure code.

[00:07:41] And so the platforms that we've invested in building are geared towards both these kinds of engineering use cases.

[00:07:49] I mean, they are separate platforms, purpose built for engineering, product development, or data modernization and migration use cases.

[00:07:56] And then the business use cases that I talked about earlier.

[00:08:00] Each of these required purpose built approaches, sometimes a bouquet of fine tuned models, you know, that are great at these very specific tasks.

[00:08:11] And that's what enterprises are adopting today.

[00:08:14] And partnerships with hyperscalers and other major players out there, they're also increasingly crucial for scaling some of these AI initiatives that we're talking about.

[00:08:24] So what strategies as persistent systems implemented to maybe help maximize the impact of some of those collaborations?

[00:08:32] Because again, another big talking point right now.

[00:08:35] Yeah, absolutely.

[00:08:36] And see, the approach for companies like us, right, not just us, should always be that you have a lot of innovation happening in these major hyperscaler companies.

[00:08:49] And the approach for us from day one has been, how do we become an orchestrator of choice of this hyperscaler ecosystem, whether it's hyperscalers or other specific niche partners that we have.

[00:09:00] So we have actually strengthened our relationship over the last two years with all of the major hyperscalers that we work with.

[00:09:09] We've been working with them for 20 plus years, in some cases, 30 plus years.

[00:09:13] But with the advent of generative AI, there was a renewed figure to how to get enterprises to adopt the hyperscaler services rapidly, make use of these technologies with the enterprise data securely.

[00:09:29] And that's where our focus has been.

[00:09:31] So if you look at today, how this has paid off is that every one of these hyperscalers recognize us as a preferred partner in their public events, whether it was AWS giving us a shout out at reInvent or at Google Next as being recognized as an innovation partner and so forth.

[00:09:49] And our focus always has been that customers now see us as a trusted partner to implement these AI services for them.

[00:10:00] So even the tools and platforms that we have created behind the scenes, they consume and they orchestrate across both the hyperscaler large foundation models, as well as smaller or open source models that the enterprises want to host.

[00:10:14] So that's how we are going about this today.

[00:10:18] And one of the things that made Persistent stand out to me and why I wanted to invite you on this podcast today is the fact that you're championing this platform driven approach to enterprise AI, because in your belief, this strategy is enabling faster, more effective outcomes for your clients, which will be music to the ears of many business leaders listening.

[00:10:39] But can you tell me a little bit more about how this approach is able to achieve and unlock some of these outcomes?

[00:10:44] Yeah, absolutely. And I think the basic thinking behind this and the message is very simple.

[00:10:52] Our customers understand their business better than us or anybody else.

[00:10:56] They should focus on the business impact they want to drive, which use cases and workflows they want to transform and how they govern their data.

[00:11:08] They shouldn't have to worry about the plumbing, which needs to go into place, which is often very general purpose.

[00:11:16] For example, how do you orchestrate across multiple models?

[00:11:20] How do you build a robust AI agent framework?

[00:11:23] How do you get your data in shape to benefit from AI?

[00:11:27] How do you do things like, you know, just targeted things like analyzing and extracting data from different file formats?

[00:11:38] These are, you know, very general purpose building blocks, which each business shouldn't have to worry about.

[00:11:44] That's what they lean on us for.

[00:11:46] And that's what we are platformized.

[00:11:48] Today, we have three major platforms that our customers benefit from.

[00:11:53] Number one, we are completely transforming digital engineering with a platform called Saswa.

[00:11:59] Saswa comes in with both generative and probabilistic and deterministic engines, which have several themes.

[00:12:08] So if you're trying to build new software and ideate, you know, you have a set of agents that are great at that.

[00:12:15] If you're trying to modernize a COBOL set of applications, maybe you've got 50 COBOL applications.

[00:12:22] Nobody knows what's in them.

[00:12:24] The people have moved on and so forth.

[00:12:26] And you want to modernize the Java and move to the cloud while you're at it.

[00:12:30] That's a different theme.

[00:12:31] So for a whole bouquet of such themes, we have created purpose-built set of agents.

[00:12:36] And these agents are trained on open source repositories, our own product repositories and so forth.

[00:12:44] But when we come in to serve a particular customer, we fine-tune them on the customer's repositories.

[00:12:50] So everything that they produce is very accuned to the customer's code base.

[00:12:55] The second platform that we have, which is iAura, is built by data engineers for data engineers.

[00:13:01] We've been doing data for 33 years of our existence.

[00:13:04] So we understand whether it is data ETL or quality, lineage, governance, privacy, compliance.

[00:13:12] All of these are very specialized tasks.

[00:13:16] And there have been a lot of platforms built around it.

[00:13:18] With the advent of Generative AI, we are able to accelerate the journeys of these data engineering teams

[00:13:24] with purpose-built AI agents powered in this iAura platform.

[00:13:28] Again, leveraging our expertise to build that.

[00:13:30] And lastly, Generative AI Hub, which is really the orchestration layer, whether it is on FinOps, whether it is on user provisioning,

[00:13:40] whether it is on application visibility or building quick and highly scalable AI agents or evaluating them.

[00:13:48] Gen AI Hub brings all of that to the table.

[00:13:50] So customers and our teams then focus on the business use cases, not on this foundational layer.

[00:13:56] And that's the strategy that has succeeded very well and actually differentiates us as well.

[00:14:02] If you see all the ratings, most recently being the ISG rating that came out,

[00:14:07] and before that, the HFS Generative AI services rating, both puts us in the leadership category ahead of many of our competitors

[00:14:17] and in line with competitors that are 10X our size because of this very differentiated strategy.

[00:14:24] And of course, I don't want to make it sound too easy because it is a very complicated journey.

[00:14:28] So what would you say are the key challenges that you see businesses facing when transitioning from that project-based AI adoption

[00:14:36] to integrated platform strategies?

[00:14:39] Anything you can share around that and how ultimately we can make their lives easier today by talking about how they can overcome some of these hurdles

[00:14:46] and some of these challenges?

[00:14:48] Absolutely.

[00:14:49] And none of this is easy.

[00:14:51] And I think the number one challenge or opportunity that businesses should take is having the clarity of,

[00:14:58] you know, how do you want to create that disproportionate impact, right?

[00:15:01] So businesses should not look at the AI and Generative AI play as purely an efficiency or optimization play, right?

[00:15:10] Or a workforce reduction play.

[00:15:12] That is a very narrow view of the opportunity that exists.

[00:15:18] I think there is a whole spectrum of this around creating stickier, more delightful end-user experiences,

[00:15:28] whether it is for your employees or for your customers.

[00:15:31] You know, getting those jobs done more efficiently, but also wanting them to come back and continue to use your product.

[00:15:38] It's a huge differentiation that not many companies are getting right today.

[00:15:43] The second element here is, can Generative AI now open adjacencies, you know, to your business that you're already catering in?

[00:15:53] How do you think through that and how do you bring clarity to what those adjacencies are?

[00:15:57] How do you move up the value chain in your own industry, for example, right?

[00:16:03] And for you to do all of this, first, you need to get your data in shape.

[00:16:06] So the number one challenge or, you know, pre-work that you have to do before AI adoption is getting your data in shape.

[00:16:14] Whether it is understanding what's in store, whether it is harmonizing the different data formats or cleaning the data so that you are able to train and fine-tune the AI applications better,

[00:16:29] all of that legwork has to be the second leg.

[00:16:32] So identify the opportunity, get the data in shape, and finally, making sure that your compliance and your data security and privacy framework

[00:16:41] also includes significant responsible AI, you know, both philosophy, controls, and training,

[00:16:49] and actual implementable things that you bring to the table in the responsible AI frame.

[00:16:56] So these are the three things I think enterprises should focus on, and many of the mature ones are thinking of this.

[00:17:04] And so as AI continues to influence industry-specific solutions, I'm curious, from what you're seeing,

[00:17:12] are there any other trends that you're observing in how businesses are tailoring AI technologies to meet their unique business needs?

[00:17:19] Anything you can share around what you're seeing and what you're talking about here too?

[00:17:23] Yeah, sure. So I'll give very diverse examples, right?

[00:17:27] We have seen companies embark on modernization journeys with their data stack.

[00:17:33] There was this customer, they had 40,000 plus reports, right?

[00:17:38] And that was spread over, I think, something like 14 different tools for BI and analytics that they were using.

[00:17:45] Many of these reports, there was not enough intelligence or documentation.

[00:17:49] What is in those reports? What are the data models? What are those queries?

[00:17:54] So they've, you know, even embarking on a modernization journey was a no-go before it even started,

[00:18:00] because you didn't know what you were going to break.

[00:18:03] With the kind of tools I talked about that we brought to the table,

[00:18:06] they were able to understand, first of all, what's in these reports.

[00:18:10] Can I rationalize some of this? Do I need 40,000 different reports?

[00:18:14] Can I blend, merge, remove some of these and not even require that?

[00:18:19] Can we create new data access experiences so that some of these reports are not even required in the future

[00:18:27] where people can just query on demand and get what they want?

[00:18:31] We help do all of that exercise and embark on a journey powered by generative AI in understanding

[00:18:37] and then modernizing the data and reporting.

[00:18:41] And so this is on the internal side.

[00:18:43] There's a company that we are working with who's able to, their field sales,

[00:18:48] have a software that we help them create,

[00:18:52] where they are able to on the fly figure out what their customers are searching for

[00:18:57] and shopping for elsewhere.

[00:18:58] And this is in a medical equipment space.

[00:19:00] And create, you know, very custom outreach briefs

[00:19:05] with the relevant product offerings from their own company

[00:19:09] and the pricing and a very tailored pitch

[00:19:12] that could be sent to these customers very effectively.

[00:19:15] They are taking revenue growth focus using AI in this manner.

[00:19:20] We have another company that is looking at transforming

[00:19:24] transforming the way construction projects and their risks and project management takes place.

[00:19:30] And how can AI agent help identify these risks better?

[00:19:34] How they can give insights that typically humans would derive

[00:19:37] by doing a lot of search across different data sources.

[00:19:41] So we have a very wide range of companies.

[00:19:44] And before I go, a very interesting one is things like drug research, right?

[00:19:50] These are things that fundamentally affect human lives at scale.

[00:19:56] A lot of prep work goes in over months and years in doing literature search,

[00:20:03] in identifying matching data or content that you have to bring to the table

[00:20:09] in drafting some of these clinical protocol things or identifying the right molecules.

[00:20:13] All of these foundational activities are being disrupted by generative AI today.

[00:20:21] Slowly, because there's a lot more scrutiny that you have to bring,

[00:20:24] a lot more guardrails that you have to bring to this table.

[00:20:27] But that's how companies are adopting it as well.

[00:20:31] And as for yourself, as we look ahead to 2025,

[00:20:35] how do you see the role of persistent systems evolving

[00:20:38] as companies navigate these complexities of implementing AI at scale

[00:20:42] and everything we've talked about here?

[00:20:45] What's next for you? How are you evolving?

[00:20:48] Well, we have always seen ourselves as a strong technology partner to our customers.

[00:20:55] So our endeavor is always going to be bringing the best of our partners

[00:20:59] and best of what we build in platforms to help enterprises adopt this securely.

[00:21:05] A few shifts that we're going to see is we do see the beginnings of,

[00:21:10] and you would have worded this phrase elsewhere, services as software, right?

[00:21:14] Instead of software as a service.

[00:21:16] We have been talking about this a little bit earlier as well

[00:21:19] in terms of delivering outcomes instead of just software.

[00:21:23] And we see that trend accelerate.

[00:21:25] So our focus is on building a set of purpose-built agents,

[00:21:30] making it easier to build these AI agents for bespoke use cases

[00:21:34] so that we are the right partner of choice in building that new agentic world,

[00:21:39] which can take entire workflows and outcomes and deliver those for a human

[00:21:44] to supervise, to validate, and approve.

[00:21:47] So those are the kind of things that we are trying to prepare for.

[00:21:52] And fundamentally, the business of digital engineering itself

[00:21:56] is already undergoing this change, right?

[00:21:58] So we hope to leverage this to do a lot more revenue per employee,

[00:22:04] if you will.

[00:22:04] And that's how we drive growth and differentiation.

[00:22:09] Be at a table where the employee strength is no longer a bar for us to not compete.

[00:22:17] I take on much larger multi-year transformation projects

[00:22:21] powered by these platforms that we talked about.

[00:22:24] That's how we see the opportunity.

[00:22:26] Well, best of luck for the year ahead and unlocking some of those opportunities.

[00:22:32] And for everyone listening, they could be a business leader,

[00:22:35] a stakeholder in a business department, just looking to leverage AI.

[00:22:40] I always like to give everyone listening a valuable takeaway.

[00:22:42] Is there any advice you'd give to an organization

[00:22:45] just at the beginning of their AI journey,

[00:22:48] particularly those seeking to move beyond experimental projects

[00:22:51] to scalable platform-driven approaches?

[00:22:55] Any advice that you would offer those people listening?

[00:22:58] Yeah, very simply, I would say, you know, take the long view on this.

[00:23:02] This is not a fad, nor is it, you know, something, you know,

[00:23:07] you can cut through the hype and there is substance here.

[00:23:11] So take the long view, make generative AI part of your broader AI strategy,

[00:23:17] figure out over the next three years, what do you want to be known for?

[00:23:21] How do you want the North Star to be defined?

[00:23:24] And then, you know, work with, you know, the right partner to figure out

[00:23:28] what is the AI foundation you need to put in place

[00:23:31] so that you can experiment safely and securely

[00:23:34] without getting caught up in the regulatory compliance trip fires, if you will.

[00:23:40] So that's the succinct advice.

[00:23:42] But do not ignore the rapid scaling and adoption of AI

[00:23:48] that's happening around you.

[00:23:51] Well, thank you so much for taking the time to sit down with me

[00:23:54] and share your insights with everyone listening today.

[00:23:57] But I'm going to be greedy now and ask you

[00:23:58] to leave one final gift for everyone listening.

[00:24:01] We are in the middle of the holiday season.

[00:24:02] I have an Amazon wishlist where I ask my guests to leave a book

[00:24:06] that they'd recommend or mean something to them

[00:24:08] that we can add to that list and they can check out.

[00:24:11] What book would you like to leave and why?

[00:24:14] All right.

[00:24:14] So this is a bit unorthodox.

[00:24:16] It's not a business book or a self-improvement book

[00:24:19] or a leadership book.

[00:24:21] So I've read this book written by Jules Wan

[00:24:26] called Off on a Comet.

[00:24:28] Actually, the English version is called that.

[00:24:29] He wrote this in 1877.

[00:24:31] He called it Actor Sarvadak,

[00:24:33] which is the name of the main character in the book.

[00:24:36] And it's about space exploration, right?

[00:24:39] And I've read the Marathi, which is a regional language here,

[00:24:43] the Marathi translation of this when I was a kid.

[00:24:45] And it's still left a lasting impression.

[00:24:47] It's a book where there is a comet that comes by

[00:24:53] and passes close to the Earth

[00:24:54] and takes off a chunk of Earth on its tail.

[00:24:57] And there are 40 or 36 people on it,

[00:25:00] different nationalities.

[00:25:01] What they experience over the next two years,

[00:25:04] you know, as the comet moves around through space.

[00:25:06] A fascinating thing about this book,

[00:25:09] in 1877, he wrote this.

[00:25:11] When there was no space travel,

[00:25:13] there was not even any flight travel.

[00:25:15] And it teaches you a lot about how societies form,

[00:25:19] how these different people almost create a new society

[00:25:21] in a world,

[00:25:22] they take some time to understand

[00:25:24] how they think through first principles

[00:25:26] to figure out what is going on around them.

[00:25:29] There's physics in there,

[00:25:30] there's geology in there,

[00:25:32] there's astronomy in there,

[00:25:33] and there's fantasy and fiction in there.

[00:25:36] And if you read this book,

[00:25:38] there's English translations called Off on a Comet.

[00:25:40] It will take you on a phenomenal journey

[00:25:42] that will leave you in a happy mood in the holidays.

[00:25:46] And it has a,

[00:25:47] I won't tell you what the ending is,

[00:25:50] whether it's happy or not,

[00:25:51] but it'll leave you happy,

[00:25:53] is what I'd say.

[00:25:55] Well, I'm so glad you didn't leave any spoilers there

[00:25:58] because I was hanging on your every word.

[00:26:00] And it's certainly a book

[00:26:01] that I'm going to be checking out over the holidays.

[00:26:04] So I hope everyone listening will as well.

[00:26:06] And for anybody listening,

[00:26:07] wanting to dig a little bit deeper

[00:26:08] on Persistent and everything we talked about there,

[00:26:11] where's the best place for people to check out

[00:26:14] and find out more information?

[00:26:16] Well, the website is thebestplacepersistent.com.

[00:26:20] Everything we do,

[00:26:21] including our work in AI is right there.

[00:26:24] So go check us out

[00:26:25] and you'll learn more about us.

[00:26:29] Well, so many big takeaways

[00:26:31] from our conversation for me,

[00:26:33] from discussing the next wave of enterprise AI,

[00:26:36] AI's dual role,

[00:26:38] the power of partnerships,

[00:26:39] and how indeed you at Persistent

[00:26:41] are bringing all this together

[00:26:42] with this platform-driven strategy

[00:26:44] that ultimately helps you

[00:26:46] and your clients deliver better,

[00:26:48] faster results.

[00:26:49] And in an age where everyone's talking

[00:26:51] about the ROI from AI projects,

[00:26:54] I think that is so important.

[00:26:56] And more than anything,

[00:26:57] you've even left us with a great book

[00:26:58] that I need to check out, ASAP.

[00:27:00] So thank you so much

[00:27:01] for sharing your insights today.

[00:27:03] Thank you for having me, Neil.

[00:27:05] Always a pleasure.

[00:27:06] So today we explored

[00:27:07] Persistent Systems' strategic approach

[00:27:10] to leveraging AI

[00:27:11] through partnerships

[00:27:12] and partner-driven solutions,

[00:27:14] emphasizing their commitment

[00:27:15] to delivering faster

[00:27:17] and more effective outcomes.

[00:27:18] And I think it's clear

[00:27:19] that the journey towards

[00:27:20] an AI-integrated future

[00:27:22] is both promising and challenging.

[00:27:24] But as we all collectively

[00:27:26] continue to navigate these developments,

[00:27:28] this is where I encourage you

[00:27:30] to reflect on how these changes

[00:27:32] might just impact your own sector.

[00:27:35] What steps can your organisation

[00:27:37] take to harness the power

[00:27:38] of AI effectively?

[00:27:41] As always, let's keep

[00:27:43] this conversation going.

[00:27:44] Explore these possibilities together.

[00:27:46] This is a dialogue,

[00:27:48] not a monologue.

[00:27:49] So email me,

[00:27:50] techblogwriteratoutlook.com,

[00:27:52] X, Instagram, LinkedIn,

[00:27:53] all those usual places.

[00:27:55] Just Neil C. Hughes.

[00:27:56] Easiest guy in the world to find

[00:27:57] if you do have anything

[00:27:58] you'd like to share.

[00:27:59] But as we're head down

[00:28:01] and racing towards 2025,

[00:28:04] it's time for me to take a breather

[00:28:06] before I get tomorrow's guest on.

[00:28:08] So thank you for listening today.

[00:28:09] And I'll speak with you all again

[00:28:11] tomorrow morning.