Enterprise Automation : The New Strategic Differentiator - with Richard Davies

FULL TRANSCRIPT

This transcript is based on the episode’s English auto-captions and has been formatted for readability. Please allow for occasional transcription errors in names, acronyms and specialised terms.

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Globally enterprise automation market valuation in 2024 itself was around 75 billion. >> You know there's a combination of legacy systems and even systems that haven't yet been automated that are causing an increasing burden on organizations from a productivity standpoint. It's not always clear whether RPS still continues to play a role >> [music] >> um in in today's automation uh landscape >> on the cost side of of AI. I think a lot of people get surprised that there's a significant variation even in the cost base and the cost models of using AI as well and they definitely need [music] to be factored in. >> But more importantly, it is pushing business leaders to rethink how the work itself gets done within the enterprises. >> Hello and welcome to the enterprise tech talk podcast. I'm your host Saumitra Kalikar. On this podcast, we explore various enterprise technology topics and emerging technology trends and to try and try to demystify them through open and honest conversations.

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Today we are going to discuss an interesting topic something that is reshaping business and technology landscape at a lightning speed. It is about enterprise automation. Now automation itself is not new. However, we are moving well beyond just scripting the tasks or bolting on the bots to a much more strategic capability. And with emergence of AI based automation, it is promising to deliver better pure customer experience, improve compliance, improve operational resilience, but more importantly, it is pushing business leaders to rethink how the work itself gets done within the enterprises. So to unpack this transformation, I have with me today Richard Davis. Richard is um he is the CTO APAC for out systems. Um while at our systems he has led major customer acquisitions and expansions.

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He's passionate about uh connecting business vision to um uh technology realities, aligning people, process and platforms to drive meaningful outcomes. Richard, welcome to the podcast. >> Thank you very Thank you very much, Simiter. It's great to be here. >> So, before we get started and take a deep dive into our for our conversation, would you mind providing some more background about yourself? Um, what engages and excites you about technology and how it impacts business? >> Yeah, sure. Look, I've been um involved in IT for for a long time. Um initially more as a technologist, as a developer um but especially in the last couple of decades. I'm I mean I'm still interested in the the nuts and bolts, but I'm particularly interested in how to make sure that technology can deliver real business value, which is where things tend to get quite quite complex.

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It's far more than just the actual technology itself that you need to be thinking about there. >> Absolutely. Absolutely. Now, good. So in this episode we will explore uh what is changing in enterprise automation, what is the hype versus reality, what is driving real ROI and where the business leader should think and act now um to drive to be ahead of the game. Um so let's Richard let's start with a big picture >> right. Um >> it's about um there is a as I said at the start enterprise automation is not new but there is clear uh sense of urgency with at executive board level about automation and when I was doing some research for for this particular episode I noted down couple of data points one was around market valuation so the globally enterprise automation market valuation in 2024 itself self was around 75 billion and and when we talk about the growth rate growth projections for for enterprise automation there's a much higher and healthy kagar um of about 60% yearonear all the way up to 203132 now these data points indicate that um there is a very clear and strong commitment from enterprise leaders to in investment to automation for foreseeable future. So in your view what is actually um uh why this this uh automation is becoming such a higher priority conversation topics at at executive level and what are the key drivers for that? Um what kind of internal and external drivers are making that shift?

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>> Um I think there's a a couple of them. Um again some are sort of longerterm drivers that this is part of an ongoing um challenge around legacy modernization that you know there's a combination of legacy systems and even systems that haven't yet been or automated that are causing an increasing burden on organizations from a productivity standpoint. point and then at the other end um more recently in the last few years there's obviously been this big push from artificial intelligence and and I think it's it's somewhat a convergence of those two factors coming together that's really making um executives think actually now is perhaps the time that we can we can start to really make a make a dent in some of that legacy and really move forward with some of our automation initiatives. >> Yeah. Yeah. And you talked about AI and so with emergence of AI based automation is it is it u just is it fundamentally changing the way automation is done or is it just accelerating the just the old ways of automation models.

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>> Um I mean there's a few different angles to tackle it from. I think um one of the challenges with AI as we alluded to earlier is not actually the technology. It's there's a very big change management issue in organizations around adoption >> and and that's putting a bit of a natural break on what can can be done and is is being done currently. >> There are also a couple of areas that it's useful to split out in terms of where it's impacting automation. you can look at say individual sort of more fine grain processes within an organization and look at how those could be modernized and updated with AI. So that's certainly one approach. Um and that's and that's starting to produce some interesting results. Um not always positive. You know I think you know people are starting to realize that that there are there are challenges associated with that. And then there's the um other side which is around the tooling which is being used in general um for the whole software development life cycle in order to improve productivity which is again um an indirect but very significant boost to to automation as well especially in tackling some of those things like the the the legacy debt problem. So I think you know there's a bit of a convergence of of some some factors that are um you know c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c culturally making it difficult to adopt um that there are some factors where organizations are playing around with some you know sort of smaller scale use cases but there is also opportunity coming in in terms of that that automation efficiency in the sort of tools that organizations are using.

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>> Yeah. Yeah. And is that um the high priority and highest high priority expectations from enterprise leaders that you see that uh uh productivity efficiency are the main drive drivers of when when some organizations think of a big enterprise automation program. Um I think there's definitely that vision is fairly common at senior levels within an organization within the the executive. There's that expectation that um delivery timeline should be improving as as this technology rolls out. Again, I think there's a bit of a an expectation gap or reality gap. um once things actually hit the ground um in implementations that there are still quite a lot of >> challenges that aren't currently being addressed by some of the the you know automation tools. There are some very interesting ones out there. Some, you know, some great interesting AI capabilities, but they're not necessarily moving the needle as much as the executives might expect in terms of >> Yeah. Yeah. Yeah. And this has been challenge.

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Uh I think this is a recurring challenge as I think there is very talk when we talk about any AI based um initiative in general. Um but then how do you see expect um organ the business leaders or executives uh think of measuring the success of those automation programs if if the if it is it's not so tangible. So I think there is tooling coming on board to help with that. So some of it again is not is not actually that new. We've been doing you know process re-engineering for a long time and you know there's existing you know practices around how to to measure and um improve productivity if we look at it sort of at a at a process level. Um, >> when you're looking to incorporate AI, however, I think there are a few things to be aware of. You do obviously need to be able to track um like let's say we we're dealing with a with a process improvement sort of more low-level scenario. you do need to be able to track um not just the potential costs that you're taking out from the human activities, but the difference potentially in error rates. Um you know, and I think actually that's another thing about AI is errors errors occur in human processes. They're not just a new thing for AI. We we need to sort of weigh them up um objectively in terms of measuring what those error rates are on both sides. it's not just on you know the AI side and how those errors are potentially mitigated which which is again nothing nothing new we we have exceptions that need to be managed but then also looking at other things like on the cost side of of AI I think a lot of people get surprised that there's a significant variation even in the cost base and the cost models of using AI as well and they definitely need to be factored in because that's often where organizations are are hit by a bit of a nasty surprise that they may have done something that could technically be successful, but when they look at the overall ROI and they factor in the new costs of what the AI is doing, then they become much less attractive or even potentially, you know, get they get negative returns on it. So that's that's one aspect and the the other is a little bit more strategic I would say which is you know that's looking at a process improvement perspective. The other is to think strategically from an organization in terms of how do you structure and what kind of use cases do you target to do this automation in >> um looking at your existing landscape obviously taking that into account >> but also looking at again if you're going to leverage AI looking at what kind of governance capabilities may be required for specific use cases and whether those can then apply to to a whole range of of use cases and what actually required because we find that there's often a quite significant step-wise increase in the amount of governance that's needed for particular use cases. You can start off with some pretty easy ones and then as you move up to dealing things with things like privacy and and knowledgebased management, it becomes significantly more challenging and you do need to put a little bit of forward planning into those use cases as well, I think.

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>> Yeah. Yeah, that's that's really great point because these things are typically missed when you do the ROI um or or even a TCO for for AI implementations, right? Because organizations typically focus on okay, I we need to build these models and then we need to roll those out etc. But there's lot more we you need to do around that um that is generally missed out. Um and so is there something here that we we should demystify our enterprise leaders as to how they should think of ROI in general for these automations uh programs because sometimes many times I would say business leader think of an initiative would deliver immediate benefits when it comes to automation. Um but it is not always necessarily the case right. Um and and the other thing is in terms of um the the KPIs etc that you can measure around automation they are not well very well defined. So is is there something you want to unpack for tech leaders or business leaders on that?

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>> Yeah sure. Look, I think part of the challenge around um tracking the a KPIs I mean look some of I agree some of them can be quite hard to track like how do you you know exactly quantify something like productivity. Um some of them though I think are hard to track simply because the technology landscape is often so fragmented. Like we know for instance that um like take take you know a a fairly lengthy process say like a procure to pay process there there are multiple steps in it often those steps are managed um some of them outside the system like in emails um it's it's it's those gaps where the KPIs can't be tracked which also creates creates issues. That's one of the reasons why I'm very keen on the idea of having platforms that can host a lot of the the solution um because they actually make those core KPIs like time and cost >> easier to track. And the other thing is too, so you know, one of the things that we do, for example, if we're migrating a human workflow to um say an automated or semi-automated oric workflow is you don't necessarily, at least while you're building the project, have have time or the metrics to um get a great detailed view. But you want enough to know if you're going in the right direction, if things are getting better or getting worse. So if you can in enough control over around um say time and cost of specific activities and some of the other key cost drivers on say the automation side, whatever tooling you're using, the AI cost, things like that, at least you can get that ongoing feedback. And that's also what's really often missing in a lot of projects is, you know, having having that moment, you know, often it happens a few months down the track where things are are finished and then we realize actually what the what the cost was and and what the potential benefit was. If we can even get something directional, even if it's fairly coarse grain during the build, get enough metrics that we know we're going in the right direction, that can be a significant help to the project as well. And that's what we focus on in our systems is being able to give you enough that you know are things getting better or are they getting worse. >> Yeah. Yeah. Yeah. Well, that's a good point. Um let's let's shift our focus to a bit of technology landscape with the when it comes to enterprise automation and as as I said again at the start it's it's automation itself is not not the new thing. Um if you recolct and you would recollect very well that six eight eight to 10 years back there was so much hyper on RPAs >> um right uh and then uh we moved on and there was emergence of low code no code platforms and now we have this whole auto AI based automation platforms right u so um do I think there is something to to clarify here for our um for our listeners as well because it's not always clear whether RPS still continues to play a role um in in today's automation uh landscape. Is it dead or is this we are building on top of it and the other thing uh I think there is not enough clarity within uh within most of the listeners uh within when it comes to low code and no code platforms typically you would see these terms used together hand in hand low code no code platform but so it will good um to clarify what is some some difference between what is a low code versus what is a a no code platform. Yeah. Yeah. Absolutely. And look, you know, to start with your first point, I I mean, I don't believe RPA is dead. I think though, to your point, it has been around for quite a while and what would maybe class as lowhanging fruit has already been pinged. But I think it's maybe getting another lease of life potentially because a lot of the innovations that are happening in the AI space really critically rely on data which is often locked into legacy systems and RPA is still a perfectly valid mechanism for getting that data out of those systems and it may not have had that level of importance in the past. So, you know, I think there there may be a bit of a reinvigoration of of of RPA's role in that sense.

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>> Um, in terms of low code, no code, you're right, people tend to say it in one breath and it's just like one one thing. What I have observed over my last decade in that industry is there is a huge amount of diversity still across the range of capabilities and the sort of focus areas within what is broadly called low code no code. Um there are very niche products. There are products that come often say from a BPM background that are now low code no code products. There are products that come from say a domain centered background like CRM or ITSM which have now evolved into low code no code. And then there are sort of more pure play which is you know where our systems uh where I work comes from which is designed from scratch to be that kind of platform. and the sort of things that you build on them are quite invariable. So again, it's really getting back to the typical um problem of, you know, you've got to use the right tool for the right job. Um for instance, our platform is much more enterprisegrade. You wouldn't use it to build your own personal website and and e-commerce. But like on the other hand, you could use it to build a core system, which our customers do, and you would use some of the lower-end low code, no code platforms for that. It's about using the right tool for the right job, I think, and looking in seriously what the differences are.

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>> Yeah. Yeah. Yeah. No, that's a good point. And I think couple of points you implicitly mentioned there which I want to elevate a bit. You said um RPA what RPA requires was data and that is not going away. the need for uh accurate data and uh I think uh when it comes to process automation important the the need for a very welldefined processes all right itself is is important prerequisite so even if you have an AI based automation platforms nothing is silver bullet as we all know in technology landscape those foundations about having a good data uh landscape and good enterprise process repository etc those are fundamental requirements or building blocks on which these this kind of these platforms autoure platform would really excel. >> Absolutely. And I I think but it is getting a little bit more complex as well because you you're still dealing with say especially in the RPA space and in the general integration space structured data. What's really making that a lot more complex with AI is you now need to bring in the unstructured data. Yes. and and you know merge that to to produce something useful as well and that's where there's that additional level of complexity and governance that's required right >> yeah yes yeah absolutely and we we'll come to the governance point and but I just want to touch base with a little bit about on on technology front on AI agents because no discussion happens nowadays without AI agents when it comes to >> do you do you see it is still a bit early days when we talk about rolling out AI agents for automation or any enterprise use cases um or are we it it is there in terms of maturity that we organizations can start start thinking of rolling out engines.

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So I have seen organizations roll out successful production agentic AI solutions but I would say that the ones that have done so are in a still very small minority. >> Y >> what that says to me is at least in some use cases and domains it absolutely is technically possible otherwise they wouldn't be able to do it at all. But the fact that the majority of organizations are nowhere near that clearly says that you know there are there are challenges which which are not broadly being addressed and my impression is um there are definitely technical challenges. So not every use case is is easy or viable for AI. So you know you do need to pick your pick your targets quite well. Um regulatory Mandates also vary from industry to industry and they can also put a break on innovation. Um I mentioned already but I stressed it again culture again is a is a big one.

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>> Um you know having having the the culture within an organization it is definitely quite threatening to a lot of people as well AI it's still quite new to a lot of people even if they're not threatened by it. So, you know, getting that level of understanding all of those uh barriers and you know, we we I did mention it again before that governance is still being established in a lot of organizations and I believe that's also one of the keys to unlocking value safely around AI and those foundations simply aren't there in in many cases as well. And then as I mentioned earlier with the ROI, the economics may not be there either that you know replace something that's uh a human task with an agentic one. It might work technically but it may not work economically. >> Yeah. Yeah. >> Yeah. Yeah. Okay. Uh let's move to I think on on this point move to um the next uh topic uh and let's unpack a little bit around governance which you mentioned a couple of times anyway. uh because um we with with the advance of this AI based technology etc there's definitely some consideration on on how we build operating models and some rules around and and governance models around this um but uh and uh let me unpack this from execution governance and operational governance perspective >> y >> uh from execution governance perspective um you must have come across very good success stories for large automation program imple implementations but at the same time you must have seen some failures I would say uh drastic failures as well. So um from when it comes to the execution of these automation programs what comes to your mind as what is it that good automation programs do well and and what the the the programs which fail actually lack or miss out on?

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>> That's a very big question. Um >> yeah I mean it's a big big production. Maybe you can unpack that from um the rules operating model and uh the the majority of the business and so on. >> Exactly. And that's and that's actually what makes it complex is is all of those >> variables which is why I'm a bit cautious you know about what I say because a lot of organizations are coming from very different starting points. What I think again as a general principle is probably quite useful is to um have in mind and and spend a little bit of time thinking about what your target state is. So you might for instance say that um and and we've seen this in in successful models is they would operate a center of excellence for example would be quite key to to to that and in fact when you're talking about governance more generally the governance capabilities really do need to be centralized to work effectively. Um but again recognizing on day one and I think this is a really good example too that if you invest a significant amount in say building up that capability you're going to miss out on delivering the value that you need to keep the the program of whatever you're doing going right you do need to be staging these capabilities and that's why I say you know understanding the target state is very important in terms of what you need from a from a governance capability And then understanding how you're going to build your maturity map through to that end target state is also I believe important because you've got to always have a bit of a balance. You know the the days of >> very large you know multi- tens of million dollar IT projects is just not around anymore.

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Businesses insisting on value being delivered in much tighter time frames which I personally agree with as well. But that means you've got to be thinking very cleverly around how you lay down those foundation layers as part of a program because you can't just go and lay the entire foundation all up front. They just it just doesn't give you the returns right. >> Yeah, I agree and I agree. uh there's a uh when it comes to governance where also it's becoming important in my view is uh uh the whole concept about citizen development uh within the enterprises and and uh particularly with this AI based automation where end users can potentially do their own um automation flows etc and right for uh within their specific um operational limits of course Right. Um and that raises a question as to how how do you balance between the autonomy you want to give those to those teams versus risk of creating a potentially a so-called I would say chaos um right where big mess where it becomes unmanageable right so how do you balance it correct correctly >> exactly and this again just relating to what you're talking about earlier I mean this does come up a lot in the low code no code space as well because there's a >> an association between that technology and citizen development. Um I [snorts] definitely believe that citizen development has its place but in a large scale enterprise automation um you have to be very careful. I mean the obviously the the benefits of any kind of effective citizen development is is huge scalability. one of one of our customers actually um in um Malaysia large customer Petronus they've created a a kind of a tiered model which I think works very well where they they have a standard onboarding process for everyone from a sort of a an individual contributor in the business right through to their centralized IT function. So you know sort of graded levels of technology expertise and also respon responsibility and they have a sort of a standard process that everything >> goes through and then can be effectively >> routed to the right level of competence.

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You know it's it's at least eyeballed by a central governance team. It could be given to a team that's more citizen developer heavy if it's deemed to be reasonable but it still has that governance layer on top of it. Yeah. And I've seen that work quite well in practice. So you at least even if you are doing you know a combination of citizen and professional development you still need a a governance process that sits on top and at least reviews who's doing what at the minimum. >> Yeah. Yeah. Yeah. And maybe that governance process can also um uh develop certain patterns or guard rails uh etc and standards which organization should follow um even under the citizen development framework right um let's move on to um uh the the use cases themselves right um um so in your view I mean if organization is is um exploring ing to initiate a big automation program, right? And there are always use cases which are potentially customerf facing, some of the back office, some of the core technology operations etc. Right? So um what how do you see um businesses should think of initiating such programs to a point where they can deliver continuous value, demonstrate wins and build on top of it.

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Right? So what what are generally quick wins? Is it back office generally a quick win or is it customerf facing or or purely technology operations uh uh remmit? >> Yeah, again it's interesting and again obviously there's variation between organizations but what I would say is as a general rule that often you know as we were talking about earlier organizations need to sort of establish those quick wins. So the the outside in pattern is is one of the more common ones we see where there will be a focus on first of all refreshing the user experience whether that's customer or internal facing >> partly because um again with appropriate tooling and platform that sets a um uh you know a a good target for what can be improved within a reasonable time frame and it then sets you up well for future future enhancements that often need to be done under the covers as well. So that's, you know, that pattern has been around for a long time. We're definitely seeing that because it often gets you those quick wins and then it and then it creates that that breathing space for you to often do a lot of the the harder investment in around things like RPI and integration that are that are more complex and time consuming and >> Yeah. >> Yeah. Yeah. Yeah. Yeah. I agree. Okay.

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Um so as automation expands right for an organization there's always this consideration on human element uh and you mentioned a bit on this which I want to unpack a bit more is to how organization should start thinking about any emerging new skills requirements new roles etc to make automation not a just oneoff project but as a as a as a strategic capability for the organization right so um do you have any any views on that what kind of new skills and what kind of any specific roles that you think are emerging to make this as a strategic operational capability. >> Yeah. And in fact, I'm going to broaden that a little bit because I mean I think some of the the technologies and the the roles that existed to date are still valid. Obviously with AI coming in what I'm what I'm seeing often in organizations not everywhere but this is probably the predominant model is organizations are hiring you know an AI team to to build out the new tools and capabilities which is you know obviously you know there are skills that are required um in that area not necessarily however deep um AI skills when you're talking about things like Gen AI um you know prompt engineering and things like that is a is a distinct skill in itself that in some ways is more akin to psychology than it is to IT development.

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Um but I think the challenge that I'm seeing um organizationally is those skills are often being isolated and that is creating a couple of problems. Those teams at the end of the day still need to have an end result which is some kind of business application and if they are not integrated into that application build factory then there's that disconnect that starts to happen between them. So keeping them closely integrated into that is important and that's one of the things for example from a plat platform perspective we've tried to do without systems is the AI agent build is just part of the regular application build platform so that teams can collaborate but the other the other reason for that is I think not just collaborating from an IT perspective but collaborating with the business is critical to address some of those those change concerns as well. And a lot of the AI capabilities that are um focused on software life cycle are really mostly just for developers. I mean, they're tools for developers to build code, document code, test code.

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They're all coding co-pilots. And I think one of the interesting capabilities that that we are are developing is the ability to quickly generate a requirements document and a full application that the business and it can collaborate on. I think the more that your team that's doing Agentic is having conversations with the business rather than going off to one side and doing things, the the more collaborative it'll be and the faster you'll get to an end solution. And and one more element about human element uh um uh it will be good to discuss briefly would be um the whole um the change management particularly when it comes to the automation program execution and oper operationalizing this as a strategic capability because um people in general are resistance to change as we all know and with automation in particular there is uh the potential risk of people building some anxiety about about this change these changes as to whether there would be potential impact to their careers, jobs and so on and so forth. So in a successful automation program in my view should also make sure that there is confidence is instilled within their um within their employees that this is not about your your roles your jobs it's about um making overall overall organization more productive right and giving you opportunities to discuss uh to to build something new. So um what's what's your take on this as to how organizations should u address this kind of softu angle to to these whole automation programs?

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>> Yeah, look um again I mean I come from a technology platform perspective but we do see this as a common barrier. >> Um one of the I think there's a couple of ways that we've approached it. Um one is you look at the work that many employees do and you can divide it into what are relatively low value and highv value tasks >> and really I think emphasizing to the employee as well that the usually it's the low value tasks are the ones that you know automation is targeting and that's to the benefit of both the organization and the employee. Usually the low value tasks are the the boring mundane ones that they don't really enjoy doing anyway and the high value ones are the ones that you know will will bring them more satisfaction and you know also improve the business. So having that kind of alignment on on on that messaging is also is is critical.

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The other the other one and this came from for example we use out systems internally to um for instance the very first use case which is a fairly common one was around support ticket deflection using AI and we're able to achieve you know a significant boost from about 10% to 40% of the tickets that that were deflected. Now what that meant for us wasn't in fact that we had to lay off any support engineers. It simply meant that we did not have to be hiring as we as we grew as well. So I think those conversations are obviously much better to have upfront that this is this is the goal. You know we are aiming to improve our automation but if we do that it will make us far more efficient. We will grow faster but we'll still need to keep the people because we'll have a higher volume of work coming in. And what we want you focusing on is that highv value work not the low value stuff. That's what we're going to target. Yeah. Yeah.

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Yeah. Yeah. Yeah. Many times we we miss on this this particular point because u there's so much an organization can do and there's just a backlog of work that organizations can't do because people are just occupied with this mundane work. Right. And with freeing up their time there's so much backlog that can be freed up and people can focus on more creative work. Absolutely. >> Absolutely. >> Okay. I think um we are moving we are almost nearing towards the towards the closure but there's one thing I want to understand from you because you are think into the the whole technology industry of automation platforms right um so I want to understand a bit more on and this will be good for listeners as well as to so what are the emerging trends um uh which are there when it comes to automation platforms and technologies over the next 2 three years that organ pressure should look forward to. >> Um there's definitely some speculation um as to the time frame. I mean I think that's the the you know the big the big question mark. I mean there's for instance um been a number of statements in the last year or two that SAS is dead which is interesting and I think that's that's really coming from the the the the base belief that you know productivity in terms of building code will become so efficient that you won't need that kind of foundation. Now again, I think it's one of those things where it's definitely not the reality. Now, having said that, I do believe there is more and more potential um to to be building with appropriate guard rails rather than just buying software. And at the end of the day, you know, most organizations want the best of both worlds. They want the safety and security of what SAS provides, but they also want the flexibility of doing it themselves, but without that productivity tax that often goes along with it. So I do feel there is a kind of convergence beyond. So it's really that that binary build versus buy is starting to become more fuzzy that there are solutions especially backed with AI that are really aiming to give the best of both worlds where you get a solid platform um but you can quickly build extensions on top of maybe some core assets is is what more the reality looks like now. That's probably for me one of the big ones, right?

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>> Yeah. Yeah. Yeah. And so maybe a a question on behalf of all software engineers for you. Um so uh the so we have this low code and no code platforms which are good to develop quickly new apps right. Uh >> y >> now there is definitely emergence of wipe coding u uh and of course it is it is still new. We all know that uh it has its own challenges but it is maturing. All right. Um um so we and web coding allows you to just build apps using prompts essentially. So how do you see evolution of your platforms local no code platforms um in in this this new world where do you see these are evolve these will eventually evolve into a more prom based software engineering platforms in the future? Yeah, absolutely. And look, they already are. You know, some of some of the, you know, more um mature platforms like like out systems are already in that space. And I think one of the areas that um we're addressing is aligned with some of the challenges around vibe coding.

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Vibe coding can let you build an application very quickly. You know, there's like you'll see Instagram posts about people building an app, the train, and and they're deploying it. That's not the kind of app you would deploy into an enterprise, right? Um, so there are a few things that really need to be addressed. One is obviously the security and the guard rails around that app. Making sure that's rock solid is really critical. And one of the areas where you know platforms like ours already have I guess an advantage there is that's just part of the platform. In a lot of cases if you've got a m mature platform you just leverage the security guard rails largely that are there. But one of the other aspects is um integration. I mean again we don't want to have an explosion of you know shadow IT as a result of all of these vibecoded apps as well and one of the symptoms of that is a whole um population of apps that are simply not integrated into the core systems as well. So having some kind of vibe coding platform that is aware of um your ecosystem is also critical. And then and then the last one is and this is a real challenge I think for vibe coding in general is there's already a ton of IT artifacts out there in most organizations and talking about that problem of you know how do we how do we integrate into everything a standard AI tool no matter how good it is often has challenges just getting at those IT artifacts because they're just everywhere in lots of different technologies and platforms. So that's really where I think a mature um low code platform like us has a big advantage simply because everything's in one spot like just having everything in one spot is often a huge advantage because you don't have to go over here for the data there for the APIs there for the UI standards it's just there. So I think if vibe coding is going to see the biggest advances where there's a there's a um co coallescence of all of those capabilities into a single coherent platform and then then you'll be able to see significant advances in it I think.

[00:45:49]

>> Yeah. Yeah. Well, I think we are almost on time, Richard. But before we go, is there any advice you want to leave with our uh this some of those could be senior leaders as well as to how they should approach this whole automation programs uh for their organizations? >> Yeah, look, I think um the key messages are, you know, have a have a target vision, but start small and think incrementally how you're going to get there. Um, think about the value that you'll be able to deliver at each stage. I mean, that's an economic imperative. Business will not support unless you can can demonstrate that value. And especially with regards to the new a AI world, I would recommend in focusing on communication and collaboration between teams to to address that um you know those challenges and then beyond that focus on a on a capable platform that can then provide the the the capabilities that you need to underpin those organizational changes. Those would be sort of the key principles that I think um >> you know a lot of organizations could could could follow um and would help their success.

[00:47:12]

>> Thanks Richards. Thanks for your time today and I think uh we covered a lot today and I think um it hopefully clarifies many um many points for for our uh listeners um in terms of how they should be approaching enterprise automation what's coming in the future uh the governance and risk aspects around this uh the people process aspects around this etc. So thanks thanks for your time again. Thank you very much Symmetra. It was a pleasure talking with you. >> Hope you like today's episode. Please subscribe us on YouTube, LinkedIn and X platforms. [music] Also, if you are passionate about any such enterprise technology topics and want to participate [music] in the discussion, please reach out at inquiry@ enterprisete.com. Also, please visit the website www.enterprisette.com [music] Enterprised techttoart.com for more