3552: How CI&T Is Turning AI Ambition Into Measurable Business Results

[00:00:04] - [Speaker 0]
Welcome back to the tech talks daily podcast. Now today's episode is a little different because I'm joined by not one, but two guests from CI and T. They're a global AI and tech acceleration partner that has quietly built a strong reputation for turning strategy into real measurable incomes. And my guests today, they form a new leadership pairing tasked with strengthening CINT's presence across the region and helping clients move beyond AI experimentation into real scalable impact. And CINT are also marking its thirtieth year in business and has just delivered its fourth consecutive quarter of double digit organic growth even in a tough economic climate.

[00:00:53] - [Speaker 0]
And that performance raises an interesting question. What are clients really asking for right now? Why are so many organizations still struggling to turn AI ambition into real business results? So in my conversation today, I wanna learn what AI led transformation looks like when it works, Talk about ROI, legacy constraints, cultural change, and why speed matters only when it's tied to outcomes that people can measure. And hear more about what business leaders across Europe are talking about, their confidence gaps in AI adoption, and also learn a little bit more about CINT's proprietary Gen AI platform called CINT Flow and how that is helping bridge the gap between proof of concept and going into production.

[00:01:40] - [Speaker 0]
So this is a conversation grounded in real programs, real results, and real lessons from the field. Before we go into today's episode, I just wanna give a quick shout out to my good friends at Denodo. The data world is louder than ever. Yeah. AI hype, lake house complexity, and pressure to deliver more with less.

[00:02:01] - [Speaker 0]
But my friends at Denodo, they're helping enterprises make sense of it all. And you can learn more @denodo.comslashaws. But enough from me. It's time for me to officially introduce you to my guest now. So a massive warm welcome to the show.

[00:02:18] - [Speaker 0]
We got not one but two guests joining me today. To begin with, Melissa, can you tell everyone listening a little about who you are and what you do?

[00:02:26] - [Speaker 1]
Great. Yes. Thanks so much, Neil. So Melissa Smith Machado. So I've spent most of my career working at this intersection of strategy, technology, and customer experience in both digital consultancies and creative industries.

[00:02:39] - [Speaker 1]
So at the moment, obviously, I'm the chief strategy officer for EMEA for CINT. And that means I'm responsible for shaping the strategy for growth across the region, but also growing our strategy practice. So in a way that that really is about helping businesses use technology, particularly data and AI and digital platforms, to drive growth in a way that's genuinely customer led and execution ready, I would say, rather than just kind of theoretically sound. So, yeah, it's really for me, the core of my work is really about bridging the gap between ambition and execution and and kinda aligning, like, the brand, the customer experience, and the technology so that, like, organizations can really create meaningful change and see a commercial impact. So it's, yeah, it's really about strategy that works in the real world.

[00:03:27] - [Speaker 1]
And then outside of work, I'm a mom of two small people. And so I think that also gives me a a practical lens on whether digital experiences actually work when time and attention are quite limited.

[00:03:38] - [Speaker 0]
Absolutely. Love it. Thank you, Melissa. And, you're joining us as well today. Today, I'm listening a little about you too.

[00:03:44] - [Speaker 2]
Thanks, Neil. Hi. I'm Alex Cross, chief technology officer for CI and T in EMEA. I am focusing on building our technology capability and the technology strategy behind it and also our partnerships for the region, a pretty long history in consultancy, various roles, various leadership roles, directing different parts of different consultancies and working with my clients very closely as a tech leader as well. And hoping to bring that to CONT as we go forward together growing in the EMEA region.

[00:04:10] - [Speaker 0]
A pleasure to have you both join me today. And be before you came on the podcast, I was doing a little research like I always do on my guests. And one of the things that really stood out to me was that you guys have just delivered a fourth straight quarter of double digit organic growth, which is just phenomenal, especially considering the current economic climate. But how does that performance change the expectations from clients, and and what does it reveal about the the demand for AI led transformation across enterprise markets right now? It it feels like there there there's a real hunger for this stuff now.

[00:04:43] - [Speaker 2]
I think the first thing is it kind of speaks to the fact that our customers have seen a lot of value in the partnerships that we've built with them and the utility of our of our platform capability, our AI first delivery approach has been a really big success and they've really seen the value of that. I think a lot of our competitors, lot of consultancies, and other services providers in our space are trying to kind of deliver what we've been able to deliver over the last few years already for their customers, and I think that's been really borne out by the the facts and figures we've seen. So I suppose the one thing I would I would highlight in particular is we have a real bias towards action with our customers, and they've really seen that. So we've had a lot of really, really fast innovation success with our customers. Our folks are really kinda super collaborative, and that's meant that when we found some new way of delivering value to them, they get it very, very fast from us.

[00:05:38] - [Speaker 2]
They get it fast through our platform, through our services, and I think that is the kind of kind of, I guess, cultural foundation that it's quite hard to replicate. And I'm I I think bringing that to EMEA is kind of the challenge that Melissa and I have. But it's like an it's it's gonna be a really exciting journey and we have a lot to work with.

[00:05:56] - [Speaker 1]
Yeah. I think I would I would also add it's really around this this kind of AI transformation end to end. Right? I think it's looking at although we're quite a a tech company, I think that what we do is allows us to kind of scale the AI solutions and really prove the return on investment in the real world. You know, in in some of our our clients are very large financial ex institutions.

[00:06:24] - [Speaker 1]
They had challenges saying, look, you know, we're $500,000,000,000 financial institution, but, you know, we're really losing the digital innovation race, you know, against some of the challenger banks. Right? And and and this is, you know, one of the banks is Itau, which is, you know, the, I think, thirteenth largest bank in the world and and largest in Latin America. And they had legacy mainframes, you know, more than, like, 2500 coupled systems. You know, they had kind of digital launches could take up to two years, which obviously the challenger banks, you know, were able to release these updates weekly.

[00:06:55] - [Speaker 1]
Right? So so they were really constrained by risk averse governance, you know, these padded timelines, and and and also not involving their customers in the in the product development. Right? So and what CINT did was really rearchitected all the core systems. Right?

[00:07:10] - [Speaker 1]
So there's the architecture bit from monolithic to micro microservices, you know, introducing automated testing and an open architecture that's aligned with open banking. But, also, it's really helped shift the organization from function based teams to multidisciplinary. Right? Like building customer centric communities, like embedding CX metrics as well into the delivery, and and also changing that mindset, right, from this very, you know, we can't get anything wrong, we can't fail, you know, to this culture of learning and empowerment and shared accountability. So actually, it's and it's really by embedding, you know, the teams within the clients that we were able to make that change.

[00:07:49] - [Speaker 1]
And some of the results were actually quite impressive. So obviously, productivity increased quite a lot, like 48% productivity gain, right, 89% reduction in the time required to implement new features. Right? So the the whole modernization backlog reduced from eight years to three. But also, you know, more importantly, there were more than 1,000,000 new digital accounts opened.

[00:08:13] - [Speaker 1]
You know, the NPS increased by 27 points. There are also $550,000,000 uplift in investment revenue, 78% lower mortgage cost to serve, and also kind of a a reduction in the physical branches. So if you actually look at kind of technology productivity impact in terms of how they're able to actually launch new products and innovate now, but also the commercial and customer impacts together, then actually that's really powerful.

[00:08:38] - [Speaker 0]
It really is. And I I think in a year where everyone's been talking about the elusive ROI and measurable impact from transformation and AI projects, etcetera. The way that you revealed so many different stats there and real tangible value and measurable difference is phenomenal. And and, Melissa, you are focused on strengthening CI and T's value proposition in the region and shaping or helping to shape that go to market strategy. So I'm curious from everything that you're seeing and hearing and the conversations that you're having with your clients across Europe about the outcomes that they want and the obstacles that still make AI adoption harder.

[00:09:17] - [Speaker 0]
What are you hearing here? Are there any trends in the kind of things that they're talking to you about?

[00:09:22] - [Speaker 1]
So what I'm hearing quite clearly, I think, is that, you know, the excitement around AI is is real. Right? But the confidence around the ROI is not. Right? So most organization want outcomes.

[00:09:34] - [Speaker 1]
It's they don't want headlines or proof of concepts or, you know, the theoretical strategies that just can't be executed. They're asking quite practical questions, like, you know, where does AI really change performance? How do we scale it? You know, how do we justify the investments? But one of the the gaps, I think, I I see to adoption is that gap between the early experimentation and the real world impact.

[00:09:59] - [Speaker 1]
And, you know, the proof of concepts, it's it's I think a lot of people are still at that stage, and they can look very promising in controlled environments. But you know, when you try to scale them, when you try to apply them to live data and real customers and stuff, then then it becomes a lot more challenging. And, also, AI rarely delivers value in isolation. It usually connects, you know, to broader changes in data, processes, and ways of working. Right?

[00:10:26] - [Speaker 1]
And the the other thing is and there's also a a pattern where I think organizations default to this tech first mindset. Right? So expecting IT alone to deliver the AI value, and that often leads to building solutions before being really clear on the problem. And also if you wait to fix your entire legacy estate first, then, you know, you might still be in the same place many years from now. Right?

[00:10:53] - [Speaker 1]
So I think our focus at CINT has become quite deliberate. Right? So it's it's focusing on being AI enabled rather than AI assisted. Assisted. So it means that it's about using AI not just to make work faster or to, you know, speed up existing processes.

[00:11:08] - [Speaker 1]
It's about transforming how work is done, reshaping workflows, right, embedding AI into the fabric of value creation. And I think that's reflected in how we're shaping our go to market in EMEA. So we're moving towards this more productized approach, like productized propositions that are anchored in real problems that clients are trying to solve. So, like, improving efficiency and resilience, but accelerating growth through more personalized experiences, obviously, legacy platforms so that, you know, we can move faster, but also creating entirely new products and business models. Right?

[00:11:40] - [Speaker 1]
Each is designed to deliver a clear measurable outcome and then help clients move faster, but also with confidence. So that's, yeah, that's how we're looking at it.

[00:11:50] - [Speaker 0]
And, Alex, you're building the technology and data capability across EMEA while also deepening partnerships with companies like AWS. So I'm curious from your vantage point here, how do you see CI and T flows fitting into these programs? And and what does it enable that clients could not achieve with those isolated tools that we're talking about here? Tell me a little bit more about the difference that you're seeing.

[00:12:15] - [Speaker 2]
That's a great question. Thanks. I suppose let's start with flow. I mean, that's a that's relatively new platform. It's it's not got a a ten year pedigree pedigree or anything, but it's something that we've built very rapidly in response to real customer needs and as a foundation for our services.

[00:12:31] - [Speaker 2]
So what I see it as, it codifies a huge volume of knowledge, expertise, and accelerators that we've kind of built built up over the years that is a massive differentiator for CINT. So it allows it allows us to to kind of bring all that knowledge and capability to our customers very, very quickly. I have to say it's kind of been amazing over the last few months to see that our people are kind of continuing to contribute and use this platform systematically. It's really easy, you know, in my experience and having worked with some very large enterprises to see like IP reuse initiatives, particularly technical IP reuse initiatives kind of fall flat and not go anywhere. You get the not built here phenomenon and that kind of stuff, adoption and reuse, very, very difficult problems.

[00:13:14] - [Speaker 2]
But CIT is just a really super collaborative place at its core, and I've seen that really paying off with Flow. When we bring that to our customers, I've seen it manifest in a few ways in in terms of value to them. So the first, I suppose, is that kind of sort of platform hurdle that Melissa was kind of alluding to before, right, that we've got this challenge of, well, we want to bring some AI value to, you know, our our business stakeholders across the enterprise, but we need to build a massive AI platform and tooling and kind of ways of working and methodology first. Like, that's a very common starting point, and and it's a place a lot of, you know, a lot of businesses are still in today, even a few years after kind of the sort of CGNAI used in anger. But for us, with Flow, we can kind of bring that with us.

[00:13:59] - [Speaker 2]
We can have it ready to go on day one, which means we can deliver value to the business directly very, very quickly from that initial beginning of an engagement with a new customer or a new project. We can help customers with that as well to tackle those strategic data and modernization challenges in parallel in the background. But being able to get started really fast is super useful for being able to prove value to our customers and to, you know, to to to drive that focus on the business and build that bridge from IT into the business. The second thing I guess I'd highlight about it is our customers use it as well. Right?

[00:14:31] - [Speaker 2]
It's not just something it's like a mystery box that we bring with us as a as a services provider. Right? We we the customer gets their intent on it. They use it. There are different kind of way applications, web applications, desktops, and so on, IDE plug ins that allows our customers to learn our ways through our platform.

[00:14:49] - [Speaker 2]
So when we've got a particular way of using agentic AI to deliver fast, to do modernization, to evolve business processes, for example, we our customers can join our teams and do that themselves and learn how this stuff really works because I get a lot of people have seen this sort of promise of this amazing new way of working and so on, but a lot of customers haven't really experienced that themselves or they've had to sort of model through and reinvent the wheel. We just bring it all with us. They can come and join us and learn by doing it alongside. The actual platform itself, I mean, haven't really described it that much so far. So I I should say, I mean, it's it's kind of a multi model kind of gateway platform for AI services, and it has a bunch of kind of tools around it and you could sort of ecosystem of our accelerators and and capability that we bring to customer projects.

[00:15:31] - [Speaker 2]
We're really quick at tailoring it to new customers as well into their needs as we find them. And that's that kind of I mean, that's a major means of capability growth and evolution for us. We can be so nimble. We can quickly adapt the platform, the technology to the challenges we see as they emerge, and we can be proactive and bring new things to our customers. And that's that's again been, I guess, a secret to our growth as well is that we're really, really on it on that kind of stuff.

[00:15:55] - [Speaker 2]
So I'm hoping to harness all that for the EMEA region. I I why don't you mention AWS? I mean, part I'll talk about partnerships briefly if if you don't mind. So, I mean, that that's a super important part of the regional strategy as well. We've built excellent partnerships and capability over the last few years with all the major hyperscale cloud vendors and data platform vendors.

[00:16:14] - [Speaker 2]
We're AWS advanced tier consulting partners. For example, we're one of only 20 partners in Amazon's Gen AI partner in innovation alliance globally, and we're Databricks elite consulting partners as well, for example. For me, I I kinda just have the task of bringing that strength of capability across to our clients in this region. So, I mean, that's slightly daunting because we've done so amazing, amazingly well in the other regions, but I'm really excited about it. And it's it's an amazing place to start and to ingredients to work with.

[00:16:41] - [Speaker 2]
In terms of the way Flow fits into that, I mean, do use our partners tools and often with like Flow alongside. So for example, we you know, we're looking at a we plug into AWS transform for example. We're using Amazon Bedrock for AI development natively, but Flow lets us kind of bring the both best of both worlds. We've got the kind of powerful native tooling that those vendors and our partners provide with the kind of wider flexibility and the sort of codified expertise and ways of working that we have in Flow. So if you think about like, say, AWS Databricks, these might be the target platform for something that we're doing for a customer.

[00:17:13] - [Speaker 2]
They might be where the AI workloads are gonna run, where the agents are gonna do their thing, where we're gonna build out those age platform and get those analytics and data products going. Flow is the tool set that we use to get the customer there super fast to innovate really, really quickly. I mean, we've got like a like an 80% success rate getting AWS AI POCs to production with quite a large set of customers. We've used Flow to accelerate that. We've used Flow to accelerate legacy and data modernization onto those platforms.

[00:17:40] - [Speaker 2]
We bring those things together. That's been a super powerful story for us, particularly over the last few years. So again, I just have to do that in EMEA now.

[00:17:48] - [Speaker 0]
Make it sound so easy. A little easy. Yeah. And a quick thank you to the sponsor that supports every podcast across the Tech Talks network and every episode because their help allows me to publish 60 interviews a month with founders and technologists who are keeping this industry moving. And this month, I'm partnering with Alcor.

[00:18:08] - [Speaker 0]
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[00:18:46] - [Speaker 0]
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[00:19:16] - [Speaker 0]
But now on with today's show. When researching CI and T Flow, I read that it has shown efficiency gains close to 50% in some early deployments, which, again, incredibly impressive stats there. But I'm curious. What does that look like in practice for a client, maybe a potential client that could be listening? And and how do you make sure that those gains translate into impact that leadership teams can measure?

[00:19:41] - [Speaker 0]
Because, again, this year, we've been talking about that businesses not been able to get that ROI that they've been looking for, but it all comes down to what you can measure as the old IT approach of you can only improve what you measure. Right? So tell me more about that.

[00:19:54] - [Speaker 2]
So so I'll start, and I'll I'll I'll turn to Melissa as well in a second. But the so the the first thing is we I mean, before flow was even a thing, we developed our own means of systematically measuring, I guess, business complexity and and how we do scoping as a as a vendor and as a service provider. So when we're thinking about how long it takes us to deliver something, we have like an objective means of measuring that and the effort required for something in the abstract. And that's been that's been super useful as a means of benchmarking our own performance and helping customers understand, you know, whether we're improving, whether there are blockers. Our management right up to the CEO are lean thinkers, and they've really embraced that all the way down.

[00:20:34] - [Speaker 2]
And that what that's meant is that we've got that kind of focus on measurement everywhere, that focus on optimization on being really super fast and hyper productivity. It's all the way through the business. The value that Flow has kind of brought to that is that because that's a platform we're using with most of our customers and it's it's by no means obligatory, but it happens to be something that adds a lot of value. When we've got that, we use that as a measurement platform as well. So we've got our own system for tracking our performance, but also tracking the performance of the solutions we build for our customers.

[00:21:06] - [Speaker 2]
So we've used that, for example, to keep an eye on the solutions we've built in in productionized AI services, AI agents, for example, so that customers can see whether those things are delivering value, whether they are getting the return, the efficiency gains, whether the cost is coming out and so on. So that's been very useful. And again, that's, for most effective, part of the interest part of what drew me to CINT was that's a problem which lots of customers have, lots of service providers have, and just having solved it and bringing the solution on day one or, you know, in the sales cycle is is incredibly powerful. Being able to surface that and knowing that we're gonna measure things and we have a standard way of doing it, I find that really useful. Our customers find it really compelling and it lets them sort of see that we've improved, that they're improving, and to see, you know, where we're adding the value in in contrast to perhaps some of our competitors.

[00:21:54] - [Speaker 2]
Melissa, do wanna add anything to that?

[00:21:56] - [Speaker 1]
Yeah. I think I mean, one of the the the other things around this is that it means we can deliver value faster. Right? So I think building on that, the real payoff is is not just obviously the efficiency gains and the way we're measuring things, but it's it's, you know, seeing these outcomes that that matter sooner. And it's an it's an important distinction, like speed.

[00:22:18] - [Speaker 1]
So I think you think of CINT, a lot of a lot of things about CINT kind of it it sort of almost lives and breathes speed. Right? But the important distinction is that it's not just speed for the sake of speed. Right? I think, you know, you think of clients, a lot of them don't necessarily wanna go faster.

[00:22:36] - [Speaker 1]
Right? Because because speed is also risk. Right? It's quite scary. But actually, you know, do do they want their problems solved faster?

[00:22:43] - [Speaker 1]
Right? Do they want to see results faster to to prove that, you know, kind of they're on the right track and you're going in the right direction or to be able to pivot, you know, sooner if you need it? So I think it's that's the thing that's quite interesting. It's it's speed that's controlled, precise, it's measurable, and the the impact is then amplified when that speed is anchored in clear strategy and business use cases. So, you know, faster execution means quicker feedback, earlier signals on what's working, as as I mentioned, and the it like, ability to kind of pivot, you know, if if the momentum is lost.

[00:23:16] - [Speaker 1]
Right? So it's it's that's when speed becomes more than just efficiency. It becomes this source of relevance and resilience and and long term value.

[00:23:25] - [Speaker 0]
And I think many organizations say they want responsible innovation, yet many struggle to connect strategy with execution. There are so many examples out there of AI projects that are stuck in testing, struggling to make it out of production, struggling to make it into production, into those live environments. So how do you the two of you plan to guide clients from vision to working solutions that scale across a business without creating new friction? And I appreciate it's probably an episode entirely on its own. It's an incredible balancing act, but so many businesses around the world are struggling with this concept right now.

[00:24:02] - [Speaker 0]
So tell me more about about how you're doing this and succeeding and having measurable results at the end.

[00:24:08] - [Speaker 2]
We do lots of POCs for customers. I think that's the same as any services vendor, and particularly with our our AWS partnership. We've been doing a really large variety with with customers who are perhaps new to the platform or who are new to AI in general. But we've been able to find a way of kind of filtering out the good use cases and helping them evolve really, really quickly. And we always kind of we we we it it for us, it's almost like a a drive to production.

[00:24:33] - [Speaker 2]
So that that that comes back to that sort of lean thinking, the commitment to DevOps at an organizational level. It means that when we find customers who are interested in engineering to AI, they have POCs they wanna try, we're firstly good at helping them build the business case, good at helping them find the good use cases to actually invest in, but we're also good at that kind of, you know, really pushing forward engineering, thinking about production from day one and getting there as soon as possible and delivering the value and evolving really, really fast. Because we've got, our own platform that we can bring with us in Flow, that also means that we don't have a lot of difficult setup costs for our customers. So the cost from, you know, of that journey from an idea to a production system that we can then evolve and and and put in the hands of business users, it's really fast. Like, it's the order of maybe six, eight weeks for some of these things.

[00:25:23] - [Speaker 2]
Right? It's really, really quick. And that has been, I think, very refreshing for our customers, but it's been really powerful and and and and useful for us as well.

[00:25:31] - [Speaker 1]
When we're talking about this yesterday, you sort of, you know, mentioned this quote that you sometimes use from MIT Sloan kinda ran I think it's something like when digital transformation is done right, it's like a cat caterpillar turning into a butterfly, but when it's done wrong, all you have is a really fast caterpillar. And I I kinda really like that analogy because I think when you what I see is often, you know, organizations are trying to innovate really quickly and handing over tools or prototypes and expecting people to just adopt them. Right? And that's that's basically the fast caterpillar. Right?

[00:26:05] - [Speaker 1]
And I think the challenge and the opportunity is kind of guiding clients to the butterfly. So, again, connecting business and IT, embedding solutions into the way that work actually gets done and solving these real complex problems that can then scale across the organization. And I think another part that I think is quite important is this experience layer. Right? I think it's often missing.

[00:26:32] - [Speaker 1]
Right? I mean, we we kind of move from a lot of the tech platforms into kind of very, you know, user centered design and things like that. And I feel like that's sort of almost been lost a little bit with AI. Right? There's often very little thought given to how people experience these tools in practice, how intuitive they are, how they fit into your day to day workflow, and and, you know, whether they're actually generally there to help people make better decisions.

[00:26:56] - [Speaker 1]
You know, and so I think without this experience layer, like even, you know, the best AI will struggle to actually deliver the real impact. So yeah.

[00:27:05] - [Speaker 2]
Yeah. I I love the caterpillar butterfly thing. I've I've found a million uses for that so far. It's my new favorite quote. I think the the other point I mean, you you asked about friction, Neil.

[00:27:13] - [Speaker 2]
I mean, I think that the other side as well is the collab the the point of collaboration with the business. I mean, we're when we're talking earlier, the the challenge all the time is we've got kind of IT on the one side and some business folks who are waiting, but have been given access to some tools and and told to go go use them somehow. I think for us about the time, we've been able to sort of bridge that gap very, very deliberately, very effectively, and very quickly and collaborating well with those people on the business side, finding a kind of mutual challenge that we're solving together that helps us find and deliver the value faster, but it also minimizes the friction for them because they're invested, they're working with us. So that's generally the the approach we've taken that I would recommend for this.

[00:27:53] - [Speaker 0]
And we are also at that magical time of the year. We're starting to think about the next twelve months, the things we can do differently, the improvements we can put into place. And as CIMT marks its thirtieth year and continues to grow its global headcount, I've I've got to ask from your vantage point here, what does the next chapter look like for the company, and how do you see the EMEA region shaping that future? Because it's looks like exciting opportunities ahead, but you got planned? Any teasers you can leave us with as well?

[00:28:22] - [Speaker 1]
I mean, I think the next chapter is about building on the capabilities we've developed and scaling them to solve, you know, business challenges our clients, like, face today and tomorrow. Think over the past few years, we've pivoted to becoming more of an AI transformation partner, which is codifying knowledge into platforms and frameworks that can kind of help us move quickly from strategy to execution. But I think, you know, the next phase is about bringing these more productized services, you know, as I mentioned before, and business solutions to the market, tackling kind of the tricky business challenges in a way that really integrates technology and business seamlessly. I think it's really about bridging that gap between business and IT and making sure that that the solutions or or services, whatever that we deliver, are embedded into, you know, kind of the operations so they can really drive impact. And I think in EMEA, we've got a real opportunity be you know, as kind of a a a relatively new leadership team, would say we've got growth, strategy, marketing, and technology working closely together as one team.

[00:29:29] - [Speaker 1]
And so I think by bridging these disciplines internally, we can also lead the way in helping our clients do the same. You know, it's about adapting and learning, but showing how the strategy and tech can come together really well in in alignment, obviously, with CINT's broader evolution globally.

[00:29:46] - [Speaker 2]
I I agree. I think the the only thing I'd add is the the I mean, we've we've been very successful over time, especially with the in the last few years with Flow and everything in in discovering, you know, amazing ways to to innovate and to reuse our knowledge and capability when it comes to engineering and technology and technology transformation. But as we're doing a lot more business oriented work with our customers, we're discovering things about, you know, common business challenges and and and and the trickiest stuff that we can that we can reuse. So it's really the next logical evolution for us is being able to bring kind of what whereas we brought kind of technology solutions to customers in the past. It's really more about bringing industry focused business solutions to them going forward.

[00:30:29] - [Speaker 0]
Well, thank you so much for both of you for spending a little time to sit down with me and talk about this in great detail. So many big takeaways. And for business leaders and people listening all around the world that wanna dig a little bit deeper on some of the areas that we explored today, maybe contact you or your team, or just keep up to speed with some of the developments of the kind of things that you're doing. Where would you like to point everyone listening?

[00:30:52] - [Speaker 2]
So I think the the best place would would be to to head to our website, and we've also got our our LinkedIn presence as well. We've got channels on YouTube where you can keep up to date with all this developments on Flow and our business solutions. But, yeah, the usual places you might expect, CI T websites could best go search for us with it.

[00:31:07] - [Speaker 0]
Awesome. I will add links to everything you mentioned there. And before we spoke today, I was aware of CI and T as its global AI and tech acceleration partner. But one of the big takeaways for me today is we're not talking about AI or tech for tech's sake or the the next shiny thing. We're talking about client programs that drive scalability, that maximize impact, and most importantly of all, deliver measurable outcomes.

[00:31:33] - [Speaker 0]
And I know many business leaders have been burnt by not doing those things last year, and, hopefully, this will be food for thought for many people listening. So I urge them to check you guys out. But more than anything, Alex, Melissa, thank you for starting this conversation today.

[00:31:47] - [Speaker 2]
Thank you.

[00:31:47] - [Speaker 1]
Thank you so much for having us.

[00:31:50] - [Speaker 0]
Listening to Melissa and Alex there, I think one of things that stood out for me is how clearly they both articulated the difference between AI for its own sake and AI that actually changes how businesses perform. The focus on outcomes, measurement, and speed with purpose feels especially relevant right now, especially when so many leaders are under pressure to show value, not just vision. And I think the way that they describe CIT flow as a a bridge between strategy, engineering, and day to day execution, I think that will resonate with organizations that have been stuck between experimentation and scale. And, of course, that emphasis on collaboration, experience, and shared accountability is also something that came through strongly. So a big thank you to Melissa and Alex for joining me today, sharing their practical insights into what AI transformation really looks like when it delivers measurable impact.

[00:32:49] - [Speaker 0]
And to everyone listening, thank you for tuning in to another episode. As you look into your own AI initiatives into the new year, are they helping you move faster in the right direction or just turning the same processes into a faster version of the past? Love to hear your thoughts. Techtalksnetwork.com. You'll find 4,000 interviews, a range of podcasts, ways of leaving me an audio message.

[00:33:12] - [Speaker 0]
But that's it today. Big thank you to each and every one of you, and I'll speak to you again tomorrow. Bye for now.