Hello, everyone, and welcome to this discussion. Our session today is on AI driven transformation and insights from the c suite. I am your moderator. My name is Namrata Shah.I have basically been in the IT industry for over two decades. I've worked with several Fortune hundred and five hundred organizations, and I have a deep passion for technology. My passion was recognized by Microsoft and AWS. Hence, I became a Microsoft MVP and an AWS hero. And I basically evangelize technology literally every single day of my life. I run my own YouTube channel. The name of the channel is my name. Hence, if you would like to learn technology, feel free to join my journey. We have two wonderful panelists today who are going to join us and share insights from their experiences. So grab as much as you can and learn from the experiences. Remember, intelligent people learn from others' experiences. So with that, I am going to request Amira to please go ahead and introduce herself.

Thank you. I'm Amira Yousef. I'm the chief digital transformation officer, UPPA Arabia here. I look after the technology, AI and data team, and digital products. I have experience across twenty five years across The UK, The US, and The Middle East. I worked for big tech and startups. I spent eight years with Microsoft, working on Windows 10 launch, Azure, and Edge AI before it was cool. I'm happy to be here with you guys.

Absolutely. Thank you for being here, Amira. And, Tendu, please go ahead and introduce yourself.

Hi, everyone. I'm Tendu Yogurtu, and, I have been in the enterprise software space with a specialization in data and AI, for about, twenty five years. I recently served as chief technology officer at Precisely where I led technology innovation and strategy, and product innovation, for a global enterprise platform serving thousands of organizations, including 95 of Fortune 100. My work has focused on helping companies scale AI responsibly through strong data foundations, governance, and operating models that connect AI to measurable business outcomes. I also serve on several, ventures and university advisory boards, and I'm a a Forbes Technology Council member.

Awesome. Thank you, ladies. So our first question for today and, Amira, I would like to start with you. So something that I believe all of us know is that AI is basically reshaping every aspect of our life, every industry. There's a lot of hype around AI, both positive and negative. But beyond this hype, from your experience, what is that one shift? Leaders must truly understand right now about how AI is changing business strategy, and most importantly, how does AI change value creation for any business? So how is this transformation impacting business?

Yeah. It's a great opening question. Thank you for that. I think the core of it, it's no longer a technology strategy. So I think the sooner people realize that, the more effective it's gonna be. It's not a side innovation. It's not a side tool. It's not a pilot. So really have to think about it from what is the business strategy, what is the operating model in this new world? Because it does change how we work, how the processes happen. It's no longer just, hey. It's something tech. I'm gonna put something on the top. So I think that's kind of the biggest shift that I would say, really, people have to understand and kind of mentally, like, shift that paradigm because that's the only way you're gonna lock value. The other thing that I would say beyond the hype is, I mean, AI has a lot of capabilities, but I would say, I work in insurance and health care.

So these are legacy systems, legacy industries. The reality then kicks in because you do not have the foundation. The data's not there. We are still on prem. You cannot realize the value of AI. So in a way, it accelerated the transformation for legacy systems systems because for once, they have it's like, oh my god. I'm missing out. I need to kind of this is what I've been looking for. If I want fraud, waste, and abuse, if I want preventative health, I mean, I need to get my house in order. It's almost the analogy is in some industries that COVID accelerated the move to the cloud. Sure. To me, in legacy systems such as as mine, it's really AI accelerated ironically, but it was like, oh my god. We need to get there.

So I think these are the two kind of beyond the hype is the realities is it's not just AI I'm gonna put a tool on top. It's really how does it transform the business. And then kind of inserted industries is like, oh my god. I need to transform my my technology agenda even before I get to the AI. So I think these are the two key areas that, you know, you have to mentally shift to realize the value of AI.

Sure. No. Thank you for that, Amira. Tell you. What are your thoughts around this?

I couldn't agree more with Amira, because, I have been seeing this shift, that now AI is becoming an operating capability, not a technology capability. And, Amira, to your point, we have seen this shift, for modernization efforts for cloud, modernization specifically, and also getting your data house in order. Another piece that is, the organizations who are really bringing AI into decision making, the workflows, rethinking about the customer experiences rather than treating it as a stand alone innovation initiative are the ones that are really, seeing value. That shift changes how leadership teams allocate the investment and how, the organizations are thinking about the governance and also developing their talent. It touches everything. And we are also seeing in parallel, the scale of capital commitment is becoming structural. Hyperscalers alone are planning roughly $700,000,000,000 in AI infrastructure spanning in 2026. Organizations now need to operating models that match that level of commitment.

Sure. No. That's that's wonderful. Thank you for sharing that. Tendu, being a CTO of a large organization, I'm sure that you have a lot of enthusiastic engineers who want to basically experiment with technology. And you being in the c suite, an obvious question that comes to my mind is, how do you ensure that your AI initiatives stay anchored to measurable business outcomes? Because that's what the business is looking from you being the CTO of the organization. And it does not become some tech frenzy or a technology experiment because your engineers want to do a lot of innovation. They want to experiment. So how do you balance both the sides ensuring that the team and you and the business and the organization stays absolutely focused on measurable business outcomes?

Namrata, this is such a critical question for every innovation cycle, and, we have lived through that at different pace, through big data, through cloud, now agentic AI. Experimentation is essential because especially now, with agentic AI, in many cases, we don't yet know exactly what success will look like at the start. Some of the most valuable outcomes are the ones you didn't initially predict. For example, we, were, actually running a pilot for our product support team with generative AI and using techniques, like, rack, etcetera. Mhmm. And our goal was reducing the mean time to resolution in product support. When a customer comes, you wanna, get their issues resolved as fast as possible. But we discovered during that pilot, the bigger impact is actually significant reduction in escalations to engineering, which creates broader operational value across the organization.

So in that sense, you do want to experiment and pilot in a time box manner. The key is that experimentation still starts with a business problem.

Correct.

That's the truth for any innovation cycle. Your innovation is not about technology for the sake of technology. You are, looking for a business problem, whether that's for your nonprofit or commercial organization. The mistake is when experimentation has no structure, ownership, connection to the business value. And, the organization seeing real impact are clear about what type of business challenges they are addressing, whether that's related to growth or to efficiency or customer experience or, actually, risk reduction. Mhmm. But clearly, that clarity shapes the experimentation even when the exact metrics can evolve. They also treat AI as business transformation, which we talked about, which means the successful programs are most often cross functional from the beginning. So it's not, just engineering looking, because if you have to transform a go to market workflow, you need to bring the domain expertise, into that problem. Correct.

We had actually when I was, precisely had a recent Drexel University and precisely AI Readiness study, that study showed over half of organizations identified AI as the primary driver of their data strategy, yet only 31% reported having metrics tied to business outcomes. That gap is where many initiatives stall and, are not successfully, leading to production deployments.

Sure. No. That makes sense. And, Amira, being the CPO of a large organization like Bupa, Bupa has been around for so long. Would you agree with what Tendu is saying? Because one, you want first, you are a CTO, so you look at it more from a product standpoint. Tendu is a CTO, so she looks at it more from a technology standpoint. So So I would like to get your perspective around this. How do you put the biz you know, how do you put, or basically combine or associate your business outcomes to any kind of AI initiatives or innovations that happens within Bupa?

So I would say I would, plus one for everything, you said because I mean, there is a lot of alignment because, for me, it's always like any technology. It starts with the business problem. What are we trying to solve? It doesn't matter which really function. I I do think beyond the hype initially was like they thought AI is a button, but it's not. But I would say for me, the core is what are we trying to solve? And some of the things that we're trying to solve, for example, I'm gonna use I'm gonna stick with fraud, waste, and abuse because it's a very big initiative for us. There's huge abuse in the system, in the kingdom here, And this is one of the key areas that we need to really think about it.

So this is for us, it's not about the AI. It's just AI accelerates us because it's like, imagine this was done manually previously. Someone is manually looking at, okay, was this a correct claim or not, whether I should reject it or not, or through some automation, for example, using RPA. But now with AI, it's like, oh my god. Like, we can do that a lot faster, a lot. So, again, it is grounded in the business problem. I think from the metrics, it was because right now, the one problem in AI, you don't still know the cost upfront in, at a production level. So, again, there's variation on how much you're gonna invest in AI in order to get the return on investment. So I would just say this is one thing that, to keep in mind because, again, the pricing changes at this point in time.

And the last thing I would just say is I really think that you have to have a business owner and technology owner because it cannot be pointing that, you know, tech is doing it and needing it and, we're gonna sit back. It's really kind of hand in hand because you need to kind of identify those user flows, what are what is important. And the tech is kind of by the side to realize this value, from that perspective. And just to stick with the fraud, waste, and abuse, example, it is really how much you are retrieving. So we have a target, and it's kind of how much are we, identifying that there is abuse in the system from that perspective. So that is kind of the measurement in the, process from us is because you could put all the AI, but if you're not realizing the end goal, then Mhmm.

It was not effective. So

No. That's awesome. Thank you for sharing that. Amira, my next question actually is quite a pivot from there. So, of course, working with BUPA, I personally also work with BUPA in my career, BUPA UK. And being a legacy organization, I believe it's been around for almost seventy nine or eighty years. What are some of the leadership behaviors and organizational mindsets that matter the most when, especially being the CTO, you are guiding your teams through this AI driven transformation or change. Because remember, to get any kind of transformation for such a legacy organization that has been around for so long, that can be very difficult. I'm sure there's a lot of resistance. All organizations have in, you know, in in whatever shape, form, or whatever percentage.

So what is the guidance that you give to your teams as to, hey. This is how we are going to go over here. This is a mindset you should have. This is a behavior that I'm expecting from all of you.

Yeah. It's a great question, and I I would say the added lens when it comes to, legacy organizations. I would just say the biggest shift is, like, even in 2026, there's a lot of manual work. That is the reality.

Okay.

And one of the key things that I keep on, sharing with my team is when we're gonna implement any AI or technology, we cannot follow what is the manual process today. You don't have to rethink how we are going to reinvent and transform the business. So kind of my guidance is and across the board to the board is it's really business transformation over technology transformation. Because the business has to transform how we are going to do things in this new AI world before we are able in terms of to implement different areas. So that's one for me. It is it's a business transformation over a technology transformation. The second thing is really kind of I guide them as what problem are we trying to solve. Because, again, there are different techniques for what we want to, solve.

So another example I'm gonna say is we want to reduce the call center, using some agents in the application. So we have to kind of walk through what are these different scenarios. It's not just like we'll put AI. It's we have to think through the different kind of areas. And the last thing is that I give them their psychological safety. There's a lot we still don't know how the model will works. What is the outcome? Is it gonna behave consistently? So that there is a permission that some of these experiments will fail, and some will succeed that will take them to production. So these are the three areas that I guide my team as when they're thinking about, really kind of how to implement AI, technologies.

Sure. Wonderful. And, Tandy, being the CTO, how do you guide your teams in your organization? Do you face the same challenges as as Amira?

I think, I look at this, beyond the technology organization, Namrata, because we have to step back and, think about the business hypothesis, that the the organization, leads with. Right? And this is actually very important question for boards also. I have written this recently because you are investing, with a business hypothesis, and that business hypothesis may not hold anymore Mhmm. For several reasons.

Is it?

Because there might be different set of competitors that you need to think about. There might be different ways of connecting multiple workflows that, you have in the organization. You have to step back truly instead of just layering AI to the existing workflows and almost magnifying the problems you already had with your data readiness or the inefficiencies, the way you design those workflows. And if you remember, in the early days of cloud, we have seen this. People were just taking their legacy, cloud legacy workflows and the processes from on prem and, doing lift and shift, which didn't go anywhere. Mhmm. We had to redesign those. We are, we have to do a big, big, step back in that sense. So that's number one. Number two is that the most important leadership responsibility is we need to have the organization adapt as AI changes how work gets done.

That means we need to invest in the skills across the company and not just with r and d, not just with the technology organizations. We need to upscale everyone

Correct.

Because that's gonna actually create so much business value. Yes. Some roles will become more automated. But at the same time, new opportunities emerge for those, people who may not be doing that, job the way that they were doing before. And you need to proactively engage them, so that they have these new skills. They understand the art of the possible and contribute in different ways, and they can work with AI effectively. And, three, because you are bringing those teams outside of technology organization to use AI in a responsibly and confidently, confident manner, you may actually see that they are bringing their business domain understanding and imagine ways that you haven't in the technology organization imagined yet.

That's the power now. Right? Because we talk about democratization of AI. But with generative AI and, even with the agentic AI now, this is no longer captured just in technology teams. Anybody can actually make use of it with natural language. And you are bringing that domain expertise, combining with technology, the creativity can burst. So technology evolves quickly, but we also need to really trust and upscale, the workforce, and make them ready, to evolve with that, technology as well.

Yep. And I think they knew that that's absolutely critical. Yes. Having the business knowledge innovation, and most importantly, I think both both of you mentioned is reinventing or the word that I personally use is reimagining the whole process. Right? Because a lot of these processes were designed in an era where there was no AI. Of course, we had classical AI, but no agentic AI or gen AI. With gen AI coming into the picture, agentic AI coming into the picture, now you have to reimagine the whole process as to where you can change it, what all optimizations can be made. So absolutely. Tell you that basically I might

Sorry. I just like to add one point to build on what tendered set is also, like, the skill set is a real challenge because it's like for example, if you want to roll out to the organization an AI tool, you really have to invest. Like, what does it mean? How does it implement? How does it how do you use it? How do you get most of it just versus I just have a bunch of tools that I'm just, like, adding. Because I I would say this is one of the things is AI literacy across the organization is very critical. So I think that's another area that I'm looking at is, like, how do we make entire organization understand even when they're asking for, I need just AI projects.

It's like, what does it mean or what does it take or how does it improve, your productivity, for example. So that educational piece because I think in software development projects has been there for a while. People understand is, like, how do I want to build an app. This is how I think about it. But for AI, it's a bit different, so that kind of literacy is very important.

Awesome. So my next question, Tanya, is going to be for you, and you kind of touched upon this a little bit. Now, of course, being the CTU and working with engineers and responsible for technology, all of your teams are super duper excited, I'm sure, for any kind of innovation. But you yourself mentioned in your previous response that, hey. You have to have responsible AI. You have to have governance. You have to build in ethical guardrails. Now a lot of people, including myself, and because I have been consulting for more than twenty three years now, and I've worked with so many organizations. I have seen a lot of organizations. They basically get slowed down by this whole process. Right? And whatever they're doing is right. See, their intention is to protect their business. So they want responsible AI. They want to have AI governance over there.

They want to ensure that the required guardrails are there. But while they are trying to put all of this in place, what I have seen as well personally from my experience is it slows down innovation. So from your experience, how have you managed to balance both the sides? Like, hey. Yes. I want to do all of this. I wanna protect the business. I want to ensure that the things are done right. Okay? And the right things are done both both the contexts. But at the same time, we should not slow down innovation and all the process reimagining that we are we are discussing about. So how have you balanced both of these sides?

So first of all, I have seen a question also on the chat along these lines. This is a time that you have to lead with the innovation first mindset. And governance, is going to be important, but think about responsible AI and governance as an accelerator. Because we have seen governance and data quality and data readiness, all of these, over the last, two gate gate, two decades as, these projects five years, ten years, we don't have that time. And, you have to reimagine your products, your you reimagine your services, reimagine your governance as well in in that sense. So responsible AI and governance go hand in hand, and, that can be only achieved if the organization operates in a way that governance is embedded in everything and not as a separate compliance exercise. We talked about upskilling teams. They have to understand what guardrails mean. Like, you have a child running through the stairs. You have guardrails on the stairs.

You won't let them jump heads down and fall. Right? You have to really, let the organization understand what happens if you don't have that those guardrails. So basic education and upskilling is still very important. And, this is for the safety and the Sure. Also trust of the organization, credibility of the organization. In my experience, one of the mass most effective approaches was, for the size of the organization that I was with, establishing a cross functional AI council that's oversaw every AI initiative. So we almost crowdsourced for everybody in the organization, not just technology, all employees to be able to initiate a AI project and pilot. However, we were very rapidly seeing which model, is included. What is the use case? What's the high level, business benefit that we would like to see from this and, have legal information security, data privacy, marketing as part of that AI council that almost, like, also aligned us across these functions, how to think about it, and, bring that wheel of oversight faster and faster, each time.

So that council helped us create that visibility across the initiatives, identify common patterns, provide safe environments. For example, from the technology organization, we created safe sandboxes with the AWS bedrock, with the, Google, Vertex so that teams can actually experiment faster in those pilots in safe, environments before going into production. And then you cannot say this is one model fits all. It depends on the size of your company, but it is not just the size. Governance also goes hand in hand in the risk profile. If you're a insurance company, your risk tolerance is different as a financial services organization than a EdTech, advertisement technology company. So you have to make that judgment at the risk profile. Who am I serving? What are customers going to also demand from me?

What are investors are going to demand from me, for, those, models in use? How data is being protected and how, potential compliance, related risks are being managed. The European Union AIX high risk system obligations approach their August 2026 enforcement timeline. With that, for example, penalties can reach up to 7% of worldwide annual turnover, yet over half of organizations still lack the systematic inventories of AI systems in production. So governance gap is becoming a material exposure as we see these, regulations come through, but you have to evaluate that in the scope of your business, your risk profile, and, also your use cases instead of just having a blanket governance, structure. And there's Absolutely. Yeah. Namrata, there's another dimension to responsible AI, which will take another panel by itself that's around the bias and transparency. And, that also comes into mind depending on your organization's nature. You have to think about the data bias and algorithm bias as well.

Sure. And, Amira, you actually come from a regulated organization. I work for a regulated organization, as well. So I'm sure that for you having guardrails and ethical AI and governance is all part of the strategy. So how do you balance your innovation now to ensure that it's not bogged down by all that? Yeah.

I think it's a very, great question that is very tricky because we are actually it's not just we're not regulated by one regulator. In The kingdom, we are regulated by the financial institution, by insurance, by the health organization. There's an AI regulator, and there's also a cybersecurity regulator. So, actually, we have to kind of be compliant to multiple regulations. So it's quite becomes, really kind of and there's quite a lot of fines, so you have to really be, very compliant there. The way we approach it is, like, in terms of the degrees because there is when it comes to patient data, of course, this is kind of, like, zero tolerance to risk. Like, you have to kind of maybe be very compliant, have to think through the pilots.

But there are some areas as for example, when it comes to employee productivity that there's no sensitive data. There's a higher tolerance. Like, it's like there's not, you know, I can take a little risk from the risk profile there, what we do. We also have in terms of an added kind of complexity in McKinsey, we have to have the data residency in country. Again, not all of the AI tools are in the market. So, actually, innovation becomes very, complex and difficult. But I always say with that comes some creativity Because there are some nonsensitive data or POCs that we can do it, externally with, with reducing the risk profile. But really kind of for the patient data, we cannot take, this is kind of almost zero risk.

So we have to kind of be very, kind of careful where we do apply or not apply, AI. So it's, I would say even before the governance, we have additional risks from that perspective. But with creativity, you can identify what can we do today versus in the future as we kind of work through these regulations because there's also some kind of efforts to lobby the regulation as, well, if we don't apply AI to the industry today, we're holding the whole industry behind.

So there's also kind of how can we do this ethically without kind of just halting all the innovation. So Skyeham, that trade off, we're kind of now working with the regulations as an industry, because we will be left behind. It's like, every kind of, follow all these regulations.

Okay. Wonderful. Now my next question to you, and I also requested the audience to post some questions. I think we answered Camilla's question. Hopefully, I checked with her. So, Camilla, Camilla, do post your thoughts over there. Audience, whoever is listening to this, please post your questions over here. We will take as many as we can. But, ladies, my next question to both of you since this is AI driven transformation, and any kind of transformation is very, very difficult. I'm sure that when when both of you started this AI driven transformation in your organization, you had anticipated certain things. Like, hey. This is how I expect this transformation to proceed further. This is what I'm I am thinking that, hey. These are some of the areas that we need to work on. These are some of the areas that we are really good at.

But when you actually started executing the transformation, what was the most hardest thing about the whole process than what you had expected? So, I mean, I don't know. Amira, you wanna start? Tendu, you wanna start? Either one of you.

Sure. I can go. I think the hardest for me that, it was really the regulation, because I never worked in a regulated market before. So that kind of in a way, it was the hardest and took me by surprise, because, it does require to rethink about how do you do the transformation and what starts first versus the other. The other kind of part is, I would say is the cultural part. While it was high on the agenda, but in order to transform how we do things, that kind of was another area that, you know, on the ground, there was a lot of resistance in terms of, no one why do we wanna change the process or, the way we do things? It's working. We just put AI on the top. So I think for me, these were the two kind of main, that also surprised me how much the regulation and the resistance in terms of how we do things in order to leverage this was kind of a surprise to me.

The two that I expected was the kind of it's a very legacy system. So things are twenty five year old systems and the data fragmentation. So I think the technical that I did expect, I mean, maybe it was, more than I expected, but these are kind of, the areas that what make the AI transformation harder. Because, we just had a workshop this morning, and we're talking about all the AI road map. And I was like, I have two key blockers right now. We're fully on prem. We're not even on cloud. And the data is not modern. This is completely fragmented. And I was like, I need to enable these two right now this year before we kind of really do these big AI transformation like Broadway, cinema use. So it's kind of how do you manage the business and the culture as in understanding these dependencies while realizing the value at a leader stage.

So these are the top of mind that I think about, on a daily basis, now.

Sure. And, Tanya, your thoughts on this? What was the toughest part about the transformation?

So many challenges and so many things harder about it. However, I will pick, I'll pick, one, because, it's important. Now one of the harder aspects of the AI transformation, has been managing the pace mismatch between innovation and what you can bring to market and the customer adoption. And, why is that important when you are going through that AI transformation? I will give an example. What I have done is that I partnered with a AI native company in Silicon Valley and, our cloud provider to say, if we were starting to build this data management platform that we offer to our customers for trusted data with the agentic AI first mindset, how would we reimagine this?

Because when we created this, data management platform, we didn't have AI agents. But this now gives us an opportunity to to connect the context from, one, aspect of data integration, bringing data from legacy systems as Amira was giving in our example, to delivering it with higher accuracy or bringing third party data. How would we imagine this in a multi agent environment where we can bring hyper personalized experience to customers and personas depending on if somebody is a marketing analyst versus a data engineer? And we did that work. But I am a big believer. And you show demos to customers. Everybody says, oh, we like it. Right? But that doesn't mean that they will adopt it. So I'm a big believer. When you go through these innovation cycles, you have to have customer validation with real working MVP, level products and get that, feedback.

However, when you try to do that, there was so much hesitation in terms of, oh, I don't know if I want my data, in this, agentic, framework. There were so many questionnaires and, things that we had to answer about the frameworks or the foundations that we are using. And you suddenly assume that you are gonna have a number of customer validations, and the the realized customer adoption is actually not going to necessarily match the speed that you can bring that to market. Having said that

Sure.

At the same time, those same customers want you to share the your AI strategy and road map with them because they wanna invest in your future. They don't wanna invest if you are gonna stay behind. Right? That's the that's the hard part. You cannot have this. It's it's becoming like the plumbing in your house because everybody expects, and there's so much miscommunication about when you need AI agents. Is this a hammer looking for a a nail, or do you really have a use case that will, benefit from it? But you are kind of balancing the expectations from your customers. They want you to be leading with it, but they don't wanna participate because they have a different adoption cycle because of their priorities or the, being in the highly regulated industries.

I think that was a key challenge that, required the balancing innovation velocity Mhmm. With the customer readiness and also, their governance expectations.

Sure. So, ladies, we have ten minutes, and we have about three questions. So I'm gonna take one question from the audience. I think it is Mia Mia Partal. I hope I'm pronouncing your name correctly. And she is asking, how can companies ensure that AI amplifies diverse voices rather than reinforcing existing biases? And I did ask her and did check with her this human biases or model biases. She said both. So not sure if you guys have you know, both of you ladies have had any experiences, but is there some way that you have ensured that, hey. We can amplify diverse voices and reduce the bias in the whole process.

I can start, perhaps, Mia. Thank you for your question. So the first dimension of advice is with data. You need to make sure that you are bringing all the relevant data for a business, problem. What does that mean? If you are covering, for example, your customer trends, globally, if you exclude Asia, and just look at the, Americas and EMEA data, you are gonna have a bias because you don't know the customer behavior in Asia. If you are excluding, a specific gender in your data, again, you are creating a bias. So you need to make sure that you're including all relevant data, and, that data has, many dimensions, from gender, race, ethnicity, bias, if that matters for that business problem, that you are solving as well as geographic and, product related dimensions as well. So the second piece is algorithms. And sometimes, we don't have control over the algorithms. Right? Because we are using publicly available models.

So you can do your research if those models are publishing any data related to how they handle algorithmic bias, there are methods that, many of the companies, deploy. And you can find actually publicly, especially for hyperscaler companies like Amazon, Microsoft, and Google, how they handle that algorithmic bias, whether it's enough or not is questionable. But, still, you will find that we don't have much control unless you are writing yourself. And the third question of that is about we have to be part of solutions. If we want our voice and diverse voices to be heard, we have to be in the creation of these technologies. We have to be, participation participating in, the workforce creating AI technologies because, otherwise, it will be created with a bias in every dimension how those offerings are going to be brought to market.

That's why it's important that, actually, we focus on upskilling the, minorities and, typically, underrepresented groups, which are new digital skills that is required.

Yeah. Thank you, Tendi, for that. I think I'll have to move to the next question now. And, Amira, I would want your opinion around this. So one of the questions that Gabrielle has asked over here is, how can organizations ensure AI transformation also includes employee participation in the decision making process. And I think this goes on the same lines as Tendi was saying. Her third point that we have to be part of the process. But coming from a regulated legacy organization, how do you ensure that your employees are included in the decision making process?

I think it's, like, the way I think about it is on three layers. Because one is when it comes to productivity or the products we're delivering or the process. And for me, it's really when it comes to the broader perspective of the employee, it will meet comes productivity. And this is where we would need to engage with our employees. It's like, what would help them? What would elevate their day to day? And that's where they should actively participate in terms of what do they require, what do they need, what are the pain points that we need to help them with these. Because, again, with the highly regulated, I think there's degrees of the data. Anything that is internal exact for example, in productivity, it doesn't include patient data. It's absolutely okay. But, so I definitely think in the productivity layer, employees can and should be because, again, you just don't want to implement certain tools or bring tools to the table that are not useful to them.

So they should be part of, what are the pinpoints that, again, we're solving. So it goes back to our beginning of the conversation, what business problem are we trying to help for the employees. I think when it comes to the processes, it's a bit different. I think if it's a process that we're automating for one of, for example, the core insurance system that they're using, absolutely, they have to be part of it. Because if we're gonna automate something, we have to get the user adoption to be part of the conversation. Similarly, if it's in the product, you have to get the customer perspective. So they have to be in the process. It's almost like, their voice must be heard.

Sure. And my last question for today, this is going to be call to action to everybody who is listening to this particular recording of presentation live. As leaders, especially women leaders and driving AI transformation in your organization sitting right here in 2026, what would be your one piece of advice to everybody who's listening to this conversation? What is that one thing that they should start doing immediately? And what is that one thing that they should stop doing if they are doing it immediately? Because as we when we started, we said, right, that intelligent people learn from other people's experiences. So from your experience, real brief, both of you, one thing that people should do so that they don't waste their time on it and one thing that they should stop so that, again, they don't waste their time on it doing something that's not going to yield anything to them. So, Amira, let's start with you, and then I'll go to tend you, and then we end this I should give two minutes.

For me, really, you have to start, if you're in a legacy industry, start with the data foundations. Like, absolutely, that's kind of the top of the agenda. And I would just stop, chasing ad hoc pilots, with no real value just to get started. So this would be my advice.

Okay. That's so I'm coming for the CTO. Now, Dan, you being the CTO. Do you have a reverse idea, or what do you think?

So first of all, data foundations is really important. But, also, I think, stop measuring success, with the number of pilots or experimentation and start thinking about beyond productivity gains and efficiencies. Start thinking about what this means for yourself personally, and what this means for the business in the bigger score scope so you can understand how you can actually be even more creative than you have ever been and, bring something unique to your customers or to your organization.

Awesome. I think we are right on time, ladies. And so thank you very much. This was a wonderful session. Thank you for all the insights. And everybody in the audience, whoever has joined us or whoever is listening to this recording in the future, hopefully, the session was very helpful, very insightful. Do connect with all of us on LinkedIn, and feel free to reach out. Thank you so much. Appreciate it. Thank you. Thank you for joining.