How are we transforming industries, empowering people, and creating the future? Obviously, it starts with one decision at a time.So it's very important to, really look at what roles do we play and how is that done. Imagine the world where your smartest team member never sleeps, process billions of data points in one second, and also continuously improves. That team member, it is artificial intelligence. But, obviously, we don't wanna just to start with that real question is how AI can do and what AI cannot do for us, but it is all about what are we willing to do with it now that it is available. Right? So I wanna make sure that today we're really talking about and sharing AI as more, obviously, than a tool. And there's also the momentum that we can also either build and chase or lead together with.

And how you can, as a leader, catalyze that change and transformation that is so essential and necessary for your organization, not as a theory, but also truly as a daily practice. Because we're seeing this, a lot of times throughout organizations where, we treat AI as a tool and also in very fragmented silent environments. So let elaborate a little bit about that. Obviously, the AI era has already arrived, whether we're ready or not. And it's interesting to see statistics just from 2024 by McKinsey that we already have over 77% of companies that are currently exploring or using some ways AI. Question is, is AI now core to productivity or strategy or innovation, or it is organizations that are truly not fully yet utilizing their full potential and what the opportunity they have to improve. So for example, when we look at things, I really wanna stress something that I found to be very meaningful.

We're not here obviously to talk about AI, what it might do. But we wanna talk about what AI and discuss it, what it must do, and how you can be part of that, and how can you lead that transformation. But what's substantial for that? Obviously, it begs the question, are we truly ready to accelerate AI momentum, or are we stuck in passive implementation? So many of you might be thinking, what's the difference and what does all means? So let me explain. We are not obviously looking to AI to just look at it as a tool, but also how is actually becoming a true fledged AI transformation within every organization that we touch. And obviously, how is that applicable to you? From theory to sustainable impact.

So when we look at a passive implementation, we're also seen as missed potential, that it's equal and anyways, obviously, as you see massive potential, there is the passive implementation. Many organization treat AI as a checklist and checkbox, not as a strategy. And as strategist, I can definitely see, how that really hurts every single one. And, also, implementation often focuses not only just on software deployment and or automation that is existing, as a part of the process, which which really reflects an organization designing to employ readiness. How much of that readiness exist, around for their employees, teams, and or organization? And then also how low ROI and workforce resistance and also, to be honest, siloed adoption is impacting of in a reversely, implementation of AI. Right? I'm sure you can relate to at least some of those very common pain points that we're seeing across the board regardless of industry.

So question is, that I often ask, how do you truly treat AI? And this is something that I arrived to. When AI is isolated and when it's used by one member or initiative, it becomes the tool. But when AI is used in integrated ways across the whole value chain with across the whole organization, then becomes transformational engine. So let me repeat this again. When AI is used and is installation, with one specific team or very small initiative, it becomes the tool. But when it's integrated across value chain, as a result, it becomes transformational engine. So now we wanna look at it. What do we have currently, right, in our own environment? Do we have just a tool or we have a transformational engine?

Or maybe we don't have neither of those yet. And then how can we tap into possibilities? Right? So what is the really acceleration of AI momentum looking like? Obviously, it has to come from cultural shift, not just from technical upgrades. A lot of times we see new tools, new shiny objects. Right? But then they don't necessarily go anywhere or they don't go to level that they could. So, obviously, requires bold leadership. Where is that bold leadership? Where is the cross functional alignment? And, also, how do we measure the impact? And, also, how do we end with conversation on the core decision making? Who is part of the dialogue? How is this integrated in strategy, operations, and workforce planning? And, obviously, we wanna know as always, here are these two around sustainable impact because theoretical decisions are no longer obviously enough.

We wanna see them practically, but we also wanna see them sustainably. Right? And how do we move from those experimentations to expert enterprise wide opportunities of integration? Because if it's only one group using it reflecting on the tool, we're missing to bring everybody else on that journey along with us. Don't you agree with that? So we obviously wanna create sustainable impact where AI is aligned with business strategy, ethical governance, which is also huge right now, people first on transformation when they're also at the forefront. Right? And then also continuous learning ecosystem where everybody has a opportunity to learn in the best way that is conducive for them, but also practice and play around with the tool. Right? So when I'm asked, and most recent conversation I had, how do you see this? I always say the future does not belong to those who implement AI.

That's not enough. We know that. Right? And implementation itself doesn't mean anything unless it's, again, properly measured, it's sustainable, all of those elements that I just mentioned. Right? Or it's not properly governed. It's not really looking from people first to outrun transformation and also con continuous learning improvement. Right? What actually belongs to those who are using to reimagine what is possible? So would you go with Journey with me just to see for a second what is possible and how this really can impact you and your team and in such a positive light? So obviously, the key takeaways for transformation, I summed them up in the last two years or so, since obviously AI took a much broader, bigger scope of utilization into five pillars. I find these five pillars of transformation so essential.

So to move from passive AI, which we're talking about, to AI momentum that so many organizations, after they discover what it means and how this can look and change their working environments, are we seeing these five pillars as a cornerstone for that transformation? First one, obviously, is coming from driving organizational change. You guys are not surprised by that, I'm sure. But the point is, are you moving the from those siloed aspiration to integrated transformation? That is the huge challenge for so many organizations. The larger organization it is, the more difficult it is. Are you rethinking workflows, decision making? Are you also value chains, processes and integrated properly? Are you aligning this AI with your business goals and objectives and also specific outcomes? Yeah. Right? Not just technical potential that AI as a tool may have. And are we enabling change through management strategies to support adoption? We also have a managing risk to show long term impact.

And with this particular pillar, as a second one, we establish AI governance. We're looking at ethics. We're looking at transparency and all those various central frameworks, right, that are needed to move the needle, which includes also mitigating algorithmic biases as well as data misuse because we see that already happening right now. Building accountability in everyone's AI life cycle, stage is so essential because obviously, we wanna avoid those typical traps where so many organization get stuck. And then also we wanna focus on resilience, not just the quick wins. Are we having individual resilience and also are we having team resilience and organizational resilience in that ecosystem that is going to drive that, from that real world application? And then the fourth pillar, obviously, it's so important where we see a lot of lagging or lack of visibility, which is leadership involvement.

It's not just enough, obviously, to have, strategy that starts at top, that we get executive sponsorship, but if it's critical also to have visibility and inclusiveness and, frankly, accessibility. How many of you can raise the hand and say you have very visible, presence at if you are in c suite, or you have your c suite leaders accessible, visible, and available to you, when it comes to these very important topics and ongoing process process, as a result to keep not only pulse check, but also to live with that vision, whatever that vision is being set for, not just those matrixes that we're seeing.

That is also being cultivating culture, which I find curiosity is essential, experimentation, and also, frankly, psychological safety. We can obtain psychological safety if we don't have a leadership be involved. And then how do we bridge the gap between tech, people, and performance? So when we add those three components together, then we also have the final, pillar that I found. The fifth pillar, also very important, is fostering a future ready workforce. If we don't have a future ready work workforce, obviously, how is this going to then be sustainable? Right? So it's important to invest in risk scaling and upscaling for AI fluency. It's also important to promote human AI collaborations across all levels, build team, and also agile data literate and change ready environment.

And it's also focused on mindset, right, not just the skill set. Because if we are just focusing on one without enhancing others, we'll never really fulfill what is possible. But the bigger issue is now how do we drive organizational change? It's different when you have some individuals that are very motivated and want to do this, but then how do you do this across the board? So that's why I see as a AI transformation actually equal to the business transformation. So when that is done, rethinking the roles, processes, and metricses, empowering across functional collaboration, moving from that AI as a project and looking this as a AI as a strategic lever. When we know which lever to push, we have opportunities.

For example, we had a Fortune 100 logistic company that was decreased and reduced their delays by 38% after the aligned AI adaptation, with cross functional departments and through collaboration with their teams. And they made a tremendous change management, obviously, improvement and also workshops that are allowing for discussion and collaboration with the teams. Imagine that. 38% AI adoption was cross functional department collaboration and change. When that aligned, they had so much reduction and delays with their as a logistic companies. And we guys know how impatient we get if we don't get our package a little on large big order and something that we're so originally needs depending who who it is, right, in which industry. So it's very important to really look at this from as it says, that is not due to algorithm alone, but is also due to organizational readiness. And one of the assessments that we recently did actually that helped to really identify how truly ready your organization is. It was a eye opening.

What was c suite maybe thinking or different departments versus as overall organization? You might get a completely different output and, obviously, run into some surprises there. Right? So now really brings another very important topic, which is, excuse me, how do you manage the risk? Right? We know know that all of these, proposed, recommendations and best practices are also bringing a lot of risk levels. Right? And how do we ensure long term impact, where that impact is gonna go? And I really wanna point out that I when I see AI risk, it does not necessarily equal to just cyber risk. Right? We also have a bias with algorithms, ethics, and decision making. But more than anything, we also need a transparency and explainability. How can you explain something?

Where is the root cause and cause and effect, right, and how that looks like? So for example, how do you build internal AI governance committee? Where where where is really crucial? And then also playing the data accountability policies in place. I think those two could be phenomenal ways of solution for people meeting and establishing something in the framework wise together. And on the flip side, also, develop further policies that's going to minimize the risk. So for example, we're gonna use this example from 2023 with health care company that use AI as a tool and then also founded to to deprioritize, minority patients of critical care due to skewed historical data. And what was happening is that AI is a mirror. Right?

Whatever we put here, whatever reflects on the data, it shows this decision, and we'll make sure that it reflects something that we feel very strong about it, that that what we wanna reflect on, that we're fine trustworthy, that we're fine also, our our entity organization that others wanted to either be part of or do wanna do business with.

So that's why it's so important to have the covenants committees and really think through through this much more, effectively. Also wanted to make sure that we understand the real world application of AI. So, for example, I wanna extend on additional four examples that I think will be, very, impactful and some really solid outcomes. So for example, we had a client with health care that we were being involved, partially, on this success. It's predictive diagnostics. I love about this because if you can preventatively look at how you can, diagnose accurately, your patients based on tests, for example, thirty percent faster early cancer detection was one of the biggest opportunities. How much lives can be impacted just with that, you know?

And, unfortunately, cancer attaches every family wherever we're a part of it, and so important to really look at what that's look like in the real world. The other one was public sector disaster response optimization, which I really love because we've seen fair amounts of disasters that have been, happening. And then how can you save not only 50,000,000 savings about FEMA, FEMA funds allocation, but also leverage and reposition those monies for something else that obviously they're needed. So when we optimize things, we deploy faster, and it's definitely a great tool for crisis intervention, which we, again, have to be prepared for. Right? From financial standpoint, I found this very intriguing that credit remodeling, risk modeling, it's actually really good because also can help reduce default rates and anticipate what those, the possibility of default, behaviors is and patterns and and before they occur and how proactively and preventatively, we can engage with those clients.

In addition, for example, manufacturing, predictive maintenance, that's something that I also love because, again, it's proactive and helps help produce 48% of reduction of downtime. So imagine if when you have a workforce already able to work and due to some team being broken or delays or frustration of system, being down, now with the reduction of that downtime, how much more actual organization only can be profitable, but also how much can impact on that bottom line.

So those are not obviously pilot projects that we're talking about, but there are the projects, that are produced in real or ROI. And that is the beauty that I really wanna make sure that everybody who is watching watching and listening can really see how truly ROI is bringing everything to the table here, with the right, KPIs and other indicators associated with that. But something that I really wanna stress out in this, conversation with you is, how important is to have a leadership in delt enrollment. That's, to me, it's not negotiable. And every time when we see a lack of leadership presence, even if there maybe did buy in and sponsor something, but then that kind of their visibility fades away. We see that, solely of as AI is being seen as a tech team's problem or project or delegation. I can tell it right away that's gonna be a fiasco, and it's not gonna go as far.

So it's very important for leaders to set that vision and not use the AI as a jargon, which we see so much, not only on LinkedIn and other, publications, but we see this also in conversations in the meetings. And then we assume that we know what we're talking about, but we truly are not bridging those gaps between tech and business outcomes. So that is why more than ever we need that. Right? It's also important to see the right actions because if we don't see the right action by leadership that are defined in these success and metrics, you don't need to be an engineer in my mind, but you must be architect of the vision. That's where the leadership come in. Right? They can arch our our key architects of the vision that is so often so much needed. And and for me, I also see this that transformation does not begin with IT.

Odd, it's very important to understand that even if it's related to technology, it does not begin with IT. Actually, that begins with the c suite. So when that is aligned with CMOs, CTOs, and all the rest of the c suite, then we have something so tangible, meaningful, and frankly, very impactful. So it's also important to leadership as lead AI. I believe that it's not only enough to just to have a champion, a strategic imperative, and alignment for that that comes in as just instead of the technical task. But I also believe it is important from clarity purposes, from conversation, dialogue, complexity of those, issues as things are happening in real time, but also, as I mentioned, the bridging the gap. If obviously, it's in it's important to have the drivers behind it. So when when we really think about it, leadership actions truly do matter. Right?

We notice things when they're lacking, but we also notice things when they're there. Right? So, having more of that, it truly makes a makes a huge difference. It's important also to ask questions. As a CEO, yourself, senior leader, ask yourself, am I embedding this AI into a strategic road map and operation or not? Am I allowing this to happen? Am I seeing the value and where where I'm lacking in my own assumptions? Do I personally understand the risks, the possibilities? And as frankly, leadership implications or where we truly are headed and how far we can go. And, we're also as organization bringing the confidence and competence in the same time for that AI adoption. So AI leadership starts and success always is with us, right, within you. Right?

Even though may not be necessarily the sole responsible for that, but it's definitely a great way to bring that forward. So now brings another question. So how do we foster future ready workshops and also AI replace humans? And this is the questions that we get a lot of times, but it's very important to redefine work. Right? So how do we prioritize, up scale, promote human collaboration? How do we bring everything to forefront? And how do we also encourage innovation? How do we also encourage the cognitive skills and key aspects of it of critical thinking? And this is the skills that I find fundamentally most necessary. We need to have a critical thinking, data literacy. We obviously change agility, that's important, and frankly, empathy and digital ethics. How do we also leverage and utilize this?

And we can say that right now, we're seeing the 78% improvement in productivity and in the internal mobility happens as a result of of, properly leveraging AI. So quick thing is now the shift that happens between the tools that we're talking about, right, how do we use AI, and the momentum. How do we shift from tools to momentum? So it's very important to have the momentum mindset that really is consistent of culture, systems, and people. So when you have that shift of, from tools to momentum, how is the culture looking like, what systems we are using, and how effective they are, and then also where it comes to people factor. Factor. And the culture that embraces those changes, system supports obviously that. And one of the biggest things is, is your organization structured to innovate or stagnate? And that is very crucial. Crucial.

And we also can very quickly assess that and understand who is embracing the cultural, change that is normalizing experimentation, as I mentioned earlier, recognizing a a reward in innovation and the roles and titles, but also digital confidence in everyday decision making and also build on psychological safety as I mentioned earlier.

But this is very crucial. Systems to support experimentation, it's important to know where is the low risk, what who is playing in that AI sandbox, and then also where the prototypes are coming from. Also integration, other elements, KPIs, velocity that comes with that, and then also, of course, empowering the people and empowering yourself. But one thing that we see constantly is that people in part use AI meaningfully, and it's such a beautiful way to see when we have AI fluency. How is this bringing, obviously, growth change from tech perspective? Where is no judgment? Where is the empowerment on all levels with c suite and design solutions that are kind of, melding in a beautiful way and then promotes functional AI ambassadors, innovation champions, and also remove the fear.

One thing if you could do is to remove the fear because AI is not replacing humans. We're seeing this so much that people are freaking out And it is also obviously important to know it is augmenting them. It's supporting them and finding best ways to do things better, more effectively. So when it comes down to this, just ask yourself, is your organization structured to innovate or stagnate? And then also, how much is being, again, affected by, elements of the human, fear factor and what can you do to eliminate that? And then also, finally, we wanted to make sure that we are having proper action. Right? So trust, leadership, and bold choices are fundamental.

We call that fits in overall strategy. How do you make that happen? And, also, how do you, as a result, lead effectively through these pilot programs, through these clearly defined goals, through feel clearly measurable KPIs? And then also current functional AI strategies roundtables that we found tremendously to be effective. Are you some of those? Are you investing in AI literacy across leadership and frontline teams? And are you also building internal culture that is innovating and also pin fostering accountability? So with that in mind, we wanna make sure that we're open here to discuss, ask, answer any questions. And I just wanna say that future is automated. It's not automated. It is activated. We have now tremendous opportunity, and let's lead it together.

And I will have be happy to share some of the data statistics that we see being tremendous impactful of these AI pilot programs and all business outcomes that I was mentioning here. Anybody, any questions? Angela, do you have any questions? Anna, anybody else? Thank you so much. Thank you, Angela. I'm happy to answer any questions that you may have. I know I'm getting close, so obviously on top of my, time here, and I wanna make sure before I close, be able to answer any questions you may have. If you don't, feel free to, again, contact directly on LinkedIn and also one on one privately. I'm super excited to see so many of you and, look forward to learning as well from all of you, in this, fantastic, frankly created event. Woman in tech, thank you so much for hosting me and having this great opportunity to present on behalf of Black Acilia Institute.