Good afternoon, everyone. So my name is Nari Bazeo, and the topic of this session is area adoption, the key to unlocking value for organizations and leaders.Now, ultimately, we are living through one of the most consequential shifts in modern organizations. AI is no longer emerging. It's actually embedded. It's scaling, and it's shaping how organizations compete, decide, and lead. And yet, despite billions invested globally, many leaders are asking the same question. Why aren't we seeing the value that we expected? And it's simply because the AI conversation has progressed far faster than AI adoption. So across industries such as, you know, consumer goods, health care, transport, retail, Organizations have the tools, the platforms, and the ambition, but what they often lack is something far more fundamental. And that is leadership that treats AI adoption as a strategic human endeavor, not just a technical deployment.

And this is what I'm here to talk to you about today because AI adoption is not just a technology challenge. It is the key leadership challenge of our time. Now let's reframe AI for a moment. And, you know, it's still too often positioned as, you know, an IT implementation, an innovation lab initiative, or a pilot running alongside the business. And organizations that treat it that way are almost always likely to stall. Why? Because AI doesn't sit neatly in one function. It reshapes how decisions are made, how work gets prioritized, how expertise is shared, and how power and accountability show up in organizations. In other words, AI rewires the organizational operating system.

Now in practice, this means that AI quietly changes whose judgment is trusted, how confident people feel making decisions, and how quickly organizations can respond to change. And that's a strategic shift, whether leaders intended or not. And that's why the most successful organizations anchor AI adoption around the decisions that matter most, the value that they want to unlock, and the behaviors that leaders want to see. An organization seeing impact ask very specific business questions. Like, where do we lose time because decisions are slow or contested? Where do we rely on gut instinct because insight isn't accessible? And where is critical knowledge trapped in too few people's heads? AI isn't about doing more faster. It's about making the most valuable decisions better. And that's why AI adoption belongs on leadership agendas, not just technology roadmaps.

Now let's talk about value because AI doesn't and shouldn't get a free pass. Most ROI conversations start and end with efficiency. Tasks automated, time saved, costs reduced, and, yes, that matters. But efficiency alone really transforms organizations because efficiency optimizes the present, and value comes from reshaping the future. And here's what I often see. Organizations launch AI tools framed around productivity. People try them once or twice and then slowly revert to old habits. Why? Because people don't feel ownership of efficiency targets. They feel ownership of outcomes they care about. The organization seeing sustained return from AI are unlocking something deeper. Faster, more confident decision making, teams spending less time searching and reconciling, and more time on judgment, creativity, and problem solving, and innovation happening closer to the front line, not just in labs. AI's real return is not just speed. It's judgment at scale. And when implemented well, AI removes the friction from work so people can focus on what humans do best.

Sense making, collaboration, curiosity, leadership. And I think great AI creates headspace. People think more, challenge more, and connect ideas more easily. And that's where innovation lives. And it's visible in how confident teams feel, how quickly ideas move, and how adaptable organizations become. And that's when AI stops being a tool and starts becoming an enabler of growth. Now here's a truth many organizations are still confronting. They are data rich, but insight poor. Data exists everywhere. Right? Emails, research, reports, operational metrics, customer signals, but very little of it actually shapes decisions day to day. Why? Because data is often fragmented, inaccessible, hard to trust, or locked into expert silos. AI changes that equation, and let me give you a simple example. So in one organization I worked with, teams were spending days preparing for decision forums, pulling data from multiple systems, reconciling versions, debating which numbers were right.

AI didn't replace those teams, but it did remove the manual aggregation, surface patterns faster, and allow conversations to focus on what to do next, not whose data was correct. The value wasn't just speed. It was better conversations. And when applied with intent, AI doesn't just analyze data. It demo it democratizes access to insight, and it puts better answers closer to the people making decisions. But and this is critical. This only works when people trust the outputs. And trust is built through transparency, context, and leadership role modeling. And that brings us to leadership. So here's the good news for leaders everywhere. You do not need to be able to build AI to lead in an AI enabled world. The leaders driving adoption ask different questions. It's not how does the model work, but it's what decisions does this improve? Where must human judgment remain essential?

How do we ensure responsible transparent use? And what confidence do our people need? Leadership in the age of AI is not about control. It's about creating clarity, building trust, and modeling responsible use, and most importantly, creating psychological safety. Because people adopt AI when they feel safe to experiment, safe to question outputs, and safe to learn in public. And this is why AI adoption follows behavior, not strategy documents. People watch how leaders respond to uncertainty, and they move accordingly. And finally, this brings me to why this community matters so much. AI systems are shaped by the data they are trained on, the assumptions behind their design, and the perspectives shaping the development.

If those perspectives are narrow, the outcomes will be too. Women in tech have a critical role to play in challenging bias, designing for inclusion, and ensuring AI amplifies opportunity, not inequality. And this is not about representation for its own sake. It is about better outcomes. Because inclusive AI earns trust faster, serves broader markets, and delivers more sustainable value. Ethical AI does not happen by default. It happens because leaders choose it. So as I close, I want to take us out of theory and more into more the world we're actually standing in now. We are not at the beginning of the AI conversation anymore. We are not at the end. We're in the middle.

That decisive moment where technology has moved faster than leadership norms, governance, and confidence. This is the moment that determines whether AI becomes a force multiplier for human potential or another system that people quietly work around. And AI will not replace leaders, but it will to redefine leadership. The future of AI adoption will not be written in algorithms, and that's what I want you all to hear really clearly. And this brings us to you because communities like ours do not just reflect the future, they shape it. And when AI changes how organizations has worked and, you know, how things have developed, that's gonna be down to us, and that's the future that we are standing up for.

Thank you,