Hi. Good morning, good afternoon, and good evening. Wherever you may be joining from, welcome.My name is Carol Kim, and I'm thrilled to be here to share insights on one of the most transformative forces that's shaping our future, artificial intelligence. As the director of, leading technology data and AI for IBM's global real estate organization, I've had the front row seat to how AI is not just a game changer. It's a new way to play the game entirely. So this is the agenda that I will go through today. Let's just dive right in. Okay. Let me start with the stat that might sting a little bit. The average human attention span is now shorter than of a goldfish. You heard that right. Humans' attention span is now eight seconds compared to a goldfish being nine. Think about that.

In a world overflowing with data, we're all trying to make smarter decisions faster than ever with less focus than a goldfish. So this isn't just a personal challenge. It's a leadership crisis. Every day, we juggle with mountains of data, make rapid fire decisions, and face relentless demands. So it feels very chaotic. But here's the irony. The very thing that over that is overwhelming us, which is data, is also our way out. With AI as our guide, we can cut through the noise and find clarity, focus, and impact. Today, I'll show you how. Alright. Let's get straight to the point. AI is no longer a luxury. It's mission critical. Consider this. The global data sphere is projected to reach 175 zettabytes this year, up from just 33 zettabytes in 2018. To put that into perspective, if data were a stack of dollar bills, it could reach the moon and back nearly 5,000 times. Yet, 80% of enterprise data goes unused.

That's eight out of 10 potential insights left untapped. It's like sitting on a gold mine and refusing to pick up a shovel. And while many organizations have invested in AI, most are stuck in experimentation mode. According to Gartner, 53% of AI projects never make it past the prototype phase. Why, you ask? Because many start with the shiny object syndrome, chasing something that's shiny focusing on the tool and not the problem. And let's not forget the $206,000,000,000 spent on AI systems in 2023. Unfortunately, much of that investment is wasted due to poor implementation and siloed strategies. So this is the opportunity. Today's computing power can process trillions of operations per second. We can analyze data at a scale that's unimaginable a decade ago. But without a strategy, even the best tools are just expensive goldfish bowls. So AI done right isn't about replacing humans. It's about enhancing human intelligence.

It helps us uncover patterns, transform data into action, and make smarter decisions. So the question isn't whether you should embrace AI. It's whether you can afford not to. So let me tell you what this looks like in action. At IBM, the global real estate team manages 42,000,000 square feet of real estate. That's equivalent to 730 football fields. Massive. So every maintenance task, whether it's an HVAC repair or replacing a light bulb, is logged as a work order. And managing this at scale is very challenging and expensive. So one day, one of our facility managers said, we're drowning in work orders, and I spend so much time just sorting through them. So that's when we turn to AI to transform our operations. So enter AI number one, which is the problem code assistant.

Imagine a tireless assistant reading the maintenance tickets and assigning the accurate problem codes. No human errors, just speed and precision. It's making sure that you send a plumber and not an electrician the first time. So the system powered by IBM's Watson x uses a configurable LLM based pipeline with our IBM Granite model. The IBM Granite model is a open source, open, weight AI foundation model that was developed by IBM for enterprise applications. And this achieves over 80% accuracy even with small data samples. So what does that mean? That means every maintenance tasks gets categorized correctly the first time, eliminating the costly re rework and delays. Our second AI was the duplicate detector.

Ever get that deja vu feeling with tasks? Our duplicate detector uses AI to flag repetitive work orders, stopping us from wasting resources on redundant jobs. So it doesn't just save us time, it helps us uncover deeper root causes, allowing us to address, systematic inefficiencies. So in less than a year, we were able to reduce the catch all category of other work orders from 54% down to just 8.8%, which results in a much cleaner data and actionable insights. And by saving just a few minutes per work order, we've reclaimed ten thousand hours annually. So each hour saved isn't just time, it's money. Whether it's the cost of labor, reducing downtime, or improving the longevity of the asset. AI's impact translates directly to the bottom line, And these optimizations allow us to reinvest in higher value projects, making every dollar work harder.

And this is the power of AI in action. It's not just solving problems, but driving tangible, measurable savings. So now how can you replicate the success? So let me introduce the three c's of AI transformation. And hopefully, this will guide you into turning chaos into clarity. The first pillar, I call it cut the noise. So too many projects fail because they start with the tools and not the problem. So start with one problem that matters. At IBM, we didn't ask how can we use AI. Instead, we asked why are 54% of the work orders labeled other, and how can we fix that? And can't forget data governance, which is equally important. If AI is a rocket, the governance is the GPS. You have to know where your data lives, who owns it, and what your models are doing. The second pillar, I call it connect the dots.

You don't need more models. You just need the right ones that were built responsibly with reuse in mind. So at IBM, we created a centralized data and AI platform, and you could think of it as a Netflix for AI models. So instead of binging on crime dramas, now teens can go on this data and AI platform and binge on models, data, and tools, focus on revenue optimization or operational efficiencies. So the results of this is reuse and not reinvent. Because remember, every time you try to reinvent the wheel, someone else is halfway down the highway. The third pillar, I call it combined strengths. So AI isn't just a technology team sport. It's an everyone team sport, and collaboration is the secret sauce. In my IBM AI examples earlier, the global real estate professionals partner with the IBM research team, the software product team, and CIO to develop the problem code assistant and the duplicate detector.

Our facilities manager didn't need to know how to write Python, but his decades of expertise helped our research team uncover inefficiencies that no algorithm could have identified on its own. And these AI capabilities are now embedded into our software tool for our internal and external clients for their benefit and reuse. And that's how you build a resilient enterprise by making humans and machines better together. So collaborative intelligence is about unlocking potential at the intersection of expertise and technology. So by combining the domain knowledge with AI's analytical power, we don't just solve problems. We uncover opportunities that neither humans nor machines could achieve alone. So let me move on to how you can put the AI blueprint into action. First, define the problem. I'm not asking for the technical wish list, but the business challenge here. Go and ask your stakeholders, what keeps you up at night?

And make sure you define the problem. Number two, map your data. You don't need perfect data to start, but you do need to know where it lives and how to access it. And everyone has those Excel islands in your enterprise. They are gold mines waiting to be tapped. My recommendation here would be ask your teams. Where do they feel the data bottlenecks in their day to day work? And that's where you can start. Number three, build for reused. We talked about this earlier, but think ecosystems and not silos. So you need to invest in systems that allow you to build once and reuse everywhere, which helps speed up adoption and scale. The fourth one is create a cross functional council. Diverse teams definitely drive better AI outcomes.

Involve all your teams, finance, HR, operations, IT, and make them cocreators with you, not just spectators. The magic of AI is in pairing human intuition with the machine precision. And last but not least, number five is start small and scale smart. So aim for what I like to call a Monday win. Something that your team will notice next week, not next year. So start small or, I sometimes call it minimum viable brilliance. It's less overwhelming, and it helps get everyone on board that much faster. Alright. So AI isn't here to replace us. It is here to elevate us, but only if we choose to lead with courage and purpose. And this isn't just a technological shift. It's a leadership revolution. We are standing on the edge of a once in a generation opportunity to refine how we work, innovate, and create value. So imagine the possibilities.

What would your business look like if every decision was smarter, every process was more efficient, and every team member was empowered to do their best work. The world isn't waiting. The organizations that act boldly today will be the one shaping tomorrow. So here's my challenge to you. Take the AI blueprint. Start small, but think big because we have to get it to scale, and lead with purpose and vision. Because the future isn't something we enter, it should be something that we create. So together, let's combine the brilliance of the human and the power of AI to build smarter businesses, stronger communities, and a better world. So the question isn't whether you're ready, it's whether you're brave enough to begin. Thank you for your time.