Hello everyone, and thank you for joining this session. My name is Terrance Stan, Joseph Ra. I specialize in enterprise data architecture, cloud data engineering, and AI driven business intelligence. I bring close to 25 years of experience in building data systems for global organizations. The title of today talk is Yaya Driven Enterprise Intelligence. Enabling real time decision making. In ops, the topic is important because businesses cannot depend on slow reports anymore. By the time traditional insight reach leaders, the opportunity may already be gone. What organization need is real time intelligence data that is processed instantly analyzed with the ai. And acted on right way. The next 30 minutes, I will walk you through four areas, how enterprise intelligence has evolved. The building blocks secured the role of responsible ai, and finally, your practical roadmap to make it real. By the end, you will see how a and ML lops together enable smarter, faster, and more reliable decisions. Here is the agenda for today's session. We will start with evaluation of enterprise intelligence. I will explain how organization move from retrospective reporting to real time decision systems and why these safety is necessary next. We will explore the building blocks of AI driven intelligence. This includes distributed processing, machine learning models and automated workflows. They form the core technology that makes real time decision possibles. After that, I will discuss responsible, yay, a implementation as yay A becomes more powerful. It also raises ethical questions. We need governance, transparency, and trust to make sure these systems are used responsibly. Finally, we will cover a practical amount of blueprint. This includes architecture, integration strategies, and an implementation roadmap that organizations can follow. My goal is not only to explain concepts. But also to give you a practical takeaways, by the end of this session, you should have a clear picture of how enterprise intelligence can transform organizations. I, let's begin with the sif from retrospective intelligence to real time intelligence for many years. Traditional business intelligence only looked backward. It was based on historical data and shared through periodic reports daily, weekly, or monthly. The common issue was delayed. By the time leaders received this report. The situation had already changed. Traditional BA answered the question, what happened, but gave little guidance on what should we do now? Real time enterprise intelligence changes this. It takes continuous data steams and process them as they arrive. This allow organization to react immediately, not hours or days later. Yay. A take this even further. Predictive analytics highlight what is likely to happen while prescriptive analytics recommend the best Next action. Some of these actions can even be automated. For example, fraud detection system systems, it can block suspicious activity in real time without human review. The sift from descriptive reporting to predictive and prescriptive intelligent is the core difference between yesterday's BI and today's AA driven systems. Enterprise intelligence become powerful when poor parts come together. The first is CAM models. These are algorithms that recognize patents, predict outcomes, and generate useful insights. Without them, the data stays raw and underused. The second is Yamal Lab's practices. This ensures model. Don't just stay in research. They are deployed, monitor, and updated as needed. YMO allows, makes AA relatable and ready for business use. The third is realtime processing. Instead of waiting for overnight report, data is processed as soon as it arrives. This give business agility and immediate responses. The fourth is government. Governance provides rules, accountability and compliance. It makes sure YAYA systems are ethical, explainable, and trusted. When all four intersect Y models, your labs realtime processing and governance, we get enterprise intelligent that is fast, scalable, and trustworthy, missing even one piece weakens the system. Together, they create the foundation for smarter, faster, and more sustainable decisions. Yay. Driven, intelligent is built on three main building blocks. The first is distributor data processing. Large amounts of data need to be processed quickly and reliably. Tools like a Kafa Spark Streaming and road services make this possible. They handle steams of information at scale with very little delay. The second is machine learning models. This can be symbol. Statistical models are advanced deep planning net worth. They find patterns, predict outcomes, and recommend actions. Models can focus, demand, produce, customer churn, or detect fraud in real time. The third is automated decision workflows. Once insights are generated, they must be turned into actions. Rule engines, region re enforcement landing, and automation tools help systems act on insights without waiting for humans. Especially for routine decisions. When combined these three elements, processing, modeling, and workflows turn raw data into real time intelligence, they are the foundation of enterprise systems that can add, not just report many organization. Start their Yay journey with small pilots. These pilots often sit in silos managed by different teams with the little coordination. This leads to duplicate efforts, inconsistent practices, and wasted resources. The next stage is integrated platforms. Here, organization move away from scattered pilots and use shared infrastructure. They standardize processes, connect data pipelines, and encourage collaboration across departments. This reduces duplication and increases efficiency. The most advanced stages. Enterprise intelligence at this level. Yay. A models, data and workflows all operate together. Insights from one area, such as supply chain can directly inform another area like finance. Customer insights can influence product decisions in the journey moves from fragmented pilots to integrated platforms, and finally to enterprise intelligence. The real value comes at the final stage when embedded into everybody business process. That's when organization unlock the full power of intelligence. Let me ask you this. How often have you tried to get the data and felt stuck with technical tools? Most people don't want to write queries. They just want answers. This is where natural language interfaces play a role. They let people interact with systems using normal conversation, for example. Yeah, manager can ask, what were our sales last week? Under this, the system can answer instantly. Even with the chart. These enterprises do more than qa. They understand intent, recognized domain terms and work across multiple models like text chart or wise. They act as the bridge between humans and ai. The benefit is democratization, cell service, analytics, operational health, workflow automation, all become available to employees at every level. Not just technical experts by lowering barriers, natural language, inter interfaces bring AI into daily work. They make interface intelligence easier to use, so that data driven decision become part of everyone's job. Now let us see how these ideas work in real industries. In financial services, Yaya detects fraud in real time, often stopping it before damage is done. Algorithm trading reacts faster than any human and banks personalize services for each customer. In healthcare, yay. A helps doctor with clinical decisions. It predict patient's risk resource use in hospitals, and even support personalized treatment plans in retail companies use a, a for dynamic pricing, inventory optimization and personalized shopping. It also improves supply chain flexibility. Helping stores stay stock even during disruptions in manufacturing. Predictive maintenance prevents costly breakdowns. Quality checks becomes automated, and production is optimist for efficiency. Each industry has unique needs, but the foundation is same. Real time data a model, and automated workflows. This source, the flexibility of enterprise intelligence, no matter the field. These tools help organization move faster, serve customers better, and save cost. Now let me ask, would you trust your system? That is fast because, but unfair, probably not. That's why responsible Yay is the foundation of enterprise intelligence. Businesses depend on trust, and trust comes from doing yay the right way. Governance is the first. Clear rules and review processes. Make sure someone is accountable for every bottle. Fairness means testing for bias and ensuring decisions are balanced across different groups of people. Explainability allows leader to understand why the system gave you a certain output instead of treating it like a black fox, complaints ensure we follow laws like GDPR, industry rules and privacy protections with those responsible AI system risk failure lawsuits or loss of reputations. With IT organization gain trust from customers, regulator, and employees responsible. Yay is not optional. It is what makes enterprise INT intelligence safe, ethical, and sustainable. One of the hardest challenging is balancing performance with ethics. Businesses always want more accuracy, but sometimes the most accurate model is not the fairest or easiest to explain. The first step is to map trade off, recognize where accuracy might conflict with fairness and privacy. The second step is multi objective optimization. Discerning models that optimize across several goals, not just accuracy. The third step is stakeholder input, legal teams, ethics boards, and even end users should have a say in what is acceptable. Finally, we need continuous evaluation. Our society changes. What is fact today may not be fact tomorrow. The goal is not for perfection in one area, but balance across all by making this decision openly and documenting them. Organization build DIA, systems that are strong in performance, but also ethical and trusted. That balance is what sustains enterprise intelligence over time. Let's look at what the architecture of enterprise intelligence actually looks like. It begins with cloud native infrastructure. Containerization makes sure models run the same everywhere. Kubernetes managers scaling and serverless functions allow even driven processing. And infrastructure as code keeps everything reproducible. The second layer is data architecture, even streaming backbones, like Oris carry data in real time. Features, tools, keep models supplied with fresh inputs, laco patents, campaign structure, and unstructured data in one system. Data contracts ensure consistency. And prevent errors together. This creates a strong foundation data flow continuously. Models always have the latest information and the infrastructure grows as business needs expand. Think of it like building not just a single building, but a whole city of intelligence. Each part connect supports others and scales together. That is a kind of blueprint needed for modern enterprise intelligence. Now that vehicle seen the architecture, let's talk about how machine learning integrates into business systems. First step is continuous integration. Every time your model or data pipeline changes. It must be tested automatically this quality and avoids the prices in production. The second step is continuous delivery models move from development to production in a repeatable process with version control and validation. Gates, this prevents error when rolling out new updates. The third feature is management. Using feature flax are canary deployments. Organization can test new models on a similar scale before rolling them out to everyone. Finally, monitoring and observation is essential. We track not the system performance, but also businesses outcome data drift and model accuracy integration ensures ideas don't stay in notebooks. It turns experiments into working systems that support real business decisions. We thought integration innovation starts with it. AI becomes part of everyday operations. I should DAA make decision fully on its own. The answer depends on the situation. Okay. Some cases work well with full automations. These are high volume row risk decisions like filtering, spam or flaking duplicate records. Other cases need a human in the loop. Yaya give recommendation, but humans approve the final decisions. This is common in medium risk areas like loan approvals or medical test reviews. Then yeah, there are Yaya augmented decisions here. Yaya provide insights, but humans stay in control. For example, your Dr. May use AA to highlight areas in the scan, but doctors still makes the final energies. The key is matching automation with risk. Low risk tasks can be automated. Highest stakes tasks must involve people. These balances give organization both speed and accountability. A e is powerful, but humans add judgment Together, they create the right mix of efficiency and trust. I, how do we bring everything together? The answer is through a clear roadmap. Step One Foundation between three to six months. In this stage, organization set up ML ops, infrastructure, governance frameworks, steaming capabilities, and an initial catalog of models. Step two, integration anywhere between six to 12 months. Here, models connect to real systems. Low risk decision can be automated. Monitoring is deployed and retaining pipelines are built. Step three expenses, 12 to 24 months at this age organization adopt advanced methods like reinforcement learning or natural language interfaces. Yay. A capabilities expand across departments and scale across the enterprise. The key is not to rush. Organizations succeed by starting small, proving value in yearly use cases, then expanding step by step. Yeah. Roadmap, provide direction, ensures steady progress and builds trust in the system. So how does this work move the field forward? First, we are building unified. Cloud native YAML ops platforms. Instead of scattered tools, models and workflows run on scalable and cloud systems that connect across the enterprise. Second, we enabled real time intelligence. Instead of waiting for reports, leaders get immediate insights and prescriptive actions. This level of responsiveness was not possible in the past. That we design, adoptive and resilient EA systems, this system do not break easily. When condition change, they are self-optimizing, fault tolerant, and reliable. Finally, we embed ethical EA by design, fairness, explainability and complaints are built into the system from the start together, these advances. Change enterprise intelligence from your reporting tool into your real time trusted decision engine. This is not just evaluation. It is a step change that pushes the entire field forward. Let's look at the measurable research organization have achieved with AI driven enterprise intelligence. First, forecasting accuracy. BU by about 30 percentage. This means companies predict, demand, supply, or risk more effectively leading to better planning and fewer surprises. Second, reporting efficiency increased by 90 percentage reports that once took our third days now generated almost instantly. Leaders can access insights quickly and make timely decisions. That companies achieve total to 15 percentage revenue preservation by predicting risk earlier and optimizing operation, they avoided losses and capture opportunities that might have been missed. These numbers are not just statistics. They represent real business outcomes, better planning, faster decision, and stronger financial performance. They also prove that AI driven intelligence is more than theory. It delivers enterprise level transformation in real environments for Fortune 500 companies and beyond. These impacts are clear evidence of value. So what does it mean for the wider industry? It meets. These models are not just useful in one place. They are shaping new standards. These frameworks are now being used as a reference architectures. They are seated in case studies, studied in professional forums, and adapted by multiple organizations. This will source their reliability and broad relevance. Many companies adopt the same approach. It validates the model. It proves that unifying Yay ops real time data and governance works in practice. This adoption also creates momentum as more organizations follow these practices, technology vendors and platform adopt their tools to support them in the innovation spreads becoming an industry now rather than an isolated success. This is a real power of this work. It doesn't just transform one company. It raises the bar for how entire industry thinks about yay driven enterprise intelligence. I, before we close, let us review the key points. First, yay. Driven enterprise intelligence transform decision making. We move from backward looking reports to real time predictive and prescriptive systems. Second, integration is essential. The real value comes when organization move beyond isolated pilots and adopt unified frameworks across the enterprise. That responsible, yay, responsible, yay, is not just optional. Ethics, governance and transparency must build in from the beginning. Test is the foundation for adoption. Finally, ops maturity is the enabler consistent practices for model development deployment. On monitoring 10 yaya from one time our projects into sustainable capabilities. This takeaway summarized the journey from past focused PA to integrated, responsible, and scalable enterprise intelligence. This is how organization can stay competitive in a data driven world. We have covered a lot today. From the evaluation of enterprise intelligence to the building blocks responsible, yay the architecture, and finally, the roadmap to make it all real. Enterprise intelligence is no longer about looking back. It is about acting now and preparing for what comes next. Yay. And LOP ops together allow organization to make smarter. Faster and more reliable decisions. These are the decision that save growth, reduce risk, and create real competitive advantage. Thank you all for your time and attention. I hope you found this as useful. Thank you.