Adaptive Cognitive AI & Big Data Systems for Dynamic Enterprise Environments

Businesses are increasingly adopting cognitive artificial intelligence (AI) systems powered by Big Data to navigate today’s complex and fast-changing environments. Unlike static AI models, cognitive AI simulates human-like reasoning and learning, enabling continuous adaptation to real-time data, evolving user needs, and dynamic external conditions. The integration of Big Data enhances cognitive AI’s ability to learn from vast, diverse, and high-velocity data streams—making models more accurate, context-aware, and resilient. In this talk, I will focus on the critical factors that drive adaptability in cognitive AI, including data-driven learning mechanisms, feedback loops, and human-in-the-loop interaction. We explore cutting-edge techniques such as transformer-based models, reinforcement learning, and neuro-symbolic architectures, analyzing how each benefits from Big Data to improve scalability and responsiveness. Real-world business use cases—such as data center energy optimization, financial fraud detection, and intelligent customer support—illustrate the power of Big Data-enabled cognitive AI in delivering high-impact outcomes. Key enablers like data quality, transparency, user feedback, and system explainability are examined to understand how they shape learning trajectories and user trust. These components are vital for building adaptive enterprise AI systems that not only react to change but proactively anticipate it. I will conclude with a set of design principles for deploying enterprise-ready cognitive AI systems that continuously learn from Big Data, align with user expectations, and drive operational excellence. When supported by iterative feedback, robust data infrastructure, and ethical governance, Big Data-infused cognitive AI becomes a strategic asset, empowering organizations to make smarter, faster, and more adaptive decisions.
Who is this presentation for?
Technical and Business Audience
Prerequisite knowledge:
BigData, AI and basic technical awareness
What you'll learn?
The audience will learn how cognitive AI systems leverage Big Data to continuously adapt to dynamic business environments. They’ll understand key technologies like transformers and reinforcement learning, and how data quality, feedback, and transparency shape system performance. Real-world case studies will highlight design principles for building resilient, enterprise-ready AI.

Jyoti Shah is a seasoned technology leader with over two decades of experience in application development, digital transformation, and AI innovation. As Director of Application Development at ADP, she combines deep technical acumen with strategic vision to drive scalable enterprise solutions. With 15 years as a full stack developer, Jyoti has mastered modern technologies including GenAI, React, Angular, Java, and JavaScript. In recent years, she has led AI-powered initiatives that optimize client engagement and sales intelligence. Jyoti is also a passionate advocate for inclusion and community growth—she is one of the leaders in the IWIN (International Women's Inclusion Network) Women in Technology chapter at ADP and actively volunteers across multiple social causes. A committed mentor and hackathon judge, she is known for nurturing talent, fostering cross-functional collaboration, peer reviewing other’s work and aligning technology with business value. Jyoti’s leadership lies at the intersection of innovation, operational excellence, and impact-driven development.