Just like the discovery of fire sparked previously unfathomable progress for humans, AI and generative AI offer fundamentally new ways for businesses and society to tackle major challenges and unexplored opportunities. Analytics leaders are central to realizing this shift. But they risk getting left on the side lines if they don’t follow the right approach. Knowing how to prioritize use cases, help employees prepare for the change, and establish responsible AI strategies are key. Join this session to learn what it takes to rewire your business and harness the fire that is fueling the future.

How do we develop AI models and systems taking fairness, accuracy, explainability, robustness, and privacy into account? How do we operationalize models in production, and address their governance, management, and monitoring? Model validation, monitoring, and governance are essential for building trust and adoption of AI systems in high-stakes domains such as financial services, manufacturing, and healthcare. In this talk, we will present risks associated with operationalizing LLMs and other Generative AI-based applications, highlight challenges faced by various stakeholders when operationalizing AI/ML models from a human-centric perspective, and emphasize the need for adopting responsible AI practices not only during model validation but also post-deployment as part of model monitoring. We will then present a brief overview of techniques and tools for monitoring deployed ML models, industry cases.

As GenAI continues to advance and reshape industries, this session offers valuable insights and practical applications that will empower you to navigate this revolution with confidence. We will talk about the value chain, innovative use cases, and discuss how various functions like sales, marketing, customer service, product development can unlock untapped potential with GenAI.

What should we do about GenAI and can we afford to wait ? That’s the question most CEOs and boards are asking which is causing a swirl of activities in most organizations. Working with clients, academia and hands on building; we have collected a set of learnings that should help data analytics leaders with some pointers.

Generative AI has become the focal point of interest and captivated the attention of business leaders worldwide. While it is a topic of discussion, businesses are actively considering its implementation. However, many companies are facing challenges to conceptualize practical use cases. In this session, we will share real-world examples that are relevant to your organization. We will explore the impact on business, associated risks, technological architecture alternatives, required changes in data processes, and privacy considerations. Boards and executive teams seek answers to these aspects before scaling pilot projects into production. By the end of this session, you will gain insights into the foundational components necessary to actualize Generative AI across your enterprise. You will also have the opportunity to connect with other industry leaders, collectively evaluating the benefits and risks, and working together to shape our perspective about the new future.

On one hand, generative AI presents an opportunity for data teams to innovate and create value. The technology can assist in data augmentation, improve data privacy, and provide tools for exploring hypothetical scenarios and solutions. However, it also raises challenges in terms of data integrity, authenticity, and ethical considerations. The talk will further examine these threats, such as the potential for data manipulation, the rise of deepfakes, and the risk of bias in generated data. Alongside, it will address strategies for harnessing the power of generative AI effectively while mitigating its associated risks. The session aims to equip data professionals with insights on how to adapt to this new AI frontier and leverage it to drive their data strategy.