Day 3

In this presentation, we will talk about the multifaceted applications
and capabilities of Artificial Intelligence (AI) in today’s digital world.
Commencing with a foundational overview, it will help the audience
dig deeper into the various technological underpinnings that enable
computers to perceive, comprehend, act, and ultimately, learn.
As the narrative unfolds, we will then touch on the evolutionary
trajectory of analytics, machine learning, and AI—connecting it to a
pivotal inflection point that promises to redefine the landscape of this
ongoing technology revolution.
A key focus of the presentation will also revolve around responsible AI
which is one of the six enterprise adoption essentials every business
leader should know. More so, we will accentuate AI’s real-world
impact through 5 compelling case studies and exemplars drawn from
the business sphere.
By the end of the presentation, we hope to encourage companies to
use breakthrough advances in AI to set new performance frontiers—
letting them redefine themselves and embrace technology
disruption.

Join our speaker as she shares their journey from traditional rules-based Customer Lifecycle Management (CLM) campaigns to the realm of AI-driven marketing. Explore the transformation that has reshaped customer engagement through personalized “Next Best Offers”. This session will cover the challenges, strategies, and outcomes of this pivotal shift. Gain valuable insights into the critical steps and best practices that guided them, including the seamless integration of AI within existing teams and workflows.

AI is transforming the way we work, and business outcomes across industries. In many industries like Finance, e-Commerce, Marketing, Transportation, Telecommunications, etc., artificial intelligence and machine learning have been successfully integrated into operations to optimize processes, enhance efficiency, and reduce operational costs to improve business results. The application of AI on industrial applications, on the other hand, has traditionally lagged behind. While there are some successes in very specific sectors like manufacturing, the broader implementation in process plants, energy, and other utilities is very limited compared to other industries.

While the potentials of AI has been demonstrated in many industries, there are challenges inherent to industrial applications. Firstly, data security and availability are key hurdles. Industrial settings often involve sensitive data and are often siloed as a result. Collecting data to develop accurate AI models, therefore, becomes a formidable task. Secondly, the domain knowledge is very limited. There are very few people who can verify the accuracy of results of AI models. And lastly, there is a need for upskilling the workforce to harness AI effectively in industrial applications.

Despite these challenges, AI continues to promise remarkable benefits that improve safety, productivity, and resiliency. As the industry continue to adapt, overcoming these obstacles will be crucial to realizing the full potential of AI in the industrial sector.

NVIDIA’s revenues and market capitalization have grown at a significantly higher rate the last 10 years than its employee headcount. Besides dominating the GPU market for gaming and artificial intelligence, NVIDIA’s supply chain efficiencies and innovations have significantly contributed to its sustainable meteoric growth. In this talk, we will go over the four distinct technologies and models that have powered NVIDIA’s supply chain systems. At the core is an advanced supply chain planning system that has automated processes and facilitated data management. Deep learning (DL) and machine (ML) learning models have helped in increasing the accuracy of forecasting demand for NVIDIA’s products worldwide. Mathematical optimization and primarily mixed-integer programming (MIP) models have helped in optimizing supply against the demand to increase revenues while decreasing costs. Finally, generative AI or large language models (LLM) are in rapid development to enable supply chain planners to quickly understand the results of DL, ML and MIP models to be able to make difficult business decisions

This talk explores the profound impact of Generative AI on various industries and its transformative potential for businesses. It delves into how AI technologies are reshaping the way we work, create, and innovate. From content generation to automation, it uncovers real-world examples of how organizations are leveraging Generative AI to gain a competitive edge, whether you’re a tech enthusiast or a business leader.

This will be based on IBM’s CEO Study 2023. How CEOs choose to adopt generative AI may be one of these defining moments. Making the right investments in this emerging tech tech could deliver a strategic advantage that pays massive dividends. But the wrong bets could open the door to data privacy concerns, legal liabilities, and a whole host of ethical issues.

CEOs know they need to tread carefully, but they also feel the need to act fast. 64% say they face significant pressure from investors, creditors, and lenders to accelerate adoption of generative AI. Consequently, investment in generative AI is expected to grow by 4 times over the next two to three years—although it remains, at least for now, a fraction of total AI spend.

What will help CEOs accelerate the adoption of generative AI? How can they scale safely and responsibly?