Day 2 Hall 1

What does it take to ensure our workforce is always one step ahead of the industry need? If tech evolution is a continuous process, how can L&D keep up with the curve? Is it time to treat ‘skilling’ as a business investment and not an expense? So many questions. The bottom line? To build organizations that are capable of innovating. We must unlearn our conventional notions of leadership and rethink how continuous learning can happen.
Sumanth Palepu, Senior Director-Edology, in his keynote address will answers these questions and present you solutions on cultivating life-long learning.

Creating ML models needs an understanding of the tools and domain knowledge. Given the difficulty in getting data scientists that understand your domain, several AI/ML initiatives never see the light of day. Another challenge is data preparation and real time operational data availability.

Qlik AutoML is our automated machine learning capability that allows business analysts to generate models, make predictions, and test scenarios all within a simple, no-code user experience. With this capability, you can transform your business analyst, who already understands your domain and business, into a citizen data scientist. In addition, Qlik Data Integration helps you leverage real time streaming data from your operational systems streamlining your data pipelines with minimal IT resources.

Businesses must make crucial decisions when they should. And it can only take place if businesses are able to utilise the value of data in real time. They have the ability to quickly and digitally crunch information, examine trends, obtain insights, and make judgments. Data may speed up decision-making within a company and be a growth multiplier for businesses. The panel will be frank about the difficulties businesses have using data to its full potential.

Through the rise of digital commerce, marketers and advertisers have relied heavily on third-party cookies to target audiences online. However, plans to phase out third-party cookies by the end of 2024, has left marketers unsure of how to deliver personalized experiences at scale; this could end up costing up to $10 billion in ad revenue.

As the death of third-party cookies looms, the need of the hour is to find an AI-enabled approach to build data strategies and identity platforms to keep delivering personalized experiences.

Merkle’s Geetha Apathotharanan & Sonia Thakurani share an innovative AI-enabled enterprise identity solution to help brands create unique customer IDs and map customer journeys using first-party data; thus enabling more contextual and personalized customer experiences in a cookie-less world.

Artificial Intelligence (AI) is transforming industries and solving important, real-world challenges at scale.
According to McKinsey, in less than 10 years, AI may be the number one driver of global GDP growth. This is a
staggering prediction. Over this next decade, we will see incredible adoption and innovation; in fact,
applications that aren’t AI-enabled may feel broken.

Google has consistently been recognized as the industry leader in AI/ML. This session will offer an overview of
how we at Google are using AI to power some of our own planet-scale applications that touch over 1B+ users
each, how we are helping other organizations realize the full potential of AI, challenges in scaling AI and our
latest technology innovations that make these fascinating applications of AI possible at scale.

One thing that comes to mind as an after-effect of automation or AI is making human resources obsolete or redundant. However, this is entirely not true. Throughout the history of technological advancements, we have seen humans still being employed, and in fact, more employment has been generated because of these technological disruptions.

In this fireside chat, we will discuss:

Chetan Bhagat’s views on technology disruptions and how he stays up to date with the latest happenings in the world of AI and ML, alongside discussing some of the tools and platforms he uses as a writer and how it has helped him.
Also, talk about how India can be ahead of the curve, surpassing these negative connotations. How can India build those capabilities? Why are students going abroad for studies and job opportunities? What is missing in India, particularly in the educational system?

Segment 02: Escaping Corporate Life: A Fireside Chat with Chetan Bhagat

Chetan Bhagat has spent a fair bit of time working in a corporate setup before venturing into a creative endeavour in 2009 to write full-time. He also runs a podcast called ‘Deeptalk,’ where he has interviewed some renowned CEOs and celebrities.

We will touch upon:

His learnings, struggles, views on corporate work culture, leadership style, and the success mantra he follows in life and how it has changed over the years.

Some of the struggles and challenges that CEOs have encountered in their career, especially in India, and some leadership philosophies and values to become a successful entrepreneur or a leader.

Also, discuss some of the characters in his books inspired by colleagues and bosses.

Segment 03: Rapid Fire questions on some candid topics

Today in modern data architectures we hear a lot about a new sensation called Data Lakehouse. This talk firstly addresses what is a lake house, what are the perceived missing aspects and how Vertica can help going beyond.

AI enables businesses to make faster decisions, improve operations, and create new revenue streams. However, the full potential of data science and AI can be leveraged by optimizing and automating them. Feature Store is a centralized environment for organizing, storing, and managing ML features. Feature Store enables enterprises to discover, distribute, and deploy ML features at scale, resulting in faster development, deployment, and operationalization. During his keynote, Soumendra will speak about accelerating AI adoption and Feature Store’s role in rewiring business impact during his keynote speech.

While investments in AI continue to rise worldwide, companies still struggle with the ground level gains.
Misalignments between business and technology and a lack of planning ahead are key issues to not realising the potential of AI adoption.
As businesses think about adopting AI, they need to look at it in terms of what current problems can be solved with the AI&ML approach-not all can! Looking at gains and pains for clearly articulated AI&ML business use cases helps develop a short and long term strategy for AI adoption. Concentrating on high gain and high pain problems early on in the adoption journey can completely derail the entire initiative!
How execution leaders think about people, data, performance and productionization of initiatives seals the case for how successfully AI will generate revenue for the company. The AI adoption process needs to be thought of more like a new type of process where change management, implementation and last mile execution are critical to success. Top level executives have to drive change downwards and hiring a data science head and team does not ensure that happens.
Planning before jumping into AI changes the 85% failure rate to success. There are practitioner frameworks and solutions available that can help companies not just adopt AI but drive revenue from it.