Episode 2  |  39 Min  |  February 05

Develop AI strategy for your organization with Dr. Kavita Ganesan

Share on

Engaging topics at a glance

  • 00:12:19
    Key messages in the book: The business case for AI
  • 00:12:58
    What should enterprise leaders look into when implementing AI
  • 00:15: 25
    What problems can be solved with AI?
  • 00:16:13
    Importance of data in AI
  • 00:19:30
    Things to consider when going with AI in production
  • 00:20:48
    What makes a problem AI suitable?
  • 00:24:35
    Success rate of AI projects
  • 00:25:37
    What causes failure of AI projects?
  • 00:28:14
    What is preventing AI success?
  • 00:30:20
    Data integration problem

“Develop AI strategy for your organization” with Dr. Kavita Ganesan, where she discusses things to consider when implementing AI.

Many programmes, specifically AI-based programmes, start with the right intentions but often fail when they go into production. And, to explore this topic, we had an insightful discussion with our guest in this episode to understand why this happens and how it can be solved.

One key problem is that they’re not solving the right problems with AI. People think of a cool idea, and then they come up with an AI solution, and once they’ve developed it, there isn’t a consumer for it.

– Kavita Ganesan

Most of the AI initiatives today fail to make it into production because people are not solving the right problems with AI, and there is a lack of understanding of what AI is at the leadership level.

The perception that Gen AI can solve every problem is inaccurate, and understanding this is crucial for enterprise leaders. There are many other AI techniques that can solve business problems and it’s important to have a general understanding of what AI is and what types of problems it can solve. As implementing AI is not only cost intensive, but it also comes with many risks.

Nowadays, many people think that because of Gen AI, we don’t need to collect data; we don’t need a data strategy; data is just gone. But that’s far from the truth.

– Kavita Ganesan

After the emergence of Gen AI, contrary to what many people think today, data collection is still a very integral part of AI initiatives in order to fine-tune the models for company-specific problems.

When deciding on the application of AI, it is advisable to use it for intricate issues that require numerous narrow prediction tasks. In such cases, a large amount of data points needs to be evaluated for making decisions, which could be challenging for human minds to process.

It’s important for companies to have a strategic approach while implementing AI. Instead of just focusing on the latest trends (like implementing Gen AI for all the problems), companies should identify the problems that need to be solved in their business in order to have a huge business impact.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

Latest podcasts

Episode 10  |  61 Min  |  February 05

What you should know about LLM’s with Anupam Datta, Co-founder TruEra, and ex-CMU

What you should know about LLM’s with Anupam Datta, Co-founder TruEra, and ex-CMU

Share on

Engaging topics at a glance

  • 00:09:15
    Introduction
  • 00:13:40
    What is a Large Language Model (LLM)?
  • 00:18:40
    Is LLM a form of intelligence?
  • 00:20:25
    Comparing how LLMs learn than human learning.
  • 00:22:50
    How LLMs differ from one another?
  • 00:27:56
    What to consider when choosing LLMs?
  • 00:44:05
    Can LLMs retrieve past human knowledge?
  • 00:51:45
    How can companies harness power of statistical models?
  • 00:53:05
    Key things to keep in Mind when integrating LLM into the business.
  • 00:56:10
    Conclusion

Join us in this episode featuring Anupam Datta, Co-founder and Chief Scientist, TruEra, as we dive into the evolution of LLMs and what they hold for the future!

This world of generative AI has caught us by storm. And as enterprise leaders in your companies, understanding the technology behind generative AI will give you a competitive advantage as you plan your companies and businesses. And to help you do this, we will unpack a technology, large language models (LLMs), that powers AI today and represent a paradigm shift in this field of Artificial Intelligence.

LLMs can craft meaningful responses across many domains. Their performance has notably improved recently thanks to the substantial increase in model size and data volume.

With the increasing acceptance of this technology, numerous companies are unveiling various Large Language Models (LLMs). It's important to recognize that opting for the largest or highest-performing LLM isn't always the most suitable approach. Instead, one might prefer LLMs that excel in specific tasks relevant to their application. As a leader in the enterprise, it's crucial to integrate this understanding into your company's strategy, aiding in identifying the appropriate LLMs to match and adapt for your applications. Achieving equilibrium between LLM selection, cost considerations, and latency considerations stands as a pivotal concern for enterprises. Equally essential is the thorough validation and assessment of generative outputs, serving as a safeguard prior to embarking on consequential choices. Hence, the undertaking of reliability testing at this current juncture is paramount.

Furthermore, enterprises need to consider a few other key aspects in this evolving landscape of LLMs as they build out LLMs. Starting with a well-defined business use case that offers real value is crucial. As LLMs move from development to production, it's important to establish thorough evaluations and observability throughout their lifecycle. Education across the organization is vital to implement LLMs effectively. Companies should train their workforce to adapt to this changing technology stack. Fostering a community around responsible AI development and evaluation can contribute to a better understanding and management of LLMs. With these steps, enterprises can navigate the complexities of LLMs and harness their potential for positive impact.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

Top trending insights

Episode 7  |  45 Min  |  February 05

How AI will impact your business with Harvard Professor, Shikhar Ghosh

How AI will impact your business with Harvard Professor, Shikhar Ghosh

Share on

Engaging topics at a glance

  • 00:10:30
    Introduction
  • 00:13:35
    Why AI is so disruptive?
  • 00:16:30
    How businesses and governments accept this new reality?
  • 00:19:20
    How enterprise leaders should approach the AI transformation?
  • 00:21:40
    New business models shaped with AI
  • 00:27:15
    Emotions, decisions, and algorithms
  • 00:34:35
    Are we ready yet?

Join us in this episode featuring Shikhar Ghosh, Professor, Harvard Business School, as we explore how AI can fundamentally impact business and society!

In the ever-evolving landscape of technology, artificial intelligence stands as a true disruptor, poised to reshape not only our businesses but also the very fabric of society. In a captivating podcast discussion with Shikhar Ghosh, Harvard Business School professor, we delve deep into the riveting world of AI, exploring why its impact is so seismic, how enterprise leaders should navigate this new frontier, the question of human relevance in the age of AI, and whether we are truly prepared for this transformative journey.

We will uncover the essence of AI's disruptive power and provide compelling insights into the sheer transformation that AI can herald.

Be prepared to be guided through the stormy seas of AI influence on businesses. Our expert highlights the critical importance of a well-defined AI approach. Enterprise leaders must be agile and proactive, recognizing that AI is not merely a tool but a transformational force. We will discuss how to approach AI with an open mindset, viewing it as a catalyst for innovation rather than just a threat.

We will also see why leaders should maximize the upside of AI. This underscores the value of human-machine collaboration, emphasizing that AI augments human capabilities rather than replacing them entirely. It's a matter of harnessing AI's analytical prowess to inform decision-making and free up human resources for more creative and strategic pursuits.

One of the most intriguing segments of the podcast explores the question that lingers in the minds of many: Will humans remain relevant in the age of AI? This is discussed with nuances that business leaders can take a leaf from and be proactive in embracing AI wisely and effectively.

In a world teetering on the precipice of AI-driven transformation, this podcast offers a compelling exploration of why AI is the disruptive force of our era. It presents an alluring narrative that transcends the technical jargon, making the topic accessible and engaging for both the tech-savvy and those new to the AI landscape. As we listen to Professor Shikhar’s captivating insights, we are left with a resounding question: Will we embrace AI as a catalyst for positive change, or will we be swept aside by its inexorable tide of disruption? The answer may very well determine the fate of businesses and society as we know it. Find out more, tune in to the full podcast and embark on a journey into the future of AI, business, and our shared human experience.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

Co-create for collective wisdom

This is your invitation to become an integral part of our Think Tank community. Co-create with us to bring diverse perspectives and enrich our pool of collective wisdom. Your insights could be the spark that ignites transformative conversations.

Learn More
cocreate-halftone