Join us as we explore the impact of AI on product innovation and marketing with Set Godin, Bestselling author.

In this episode of Unpacked, marketing guru Seth Godin delves into the profound changes and opportunities AI brings to marketing and product development. The conversation emphasizes the necessity to adapt and harness AI’s potential to build remarkable products.

I think AI is the biggest change in our world since electricity. And I think it has been under-understood and we’re not even coming close to understanding what it’s going to do.

– Seth Godin

Seth begins by discussing the evolution of marketing, highlighting the transition from traditional methods to more innovative approaches driven by technology. He underscores that the essence of marketing remains the same: understanding and meeting customer needs. However, the tools and strategies have significantly evolved, with AI playing a pivotal role.

AI’s impact on marketing is multifaceted. Seth explains that AI enables more precise targeting and personalization, allowing marketers to deliver tailored experiences to consumers. This not only enhances customer satisfaction but also drives engagement and loyalty. He stresses that marketers must embrace AI to stay competitive, as it offers insights and capabilities that were previously unattainable.

Building on this, Seth explores how AI can aid in creating remarkable products. He asserts that AI can streamline product development processes, from ideation to execution. By analyzing vast amounts of data, AI can identify trends, predict consumer preferences, and optimize designs. This accelerates innovation and helps companies bring products to market faster and more efficiently.

If you are not using AI as that sort of intern slash clerk slash assistant slash brainstorming partner, you are wasting a lot of your time.

– Seth Godin

A key theme throughout the podcast is the importance of being remarkable. Seth emphasizes that in a world saturated with options, standing out is crucial. Remarkable products are those that elicit conversations and inspire loyalty. AI can help identify what makes a product remarkable by analyzing consumer feedback and market trends, enabling companies to refine and enhance their offerings continuously.

Seth also touches on the ethical considerations of using AI in marketing. He advocates for transparency and responsible use of AI, ensuring that consumer trust is maintained. Marketers should be mindful of privacy concerns and strive to use AI in ways that benefit consumers without compromising their rights.

The conversation then shifts to practical advice for marketers looking to leverage AI. Seth suggests starting with small, manageable projects to gain familiarity with AI tools and build internal expertise. He also recommends collaborating with AI specialists and investing in training to bridge any knowledge gaps.

And now that we have the chance to have a trillion dollar workforce of AI assistants who will work for us for free, how are we gonna use it? What are we gonna use it for? What would make it worth it?

– Seth Godin

Seth concludes with a call to action for marketers. He urges them to embrace AI not just as a tool but as a transformative force that can elevate their marketing efforts and product development. By being proactive and innovative, marketers can create remarkable products that resonate with consumers and drive business success.

In summary, the podcast with Seth Godin provides valuable insights into the intersection of AI and marketing. It highlights the potential of AI to revolutionize product development and underscores the importance of creating remarkable products in today’s competitive landscape. Seth’s practical advice and call to action inspire marketers to embrace AI and leverage its capabilities to stay ahead of the curve.

“Developing GenAI Strategy” with guest Omid Bakhshandeh, AI Scientist with a PhD in Artificial Intelligence, discusses how organizations can foray into adoption of GenAI.

Whether you are the company's CEO or leading a business unit, if you're asking yourself the question, should I develop an AI strategy? That's the wrong question because today, we know that if you don't have an AI strategy, the odds of you being successful in the next couple of years will diminish. So, the right question is, what is my AI strategy, and how fast can I deploy this strategy? To answer this question, large language models are at the heart of every company's AI strategy. In a previous episode with Professor Anum Datta, we unpacked LLMs and explored what LLMs are. In this episode, that conversation was taken to the next level, and we discussed the key things you need to know about LLMs that'll help you develop your company's AI strategy.

Looking at the current landscape of Large Language Models (LLMs), these LLMs capture vast amounts of knowledge and serve as repositories of knowledge that have given rise to foundational models. With this concept, there's no need to initiate the training of an LLM from the ground up. Instead, existing LLMs available in the market, which have already encapsulated knowledge, can be harnessed and seamlessly integrated into applications. It is beneficial for companies in most cases to follow this strategy. The inherent trade-off pertains to the risk of foregoing the utilization of established LLMs, which could result in a delay in promptly reaching the market.

On the contrary, some companies, characterized by their possession of significant volumes of unique and customized data, may contemplate the development of proprietary foundational models and specific LLMs. This strategic manoeuvre facilitates the integration of such models into their respective industries and provides avenues for potential monetization opportunities.

The key for leaders is to pay close attention to the potential use cases, data, and the support system available when building the AI strategy.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

"Exploring what should organization considering when choosing to adopt LLMs" with guest Nick Brady, Senior Program Manager at Microsoft Azure Open AI Service

AI has been at the forefront of transformation for more than a decade now. Still, the Open AI launch of chat GPT in November 2022 will be noted as a historical moment – the scale of which even Open AI did not expect – in the history of technological innovations. Most people don't realize or fully appreciate the magnitude of the shift that we're in. Now, we're able to directly express to a machine a problem that we need to have solved; equipping these technologies with the right reasoning engines and the right connectivity could bring the biggest technology leapfrog not just for enterprises but even in everyday lives.

The onset of leapfrog does bring out a few questions for enterprises looking to adopt GenAI as a part of their strategy, operations and way ahead, like:

What use cases are best suited to adopt the models?

While most customers are looking for how this could reduce business costs in their organizations, the true value is when it is used to maximize business value productivity and downstream that could lead to employee satisfaction and customer satisfaction. Any place where there's language – programming or natural language – is a good use case for generative AI, and that probably would be the most profound shift. So, if you have language, if you have a document, if you have big data where you're trying to sort of synthesize, understand what that content and what the content is, generative AI models can do this ad nauseam without any delay.

The most common metric used across the world to describe LLMs is the number of parameters; in the case of GPT 3, it is trained on 175 billion parameters, but what does this mean?

Parameter size refers to essentially the number of values that the model can change independently as it learns from data and stores all information in the vast associative ray of memory as its model weights. What's perhaps more important for these models, and it speaks to more of their capability, is their vocabulary size.

How does one decide and evaluate which would be the best-suited model for the selected use cases?

The best practice really is to start with the most powerful and advanced language model like GPT 4.0 to test, if it's even possible, with your use case. Post confirming the possibility of use case trickle down to simpler models to find its efficacy and efficiency. If the simpler model can probably achieve 90% of the way, with just a little bit of prompt engineering, then you could optimize for costs.

Organizations would have to define what quality means to them. It could be the model's output, its core response, or performance in terms of latency, where the quality of the output may not be as important as how quickly we can respond back to the user.

The key for leaders is to pay close attention to the potential use cases, test them with the best model and then optimize the model to balance the cost, efficacy and efficiency factors.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha