Enterprise AI Transformation Podcast

Rabi Jay
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Enterprise AI transformation is about making a business smarter by using AI and ML technologies to achieve business goals. AI may have been a distant dream until just a few years ago, but not anymore. We now have a fantastic opportunity in our hands to transform how we operate our businesses. But we also know that most AI efforts do not even get into production for various reasons. In this podcast, I hope to address the reasons why and provide solutions to bridge the gap between the world of prototypes and real-world AI.

Episodes

  1. 03/15/2023

    Envision Phase of an AI initiative

    Hello everyone. This is Rabi Jay here. Sharing my AI insights for the day. Last time, we spoke about the four phases of an AI initiative. Please note it can be for phases, five phases. That's not important. What is important is the list of activities one has to do because every company's journey is going to be unique and you should be able to tailor it as per your needs.  Let's look at the first phase, the Envision phase, which as the name itself says, Is all about painting the Vision. What are we going to achieve by end of this initiative? That's what it is all about. So we will have to come up with a list of AI use cases and prioritize them and also associate them with a list of business outcomes that we want to measure throughout the phase of the project. So we can see whether we are tracking good in terms of achieving those outcomes,  But here's the thing, how can a team identify these use cases without knowing what AI + ML can do? So that's another important item, we have to educate ourselves about what am AI/ML can do. So companies have to come up with training programs for their employees, to make sure they can do a good job, it is also important to have a core AI team that is coming from different business units as well as from the technology units so that they can together collaborate to kind of evangelize AI and also champion AI throughout the organization.  And also the last one which is even In more important is to identify the key stakeholders who are going to be the change agents for the organization. In other words, they are going to be people who are influential, they're probably going to come from C level or directors of VPs for their respective organizations. They can promote change through AI within the organization because change is not going to happen automatically. It has to be from the top down as well, so that's pretty much it, folks. Each and every item here is huge, probably I can write a book on it.  But anyway, I will come in the next few days and will talk about each one of those items, but this is it. The end of The Envision phase is to have a clearly defined list of use cases for AI and in the next phase we will talk about the feasibility of it and everything, until then have a good day. Bye bye.

    2 min
  2. Episode 10 - Four Phases of an AI project

    03/14/2023

    Episode 10 - Four Phases of an AI project

    Here we talk at a high level about the four phases of an AI project and some of the nuances around data and pilots.

    2 min
  3. Leveraging AI Capabilities to Achieve Business Outcomes

    03/12/2023

    Leveraging AI Capabilities to Achieve Business Outcomes

    Hello everyone, this is Rabi Jay here sharing my AI insights for today. In the last edition, we talked about the core capabilities that an organization can leverage to become world class. We looked at Netflix in particular, we also said that we need to follow a methodology or a recipe that helps to leverage those capabilities to achieve the company’s business outcomes. This methodology boils down to doing the following four things, which we discuss here.

    2 min

  4. 03/11/2023

    Enterprise AI Transformation Podcast - Intro

    Enterprise AI transformation is about making a business smarter by using AI and ML technologies to achieve business goals. Its goals remain the same as the goals of any business: reduce cost, increase revenue, or generate new business models through innovation. AI may have been a distant dream until just a few years ago, but not anymore. We now have a fantastic opportunity in our hands to transform how we operate our businesses. But we also know that most AI efforts do not even get into production for various reasons. In this newsletter, I hope to address the reasons why and provide solutions to bridge the gap between the world of prototypes and real-world AI. Incorporating AI and ML into our business processes to achieve results has become easier. After all, AI can now help us detect defects in manufacturing, translate documents, or even analyze the sentiments of our customers in a Twitter feed, all in real-time. And so much more! In this newsletter, we will discuss what it means when someone says their company will adopt AI or ML and explore the possibilities with AI. While it is true that AI may not be a silver bullet for all enterprise problems, it is also true that it can fundamentally change or even reinvent the business model when combined with other factors that I will be discussing in this new newsletter series in the next few weeks. We will also discuss things like why some companies are good at AI adoption, and some are not. I will also go through a step-by-step process that anyone can take to achieve an enterprise-wide AI transformation. Stay tuned! Disclaimer: All opinions are my own. I am speaking for myself only and do not reflect the views of my employer. It is the result of my research, and I hope those who want to learn, use, or drive AI transformation in their companies will find it useful.

    3 min
  • 4 Episodes

About

Enterprise AI transformation is about making a business smarter by using AI and ML technologies to achieve business goals. AI may have been a distant dream until just a few years ago, but not anymore. We now have a fantastic opportunity in our hands to transform how we operate our businesses. But we also know that most AI efforts do not even get into production for various reasons. In this podcast, I hope to address the reasons why and provide solutions to bridge the gap between the world of prototypes and real-world AI.

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