Generative AI in Marketing: Understanding with Examples

ChatGPT (OpenAI) has revolutionized the way we interact with artificial intelligence and made the technology more mainstream. Generative AI is expected to add trillions of dollars to the global economy with 75% of value coming from use cases in marketing, sales, software engineering, R&D, and customer ops. The survey reported that only 14% of organizations were using Gen AI for Marketing and Sales [4]. The best way to understand Gen AI in Marketing is to look at how some of the top companies leveraged this technology to stand out.

Generative AI Marketing Use Cases: Where Do You Want To Start?

Marketing has seen an explosion of innovation reshaping the field. Generative AI is one such game-changer technology that allows today’s marketers to stay ahead of the curve. It allows for innovative solutions to age-old problems by automating repetitive tasks, increasing scalability, personalizing interactions, and enhancing creativity. While 98% of executives said that Gen AI was a hot topic of discussion with their board, only 14% of organizations were using Gen AI for Marketing and Sales. I have highlighted the key Gen AI use cases in Marketing to get started.

Generative AI Marketing Tech Stack: An Explosion of Interest

Generative AI (Gen AI) Marketing Technology Stack. The number of large and small companies catering to the marketing tech stack had grown exponentially from around 150 in 2011 to 9900+ in 2022. This number has now exploded with the seemingly unlimited potential of Generative AI. ChatGPT (OpenAI) has revolutionized the way we interact with artificial intelligence and made the technology more mainstream.  Below are some of the companies that leverage Gen AI and cater to the marketing landscape

Generative AI in Marketing: Do the Benefits Outweigh the Risks?

What is generative AI? Generative AI (Gen AI) is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models learn the patterns and structure of their input training data and then generate new novel and unique data that has similar characteristics. Marketing has seen an explosion of innovation reshaping the field. Generative AI is one such game-changer technology that allows today’s marketers to stay ahead of the curve.

Docker vs Kubernetes. Can you compare them?

Interest in the relationship between Docker and Kubernetes has been extremely high. The average monthly search volume in the US alone for ‘Docker vs Kubernetes’ was 12000 searches [source: Google]. This was around 10% of searches for the ‘Kubernetes’ keyword. A very high ratio that indicates a level of confusion on this topic.

Understanding Containers and Kubernetes 101

Kubernetes is an open-source system for automating software deployment, scaling, and management of containerized applications. It has an expansive open-source ecosystem and is the market leader in this segment. All major players such as Google, Docker, Red Hat, Microsoft, AWS, Wind River and VMware have adopted/supported Kubernetes.

Differences Between Virtual Machines (VM) and Containers

As the application container and virtual machine market continue to grow, there has been a tremendous interest in how they differ, and which of them is the better approach for specific projects. The keyword ‘virtual machines vs containers’ was searched 2400 times (on average) per month in the US alone [source: Google]

Comparing Traditional Deployment vs Virtual Machine vs Containers

Virtual Machine provides an abstraction of the physical hardware. A Hypervisor allows multiple Virtual Machines (VM) to run on a single server. Each VM has a full copy of OS, app binaries, and libraries Pros: • Better utilization of resources than traditional methods • Applications are isolated Cons: • OS images are heavy (GB) and have a slow bootup process • Applications are not portable • Not Scalable • Can get expensive

Evolution of Containers: Past, Present, and Future

Containers are units of software that contains the code and all dependencies so that an application can run across platforms such as desktops, data centers, and cloud. It provides an abstraction at the application layer. Each container runs as an isolated process while sharing the same OS kernel.
Container adoption has grown rapidly, and much faster than expected.

Waterfall vs Agile vs DevOps SDLC Models

SDLC models have evolved over the years to meet customer and industry needs. Below table illustrates some of the differences between the three key models. Waterfall Model (1970) provided a linear sequential approach to managing software projects. Each phase depends on deliverables from the previous one. The sequence includes Requirement, Design, Development, Test, Deploy, and Maintenance. This model dominated for more than 2 decades