Is Generative AI Worth The Hype?

Sri Gari

Feb 5, 2024

AI Hype

We’ve all heard enough about generative AI (gen AI). There are news headlines that describe gen AI as the new electricity with more profound impact than the internet revolution. 

On one side, gen AI is being used to help with support chatbots, company wide search, and data analysis. But there are also headlines about “50 art fails that are both horrifying and hilarious”, "Generative AI fails in common sense!", or “AI creates violent imagery!"

In the world of IT and business, generative AI is like that one teammate who's always full of surprises. You ask for a sales report, and it gives you a colorful pie chart... of actual pies. It's trying hard, sure, but sometimes you can't help but chuckle. It's like that friend who's a genius but also walks into a room and forgets why they’re there. 

For us in the business, every day with AI is a bit like rolling the dice. You might get something brilliant, or you might end up scratching your head, thinking, 'Well, that's one way to look at it!' But hey, it keeps things interesting, right?"

Now with all this gen AI overload, if you are an executive, business professional, or an IT team member, you might be wondering, should I really care about gen AI?

To answer that, first let’s try understanding gen AI in simple terms.

What is generative AI?

AI Methods

Generative AI represents a paradigm shift in the field of artificial intelligence, introducing a new era of content creation. Unlike traditional AI models that are designed for specific tasks, generative AI models can create new and original content with minimal fine tuning. 

As represented in the image above, gen AI is a subset of the broader AI umbrella. Other tools in AI, such as supervised, unsupervised, and reinforcement learning models are trained on large volumes of specific examples for specific use cases. 

On the other hand, Gen AI models are trained on vast general datasets and leverage advanced techniques to discern the underlying patterns and structures within the data. This enables them to generate highly realistic and unique content that often rivals human-generated creations.

Gen AI has the potential to generate new text, code, audio, images, video from existing information. Gen AI could be leveraged to provide contextual answers to common search queries, personalized to the user. For example, a 5 year old asking the question would get a different answer than a domain expert asking the same question. 

People are using Gen AI to create marketing copy, artistic images, workflow automations, contextual suggestions, data analysis, and even feature films. Did you know that RunwayML uses gen AI to create feature films? They even have awards for AI generated films 🙂

The potential of generative AI is boundless, promising to revolutionize industries and redefine the boundaries of creativity and innovation. Gen AI can alleviate the burden of repetitive tasks, allowing professionals to focus on more strategic and creative endeavors. Moreover, it can serve as a valuable tool for enhancing creativity, providing fresh perspectives, and inspiring new ideas. 

Now that we have understood Gen AI, you might still be wondering, so what? We have been hearing about AI for decades, what’s so different about gen AI?

That’s a good question, let’s understand the difference with a specific example.

Why is Gen AI different?

Gen AI Differentiation

Let’s take a look at the above image and walk through the progression of building a customer support chatbot. You want to build an empathetic chatbot that responds differently when the user is expressing frustration.

Prior to AI, you’d have to configure a strict rule using an if and else programmatic structure to make the bot respond in an empathetic tone. As an example, you’d need to add words that are synonymous for frustration, like “disappointed”, “bad” etc, but, if the user uses a different word not in the rule, then the AI fails to respond in the right tone.

Next with the advent of machine learning (ML), techniques like sentiment analysis can be leveraged. With ML, you still need to add a rule, but this time, you can just pick a sentiment like unhappy, and then make AI respond in an empathetic way. But still there are 100s of programmatic rules + machine learning techniques that you’d need to configure to make the chatbot behave in a specific way.

Now with gen AI, instead of configuring 100s of rules, or using specific ML techniques, you can just add a prompt like “you are an empathetic AI assistant that answers customer questions in a friendly manner” and voila! your chatbot responds in an empathetic tone.

So using gen AI you can get close to a human-like answer for certain use cases, without elaborate rules and ML training techniques. What’s different is that you can leverage gen AI to increase efficiency and productivity across multiple business functions like sales, support, marketing, etc, without a ton of band-aid like rules and years worth of machine learning training.

That said, you might say gen AI could work for simple use cases, but I have more complex ones. Should I even leverage gen AI as it seems to be early, why don’t I wait until the dust settles?

What if I don’t leverage Gen AI?

Internet History

Yes, gen AI is not a silver bullet for all your problems. And it’s still nascent, and you need to pick the right use cases. And we even started this article by poking fun at gen AI.

But there is no denial that companies that are using Gen AI are reaping benefits. Employees using AI assistants finish tasks 25% faster, 72% spend less mental effort on mundane or repetitive tasks, and Forrester has predicted that 60% of workers will use personalized AI for their jobs by the end of 2025.

Most importantly, remember the beginning of the internet era? Blockbuster thought Netflix was a joke, and similarly Barnes and Noble thought Amazon was a joke. Both Netflix and Amazon successfully leveraged the internet to reduce costs, increase revenue, and boost productivity.

Gen AI today is still early, but it is progressing at a rapid pace. There are 420K+ gen AI models growing at 30% in the last 3 months. As generative AI continues to evolve at an unprecedented pace, it holds the key to unlocking new possibilities and driving innovation across industries.

And yes, some of us in the enterprise have to solve more fundamental problems before adopting gen AI. Yes, enterprises do have to keep the lights on. But imagine this, if a city only focuses on keeping the lights on, they won’t see the incoming big storm that will wash away the entire city.

Gen AI Storm

Embracing this transformative technology will be crucial for enterprises seeking to thrive in the rapidly changing landscape of the digital age. Otherwise there is a high likelihood that companies that do not adopt gen AI will be at a competitive disadvantage and be left behind.


In conclusion, Enterprises need to embrace a proactive approach. This involves fostering a culture of innovation, investing in the necessary infrastructure, and up skilling the workforce to effectively utilize these advanced technologies. 

By integrating generative AI into their operations, enterprises can unlock new possibilities, optimize processes, and drive business success in the digital age. Moreover, enterprises should closely monitor industry trends and developments in generative AI to remain agile and adaptable.

Generative AI is different from the other AI tools; it can generalize across multiple use cases. Gen AI is still new, not a silver bullet, but progressing at a rapid pace. So enterprises need to understand where gen AI can be applied effectively. Leveraged correctly, it can be used to skyrocket operational efficiency and team productivity.

Now, you ask what use cases should we at enterprises be focused on? That will be the topic of my next article, where to apply and where not to apply gen AI. If you are already using gen AI in production, what are the use cases where your company has effectively integrated Gen AI tech?