Exploring the Impact of Generative AI: Current applications, future prospects, and challenges

Much of the vaunted push towards an autonomous end goal will not truly start until more enterprises start adapting AI en masse. (Representational Image).

There has been a lot of talk lately about Generative AI and how far it is from being assimilated into our daily lives.

We have chatbots like ChatGPT, or Copilot, image generators like DALL-E and Stable Diffusion, even music generators but are these enough to say that they are truly making a difference to our lives?

Much of the vaunted push towards an autonomous end goal will not truly start until more enterprises start adapting AI en masse, when AIs expand their boundaries into being more than just conversation starters.

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In a recent poll conducted by Gartner, more than 2,500 executives identified potential scenarios where Generative AI can prove useful. Thirty eight percent said that customer experience/retention was the primary purpose of their AI investments, 26 percent said it will help grow their revenue, 17 percent will use it for cost optimization, seven percent voted for business continuity and 12 percent voted for none of the above, or felt they didn't really need Generative AI.

With the technology we have currently, this makes sense. Natural language models are good at answering questions and writing 'draft' content that can significantly speed up development processes.

They can also be used to summarize content, or change tone of a specific piece of written content. For coders, Generative AI can help speed up the process the exponentially with quick code generation, translation or verification.

In the long term, the medical field provides an exciting opportunity for Generative AI, in things such as predictive imaging that can roadmaps of a disease's progress over time. It can also help collate and sort the vast amounts of unused medical data, making diagnosis more efficient.

In the medical field, this may also help development of new drugs by speeding up the process. These can extend to other fields such as fragrances for perfumes, or new alloys in construction.

According to Gartner, more than 30 percent of new drugs and materials will be discovered using Generative AI techniques by 2025, and 30 percent of outbound corporate messaging will be dictated by AI.

There are also various applications in the fields of automotive, aerospace and defense industries where Generative AI can help speed up the development pipeline and make it more efficient, by helping out with drafts and initial designs.

Gartner says that by 2024, 40 percent of enterprise applications will have AI embedded in form or the other. By 2025, 30 percent of enterprises will have AI-augmented development and testing.

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In 2026, Generative AI will automate 60 percent of all design on websites and mobile apps and by 2027, nearly 15 percent of all new applications will be automatically generated by AI without the need for a human.

The Golden egg, however, is AGI or Artificial General Intelligence, also known as Strong AI. The models we have currently are trained to do one task well, for example - ChatGPT is a chatbot, DALL-E is an image generator and so on.

Artificial General Intelligence, if it ever happens, will be good at nearly everything a human can do and they will be able to do it faster and more efficiently. AGI isn't quite here yet, and experts are divided on when it actually will be reality, some say that it may take twenty years, while others believe it's merely a few years away.