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Introduction

The concept of Generative AI is designed to be intuitive and simple. This refers to any AI platform that generates some output in a variety of formats – text, data, 3D creations, code, etc. Combined with Large Language Models (LLMs), Generative AI has become the all-new buzz word for enterprises and professionals alike.

In fact, ever since OpenAI launched its Generative AI program ChatGPT, it has become almost impossible to ignore the buzz around this form of AI. The reason why this form of AI is gaining rapid popularity is that it is enables users using natural languages to “ask” the engines for any kind of content and get the output they need. This is opposed to previous forms of AI that were mostly used for data analysis and automation purposes.

The Generative AI market is predicted to grow at a CAGR of about 36% by 2032. In fact, venture capital firms have already invested more than USD 1.7 billion into Generative AI solutions since 2020. With the constant expansion of Generative AI’s capabilities and consecutive launches of startups based on it, it is imperative to analyze its practicality.

Here are six real applications of Generative AI as seen in enterprises.

Real Applications of Generative AI in Enterprises

Generative AI in Programming Automation

Generative AI tools by Amazon as well as ones like Copilot, have found immense acceptance among developers who want a productivity boost while coding. Generative AI helps in all three stages of creating a code, namely:

  • Code Generation
  • Code Completion
  • Code Review

The 50% reduction in time required for code generation has diminished the need to involve a large team of programmers and testers. This has helped simplify the process. LLMs, or Large Language Models, exhibit accuracy in a wide variety of languages, including Machine Language. Generative AI has been used to create multiple RPA tools that can be programmed using natural language. Thus, automation of a particular task or workflow has become significantly user-friendly and time-saving.

Coming down to the technicalities, Generative AI analyzes a large dataset of codes, trains itself from the same, and creates a neural network that generates codes based on the syntax of the code examples it has studied. It also fixes bugs, automates code refactoring, and suggests code completions to developers as they type.

Language-learning app Duolingo’s efficiency is reported to have increased by 25% with the use of Generative AI through Copliot, as per the Wall Street Journal. This efficiency boost was possible because of the time and energy saved by not having to worry about code documentation and searching for information.

Generative AI in Customer Support

Generative AI helps in creating Conversational AI models that generate human-like and accurate output in response to natural input, which in turn helps automate customer support. Conversational AI models can grasp user queries and requests and respond accordingly. Chatbots, a product of generative AI, make sure that customer support is available to consumers 24×7, even when it is not possible to manually deal with every query.

Additionally, conversational interfaces like virtual assistants and voice assistants help provide highly informed and accurate services with the needed human touch that enhances the overall customer experience.

Other services like automated emails and self-service portals for personalized suggestions have also gained immense popularity. Automated emails help respond to customers who reach out via email, and self-service portals provide customized recommendations to users based on their query history.

Generative AI in Business Insights

With regard to business insights, the questions asked by business individuals would vary widely from those asked by data scientists. A business user is more adept at determining what information is needed regarding the nuances of business. A data scientist, on the other hand, analyzes the code and programs needed to retrieve answers to those queries.

Generative AI allows business individuals to ask questions in natural language. It then translates this into Structured Query Language queries, runs it through its internal database, and provides an output in the form of a structured narrative almost immediately. Thus, this removes the need to resort to a data scientist for every query a business individual might have, along with making the process significantly more efficient and time-saving.

Generative AI for Marketing Automation

Generative AI has the capability to study and analyze large datasets  to identify the different patterns in consumer behavior, which in turn helps businesses. Enterprises can, with the help of generative AI, develop digital marketing campaigns with high-performing and SEO-friendly keywords and relevant phrases. AI tools help in creating quality SEO content by conducting keyword research, generating unique content topics, and narrowing down on the target audience.

Further, B2B Marketers have widely implemented generative AI for the automation of marketing messages and prompt emails sent to existing and potential users. It has also facilitated the testing of marketing campaigns by contesting different forms of content to determine which performs best.

Generative AI for Data 

Synthetic data refers to the type of data generated by AI that can be used instead of real data to train various machine learning models. Statistically, it is quite similar to real data but is actually not the same. Synthetic data helps create synthetic data assets that can be used instead of actual customer data, ensuring their safety.

Synthetic data is used as feed for generative AI’s natural language processor so that it can generate more human-like responses. In essence, synthetic data helps maintain the privacy of real data by using safely made copies for training ML models.

Conclusion

With the overwhelming increase in the use of generative AI technologies like GPT-4 and DALL-E 2, it is evident that generative AI is here to revolutionize the way enterprises function. As enterprises become more and more reliant on it to accelerate innovation and productivity, it is imperative to consult with experts in the field to create differentiated products and experiences.

At Pratiti Technologies, we help businesses with end-to-end software development while stressing their unique requirements. Ranging from new product development to enterprise technology and staff augmentation, Pratiti Tech is here to take your enterprise to the next level of innovation and creativity. Contact us to learn more.

Nitin
Nitin Tappe

After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

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