Exploring the Power of Generative AI for Enterprises - Application and Use Cases

5 minute read
Updated On | May 11, 2024 2:14:36 PM
Publish On | Sep 4, 2023 1:55:55 PM


It may be less than a year since Generative AI went mainstream with the launch of ChatGPT, but its ability to transform entire industries is already becoming apparent. 

The capability to create and generate text, images, audio and video technologically, through algorithms, machine learning and Artificial Intelligence (AI), is already causing a rethink of business processes that have been set in stone for generations.

For example, in 2022, just 2% of outbound marketing messages from large organisations were generated by AI, showcasing one of the Generative AI use cases. This figure is expected to reach 30% by 2025. Similarly, AI has barely been used in the film industry to date. It’s expected that by the end of the decade, a major film that is 90% AI-generated will be released.

It’s no surprise, therefore, that organisations around the world are actively seeking out the most beneficial ways to deploy this technology. According to the IBM Institute for Business Value, three-quarters of CEOs believe that organisations with the best Generative AI tools pose a significant competitive advantage. But what does this look like in practice, and what are the real-world use cases of Generative AI? This blog explores them in detail.ciklum_blog_generative_ai_for_enterprises_imagery_01

Understanding Generative AI technology

Generative AI models are generally built using several different technologies. Together they all contribute to the generation of diverse, unique assets. These include advanced neural networks like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The neural networks are made up of small, adjustable connections that work together to perform specific tasks. Large language models (LLM), on the other hand, read and understand written content, and use it as the basis for new content.

The technology has now progressed to such an extent that applications of Generative AI can now produce high-quality outputs suitable for professional use. This means that its value to businesses looking for cost savings and efficiencies in their traditional processes is continuously increasing. This alone demonstrates the practicality and potential of Generative AI use cases.

Popular applications of Generative AI 

Already, Generative AI is being used in a vast range of real-world applications and impacting daily business operations. These include (and are not necessarily limited to):

  • Analysing and understanding data sources and training data, making accurate decisions at speed
  • Seamlessly generating realistic images and text - including written content to a pre-defined length and style, and manipulating existing text to improve its tone or professionalism
  • Summarising existing written content, from articles and web pages to emails and conversation transcripts, to make it more digestible
  • Answering user questions through informative and engaging interactions with chatbots
  • Classifying content based on various criteria to enhance organisation and user experience
  • Generating, translating and verifying code to a high-quality

Generative AI use cases across industries

On a more practical level, there is an endless list of possibilities and use cases for Gen AI. When it comes to industries some are more prone to support their processes with the help of Generative AI. We will look at some of the most popular industry-specific use cases of Gen AI: 

ciklum_blog_generative_ai_for_enterprises_icon_01_finance (1) Finance

Generative AI is already playing a huge part in the finance industry supporting better performance in many vital but data-heavy processes. For example, Generative AI tools can:

  • spot fraudulent transactions by identifying anomalies in spending patterns;
  • assess credit worthiness using income and employment data; 
  • manage operational and financial risk by assessing historical data and patterns;
  • improve the efficiency of data entry, trading and pricing strategies.

ciklum_blog_generative_ai_for_enterprises_icon_02_insurance (1) Insurance

Many of the use cases listed above for the finance industry are equally useful for insurance businesses. For example, AI insights can:

  • point to areas where there may be fraudulent activity; 
  • to help inform decision-making that minimises risk;
  • and to suggest potential pricing strategies for insurance products for maximum profitability.

ciklum_blog_generative_ai_for_enterprises_icon_03_retail Retail

According to a Gartner survey, 38% of executives believe that Gen AI investments can primarily support them with customer experience and retention. The ability of Generative AI to create content means that marketing operations can be made faster and more efficient. 

Generative AI tools can work out the types of messaging that would resonate best with an individual customer. The tools can then create the content that can be distributed to them. 

 ciklum_blog_generative_ai_for_enterprises_icon_04_igaming iGaming

Generative AI is already being used by iGaming providers to create synthetic voiceovers and visuals for their gaming platforms. Additionally, the marketing opportunities that Generative AI can support are similar to those for the retail industry. 

Content and communications personalised to individual players can be generated and sent out, far beyond the capability of human marketing teams.

ciklum_blog_generative_ai_for_enterprises_icon_05_manufacturing Manufacturing

Every machine in a manufacturing setting generates data on its performance and operations. Generative AI can use that data to enable better decision–making about those machines.

For instance, it can detect issues early, allowing employees to address them before they impact reliability. Production data can also be used to generate insights for greater efficiency and lower costs.

ciklum_blog_generative_ai_for_enterprises_icon_06_healthcare Healthcare

AI's fast and insightful analysis is improving patient outcomes and ultimately helping to save lives. It can detect anomalies in health results and metrics, and spot issues and major medical problems.

Generative AI is also being used to accelerate drug development by analysing research data and recommending personalised treatments for patients.

In summary: what does the future hold for Generative AI?

The future potential of Generative AI use cases in the months and years ahead is enormous. As the technology continues to grow in capability, even more processes can be expedited and even replaced. Especially those that used to rely on human endeavour, and were expensive and time-consuming for the business. 

There will naturally be a global conversation about the ethics of AI and where its use perhaps should be limited. Whatever is decided by regulators, will play a major role in all industries for a long time to come. 

As this blog demonstrates, whether you want to solve key business problems, innovate to support better agility, or create content more quickly, there’s bound to be a Generative AI tool that suits your needs. All you need to do is work out what it is.

As a global expert in Generative AI, Ciklum can help assess your business challenges, work out how AI can help, and apply the right technologies for maximum success. Get in touch with us today to discuss your specifics, and how we can support you with our worldwide team of more than 4000 developers and IT leaders.

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