Why generative AI is the next technological breakthrough

21 June 2024 Consultancy.eu 4 min. read

In the realm of technological breakthroughs, generative artificial intelligence (generative AI) is emerging as a defining paradigm shift. Tapas Mishra from Cognizant outline why this is the case and sheds light on the some of the most promising use cases in four sectors.

Change being the only constant is a well-worn cliché. Over time, and various hype-cycles, we’ve witnessed numerous technology breakthroughs – internet of things, robotics, robotics process automation and intelligent process automation, machine learning, artificial intelligence, blockchain, quantum computing, and more.

While some of these innovations have had significant consequences, generative AI stands apart.

Why generative AI is the next technological breakthrough

Unlike its predecessors, generative AI’s applications transcend conventional boundaries, promising unforeseen possibilities and reshaping our understanding of creativity and interaction between machines and humans.

Use cases in four sectors

Generative AI is often referred to as a “general-purpose technology”. This label signifies that it provides a broad capability that can be harnessed in multiple ways, with its applications evolving over time. Just as electricity initially defied full comprehension but revolutionized human existence, generative AI opens new frontiers in machine interaction intersecting with aspects (such as creativity) that humans traditionally consider uniquely their own.

The true value of generative AI lies not in semantics but in its capacity to reshape our world. A number of use cases highlighted:

Generative AI in banking
Generative AI is a revolutionary technology that can redefine all aspects of banking operations, providing banks with a competitive edge by delivering personalized services and boosting operational efficiency. This innovative technology allows banks to make insightful, data-driven decisions, manage risks effectively, and improve customer satisfaction.

A well-known use case is risk management. In this crucial area for banks, machine learning algorithms can swiftly analyze patterns in transactions, flagging suspicious activities in real-time. This significantly strengthens security measures and minimizes potential risk for both customers and the bank.

Generative AI can meanwhile help banks to stay compliant by continuously monitoring changes in regulations and swiftly adapting internal processes to ensure that they comply with new regulatory requirements.

Generative AI in manufacturing
Generative AI in manufacturing can offer fascinating benefits to industrial companies. A digital twin for instance can help represents the real-world environment in data form, which can serve as the foundation for analytics and optimization. A digital twin can reduce development costs and time to market by eliminating the need for physical prototypes.

Generative AI can also contribute to increased efficiency and streamlined operations, while freeing up time of staff to focus on more enjoyable and value-adding tasks.

Generative AI in retail
Generative AI is transforming the retail industry in ways we never thought possible. One example is the customer experience domain. Generative AI can help analyze current market trends, consumer preferences, and historic sales data to create new product designs.

Retailers can also use generative AI to create virtual photoshoots, which can save time and money compared to traditional photoshoots, as well as automate and enhance customer service.

Other key use cases include process improvement, knowledge enhancement, and innovation.

Generative AI in life sciences
The field of life sciences has witnessed remarkable advancements in recent years, thanks to the integration of generative AI technologies.

One of the most significant applications is in drug development. With generative AI, the process of drug development becomes swift and efficient. It’s like having a supercomputer in a lab coat, predicting new compounds with medicinal properties. This is a significant departure from traditional methods of drug development, which are often slow, expensive, and fraught with failure.

Generative AI is also helping to unravel the mysteries of the human body, diseases, and genetic variants. It’s like having a magnifying glass that can see patterns and links that might elude even the most experienced researchers. This ability to analyze vast amounts of data and identify patterns is paving the way for personalized healthcare. By tailoring therapies to an individual’s genetic makeup, we can provide more effective and targeted treatment options.