Industries the world over are gaining a deeper understanding of the power, reach and capabilities of generative AI. In this article, we assess its impact on businesses and how it is poised to transform roles and boost performance across business functions, customer operations and software development.

What is generative AI?

Unlike narrow AI, generative AI (or GenAI), a subset of artificial intelligence (AI), stands out for its ability to create new content by mimicking human-like creativity.

Voice assistants like Siri or Alexa are a good example of narrow AI. They can play music on command, but that music must already have been written. Google’s Lyria system, on the other hand, uses generative AI to generate vocals, lyrics and backing tracks in the style of popular artists.

So, where narrow AI focuses on a specific predefined task or a limited set of tasks, GenAI possesses a broader scope, making it versatile in various applications. It can produce realistic images, generate human-like text and even create music.

Current business applications

Generative AI's impact extends across diverse industries and has already come a long way in the automotive and design fields.

In the automotive sector, GenAI revolutionizes vehicle design and manufacturing. By analyzing vast datasets, it assists in creating optimal designs for components, improving aerodynamics and even optimizing fuel efficiency.

Automotive engineers leverage GenAI to explore numerous design possibilities, leading to the development of safer, more fuel-efficient and aesthetically pleasing vehicles. Additionally, GenAI plays a pivotal role in simulation testing, enabling engineers to assess the performance of various design iterations in virtual environments before physical prototyping.

In the design field, GenAI is a game changer. Graphic designers and artists use it to generate unique visual elements, fonts and layouts. For architects, GenAI assists in creating innovative building designs based on specific criteria such as sustainability, functionality and aesthetics. By inputting parameters and constraints, architects can explore an extensive array of design possibilities, optimizing the creative process. This application of GenAI not only expedites design workflows but also fosters creativity and innovation.

These applications demonstrate the versatility of GenAI and show how it goes beyond routine tasks, unlocking novel possibilities and transforming traditional industries. As technology advances, its applications in automotive design and beyond are likely to further redefine the boundaries of innovation and efficiency.

Customer experience and interaction

Generative AI transforms customer interactions by personalizing experiences. Organizations use it to analyze user preferences and tailor recommendations accordingly, creating a more engaging and relevant customer journey.

For example, in ecommerce, GenAI can analyze a customer’s past purchase history, browsing behavior and demographic information to generate personalized product recommendations. This level of personalization extends beyond product suggestions to tailor the content, advertisements and promotional messages customers see, in line with their individual preferences and interests.

This nuanced approach to customer interaction enhances engagement and fosters a deeper connection between businesses and their customers, ultimately contributing to increased customer satisfaction and loyalty.

Weighing the costs and benefits of GenAI

The current hurdle in GenAI adoption lies in the significant initial costs associated with acquiring specialized talent, advanced infrastructure and high-performance software. Additionally, organizations might incur costs related to data acquisition, cleaning and storage, as GenAI heavily relies on extensive and high-quality datasets.

However, as the technology advances and becomes more mainstream, increased competition among technology providers and a growing pool of AI specialists may drive costs down. The cost of acquiring and retaining skilled personnel could reduce, while advancements in cloud computing services and the availability of more cost-effective AI solutions could make infrastructure costs more manageable for organizations of various sizes.

And, as GenAI becomes more prevalent, economies of scale may come into play. Widespread adoption could lead to the development of standardized solutions, lowering the costs associated with technology acquisition and implementation. Standardization could also facilitate easier integration with existing systems, reducing the complexity and cost of deployment.

As GenAI becomes an integral part of business strategies, organizations can expect increased efficiency, improved customer satisfaction and a competitive edge in their respective industries.

Integration with existing systems

The integration of GenAI into existing systems varies depending on the complexity of operations. Although advancements in technology are making this integration more seamless, it is crucial for organizations to carefully plan the integration process. For a smooth transition, factors like data compatibility, system architecture and employee training must be considered.

A partner like NTT DATA can offer robust infrastructure support. Leveraging our cutting-edge solutions such as Private 5G and Edge as a Service, we can enhance the integration process. Private 5G, with its secure and dedicated network, provides the low-latency, high-throughput environment essential for GenAI operations. This technology ensures a reliable and fast connection, facilitating the real-time data processing and analysis that are critical to AI applications.

Moreover, our Edge as a Service solutions contribute to the efficiency of GenAI by bringing computing resources closer to the data source. This reduces latency and enhances the overall performance of AI algorithms. It enables organizations to leverage the power of GenAI in a decentralized manner, optimizing the distribution of computing resources across their infrastructure.

Challenges in the regulatory landscape and other concerns

Implementing GenAI comes with challenges such as ethical concerns, data privacy issues and the potential for biased outcomes. Organizations need to address these concerns proactively by establishing ethical guidelines, ensuring transparent data practices and regularly auditing AI algorithms. Employee training and awareness programs can mitigate challenges related to workforce adoption.

As GenAI gains prominence, regulatory bodies are also focusing on defining frameworks to govern its use. Privacy regulations, ethical guidelines and industry-specific standards are emerging to ensure responsible AI deployment. Staying informed about these regulations is crucial for businesses to navigate the evolving legal landscape.

Future trends for business operations

The future promises deeper collaboration between humans and AI, making AI decisions more understandable. Moreover, the fusion of GenAI with emerging technologies like augmented reality (AR) and virtual reality (VR) will reshape the business landscape. As AI continues to advance, organizations can anticipate more advanced applications and increased operational efficiency.

GenAI presents immense opportunities for innovation, personalized customer experiences and streamlined operations. However, organizations must act now to prepare for this revolution by assessing and upgrading their infrastructure, leveraging technologies like private 5G and edge computing.

By doing so, they can proactively position themselves to extract maximum value from GenAI in a rapidly advancing digital landscape. While challenges exist, including ethical considerations and regulatory changes, careful preparation will ensure that organizations can harness the full potential of GenAI.


Learn more about NTT DATA’s Edge as a Service to see how we can help you deliver new business outcomes.