Economic opportunities and challenges of Generative AI

Economic Opportunities and Challenges of Generative AI

Generative AI is a step ahead in the evolution of Artificial Intelligence transforming the business landscape. Be it composing music, investment management, or designing graphics, AI has the potential to perform these tasks. Generative AI has a major potential to contribute across various sectors of the economy.

Opportunities for Generative AI

It has been found that Generative AI has diverse opportunities across four key areas including:

Customer operations

Generative AI has diverse functions in customer operations that improve the experience of the customers. It has increased the productivity of agents through AI assistants to enhance their agents’ skills. AI generative has provided services to customers by automating interactions with the customers.

Here are a few cases where Generative AI has done operational improvement:

  • Customer self-service: Generative AI chatbots inquire about customer queries and provide personalized responses to them. This has improved the quality of interactions with customers and has enabled the customer teams to resolve queries that can be resolved only through human agents.
  • Reduced response time: Generative AI can reduce the time consumption that the human sales representative spends on responding to customers by assisting them in real time.
  • Increased sales: Generative AI processes the data of customers and tries to find out the customer preferences as per their browsing histories. By gathering insights from customer information, Generative AI helps to enhance the quality of products and services.

Marketing and sales

Generative AI can generate content with different specifications that increase customer value and help retain customers at a higher scale as compared to traditional marketing techniques used. The use of Generative AI in marketing can help to overcome the problem of different datasets that involve inconsistent, unstructured, and disconnected data by interpreting the abstract data sources of varying structures. This will help to synthesize the customer feedback and customer behavior to generate marketing strategies for target customers. These can be used to synthesize trends from unstructured data in social media, academic research, and customer feedback.

Potential operational benefits from using generative AI for marketing include the following:

  • Efficient and effective content creation: Generative AI facilitates consistency from the process of ideation of content to the final stage of content drafting. It unlocks a uniform voice and writing style that signifies the brand thus reducing the time required in the process. This enhances the personalization of marketing messages for different customer segments.
  • SEO optimization: Generative AI can help to optimize SEO and sales technical components used as marketing techniques to increase sales.
  • Product discovery and search personalization: By browsing customer histories, Generative AI can leverage customer preference to generate the relevant product and provide personalized descriptions of the product. This enables the retail and travel organizations to improve their e-commerce sales.

Software engineering:

Software engineers can use generative AI for augmented coding and use natural language for Large Language Models (LLM) to develop different applications. With Generative AI the scope of software engineers has expanded making the machine language convenient for them. Information Technology is a significant department in every organization and has been growing in a massive scale.

Potential operational benefits from using generative AI for software engineering include:

Increase in product value: Be it a gadget or automobile, the use of Generative AI has increased the value of a product by upgrading and enhancing its product features. For example: In vehicles, digital features such as parking assistance and adaptive cruise control increase the value of the product.

Product R&D:

Generative AI has the potential to generate generative design techniques in product research and development. Foundational models along with Generative AI can have a wider scale of applications in product research and development. These can increase the number of products where the generative design can be applied. As of now, the foundation models lack the capabilities to design products across various industries.

Potential operational benefits from using generative AI for product R&D include:

  • Enhanced design: Generative AI helps in designing the product by efficient selection and use of materials.
  • Improved product testing and quality: Using Generative AI in generative design, the quality of the product can be enhanced. Generative AI can also accelerate the testing time of complex products.

Challenges of Generative AI

Along with the huge opportunities, Generative AI is not without challenges. Here we have enlisted a few challenges of Generative AI:

Ethical Considerations

One of the challenges that Generative AI may face is addressing ethical considerations. It is important to ensure that AI follows ethical guidelines and does not generate offensive material. Developers must work to prevent data bias which will lead to transparency and fairness in the Generative AI systems.

Computational Resources

The computational resources required for deploying large generative models is one for a major challenge for smaller firms. Training these models requires excessive resources as powerful hardware and a large computational infrastructure.

Security Concerns

As these models can be vulnerable to adversarial attacks, security concern is a major challenge. Adversarial actors may exploit the models to manipulate the result, leading to misleading content.
With technological advancements and regulatory guidance, Generative AI has been growing responsibly and innovatively. Generative AI is poised to redefine our lives not just on a professional level but also from a personal perspective in the future.




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