NTT’s CFO Sheds Light on Enterprise Generative AI Challenges, Energy Consumption, and Pricing

Artificial Intelligence keeps improving. Many businesses are looking to integrate generative AI models into their systems to increase efficient operations. However, with the rise of generative AI comes a host of challenges that organizations must navigate. In this guide, we’ll take you through what generative AI is, the challenges it poses, and how businesses can overcome these challenges to achieve successful adoption.

What is Generative AI?

Generative AI is a type of Artificial Intelligence that’s capable of creating new, original content, such as images, music, and text, without human input. It’s achieved through the use of deep learning neural networks, which can learn to create realistic content based on patterns in pre-existing data.

Generative AI has a wide range of applications, from creating art to improving customer service experiences. It’s also being used in research and development, as well as in the creation of new products.

The Challenges of Generative AI Adoption

Despite the many benefits of generative AI, its adoption poses several challenges to organizations. Some of these challenges include:

Energy Consumption

One of the main challenges of generative AI is its high energy consumption. The training of deep learning models requires a vast amount of computing power, which can lead to high energy costs. As organizations look to integrate generative AI into their operations, they must find ways to reduce their energy consumption to remain profitable.

Value-Based Pricing

Another challenge of generative AI adoption is the complexity of pricing. As generative AI is relatively new, it can be challenging to determine its value to an organization accurately. This lack of clarity makes it difficult to establish value-based pricing, which is crucial for profitability.

Ethical Concerns

Generative AI also raises ethical concerns, such as the potential for bias in the data sets used to train AI models. In addition, generative AI can be used to create fake news and other forms of disinformation, which can have serious consequences. Organizations must develop ethical guidelines to ensure the responsible use of generative AI.

Edge-Based Computing

Generative AI models require large amounts of data processing, which can create latency issues when working with cloud-based systems. Edge-based computing is a solution that brings data processing closer to the source of data, reducing latency issues. However, edge-based computing also poses challenges, such as the need for powerful computing resources at the edge.

Complexity of Implementation

Finally, implementing generative AI models can be complex and require specialized knowledge. Organizations must have access to skilled data scientists and machine learning engineers to build and maintain generative AI models.

Overcoming Generative AI Challenges

While generative AI adoption poses several challenges to organizations, there are ways to overcome these challenges. Here are some strategies that businesses can use to achieve successful adoption of generative AI:

Prioritize Energy Efficiency

To reduce energy consumption, organizations can prioritize energy efficiency by investing in low-power hardware and software solutions. They can also design their data centers to optimize energy use, such as using renewable energy sources.

Establish Value-Based Pricing

Organizations can establish value-based pricing by conducting market research and analyzing the competition. They can also offer free trials to customers to gather feedback and refine their pricing strategy.

Develop Ethical Guidelines

To ensure the responsible use of generative AI, organizations must develop ethical guidelines. These guidelines should address issues such as bias in data sets, disinformation, and the protection of personal data.

Implement Edge-Based Computing

Organizations can implement edge-based computing by investing in powerful computing resources at the edge. They can also use cloud-based systems in conjunction with edge-based computing to achieve optimal performance.

Partner with Skilled Data Scientists and Machine Learning Engineers

To overcome the complexity of implementation, organizations can partner with skilled data scientists and machine learning engineers. These professionals can help build and maintain generative AI models, ensuring successful adoption.

Conclusion

Generative AI has the potential to revolutionize the way businesses operate. However, its adoption poses several challenges that organizations must navigate to achieve successful integration. By prioritizing energy efficiency, establishing value-based pricing, developing ethical guidelines, implementing edge-based computing, and partnering with skilled professionals, businesses can overcome these challenges and unlock the full potential of generative AI.

FAQ

What is generative AI?

Generative AI is a type of Artificial Intelligence that can create new, original content without human input.

What are the challenges of generative AI adoption?

Some of the challenges of generative AI adoption include energy consumption, value-based pricing, ethical concerns, edge-based computing, and complexity of implementation.

How can businesses overcome these challenges?

Businesses can overcome these challenges by prioritizing energy efficiency, establishing value-based pricing, developing ethical guidelines, implementing edge-based computing, and partnering with skilled professionals.

First reported on VentureBeat

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