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Shifting from AI hype to practical, ethical, and sustainable implementation

Simon Osuji by Simon Osuji
September 4, 2024
in Artificial Intelligence
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Shifting from AI hype to practical, ethical, and sustainable implementation
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Regardless of the hype cycle, AI is no longer a distant dream but a tangible reality. For decades, way before generative AI, this technology played a critical role in transforming industries and reshaping the way we do business. However, amid the buzz and excitement, shifting your focus from the hype to the practical implementation of AI is critical for any successful deployment of this technology within your projects.

The following is a short excerpt summarising a segment of an episode from the AI Geeks Podcast between the veteran, serial technology entrepreneur (Silicon Valley, Europe and Asia) Henry Nash, and the London-based serial tech entrepreneur Tim El-Sheikh (AI Geeks founder and host):

Understanding the real cost of AI

The debate often revolves around the integration of AI into our environment and the tools required for it. While these aspects are indeed important, the long-term cost of running and maintaining AI in production is a crucial factor that will determine the success of your AI strategy. 

The real winners in this AI race will be those who can strike the best balance between accuracy, trustworthiness, and cost-effectiveness while maintaining an optimal aperture for the flow of information.

From science experiments to real-world applications

The fascination with large language models (LLMs) and their capabilities often leads to overlooking the real-world applications of AI. Teams should focus on how they can use AI to perform valuable work and accelerate businesses by augmenting their people and processes. The hype cycles around AI often present it as a magic solution that will replace all existing systems and processes, which is far from reality. 

The real challenge lies in making AI practical and achieving a reasonable return on investment (ROI). In simple terms, you should treat AI as a journey, not as a product. It’s nothing like building a website or an app, it’s much more complex in most scenarios and, thus, costs significantly more to deploy and manage.

The future of AI is ethical implementation and skill augmentation of people, not replacement

The typical, “tech-bro” AI hype often revolves around the idea of replacing workers. However, as mentioned above, the real value of AI lies in augmenting human capabilities, not replacing them.

For instance, in the HR sector, AI can handle the mundane questions that HR personnel get asked every day, freeing them up to focus on more strategic HR management and worker welfare. This approach not only enhances productivity but also respects the value of human roles and expertise, as demonstrated by Tim’s company Nebuli.com and their Nano for Teams enterprise generative AI ecosystem for HR and other departments.

To achieve a reasonable ROI, your deployment must involve identifying and pinpointing the right areas where AI can help and putting your efforts into those. The true value of AI is achieved by building an ecosystem that helps people (i.e. employees, decision-makers, and end users) become better, happier, more productive, and more data-driven, empowered by a culture of collective innovation, creativity, and agility. It is about human-machine intelligence, not just machine intelligence!

Listen to the full, two-hour discussion covering the above point in more detail, with a segment about quantum computing as the potential upcoming star of the tech world. You can listen for free via AIGeeks.com.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.
Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, artificial intelligence, ethics, large language models, llm, Society



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