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Saturday, July 20, 2024

Identifying the True Competitive Advantage of Generative AI Co-Pilots

July 20, 2024

In the context of the widespread application of generative AI, many organizations are experimenting with this technology in an attempt to gain a competitive edge. However, most of these initiatives have not yielded the desired results. This article will explore how to correctly utilize generative AI co-pilot tools to achieve a genuine competitive advantage in specific fields.

Current Application of Generative AI in Organizations

Generative AI has attracted significant interest from enterprises due to its ease of use and broad application prospects. For example, a bank purchased tens of thousands of GitHub Copilot licenses but has made slow progress due to a lack of understanding of how to collaborate with this technology. Similarly, many companies have tried to integrate generative AI into their customer service capabilities, but since customer service is not a core business function for most companies, these efforts have not created a significant competitive advantage.

Pathways to Achieving Competitive Advantage

To achieve a competitive advantage, companies first need to understand the three roles of generative AI users: "acceptors," "shapers," and "makers." Since the maker approach is too costly for most companies, they should focus on the sweet spot of improving productivity with off-the-shelf models (acceptors) while developing their own applications (shapers).

The near-term value of generative AI is largely related to its ability to help people perform their current tasks better. For example, generative AI tools can act as co-pilots, working alongside employees to create initial code blocks or draft requests for new parts for field maintenance workers to review and submit. Companies should focus on areas where co-pilot technology can have the greatest impact on their priority projects.

Examples and Application Areas of Co-Pilots

Some industrial companies have identified maintenance as a critical area of their business. Reviewing maintenance reports and spending time with frontline workers can help determine where AI co-pilots can make a significant impact, such as quickly and early identifying equipment failures. Generative AI co-pilots can also help identify the root causes of truck failures and recommend solutions faster than usual, while serving as a continuous source of best practices or standard operating procedures.

Challenges and Solutions

The main challenge of generative AI co-pilots lies in how to generate revenue from productivity gains. For example, in the case of a customer service center, companies can achieve real financial benefits by stopping new hiring and utilizing natural attrition. Therefore, defining a plan to generate revenue from productivity gains from the outset is crucial for capturing value.

Generative AI co-pilot tools can significantly improve productivity in specific fields, but to achieve a true competitive advantage, companies need to clearly define their application scenarios and develop corresponding revenue plans. By effectively utilizing generative AI, companies can create unique competitive advantages in key business areas.

TAGS:

Generative AI co-pilots, AI competitive advantage, AI in customer service, GitHub Copilot integration, productivity gains with AI, AI in maintenance, generative AI applications, AI tool adoption strategies, business productivity improvement, revenue generation from AI