Shanghai News, March 1 (Reporter Jiang Yu) PricewaterhouseCoopers recently released the report "Are You Ready for the "Early Bird" Strategy of Generative AI", which aims to provide practical reference for enterprises to formulate generative AI strategies, help enterprises seize opportunities, and lay a solid foundation for success.
Victor Chow, PwC's Global Technology, ** and Telecommunications (TMT) Industry Leader and PwC's Chinese AI Leader, said: "Although generative AI is still in its early stages, the industry has been trying to keep up with the development and potential of this new technology since the launch of ChatGPT. According to a survey published by PwC, the vast majority of business leaders in APAC see generative AI as a catalyst for business transformation, which will drive efficiency, innovation and transformational change. By focusing on the leaders who are at the forefront of their early deployments, we've identified six key takeaways to help companies stay ahead of the curve with generative AI applications. ”
According to the report, there is a wrestling within companies, both globally and in Chinese mainland and Hong Kong: executives eager to leverage generative AI technology for competitive advantage, while technological, legal and other leaders are concerned about potential risks. Businesses can benefit greatly from dealing with such conflicts effectively, while at the other end they can lead to stagnation or reckless behaviour due to disagreements, resulting in significant potential costs. Looking at the Chinese mainland and Hong Kong markets, most companies are open and willing to embrace generative AI for a greater competitive advantage. Typically, they select small scenarios to pilot, validate results, and calculate ROI to inform executives in deciding whether to fully apply generative AI.
Weifeng Zhang, PwC China Financial Services Consulting Services Partner and AI Solutions Advisory Partner, said: "There are also challenges and complexities in the application of generative AI. For example, in the past year or so, large models in Chinese mainland have blossomed, which has not only accelerated the evolution of local large models, but also brought technical challenges to enterprises in selecting suitable large models. At the same time, the cost of computing power required to deploy large models is also an important factor restricting enterprises from taking the first step. ”
The rapid increase and growing adoption of generative AI capabilities has a significant impact on enterprises' digital transformation efforts. Since the primary output of generative AI is digital, such as digitized data, assets, and analytical insights, these outputs maximize their impact when applied to, and combined with, existing digital tools, tasks, environments, workflows, and datasets. If a generative AI strategy is fully integrated with a holistic digital strategy, it can bring significant benefits to businesses. On the other hand, given that generative AI and its distributed nature are easy to stimulate exploration, some experimental research that is detached from extensive work is also more likely to sprout and accelerate the creation of digital value.
Pilots of generative AI are significant. This technology is versatile and can be used in a variety of ways to meet a variety of needs. This versatility means that people who are familiar with the business may be the easiest to identify the best value for business use cases. As a result, centralized control over generative AI application development is likely to lead to the neglect of specialized use cases that can cumulatively deliver significant competitive advantage.
Scaling is critical for companies looking to take full advantage of the benefits of generative AI, but there are at least two challenges: first, the diversity of potential applications of generative AI often leads to extensive pilots, which are necessary to identify potential value, but can trigger a "1+1<2" effect; Second, the large-scale implementation of the pilot often requires a cross-departmental strategic organizational perspective, so the participation of senior leaders is indispensable.
Generative AI's ability to find relevant information, perform repetitive tasks quickly, and integrate with existing digital workflows means that this technology can improve efficiency and productivity across departments and businesses almost instantaneously. It has deepened many leaders' awareness and interest in AI synergies, and companies can use these productivity to do three things: reinvest in productivity, improve the quality, quantity, or speed of product delivery and service, and achieve more output with the same input in a broad sense; keep output unchanged and reduce labor input to reduce costs; Pursue a combination of both.
The right solution varies from industry to industry, business, or even department, and developing a productivity plan to clarify the answers to the questions "what time frame to solve" and "how to deal with employees who are eliminated by generative AI" are key.
The PwC survey found that about half of business leaders surveyed want generative AI to improve their ability to build trust with stakeholders, and about 60% want generative AI to improve the quality of their products or services. However, another PwC workplace survey revealed that many employees were skeptical or unaware of the potential impact of these technologies on them. For example, fewer employees (less than 30%) believe that AI will create new jobs or skill development opportunities for them.
Commenting on this, PwC Hong Kong AI & Emerging Technologies Advisory Leader, Delin Lee, said: "Numerous studies have shown that employees are more likely to adopt what they have co-created, highlighting the need to put people at the heart of generative AI strategies. To ensure that the full potential of generative AI can be fully utilized, companies should prioritize measures to engage employees in the creation and selection of AI tools, and invest in AI education and training to foster an innovation culture that supports 'human-intelligence' collaboration and data-driven decision-making. ”
A recent analysis by PwC found that companies with a clear ecosystem strategy are more likely to outperform those that don't. In the process of experimenting with AI, it is especially important to think outside the corporate mindset, such as how companies, service providers, customers and other partners intend to use this technology to improve their services, the impact of their AI applications on their own "early bird" strategy, whether new conditions and requirements will be put forward for enterprises, and whether closer cooperation in AI can lead to stronger and powerful new opportunities. (ENDS).