Today, as technological development advances rapidly, generative AI is transitioning from being a hot topic to actual application. Major companies are no longer asking what generative AI is, but rather how to improve the return on investment (ROI) from deploying this technology. This shift indicates that the disruptive potential of generative AI is increasingly recognized by the industry. From startups to industry giants, every company is considering or piloting generative AI use cases to enhance their competitive advantage.
The emergence of groundbreaking generative AI tools, such as ChatGPT, GitHub Copilot, Stable Diffusion, and China's homegrown tools like ERNIE Bot and Pangu, is the result of massive investments and dedicated research in machine learning and deep learning technologies.
Generative AI tools are powered by foundational models and are widely applicable to both transactional and creative activities. As large language models continue to improve rapidly, the capabilities of generative AI have made significant progress, poised to reshape knowledge-based work across various industries and functions. It is also expected to drive the transformation and performance improvement of key areas such as marketing and sales, customer operations, and software development.
To estimate the economic and social value of generative AI, we focused on two major areas:
1. The application of over 60 generative AI use cases across 16 business functions;
2. The impact of generative AI on 850 occupations and over 2.100 specific tasks.
Research shows that the value creation potential of generative AI is astonishing. By 2030. it is expected to contribute $7 trillion in value to the global economy, 50% higher than the potential economic benefits of traditional AI or analytics. As a leader in AI research, China is expected to share in one-third of generative AI's total benefits through strategic investments. As the world moves towards an AI-driven era, unlocking the full potential of generative AI will become a critical priority for organizations to achieve commercial success.
Generative AI Will Empower Numerous Industries and Functions
The value creation potential of generative AI is undeniable, but its benefits depend on multiple factors, including the proportion and significance of different functions within an industry and the revenue scale of the sector. Globally, the high-tech industry is where generative AI's impact will be most profound. In China, the most representative industries will be advanced manufacturing and electronics/semiconductors.
The primary value created by generative AI can be summarized by the "4Cs":
1. Concision (Virtual Expert):** Generative AI can summarize and distill insights from unstructured data sources, facilitating the dissemination of expertise. It can also interpret texts and transcripts, creating embedded texts to support reference queries.
- *Example:* In financial performance analysis, generative AI can provide targeted external financial data and internal performance summaries, annotate financial statements, and improve financial planning and analysis efficiency, potentially reducing financial costs by 4% to 7%.
2. Coding and Software Development:** Generative AI can drive code refactoring, accelerating mainframe migrations. It can interpret and generate code, automate tasks such as documentation, testing correction, and software development processes.
- *Example:* In automating or enhancing data management, generative AI can automate parts of data labeling, data cleaning, and document generation, helping users identify anomalies in unlabeled data and create documentation for newly acquired datasets. This speeds up data processing, improves data quality, and can reduce data management costs by 5% to 10%.
3. Content Creation:** Generative AI can create initial drafts of various forms of content, including generating text, images, and other information media. It can also automate the creation of contracts, bids, and visual elements, accelerating the pace of R&D.
- *Example:* When creating marketing content for various media channels, generative AI can assist with creative generation (e.g., storyboarding) and large-scale content creation. It can help users write emails in different languages for various channels and offer personalized product/service recommendations, potentially reducing marketing costs by 5% to 8%.
4. Customer Engagement:Generative AI can help create highly personalized customer experiences, such as optimizing customer service through chat features and expanding the application of customer service chatbots, accelerating customer outreach and data collection.
- *Example:* In chatbot use cases, generative AI can enable natural conversations, better handle foreign languages and dialects, create automated self-service systems, and provide virtual agent support. It can more effectively answer customer inquiries, accurately identify root causes of issues, and reduce customer operations costs by 9% to 11%.
The Future of Work Reshaped by Generative AI
For decades, technological advancements have continuously reshaped the nature of work, empowering workers to accomplish tasks faster and more accurately. Generative AI is set not only to continue this trend but to do so with an unprecedented impact.
As generative AI is gradually adopted, the pace of work automation will accelerate significantly, and the "midpoint scenario" of automation could arrive 10 years earlier than expected, with 50% of work hours projected to be automated. Since 2017. the McKinsey Global Institute (MGI) has been closely monitoring the impact of automation technologies on work. MGI’s analysis reveals that, currently, tasks that account for 50% of workers’ time could be automated by 2030. In China alone, approximately 220 million jobs could be reshaped by automation technologies such as generative AI. These staggering figures underscore the massive impact generative AI will have on workers.
The disruptive potential of generative AI will affect different roles across industries, with varying degrees of impact on different types of jobs. White-collar jobs are expected to be more significantly impacted than blue-collar jobs.
Overall, generative AI will promote workforce transformation and upgrading, create new ways of working, and significantly boost productivity. As generative AI continues to evolve, businesses and workers must efficiently adapt and apply these technologies. Organizations must embrace AI trends, redefine job roles based on the potential of generative AI, and cultivate new skills. Likewise, workers need to adopt a lifelong learning mindset, actively enhancing their skills to thrive in an AI-enabled future.
The future of work also demands a reassessment of the current education system. Future talent must possess digital literacy to fully leverage generative AI and related technologies. There should be a stronger emphasis on developing problem-solving abilities and critical thinking, rather than focusing on rote memorization. Additionally, as jobs that require basic cognitive skills are increasingly automated, the importance of social and emotional skills will grow, and education must integrate these skills into talent development.
Conclusion: The Challenges of Deploying Generative AI
To unlock the full potential of generative AI, organizations must overcome several challenges to successfully deploy this technology on a large scale. Due to the long time required for widespread adoption and constraints in economic feasibility, the speed of AI adoption varies across countries. Over the past five years, AI adoption globally has grown significantly. In 2017. 20% of companies deployed AI technologies, a figure that rose to 50% in 2022. However, China’s AI adoption rate is only 41%, lagging behind leading countries such as Germany, the U.S., and Japan, leaving room for improvement. In terms of automation in job functions, China is projected to reach the 50% automation adoption rate about two years later than more developed countries.