The future of generative AI, figuring out whether it is a bubble or not with numbers
Haebom
3
According to a McKinsey report, Generative AI is growing rapidly, and the technology is expected to add up to $4.4 trillion in value to the global economy annually. The technology is expected to impact a wide range of industries and occupations, with a particularly large impact in marketing and software development.
In addition to the McKinsey report, recent Deloitte and domestic and international market analysis data or reports show that the generative AI market is different from the existing buzz in terms of actual utility, unlike existing cryptocurrencies or metaverses. In this article, we will take a look at the future of generative AI based on reports and charts published by McKinsey and various companies.
What activities will add the most value to the organization?
How do workers feel about this technology?
What safeguards are needed to ensure responsible use of this technology?
The era of generative AI launched by chatGPT
Generative AI can perform a variety of tasks, from chatbots like ChatGPT to various content creation, unlike existing AI. This article is a chart of how companies can utilize Generative AI, and the risk factors and management methods to consider when introducing this technology. This chart is a timeline of the process in which ChatGPT was released, LLM gained attention, and the discourse on generative AI began.
Is the pace of development of generative AI already at a singularity?
The road to human-level performance is getting shorter and shorter. For most of the skills shown in this chart, Generation AI will be 10 years ahead of the median human performance by the end of this period. And its performance will rival the top 25% of people who can complete all of these tasks before 2040. That’s 40 years faster than experts thought.
Knowledge workers can also be automated.
While previous waves of automation technology have primarily impacted physical work activities, the AI generation is likely to have the greatest impact on knowledge work, particularly activities involving decision making and collaboration. Professionals in fields such as education, law, technology, and the arts are likely to have their work done faster than before. This is because generative AI can predict and dynamically use natural language patterns.
Specialized small apps, not super apps, are making a comeback.
Apps that address specific use cases are proliferating. Next-generation AI tools can already generate most text, images, video, audio, and coded content. And companies are developing applications to address use cases across all of these areas. In the near future, applications targeting specific industries and functions are expected to deliver the following value:
The most notable aspect of generative AI is its convergence with other fields.
In the coming AI era, the combination and importance of various business functions, as well as the scale of industrial sales, will vary depending on various factors. In particular, the marketing and sales fields are expected to benefit the most from the AI era. However, for high-tech and financial industries, the hasty introduction of AI can be dangerous.
Industry-specific concerns about the introduction of artificial intelligence
This chart includes a spotlight section detailing how we identify the use cases with the highest value potential in the banking (financial), life sciences, retail, and consumer goods industries. This scale provides a good framework for evaluating your industry.
The market hasn't even started yet. You're in the lead.
Despite the advent of generative AI, most organizations are not yet using it. When we asked marketing and sales leaders how often they thought their organizations should use generative AI or machine learning for commercial activities, 90% thought it should be at least “often.” Given that marketing and sales are the areas where we’ve seen the greatest impact, that’s not surprising. But 60% said their organizations rarely or never do this.
Marketers are struggling to figure out how to get on board with the new paradigm.
Marketing and sales leaders are most enthusiastic about three use cases. Our research shows that marketing and sales leaders expect at least a medium impact from each of the AI use cases we proposed, and they are most enthusiastic about lead identification, marketing optimization, and personalized outreach.
Artificial intelligence and developers are pretty good partners.
Software engineering, another big value proposition in many industries, can be made much more efficient. When we tested a generative AI-based tool on 40 of McKinsey’s own developers, we found dramatic speedups in many common developer tasks: Documenting code features for maintainability (taking into account how easily code can be improved) can be done in half the time, new code can be written in almost half the time, and existing code (called code refactoring) can be optimized in almost two-thirds of the time.
Artificial intelligence makes developers happy.
AI-assisted development can make developers happier. Our research found that equipping developers with the tools they need to be most productive significantly improved their experience, which in turn can help companies retain their best talent. Developers who used generative AI-based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow.
The number of people using AI tools will only increase.
Workers are becoming more resilient to using generative AI tools. A new McKinsey survey finds that a majority of workers across industries and geographies have used AI tools at least once, either on or off the job. That’s a remarkable leap in adoption, coming in less than a year. One surprising finding is that baby boomers report using generative AI tools for their jobs more than millennials.
Organizations still need to be able to read and write more AI
As organizations begin to set goals related to AI, employees are demanding more workers who can read, understand, and write AI-related texts. As general AI and other applied AI tools begin to provide value to early adopters, the gap between the demand and supply of skilled workers remains large. To remain competitive in the talent market, organizations must develop superior talent management skills and provide a rewarding work experience for the workers they hope to hire and retain who can read and write AI-related texts.
What should you be aware of when introducing AI tools into your organization?
Organizations should proceed cautiously. The promise of AI is powerful for many. But like any new technology, AI does not come without potential risks. For one, AI has been known to produce content that is biased, factually incorrect, or illegally scraped from copyrighted sources. Before adopting AI tools for use in an organization, organizations should consider the reputational and legal risks they may expose. One way to reduce risk is simple: a real human must verify any generative AI output before it is published or used.
AI could ultimately increase global GDP
McKinsey found that generative AI could significantly increase labor productivity across the economy. But to reap the benefits of these productivity gains, workers whose jobs are affected would have to shift to other work activities that would allow them to at least match their 2022 productivity levels. If workers are supported to learn new skills and, in some cases, change jobs, strong global GDP growth could lead to a more sustainable and inclusive world.
Generative AI shows a small part of the value potential that can be gained from AI.
Gen AI is a big step forward, but traditional advanced analytics and machine learning continue to play a big role in optimizing tasks and continue to find new applications in a variety of fields.
Organizations undergoing digital and AI transformation would do well to keep an eye on generative AI, but that doesn’t mean they should rule out other AI tools. Just because it’s not a hot topic doesn’t mean it can’t work to improve productivity and ultimately deliver value. That said, it’s a good idea to quickly find or build the right AI tool for your organization.
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