Generative artificial intelligence is changing the business world. More businesses are now looking to incorporate generative AI tools to reduce costs, optimize operations, as well as find new revenue streams.
Generative AI is artificial intelligence technology that enables users to generate texts, images, sounds, animations, and other types of data based on a variety of inputs.
According to a 2023 survey by consulting firm McKinsey, one-third of survey respondents said their companies are using generative AI (GAI) in at least one business function.
There is so much hype around AI technology that 40% of the survey respondents said that their companies will increase their investments in overall AI.
Technology heavyweights like Google, Adobe, and Meta have released several GAI tools in the past year. Google has incorporated the technology to help users write emails and edit photos. Adobe launched image-generating tools to compete with front runners such as Midjourney. Meta rolled out a generative AI tool to help advertisers to generate several versions of their advertisements.
Google CEO Sundar Pichai is so convinced about the AI revolution that he said that “AI will be the biggest technological shift we see in our lifetimes” and spoke of Google pivoting to an “AI-first company.”
Are Jobs Safe From Generative AI?
Let’s talk about a worrying theme that everyone associates with the growth of AI: job losses.
Which industries are the most vulnerable to AI-related job losses, and which job markets will come out relatively unscathed?
According to McKinsey, the emergence of generative AI is expected to automate 60 to 70 percent of work activities. However, the consulting firm said that “this doesn’t necessarily translate into the automation of an entire role.”
Jobs in the service operations sector is expected to be the one most affected by the growth of generative AI. The world will likely see a decrease in workforce in customer care and back-office support as humans are replaced by AI.
McKinsey’s survey revealed that marketing, sales, product development, and software engineering are the most likely to use generative AI tools. Thus, job markets in technology and financial services sectors are expected to see disruptive change from gen AI.
“Industries relying most heavily on knowledge work are likely to see more disruption—and potentially reap more value,” said McKinsey.
Job markets in manufacturing-based industries such as aerospace, automotives, and advanced electronics are expected to be least impacted by the emergence of GAI.
“This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to gen AI’s strengths in language-based activities, as opposed to those requiring physical labor,” wrote McKinsey.
Generative AI Use Cases in The Office
Here are the most popular use cases of GAI, according to 1,684 participants from different regions, industries, companies, and specialties surveyed by McKinsey:
- Crafting first drafts of text documents
- Personalized marketing
- Summarizing text documents
- Identifying trends in customer needs
- Drafting technical documents
- Creating new product designs
- Use of chatbots for customer service
- Forecasting service trends
- Business opportunities in generative AI
Corporations see AI technology as the biggest disruption to come since the internet. Many are using AI to cut costs, while front runners are looking to create new markets to reap rewards from.
McKinsey’s survey showed that only 23 percent of respondent said AI contribute to at least 5 percent of their company’s earnings before interest and taxes (EBIT). The data suggests that AI technology is still in its early innings and has enormous room to capture value.
Companies that are leading the AI revolution are playing the long game and are prioritizing the creation of new revenue sources over cost reduction. McKinsey refers to them as “AI high performers.”
These companies are creating new AI-based products, revamping existing products with AI capabilities, using AI in product development, supply chain management, product-development-cycle optimization, and human resource performance management.
“We continue to see a set of AI high performers that are building out the foundations and capabilities that allow them to generate value.
One way to interpret this is that “the rich are getting richer” when it comes to extracting value from AI. We’ll be interested in seeing whether the great interest in generative AI opens the door to higher overall adoption of AI going forward,” said Michael Chui, Partner at McKinsey Global Institute.
McKinsey’s survey also indicated that most companies were not fully prepared to deal with risks related to GAI adoption. Only 21 percent of respondents said that their companies had established policies that govern an employee’s use of GAI at work.
While less than 40% said their organizations were actively working to mitigate risks related to inaccuracy, cybersecurity, and intellectual property infringement arising from generative AI.
In order to unlock the full potential of generative AI technology, there is a rising need for companies to place governance and operating model for AI use, learn how to best manage third parties, and find balance between short-term gains and long-term growth.
“The real trap, however, is that companies look at the risk too narrowly. There is a significant range of risks—social, humanitarian, sustainability—that companies need to pay attention to as well. In fact, the unintended consequences of generative AI are more likely to create issues for the world than the doomsday scenarios that some people espouse,” said Alexander Sukharevsky, Senior partner and global leader of QuantumBlack, AI by McKinsey.
The wave of generative AI is a transformative force with vast potential for businesses, but it also presents challenges.
As industries increasingly adopt these technologies, it is essential to strike a balance between automation and human expertise, ensure ethical use, and prioritize ongoing education and reskilling to harness the full potential of generative AI while mitigating its drawbacks.