How businesses are actually using generative AI

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How businesses are actually using generative AI

The Economist | Feb 29, 2024

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It has been nearly a year since Openai released gpt-4, its most sophisticated artificial-intelligence model and the brain-of-sorts behind Chatgpt, its groundbreaking robot conversationalist. In that time the market capitalisation of America’s technology industry, broadly defined, has risen by half, creating $6trn in shareholder value. 

At the same time, big tech’s sales of AI software remain small.  For the AI stockmarket boom to endure, these firms will at some point need to make serious money from selling their services to clients. Businesses across the world, from banks and consultancies to film studios, have to start using Chatgpt-like tools on a large scale. When it comes to real-world adoption of such “generative” AI, companies have trodden gingerly. Yet even these baby steps hint at the changing nature of white-collar work.

Previous technological breakthroughs have revolutionised what people do in offices. Could generative ai prompt similarly profound changes? A lesson of previous technological breakthroughs is that, economywide, they take ages to pay off. The average worker at the average firm needs time to get used to new ways of working. The productivity gains from the personal computer did not come until at least a decade after it became widely available. So far there is no evidence of an AI-induced productivity surge in the economy at large.

That is unsurprising. Most firms do not currently use Chatgpt, Google’s Gemini, Microsoft’s Copilot or other such tools in a systematic way, even if individual employees play around with them.  Some corporate giants are frantically experimenting to see what works and what doesn’t. They are hiring  AI experts by the thousand.  

Capgemini, a consultancy, says it will “utilise Google Cloud’s generative ai to develop a rich library of more than 500 industry use cases”. Bayer, a big German chemicals company, claims to have more than 700 use cases for generative ai.  This “use-case sprawl”, as one consultant calls it, can be divided into three big categories: window-dressing, tools for workers with low to middling skills, and those for a firm’s most valuable employees. Of these, window-dressing is by far the most common. 

Tools for lower-skilled workers could be more immediately useful.  Routine administrative tasks likewise look ripe for ai disruption.  Giving AI tools to a firm’s most valuable workers, whose needs are complex, is less widespread so far. But it, too, is increasingly visible. Lawyers have been among the earliest adopters.  Some companies are using the technology to build software.

As with earlier technological revolutions, fears of an AI jobs apocalypse look misplaced. So far the technology appears to be creating more jobs than it eliminates. 

Though such developments will not translate into overall productivity statistics for a while, they are already affecting what white-collar workers do. Some effects are clearly good. ai lets firms digitise and systematise internal data, from performance reviews to meeting records, that had previously remained scattered. 

AI adoption may also have certain unpredictable consequences.  Polling by IBM, a tech firm, suggests that many companies are cagey about adopting AI because they lack internal expertise on the subject. Others worry that their data is too siloed and complex to be brought together. About a quarter of American bosses ban the use of generative ai at work entirely. One possible reason for their hesitance is worry about their companies’ data.

Ultimately, for more businesses to see it as an open-and-shut case, generative ai still needs to improve.  Still, even the typewriter had to start somewhere.

3 key takeaways from the article

1. It has been nearly a year since Openai released gpt-4, its most sophisticated artificial-intelligence model and the brain-of-sorts behind Chatgpt, its groundbreaking robot conversationalist. In that time the market capitalisation of America’s technology industry, broadly defined, has risen by half, creating $6trn in shareholder value.
2. At the same time, big tech’s sales of AI software remain small.  For the AI stockmarket boom to endure, these firms will at some point need to make serious money from selling their services to clients. Businesses across the world, from banks and consultancies to film studios, have to start using Chatgpt-like tools on a large scale.
3. Previous technological breakthroughs have revolutionised what people do in offices. Could generative ai prompt similarly profound changes? A lesson of previous technological breakthroughs is that, economywide, they take ages to pay off. The average worker at the average firm needs time to get used to new ways of working.

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Topics:  Technology, Artificial Intelligence, Productivity