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Data firm Gartner is famous for mapping the “hype cycles” of various technologies. In this year’s edition, generative artificial intelligence has passed the peak of inflated expectations and is now sliding into the trough of disillusionment. The slope of enlightenment and the plateau of productivity are not reached until much later.
OpenAI’s ChatGPT announcement three years ago certainly sparked an avalanche of excitement about the potential of generative AI. The adoption of this technology is one of the fastest in history. According to the company, ChatGPT currently has more than 800 million weekly active users. Users have marveled at the chatbot’s uncanny ability to perform tasks as diverse as writing a plausible sonnet about a pet goldfish, summarizing a complex legal document, or crafting a passable corporate presentation.
However, these basic models also have some obvious flaws, most notably their tendency to hallucinate or, more precisely, fabricate facts. In countless earnings calls, corporate bosses have extolled the potential to improve productivity by implementing AI into nearly every business function. But they are also wary of the risks that generative AI poses to data security, customer confidentiality, and corporate reputation. The excitement generated by the introduction of AI agents has also hit the hard wall of reality: nothing is as simple as programmers imagine.
Several recent reports suggest that generative AI has so far failed to live up to its lofty early promises, leading to widespread disillusionment among businesses.
When asked to identify “failures” in a large-scale language model, ChatGPT itself answers: “Technology is powerful in itself, but without proper preparation, it becomes a liability rather than an asset.” That seems to sum up pretty well where we are today.
With proper preparation and wise implementation, generative AI can be a great productivity tool
But with proper preparation and smart implementation, generative AI can be a great productivity tool.
This is perhaps most evident in the performance of large US technology companies, which are most aware of the potential and flaws of AI models. As research firm Alpine Macro points out, these companies are currently enjoying an “unemployment profits boom” reflecting accelerating productivity gains.
Tech companies may have overhired during the coronavirus pandemic when the world went online, but they’ve been cutting jobs since then. But Alpine Macro points out that technology jobs have been in a recession for more than three years now, suggesting there’s something else behind it. “We suspect that job losses in the tech industry are primarily driven by the exclusion of AI,” wrote Chen Zhao, the company’s chief global strategist.
Interestingly, this phenomenon appears to be spilling over into the broader economy. U.S. private sector employment remains 5% below pre-pandemic trends, according to Alpine Macro.
Reduced labor supply due to the mass deportation of undocumented workers may also have encouraged more companies to invest in technology. Alpine Macro estimates that productivity growth is now more than double what it was in the 2010s.
So how can individual businesses maximize the potential benefits of AI while minimizing risk?
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One company that is applying AI decisively is Mimecast, a global cybersecurity company with over 40,000 customers. The company has leveraged AI to enhance its service offerings, help detect cyber threats, and improve enterprise productivity.
All of Mimecast’s 2,400 employees are encouraged to adopt this technology and are provided extensive training to ensure its ethical and responsible use. Mimecast has been working with Pair, a training startup, to assess the capabilities of its employees and improve their skills.
Tim Seamans, vice president of AI and business transformation at Mimecast, said the company’s workforce could be divided into early adopters, cautious optimists, and skeptics.
But the company has systematically helped its employees move up the AI value chain. 96% of our staff now incorporate this technology into their daily workflow, significantly increasing their productivity. “It’s in everyone’s hands,” he says.
Change is continuously being driven from the top of the company, with CEOs creating their own AI agents and sharing them with colleagues to drive discussion about business use cases.
Mimecast also benchmarked each division against industry adoption rates to reveal the company’s strengths and weaknesses. The biggest gains often come in less obvious areas such as customer service, finance, human resources, and sales.
As Seamans points out, and many other executives have emphasized, implementing powerful new technology is about more than just finding ways to use the technology itself. It also involves changes in corporate culture, labor practices, and business organization. Only then can companies climb the slope of enlightenment.
Video: Can generative AI live up to the hype? | FT Tech
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