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Lessons from the frontiers of AI adoption
It is becoming ever more common for bosses to talk up their artificial-intelligence efforts while wielding the axe. Last month Enrique Lores, chief executive of hP, said that the computer manufacturer would cut around 5,000 jobs within three years as it embeds “AI in everything we do”. The same day Marguerite Bérard, boss of ABN Amro, a Dutch bank, unveiled sweeping lay-offs of her own, declaring that her company was “embracing AI to improve client services and reduce costs”. According to Challenger, Gray & Christmas, an employment firm, AI was cited as a cause in a fifth of the lay-offs announced by American companies in October.
Much of this is posturing. A company looks better in investors' eyes if it attributes staff cuts to its technological prowess rather than pandemic-era over-hiring (often a more probable explanation). So far, the evidence that AI is changing the labour market in a big way remains weak.
Yet that could change once companies adopt the technology more widely. Over the past few years plenty of researchers have sought to identify which jobs are most at risk by speculating about what types of tasks ChatGPT-like AI will be best able to take on and identifying the occupations where these are most prevalent. Another approach, however, is to look at the jobs where adoption of AI is already gaining pace, and to identify what links them. Two stand out.
First is computer programming. Some two-thirds of coders say that they use an AI tool at least once a week, according to data from Stack Overflow, an online forum. Microsoft's GitHub Copilot, one coding tool, has 26m users worldwide. Venture-capital (VC) spending is pouring into rivals, such as Windsurf and Cursor (see chart). According to Anthropic, a model-maker, a third of queries sent to its chatbot relate to computer programming.
Second is customer service. A survey by Gartner, a research firm, found that 85% of customer-service managers planned to experiment with ai this year. Companies from IBM, an IT giant, to Lufthansa, a German airline, are injecting the technology into their customer-service operations. VC investors are also backing AI startups targeting the occupation, such as Cresta and Sierra, though they have focused on it less than coding. The share price of Teleperformance, a French customer-service outsourcer, has slid by three-quarters since the launch of ChatGPT in 2022 amid expectations of looming upheaval.
What links these jobs? Consider first the nature of the work. Both involve plenty of repetitive tasks, but so do many others. In addition, however, the tasks performed by coders and call-centre agents tend to be “context-light”, meaning that those who do them don't need a deep understanding of the company, notes Kabeh Vaziri, of Gartner. They are also “easily verifiable”: programmers can run tests on chunks of code to ensure that they work; call-centre supervisors can look at whether a customer's problem was resolved and how happy they were after the interaction.
A second factor that makes these two occupations particularly fruitful terrain for AI is the abundance of available data that can be used to train models. Github Copilot has an enormous repository of code to learn from; customer-service units often have years of transcripts. Other information, such as “upvotes” in coding forums, can help the ai system judge an answer and improve the model.
A third commonality is that both occupations are big prizes for AI companies to target, encouraging investment in tailored software. In America 3m people work in customer service, typically in call centres, and another 2m are software developers. Cut out manual jobs and both occupations are among the country's five most common
The links between coding and customer service offer clues as to where AI adoption may take off next. Junior bankers and lawyers, who are less numerous but handsomely paid, are already in the startups' sights. What is more, the cost of using AI is plummeting as models and hardware become more efficient, which may lead to a wider range of occupations being targeted. At the same time, big businesses are busily sorting out their siloed, disorganised data, which should help with developing custom tools for white-collar workers. The AI of tomorrow will probably be both more specialised and more widespread. When that happens, bosses who blame the technology for lay-offs may no longer strain credulity.
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Source: HindustanTimes
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