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A financial analyst turns to a chatbot to study the possible risks and returns from an investment programme. A job that would have taken 50 minutes is completed in 10.

That is one of the real-world cases that AI company Anthropic claimed to find recently when it asked its Claude chatbot to analyse how it was being used at work. But showing how one-off tasks like this translate into real-world business value for employers is not so simple.

This is set to become one of the main fronts in the battle between leading AI companies in 2026. OpenAI chief executive Sam Altman said recently that his company was shifting its focus to enterprise customers as it looks to boost its revenues — a market where Anthropic is currently ahead.

Yet even if people are starting to take to generative AI at work, most companies aren’t yet able to measure whether the technology makes individual workers more effective in their jobs, let alone trace any productivity gains at the level of the company as a whole.

Looking for the effects of a new technology like AI in the broader economy is even harder. IT’s impact on overall labour productivity has been famously difficult to identify from the official data. The impact of digital technologies did not show up in US economic data for years, until productivity growth started to rise steadily after the late-1990s. By the beginning of this decade, though, growth had fallen back to its earlier level, at around 1.5 per cent a year.

The encouraging news for the AI companies and their investors is that many people are starting to find uses for generative AI in their working lives. Summarising a long report, drafting a marketing presentation and analysing financial data are the kinds of things workers have tried for the first time this year. If and when any of these use cases take hold widely, the effects in terms of AI model usage could be significant.

So far, generative AI has one “killer app” at work, in the form of the coding assistants used by software developers. Its effects have been explosive. In May this year, 11 per cent of all the tokens generated by large language models were related to coding, according to a study of AI model usage by OpenRouter. By November, that proportion had soared to around 50 per cent.

Workers themselves certainly believe AI is starting to make them more effective. Earlier this month, OpenAI said workers it surveyed found AI saved them 40-60 minutes a day. That is up from the 2.2 hours a week that workers believed they were saving in a similar study conducted by the St Louis Fed a year ago.

Self-reporting like this is highly subjective, which makes Anthropic’s study of real-world tasks potentially more revealing. Based on 100,000 work-related conversations, Claude estimated it was slicing 65 minutes off the 85 minutes an average task would have taken.

Yet demonstrating virtuosity on individual tasks doesn’t translate directly into business advantage for customers, as Anthropic is the first to admit. The figures don’t show, for instance, how much extra work goes into checking the output from chatbots, or how overall quality affects the results.

Also, a single job may lead to more than one chat session. The ease and speed of getting a result out of a chatbot might lead workers to produce many more reports or emails, leading to a cascade of unproductive “workslop”. Nor can Claude tell what workers do with any time the technology has managed to save them.

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Another drawback is that the task-based analyses that lie at the heart of most studies of technology’s impact on productivity fail to capture the reality of working life. For most people, work doesn’t fall into discreet, self-contained segments. Looking at single tasks in isolation, as Anthropic admits, doesn’t capture the tacit knowledge and personal relationships that affect how work is done, or the connections between different tasks.

That appeared to explain the counter-intuitive results of one study this year, which found that a group of experienced developers took 19 per cent longer to complete a task when they used an AI coding tool.

The full benefits of generative AI will only become apparent when companies have redesigned entire work processes to make best use of the technology, and when they have overcome the cultural barriers that always stand in the way of this kind of change. But with workers starting to take to experiment with AI, the race is on.

richard.waters@ft.com