"Early initiatives focused on using generative AI to produce code faster, but writing and testing code typically accounts for about 25 to 35 percent of the total development process, the report states, so speeding up this stage alone is not going to be effective at reducing time to market. Perhaps greater value will be found from applying generative AI across the entire development life cycle?"
"Meanwhile, another recent study from nonprofit research group Model Evaluation & Threat Research (METR) found that AI coding tools actually made software developers slower, despite expectations to the contrary, because they had to spend time checking for and correcting errors made by the AI. This is perhaps what Bain & Co means when it notes that the time saved often isn't redirected toward higher-value work, so even the modest gains that have been made have not translated into positive returns."
Generative AI adoption in software development is widespread but has yielded only modest productivity gains, often around 10–15 percent for teams using AI assistants. Developer adoption within firms remains low despite two-thirds of companies rolling out GenAI tools. Some studies found AI coding tools slowed developers because additional time was spent checking and correcting AI-generated errors. Writing and testing typically consume only 25–35 percent of development effort, so accelerating coding alone limits time-to-market impact. Applying generative AI across discovery, planning, design, testing, deployment, and maintenance and changing processes could unlock greater value.
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