According to Andy Jassy, Amazon CEO, Amazon Q, Amazon’s generative artificial intelligence assistant for software development, has had a clear impact on Java upgrades.
‘The average time to upgrade an application to Java 17 plummeted from what’s typically 50 developer-days to just a few hours,” said Jassy. “We estimate this has saved us the equivalent of 4,500 developer-years of work.”
“And, our developers shipped 79% of the auto-generated code reviews without any additional changes,” he noted.
Beyond that, “the upgrades have enhanced security and reduced infrastructure costs, providing an estimated $260 million in annualized efficiency gains,” Jassy said.
That is a good example of the expectation that GenAI will in many initial cases--perhaps most cases--be used to support ongoing business practices.
While arguably helpful, are hard to quantify in ways other than “productivity” improvements, such as doing things faster. Amazon just happens to be able to apply GenAI in a way that is quantifiable, in this instance.
Most firms will not have such outcome-oriented results.
“Earlier this year, we integrated GenAI into the handheld devices in our stores, providing our team with rapid access to best practice documentation and the ability to quickly receive straightforward responses to common questions like, how do I sign a guest up for a Target Circle card, and how do I restart the cash register in the event of a power outage,” said Michael Fiddelke, Target CFO and COO. “Since the full chain rollout of this new tool, our team members have leveraged the technology more than 50,000 times, giving answers in a highly efficient average chat time of less than one minute.”
We can agree that is a productivity enhancement, but likely also agree that it is virtually impossible to correlate with financial results or operational outcomes.
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