"Jain said he had tried to automate internal workflows at Glean, including an effort to use AI to automatically identify employees' top priorities for the week and document them for leadership. "It has all the context inside the company to make it happen," said Jain, adding that he thought AI would "magically" do the work. The idea seemed simple, but it hasn't worked."
"Jain pointed to another bet that fell short: building and fine-tuning a custom model for a specific use case within Glean's product. That effort "didn't really pan out," ultimately pushing the company back toward existing foundation models that were easier to deploy, he said. "It actually takes much longer than you know to actually generate success," he added."
"Ghodsi, whose company sells a data and AI platform, said: "It's not just you can just unleash the agents, and it just works." Making AI useful inside an organization is "an engineering art," requiring careful evaluation, production work, and strong teams to support it, he added."
Arvind Jain attempted to automate internal workflows at Glean, including using AI to identify employees' top priorities each week and document them for leadership. The system contained company context but failed to perform as expected. A custom model built and fine-tuned for a specific product use case also did not pan out, prompting a return to existing foundation models that were easier to deploy. Ali Ghodsi warned that unleashing agents does not make AI work by itself. Making AI useful inside organizations requires careful engineering, production work, evaluation, and strong teams, and success often takes considerably longer than anticipated.
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