Tech professionals across Big Tech and startups are using AI to collapse hours of drudgery into minutes. They draft polished documents, summarize months of meetings, review code, and automate repetitive reports. Yet faster work doesn’t always translate to lighter workloads. Time saved in one area is often immediately poured into the next challenge — or spent upfront building the very automations that promise future relief.
Business Insider spoke with six tech workers about how AI is reshaping their days. Here’s what they shared (edited for length and clarity).
The time saved just gets reinvested into the next problem
**Priyanka Devi Ramesh**, a business intelligence engineer at Amazon (30, Virginia), has seen the biggest gains in document writing. Using Amazon’s internal AI tool Pippin, she now turns rough thoughts into polished technical or customer-facing documents in just 15–20 minutes — work that previously took over an hour.
On the technical side, tools like Kiro help her brainstorm and refine logic quickly, while she’s building agents in Amazon Quick to handle common customer questions and surface data insights automatically.
Still, Priyanka says AI hasn’t reduced her overall workload. “We’re constantly cleaning up messy data and finding new opportunities to automate,” she explains. “The time saved in one area simply gets reinvested into the next problem.”
AI turns months of meetings into quick summaries
**Prerit Pathak**, a security engineer at Google (27, New York City), relies heavily on Gemini for note-taking and summarization. He used to scribble shorthand notes during calls. Now he lets Gemini capture them, and reviewing six months of meeting history that once took 1–2 hours now takes just 5–10 minutes.
Building automation is adding hours — for now
**Sarthak Gupta**, a data scientist at Amazon (29, Seattle), uses AI to construct end-to-end automation pipelines for recurring work. A monthly stakeholder report that used to require 8–10 hours over several days — pulling data, cleaning it, creating visualizations, and writing summaries — is now mostly handled by an AI pipeline. He spends about 45 minutes reviewing and adding context.
However, Sarthak is currently working *longer* hours. “We’re in the middle of an automation phase,” he says. Building pipelines, integrating tools, validating outputs, and onboarding teams is front-loaded work. The payoff — collapsing days of effort into a single click — will come later.
From messy ideas to structured plans in minutes
**Tanvi Pisal**, a UX designer contracting for Apple via Red Oak Technologies (29, San Jose), uses AI in the early stages of product work. What once took 3–4 hours of drafting product requirement documents, brainstorming user stories, mapping edge cases, and outlining scenarios now takes about 30 minutes.
She starts with rough notes or messy drafts, and AI quickly turns them into structured, polished documents ready for feedback and iteration.
Getting to the starting line faster
**Udit Mehrotra**, head of product at Amazon (30s, Seattle), says AI has transformed how he writes the detailed product documents that kick off every major initiative at the company. Previously, the first 1–2 hours were spent building the basic structure and scaffolding.
Now he can feed in the customer problem and constraints and receive a solid first draft in minutes — often more comprehensive than what he would have produced manually under time pressure. The real work — strategic judgment, tradeoffs, and deep context-driven decisions — still falls to him, but he reaches that high-value thinking much faster.
What used to take a week now takes a day
**Iren Azra Zou**, a software engineer at trucking logistics startup Double Nickel (20s, New Jersey), uses tools like Claude for the majority of her coding. “It feels like what used to take a week can now take a day,” she says.
AI also handles most code reviews (except for particularly risky changes), providing rapid feedback loops instead of waiting days for human input. This has dramatically sped up iteration and reduced the time she spends reviewing others’ code. While there are tradeoffs to less human oversight, the speed of innovation has been a major win for the team.
Across these experiences, one theme emerges clearly: AI is a powerful accelerator, but it hasn’t delivered widespread reductions in working hours yet. For many, it simply raises the ceiling on what can be accomplished in the same amount of time — or requires significant upfront investment before the gains materialize. The result? Many tech workers feel just as busy, even as individual tasks fly by faster than ever.