How Tech Workers Are Using AI to Save Hours of Work Each Week

Ask a tech worker how AI has changed their jobs, and chances are they’ll answer with a single number: hours saved.

In interviews with Business Insider, Big Tech software engineers, product managers, and data scientists described using AI to compress hours of work into minutes. They use it to draft documents, summarize months of meetings, review code, automate reports, and more.

Faster doesn’t always mean easier, however. One Amazon data scientist said AI is adding hours to his workweek as he builds the automation systems that should eventually save him time. Another Amazon employee said any time saved is quickly redirected to the next project.

Here’s how six tech workers said AI is saving them the most time. (Their responses have been edited for length and clarity.)

Business Insider is speaking with workers who’ve found themselves at a corporate crossroads — whether due to a layoff, resignation, job search, or shifting workplace expectations.

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The time AI saves me gets reinvested into the next problem

Priyanka Devi Ramesh is a business intelligence engineer at Amazon. She’s 30 and lives in Virginia.

Document writing is where AI has had the greatest impact. With the help of an AI tool called Pippin, it’s become easy to translate my thoughts about the projects I’m working on into polished documents that can be technical or customer-facing. This saves a massive amount of time — I spend hardly 15 to 20 minutes max to write and finalize a document that would have previously taken me well over an hour.

On the technical side, I use Kiro and Amazon Quick. Kiro is great for brainstorming ideas and making logic updates in minutes. I’m building agents within Amazon Quick to automate common customer questions about dashboards and to surface insights from data.

AI hasn’t reduced my work time. We’re constantly looking for ways to clean up messy data and finding opportunities to automate wherever possible — so the time saved in one area gets reinvested into the next problem.

AI helps me make sense of months of meetings at Google

Prerit Pathak is a security engineer at Google. He’s 27 and lives in New York City.

I use Gemini for a variety of purposes, but recently it has bolstered my note-taking.

I used to take shorthand notes during meetings to record interesting or important information. Now, I let Gemini take notes on my work calls, and the improvement has been incredible. A summarization task — such as understanding what happened over the previous six months — that once would have taken one to two hours now takes five to 10 minutes.

I’m working longer hours now, so AI can save me time later

Sarthak Gupta is a data scientist at Amazon. He’s 29 and lives in Seattle.

AI has been most helpful with building end-to-end automation pipelines for recurring workflows.

It used to take 8 to 10 hours over a couple of days to create a monthly stakeholder report that involved pulling data, cleaning it, generating visualizations, and writing the summary. Now, an AI pipeline handles the data pulls, transformations, and dashboard refreshes. I spend maybe 45 minutes reviewing the output and adding context before sending it out.

However, my overall working hours right now are running longer than normal. The reason is that we’re in the middle of an automation phase. Building the pipelines, integrating the AI tooling, validating outputs, and onboarding all of this into existing workflows is front-loaded work, and that upfront investment is real. The payoff comes later, when the same task that took a couple of days collapses into a button click.

So in the short term, AI is actually adding hours to my week, not subtracting them. I’d expect that to flip once the foundational pipelines are stable and the automation is doing the heavy lifting on its own.

AI helps me turn messy ideas into polished plans

Tanvi Pisal is a UX designer working as a contractor for Apple via Red Oak Technologies. She’s 29 and lives in San Jose.

One of the biggest ways AI saves me time is in early-stage product thinking and documentation.

As a product designer, I used to spend hours drafting product requirement documents, brainstorming user stories, mapping edge cases, outlining use scenarios, and refining ideation before I even got to visual design.

Now, I can start with rough notes or a messy draft, and AI helps turn that into a much more structured document in minutes. What used to take me three to four hours can often be reduced to 30 minutes with feedback and refinements.

AI gets me to the starting line faster at Amazon

Udit Mehrotra is a head of product at Amazon. He’s in his 30s and lives in Seattle.

Writing product documents is where I’ve seen the biggest change. Every major initiative at Amazon starts with a written document, and for years, the first hour or two of that process was building scaffolding: setting up the structure, filling in the sections you know by heart, and building something worth reacting to before you could get to the actual thinking.

Now I can use AI to input the customer problem and constraints and get a solid first draft in minutes. What surprises me is that it’s often more comprehensive than what I’d have written on my own under time pressure.

Getting from 80% to 100% is still where the real work lives, and AI doesn’t change that. The strategic judgment, the tradeoffs between what customers need and what’s technically feasible, the decisions that require years of accumulated context about a specific customer problem — that thinking still takes the same depth and care it always did.

What has changed is that I arrive at the starting line faster, with a more complete structure to react to and push against. The quality of the final document is often better as a result, not because AI did the hard thinking, but because I spent more of my time on it.

What used to take a week can now take a day

Iren Azra Zou is a software engineer at the trucking logistics startup Double Nickel. She’s in her 20s and lives in New Jersey.

I use AI, mostly Claude Code, for the majority of my coding. It’s honestly hard to quantify the time savings; it feels like what used to take a week can now take a day.

We also rely heavily on AI to review and provide feedback on code, unless a change is particularly risky. That alone saves a huge amount of time. Instead of waiting days for human reviews, you get multiple rounds of feedback within hours. It also means I spend less time reviewing others’ code, which probably saves me several hours each week.

There are tradeoffs — less human review can have downsides. But right now, the speed of iteration and innovation is incredibly valuable for us.

Do you have a story to share about how you’re navigating a career crossroads? If so, please reach out to the reporter via email at [email protected], or via Signal at jzinkula.29.