Your Employees Are Wasting $28,500 a Year on Data Entry. An AI Computer Use Agent Fixes That Today.

The Numbers Are Embarrassing and Nobody Wants to Say It Out Loud

Smartsheet surveyed workers and found that over 40% of them spend at least a quarter of their entire work week on manual, repetitive tasks. Data entry. Copying between systems. Re-keying information that already exists somewhere else. Clockify's 2025 research puts it even more bluntly: employees burn 11.43 hours per week on recurring tasks they were never hired to do. That's nearly a full third of a 40-hour week. Gone. Vaporized. Paid out in salary for work that produces zero creative or strategic value. Think about what that means at scale. A 50-person operations team where everyone wastes 11 hours a week on data entry is collectively losing 550 hours every single week. That's 13 full-time employees doing nothing but copy-pasting. You wouldn't hire 13 people to do that. So why are you letting it happen anyway? The answer, for most companies, is inertia. The old tools were too hard to set up. RPA required developers. API integrations required budget approvals. So the spreadsheets multiplied and the manual work just... stayed. That era is over.

Why RPA Failed You (And Why AI Computer Use Is Different)

Let's talk about RPA for a second, because a lot of teams tried it and got burned. Traditional robotic process automation tools like UiPath or Automation Anywhere work by scripting exact UI interactions. Click this button at pixel coordinate X,Y. Read this field. Write that value. It works great until the UI changes, the vendor updates their software, or someone moves a button three pixels to the left. Then your automation breaks and you're back to square one, paying a developer to fix it. That's not a knock on those companies specifically. It's a fundamental architectural problem with rule-based automation. It's brittle by design. AI computer use is a completely different animal. A computer use agent doesn't follow a hardcoded script. It looks at the screen, understands what it sees, decides what to do, and acts. The same way you do. If the button moves, the agent finds it. If the form layout changes, the agent adapts. If there's a popup it didn't expect, it handles it. This is why the shift from RPA to AI-powered computer use agents isn't incremental. It's a different category entirely.

What AI Computer Use Actually Looks Like for Data Entry

• ●Pulling invoice data from PDFs and entering it into your ERP, no API required, the agent reads the document and types into the fields like a human would
• ●Cross-referencing two systems that don't talk to each other, say your CRM and your billing platform, by literally opening both, reading one, and updating the other
• ●Logging into supplier portals, downloading reports, and populating internal spreadsheets automatically on a schedule you set
• ●Filling out government forms, compliance submissions, or regulatory filings by reading source data and typing it in accurately, every time
• ●Processing high-volume order entry from email attachments or web forms into backend systems at a speed no human team can match
• ●Catching and correcting errors in existing data by comparing records across systems and flagging or fixing discrepancies without you asking
• ●Running these tasks in parallel using agent swarms, so what took your team 8 hours takes the AI 20 minutes

The Competitor Situation Is Honestly Kind of Wild Right Now

Anthropic's Claude Sonnet 4.5 scored 61.4% on OSWorld, which is the gold-standard benchmark for real-world computer task completion. OpenAI's Operator, which launched to a lot of fanfare in January 2025, has already been absorbed into ChatGPT and researchers from the Partnership on AI documented it making OCR mistakes and taking screenshots instead of reading text properly during testing. These are smart companies with enormous resources and their computer use agents are still struggling with tasks that seem basic. The gap between 'demo video' and 'actually works reliably in production' is enormous for most of these tools. That's not me being mean. That's just the benchmark data talking. If your computer use agent can't reliably complete real desktop tasks, it's not ready to touch your business-critical data entry workflows. The stakes are too high. A wrong entry in your ERP isn't a minor bug. It's a wrong invoice, a missed payment, a compliance problem, a customer getting charged the wrong amount.

Why Coasty Exists and Why 82% on OSWorld Actually Matters

I'm going to be straight with you. I work for Coasty. But the reason I work for Coasty is because I spent time looking at the benchmark numbers and the product capabilities and the gap was obvious. Coasty scores 82% on OSWorld. That's not a marketing number. OSWorld tests 369 real desktop tasks across file management, web browsing, and multi-app workflows. It's the closest thing the industry has to a real-world report card for computer use agents. 82% versus 61.4% from Claude Sonnet 4.5 is not a small difference. That's the difference between an agent that handles your messy, real-world data entry workflows and one that works great in demos and falls apart on Tuesday afternoon when the vendor portal loads slowly. Coasty controls real desktops, real browsers, and real terminals. Not API wrappers pretending to be agents. Actual computer use. You can run it as a desktop app, spin up cloud VMs, or use agent swarms to parallelize work so 50 data entry tasks run simultaneously instead of sequentially. There's a free tier if you want to test it without a purchase order. BYOK is supported if your security team won't let you send data to third-party models. It's built for the people who actually have to make this stuff work in production, not just the people writing press releases about it. If you're serious about automating data entry and you want the best computer use agent available right now, the benchmark doesn't lie. Start at coasty.ai.