Your Employees Are Losing $28,500 a Year to Data Entry. A Computer Use AI Agent Fixes That Today.

The Scale of This Problem Is Actually Embarrassing

Let's stack the numbers up so they're impossible to ignore. The average employee burns 4 hours and 38 minutes every week on duplicate, repetitive tasks, according to Clockify's 2025 research. That's over 240 hours a year. Per person. McKinsey found that 50% of workers who lack automated systems cite manual data input as their single biggest time drain. And the errors that come with all that manual work? A 4% error rate on data entry translates to $240,000 in annual costs for a mid-size supply chain operation, per a 2025 analysis from OrderEase. That's not a rounding error. That's a salary. That's two salaries. The dirty secret is that most companies know this. They just bought the wrong solution to fix it, and now they're too committed to admit it.

RPA Was Supposed to Save Us. It Mostly Didn't.

• ●Traditional RPA tools like UiPath work by recording exact screen coordinates and pixel positions. Change the UI even slightly and the bot breaks. Completely. Immediately.
• ●Gartner estimated that over 50% of RPA projects fail or stall before delivering meaningful ROI. The maintenance burden alone kills the business case.
• ●UiPath's own documentation explicitly warns that 'UI automation activities relying on screen coordinates could fail intermittently.' That's the vendor telling you their product is fragile.
• ●The average enterprise RPA deployment takes 6 to 18 months to go live. By the time it's running, the software it was built for has already updated three times.
• ●Knowledge workers spend nearly 4 hours daily on repetitive admin tasks including data entry, per a 2025 MarketsandMarkets analysis. RPA was meant to kill that number. It barely dented it.
• ●92% of employees say workflow automation increased their productivity, per Clockify. The problem isn't that people don't want automation. It's that the tools they were given are garbage.

Big Tech Tried to Solve This Too. It's Complicated.

When Anthropic launched Computer Use and OpenAI shipped Operator, everyone lost their minds. Finally, AI that can actually use a computer. And to be fair, it was a real step forward. But the honest reviews told a different story. Tim Lee at Understanding AI ran both tools through real-world tasks in 2025 and called computer-use agents 'a dead end' in their current form from those providers. He tried ordering groceries with Operator and Claude's computer use agent. Both failed. A writer at Where's Your Ed At put it even more bluntly, saying Anthropic has 'categorically failed' to deliver on the agent promise. These aren't fringe takes. These are people who wanted these tools to work and were disappointed. Claude 4.5 Sonnet scores 61.4% on OSWorld, the gold standard benchmark for real-world computer tasks. That's not bad. But it's not good enough to trust with your accounts payable queue either. The bar for automating data entry isn't 'impressive demo.' It's 'doesn't screw up my books.'

What Actual AI Computer Use Looks Like When It Works

Here's the difference between a gimmick and a real computer use agent. A real one doesn't care what app you're using. It sees the screen the way a human does, reads the context, and takes action. It doesn't break when a button moves three pixels to the left. It doesn't need you to pre-map every workflow in a visual editor before it can do anything. It handles the messy, unstructured reality of actual business software, which is never as clean as the demo. The tasks that eat the most time in data entry workflows are exactly the kind of tasks a proper computer-using AI was built for: copying data between systems that don't have APIs, filling out web forms, processing PDFs into structured records, updating CRM fields after a call, logging expenses from receipts, reconciling spreadsheets. None of these are hard for a human. All of them are mind-numbing. And all of them can be handed off to a computer use agent that actually works, today, without a six-month implementation project.

Why Coasty Exists

I've tried a lot of these tools. Coasty is the one I'd actually put in front of a real workflow. It scores 82% on OSWorld, which is the highest score of any computer use agent, period. Nobody else is close. That number matters because OSWorld tests agents on real, open-ended computer tasks, not curated demos. It controls actual desktops, browsers, and terminals. Not API wrappers pretending to be agents. You can run it as a desktop app, spin up cloud VMs, or deploy agent swarms to run tasks in parallel if you're dealing with high volume. For data entry specifically, that parallel execution is a big deal. Imagine processing 500 invoices simultaneously instead of one at a time. There's a free tier if you want to test it without a procurement conversation, and BYOK support if your security team has opinions about where your API keys live. It's at coasty.ai. The setup is not a six-month project. That's the point.