Your Enterprise Is Burning $47K Per Employee on Tasks a Computer Use Agent Could Do Overnight

The RPA Hangover Is Real and Nobody Wants to Admit It

Let's talk about the elephant in the server room. Enterprises spent the last decade dumping billions into robotic process automation. UiPath alone hit a $35 billion valuation at its peak. The pitch was simple: automate repetitive tasks with bots that follow rigid scripts. And for a narrow set of perfectly structured, never-changing workflows, it kind of worked. But here's what nobody in the RPA sales cycle told you. Every time a UI changes, the bot breaks. Every time a vendor updates their portal, someone files a ticket. Every time a process has even slight variation, the whole thing falls over. Gartner estimated that RPA maintenance can eat up to 40% of initial implementation costs annually. You're not automating work. You're creating a new category of fragile, expensive infrastructure that needs its own team to babysit. The promise was 'set it and forget it.' The reality was 'set it, break it, fix it, repeat.' Traditional automation was built for a static world. Enterprise work is anything but static.

Why 42% of Companies Bailed on AI Projects in 2025

• ●42% of companies abandoned most AI initiatives in 2025, up from just 17% in 2024, according to recent enterprise research. That's not a blip. That's a crisis.
• ●Most failed AI projects tried to bolt AI onto broken workflows instead of replacing the workflows entirely. Chatbots on top of chaos are still chaos.
• ●API-only AI tools can't touch legacy software. If your ERP doesn't have a modern API, your AI assistant is useless. Computer use agents don't need an API. They use the screen, just like a human would.
• ●Pilot programs died because they were too narrow. A computer use agent that handles one task in one department doesn't justify the budget conversation. Swarms of agents running in parallel across the entire org? That's a different math problem.
• ●Leaders moved too slowly. McKinsey's 2025 workplace report found the biggest barrier to scaling AI isn't employee resistance. It's leadership that isn't steering fast enough. By the time the committee approves the pilot, the company that moved fast already automated three departments.

The Big Players Are Fumbling This, Too

You'd think the Anthropics and OpenAIs of the world would have this figured out. They don't, at least not for enterprise. Anthropic's computer use tool is API-first, which means it requires developer setup before a single non-technical employee can touch it. That's a real barrier. OpenAI's Operator launched with fanfare in January 2025 and has since been folded into ChatGPT agent, but critics were quick to point out that even the best model they tested struggled badly with anything beyond simple, linear web tasks. One widely-shared analysis from Understanding AI bluntly titled a piece 'Computer-use agents seem like a dead end' after testing the major players. The complaints were consistent: agents that hallucinate clicks, fail on multi-step workflows, and can't recover gracefully when something unexpected happens on screen. And on OSWorld, the benchmark that actually measures how well an AI agent handles real computer tasks across real operating systems, the scores from most players are embarrassingly low. The gap between 'demo video' and 'production-ready enterprise tool' is enormous. Most vendors are living in the demo video. OSWorld scores don't lie, and most of the field is sitting somewhere in the 30-50% range, which means they fail more than half the time on standardized tasks. That's not enterprise-ready. That's a science project.

What 'Enterprise-Ready' Actually Means for Computer Use

Here's what enterprise teams actually need from a computer use agent, and why the checklist matters. First, accuracy. Not 'pretty good.' Not '70% of the time.' Enterprise workflows have real consequences. A computer-using AI that fills out a form wrong 30% of the time isn't saving you money. It's creating a new audit problem. Second, parallelism. One agent doing one task is a toy. A swarm of agents running dozens of workflows simultaneously across cloud VMs is actual enterprise throughput. Third, flexibility. The agent needs to work on real desktops, real browsers, real terminals, and real legacy software without needing a custom integration for each one. Fourth, accessibility. If only your DevOps team can deploy it, you've just built a new bottleneck. The whole point is to give power users and operations teams the ability to automate without writing code. Fifth, cost control. Enterprise procurement hates surprise bills. BYOK support and transparent pricing aren't nice-to-haves. They're table stakes. Most computer use tools check one or two of these boxes. Very few check all five.

Why Coasty Exists

I'm going to be straight with you. I've looked at the field. I've watched the benchmark scores. I've read the post-mortems from failed enterprise pilots. And the reason I use Coasty is pretty simple: it's the only computer use agent that scores 82% on OSWorld. That's not a marketing claim. OSWorld is the industry-standard benchmark for AI computer use, and 82% is the highest score in the field, higher than Anthropic, higher than OpenAI's CUA, higher than anything else currently shipping. That gap matters in practice. It means fewer failed tasks, fewer human check-ins, fewer exceptions to handle manually. Coasty controls real desktops, real browsers, and real terminals. It runs on a desktop app or cloud VMs. It supports agent swarms for parallel execution, which means you can run 20 workflows simultaneously instead of queuing them up like it's 2015. There's a free tier so you can actually test it without a procurement nightmare, and BYOK support for teams that have strong opinions about which model they're running. It's not trying to be a chatbot with some extra features. It's built from the ground up as a computer-using AI for people who need work to actually get done. The a16z piece on the rise of computer use called this category 'a step-change beyond browser automation and RPA.' Coasty is what that step-change looks like in practice.