Why Your Enterprise AI Automation Is Failing (And Why RPA Is Dead)
95% of enterprise AI pilots fail to deliver ROI according to a July 2025 MIT study. That's not a typo. Three out of every four AI projects you've seen announced are dead on arrival. At the same time, manual data entry costs American companies $28,500 per employee every single year. Your team is copy-pasting, typing, and fixing errors that cost you millions. This is absurd.
The $28,500 Per Employee Problem
A 2025 survey by Parseur found manual data entry costs U.S. companies $28,500 annually per employee. Think about that number. One full-time person's salary every year spent on work an AI agent could finish in minutes. Most companies would fire an employee who waste that much money. They just let it happen because nobody has built a reliable computer use agent that actually works at scale. You're paying people to do what a computer-using AI can do better, faster, and cheaper.
Why 95% of AI Projects Fail
- ●RPA vendors changed their names and kept selling the same broken scripts
- ●Anthropic Computer Use and OpenAI Operator can't handle real enterprise environments
- ●Most tools still rely on brittle APIs that break when something changes
- ●Companies hire consultants to build systems that don't scale or integrate properly
- ●Internal teams lose confidence after first failure and stop trying
MIT found 95% of generative AI pilots fail. The problem isn't AI. It's that vendors are selling 2020 thinking wrapped in 2025 buzzwords.
RPA Is Dead. Call It What It Is.
RPA vendors spent 2025 pivoting to agentic AI and computer use. They changed their marketing. They added screenshots and 'vision' capabilities. They didn't fix the fundamental problem: they sell rigid rules that break when you touch anything unexpected. Your finance team uses RPA to move invoices between systems. The vendor charges thousands per bot per year. If the invoice layout changes by one pixel, the bot fails and someone has to fix it manually. That's not automation. That's digital janitorial work.
The Real Difference Is OSWorld
Computer use agents should be judged by how well they handle open-ended tasks on real desktops. The OSWorld benchmark measures this. Coasty scores 82% on OSWorld, higher than every competitor and above the human baseline. Other tools struggle to reach 40%. The gap isn't theoretical. It's the difference between an agent that can actually help your team and one that needs constant human supervision. Coasty controls real desktops and browsers. It handles complex workflows that other tools give up on. That's what enterprise computer use needs.
Why Coasty Exists
Enterprise teams don't need another tool that promises the world and fails in production. They need an AI agent that actually works. Coasty is built for real desktop environments, not synthetic benchmarks. It runs as a desktop app or in cloud VMs. You can deploy multiple agents in parallel for large workflows. BYOK is supported so you can keep your data where it belongs. The free tier exists because we want you to see the difference before you spend a dime. Coasty isn't a side project. It's the result of treating computer use agents like serious infrastructure instead of experimental toys.
Stop funding the 95% failure rate. Manual data entry costs you $28,500 per employee. RPA is selling you scripts from a decade ago. Computer use is where enterprise automation actually lives, and Coasty is the only agent that proves it day after day. Try the free tier at coasty.ai. See what 82% on OSWorld actually looks like in your environment. Then tell me you're still paying people to copy-paste data in 2026.