Automation Anywhere vs AI Agents: Your $750K RPA Investment Is Already Obsolete

The RPA Lie Nobody Wants to Admit They Bought

Here's the pitch Automation Anywhere gave every enterprise buyer for a decade: 'Your employees do repetitive work. Our bots will do it instead. Costs drop, productivity soars, everyone wins.' It sounded great in a boardroom. It collapsed on contact with reality. Ernst and Young's internal research put RPA project failure rates at 50%. Forrester found that maintenance consumes 60% of ongoing RPA budgets. A LinkedIn analysis from early 2025 cited a 40% failure rate on individual bot tasks, meaning bots were kicking work back to humans nearly half the time. Think about that for a second. You bought automation software that fails almost half the time and spends most of its budget just staying alive. That's not automation. That's a very expensive way to create a new category of IT support tickets. The core problem is architectural. RPA bots are brittle by design. They don't understand what they're looking at. They memorize coordinates and sequences. Change the UI, update the app, rename a field, and the bot dies. Every software update at your company becomes a potential incident. Your IT team stops shipping features and starts babysitting bots. Automation Anywhere didn't solve the repetitive work problem. They just moved it upstream.

The Numbers That Should Make Every CFO Furious

• ●40% of workers spend at least a quarter of their work week on manual, repetitive tasks, according to Smartsheet research. RPA was supposed to fix this. It didn't.
• ●Manual data entry alone costs U.S. companies $28,500 per employee per year, per a 2025 Parseur report. That's not a rounding error. That's a salary.
• ●The average employee burns 4 hours and 38 minutes per week on duplicate tasks, according to Clockify's 2025 research. Across a 200-person company that's 900+ hours of pure waste every single week.
• ●Ernst and Young found a 50% RPA project failure rate. Half. The projects don't even finish.
• ●Forrester: maintenance eats 60% of ongoing RPA budgets. You're paying more to keep the bot running than you paid to build it.
• ●Gartner predicted in June 2025 that over 40% of agentic AI projects will be canceled by end of 2027, mostly because companies are building on the wrong foundations. RPA-first thinking is one of them.
• ●A16z called it in November 2024: the original promise of RPA, turning operations headcount into real automation, was never actually delivered. AI agents are the first technology that can actually do it.

Automation Anywhere's 'AI Agent' Pivot Is a Tell

Watch what companies do, not what they say. Automation Anywhere has spent the last 18 months aggressively rebranding around 'AI agents' and 'generative AI-powered automation.' Their blog is full of it. Their conference keynotes are full of it. Why? Because they can read the room. They know scripted bots are losing. They know their enterprise customers are asking hard questions about ROI. So they slapped an LLM on top of their existing platform and called it agentic. But here's the problem with that approach. Real computer use AI isn't a feature you bolt onto a legacy RPA platform. It's a fundamentally different architecture. A true computer use agent doesn't memorize click sequences. It sees the screen the way a human sees it, reasons about what it's looking at, decides what action to take, and adapts when things change. It handles exceptions. It navigates ambiguity. It doesn't file an IT ticket when someone updates the CRM. Automation Anywhere adding 'AI' to their marketing doesn't change the fact that their underlying bot infrastructure was built for a world of static UIs and predictable workflows. That world is gone. The medium is different now. The tool has to be different too.

What Real Computer Use Actually Looks Like

The term 'computer use' has a specific meaning in AI circles now. It refers to agents that control real desktops and browsers the way a human would, by seeing the screen, reasoning about it, and taking actions. Not API calls. Not pre-mapped workflows. Actual visual understanding of whatever is on the screen. This matters because most enterprise software doesn't have a clean API. Your legacy ERP, your ancient claims processing system, your custom internal tools built in 2009, none of them were designed for programmatic access. RPA tried to solve this with screen scraping and coordinate mapping, which is why it breaks constantly. Computer use AI solves it by actually understanding what it's looking at. The OSWorld benchmark is the current gold standard for measuring how well AI agents handle real-world computer tasks. It throws agents at genuine desktop workflows, the kind of messy, multi-step work that real employees do every day. The gap between the top performers and everyone else on that benchmark is enormous, and it directly predicts how useful an agent will be in production. An agent scoring in the 80s on OSWorld is operating in a completely different category than one scoring in the 30s or 40s. The difference isn't marginal. It's the difference between an agent that works and one that creates new problems.

Why Coasty Exists

I'm not going to pretend I don't have a dog in this fight. I work at Coasty, and I think it's the best computer use agent available right now. But here's why I think that, and you can verify it yourself. Coasty scores 82% on OSWorld. That's the highest score of any computer use agent on the market, higher than Anthropic's computer use implementation, higher than OpenAI Operator, higher than anything Automation Anywhere has shipped or announced. That number isn't marketing. It's a benchmark on real-world desktop tasks, run by independent researchers. Beyond the benchmark, the architecture is what actually matters for enterprise use. Coasty controls real desktops, real browsers, and real terminals. Not a sandboxed simulation. It ships as a desktop app, runs on cloud VMs, and supports agent swarms for parallel execution, meaning you can run multiple tasks simultaneously instead of queuing everything through a single bot. There's a free tier if you want to test it before committing. BYOK is supported if you have data residency requirements. The point isn't that Coasty is perfect. The point is that when you're comparing it to an Automation Anywhere bot that fails 40% of the time and costs 60% of its budget in maintenance, the bar isn't that high. A computer use agent that actually understands what it's looking at beats a scripted bot every single time. Go to coasty.ai and see what 82% on OSWorld looks like in practice.