Automation Anywhere Is Charging You $300/Month Per Bot While AI Computer Use Agents Do It Better for Free
Manual data entry alone costs U.S. companies $28,500 per employee per year. That's the problem. Now here's the insult: Automation Anywhere's solution to that problem starts at roughly $300 per month per bot, requires a certified developer to build it, and breaks the moment someone at Salesforce redesigns a button. You're paying enterprise prices to automate a problem that still requires a babysitter. This is the dirty secret of the RPA industry, and it's why 30 to 40 percent of bot capacity inside most large enterprises is spent on maintenance rather than actual work. The bots aren't automating your business. They're creating a second job for your IT team. AI computer use agents are a fundamentally different answer to the same question, and the gap between the two approaches is now impossible to ignore.
What Automation Anywhere Actually Sells You (And What You Actually Get)
Automation Anywhere is a fine company. Smart people work there. But let's be honest about the product model. You buy a license. You hire an RPA developer or pay a consultant. That developer hard-codes a bot that follows a rigid, pixel-perfect script across your desktop or browser. It works great, until the software it's automating gets a UI update. Or someone changes a field name. Or the page loads 200 milliseconds slower than usual. Then the bot fails silently, someone notices three days later when the reports are wrong, and your developer spends a week rebuilding a workflow that should have taken an afternoon. This isn't a horror story. This is Tuesday for most enterprise RPA teams. The Kognitos research is blunt about it: 30 to 40 percent of bot capacity goes toward maintenance overhead. You're not getting automation ROI. You're getting an automation treadmill. And the pricing reflects none of that hidden cost. Entry-level Automation Anywhere plans start around $300 per month per bot, and real enterprise deployments routinely run into six figures annually before you factor in developer salaries, consultant fees, and the ongoing cost of keeping your bot estate alive.
The Core Problem With RPA That Nobody Wants to Admit
- ●RPA bots follow fixed scripts. Change the UI by one pixel and the bot breaks. No judgment, no recovery, no adaptation.
- ●Smartsheet research found workers waste a full quarter of their work week on manual, repetitive tasks. RPA was supposed to fix this. It's been 15 years.
- ●30-40% of bot capacity in most enterprises is consumed by maintenance, not productive work, per Kognitos analysis.
- ●Traditional RPA can automate at best 70% of a process, per Zamp's analysis. The other 30% still needs a human.
- ●Gartner predicts over 40% of agentic AI projects will be canceled by 2027, but the reason isn't AI agents failing. It's companies applying old RPA thinking to new tools.
- ●Automation Anywhere, UiPath, and Blue Prism are all scrambling to bolt AI onto their platforms now. That's not a product strategy. That's a panic response.
- ●The average enterprise RPA project costs $750,000 or more in maintenance alone over three years, according to analysis from Duvo.
"30-40% of bot capacity is consumed by maintenance, not work. You're not automating your business. You're running a bot repair shop."
What a Real Computer Use Agent Actually Does Differently
Here's the thing about a genuine computer use agent: it doesn't follow a script. It looks at the screen the same way a human does, understands what it sees, decides what to do next, and executes. If the UI changes, it adapts. If something unexpected appears, it handles it. This isn't marketing language. This is the fundamental architectural difference between RPA and AI computer use. RPA is a macro recorder with a fancy dashboard. A computer-using AI is closer to a junior employee who can actually think. The benchmark that matters here is OSWorld, which tests AI agents on real computer tasks across real applications. Claude 4.5 Sonnet scores 61.4% on OSWorld. UiPath's Screen Agent, which runs on Claude Opus 4.5, recently claimed a top ranking on the same benchmark. These are real numbers on real tasks, not vendor demos. And the best computer use agents are already well past the 80% mark. That's not a small improvement over RPA. That's a different category of tool entirely.
The RPA Vendors Know They're In Trouble
Watch what Automation Anywhere and UiPath are actually doing right now, not what they're saying. Both are aggressively rebranding around AI agents. Automation Anywhere calls itself 'the number one provider of agentic automation' on its homepage. UiPath just announced its Screen Agent benchmark results with considerable fanfare. Microsoft added computer use capabilities to Copilot Studio in April 2025, explicitly citing it as a solution to 'common RPA challenges.' These aren't companies confidently evolving their products. These are companies watching their core value proposition get eaten alive by a new paradigm and trying to rebrand fast enough that customers don't notice. The problem is that slapping an AI layer on top of a brittle RPA architecture doesn't fix the underlying brittleness. You still have the same licensing model, the same developer dependency, the same maintenance overhead. You just have a better-looking pitch deck. Real computer use AI is built from the ground up around vision, reasoning, and adaptive execution. That's not something you retrofit onto a 2012-era bot framework.
Why Coasty Exists, and Why the Benchmark Actually Matters
I've looked at a lot of computer use agents. The OSWorld benchmark is the closest thing we have to an objective test of how well an AI can actually operate a computer on your behalf. It covers real applications, real tasks, and real failure modes. Coasty scores 82% on OSWorld. That's not a rounding error above the competition. That's a meaningful lead over Claude 4.5 Sonnet at 61.4%, and it's higher than every other agent currently on the benchmark. What makes that number meaningful in practice is what Coasty actually does with it. It controls real desktops, real browsers, and real terminals. Not API wrappers. Not simulated environments. Actual computer use on your actual software stack. You can run it on a desktop app, spin up cloud VMs, or deploy agent swarms for parallel execution across multiple tasks simultaneously. That last part is worth sitting with. Parallel agent swarms mean you're not just replacing one human doing one task. You're replacing an entire workflow team doing dozens of tasks at once. There's a free tier if you want to test it without a procurement process, and BYOK support if you're particular about your model stack. The pricing model is not 'pay us $300 per bot per month and then hire a developer.' It's built for the way AI computer use actually works.
Here's my honest take. Automation Anywhere built something genuinely useful for its era. RPA solved real problems in the 2010s when the alternative was pure manual labor. But we're in 2025, and the era of paying enterprise software prices for brittle, script-based bots that need constant maintenance is over. AI computer use agents can see your screen, reason about what they see, and execute tasks adaptively without breaking every time your SaaS vendor ships an update. The productivity math is brutal for the old model. Workers still waste a quarter of their week on repetitive tasks despite a decade of RPA promises. $28,500 per employee per year in wasted manual data entry costs. A maintenance overhead that swallows nearly half of what you paid for. That's not a technology problem anymore. That's a choice. If you're still evaluating Automation Anywhere in 2025, you owe it to yourself to at least run the comparison. Start with Coasty at coasty.ai. The free tier is real, the 82% OSWorld score is real, and the difference between a brittle bot and a genuine computer use agent will be obvious within the first hour.