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What is Robotic Process Automation (RPA)?

Software robots that use your apps the way a human would.

Robotic Process Automation (RPA) is software that automates repetitive digital tasks by controlling user interfaces — clicking buttons, typing into forms, copying data between applications — exactly as a human would. It was pioneered in the mid-2000s by vendors like Blue Prism and UiPath, and solves the integration problem for legacy systems that have no API by driving their UIs instead.

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RPA's core idea is to record or script a sequence of UI actions — open this app, click this field, copy the value, paste into that other app — and replay it on schedule or on trigger. The 'robot' is a background process that has access to the user's screen and input devices (or runs headless in a virtual desktop). Unlike workflow automation which connects apps via APIs, RPA is app-agnostic: if a human can do it, an RPA bot can do it. RPA emerged specifically to address the enterprise integration crisis of the 2010s. Large banks, insurers, and government agencies ran hundreds of legacy systems — mainframes, green-screen terminals, old client-server apps — that had no APIs and would cost millions to replace. Rather than rebuild, RPA vendors offered a shortcut: let bots drive the existing UIs. A bank could automate loan processing without migrating off its 1990s core banking system. This is why the big three RPA vendors (UiPath, Automation Anywhere, Blue Prism) grew to billion-dollar valuations — they monetized the enterprise technical-debt overhang. Technically RPA has two flavors. Attended RPA: a bot runs on a human's desktop and augments their work — for example, opens all the apps needed for customer onboarding when the rep starts a call. Unattended RPA: a bot runs headless in a virtual desktop (usually a Windows VM), processes queues of work 24/7 with no human involvement. Unattended is where RPA produces the big labor savings and is what most enterprise programs deploy. The fundamental trade-off: RPA is powerful but brittle. A bot trained to click a specific button at specific coordinates breaks when the UI changes — a new field added, a redesigned dialog, a different Windows theme. Enterprises with large RPA portfolios spend significant effort on maintenance: monitoring for failures, updating scripts after UI changes, handling 'exception' flows the bot wasn't designed for. The industry phrase is 'RPA tech debt'. 2020-2023 saw RPA vendors add AI and ML to make bots more adaptive: computer vision to find buttons by appearance rather than coordinates, NLP to classify documents, ML to predict when a bot will fail. 2024-2026 is the LLM integration wave — UiPath AI Center, Automation Anywhere Co-Pilot, Blue Prism Bots+LLM — which is gradually shifting RPA from scripted action sequences to reasoning agents that can handle UI changes on their own. This convergence with AI agent tech is sometimes called 'intelligent automation' or 'agentic RPA'. The new entrants — Anthropic's computer use, OpenAI's Operator — are effectively 'LLM-native RPA': no recording, no scripts, just ask the model to do the UI task in English. For small businesses, these are already cheaper and more flexible than traditional RPA. For large enterprises with thousands of bots, the transition is more gradual — LLM costs at enterprise scale still exceed scripted RPA costs, and reliability isn't yet at the 99.9% level required for critical workflows. Expect the two approaches to merge over 2026-2027. For small businesses and solo founders, traditional RPA is usually overkill. A Tycoon AI employee with API tools (via MCP or Composio) plus occasional Claude computer use for edge cases covers 95% of what a small business needs, without the implementation complexity of a UiPath program.

Examples

→UiPath — RPA market leader, publicly traded, combines classic RPA with AI document understanding and LLM integration
→Automation Anywhere — major enterprise RPA platform, now with Automation Co-Pilot (LLM-assisted bot building)
→Blue Prism (now SS&C Blue Prism) — one of the original RPA vendors, strong in regulated industries like finance and insurance
→Microsoft Power Automate Desktop — free RPA tool bundled with Windows 11, good for low-end enterprise and prosumer use
→WorkFusion — RPA with heavy ML focus, used for banking KYC and insurance claims processing
→Bank loan processing — a classic RPA use case: bot pulls applications from queue, opens scoring system, copies data, routes to underwriter
→Insurance claims — bots extract data from PDFs, enter into claims system, flag anomalies — RPA plus AI document understanding

Frequently asked questions

Is RPA the same as computer use?

They do similar things — control UIs on a computer — but with different approaches. Classic RPA records a deterministic script: do A, then B, then C, always the same way. Computer use (Anthropic's Claude API, OpenAI Operator) uses an LLM to reason about what's on screen each step and decide what to do. RPA is faster and cheaper when the UI is stable. Computer use is more adaptive when the UI changes or the task varies. In 2026 the distinction is blurring as RPA vendors add LLMs and LLM vendors add computer use.

Is RPA still relevant with LLMs around?

Yes, for now, in two areas. (1) Large enterprises with existing RPA portfolios — rewriting thousands of production bots as LLM agents is a multi-year migration and the economics don't favor it at enterprise scale yet. (2) High-volume low-variation work — processing 10,000 identical forms per day, an RPA script is 10-100x cheaper than an LLM agent per run. For new deployments and small businesses, LLM-based computer use or API-driven AI agents are increasingly the better choice, and traditional RPA's market share is plateauing.

What does RPA cost?

Highly variable. Enterprise platforms (UiPath, Automation Anywhere) start around $2000-$10,000/year per bot license plus implementation consulting costs of $50K-$500K+. Power Automate Desktop is free for individual use, around $15/user/month for unattended bots. Community RPA (open source options, free Power Automate) has lower initial cost but higher maintenance. Compared to the $20-100/month cost of an AI employee in Tycoon, enterprise RPA is clearly enterprise-priced — and pays off only at sufficient scale.

Why do RPA projects fail?

Four common reasons. (1) Automating the wrong process — without process mining first, the obvious candidates are often not the highest-value. (2) Over-promising — the vendor sale pitches 80% labor reduction, reality delivers 20-40%, leadership loses trust. (3) Maintenance debt — every UI change breaks bots, and no one budgeted for ongoing bot-care. (4) Exception handling gaps — bots handle the happy path; the 15% of exception cases flood human operators and the program stalls. The Gartner estimate is that roughly 40-50% of RPA projects fail to achieve ROI targets.

How does Tycoon compare to RPA?

Tycoon operates at a higher level of abstraction. Where RPA automates specific click-by-click task sequences, Tycoon's AI employees own functional areas (marketing, ops, support) and choose from a tool set (API integrations via MCP and Composio, computer use for edge cases) to get work done. For a small business, one AI employee in Tycoon often replaces what would have been 3-10 RPA bots plus the orchestration and monitoring overhead. Large enterprises with existing RPA programs can integrate Tycoon-style agents on top — the AI employee delegates high-volume mechanical tasks to existing RPA bots and handles judgment work itself.

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