Executive Summary

Many knowledge workers waste hours on repetitive, cross-application desktop tasks because existing options (macros, scripting, or enterprise RPA) are either too technical, brittle, or expensive; IT admins and individual contributors alike face manual context switching, data leakage risks, and poor auditability. This creates a tangible productivity drag for an estimated 200M knowledge workers who could plausibly pay ~$200/year for reliable automation. You could build an AI agent that accepts natural-language instructions, composes and executes multi-step workflows across desktop apps, and runs primarily on-device with optional cloud augmentation for heavy LLM work—think conversational automation, prebuilt connectors, scheduled triggers, and audit logs. A hybrid architecture would balance privacy and capability by keeping sensitive data local while leveraging cloud models when needed. The total addressable market is roughly $40B (200M workers × $200 ACV), and current trends—LLM-driven workflow synthesis, demand for local execution, and a shift from scripting to conversational interfaces—make adoption more likely now than in the last decade, reflected in a market score of 90/100 and revenue potential of 82/100. To stand out you’ll need a local-first security posture, rock-solid cross-application reliability, and a conversational UX that non-technical users can trust; the challenges are significant (hard integration engineering, platform fragmentation, and competing with established RPA vendors and big tech), but if you can demonstrate clear time-savings and safe, auditable automation, this is a high-potential, buildable product.

Market Opportunity

Automate desktop tasks via an AI agent that runs apps and workflows targets a $40.0B = 200M knowledge workers × $200 ACV total addressable market with medium saturation and a year-over-year growth rate of Approximately 25-35% CAGR for AI-driven automation and RPA combined (industry estimates from Gartner and Grand View Research).

Key trends driving demand: AI-first productivity — LLMs enable translating natural-language instructions into multi-step workflows which increases user adoption potential.; Privacy and on-device execution — demand for local execution and data minimization is rising, creating an opportunity for hybrid local/cloud agents.; Shift from scripting to conversational automation — non-technical users prefer conversational interfaces over writing scripts, opening a larger addressable market.; Platform openness — OS vendors are adding automation APIs, making reliable on-device control easier and safer for third parties..

Key competitors include Microsoft Power Automate, AutoHotkey, Raycast.

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Automate desktop tasks via an AI agent that runs apps and workflows

Executive Summary

Market Validation

Market Opportunity

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