Automate repetitive desktop workflows by running AI agents on your PC

Executive Summary

Millions of knowledge workers spend substantial portions of their day on repetitive desktop tasks—copying data between apps, extracting information from documents, managing files and emails—which creates a clear addressable market of roughly 300 million knowledge workers and an $85.0B opportunity (about $283 per worker per year) for desktop automation and productivity tools. These problems are especially acute for roles that rely on multiple legacy desktop applications (legal, finance, operations) where existing cloud automations either can’t reach or force risky data egress. You could build an on‑device AI agent platform that orchestrates multi‑step workflows across local apps and files, pairing a visual no‑code builder with recorder-style capture and a plugin system for deep OS/app integration. The product would run small to medium on‑device models for inference to keep latency low and data private, while using LLM-based orchestration to chain steps and call tools, aligning with current trends in on‑device AI, agent orchestration, and no‑code automation. The market is attractive now because on‑device models and hardware acceleration are maturing, privacy and latency concerns are increasingly decisive buying factors, and analysts score this sector highly (Market Score 92/100, Revenue Potential 86/100). Differentiation comes from robust local execution, reliable OS-level connectors, enterprise management controls, and a genuinely usable no‑code UX; competition is medium, so technical depth and integrations matter more than pure marketing. Be honest about the hard parts: on‑device model performance, brittle integrations with closed desktop apps, and the sales motion into enterprises; pursue this if you have strong ML systems engineers, desktop integration expertise, and 12–18 months of runway, or else consider a niche vertical or hybrid approach to de‑risk initial traction.