Instead of marketing OpenClaw as a generic assistant, the real opportunity lies in creating specialized, vertical solutions for specific industries. Assisting companies in adopting these tailored computer-use agents for their niche problems is a major area for startups, as highlighted by Andreessen Horowitz.
Businesses are actively posting high-value automation jobs on platforms like Upwork. These job descriptions provide the exact context and requirements needed to build a demo with OpenClaw, creating a direct path to finding your first paying customers.
Use AI on your own process to accelerate client work. Record discovery calls, generate transcripts, and feed them into an LLM. Ask it to identify the highest-value automation opportunities and map out the step-by-step workflow based on the client's own words.
The speed of agent development allows for a new sales motion: live prototyping. Instead of just describing a potential automation, build a minimum viable version of the 'skill' during the conversation. This transforms an abstract idea into a tangible, working demo in minutes, demonstrating immediate value.
The business model is shifting from selling software to selling outcomes. Instead of creating a tool and inviting users, create pre-trained agents that perform valuable work. Then, invite companies to a workspace where this 'team' of AI employees is ready to start delivering value immediately.
Structure your AI automations architecturally. Create specialized sub-agents, each with a discrete 'skill' (e.g., scraping Twitter). Your main OpenClaw agent then acts as an orchestrator, calling these skilled sub-agents as needed. This frees up the main agent and creates a modular, powerful system.
To win over a new business client, don't try to automate everything at once. Use a design thinking approach to identify the single 'low-hanging fruit' task that provides the highest value for the lowest effort. This initial 'wedge' builds trust and opens the door for larger projects.
Go beyond using a single OpenClaw instance. Spawn multiple sub-agents to parallelize work. This can mean either having ten agents work on ten different parts of one large task, or having ten agents run ten separate instances of the same task simultaneously.