Don't try to optimize your strongest departments with your first AI project. Instead, target 'layup roles'—areas where processes are broken or work isn't getting done. The bar for success is lower, making it easier to get a quick, impactful win.

AI companies can accelerate enterprise adoption by focusing on workflows already outsourced to BPOs. This provides pre-codified standard operating procedures (SOPs), existing QA processes, and simpler change management, as replacing a vendor is easier than displacing an internal team.

Industries like law firms, insurance, and real estate are ideal first customers. They are eager to adopt AI to solve significant operational pain points but lack the in-house talent. This creates a strong market pull for an outsourced agent-building service. Avoid highly regulated fields like healthcare initially.

Before creating a new headcount for administrative or repetitive work, conduct a thought experiment: can an AI agent or an automation workflow fulfill these duties? This approach can reduce overhead and force a re-evaluation of how tasks are accomplished.

Startups building AI agents to automate work should first target outsourced services. It is easier to win business by swapping an existing third-party vendor with a ready budget than it is to persuade a company to undergo internal reorganization and headcount reduction.

1mind’s go-to-market for its AI sales engineer targets segments where a human equivalent is economically impossible, like adding a solutions engineer to small commercial deals. This strategy proves value without directly threatening existing jobs, earning the right to move upmarket later.

An AI appointment setter is an easy business to launch because its value proposition is simple. You're not selling a new concept, but rather a more efficient, cost-effective replacement for an existing, expensive full-time employee, making the ROI immediately clear to potential clients.

The business model for AI agents fundamentally shifts the value proposition from selling a tool (license) to selling an outcome (automated work). This allows vendors to tap into operational or labor budgets, not just IT budgets, unlocking a new price-for-value equation and exponentially larger contract sizes.

The most effective use of AI agents isn't just automating tasks. It's solving a critical, high-pain business problem that humans are failing at, such as SaaStr's six-figure lag in customer collections.