To overcome customer inertia with AI, don't pitch a broad platform. Instead, identify a specific, high-impact use case for their industry (e.g., 'where's my order' for retail). Deliver a pilot that shows tangible, quick value, and use that success as a beachhead to expand to other use cases.

Don't deploy an AI SDR to find product-market fit or create a sales motion from scratch. It's a tool for amplification. You must first prove that a human can successfully sell your product with a specific playbook, then feed that playbook to the AI.

Enterprise buyers are drawn to the vision of full automation ("the sizzle"), but their immediate need is improving existing human workflows ("the steak"). A startup must offer both. The visionary product gets them in the door, while the practical agent-assist tool delivers immediate value and gathers necessary data for future automation.

Start with a 'Minimal Useful Agent' that performs a simple, bounded task like drafting replies for human approval or triaging inbound requests. This 'draft and approve' model reduces risk, builds customer trust, and allows you to earn autonomy over time.

Don't try to build a complex AI agent from day one. SaaStr's AI VP of Customer Success started as a basic project management portal to replace a clunky tool. Its advanced, agentic capabilities were layered on over months as real user needs became clear post-launch.

Initial adoption of AI agents was driven by solving small, personal annoyances like ordering groceries, dubbed "computer errands." This low-stakes entry point helped users build familiarity and trust with the agent before graduating them to more complex, high-value professional work.

Before writing code, manually perform the customer's workflow as a service. This unsexy approach ensures you deeply understand the process, enabling you to build a superior automated solution later. It's about fulfilling the task first, then building the software.

Instead of immediately building an AI agent, founders should first manually perform the target workflow as a service. This process allows them to deeply understand the pain points, map edge cases, and acquire initial clients. Only after mastering the job manually should they incrementally add vertical agents to automate specific steps.

Instead of a complex, full-funnel AI integration, companies can get a faster ROI by targeting a high-leverage, contained activity. Post-sales support, like using vision AI to verify warranty claims, is an ideal starting point for tangible results and building internal momentum.