For sophisticated AI tools requiring deep business context, a purely self-serve onboarding often fails. Plurium validates its PLG motion by initially using human consultants for setup to ensure data accuracy and gather context, then building those learnings into an automated, self-service flow over time.

Instead of a traditional, linear onboarding flow, OpenAI experiments with using the model itself to welcome users. The AI can conversationally understand a user's goals and tailor its guidance, creating a dynamic and personalized first-time experience.

The traditional SaaS onboarding model of dashboards and manual configuration is becoming obsolete. By exposing a product via a CLI to a user's primary AI agent, the agent can leverage its existing context about the user to perform setup and configuration automatically, creating a superior user experience.

Instead of relying on traditional tutorials, non-technical individuals can successfully build complex AI agent teams by using a conversational AI as an interactive, patient, step-by-step coach. This approach democratizes access to advanced technology, bypassing conventional learning methods.

Instead of setup menus, users onboard Lindy through conversation, just as they would with a human. Telling it "after my meetings, I want you to update my CRM" is the entire configuration process, drastically lowering the adoption barrier for non-technical users.

Since AI capabilities are novel, users often struggle with adoption. Rather than using traditional templates or tutorials, a more effective method is to build an AI agent or operator that guides users through the process. This approach uses the AI to teach the user how to leverage AI's potential within the product's specific context.

To ease the transition to AI workflows, begin by encouraging employees to use common tools like ChatGPT with simple, conversational prompts. This builds comfort with generative responses. Only after this foundation is set should you introduce the concept of supervising small, autonomous AI agents, making adoption more natural.

Instead of pre-designing a complex AI system, first achieve your desired output through a manual, iterative conversation. Then, instruct the AI to review the entire session and convert that successful workflow into a reusable "skill." This reverse-engineers a perfect system from a proven process.

Create an AI skill that ingests your core principles, goals, and positioning from a central intelligence layer. This allows your team to query a 'synthetic you' for quick answers and guidance, freeing up your time for higher-level work and empowering them to move faster.