AI vs traditional product-led growth

AI products are probabilistic: the same input can produce different outputs depending on context, data, and model state. That makes perceived value more variable and user trust more fragile than in purely deterministic software.

At the same time, AI systems can adapt interfaces, recommendations, and workflows based on observed behavior, which creates new opportunities for personalized activation and retention loops that did not exist in static products.

In traditional PLG, onboarding often focuses on helping users discover features and configure settings. In AI PLG, onboarding must also help users build good mental models of what the system can and cannot do, and how to interact with it effectively.

Experimentation and risk look different too: small changes to prompts, guardrails, or model configuration can have outsized effects on perceived quality, safety, and trust. You need to watch not just conversion metrics but also signals of user confidence and error handling.

Activation in AI products is often tied to the first time a user gets a “good enough” output that they actually use in their real work: a draft they edit and send, a workflow that runs end-to-end, or a recommendation they follow.

Retention depends on whether the AI system becomes embedded in repeatable workflows such as projects, documents, agents, or automations that users return to, not just on whether it can produce impressive one-off demos.

Inline suggestions, completion, and automation can dramatically reduce time-to-value when they are well-tuned, but they can also overwhelm or confuse users if surfaced too early or in the wrong context.

Feedback channels such as ratings, corrections, or “compare to previous” views become part of the PLG system: they inform model and UX improvements and give users a sense of control over a probabilistic system.

Overpromising what the AI can do or failing to clearly communicate its limits can erode trust quickly and show up as sudden drops in activation or retention after initial curiosity fades.

Onboarding flows that assume users will craft perfect prompts or understand advanced configuration from day one often backfire; users churn before they ever see the product at its best.

AI products still need traditional PLG metrics such as activation, retention, and expansion, but must also add quality and trust signals: acceptance rates, correction rates, error reports, and qualitative feedback.

Experiments should be designed to monitor both business outcomes and these quality signals. A change that boosts short-term engagement but quietly increases frustration or errors can damage the motion over time.