What Not to Promise

A specific rank: AI answers have no stable position structure. Instead: commit to mention rate and average position improvement across a fixed question set.

Post-visit conversion: depends on product, pricing, landing experience and sales process. Instead: commit to isolating the AI channel and reporting explainable conversion data; write a conversion target only where both sides share the definition.

Third-party platform output: communities remove marketing content, named reviews depend on customers, editorial adoption is not yours. Instead: commit inputs — volume, channel types, standards and review process.

Quantified targets on third-party estimates: those traffic and difficulty figures are estimates. Instead: use them to prioritise, and hang acceptance on first-party analytics and server logs.

All four look committable — they have numbers and tools that measure them. That is exactly the danger: the definition mismatch or lack of control only surfaces at acceptance, when disputes are expensive.

Disagreement in the public research makes the point: one study values AI visitors at 4.4× organic, while a statistical analysis found no significant difference (p=0.794). With that spread, no specific conversion multiple has a basis.

Write the commitment table in two columns: what we control (inputs and process metrics) and what depends on others (outputs and results). The second column carries observations, not commitments.

The benefit is that mid-project variance becomes a conversation about causes rather than about whether a promise was broken.