For product management teams
Run AI marketing capabilities like a product
mAI treats marketing intelligence as a managed product: defined audience, documented build path, tested release, an evidence standard and a feedback loop.
Where AI features and marketing systems stall
- No clear owner or audience: the capability is built before anyone defines who it is for and what it changes.
- No release discipline: prototypes reach users without baselines, tests or approval limits.
- Weak feedback loops: results are reported but do not change the roadmap.
- Governance bolted on late: privacy, claims and approvals are added after the fact.
A product lifecycle for marketing intelligence
- Define: the audience, the problem, the success measures and the baseline, with assumptions written down as assumptions.
- Build and test: every model starts from one documented framework and is tested before release. See how an mAI model is built.
- Release under limits: explicit approval thresholds for budgets, audiences and claims, with write access to your systems off by default.
- Measure: Productivity + Precision (P²) read against the agreed baseline. Targets are labeled as targets, and a correlation is never treated as a cause.
- Improve: findings return to context as controlled change requests, each with evidence, risk, an approver and a way to confirm it worked.
See the architecture and the model gallery of demonstration models.
Decisions that stay with product owners
Strategy changes, audience definitions, budgets above threshold, new channels and regulated claims stay with named people. mAI recommends and drafts; people decide. Read the governance model.
