Practical AI workflows with sources, evaluation and a clear role for human review.
How to decide whether an AI-assisted draft deserves publication: audit claims and implied experience first, then added value, then voice. An annotated edit and a publish, revise or reject rubric.
Positioning decides who you are for, against which alternative, why you are better and what proves it. Messaging comes after. A worked example from generic headline to defensible claim, and where AI helps.
How to build a B2B ideal customer profile from won, lost and churned accounts and real customer language, using AI to sort the evidence while unsupported traits stay marked unknown.
A practical method for choosing a marketing team’s first AI project: start from the bottleneck, screen tasks on value, frequency, inputs, reviewability and error impact, then run one bounded pilot.
Fixed rules, a reviewed AI step, a controlled workflow or an agent: what each is, what added autonomy costs in checking and reliability, and a decision tree for choosing the simplest approach that works.
A practical research workflow that preserves sources, leaves unknowns visible and gives a person the final decision.
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