A practical guide to writing AI agent case studies that actually help buyers buy: workflow context, baseline pain, control design, metrics, human handoff, and why most AI case studies fail to build trust.

A practical guide to the proof assets behind an AI agent business: what to publish before buyers trust you, how to make expertise legible, and how to turn insight into credibility without relying on hype.

A practical guide to writing AI agent case studies that create trust, pipeline, and proof without exposing client-sensitive details or turning the story into vague marketing sludge.

A practical guide to the procurement packet behind an AI agent project: what the buyer needs to document before the deal gets stuck in security review, IT questions, ownership confusion, or budget limbo.

A practical guide to building the business case for an AI agent project: how to frame the workflow problem, estimate value honestly, account for exception costs, and justify budget without fake autonomy math.

A practical guide to the internal champion behind an AI agent project: who should own the pitch, what evidence they need, why deals stall in the middle, and how to package the project so it survives budget, procurement, and operational skepticism.

A practical guide to the discovery questions that matter before you sell, scope, or build an AI agent workflow: where the pain is, what breaks, who owns exceptions, and whether the economics are actually worth it.