AI marketing
consulting.

Most teams have tried a model on a draft, a summary, or a report. The useful question is narrower: which recurring marketing tasks should a model touch this quarter, which decisions stay with a person, and what data the model is allowed to see.

Who this is for

You are a fit when pilots are scattered across individual logins, the output quality depends on who wrote the prompt, and leadership cannot say what the team adopted. The engagement is for companies that already market, and want AI inside that system.

Marketing automation is the related service for the follow-up machinery: the emails, the stages, and the handoff to sales. AI consulting decides where a model is allowed inside that machinery. Marketing analytics is where we tie the result to pipeline and revenue.

What the work covers

A map of the work. We list the recurring tasks in research, content, sales follow-up, and reporting. Each task gets one of three marks: a person does it, a model drafts and a person approves, or it waits.

Rules the team can keep. What a model may draft. What a person still decides. Which customer data, prompts, and outputs stay inside your accounts. The point is a page the team will actually open, not a policy binder.

A small set of workflows. A few prompts and steps for the tasks you marked as ready. The free RTCOE prompter is a useful way to write one of those prompts. The consulting work is choosing which prompts are worth standardizing, and reviewing the output against your buyers.

A starting score, if you want one. The AI marketing readiness benchmark is a 20-point check on data, process, tools, and measurement. We use it when the foundation is the question. The consulting engagement is what you do after the score.

A review after one cycle. Time returned to the team, work you kept, work you reverted, and the next task that deserves a workflow.

How an engagement runs

1. Understand. How marketing work moves today, which tools people already pay for, and where a draft has helped or embarrassed you.
2. Strategize. The short list for this quarter, the rules, and the one or two workflows worth building first.
3. Execute. The workflows, a short training session with the people who will use them, and a written standard.
4. Review. What the team used, what they bypassed, and whether the output is good enough to keep.

If the readiness score says the data and the process are not ready, we say so and start there. A model on top of a messy pipeline makes the mess faster.

Frequently asked questions

Do we need to buy new AI tools?

Usually not at the start. Most teams already have access to capable models. The work is deciding where AI belongs in your marketing, which tasks stay with people, and the rules the team follows.

Will AI replace our marketing team?

No. AI speeds up drafting, research, and analysis. Judgment about your buyers, your offer, and what to publish stays with your team.

How do we keep AI-written content accurate?

Every piece has a human owner who checks facts, claims, and tone before it goes out. We set that review step up as part of the engagement.