Most marketing teams have bought the AI tools and still ship at the same speed. Everyone has a ChatGPT tab open. Nothing in the actual workflow changed, nobody trusts the output enough to send it, and there is no number anyone can point to.

That gap is the work.

What does an AI B2B marketing consultant do?

An AI B2B marketing consultant finds where AI can improve marketing, builds the system around it, trains the people who will use it, and measures whether it made the work or business result better.

The point is not more tools. The point is more useful work from the team you already have.

I run marketing this way myself: I rebuilt this site with agents, turned 19 consulting casebooks into agent commands, and built a research system that does a week of company analysis in an afternoon. The tools are open source if you want to see the workmanship before you hire it.

Book 30 minutes on my calendar to talk through your team, goals, and current AI work.

What you get

A clear AI plan

Which work should change, which work should stay human, what to build first, who owns it, and how success will be measured.

Working marketing systems

Agents, instructions, tools, data, review steps, and documents connected around real work. Not a folder of prompts people forget.

A team that can use the system

Workshops and training built around your work. People learn the tools, the process, the quality bar, and how to challenge bad output.

Proof that the change helped

Before and after measures for speed, cost, quality, use, and the business result linked to the work.

Where I help

AI strategy and team design

Choose use cases, tools, roles, rules, budget, and the order in which the team should change.

Content, AEO, and GEO

Build research, writing, review, and publishing systems that help useful pages ship faster and make the brand easier for search engines and AI answers to understand.

Campaign, sales, and lifecycle work

Use AI for research, planning, account work, sales support, campaign production, customer signals, onboarding, and expansion.

Knowledge and reporting

Give people and agents trusted context. Connect plans, customer evidence, pipeline, campaign results, and review notes so the next decision starts with what the company already knows.

AI does not fix unclear marketing

Give a strong model a weak brief and you get weak work faster.

Most failed AI projects are not model problems. Nobody chose the business goal. Context is scattered. The old process was never clear. Review belongs to whoever has time. The team cannot tell whether the new system helped.

Buying tools is easy. Changing how the work gets decided, built, checked, and measured is the job.

Choose how we work

Workshop

Use a workshop to set the AI plan, train leadership or the team, choose use cases, and leave with clear owners and next steps. Practical tool training can be part of it.

Defined project

Use a project to build and run one valuable system. Good starting points include AEO and GEO, content, sales support, account research, reporting, lifecycle, or campaign work.

Ongoing consulting or fractional leadership

Use an ongoing contract when several parts of marketing need to change, the team needs regular help, or the tools and work will keep moving.

I can guide the plan, train the team, review systems, help choose vendors, watch the measures, and keep improving what ships.

If the plan needs more execution capacity, I can bring in Grow & Close while staying close to the strategy and review.

How the work happens

Choose the result. Start with a business or team goal, not a tool.

Map the work. Find the steps, people, data, delays, and decisions inside the current process.

Build and train. Create the system, run it on real work, and teach the team how to use and change it.

Measure and improve. Compare speed, cost, quality, use, and business results. Fix what breaks. Expand only when the first system works.

What we measure

The measure depends on the use case.

For content: time to publish, review time, quality, search visibility, conversion, and influenced pipeline.
For sales support: research time, use by reps, meeting quality, conversion, and pipeline.
For reporting: time saved, data trust, decision speed, and forecast quality.
For team change: use, work shipped per person, error rates, and where senior time is spent.

We choose the measures before building. Otherwise every AI pilot looks successful.

Proof

At GTM Buddy, I used GitHub, Cursor, Claude, Codex, and Swan in daily marketing work. I built systems for sales signals, account research, meeting preparation, campaigns, and pipeline reporting.

I also maintain public agent skills, experiments, and operating notes on GitHub. You can inspect the work instead of trusting an “AI expert” label.

My broader marketing record is on the work page.

Common questions

Can you run an AI workshop with tool training?

Yes. The tools will be taught through your work and goals, not as a random product tour.

Do you build the systems yourself?

Yes. I can build key parts, work with your technical and marketing teams, or bring more execution help through Grow & Close.

Do you take ongoing AI consulting contracts?

Yes. Ongoing work makes sense when several systems need to change or the team needs a senior person to keep the plan, training, and measures connected.

Does every AI project need to create pipeline?

No. Some work should improve cost, speed, quality, or team capacity. We still name the business reason and measure it before we build.

Start with the work, not the tool

Bring one process that is slow, expensive, weak, or hard to scale. We will decide whether it needs a workshop, a defined project, or ongoing help.

Book a 30-minute conversation.