About Mike Moore

Executive experience, applied to the work ahead.

I bring 32 years of experience in technology, including 12 years in executive roles. I’m responsible for growth in my current role, spanning marketing, partnerships, and other growth initiatives. Rebuilding marketing was the first challenge: I was expected to expand what the function could deliver without adding human headcount.

I had already been applying AI tools in earlier roles. In my current work, I built a team of AI agents to help research, draft, review, and produce the work a larger marketing team would otherwise need to cover. I’m extending the same practice across growth: managing and recruiting partners, and supporting SDR work from lead intake and processing through meeting preparation and opportunity follow-through.

MOORE AI is based in Boston and works with executives and teams remotely across locations.

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The work behind the approach

Build the capacity to get important work shipped.

As I rebuilt marketing without additional human headcount, the challenge was clear: the ideas and priorities were there, but one person could only take so much from concept to delivery. I needed a way to scale the work without lowering the bar for accuracy, compliance, or judgment.

I stopped treating AI as a writing tool and began building a team of AI agents around the work: research, drafting, review, and production. I designed the workflows myself and kept refining them as real work exposed gaps. Shared brand, compliance, and approval guidelines help keep the work aligned with how we operate. AI helps move the work forward; I bring the judgment, editing, compliance check, and sign-off.

This approach helped bring organic and paid social back in-house and build a repeatable episode process. I’m applying the same thinking to partner and SDR workflows. That work is early: I’m developing partner briefs and draft growth plans, and helping SDR prepare researched account briefs and first-touch messages. People verify the context, coordinate with account owners, and make the decisions and send the outreach.

Examples from the work

Make one piece of work useful in more places.

A practical workflow helps real work move from idea to review to delivery—and gives someone else a process they can run.

Podcast production

From episode to coordinated release.

An episode becomes a coordinated package: show notes, a companion article, social posts, video assets, a web page, and a newsletter mention. AI-supported steps help research guests, develop drafts from transcripts, screen for possible compliance issues, and check that each asset is ready. I choose the episode focus, edit for accuracy and tone, and approve the package. Guest and organization review happens where needed. The video premieres first; the web page and article follow about 30 minutes later. The checklist has made scheduling straightforward enough for an intern to run. Nothing publishes without compliance review and my sign-off.

Sales enablement

A useful first answer, without a manual quote build.

Using Claude, I built a budgetary pricing calculator for a sales team. Reps can get a fast, consistent ballpark number and keep a conversation moving without waiting for someone to assemble a quote by hand.

What experience taught me

Good AI workflows need good operating judgment.

Set the rules before the prompts.

Clear brand language, compliance requirements, and approval roles do more for dependable work than clever wording alone.

Keep people accountable.

AI can draft and organize. In regulated work, a person still checks the work and signs off before it goes out.

Keep only what helps.

A process should make useful work easier. If a dashboard or automation does not help anyone act, it is clutter.

Give a small team more reach.

AI can help a lean team deliver more of the work that matters while people retain ownership, judgment, and accountability.

How MOORE AI works

Bring the work you need to move forward.

That is the same starting point I bring to executive coaching, team enablement, and workflow sprints: a real task or process, the tools and information environment you approve, and a practical way to review what AI produces.

The goal is to leave with useful work in hand and a clearer sense of how to repeat or adapt the approach. Depending on your environment, tools may include ChatGPT, Claude, Gemini, or Microsoft Copilot.

Explore the work