AI-Powered GTM Automation

AI is worth using at the points in a workflow where the alternative is a person reading something. It is a waste of credits everywhere else — a database lookup is faster, cheaper and more accurate than asking a model to guess.

This course goes stage by stage through the framework and marks the places AI genuinely wins, then builds each one — first as columns in a table, then as MCP chains your AI client runs end to end.

About this course

Nineteen lessons in nine modules. The first module gives you a test for whether a step should use AI at all. The second teaches you to think in MCP tool chains. The middle five build the four-stage framework. The last three are complete agentic workflows you can run the day you finish reading them.

The bias throughout is against AI, not toward it. Most GTM workflows that feel expensive are expensive because a model was asked to do something a filter could have done for free. Every lesson names the cheaper alternative it beat.

Who it is for. People who already run a table and want to make it smarter, and people who want to run the same work by prompting instead. If a column, an action and a run are unfamiliar words, do SyncGTM 101 first.

What it costs. Around 150 credits at the suggested volumes. The build modules carry most of it; each states a smaller volume you can run instead.

Thinking with MCP

Half this course runs through the MCP server rather than the app, so it is worth being explicit about what changes.

Once the server is connected, your AI client holds roughly 50 Sync GTM data tools. It stops being something that advises you and becomes something that acts: it searches, enriches, verifies and writes, in one pass, inside the conversation you are already having.

The mental shift is from questions to chains. Not “who should I prospect?” but “source these people, cut them to this filter, enrich the survivors, verify, hand me the table.”

Both halves of that table are why it is cheaper as well as faster. Filtering happens mid-chain and costs nothing, so you pay to enrich 25 rows instead of 200 — and a step that returns nothing simply stops that row rather than billing you for the rest of it.

Lesson 04 is the full mental model. The phrasing rules that make a chain fire the right tool — name the tool, spell out the filters, cap the run, ask for raw rows — live in the MCP prompting guide, worth reading once before your first real chain.

Skills you will gain

• Apply a test for whether a step should use AI at all before spending on it
• Express a GTM job as an MCP tool chain instead of a question
• Source accounts that resemble your best customers rather than your loudest ones
• Make a model return one of five values instead of a paragraph
• Score a row on evidence rather than vibes, and gate spending on the score
• Generate copy that references a specific fact and reads like a person wrote it
• Run warm outbound from LinkedIn engagement straight into an Instantly campaign
• Brief every meeting on your calendar before you join the call
• Keep a CRM current without anyone volunteering to clean it

In this course

Module 1 · Deciding where AI belongs

Three lessons of judgement before any spend. Skipping this module is how workflows get expensive.

• 01Where AI actually helps in GTMA test you can apply to any step before spending on it7 min
• 02The AI toolkit in Sync GTMKnowing which AI feature solves which problem9 min
• 03Credit disciplineThe cheap-first ordering that keeps runs affordable8 min

Module 2 · Thinking with MCP

The surface where AI does the assembling. Everything in the three build modules assumes this one.

• 04How to think with MCPAny GTM job expressed as a tool chain, and the ones to leave in a table10 min
• 05From prompt to reusable GTM agentA working chain saved as a skill your team can trigger8 min

Module 3 · Find

• 06Describing your ICP to a modelA written ICP that produces a usable filter, not a vibe9 min
• 07Sourcing lookalike accountsCompanies resembling your best customers, sourced not guessed10 min

Module 4 · Research

• 08Research agents that stay on taskAnswers that do not drift off the question11 min
• 09Reading unstructured sourcesFacts extracted from prose, filings and posts10 min

Module 5 · Enrich

The stage where AI does the most work per credit — classification and scoring cost little and decide a lot.

• 10AI as an enrichment fallbackCoverage on rows the providers missed9 min
• 11Classifying rowsEvery row tagged with one of a fixed set of values9 min
• 12Scoring and gatingA score that decides which rows get the expensive columns11 min
• 13Normalizing and dedupingConsistent values across inconsistent inputs9 min

Module 6 · Outreach

• 14Copy that references something realAn opening line tied to a fact on the row12 min
• 15Multi-step message sequencesA follow-up that does not repeat the first touch10 min
• 16Quality control on AI outputBad rows caught before they leave the table6 min

Module 7 · Build — AI-agentic outbound

Warm leads sourced from LinkedIn engagement, qualified for free, enriched, verified, and sequenced into Instantly. All from one chat window.

• 17Warm leads to Instantly, end to end25 verified, signal-carrying contacts in a live campaign for ~36 creditslinkedin_post_commenters → find_work_email → verify_email → Instantly14 min

Module 8 · Build — AI account researcher

The chain that starts with your calendar instead of a list: every external meeting, researched, multi-threaded and briefed before you join.

• 18Research every demo on your calendarA sourced brief per meeting, with the other decision-makers namedcalendar → enrich_organization → techstack, hiring, posts → find_people_within_company12 min

Module 9 · Build — Auto-enrich your CRM

Pull the broken records out of Attio, refill titles, companies and emails from LinkedIn enrichment, review the diff, write back only what you confirmed.

• 19Keep Attio current without touching itA review-then-write chain that never overwrites good dataAttio → linkedin_profile_enrich → find_work_email → verify_email → Attio12 min

What you will have built

A table where AI runs in four distinct roles, each earning its place:

• A classifier turning free text into one of a fixed set of values you can filter on
• A scorer producing a number from evidence on the row, with a threshold behind it
• A gate that stops the expensive columns from firing on rows below the threshold
• A writer assembling an opening line from the research answer, with a QC pass behind it

Plus three MCP chains that run without a table at all: warm outbound into a live campaign, a researched brief for every meeting on your calendar, and a CRM that repairs its own records.

The gate is the part that pays for the course. In most workflows it removes more cost than every other optimization combined.

Before you start

• A table you already run, with at least a sourcing column and some enrichment on it.
• The MCP server connected to your AI client — modules 2, 7, 8 and 9 all assume it. Two minutes, no API key.
• A sense of your best customers — lesson 06 turns it into a written ICP, and lesson 07 sources against it.
• SyncGTM 101 if any of that is unfamiliar. This course does not re-teach the mechanics.

The ordering rule

Cheap and deterministic first, expensive and generative last. A filter costs nothing, a database enrichment costs a credit, an AI research call costs more, and a generated message is the most expensive thing you can put on a row. Run them in that order and every row that gets the expensive step has already earned it.

The rule does not change on the MCP surface — it just moves into the prompt. In a table it is column order; in a chain it is the sentence that says “qualify before you spend anything”. Lesson 03 turns it into a checklist you can apply to either.

Start from a GTM agent

Every build module in this course also exists as an installable skill file — the chain already scoped to the right tools, filters and caps, triggered with a slash command:

• LinkedIn Job Lead Sequencer and Warm Leads From Posts — module 7
• Daily Meeting Prep and Account Researcher — module 8
• Attio Enricher, plus the HubSpot, Salesforce and Close versions — module 9

Read the lesson to understand the chain, then install the agent so you never type it again.

Browse all GTM agents →

Reference behind this course

• AI Agents — configuring agents and prompts
• Actions — how columns run and chain
• Credits — what each action type costs
• MCP prompting guide — phrasing a chain so the right tool fires
• MCP tools — every tool, its parameters and its credit cost

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