The AI Marketing Stack We Run in 2025 (Full Breakdown)

Deep-web search with citations, competitive intelligence and category primers now take hours instead of days. Perplexity handles fast-turnaround questions; Exa is our default for structured research with cleaner citations; Claude with a research prompt library synthesises across both.

We've cut research time on new client categories by roughly 70% while raising the quality of the strategy that comes out of it.

First drafts, tone rewrites, headline variants, section outlines. Never publish raw — always edit for voice, always fact-check, always cut 40% of what the AI wrote.

The shared prompt library is the actual productivity gain: 30 tested prompts (SEO brief, editorial brief, newsletter, LinkedIn post, ad copy variants) that produce reliably good first drafts because the prompts themselves are the accumulated intellectual property.

Concept boards in minutes, motion tests before production, style exploration in real time, voiceover iteration before booking human sessions. This quartet has changed our pre-production economics — we can show clients three moods and three voice directions in an afternoon, at essentially zero incremental cost, which raises the quality of the final creative direction we commit to.

The connective tissue between the AI tools and the systems of record. AI-triggered workflows now automate roughly 40% of our reporting pipeline — data extraction, insight generation, first-draft narrative, delivery formatting.

n8n is our default for anything complex enough to need branching logic; Zapier and Make are for straight-through automations that a strategist needs to own.

Data goes into BigQuery, insights come out of Claude with a carefully engineered prompt that reads structured summaries and returns anomaly flags and narrative. The prompt is the value; the tools are commodity.

A junior analyst with the right prompt out-performs a senior analyst without one.

The real productivity gain isn't the tools — it's the prompts your team reuses. Version them like code (we store them in Notion with change logs).

Every prompt has an owner, a test case, and a documented failure mode. This turns AI from 'individual productivity hack' into 'team capability,' which is where the real leverage lives.

Every quarter we test 5–10 new AI tools and typically adopt one. Tools that promised to replace strategists, replace designers, or 'automate marketing' have all failed in production.

Tools that speed up specific tasks with clear boundaries have all won. This is a reliable pattern — the wide-scope AI products are hype; the narrow-scope ones are infrastructure.

Full stack cost for a team of 20: roughly $2,400/month in AI tools plus $800/month in infrastructure. The productivity return is somewhere between 15 and 25 additional 'strategist hours per week' distributed across the team.

At any reasonable billable rate, the payback period is under a week.