Most 'AI for marketing' pitches in 2026 are vapourware. The studio takes the opposite stance: only ship use cases where the AI replaces a measurable, recurring human task. Lead scoring is one — an LLM ranking ICP fit on a documented signal model is faster and more consistent than a junior SDR doing it manually. ICP enrichment is another — pulling firmographic, technographic, and intent signals into a single per-account brief. Outbound personalisation works when the input is a real signal (a job posting, a funding round) rather than a token replacement. Content operations work for first-pass drafts that a senior reviews. Anything else — chatbots, generic 'AI strategy' decks, autonomous agents that send without human review — is skipped on purpose.
Who needs AI in their funnel: B2B SaaS teams with 1000+ inbound leads/month where SDR triage is the bottleneck, RevOps teams who can't afford a Clearbit/ZoomInfo enterprise contract but need ICP enrichment, founders who need a senior content-ops hand to ship 2 articles/week without hiring a full-time editor, and outbound teams running multi-domain engines who need per-prospect openers grounded in real signals. The unit of measurement is always 'time saved' or 'dollars converted' — never 'AI deployed'.
What we don't build: chatbots that paper over a broken support page, autonomous outbound agents that send without human review, generic 'AI strategy' decks, RAG systems for marketing copy nobody reads, vector databases that exist to justify a vendor invoice. The studio has shipped these for clients who insisted, watched them die in 60 days, and then refunded the next ask.
How Martechno ships it: week 1 use-case audit (which recurring tasks could AI replace, what's the human-time cost, what's the failure mode). Week 2 model selection (OpenAI, Anthropic, open-weights) and prompt engineering, eval harness with golden examples. Week 3 integration (CRM, Clay, content stack), guardrails, monitoring. Week 4 launch and handover with documented prompts the team owns. Engagements run $10k–$30k for sprint scope; ongoing ops included in retainer.
What you get: lead scoring tuned on real first-party + intent signals with weekly accuracy review, an ICP enrichment pipeline that runs on Clay or a custom service, outbound personalisation grounded in named signals (funding rounds, hiring activity, tech-stack changes), content-ops drafts a senior reviews before publishing, an eval harness so the team can detect drift, and prompts documented in the same repo as the rest of the stack. Engagements include a 30-day tuning window.
Common questions: Will AI replace our SDRs? No — it makes them faster on triage and personalisation, doesn't replace human judgment on outreach. What's your stance on autonomous agents? Skipped — every send has a human in the loop. How do you measure ROI? Time saved per task × frequency × hourly cost, plus conversion rate lift on the touched cohort. Do you train custom models? Rarely — fine-tuning makes sense only for ICP enrichment with 10k+ labelled examples; everything else uses prompting + RAG.
Why senior operators win at AI for marketing: vendor pitches collapse on contact with real B2B funnels. Senior engineers know which use cases convert and which don't, write evals that catch silent regressions, and refuse to ship anything that doesn't move a measurable metric.