I don't just talk about AI — I ship production AI tools that marketing teams use daily. Job search agents, SEO audit agents, content pipelines, ad-copy automation. Claude, GPT, Gemini, Python, and proper observability. 6 live AI agents currently running in production.
Most "AI marketing" content is either vague hype or automated spam. Useful AI is specific, scoped, and measurable — a well-defined agent that reliably handles a narrow task. The magic is in the boring engineering, not the prompts.
An agent that scores 1,000 keywords consistently beats an assistant that does "everything" occasionally. Reliability at one thing beats versatility across ten.
AI drafts, a human approves. AI ranks, a human decides. Automation without oversight is how brands end up with viral PR disasters and bad content at scale.
Every AI workflow has a clear KPI: time saved, output volume, quality score. If you can't measure it, you can't defend the investment when it's time to renew.
The honest list — categories where AI is genuinely production-ready for marketing teams in 2026, and what it still can't do well.
First-draft blog posts, ad copy variations, social captions, email drafts — all ready for human editing. Output volume goes up 3–5x with no quality drop.
Summarizing analytics dashboards into natural language, flagging anomalies, generating weekly executive summaries. GA4 + Claude/GPT = weekly analyst for a fraction of the cost.
AI agents that handle tier-1 support with escalation paths. Resolution rate for common queries now consistently over 70%, freeing the team for complex work.
Automated technical audits, competitor content analysis, keyword clustering at scale. What took 2 days now takes 20 minutes with an agent and human review.
Dynamic landing pages, email subject lines, and product recommendations tuned to individual behavior. Where classic segmentation stops, AI personalization begins.
End-to-end pipelines: brief → research → draft → edit → publish. Days of sequential work compressed into minutes, with human checkpoints at the right moments.
A well-engineered prompt is a specification. It includes context, examples, constraints, output format, and failure handling. "Prompt hacks" are beginner content — production systems use disciplined prompt design.
The biggest risk with AI isn't capability — it's shipping without guardrails. Every AI system I deploy includes explicit safety, legal, and brand protections.
No customer PII in prompts. No training on sensitive data. GDPR-compliant retention policies. Self-hosted vector stores for anything proprietary.
When content is primarily AI-generated, it's labeled. When agents talk to customers, they're identified. Brand trust compounds; one deception destroys years of it.
If AI output is worse than what a junior human would produce, it doesn't ship. Automation with a low quality floor just scales a mediocre experience.
Current marketing workflow audit. Identify 3–5 highest-ROI AI opportunities with clear time-savings estimates.
Build one narrow agent end-to-end. Validate it works, is measurable, and survives real-world inputs.
Deploy on your infrastructure (VPS, cloud, or SaaS). Add observability, error handling, cost monitoring, rollback paths.
Train the team, write documentation, establish quality review checkpoints. Tools the team can't use don't matter.
One automation at a time. Measure impact before adding the next. Compound gains quarter over quarter.
LLM providers update models, pricing changes, prompts drift. Monthly maintenance keeps production stable.
4-week sprint to ship one working AI workflow. Proof before commitment.
Ongoing AI integration across your marketing stack.
Custom agents and infrastructure for production at scale.