AI Marketer

Welcome to Marketing Fundamentals for the AI Era, the first course in the AI Marketer Nanodegree program. See why AI systems now shape what buyers learn about your brand.

Understand how the marketer role is being repositioned in 2026 — from using AI as a copywriter to thinking like an AI-enhanced marketer. Learn the four roles AI plays in modern marketing operations and how that role evolution maps to real career destinations.

Use an AI assistant as a research analyst to diagnose a brand's unfocused value proposition from customer and product evidence. Narrow it to one audience, one customer job, and one evidence-supported difference, choose a direction by excluding others, and draft a refined value proposition with every claim checked against the evidence.

Use AI to research a competitive landscape, construct a positioning map, and draft a brand positioning statement that clearly differentiates the brand from its competitors.

Use AI to synthesize customer evidence from multiple sources (testimonials, reviews, support tickets, behavioral signals) into a six-quadrant empathy map for a target segment. Audit every insight against its source, rate its evidence strength, and separate supported facts from hypotheses that still need validation.

Use AI to synthesize customer evidence and empathy insights into candidate segments, then choose one priority buyer persona and name the segments you won't target. Build an evidence-grounded persona card — goals, pains, buying triggers, objections, preferred channels — and turn it into decision rules a marketing team can use to align messaging and channel strategy.

Identify the AI-mediated touchpoints in today's customer journeys — discovery, recommendation, and delegated action — and where each tends to sit across the journey. Classify each touchpoint as current, emerging, or speculative so investment follows real customer behavior rather than hype.

Build a six-stage customer journey map for a priority persona, from awareness through advocacy, with the barriers at each stage and the AI-mediated touchpoints that shape it. Classify each touchpoint as current, emerging, or speculative from the evidence, and pick the one stage where addressing the barrier would have the most impact.

Understand AI-native channels — the layer across owned, earned, and paid media where a system, not a person, reads your material first. Learn why clarity rather than placement earns visibility there, how query-focused owned content makes a brand usable as a source, and why these channels are tiered on evidence like any other.

Inventory a brand's channels against the customer journey and build a tiered channel plan — prioritize now, test next, deprioritize — grounded in performance baselines and position fit, with declined channels and tradeoffs made explicit. Define the brand's first owned content for AI-assisted discovery and the claims the evidence can and cannot support.

Learn the test every KPI must pass — it has to be tied to a decision — and the two AI-era visibility metrics for the part of the journey your analytics can't see: brand citation rate and Share of Model. See how to measure each against a fixed query set, set an honest baseline, and hold back attribution until the data supports it.

Write strategy-linked marketing objectives and build a KPI plan that keeps only decision-linked metrics, combining traditional measures with at least one AI-era visibility signal. Attach honest baselines, including what is not yet measured, and document the first-cycle measurement plan and the claims the plan will not make.

Learn what AI systems look for when deciding whether to draw on your content, and why writing for people and for AI discovery is one job, not two. Covers entity clarity, answer-first structure with real headings, and supported claims that earn accurate representation.

Build a content strategy that maps pillars, formats, and editorial cadence to funnel stages with AI discoverability built in. Includes editorial calendar mechanics and AI disclosure considerations.

Learn why brand voice needs to be codified as loadable AI input — not just a PDF guide. Understand what a voice asset contains (a one-line position, traits with do and don't examples, approved and discouraged language) and how a claims boundary keeps AI-drafted content accurate and safe.

Define and document a brand's voice as a single loadable asset — voice traits with do and don't rules, preferred and prohibited language, and claims boundaries, packaged as a reusable prompt template. Add a human review checklist and validate the template against a test AI generation.

Wrap up Course 1 by reviewing the skills you've built and how your nine artifacts come together in the AI-Enhanced Strategic Marketing Roadmap. Leave with one practical step to apply those skills to your own brand right away.

Meet your instructor and review the course roadmap. Prepare for the rigorous AI operations work that yields reliable outcomes, from writing sharper briefs to running synthetic audience research before you spend a dollar.

Common habits can limit your AI results. Learn the mental moves that make AI work for marketing. We will explore setting goals instead of steps, the Golem problem, and the trap of overspecification.

Build a goal-rich brief using the five-element scaffold. Diagnose over-prescribed, under-specified, and vague prompts for an abandoned-cart email. Compare all four outputs to see what drives your goal.

"Human in the loop" is a common approach. Learn the alternative, human in the lead, and why being surprised by AI is a feature.

Assign an AI assistant a skeptical persona and explicit business goal to run an adversarial review on your campaign. Uncover second-order effects and document strategic revisions in a change memo.

Set up a Claude Project as a reusable base for recurring campaigns. You will learn to update documents when decisions change and open new chats in the same project for campaign consistency checks.

Marketers have four AI surfaces: chat, design, code-or-agent, and low-code. Choosing the right one for the task is essential. Learn the five criteria that decide it.

Use Claude and an image tool to take Vessl into boxing and MMA gyms. Check printed flyer concepts against the brand guide and campaign brief. Draft a short team note explaining your direction.

Hand off a marketing research task to a background subagent with clear scope and a definition of done, then audit what comes back, claim by claim and against the original request.

Learn how to assess and select AI marketing tools based on functional needs, stability, integration, and fit with your existing stack to make informed tool-selection decisions.

Score three AI copywriting tools and an in-house build option on six dimensions. Adjust the scores and weight the factors for a lean team. Write a build-versus-buy recommendation for your manager.

Synthetic personas are a widely discussed AI marketing tool. Learn what they do well and what they do not.

Seed three synthetic personas with real customer data to stress-test a product concept. Run structured interviews to separate real signal from confabulation. Assign a confidence rating to guide your live research.

Score three creative directions on four fixed dimensions using a synthetic persona panel of three personas. Identify panel splits. Deliver a ranked recommendation with a confidence level and stated limits.

Extend synthetic personas beyond 1:1 interviews and A/B tests into scaled audience research. Simulate a survey panel of 24-30 persona variants to produce a structured findings report.

Explore predictive performance analytics to understand what is mature, what is emerging in forecasting and bidding, and what is still hype.

To build a defensible forecast range, let AI detect past campaign patterns, add business context to decide what carries forward, and build low, mid, and high cases with labeled assumptions for your budget.

Check a 30-day forecast against holdout actuals to spot systematic misses and drift. Claude drafts a calibration report for a finance leader. Verify each verdict against evidence to tune or rebuild.

Claude slices customer LTV data to help you spot hidden confounds. You then use these insights to make one spend change for value-based bidding.

Take these AI skills into your next campaign. You can now turn vague asks into goal-rich briefs, pressure test creative with synthetic research, and build rigorous brand investment proposals.

Find out why AI-powered search now decides which brands get named, how this course covers Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO), and what you need before you start.

Learn what signals cause large language models to discover, trust, and cite content when responding to user queries.

Evaluate a set of web pages against GEO content signal criteria to identify citation-readiness gaps and opportunities.

Learn how to develop topical depth, credibility signals, and E-E-A-T indicators that make content citation-worthy for AI models.

Assess a brand's current authority signals and develop a plan to build topical depth and E-E-A-T indicators for a target subject area.

Learn how to structure content to directly answer conversational queries from AI assistants and answer engines.

Rewrite or restructure existing content to directly address high-value conversational queries and improve performance in AI answer engines.

Learn how JSON-LD schema markup helps search engines and AI systems understand, categorize, and surface content as rich results.

Write and validate JSON-LD schema markup for FAQ and product pages, including the Organization fields (sameAs, knowsAbout) that help AI systems resolve who a brand is.

Learn the strategies for positioning content to appear as cited sources in Google AI Overviews and other generative search features.

Rewrite and format a target page to increase its likelihood of being cited in Google AI Overviews for relevant queries.

Learn how autonomous shopping agents discover and act on product information, which machine-readable fields they depend on, how the ACP and UCP protocols build on those fields, and how shopping platforms differ in naming the pages behind their recommendations.

Read a product page the way an autonomous buyer does, encode its price, availability, and return policy as validated Product schema, and document the before-and-after change in agent discoverability.

Learn how to sample brand citations across AI platforms, what belongs in a citation baseline report, and why a frequently cited query can still be a problem rather than a win.

Design and execute a citation tracking methodology to measure a brand's visibility across major AI search platforms and produce a baseline report.

Learn how to conduct a systematic audit of a brand's AI search visibility across GEO and AEO dimensions to identify optimization priorities.

Run a structured GEO/AEO audit on a brand's digital presence and produce a prioritized findings report with recommendations.

Look back on what you can now do: diagnose and rewrite pages for citation, mark up content so machines understand it, and measure the change. Then see why the next step is making visibility work automatically, at scale.

Get oriented with Course 4, where you will build the components of an automated multi-agent marketing pipeline (intelligence gathering, content production, brand-safety auditing, closed-loop optimization, and trust evaluation), culminating in a capstone production system.

Learn how Skills move marketing operations from one-off prompts to repeatable, brand-consistent systems by codifying institutional knowledge as agent input.

Build the three foundational marketing Skills used throughout the course: Brand Voice, Product Accuracy, and Platform Best-Practices.

Learn how scheduled tasks (routines) enable recurring marketing operations (reporting, monitoring, audits, and content drafting) on a cadence.

Set up a recurring routine that invokes a marketing Skill on a cadence and produces a structured output for the marketing team.

Learn how built-in tools (web search), MCP tools, and custom tools let marketing agents reach beyond their context to gather live market data.

Build an agent that gathers competitive intelligence, brand citations, and trend signals using web search, producing a structured weekly intelligence brief.

Learn how to decompose complex marketing workflows into specialized sub-agents that coordinate via handoffs, with parallel and sequential patterns.

Build a paid-search RSA pipeline using two specialized sub-agents (headlines and descriptions) with Brand Voice and Platform Best-Practices Skills as input.

Learn how marketing agents use loaded context, persistent memory, and state passing to handle multi-session work and continuity across campaigns.

Build an agent that drafts customer case studies from raw interview transcripts, applying Brand Voice and Product Accuracy Skills to produce review-ready drafts.

Learn how agent frontmatter, system prompts, and tool restrictions shape an individual agent's role, including the independent reviewer pattern for brand safety.

Build a brand-safety auditor agent that operates as an independent reviewer, not a downstream check inside a producer agent, validating cross-channel campaign content against brand voice and compliance Skills.

Learn how real-time platform data feeds the next iteration of marketing generation: the closed-loop optimization pattern that MCP integrations enable.

Connect your agent fleet to an ad platform via MCP with a memory layer, run a 2-iteration closed loop where prior-iteration data feeds next-iteration generation, and keep campaign memory healthy over time.

Learn the three dimensions of trustworthy autonomous marketing: output quality, production stability, and regulatory compliance.

Build an evaluation framework that scores marketing agents across quality, stability, and compliance, then apply it to a provided influencer outreach research agent and add fallbacks based on findings.

Frame the operations learners can now run, the career destinations they are positioned for, and what to bring to their next job interview.