I still use one-off prompts for quick tasks. The trouble starts when the same task has to run every week, across several people, with the same inputs and quality checks.

This guide covers four workflows I use as starting points: SEO, content research, experimentation, and paid ads analysis.

I will show you how to move from prompt hacking to workflow design with one orchestrator and chained skills using Codex and Claude. If you want supporting implementation references, both Anthropic’s agent engineering guide and the OpenAI Agents guide are useful baselines. You will also get concrete AI marketing workflow examples for:

• SEO planning and production (Ahrefs + Search Console)
• content research and briefing
• growth experimentation loops
• paid ads performance analysis from LinkedIn or Meta CSV exports

Before we go deeper, one important clarification:

The GitHub setup I reference is a starter framework, not a final one-size-fits-all system. You should adapt workflows, thresholds, QA rules, and integrations to your own Umbraco setup.

Quick answer: what is an AI marketing workflow?

An AI marketing workflow is a repeatable process where structured inputs move through defined stages, model-assisted skills, QA checks, and human approvals before producing an output. A prompt creates one response. A workflow creates a reliable operating pattern your team can reuse for SEO, content, CRO, reporting, and paid media work.

That difference matters once the work repeats. A good workflow does not ask the model to magically “do marketing.” It tells the model what role each step plays, what inputs are allowed, what quality bar matters, and where a human needs to decide.

If you only need one example to start, use the SEO refresh workflow below. Search Console already provides structured inputs and a clear place to compare the recommendation with what happened later.

AI marketing workflow examples

Here is the overview before we go into the details.

Workflow	Inputs	Skills / stages	Output	QA check
SEO refresh workflow	Ahrefs, Search Console, content inventory	Keyword clustering, opportunity mapping, brief, draft, SEO QA	Keep/update/merge recommendations and article briefs	Intent fit, internal links, unsupported claims
Content research workflow	Competitor pages, sales notes, support themes	Extraction, pattern finding, angle generation, briefing	Differentiated content brief	Repeated claims, weak proof, generic angles
Growth experimentation workflow	Experiment history, conversion data, constraints	Hypothesis, design validation, results analysis, next-step planning	Prioritized experiment plan	Power, instrumentation, guardrails
Paid ads workflow	LinkedIn/Meta CSV, naming map, KPI hierarchy	Normalization, diagnostics, decision rules, action brief	Weekly keep/change/discard plan	Threshold logic, volatility, business impact

If you are unsure where to begin, start with the workflow that already has structured data. Search Console exports and ad CSVs are boring in the best possible way: they give the model less room to invent.

How to Build an AI Marketing Workflow

I use the same basic shape for most AI marketing workflows:

1. Define the input. Name the files, fields, page URLs, campaign data, or notes the workflow is allowed to use.
2. Split the work into stages. Separate research, analysis, drafting, QA, and publishing decisions.
3. Give each stage a quality bar. Tell the model what good looks like and what should trigger a human review.
4. Make the output reusable. Ask for a brief, table, issue list, rewrite queue, or decision log that another person can use later.
5. Close the loop with results. Feed Search Console, analytics, CRM, or campaign data back into the next run.

The aim is not to make every task a workflow. It is to stop rebuilding the same context and checks for work that genuinely repeats.

Why prompt-driven marketing does not scale

Prompting feels productive because it compresses effort into one interaction. The problem is that your team still has to rebuild context every time.

In real execution, that creates familiar failure modes:

• outputs vary heavily between operators
• structure and formatting drift over time
• proof requirements and claim checks get skipped
• decisions are not logged, so quality regressions are hard to trace
• lessons from previous campaigns rarely feed into future runs

This is why teams using “better prompts” still feel stuck. They improved one layer, but not the system layer.

If your work repeats weekly, you need repeatable architecture.

The shift: prompts -> workflows -> skills

Here is the practical model I use:

• Prompt: one isolated generation request
• Workflow: ordered steps, some deterministic and some model-assisted
• Skill workflow: workflow plus tool access, constraints, logging, and handoffs

Put differently: the prompt is the instruction, the workflow is the operating rhythm, and the skill is the reusable capability inside that rhythm.

This shift changes your planning questions.

Instead of asking “what is the best prompt?”, you start asking:

• Which steps are deterministic?
• Which steps require model judgment?
• Which steps need strict QA gates?
• Where should a human approve before publish or budget moves?

If you want the single-prompt version first, I use this ChatGPT prompt framework as the base. The workflow layer starts when that prompt needs to run consistently for more than one person or one deadline.

The simple setup I use in Umbraco

You do not need a multi-agent platform. You need a clear operating setup:

• One orchestrator: runs stages in order and logs each run
• Workflow skills: research, SEO, data analysis, CRO, strategy, copywriting, social content, PPC/paid search, QA, and analytics
• Model routing: use Claude or Codex based on task type
• Tools you already use: Ahrefs, Search Console, ad CSV exports, analytics, Umbraco
• One approval point: human check before risky actions
• Feedback loop: outcomes feed next week’s priorities

If this feels like overkill, start smaller: one workflow, one owner, one SLA. Expand after you stabilize quality.

The point is not to run every skill at once. The point is to chain the ones that match the job in front of you.

AI workflow example 1: SEO workflow (Ahrefs + Search Console + content production)

This is where I would start when Search Console data and a content inventory are already available.

Inputs

• Ahrefs keyword export (keyword, volume, difficulty, intent, trend)
• Search Console page + query export (impressions, CTR, average position) from the Performance report
• Existing content inventory
• internal linking targets
• brand voice and evidence requirements

Stage flow

1. Keyword clustering skill

• clusters semantically related terms
• scores opportunities by demand, difficulty, and business fit
• proposes one primary keyword + mapped secondary terms

2. Search Console opportunity skill

• maps clusters to live page/query data
• flags “striking distance” opportunities (for example position 6-20)
• identifies high-impression / low-CTR pages for title and intro rewrites
• flags cannibalization risk where multiple pages compete for same intent

3. Content brief skill

• creates structured brief with:
  • intent target
  • audience and objection profile
  • section-level keyword map
  • evidence requirements
  • internal link opportunities

4. Drafting skill

• writes complete long-form draft in your house style
• includes practical examples, not generic filler

5. SEO + editorial QA skill

• validates heading hierarchy and keyword placement
• checks unsupported claims and readability
• verifies internal links and publish metadata

6. Refresh recommendation skill

• outputs keep / update / merge / drop recommendations based on performance trend and topic overlap

Keep / update / discard logic

Use explicit rules so decisions are consistent across editors:

• Keep: stable rankings, strong CTR, still aligned to business priority
• Update: meaningful impressions but weak CTR, weak depth, or outdated examples
• Discard or merge: low value, overlapping intent, no strategic role

That is where pairing Ahrefs with Search Console gets powerful. Ahrefs gives demand and difficulty signals. Search Console tells you where your current asset base is leaking value.

If you automate this ingestion, the Search Analytics API keeps your workflow refreshable without manual exports.

AI workflow example 2: content research workflow

Prompting can generate text quickly. It does not automatically create differentiated positioning.

This workflow is for finding better angles before writing.

Inputs

• competitor pages in your topic cluster
• sales call notes or objections
• support ticket themes
• existing positioning statements

Stage flow

1. Extraction skill

• pulls thesis statements, section structure, claims, and proof style from competitor pages

2. Pattern skill

• finds repeated talking points and blind spots across the set

3. Angle generation skill

• proposes 3-5 differentiated angles tied to your market position

4. Briefing skill

• outputs production-ready brief with thesis, structure, examples, and risk notes

This keeps your content from sounding like everyone else repeating the same list post with different wording.

AI workflow example 3: growth experimentation workflow

Most teams have no shortage of test ideas. They have a shortage of test discipline.

This workflow makes experimentation compounding instead of random.

Inputs

• experiment history
• baseline conversion metrics
• segment-level data
• constraints (sample size, confidence threshold, risk limits)

Stage flow

1. Hypothesis skill

• generates prioritized hypotheses by impact x confidence x effort

2. Design validation skill

• checks power assumptions, instrumentation coverage, and guardrails

3. Results analysis skill

• reads test output with both significance and effect size

4. Next-step planner skill

• recommends next tests based on observed interaction effects

If you are already using the A/B Test Lab, this workflow plugs directly into your interpretation process.

AI workflow example 4: paid ads performance workflow (LinkedIn or Meta CSV)

This is the one paid teams usually ask for first, and for good reason.

You already export campaign data. The gap is usually decision quality and consistency after export.

For teams new to this, LinkedIn documents the export flow in Campaign Manager report exports.

Inputs

• LinkedIn Ads or Meta Ads CSV export
• naming convention map (campaign, ad set, ad, objective)
• KPI hierarchy (pipeline, CPA, ROAS, CTR, CPL)
• optional CRM outcome mapping

Stage flow

1. Normalization skill

• standardizes fields across platforms
• aligns metric names and units (spend, impressions, clicks, conversions, CPL/CPA)

2. Diagnostics skill

• computes performance by objective, audience, creative, placement, and time window
• flags instability and outliers

3. Decision skill

• outputs explicit keep / change / discard classification
• uses your threshold rules, not random model preference

4. Action recommendation skill

• proposes specific next moves:
  • pause inefficient placements
  • shift budget to stronger cohorts
  • rotate fatigued creatives
  • refine audience definitions
  • generate new hooks based on highest CTR themes

5. Execution brief skill

• produces a weekly action plan with owner, due date, expected impact, and validation metric

Keep / change / discard rules (example)

You can start with simple policy logic:

• Keep: above target efficiency for 2+ windows with acceptable volatility
• Change: near threshold but one bottleneck is clear (creative, audience, placement)
• Discard: below threshold across multiple windows with no valid recovery signal

GitHub setup: how to use the framework the right way

Reference repo: nclaursen/agentic-marketing-repo

Important: this repository is a suggested starter setup, not a finished universal implementation.

Use it as a base architecture. Then adapt:

• skill roles
• workflow stages
• KPI thresholds
• QA policies
• approval flow
• tool integrations

Recommended onboarding sequence

1. Clone repo and review structure
2. Set environment variables and model/provider keys
3. Configure one workflow only (SEO or ads analysis)
4. Run on historical data first
5. Compare workflow recommendation vs human decision
6. Tune thresholds and prompts
7. Expand to additional workflows

If you skip tuning and go straight to full automation, quality drift will catch you.

Practical implementation checklist

If you want a no-excuses starting point, use this checklist:

• define one workflow owner and one backup owner
• set input file naming standards for Ahrefs, Search Console, and ad exports
• create one shared schema document for required columns
• define QA thresholds before you run the first workflow run
• set a weekly review slot to compare recommendations vs real outcomes
• keep a short failure log with root cause and fix

This gives a new colleague a visible process to inspect instead of a folder of unexplained prompts.

What to track so workflows actually improve

You need outcome data to judge whether the workflow is helping.

At minimum, track:

• cycle time: time from input to publish-ready output
• QA pass rate: percent of runs that pass without manual rework
• recommendation follow-up: what happened after a keep/change/discard recommendation
• business impact: CTR, conversion rate, CPL/CPA, or pipeline contribution depending on workflow
• stability: how often outputs vary for equivalent inputs

If these measures do not improve, inspect the inputs, steps, and review criteria before adding more automation.

Also keep your editorial quality bar aligned with Google’s helpful, reliable, people-first content guidance, especially when workflow output scales faster than human review.

Common mistakes to avoid

• treating model choice as strategy
• skipping schemas and contracts
• automating before policy is defined
• shipping outputs without QA
• forgetting to feed performance data back into planning

Think of this as your marketing preflight check. If the process is weak, faster generation just helps you scale inconsistency.

Start with one recurring task, run it on historical examples, and compare the output with the decision a person made. Expand only when the workflow is saving time without lowering the quality of the decision.

Related reading

• ChatGPT Prompt Framework: A Practical 5-Part Template
• AEO Content Audit: How to Check if Your Content Is Retrieval-Ready
• How to Structure Content for AI Retrieval: The Retrieval Stack
• UTM Parameters: Why Tracking Breaks More Often Than You Think
• Tools
• AI Answer Readiness Checker

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