Online professional certificate
Professional Certificate in Generative & Agentic AI for Marketing
Build a 2027-ready marketing operating system for evidence, audiences, governed content, campaigns, experiments, measurement and human-controlled agentic workflows.
- Format
- Online, self-paced
- Study time
- Up to 1 month
- Curriculum
- 20 applied lessons
- Language
- English
Practical capability
Build marketing systems that remain accountable as AI capability grows.
Fresh employer research approaching 2027 points to a combined operating profile: research, audiences, content, campaigns, experimentation and measurement, held together by human ownership and cross-functional controls.
Frame decision-led research, preserve provenance and turn credible sources into reviewable recommendations.
Build permitted segment hypotheses, audience rules and journey opportunities without invented customer knowledge.
Run message briefs, context systems, responsible variants, claims checks and content lifecycle controls.
Connect objectives, audiences, channels, budgets, frequency, approvals and handoffs in one operating charter.
Pre-register experiments, evaluate AI failure modes and connect KPI evidence to explicit decisions.
Specify permissions, human approvals, audit logs, monitoring, stop conditions, fallback and rollback.
Who this course is for
A 2027-ready route for marketers who must connect AI with operating discipline.
Designed for professionals who need to make AI-supported marketing work traceable, testable, measurable and safe to hand across teams.
The operating cycle
Move from a decision question to controlled action.
Work through one connected fictional company and practise the complete sequence from evidence to business decision, including one bounded proposal-only agent workflow.
Curriculum
Four modules. Twenty applied lessons.
Evidence, Customers and Segments
Turn marketing questions into traceable evidence, explicit audience rules and customer opportunities. The module connects decision framing, source quality, segmentation and journey logic before any content or campaign is produced.
01The AI-Enabled Marketing Operating Model
Map decisions, evidence, owners, risks and outcome logic before selecting AI tasks.
Five practical steps
- Name the business decision
- Map work and evidence
- Assign accountable owners
- Classify AI assistance
- Define review and outcome rules
Primary deliverable: Marketing AI Operating Map.
02Decision-Led Research with Generative AI
Frame research around a decision, not an open-ended content request, and preserve unknowns.
Five practical steps
- Define the decision and user
- State the evidence need
- Set source boundaries
- Plan synthesis and challenge
- Specify the decision output
Primary deliverable: Research Decision Brief.
03Source Quality, Synthesis and Research QA
Evaluate provenance, recency, relevance and confidence before converting sources into a recommendation.
Five practical steps
- Inventory every source
- Score relevance and authority
- Separate fact from inference
- Triangulate material claims
- Record limits and confidence
Primary deliverable: Evidence Ledger.
04Segmentation and Audience Rules
Translate a useful segment hypothesis into operational inclusion, exclusion, consent and suppression rules.
Five practical steps
- Name the decision purpose
- Choose permitted evidence
- Define inclusion criteria
- Define exclusions and expiry
- Set activation approval
Primary deliverable: Segment Hypothesis Card.
05Journey and Opportunity Mapping
Connect customer evidence to moments, friction, needs and testable marketing opportunities.
Five practical steps
- Define the journey scope
- Map observable moments
- Locate friction and needs
- Form opportunity hypotheses
- Prioritize the next test
Primary deliverable: Journey and Opportunity Map.
Governed Content Operations
Move from one-off generation to controlled content systems. You will connect message architecture, approved context, responsible variation, rights and claims checks, and a visible content supply chain.
06Message Architecture and Governed Content Briefs
Convert evidence and audience logic into a clear message hierarchy and reviewable content brief.
Five practical steps
- Set the communication objective
- Define audience and moment
- Map approved messages
- State claims and constraints
- Assign review and success evidence
Primary deliverable: Governed Content Brief.
07Prompt and Context Systems for Marketing Work
Build reusable context that keeps AI-assisted work grounded in current sources, rules and examples.
Five practical steps
- Define the task boundary
- Select authoritative context
- Structure instructions and examples
- Add exclusions and uncertainty
- Version and test the pack
Primary deliverable: Marketing Context Pack.
08Asset Variation and Responsible Personalization
Create controlled asset variants without inventing customer knowledge or crossing consent boundaries.
Five practical steps
- Define the base asset
- Choose allowed variables
- Set personalization rules
- Generate bounded variants
- Compare against the brief
Primary deliverable: Asset Variant Matrix.
09Content QA, Rights and Disclosure
Review facts, claims, rights, brand, accessibility and disclosure before any asset is approved.
Five practical steps
- Trace every material claim
- Check rights and sources
- Review brand and channel fit
- Test accessibility and disclosure
- Record approval or required revision
Primary deliverable: Content QA Record.
10Content Supply Chain and Performance Feedback
Manage briefs, assets, versions, approvals, publication states and retirement through one operating board.
Five practical steps
- Map the content stages
- Assign state owners
- Define version evidence
- Connect performance signals
- Retire or revise responsibly
Primary deliverable: Content Operations Board.
Campaigns, Experiments and Measurement
Plan integrated campaigns as decision systems. The module joins channel roles, budgets, frequency, handoffs, experimental design, adversarial AI evaluation and honest business measurement.
11Campaign Charters and Channel Roles
Align objective, audience, proposition, channel contribution, handoffs and decision rights in one charter.
Five practical steps
- Define the campaign decision
- Confirm audience eligibility
- Assign channel roles
- Map cross-functional handoffs
- Set outcomes and owners
Primary deliverable: Campaign Charter.
12Budgets, Frequency, Handoffs and Guardrails
Make spend, contact pressure, approval thresholds and exceptions explicit before activation.
Five practical steps
- Record the approved envelope
- Set frequency boundaries
- Map approval thresholds
- Define handoff evidence
- Create an exception route
Primary deliverable: Campaign Guardrail Sheet.
13Experiment Design for Marketing Decisions
Pre-register a credible comparison, primary metric, guardrails and decision rule before observing results.
Five practical steps
- State the causal hypothesis
- Choose assignment and comparator
- Define metrics and threshold
- Record sample constraints
- Set stop and decision rules
Primary deliverable: Experiment Canvas.
14AI Evaluation and Adversarial QA
Test AI-supported work across normal, edge, failure and adversarial cases, with critical failures that block release.
Five practical steps
- Define evaluation dimensions
- Build representative cases
- Add edge and failure cases
- Run adversarial challenges
- Set release and remediation rules
Primary deliverable: AI Evaluation Rubric.
15KPI Trees, Attribution and Incrementality
Connect activity to business outcomes while separating descriptive movement, attributed reporting and incremental evidence.
Five practical steps
- Start with the decision
- Build the KPI tree
- Define metrics and sources
- State attribution limits
- Write the decision memo
Primary deliverable: Measurement Decision Memo.
Human-Controlled Agentic Workflows
Design bounded agents around marketing work without granting unattended external authority. You will specify use cases, tools, permissions, approvals, logs, monitoring, fallback and rollback.
16Selecting Agentic Use Cases
Prioritize repeatable, evidence-rich tasks where an agent can prepare or propose work within clear boundaries.
Five practical steps
- Map the candidate workflow
- Assess value and repeatability
- Assess consequence and ambiguity
- Choose the allowed action class
- Prioritize a controlled pilot
Primary deliverable: Agentic Use-Case Portfolio.
17Workflow Specifications, Tools and Permissions
Specify triggers, context, tool access, permissions, validation and termination before implementation.
Five practical steps
- Define trigger and termination
- List approved context
- Create the tool allowlist
- Set per-tool permissions
- Define validation and output
Primary deliverable: Agentic Workflow Specification.
18Human Approval Gates, Logs and Escalation
Place accountable review at consequential decisions and retain evidence for every material action or exception.
Five practical steps
- Classify every action
- Locate approval gates
- Name reviewer evidence
- Define log fields
- Route exceptions and uncertainty
Primary deliverable: Human Approval Matrix.
19Pilot, Monitor, Stop and Roll Back
Release a bounded pilot with test evidence, thresholds, manual fallback and a usable recovery path.
Five practical steps
- Set the pilot scope
- Pass critical tests
- Monitor quality and cost
- Define stop conditions
- Rehearse fallback and rollback
Primary deliverable: Agent Pilot Scorecard.
20Capstone: Controlled AI Marketing Operating System
Integrate evidence, audience, content, campaign, experiment, measurement and one proposal-only agent workflow.
Five practical steps
- Reconcile the evidence chain
- Assemble the operating portfolio
- Challenge permissions and claims
- Choose scale, revise, stop or investigate
- Brief the accountable decision owner
Primary deliverable: Executive AI Marketing Portfolio.
Applied capstone
Design a controlled AI marketing operating system.
Use the methods that fit the decision. The capstone asks for one coherent executive portfolio, not a disconnected bundle of AI outputs.
The situation
Northstar Pulse is preparing a growth initiative for small professional teams. Leadership wants evidence, a permitted audience, governed content, a credible campaign test and measurement that can support a scale, revise, stop or investigate decision. One campaign-QA agent may prepare and propose work, but it cannot publish, send, activate, change spend, export, delete, price or commit.
Your task
Build and defend the operating system for an eight-week controlled test. Reconcile sources, segment and consent rules, claims, channels, budget and frequency, experiment logic, KPI definitions, approval gates, audit evidence, stop conditions and rollback.
The people behind MTF
Meet MTF faculty and the learner community.
Explore the professional backgrounds of MTF faculty and learn more about the international community studying with the Institute.
Enrollment
Enroll in Professional Certificate in Generative & Agentic AI for Marketing
One-time course price: €10, including applicable taxes. Payment is processed securely by Stripe. No card details are stored on the MTF Institute website.
You will receive an email with access to the course. If you have any difficulties, please write to welcome@gtf.pt. Course learning environment: open the course page.
Questions and details
Frequently asked questions
Open the sections that matter to you, including delivery format, AI-supported practice and the evidence used to design the curriculum.
Who is this Generative and Agentic AI for Marketing course for?
This program is designed for marketing operations and campaign managers, content and lifecycle marketing leads, market research and customer insights professionals, and growth, performance or marketing analytics managers. It is also suitable for adjacent professionals moving into AI-enabled marketing work who need a repeatable operating method.
What does 2027-ready mean in this program?
The curriculum was rebuilt from fresh employer and current-practice evidence for work approaching 2027. It develops durable operating capability in traceable research, governed content, testable campaigns, business measurement and controlled agent workflows rather than making predictions about a specific vendor or model.
Do I need coding or data-science experience?
No. The course uses vendor-neutral briefs, maps, matrices, test plans, scorecards and decision memos. Technical integrations are treated as governed interfaces that require the appropriate specialist and approval owner.
How is AI used in the practical work?
Every lesson combines theory with AI Practice. A focused prompt may organize authorized inputs or draft the lesson artifact; a separate critic prompt challenges omissions and weak reasoning; and a human verification workflow determines whether the result is accepted, revised or rejected.
What evidence supports the curriculum?
The curriculum is grounded in an MTF Institute analysis of 100 current eligible U.S. vacancies from 98 employers and an independent review of 26 recent sources from 11 organizations. The evidence is published through two MTF Insights articles and an open Zenodo record.
Will AI agents execute campaigns autonomously?
No. The course teaches human-controlled agentic workflows. Learners define least-privilege permissions, approval gates, audit logs, escalation, monitoring, stop conditions and rollback. Publishing, sending, spending, activation, export, deletion, pricing and external commitments remain human-controlled.
What certificate and access will I receive?
After successful enrollment, you receive access to the MTF learning platform. Completing the required lessons, capstone and certificate activity provides the MTF Institute course-completion certificate for Professional Certificate in Generative & Agentic AI for Marketing.