Direct answer

The AI Digital Marketing: SEO, Ads & Sales program is an online professional certificate for marketers, business owners and commercial professionals who need to apply artificial intelligence across a complete marketing workflow. It connects customer research, search visibility, content, advertising, conversion and measurement rather than treating prompt writing as a separate skill.

The program is professional education. It is not an academic degree, diploma or credit-bearing qualification.

The business problem this program addresses

AI makes it easier to produce campaigns, copy and analysis. It does not automatically make those outputs accurate, differentiated or commercially useful. A weak brief can generate polished but generic content. An unverified research summary can introduce false assumptions. Automated advertising can spend faster without improving the offer, landing page or measurement model.

The practical challenge is therefore managerial: how should a marketer combine human judgment, reliable evidence, platform data and AI assistance inside one controlled operating system?

This program develops that capability. Learners work from a business objective to an audience hypothesis, message, channel plan, campaign asset, conversion path and measurement review. AI is used to accelerate bounded tasks. The learner remains responsible for source verification, brand decisions, privacy, platform compliance and the final commercial recommendation.

Who the program is for

  • marketing specialists who want a structured AI workflow rather than isolated prompts;
  • entrepreneurs and small-business owners building an acquisition system;
  • commercial and sales professionals connecting demand generation to revenue;
  • content, social-media and SEO practitioners broadening their analytical capability;
  • career changers who need a practical portfolio of marketing work;
  • managers supervising agencies, freelancers or internal marketing teams.

No programming background is required. Learners should be comfortable working with documents, spreadsheets, websites and common digital platforms.

What you will be able to do

After completing the applied work, a learner should be able to:

  1. translate a commercial objective into a measurable marketing problem;
  2. use AI to structure audience and competitor research while separating evidence from inference;
  3. organize search demand into useful topics, intent groups and content briefs;
  4. create and review campaign assets against a defined audience, offer and brand voice;
  5. develop advertising hypotheses and variations without confusing generated copy with validated performance;
  6. map a lead journey from first contact to conversion and follow-up;
  7. interpret campaign metrics in the context of revenue, margin and customer acquisition cost;
  8. introduce privacy, intellectual-property, disclosure and human-review controls;
  9. present a coherent AI-supported marketing plan to a manager or client.

Applied curriculum

1. Marketing objectives, offers and evidence

Learners begin with the economics of the offer. They define the customer problem, the proposed value, the conversion event and the evidence needed to judge whether a campaign is working. This prevents the common mistake of starting with a tool before defining the commercial decision.

2. AI-supported market and customer research

This block covers research questions, audience hypotheses, customer language, competitor positioning and source evaluation. Learners practise asking an AI system to organize material without allowing it to invent market facts. Every important claim must be traced to platform data, customer evidence or a named external source.

3. Search visibility and content architecture

Learners connect search intent to useful pages, articles and campaign assets. Work includes topic grouping, search-result comparison, content briefs, internal linking and quality review. AI can help classify and draft, but the learner must add original examples, institutional evidence and expert judgment.

4. Content and creative production

The program applies reusable briefing methods to landing-page copy, email, social content and campaign variations. Learners define the audience, action, constraints, tone and review criteria before generating alternatives. The goal is not maximum volume; it is faster production of material that can survive editorial and factual review.

5. Paid advertising hypotheses

This block introduces campaign structure, audience and keyword hypotheses, creative angles, landing-page continuity and experiment design. Learners distinguish the message being tested from the platform's delivery mechanics. AI-generated variations become test inputs, not evidence of effectiveness.

6. Sales funnels and conversion paths

Learners map the journey from discovery to enquiry, checkout or another defined action. They examine friction, trust evidence, calls to action, follow-up and attribution. The work connects marketing activity to a real commercial outcome instead of stopping at reach or engagement.

7. Measurement and management review

The learner builds a concise measurement plan covering source, campaign, landing page, conversion event, revenue where available and review cadence. The program discusses useful ratios such as conversion rate and customer acquisition cost while emphasizing that metrics must be interpreted in the economics of the specific business.

8. Responsible AI marketing practice

The final block covers confidential data, personal data, copyright, hallucinations, disclosure, brand risk and human accountability. Learners create a control checklist identifying what may be automated, what requires review and what information must never be placed in an unapproved public AI tool.

Portfolio evidence

The recommended final portfolio contains:

  • a one-page marketing objective and evidence brief;
  • an audience and competitor research matrix with sources;
  • a search and content architecture;
  • a campaign message and creative testing matrix;
  • a landing-page or conversion-path review;
  • a measurement specification;
  • an AI marketing control checklist;
  • an executive summary explaining the assumptions, trade-offs and next experiment.

These work products can demonstrate process and judgment. They are not a promise of employment, promotion, rankings, advertising performance or revenue.

Learning method

The program is online and self-paced. Text lessons, worked cases, templates and AI-supported exercises allow the learner to pause, inspect assumptions and revise the work. This method is deliberately different from passive viewing: the learner must produce decisions and artifacts.

AI tools may change over time. The durable capability is the operating method: define the problem, provide context, request a structured output, verify evidence, apply professional judgment and document the final decision.

How this page relates to the original gtf.pt program

This is the global canonical page for the English program previously presented at gtf.pt/en/aidigital. The program's core proposition remains the same: practical AI digital marketing across SEO, advertising and sales. The new page makes the scope, professional status, expected work and responsible-use boundaries easier to inspect.

Related learning and evidence

Admissions and program details

The program is delivered in English through online professional learning. Current enrollment availability, access conditions, tuition and the applicable refund terms must be confirmed before payment. Contact welcome@gtf.pt when the current enrollment link is not displayed.

Learning outcomes and credential

Learners who satisfy the current completion requirements receive an MTF Institute professional certificate. The certificate records professional learning; it is not an academic degree or diploma and does not confer academic credits.