Generative AI for Marketing Training Course

Participants can construct repeatable prompts that produce marketing outputs with clear audience, brand and format controls.

• STP decisions converted into model-readable audience and offer briefs
• Brand positioning statements tested against category, competitor and customer language
• Prompt briefs specify role, task, context, constraints and output format
• Few-shot prompting uses approved examples to control tone and structure
• Structured reasoning prompts request assumptions, alternatives and verification steps
• Prompt versioning records inputs, outputs, owners and approval status

Participants can use generative AI to organise research while distinguishing evidence, inference and unsupported claims.

• Search intent clusters built from queries, questions and content gaps
• AI-assisted desk research separates source retrieval from claim generation
• Source triangulation checks recency, authority, corroboration and commercial bias
• Competitive messaging audits compare claims, proof points and calls-to-action
• Hallucination checks require citations, source inspection and subject-matter review
• Insight briefs distinguish observed evidence, inference and proposed tests

Participants can generate and review campaign copy variants that meet audience, conversion and brand requirements.

• AIDA, PAS and FAB frameworks matched to campaign objectives
• Message houses organise value propositions, proof points and objection handling
• Copy prompts set audience, awareness stage, channel and conversion action
• Email sequences maintain narrative progression, frequency and suppression rules
• Landing-page variants apply message match, hierarchy and friction reduction
• Editorial review checks accuracy, specificity, brand voice and prohibited claims

Participants can brief, assess and govern AI-generated visual concepts for marketing communications.

• Creative briefs define concept, audience response, mandatories and production constraints
• Text-to-image prompts control subject, composition, lighting, style and exclusions
• Reference-image workflows preserve brand elements while generating new concepts
• Accessibility checks cover alt text, contrast, captions and readable typography
• Asset rights review distinguishes licensed material, public domain and permissions
• Creative quality criteria assess distinctiveness, clarity, relevance and brand fit

Participants can adapt AI-assisted campaign content for channel, audience and decision-stage requirements.

• Channel-content matrix maps formats to intent, audience and decision stage
• Social content systems turn pillars into serial posts and response prompts
• SEO content outlines combine entity coverage, search intent and internal links
• Paid media variants align hooks, proof and calls-to-action with audiences
• CRM personalisation rules distinguish behavioural triggers from inferred attributes
• Repurposing workflows adapt core ideas without duplicating channel-native formats

Participants can design controlled workflows for generating, reviewing, approving and publishing AI-assisted marketing assets.

• Human-in-the-loop workflow maps assign generation, review, approval and publication
• Content operations boards define briefs, prompts, assets, deadlines and owners
• Reusable prompt libraries store approved patterns, examples and guardrails
• API and no-code automation use cases assessed before implementation
• Quality assurance gates prevent unreviewed copy, imagery and data errors
• Service-level agreements set turnaround times, escalation routes and accountability

Participants can apply data, intellectual property and brand safety controls to generative AI marketing activity.

• Data classification identifies public, internal, confidential and restricted marketing information
• PII minimisation removes unnecessary customer data from prompts and uploads
• GDPR lawful basis, transparency and data-subject rights applied to AI use
• Copyright review addresses training data, outputs, trademarks and attribution
• Brand safety controls define prohibited topics, claims and image treatments
• AI governance register records tools, owners, risks, controls and reviews

Participants can measure whether generative AI improves marketing efficiency, content quality and campaign outcomes.

• Measurement framework links AI activities to funnel, efficiency and quality metrics
• Baseline performance establishes comparison points before AI-enabled content deployment
• A/B test design specifies hypothesis, variable, audience, sample and duration
• Incrementality concepts distinguish correlation, attribution and causal campaign effects
• Content scoring combines accuracy, brand fit, engagement and conversion signals
• Performance dashboards separate operational metrics from commercial outcome measures

Participants can prepare a practical adoption roadmap for introducing generative AI into marketing operations.

• Use-case roadmap sequences pilots, dependencies, owners, budgets and decision gates
• Pilot charters state hypothesis, scope, success criteria and stop conditions
• Change communications address marketer concerns, role shifts and review responsibilities
• Vendor evaluation compares model capability, security, pricing, integration and support
• Operating model defines central standards and local campaign adaptation rights
• 90-day action plans schedule adoption milestones, metrics and governance reviews