AI for Marketing, Content & Search

The quickest way to create generic AI marketing is to ask AI to write before you give it anything worth saying.

• Customer questions, review mining and community discussions
• Competitor, market and search-intent research
• Analysing interview notes and identifying repeated customer language
• Objections, jobs and motivations
• Separating evidence from AI assumptions, and building a reusable research base

Exercise: create a customer and market evidence pack from several real sources and turn it into usable insights.

• Turning research into a strategic brief: audience, problem, promise, proof
• Positioning, message hierarchy and offer development
• Campaign concepts, creative territories and content pillars
• Editorial planning
• Using AI to challenge a strategy rather than simply agree with it

Exercise: generate several campaign directions, then judge them against the same brief instead of picking the most entertaining answer.

• Content briefs, articles, landing pages and email
• Paid ads, social content and scripts
• Visual concepts and AI image and video support
• Voice and style guidance, fact checking and editing
• Original examples, experience and proof, and why generic AI copy underperforms

Exercise: build a small campaign asset set from one shared strategy and keep it consistent across formats.

People now discover information through Google, AI Overviews, answer engines, chat tools, social platforms and specialist communities.

• Search intent, topic and entity coverage
• SEO fundamentals that still matter: useful content, information architecture, internal linking
• Structured answers, original evidence, authority and trust
• AI Overviews, answer engines and AI visibility
• Being understandable and citable, schema opportunities
• Why “write for AI” should not mean writing robotic content

Exercise: take an existing page and redesign its structure for both human usefulness and modern search discovery.

• Reporting with AI and interpreting campaign data
• Asking better questions of analytics, finding patterns, turning observations into actions
• Automating repetitive reporting and content QA
• Approval, brand governance and privacy
• Building a repeatable AI marketing process

Final project: a complete AI-assisted campaign: evidence pack, audience and problem, positioning, campaign idea, landing page, one substantial content asset, an email or social sequence, ad concepts, a search and AI-discovery plan, a measurement framework, and notes on where AI was used and where human judgement changed the result.

Every session mixes short explanations with hands-on work on a real brand, product or client. Bringing your own business makes the project much more useful.

AI tools change quickly. We group tools by job (research, writing, visual, search and analytics) and review them before each cohort. Tools change; the workflow matters more.

There is already enough content. The advantage now comes from knowing something useful, structuring it clearly, developing a point of view, proving claims, creating original material and distributing it intelligently. AI can speed up a good marketing process. It can also speed up a bad one. We teach the first version.