The marketing team that still segments its audience by age bracket and sends the same email to everyone in it is competing against brands that can predict what an individual customer wants before that customer searches for it. Artificial intelligence is what makes that gap possible — processing behavioural data at a scale no team could match manually, and turning it into campaigns, pricing, and content decisions that adjust in real time.

The Arab British Fellowship Training Academy designed this advanced programme for marketers who need to move past isolated AI tools and build a coherent strategy around them: from data and automation through pricing, advertising, content, and the governance questions that come with relying on algorithms.

Who Should Attend?

• Marketing managers and teams in large companies and institutions.
• Professionals in digital marketing and digital transformation.
• Consultants and entrepreneurs seeking smart marketing solutions.
• Anyone integrating AI into marketing strategy.

Course Objectives

By the end of the programme, participants will be able to:

• Explain how AI is reshaping marketing, from fundamentals to real-world applications.
• Use big data and automation to personalise campaigns, pricing, and content.
• Apply predictive models to forecast demand, optimise inventory, and target advertising.
• Measure campaign performance and build a data-driven marketing strategy.
• Manage the privacy, ethical, and human-touch risks that come with relying on AI.

Module 1: Why Marketing Leaders Can No Longer Ignore Super Intelligence

• Core AI concepts and terminology, and the difference between AI and machine learning.
• Types of AI used in marketing, and how digitalisation is changing consumer behaviour.
• Integrating AI tools into the marketing ecosystem and its impact on communication channels.

Module 2: Turning Data Into Marketing Decisions

• Sources of marketing data and AI-based analytics tools.
• Extracting consumer behaviour patterns from big data.
• Supporting marketing decisions with market-trend models, and weighing proactive versus reactive approaches.

Module 3: Automating and Personalising the Customer Journey

• Automated email campaigns and setting up automated marketing scenarios.
• Managing the customer lifecycle through automation.
• Analysing individual preferences to personalise product and service recommendations and retargeting campaigns.

Module 4: Understanding and Designing for the Modern Customer

• Smart tracking technologies and modelling interaction with content.
• Analysing customer sentiment online.
• AI tools for customer support, intelligent interactive interfaces, and improving customer-journey stages.

Module 5: Smart Pricing and Product Lifecycle Decisions

• Dynamic pricing, demand-elasticity analysis, and predictive pricing tools.
• Forecasting a product's stage in the market and timing promotional campaigns.
• Supporting new product development decisions.

Module 6: Forecasting Demand and Optimising Operations

• Machine learning algorithms for demand forecasting and seasonal pattern analysis.
• Linking marketing with the supply chain to optimise inventory planning.
• Reducing waste, improving efficiency, and supporting production decisions.

Module 7: Getting More From Paid Advertising and Campaign Data

• Accurate audience targeting and real-time ad performance optimisation.
• Dynamic display techniques.
• Key performance indicators, ROI analytics, and smart, customised reporting.

Module 8: AI-Generated Content and Smart Distribution

• AI-based content-writing tools and generating visual content with AI.
• Personalising content by channel.
• Choosing effective channels, smart publishing timing, and analysing audience engagement.

Module 9: Social Media Intelligence and Trend Analysis

• Social media management tools and interaction and reach analysis.
• Identifying the most suitable influencers.
• Tracking trending topics and digital audience behaviour to craft better messages.

Module 10: Building the Strategic AI Marketing Plan

• Linking marketing objectives with technology, and strategic-planning support tools.
• Forecasting future marketing trends.
• Building cross-functional teams and improving internal communication for effective data use.

Module 11: Privacy, Risk and the Limits of Automation

• Protecting customer data, complying with legal standards, and ensuring transparency in data use.
• Algorithmic bias and the risk of losing the human touch in marketing.
• Balancing automation with genuine human interaction.