AI for Digital Marketing Managers Course

Session Description

AI for marketing isn’t just about what model you use. Marketing leaders need to decide how AI fits into their team, what tools to use, and how to manage AI-assisted work. In an AI-saturated market, they also have to ensure that work is impactful, on-brand, and drives results. This module also covers the legal and ethical responsibilities marketers must keep in mind when implementing AI.

Instructional Objectives

• Discuss the manager’s role as the director of AI work, from assessing how AI can support your team, to selecting the right tools and inputs, and measuring and managing AI outputs.

• Review the importance of strategy, quality, and differentiation, as well as techniques and best practices for ensuring AI marketing work stands out and drives business results.

• Outline the marketer's legal and ethical responsibilities when using AI to ensure the company, clients, employees, and others are protected and treated fairly.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Evaluate where AI can help their team improve collaboration and productivity.

• Understand what drives marketing performance and differentiation in an AI-saturated market.

• Understand marketing’s legal and ethical responsibilities around data privacy, intellectual property, and AI work product.

Session Description

This module introduces general concepts and applications for integrating AI into omnichannel campaign management. Students will learn the fundamentals and benefits of autonomous media agents, as well as how to design decision-tree workflows to create their first autonomous agent.

Instructional Objectives

• Understand autonomous media agent capabilities and learn about goal-oriented AI agents that can manage your accounts, including performance monitoring, data ingestion, analysis, strategy, recommendations, and execution.

• Break down channel silos and discover how agents can act as the connective tissue between paid media channels, such as monitoring performance across all channels simultaneously to recommend real-time budget shifts to the highest-performing networks, or automatically passing high-converting search queries to a social agent to inspire new creative.

• Transition from manual processes to automation and learn how your role adapts in the autonomous world, where you focus less on manually pulling levers and instead set objectives, insights and key learnings, guardrails, and external data to orchestrate a synchronized, multi-platform AI workforce.

• Practice responsible AI by learning about governance, first-party data protection, data clean rooms, and risk mitigation practices to establish essential guardrails for brand safety, privacy compliance, and transparent AI decision-making.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Establish foundational knowledge of agentic capabilities, workflows, and prompt architecture to effectively define constraints and goals for AI agents.

• Create autonomous agents to interpret complex, multi-touch data, enabling better business and omnichannel campaign decisions with greater speed.

• Design and deploy basic agentic decision trees for budget, creative, and media optimization based on real-time signals, user intent, and performance strength.

Session Description

This module moves from strategy into execution. By the end of this module, students will have practical, channel-specific ways to use AI across their day-to-day operations. Students will learn concrete AI use cases for content production and SEO, paid and organic social, and customer insights, with an emphasis on where AI genuinely saves time versus where human judgment and creativity still need to lead. By the end, students will have a working AI toolkit they can apply immediately across their core marketing channels.

Instructional Objectives

• Understand where AI can genuinely speed up content, SEO, and social media work, and where human judgment and originality still need to lead.

• Learn how to use AI to surface customer insights and interpret marketing performance data, rather than just generate content.

• Recognize the limits of AI-generated analysis, including correlation versus causation and missing context, and know when to apply human judgment before acting on it.

• Build a personal AI toolkit with concrete tools and prompts mapped to your own recurring marketing tasks.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Apply AI to accelerate content and SEO tasks without sacrificing originality or quality.

• Use AI to support paid and organic social workflows while preserving brand voice and audience nuance.

• Use AI to analyze marketing data, pull customer insights, and report on performance to make better-informed decisions.

• Build a personal AI toolkit mapped to recurring tasks in their own role.

Session Description

This module takes a deep dive into AI concepts and applications for budgeting and measurement, including tools that can monitor media budgets, audit performance data, and generate actionable ROI narratives. Students will learn how to design responsible, autonomous AI agents that manage cross-channel spend, performance optimizations, and dashboard visualizations to monitor performance.

Instructional Objectives

• Understand AI in paid ads and learn how AI agents manage paid ad budgets across multiple channels and how they differ from basic automated rules.

• Build automatic data pipelines and learn how to automatically pull data from Meta, Google Ads, and your online store so you can analyze it all in one place without manual CSV exports.

• Visualize performance with Gemini Canvas by learning to build interactive, low-code data visualization dashboards directly from raw marketing datasets.

• Formulate analytical prompts and master the system instructions that guide AI to analyze performance datasets, interpret ROAS fluctuations, and synthesize executive-level reporting.

• Establish guardrails for scale by identifying critical Human-in-the-Loop (HITL) checkpoints to ensure autonomous agents recommend, rather than blindly execute, budget reallocations.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Architect a functional workflow for a cross-channel pacing agent that autonomously pulls daily spend, compares it to monthly targets, and alerts media buyers to over- or under-pacing.

• Automate data gathering by establishing a reliable ingestion pipeline that replaces manual CSV exports with automated data flows from ad platforms to a reporting environment.

• Construct a functional, interactive ROI dashboard using Gemini Canvas to visualize cross-channel performance and filter metrics.

• Program a reporting agent capable of cross-referencing conversion data to generate actionable ROI narratives and cohesive weekly summaries explaining why a CPA spiked or performance degraded.

Session Description

This module teaches digital marketing managers how to use AI across the full vendor lifecycle: sourcing and shortlisting agencies or tools, comparing proposals, tightening RFP and SOW language, tracking vendor performance against deliverables, and preparing for renewal or negotiation conversations. Students will practice using AI to synthesize vendor proposals into structured comparisons and turn performance data into a negotiation brief. The module draws a firm line around what AI should never touch unsupervised: confidential data, final contract language, and the actual decision to hire or fire a vendor. Students will leave with a vendor evaluation framework and a set of prompts for their next vendor decision.

Instructional Objectives

• Map where AI can speed up each stage of the vendor lifecycle: sourcing, evaluation, contracting, monitoring, and renewal.

• Use AI to build a structured, objective vendor comparison from multiple proposals.

• Identify the confidentiality, accuracy, and bias risks of AI in vendor management, and apply a safeguard for each.

• Use AI to convert vendor performance data into a data-backed negotiation brief.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Build an AI-assisted comparison scoring three or more vendors against five or more defined criteria.

• Draft one AI-generated RFP or SOW section and identify two edits needed before sending.

• Identify two confidentiality or accuracy risks specific to AI-assisted vendor management, each with a safeguard.

• Produce a one-page negotiation brief with three data-backed talking points.

Session Description

AI is changing where competitive advantage comes from. This final module looks at what marketing strategy really means: setting direction, defining success, and aligning activity to outcomes. It covers how AI can support strategic thinking, from research to planning, and where executive judgment and human decision-making should lead.

Instructional Objectives

• Explore how AI-driven changes in market research, content generation, and analysis are reshaping competitive advantage and strategic value, and why marketing strategy matters more as a result.

• Examine the role of strategy in setting direction, defining what success looks like, and aligning marketing activity to outcomes.

• Address how AI can support strategic development through research, synthesis, and testing, and where executive judgment and decision-making are most critical.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Analyze where genuine competitive advantage comes from when AI capabilities are widely accessible, and identify the implications for their own business or clients.

• Explain the role of strategic direction in making executive marketing decisions and prioritizing activities.

• Apply AI to support strategic thinking by using it to research, synthesize information, and test ideas.

• Evaluate AI-generated strategic outputs for quality and coherence, and refine them to ensure alignment with defined goals.