AI-Powered Strategy & Operations Course

Session Description

This module builds the foundation for the rest of the course: how AI actually works, how to prompt it well, and where it is already showing up across departments like marketing, operations, sales, and customer support. This module will also help students create a roadmap for rolling AI out to an entire organization or team in a way that promotes adoption, not fear.

Instructional Objectives

• Understand how large language models work at a high level.

• Apply core prompt-writing best practices to get more useful output from common AI tools like ChatGPT, Claude, and Copilot.

• Recognize where AI is already adding value across departments like marketing, operations, sales, and customer support.

• Know how to introduce AI responsibly on a team, balancing data privacy with lasting adoption.

Measurable Learning Outcomes

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

• Explain, in plain language, how a large language model generates a response.

• Apply new prompt-writing techniques to improve future AI outputs.

• Identify at least one AI use case for each department within their organization.

• Draft a basic "do this, not that" privacy guardrail.

Session Description

This module helps marketing leaders move beyond ad hoc AI experimentation toward a deliberate, responsible strategy. Students will learn how to evaluate marketing activities across core channels (content, search engine optimization, social, and paid media) and decide where AI genuinely adds strategic value versus where it introduces risk to brand quality, customer trust, or legal exposure.

Instructional Objectives

• Understand how to use AI responsibly in marketing without compromising brand quality or originality.

• Recognize the three core AI risks (bias, hallucination, generic content) and how they show up specifically in marketing work.

• Understand how AI Overviews and AI-powered search are reshaping search engine optimization, and where to focus content strategy as a result.

• Identify the right mix of traditional and AI-specific metrics (and the tools to track them) to measure marketing performance.

Measurable Learning Outcomes

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

• Identify at least one countermeasure for each of the three core AI risks to ensure safe AI usage.

• Outline what to feed into a dedicated AI knowledge base for one marketing function on their team (e.g., email, social, customer insights) to reduce blank-page syndrome and improve brand consistency.

• Identify at least three "ownable" content types to prioritize as AI Overviews absorb high-volume, top-of-funnel keywords.

• Evaluate a website against the AI-readiness checklist and identify at least three gaps to close.

Session Description

This module teaches students how to use AI to turn customer data and feedback into actionable insights for strategic and operational decision-making. Students will explore how AI can identify patterns, segment customers, explain customer behavior, anticipate future behavior, and translate insights into recommended actions. The module emphasizes that AI is most valuable when paired with human judgment: the goal is not to outsource customer understanding to AI, but to use AI to ask better questions, analyze information faster, and make better-informed decisions. Students will also practice evaluating AI-generated insights for accuracy, bias, privacy, and business relevance.

Instructional Objectives

• Understand the role of AI in customer insights and distinguish between descriptive, diagnostic, predictive, and prescriptive forms of customer analytics.

• Use AI to analyze customer information, such as surveys, reviews, CRM data, support conversations, and behavioral data, to identify patterns, segments, needs, pain points, and opportunities.

• Translate customer insights into business decisions, using AI to generate hypotheses, identify potential actions, and recommend next-best experiences or interventions.

• Apply responsible AI principles to customer insights, including data privacy, bias, hallucinations, transparency, and the need for human validation.

Measurable Learning Outcomes

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

• Classify a customer insight problem as primarily descriptive, diagnostic, predictive, or prescriptive and explain what role AI can play in addressing it.

• Create an AI-assisted customer insight analysis that identifies at least three meaningful patterns or customer needs from a provided dataset or collection of customer feedback.

• Turn an AI-generated insight into a business recommendation, specifying the target customer, proposed action, expected business impact, and evidence supporting the recommendation.

• Evaluate an AI-generated customer insight for responsible use, identifying potential issues related to data quality, privacy, bias, unsupported conclusions, or overreliance on AI.

Session Description

This module gives students a repeatable way to turn scattered customer feedback and support tickets into improvements customers actually feel. Students will use AI to process reviews, tickets, survey responses, and call transcripts at a scale no manual read-through allows, validate every finding against the raw data, and then apply it: naming the customer-journey moment to fix, drafting stronger support responses, and closing the loop with a measure like CSAT or resolution time. The module treats AI as an analysis and drafting accelerator, not a source of truth, and keeps a human in the loop on every customer-facing reply. Students will leave able to run the full Insight-to-Experience workflow (Gather, Synthesize, Validate, Recommend, and Apply) on their own customer and support data.

Instructional Objectives

• Explain how AI processes qualitative and quantitative customer feedback (reviews, tickets, surveys, transcripts) faster than manual analysis.

• Apply the four-step workflow (Gather, Synthesize, Validate, Recommend) to turn raw feedback into a validated insight.

• Translate insights into specific customer experience improvements and stronger support responses.

• Identify ethical risks in AI-driven analysis of customer and support data, and name a mitigation for each.

Measurable Learning Outcomes

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

• Produce a one-page brief that turns a validated insight into a named customer experience or support improvement, with three findings each backed by a quote or data point.

• Draft two AI-improved support responses (a canned reply or a help-doc update) from real ticket themes.

• Map one insight to a specific customer-journey moment, with the action, owner, and timeframe to improve it.

• Identify two risks or blind spots in an AI analysis of customer or support data and describe a mitigation for each.

Session Description

This module is the tactical companion to AI Strategy Foundations. Students will walk through day-to-day workflows: how to triage what is worth automating, build AI into recurring work instead of starting cold every time, and connect AI directly to internal tools via Model Context Protocol (MCP).

Instructional Objectives

• Triage where AI is worth applying to a given task using an effort and impact framework.

• Set up repeatable AI workflows for recurring work, including when to move from a static prompt to a live MCP connection.

• Use AI to extract decision-ready insights from sales, employee, and Net Promoter Score (NPS) data.

• Turn scattered internal knowledge and messy processes into structured, AI-usable documentation and presentation decks.

Measurable Learning Outcomes

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

• Categorize tasks by the level of AI assistance required using the Walk, Run, Sprint framework.

• Set up a Claude Project or Custom GPT as a repeatable workflow and identify when an MCP connection is preferred.

• Extract actionable insights and reports using business data.

• Convert scattered documentation and messy processes into a usable standard operating procedure.

• Create a structured presentation deck from a blank page using AI.

Session Description

AI is changing how teams get work done, not just what tools they use. Managers need to know what talent and skills this shift requires, as well as how to build and nurture them. Day-to-day work requires new management practices, ranging from team collaboration to quality control. This module gives marketing leaders practical tools to grow, manage, and lead AI-empowered teams.

Instructional Objectives

• Discuss how AI is reshaping tasks and roles, and what that means for a team.

• Break down what talent and skills actually add value on an AI-enabled team, and how to build that capacity through hiring, upskilling, or outside support.

• Provide practical guidelines for managing AI-assisted work day-to-day, including workflow, team management, and evaluating output.

• Equip managers to lead AI implementation and change on their team, including culture, communication, and aligning AI use to business value.

Measurable Learning Outcomes

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

• Explain how AI is changing deliverables and workflows, including what is changing, disappearing, or newly possible for their team.

• Evaluate which skills and talent add genuine value on an AI-enabled team, and decide whether to train, hire, or outsource for them.

• Manage AI-assisted work day-to-day, including work, people, and quality control.

• Apply culture, communication, and accountability best practices to lead an AI-powered team.