AI in Marketing and Customer Experience

From AI Literacy to Agentic Execution for Smarter Campaigns, Better Journeys, and Measurable Impact in 2 days

Artificial intelligence has moved from novelty to operating reality in marketing and customer experience. Teams now use it to accelerate content creation, sharpen audience insight, personalize journeys, support sales enablement, improve service quality, and automate repetitive work. Current industry reporting shows that generative and agentic AI are now central themes in customer experience strategy, while major platforms such as ChatGPT, Claude, Gemini, Gemini CLI, Manus, and Microsoft Power Automate continue expanding practical business workflows for creation, research, orchestration, and automation.

This 2-day course is designed to demystify AI before moving into real marketing and CX application. It begins by making AI understandable in plain business language: what it is, the main types, how it “works” at a conceptual level, where it is strong, and where human judgment still matters. From there, the course moves into the real work of modern teams: customer understanding, campaign planning, content operations, personalization, service improvement, workflow redesign, and responsible adoption. The second half focuses on hands-on tool usage, prompt engineering, context engineering, and agentic automation, including live demonstrations with ChatGPT, Claude, Gemini, Gemini CLI, Manus, and Power Automate with Outlook. These are not abstract academic topics; they reflect the way leading vendors and teams are positioning AI for everyday business use.

The course is delivered from an industry-first perspective. The instructor brings over 30 years of industry experience and will use real industry-demanded content, workflows, and decision patterns rather than an academic treatment of the subject.

Learning outcomes

By the end of this 2-day program, participants will be able to:

Explain AI in plain language and distinguish between key AI types relevant to business use.
Describe, at a non-technical level, how modern AI systems generate outputs and why results vary.
Identify high-value marketing and customer experience use cases where AI can improve speed, consistency, relevance, and scale.
Evaluate where AI should assist, where humans should lead, and where governance is required.
Use AI tools to support campaign ideation, messaging, audience analysis, content adaptation, customer communication, and service workflows.
Apply prompt engineering techniques to get more accurate, structured, and useful outputs.
Apply context engineering techniques so AI outputs align better with brand, audience, goals, and business constraints.
Understand agentic automation and where autonomous or semi-autonomous workflows can support marketing and CX operations.
Compare the practical strengths of ChatGPT, Claude, Gemini, Gemini CLI, Manus, and Power Automate for different tasks.
Design an initial roadmap for leveraging AI responsibly in marketing and customer experience functions.

Prerequisites

No technical or coding background is required.
Basic familiarity with marketing, customer experience, sales support, service operations, or digital business processes.
Comfort using common workplace tools such as email, documents, spreadsheets, and web applications.
An openness to experimenting with AI tools in guided exercises and demonstrations.
Helpful but not required: prior exposure to ChatGPT, Claude, Gemini, or workflow tools.

Detailed training outline

Course foundation: AI for modern marketing and customer experience
1. Why AI matters now in revenue, brand, and service functions
2. The shift from experimentation to operational adoption
3. The difference between hype, capability, and business value
4. How AI is changing expectations around speed, personalization, responsiveness, and scale
5. The emerging role of generative AI and agentic AI in customer experience orchestration
Demystifying AI
1. What AI is in practical business language
2. What AI is not
3. The major families of AI
  1. Predictive AI
  2. Generative AI
  3. Conversational AI
  4. Recommender and personalization systems
  5. Agentic AI
4. The evolution from rules-based automation to machine-assisted decision support to generative and agentic systems
5. Common myths and misunderstandings about AI
6. Where AI creates value
7. Where AI still struggles
8. Why business users do not need deep technical expertise to use AI effectively
How AI works without getting technical
1. Data, patterns, and prediction in plain English
2. The idea of models and training
3. Why AI generates plausible language, summaries, and suggestions
4. Why outputs can be inconsistent
5. Hallucinations, gaps, and false confidence
6. The role of prompts, instructions, and context
7. Why the same question can produce different answers
8. The importance of review, refinement, and human oversight
9. The practical relationship between input quality and output quality
Core AI concepts for business users
1. Prompts
2. Context
3. Memory and session continuity
4. Files, documents, and reference material
5. Structured outputs
6. Multimodal input and output
7. Assistants versus agents
8. Workflow automation versus autonomous execution
9. Human in the loop versus human on the loop
AI opportunities across marketing
1. Market understanding and customer insight
  1. Voice of customer synthesis
  2. Persona refinement
  3. Segment exploration
  4. Trend and competitor signal gathering
2. Strategy support
  1. Campaign ideation
  2. Offer framing
  3. Positioning alternatives
  4. Messaging architecture
3. Content operations
  1. Copy drafting
  2. Content adaptation across channels
  3. Editorial planning
  4. Repurposing long-form into short-form
  5. Localization support
4. Performance marketing support
  1. Ad angle generation
  2. Testing hypotheses
  3. Landing page optimization ideas
  4. Reporting narrative creation
5. Sales and enablement alignment
  1. Battlecards
  2. Outreach support
  3. Proposal and deck support
6. Brand stewardship
  1. Tone alignment
  2. Brand guardrails
  3. Governance for consistency
7. Productivity gains and workflow compression using AI tools for marketing teams
AI opportunities across customer experience
1. Customer journey analysis
2. Experience friction identification
3. Service response support
4. Knowledge assistance for frontline teams
5. Self-service and conversational support
6. Personalization of content and communication
7. Proactive engagement opportunities
8. Case summarization and next-best-action support
9. Complaint and sentiment pattern analysis
10. Loyalty, retention, and lifecycle communication support
11. AI for experience orchestration and real-time relevance
Use-case prioritization for the enterprise
1. Quick wins versus strategic bets
2. High-volume, low-risk use cases
3. High-impact, cross-functional use cases
4. Internal productivity versus customer-facing experiences
5. Risk, compliance, and data sensitivity considerations
6. Effort versus value mapping
7. Selecting pilot opportunities
8. Measuring success criteria
Human judgment in the age of AI
1. What should remain human-led
2. Creativity, taste, ethics, and accountability
3. Review and approval checkpoints
4. Brand, legal, and reputational risk
5. Decision ownership
6. Escalation and exception handling
Responsible and secure use of AI in marketing and CX
1. Accuracy and verification
2. Privacy and confidential information handling
3. Intellectual property and content ownership awareness
4. Bias and fairness considerations
5. Transparency in AI-assisted work
6. Policy guardrails for teams
7. Governance for tools, prompts, and outputs
8. Adoption principles for enterprise environments
Leveraging AI for business impact
1. Identifying where to start
2. Building a practical AI operating model for marketing and CX
3. Defining use-case owners
4. Establishing prompt and context assets
5. Creating reusable playbooks
6. Aligning AI initiatives with KPIs
7. Building trust through controlled experimentation
8. Moving from isolated use to team-wide workflows
9. From assistant usage to embedded process redesign
Tool landscape and practical positioning
1. Understanding the current AI tool ecosystem
2. Choosing the right tool for the job
3. General-purpose assistants versus specialized workflow tools
4. Standalone chat interfaces versus connected enterprise tools
5. Browser, document, spreadsheet, terminal, and automation contexts
6. How enterprise platforms are framing AI for business workflows today
ChatGPT for marketing and customer experience
1. Positioning ChatGPT as a work assistant
2. Ideation and structured thinking support
3. Messaging and content acceleration
4. Summarization and synthesis
5. Research framing and analysis support
6. Campaign planning assistance
7. Customer communication drafting
8. Refinement through iterative prompting
9. Shared usage patterns in workplace adoption
10. Sales and marketing workflow relevance
Claude for marketing and customer experience
1. Positioning Claude for writing, reasoning, and extended drafting
2. Long-form content and synthesis support
3. Working with nuanced tone and narrative
4. Internal collaboration support
5. Marketing and collateral-related use patterns
6. Use in planning, summarization, and thought partnership
7. Fit for teams needing careful drafting and refinement
Gemini for marketing and customer experience
1. Gemini in productivity workflows
2. Workspace-connected use cases
3. Email, document, sheet, and slide assistance
4. Marketing planning and collaboration support
5. Prompting in business productivity environments
6. Cross-document and cross-app productivity opportunities
Gemini CLI in the modern work toolkit
1. What Gemini CLI is
2. Why terminal-based AI matters beyond developers
3. Local project and file context
4. Workflow support for structured assets and automation
5. Research, editing, and execution possibilities in command-line environments
6. MCP and tool-connected workflow concepts
7. The relevance of CLI-based AI agents to advanced business operations
Manus and the rise of action-oriented AI
1. What Manus represents in the market
2. From answering to executing
3. Research, content, slide, and workflow-oriented task execution
4. Browser operator concepts
5. The practical meaning of end-to-end task automation
6. Suitability for marketing operations and research-heavy work
7. Opportunities and control considerations in agentic systems
Microsoft Power Automate with Outlook and related workflows
1. What Power Automate is
2. Cloud flows and desktop flows
3. Event-driven automation concepts
4. Outlook-triggered workflow possibilities
5. Email triage, routing, alerts, reminders, and approval support
6. Integration patterns across apps and services
7. AI Builder as a low-code intelligence layer
8. Where workflow automation fits marketing and CX operations
Prompt engineering
1. What prompt engineering is
2. Why prompting matters
3. Characteristics of strong prompts
  1. Clear role
  2. Clear objective
  3. Clear audience
  4. Clear constraints
  5. Clear output format
  6. Clear success criteria
4. Instruction design
5. Prompt patterns for business work
  1. Ideation prompts
  2. Analysis prompts
  3. Transformation prompts
  4. Evaluation prompts
  5. Comparison prompts
  6. Structured output prompts
6. Iterative prompting and refinement
7. Troubleshooting weak outputs
8. Prompt libraries and reusable templates
9. Prompt engineering as an everyday managerial skill
Context engineering
1. Why context matters more than clever wording
2. The difference between prompt quality and context quality
3. Supplying business context
  1. Brand voice
  2. Customer segment
  3. Product information
  4. Market realities
  5. Objectives
  6. Constraints
  7. Source material
4. Grounding outputs with internal knowledge
5. Structuring context for better consistency
6. Context windows and practical implications
7. Files, reference docs, examples, and style guides
8. Building reusable context packs
9. Maintaining relevance while avoiding overload
10. Context engineering for enterprise team adoption
Agentic automation
1. What makes a system agentic
2. Tasks, tools, memory, and goals
3. Assistive workflows versus autonomous workflows
4. Multi-step execution
5. Tool use and action chains
6. Research agents
7. Content production agents
8. Routing and orchestration agents
9. Approval-based agentic flows
10. The business case for agentic marketing and CX operations
11. Limits, risks, and control design
12. Where agentic automation is mature enough to pilot now
Demonstration-driven application layer
1. Live use of ChatGPT for campaign and CX tasks
2. Live use of Claude for drafting and synthesis tasks
3. Live use of Gemini for productivity and workspace tasks
4. Live use of Gemini CLI for context-rich workflow execution
5. Live use of Manus for end-to-end action workflows
6. Live use of Power Automate with Outlook for triggered automation
7. Comparing outputs, effort, speed, and usability across tools
8. Recognizing where each tool fits best
9. Translating demos into real business use cases
Practical marketing workflows participants should be able to envision
1. Campaign brief generation workflow
2. Content repurposing workflow
3. Persona and insight synthesis workflow
4. Sales-support messaging workflow
5. Customer email response enhancement workflow
6. Complaint and feedback analysis workflow
7. Meeting and follow-up automation workflow
8. Research-to-summary-to-action workflow
9. Approval-aware content production workflow
Practical customer experience workflows participants should be able to envision
1. Inbox triage and categorization
2. Service summary generation
3. Knowledge-assisted response drafting
4. Escalation pattern identification
5. Lifecycle communication support
6. Experience improvement recommendation generation
7. VOC-to-insight-to-action workflows
8. Follow-up and reminder automation
Measuring value and adoption
1. Time saved
2. Throughput gains
3. Quality improvement
4. Response consistency
5. Personalization relevance
6. Team adoption indicators
7. Risk reduction measures
8. Customer impact measures
9. Operational dashboards and review cadence
Building an implementation roadmap
1. Readiness assessment
2. Team capability assessment
3. Tool selection principles
4. Use-case sequencing
5. Governance setup
6. Prompt and context asset creation
7. Pilot design
8. Stakeholder alignment
9. Scaling patterns
10. Continuous improvement loop
Closing integration
1. The future of marketing and CX work with AI
2. How roles are changing
3. What excellent teams will do differently
4. Building capability instead of chasing hype
5. A framework for sustained leverage
6. Turning AI from curiosity into operating advantage

Disclaimer

This outline is intended solely as an indicative guide to the anticipated content and direction of the course. It does not constitute a fixed or exhaustive agenda. The trainer reserves the right to revise, vary, re-sequence, substitute, cancel, or otherwise modify any part of the program content, materials, demonstrations, tools, delivery approach, or coverage at any time, as may reasonably be required in light of participant needs, class dynamics, logistical considerations, technological changes, tool availability, or professional judgment. Wherever possible, any such changes will be made with the objective of preserving the overall learning outcomes and ensuring the continued relevance and practical value of the program.

Practical, connected learning

My wider training approach brings hands-on implementation and systems thinking together, connecting technology with real operational needs.

AI in Marketing and Customer Experience