Programme overview
Technical depth: Practitioner · Practical mode: Lab
Introduction
Marketing and sales teams now have AI assistants inside the tools they already use, yet most staff use them occasionally and inconsistently, with little guidance on quality, confidentiality or disclosure. The result is small time savings and new risks. This Core Concept course gives marketing and sales staff practical, hands-on competence with general AI assistants, design tools and CRM-embedded AI, applied to everyday tasks from campaign copy to prospect research. Participants work in live tools throughout and leave with an AI Productivity Playbook for their own role.
Course Objectives
- Identify marketing and sales tasks where AI assistants save time and rank them by value, effort and risk
- Write structured prompts and reusable assistant instructions that keep outputs on brief and on brand
- Produce campaign copy, visuals, prospect research and sales emails with ChatGPT, Copilot, Gemini, Canva and CRM-embedded AI
- Check AI outputs for factual errors, bias, confidentiality breaches and disclosure obligations before use
- Connect AI steps into repeatable workflows and measure the time and quality gained
- Produce an AI Productivity Playbook with a prompt library and usage guardrails for the participant's own role
Target Audience
- Marketing staff who produce campaigns, content and performance reports
- Sales representatives and inside sales staff who research prospects and manage outreach
- Sales and marketing coordinators who support campaign and pipeline administration
- CRM and customer communications staff who maintain customer data and messaging
- Staff in small businesses and start-ups who run marketing and sales with limited resources
Course Outline
Day 1: AI in Marketing and Sales: Foundations and Use-Case Inventory
- Generative AI Capabilities and Limits: Large Language Models, Hallucination and Context Windows
- AI Assistant Landscape: ChatGPT, Microsoft 365 Copilot, Gemini and Claude
- CRM-Embedded AI: HubSpot Breeze and Salesforce Einstein Compared
- Marketing and Sales Task Audit for Automation Potential
- Use-Case Prioritisation Matrix: Value, Effort and Risk
Day 2: Prompting Methods and Responsible Use Standards
- Structured Prompting: Role, Context, Task, Format and Constraints
- Reusable Assistant Configurations: Custom GPTs and Copilot Agents
- NIST AI RMF and the NIST AI 600-1 Generative AI Profile Applied to Everyday Work
- AI Disclosure and Accountability under the ICC Advertising and Marketing Communications Code
- Data Handling Rules for Confidential, Personal and Customer Data in AI Tools
Day 3: AI for Marketing Content and Sales Workflows
- Campaign Copy and Variant Generation for A/B Testing
- Visual Asset Creation with Canva Magic Studio and Adobe Firefly
- Prospect Research and Account Briefs with AI Assistants
- Personalised Sales Email Sequences in the CRM
- Meeting Summaries and Follow-Up Actions with Copilot in Teams
Day 4: Output Quality, Risk and Workflow Optimisation
- Output Verification Checklist: Facts, Claims, Figures and Sources
- Bias and Cultural Sensitivity Review of AI-Generated Content
- Content Provenance and Labelling with C2PA Content Credentials
- Workflow Automation with Zapier and Power Automate
- Time-Saving and Quality Baseline Measurement for AI Use Cases
Day 5: Lab Practice and the AI Productivity Playbook
- Consumer Brand Campaign Lab: From Brief to Multichannel Assets
- B2B Sales Lab: From Target Account List to Outreach Sequence
- Team Prompt Library Build in a Shared Workspace
- AI Productivity Playbook Drafting
- Peer Demonstration and Playbook Review
Skills You Will Gain
- Structured Prompt Writing
- AI-Assisted Copywriting
- AI Image Generation
- AI Prospect Research
- AI Output Verification
- Workflow Automation
- Responsible AI Use
- Productivity Measurement
Why Attend This Course
- Return with an AI Productivity Playbook and a tested prompt library for the tasks you do every week
- Complete a real campaign and a real outreach sequence with AI tools during the course, hands on
- Know which data must never go into an AI tool and how to check outputs before they reach a customer
- See how marketing and sales staff in other sectors and countries are using the same tools
Conclusion
AI tools improve productivity only when they are applied to the right tasks, directed with clear instructions and checked before their output reaches a customer. This course moves from identifying high-value use cases, through prompting methods and responsible use standards, to hands-on content, research and sales workflows, and the verification and automation that make them reliable. The final day turns this practice into an AI Productivity Playbook that participants take back to their teams and put to use from the first week.