The AI Productivity and Automation for Non-Technical Staff Training Course is a practical, workplace-focused program designed to help employees use artificial intelligence and automation tools safely and effectively in their day-to-day work without requiring programming or technical expertise.

The course introduces participants to generative AI, effective prompt engineering, AI-assisted communication, document processing, spreadsheet analysis, reporting, presentations, meeting support, research, customer assistance, content creation, internal knowledge management, and low-code workflow automation.

Throughout the program, participants learn how to identify appropriate opportunities for AI and automation while maintaining human oversight, data privacy, accuracy, security, and responsible AI practices. Practical business scenarios enable participants to apply AI to repetitive and information-intensive tasks and develop reusable workflows that can improve individual and team productivity.

The program concludes with a practical capstone project in which participants design and present an AI-assisted productivity or automation solution applicable to a realistic workplace process.

 

Duration 10 Days – 70 hrs.

 

Objectives

• Explain the fundamentals of artificial intelligence and generative AI in practical business terms.
• Identify appropriate and inappropriate workplace applications of AI.
• Recognize the strengths, limitations, and risks of AI-generated outputs.
• Create clear, structured, and reusable prompts for common workplace tasks.
• Use AI to draft, rewrite, summarize, classify, and prioritize business communications.
• Apply AI to extract, summarize, compare, and organize information from documents and PDFs.
• Use AI to support spreadsheet analysis, formula generation, data interpretation, and business reporting.
• Generate professional reports, executive summaries, presentations, and supporting content.
• Transform meeting information into summaries, minutes, action items, and follow-up tasks.
• Identify repetitive business processes suitable for automation.
• Design simple low-code workflows involving emails, forms, files, approvals, notifications, and tasks.
• Conduct structured research and verify AI-generated information against reliable sources.
• Use AI to assist with customer or member inquiries while maintaining appropriate escalation controls.
• Create business and marketing content for different audiences and communication channels.
• Understand the fundamentals of AI-powered internal knowledge and policy assistants.
• Apply privacy, confidentiality, security, copyright, and responsible AI principles.
• Develop a practical AI productivity and automation solution for a workplace use case.

 

Target Audience 

• Administrative and office staff
• Executive assistants and coordinators
• Human Resources personnel
• Finance and accounting staff
• Sales and business development teams
• Marketing and communications personnel
• Customer service and support teams
• Operations personnel
• Procurement and logistics staff
• Project coordinators and project support teams
• Supervisors and team leaders
• Managers and department heads
• Business analysts and reporting personnel
• Employees responsible for documentation, reporting, research, or repetitive administrative processes
• Non-technical professionals who want to improve productivity through AI and automation

 

Prerequisites 

• Basic computer literacy.
• Familiarity with common office productivity applications.
• Basic experience using email, documents, spreadsheets, presentations, and web browsers.
• Basic understanding of their organization’s common business processes and workflows.
• Access to the AI and productivity tools approved for use during the training.
• No programming or software development experience is required.

Course Outline 

Module 1: AI Fundamentals and Prompt Engineering 

• What AI and generative AI are
• Large language models in plain language
• AI copilots, chatbots, agents, and automations
• Strengths and limitations of AI
• Hallucinations and unreliable outputs
• Human review and accountability
• Anatomy of an effective prompt:
  • Role
  • Task
  • Context
  • Input
  • Constraints
  • Output format
  • Examples
• Zero-shot, one-shot, and few-shot prompting
• Follow-up and refinement prompts
• Creating reusable prompt templates

Hands-on activities

1. Improve a vague prompt into a structured workplace prompt.
2. Ask AI to rewrite the same announcement for employees, managers, and customers.
3. Compare AI results with and without examples and formatting requirements.
4. Build a reusable prompt library for common office tasks.

 

Module 2: Email Integration and Automation 

• Email triggers and automated actions
• Inbox rules versus workflow automation
• Shared mailboxes and departmental inboxes
• Automatic acknowledgment messages
• Email-to-task automation
• Saving attachments automatically
• Email notifications and approval workflows
• Escalation and follow-up reminders
• Avoiding automation loops and duplicate messages
• Monitoring failed workflows

Hands-on activities

1. Automatically save incoming attachments to a designated folder.
2. Create a task when an email contains a specific subject or category.
3. Send an acknowledgment for received requests.
4. Add a manager-approval step before sending sensitive replies.

Module 3: AI-Assisted Email Drafting, Summarization, and Classification 

• Drafting new messages
• Rewriting messages for tone and clarity
• Shortening lengthy emails
• Summarizing email threads
• Identifying the sender’s main request
• Sentiment and urgency detection
• Email categories and routing
• Suggested responses
• Multilingual email assistance
• Risks of automatic sending

Hands-on activities

1. Rewrite an unclear email into a professional response.
2. Summarize a long email conversation into five bullet points.
3. Extract deadlines and responsibilities from an email.
4. Classify sample messages as inquiry, complaint, request, approval, or spam.
5. Create an email-response template that requires human approval.

 

Module 4: Document and PDF Processing                

• Text-based versus scanned PDFs
• Optical character recognition
• Document summarization techniques
• Executive, section, and audience-specific summaries
• Data extraction from forms and reports
• Contract and policy analysis
• Document comparison
• Identifying missing information
• Creating checklists from procedures
• Citation and traceability
• Handling confidential documents

Hands-on activities

1. Summarize a policy document for new employees.
2. Extract names, dates, obligations, and deadlines from a sample PDF.
3. Compare two versions of a procedure.
4. Convert a long policy into a one-page checklist.
5. Validate each summary statement against the original document.

 

Module 5: Excel and Data Analysis 

• Preparing data for AI analysis
• Tables, headers, data types, and missing values
• Formula generation and explanation
• Sorting, filtering, and conditional formatting
• Pivot tables and summary reports
• Descriptive statistics
• Variance and trend analysis
• Finding duplicates and anomalies
• Forecasting basics
• Chart selection
• Data validation and error checking
• Avoiding unsupported conclusions

Hands-on activities

1. Clean an untidy sales worksheet.
2. Ask AI to generate and explain SUMIFS, COUNTIFS, XLOOKUP, and IF formulas.
3. Build a department-level summary using a pivot table.
4. Identify the highest-performing products and declining categories.
5. Create a management dashboard and written executive summary.

 

Module 6: Report and Presentation Generation

• Defining the report’s audience and objective
• Executive summaries
• Report structure and narrative flow
• Converting spreadsheet findings into written insights
• Generating charts and commentary
• Presentation storyboarding
• Slide titles that communicate conclusions
• Speaker notes and talking points
• Brand and formatting consistency
• Fact-checking and source attribution

Hands-on activities

1. Convert spreadsheet findings into a two-page report.
2. Create an executive summary for senior management.
3. Transform the report into a 10-slide presentation.
4. Generate speaker notes for each slide.
5. Review the output using an accuracy and quality checklist.

 

Module 7: Workflow and Task Automation 

• Automation opportunities
• Trigger-action-condition model
• Scheduled versus event-based workflows
• Approvals and human-in-the-loop controls
• Email, forms, files, spreadsheets, and task integration
• Notifications and reminders
• Data validation
• Error handling and retry behavior
• Audit logs
• Workflow maintenance
• Measuring time saved

Hands-on activities

1. Draw the current version of a manual approval process.
2. Redesign the process as an automated workflow.
3. Build a leave-request or purchase-request workflow.
4. Add approval, rejection, and notification branches.
5. Test normal, missing-data, duplicate, and failure scenarios.

 

Module 8: Research and Information Gathering 

• Turning broad topics into research questions
• Source discovery and evaluation
• Primary versus secondary sources
• Current versus outdated information
• Cross-checking important claims
• Detecting fabricated references
• Competitive and market research
• Policy and regulatory research
• Comparison matrices
• Research summaries and briefing papers
• Copyright and proper attribution

Hands-on activities

1. Create a research plan for a proposed service.
2. Compare three vendors using predefined criteria.
3. Verify five AI-generated claims using trusted sources.
4. Produce a one-page research brief.
5. Label findings as fact, interpretation, assumption, or recommendation.

 

Module 9: Customer and Member Inquiry Assistance 

• Frequently asked questions
• Intent classification
• Knowledge-based response generation
• Tone and empathy
• Personalizing responses safely
• Complaint handling
• Multilingual assistance
• Confidence thresholds
• Human escalation
• Prohibited or sensitive responses
• Logging and quality monitoring

Hands-on activities

1. Create an approved FAQ knowledge set.
2. Draft responses to common customer questions.
3. Classify inquiries by department and urgency.
4. Design an escalation rule for complaints and sensitive cases.
5. Review responses for accuracy, tone, privacy, and policy compliance.

 

Module 10: Marketing and Content Creation 

• Audience personas
• Brand voice and tone
• Content ideation
• Social media posts
• Email campaigns
• Website and blog content
• Headlines and calls to action
• Content calendars
• Image-prompt fundamentals
• Search-friendly content
• A/B test variations
• Copyright, disclosure, and misleading claims

Hands-on activities

1. Define a brand voice guide.
2. Create a one-week content calendar.
3. Turn a product announcement into:
   • An email
   • A social media post
   • A website article
   • A customer FAQ
4. Generate three headline and call-to-action variations.
5. Review the content for unsupported or exaggerated claims.

 

Module 11: Internal Knowledge and Policy Assistant 

• Knowledge bases and document repositories
• Document naming, classification, and ownership
• Search-based AI assistants
• Grounded answers
• Source citations
• Access permissions
• Document version control
• Content review and expiration
• “I don’t know” and escalation behavior
• Monitoring frequently asked questions
• Avoiding cross-department information leakage

Hands-on activities

1. Organize a small HR or operations knowledge base.
2. Develop 15 test questions based on internal policies.
3. Design an assistant response format containing:
   • Direct answer
   • Source document
   • Relevant section
   • Last-updated date
   • Escalation contact
4. Test questions whose answers are missing or ambiguous.
5. Create a knowledge-maintenance checklist.

 

Module 12: Data Privacy, Confidentiality, and Responsible AI 

• Personal and sensitive personal information
• Confidential business information
• Public versus internal versus restricted data
• Data minimization
• Anonymization and redaction
• Organizational AI policies
• Access control and least privilege
• Data retention and sharing
• Bias and fairness
• Transparency and explainability
• Intellectual property and copyright
• Human accountability
• Social engineering and prompt-injection risks
• Incident reporting

Hands-on activities

1. Classify sample information as public, internal, confidential, or restricted.
2. Redact sensitive information before submitting a document to AI.
3. Identify privacy and security risks in sample scenarios.
4. Perform a responsible-AI review of an automated workflow.
5. Build a pre-submission safety checklist.

Safety checklist

Before using AI, participants should ask:

• Am I authorized to use this information?
• Does the input contain personal or confidential data?
• Can the information be minimized or anonymized?
• Is the selected AI tool approved by the organization?
• Can I verify the answer using an authoritative source?
• Does a person need to approve the result?
• Could the output unfairly affect an individual?
• Should the activity be recorded for audit purposes?