Faysal AzizEnquire
FA-0005Agentic & Generative AIAI for Leaders & BusinessData & Analytics

Everyday AI at Work

Practical Prompting, Data Insights and Business Use Cases - 1 day

Introduction

Why this course

Artificial intelligence is moving from experimentation into everyday professional work. Employees are increasingly expected to use AI to communicate more effectively, analyse information, prepare decisions and improve routine processes. The real value of AI, however, depends on the user’s ability to provide clear instructions, interpret the results correctly and recognise where AI can support a genuine business need.

This practical one-day course introduces AI in clear, non-technical language before moving into productive prompting, AI-assisted data analysis and real-world business applications. Particular attention is given to practical examples and business use cases that participants can connect to their daily responsibilities. The course also helps employees identify potential opportunities for future improvement and innovation within their own functions.

The programme will be delivered by an instructor with over 30 years of industry experience. Training content will focus on current industry-demanded practices and realistic workplace applications rather than an overly academic discussion of artificial intelligence.

Learning outcomes

Learning outcomes

By the end of the course, participants will be able to:

  • Explain generative AI and large language models in practical terms
  • Recognise appropriate workplace applications of AI
  • Construct clear and effective prompts for common business tasks
  • Improve AI responses through structured follow-up prompting
  • Use AI to summarise, compare and interpret business information
  • Apply AI-assisted data analysis to identify patterns, trends and anomalies
  • Understand real-world AI applications across selected business functions
  • Analyse AI use cases and case studies from organisational environments
  • Identify opportunities to adapt AI approaches to daily work
  • Evaluate potential AI opportunities based on value, feasibility and risk
  • Verify AI-generated information and recognise unreliable outputs
  • Apply responsible practices relating to confidentiality, accuracy and human oversight
Prerequisites

Prerequisites

  • Basic familiarity with email, documents and spreadsheets
  • General confidence using web-based business applications
  • No programming or technical AI experience required
  • Access to an approved generative AI platform where available
  • Willingness to participate in practical AI activities
Training outline

6 modules

·
01Understanding AI in the Modern Workplace8 topics
  1. Artificial intelligence and generative AI
  2. Traditional automation and AI-enabled work
  3. Machine learning and large language models
  4. How AI generates responses
  5. Strengths and limitations of AI
  6. Human judgement and accountability
  7. Suitable and unsuitable workplace tasks
  8. Current organisational adoption patterns
02Everyday AI Productivity8 topics
  1. Drafting and refining workplace communications
  2. Summarising reports and meeting notes
  3. Converting notes into structured documents
  4. Research and information organisation
  5. Brainstorming and idea development
  6. Planning and task prioritisation
  7. Tone and audience adaptation
  8. Reusable AI-supported workflows
03Prompting and Data Analysis for Daily Workflows4 topics
  1. Productive Prompting
    1. Clear instructions
    2. Relevant business context
    3. Roles and audiences
    4. Constraints and boundaries
    5. Output formats
    6. Reference information
    7. Iterative prompting
    8. Follow-up instructions
    9. Reusable prompt templates
  2. AI-Assisted Data Analysis and Insight Extraction
    1. Preparing information for analysis
    2. Summarising tables and reports
    3. Identifying patterns and trends
    4. Comparing categories and reporting periods
    5. Detecting anomalies and inconsistencies
    6. Generating questions from business data
    7. Extracting operational insights
    8. Producing management-level summaries
    9. Translating findings into possible actions
  3. Practical Examples and Business Use Cases
    1. Written communication workflows
    2. Report and document analysis
    3. Meeting information consolidation
    4. Data interpretation and summarisation
    5. Decision-support preparation
    6. Repetitive information-processing tasks
  4. Validation and Quality Control
    1. Verifying calculations
    2. Checking interpretations
    3. Separating facts from assumptions
    4. Recognising incomplete analysis
    5. Protecting sensitive data
    6. Maintaining human review
04AI Use Cases and Case Studies6 topics
  1. Real-World AI Applications
    1. Organisational AI adoption
    2. AI-supported process improvement
    3. Operational limitations
    4. Adoption and change considerations
  2. Engineering Use Cases
    1. Technical document summarisation
    2. Requirements clarification
    3. Design review preparation
    4. Engineering knowledge retrieval
    5. Root-cause investigation support
    6. Maintenance documentation
    7. Technical communication improvement
  3. Customer Service Use Cases
    1. Customer enquiry classification
    2. Response drafting
    3. Knowledge-base retrieval
    4. Conversation summarisation
    5. Customer sentiment identification
    6. Recurring issue analysis
    7. Escalation support
    8. Service-quality reporting
  4. Supply Chain and Order Fulfilment Use Cases
    1. Order-status summarisation
    2. Order exception identification
    3. Demand and inventory insight support
    4. Supplier communication preparation
    5. Delivery-risk monitoring
    6. Operational report analysis
    7. Process bottleneck identification
    8. Cross-functional coordination
  5. Business Case Study Review
    1. AI-enabled approach
    2. Data and information requirements
    3. Employee involvement
    4. Expected benefits
    5. Operational risks
    6. Success measurements
    7. Lessons for organisational adaptation
  6. Adapting AI Approaches to Daily Work
    1. Repetitive workplace activities
    2. Information-heavy processes
    3. Communication-intensive tasks
    4. Data interpretation requirements
    5. Workflow friction points
    6. Improvement opportunities
    7. Innovation opportunities
    8. Small-scale pilot possibilities
05Responsible and Safe AI Use8 topics
  1. Hallucinations and factual errors
  2. Bias and incomplete context
  3. Confidentiality and data protection
  4. Intellectual property considerations
  5. Transparency and disclosure
  6. Verification and source checking
  7. Human approval requirements
  8. Risk-based AI usage
06Building Sustainable AI Work Practices8 topics
  1. Selecting appropriate tasks for AI
  2. Combining AI with professional judgement
  3. Creating a personal prompt library
  4. Standardising repeatable workflows
  5. Sharing effective practices
  6. Measuring time and quality improvements
  7. Identifying future improvement opportunities
  8. Supporting responsible AI innovation
Disclaimer

This training outline is provided as a general framework for programme delivery. The trainer reserves the right to amend, reorganise, substitute, expand or omit particular topics where reasonably necessary to accommodate participant requirements, operational circumstances, technological developments, available training time or changes in accepted industry practice. Such adjustments may be made without prior notice, provided that the principal objectives and intended learning outcomes of the programme are substantially maintained.

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Everyday AI at Work
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