Artificial Intelligence Tools for Productivity
Course ID: 260119 0101 021ESH
Course Dates : 19/01/2026 Course Duration : 5 Studying Day/s Course Location: London, United Kingdom
Language: Bilingual
Course Category: Professional and CPD Training Programs
Course Subcategories:
Business Decision Making Organisational Effectiveness Performance Management Professional Practice & Standards Strategic Management
Course Certified By: ESHub CPD & LondonUni - Executive Management Training
* Professional Training and CPD Programs
Leading to:
Executive Diploma Certificate
Leading to:
Executive Mini Masters Certificate
Leading to
Executive Masters Certificate
Certification Will Be Issued:
From London, United Kingdom
Course Fees:
VAT varies by course location and participant nationality.
Date has passed please contact us Sales@e-s-hub.com
Introduction
This five-day course teaches practical use of AI tools to increase everyday productivity. You will learn how to choose tools, set them up securely, and apply them to real work tasks. Training focuses on hands-on exercises and reusable templates. The goal is immediate, measurable improvement in routine work.
Objectives
1. Identify the right AI tool for a given task and evaluate trade-offs.
2. Create and refine prompts and templates that produce consistent outputs.
3. Configure basic security, access controls, and data-handling settings for team tools.
4. Build and deploy at least two automated workflows that reduce manual steps (examples: email triage, report generation).
5. Implement a simple quality-check and governance process to monitor and improve AI outputs over time.
2. Create and refine prompts and templates that produce consistent outputs.
3. Configure basic security, access controls, and data-handling settings for team tools.
4. Build and deploy at least two automated workflows that reduce manual steps (examples: email triage, report generation).
5. Implement a simple quality-check and governance process to monitor and improve AI outputs over time.
Who Should Attend
Product managers
Project managers
Business analysts
Marketing managers (responsible for content and campaigns)
Executive assistants and operations coordinators
Training Method
• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Post-assessment
If Applicable:
• Each participant receives a 7” Tablet containing a copy of the presentation, slides and handouts
Program Support
This program is supported by:
* Interactive discussions
* Role-play
* Case studies and highlight the techniques available to the participants.
Course Agenda
Daily Schedule (Monday to Friday)
- 09:00 AM – 10:30 AM Technical Session 1
- 10:30 AM – 12:00 PM Technical Session 2
- 12:00 PM – 01:00 PM Technical Session 3
- 01:00 PM – 02:00 PM Lunch Break (If Applicable)
- Participants are expected to engage in guided self-study, reading, or personal reflection on the day’s content. This contributes toward the CPD accreditation and deepens conceptual understanding.
- 02:00 PM – 04:00 PM Self-Study & Reflection
Please Note:
- All training sessions are conducted from Monday to Friday, following the standard working week observed in the United Kingdom and European Union. Saturday and Sunday are official weekends and are not counted as part of the course duration.
- Coffee and refreshments are available on a floating basis throughout the morning. Participants may help themselves at their convenience to ensure an uninterrupted learning experience Provided if applicable and subject to course delivery arrangements.
- Lunch Provided if applicable and subject to course delivery arrangements.
Week 1
Day 1: Assessing and Selecting AI Productivity Tools
Mapping the AI productivity landscape
A. Categorizing tools by function (e.g., writing, coding, design, scheduling)
B. Evaluating the maturity, reliability, and user base of popular AI platforms
C. Identifying tools that integrate seamlessly with existing software stacks
Evaluating trade-offs and total cost of ownership
A. Comparing free, freemium, and enterprise-tier AI subscriptions
B. Assessing the learning curve and training requirements for new tools
C. Calculating the potential time savings versus subscription and implementation costs
Conducting structured tool trials
A. Defining specific, measurable tasks to test during the trial period
B. Gathering feedback from a small group of power users
C. Documenting pros, cons, and dealbreakers for each evaluated tool
Day 2: Prompt Mastery and Template Creation
Advanced prompting techniques
A. Using chain-of-thought prompting to solve complex, multi-step problems
B. Applying role-playing prompts to get highly specialized outputs
C. Utilizing delimiters and structured formats (e.g., JSON, Markdown) for precise control
Building a personal and team prompt library
A. Creating standardized templates for recurring tasks (e.g., status reports, code reviews)
B. Organizing prompts by category, tool, and expected output
C. Establishing a version control system for evolving prompt templates
Optimizing prompts for consistency
A. Testing prompts across multiple scenarios to ensure reliable results
B. Refining instructions to eliminate ambiguity and reduce hallucinations
C. Documenting known limitations and edge cases for each template
Day 3: Automating Routine Workflows
Automating email and communication management
A. Setting up AI rules to triage, categorize, and draft responses to routine emails
B. Summarizing long email threads and extracting key action items automatically
C. Scheduling and optimizing calendar invites based on participant availability
Streamlining document and report generation
A. Connecting AI tools to databases to auto-generate weekly performance summaries
B. Using AI to format and proofread large batches of documents simultaneously
C. Automating the creation of meeting agendas and post-meeting minutes
Integrating AI with no-code/low-code platforms
A. Using platforms like Zapier or Make to connect AI APIs with everyday apps
B. Building simple automated workflows (e.g., form submission → AI summary → Slack notification)
C. Testing and debugging automated workflows to ensure reliability
Day 4: Secure Configuration and Data Handling
Configuring AI tools for maximum security
A. Adjusting privacy settings to prevent data from being used for model training
B. Implementing role-based access controls for team AI accounts
C. Enabling audit logs to track AI tool usage and data inputs
Safe data handling practices
A. Identifying and redacting PII (Personally Identifiable Information) before prompting
B. Using synthetic or dummy data for testing and template creation
C. Understanding the legal and compliance implications of AI data processing
Managing third-party AI integrations
A. Reviewing API permissions and data sharing agreements
B. Regularly auditing connected apps and revoking unnecessary access
C. Establishing protocols for reporting and responding to potential data leaks
Day 5: Quality Control, Governance, and Continuous Improvement
Establishing quality assurance checkpoints
A. Defining clear criteria for what constitutes an "acceptable" AI output
B. Implementing mandatory human-in-the-loop reviews for critical tasks
C. Creating feedback mechanisms to flag and correct poor AI performance
Developing team AI governance policies
A. Drafting clear guidelines on acceptable and prohibited AI use cases
B. Assigning ownership for AI tool management and policy enforcement
C. Communicating governance rules effectively to all team members
Measuring and optimizing productivity gains
A. Tracking baseline metrics before and after AI tool implementation
B. Conducting regular retrospectives to identify new automation opportunities
C. Scaling successful individual workflows into standardized team practices
