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AI+ Product Manager Fundamentals™ eLearning
Formerly known as AI+ Product Manager™ <br> <br> Innovate Products Faster with AI-Enabled Management
Product Innovation: Leverage AI to drive product development and market fit
Concept to Execution: Explore AI applications in real product lifecycle scenarios
Market Advantage: Bridge the gap between tech innovation and customer needs
Leadership Ready: Prepare for leading roles in fast-evolving product ecosystems
Certification Overview
Course Introduction Preview Module 1: Introduction to Artificial Intelligence (AI) for Product Managers
1.1 Understanding the Basics of artificial intelligence
1.2 Importance of AI
Module 2: Fundamentals of Machine Learning
2.1 Introduction to Machine Learning
2.2 Data Preparation in ML model
Module 3: AI Product Development Lifecycle
3.1 Exploring How AI Can Be Leveraged in Ideation and Conceptualization
3.2 Prototyping and Testing: Explore Methods for Prototyping and Testing AI-driven Products Effectively
Module 4: AI Ethics and Bias
4.1 Understanding Ethical Considerations: Examine the Ethical Implications of AI Products and the Responsibility of Product Managers
4.2 Mitigating Bias: Learn Strategies to Identify and Address Bias in AI Algorithms and Products
Module 5: AI Implementation Strategies
5.1 Integration with Existing Products: Explore Methods for Integrating AI Features into Existing Products Seamlessly
5.2 Stakeholder Management: Understand How to Communicate AI Initiatives Effectively with Stakeholders and Gain their Support
Module 6: AI Metrics and Performance Evaluation
6.1 Key Performance Indicators (KPIs): Identify Relevant Metrics for Measuring the Success of AI-driven Products
6.2 Performance Evaluation Techniques: Learn Methods for Evaluating the Performance of AI Models and Products
Module 7: AI Regulation and Compliance
7.1 Regulatory Landscape: Explore Current Regulations and Frameworks Relevant to AI Products
7.2 Compliance Strategies: Develop Strategies to Ensure AI Products Comply with Regulatory Requirements
Module 8: Future Trends in AI and Product Management
8.1 Emerging Technologies: Discuss Upcoming Trends and Technologies Shaping the Future of AI and Product Management
8.2 Strategic Planning: Learn How to Anticipate and Adapt to Future Changes in the AI Landscape to Drive Product Innovation
Optional Module: AI Agents for Product Management
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
ChatGPT
AI Fairness 360
Power BI
IBM Watson OpenScale
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€225
E-Learning
max 999
1 dag
AI+ Project Manager Fundamentals™
Nieuwegein
ma 2 nov. 2026
Formerly known as AI+ Project Manager™ <br> <br> Streamline Project Success: AI-Enhanced Intelligent Solutions
Real-Time Integration: Learn to apply AI in project planning, decision-making, and execution
Advanced Curriculum: Covers AI algorithms, ML, and resource allocation tools
Multi-Disciplinary Focus: Tailored for complex, cross-functional project scenarios
Leadership Readiness: Empowers professionals to lead AI-driven project success
Course Overview
Course Introduction Preview Module 1: Introduction to Artificial Intelligence (AI) in Project Management
1.1 AI Fundamentals Preview
1.2 AI in Project ManagementPreview
1.3 Key AI Technologies
1.4 Benefits and Challenges
1.5 Future Perspectives Module 2: AI Tools for Project Management
2.1 Overview of AI Tools Preview
2.2 Artificial Intelligence Tools in Action: Enhancing Project Management Efficiency Preview
2.3 Selecting AI Tools Preview
2.4 Implementing AI Tools
2.5 Case Studies Module 3: Data-Driven Decision Making
3.1 Importance of Data in Artificial Intelligence Preview
3.2 Data Analysis Techniques Preview
3.3 Applying Data Insights to Project Decisions
3.4 Tools for Data Visualization and Reporting
3.5 Challenges and Best Practices Module 4: AI for Enhancing Team Collaboration and Productivity
4.1 AI-Enhanced Collaboration Tools
4.2 Boosting Productivity with AI
4.3 Managing Project Knowledge with AI
4.4 Overcoming Collaboration Challenges
Module 5: Ethical Considerations and Bias in AI
5.1 Understanding AI Ethics
5.2 Identifying and Mitigating Bias
5.3 Developing AI Governance
5.4 Case Studies
Module 6: Implementing AI in Projects
6.1 Strategies for AI Integration
6.2 Choosing the Right AI Tools
6.3 Project Data Preparation for AI
6.4 AI Implementation Plan
6.5 Monitoring AI Integration
6.6 Evaluating AI Outcomes
6.7 Risk Management in AI Projects
6.8 Workshop: AI Tool Deployment
Module 7: Future of AI in Project Management
7.1 Emerging Trends in AI and Project Management
7.2 AI and the Evolving Role of the Project Manager
7.3 Sustainability and AI in Projects
7.4 Adapting to Future AI Development
7.5 Predictive Analysis and Future Planning
Optional Module: AI Agents for Project Management
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
Hive
Wrike
Trello
ClickUp
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€995
Klassikaal
max 12
1 dag
AI+ Project Manager Fundamentals™ eLearning
Formerly known as AI+ Project Manager™ <br> <br> Streamline Project Success: AI-Enhanced Intelligent Solutions
Real-Time Integration: Learn to apply AI in project planning, decision-making, and execution
Advanced Curriculum: Covers AI algorithms, ML, and resource allocation tools
Multi-Disciplinary Focus: Tailored for complex, cross-functional project scenarios
Leadership Readiness: Empowers professionals to lead AI-driven project success
Course Overview
Course Introduction Preview Module 1: Introduction to Artificial Intelligence (AI) in Project Management
1.1 AI Fundamentals Preview
1.2 AI in Project ManagementPreview
1.3 Key AI Technologies
1.4 Benefits and Challenges
1.5 Future Perspectives Module 2: AI Tools for Project Management
2.1 Overview of AI Tools Preview
2.2 Artificial Intelligence Tools in Action: Enhancing Project Management Efficiency Preview
2.3 Selecting AI Tools Preview
2.4 Implementing AI Tools
2.5 Case Studies Module 3: Data-Driven Decision Making
3.1 Importance of Data in Artificial Intelligence Preview
3.2 Data Analysis Techniques Preview
3.3 Applying Data Insights to Project Decisions
3.4 Tools for Data Visualization and Reporting
3.5 Challenges and Best Practices Module 4: AI for Enhancing Team Collaboration and Productivity
4.1 AI-Enhanced Collaboration Tools
4.2 Boosting Productivity with AI
4.3 Managing Project Knowledge with AI
4.4 Overcoming Collaboration Challenges
Module 5: Ethical Considerations and Bias in AI
5.1 Understanding AI Ethics
5.2 Identifying and Mitigating Bias
5.3 Developing AI Governance
5.4 Case Studies
Module 6: Implementing AI in Projects
6.1 Strategies for AI Integration
6.2 Choosing the Right AI Tools
6.3 Project Data Preparation for AI
6.4 AI Implementation Plan
6.5 Monitoring AI Integration
6.6 Evaluating AI Outcomes
6.7 Risk Management in AI Projects
6.8 Workshop: AI Tool Deployment
Module 7: Future of AI in Project Management
7.1 Emerging Trends in AI and Project Management
7.2 AI and the Evolving Role of the Project Manager
7.3 Sustainability and AI in Projects
7.4 Adapting to Future AI Development
7.5 Predictive Analysis and Future Planning
Optional Module: AI Agents for Project Management
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
Hive
Wrike
Trello
ClickUp
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€225
E-Learning
max 999
1 dag
AI+ Project Management Practitioner™ eLearning
Build stronger project foundations with AI+ Project Management Practitioner ™ by combining AI-assisted planning with practical decision support.
Intelligent Project Operations: Discover how AI enhances planning, scheduling, task prioritization, and progress tracking to reduce manual effort and improve project consistency.
Predictive Planning & Resource Optimization: Use data-driven insights for timeline forecasting, workload balancing, capacity planning, and early risk detection to keep projects on track.
Governance, Compliance & Risk Awareness: Understand how AI supports documentation accuracy, change control, audit readiness, and ongoing risk monitoring in project environments.
Leadership Foundations for AI-Augmented Projects: Build skills to lead teams using AI-enabled workflows, including automated reporting, real-time insights, and improved stakeholder alignment.
Module 1: Project Management Overview
1.1 Introduction to Project Management
1.2 Project Management Lifecycle
1.3 Advanced Project Management Tasks
1.4 Project Management Frameworks
1.5 Project Manager’s Roles and Responsibilities
Module 2: Introduction to AI and ML
2.1 Introduction to Artificial Intelligence (AI)
2.2 Introduction to Machine Learning (ML)
2.3 Neural Networks
2.4 AI and ML Applications and Trends
2.5 Case Studies on AI and ML Projects
Module 3: Data Driven Decision Making
3.1 The Importance of Data in Artificial Intelligence
3.2 Data Analysis Techniques
3.4 Applying Data Insights to Project Decisions
3.5 Tools for Data Visualization and Reporting
3.6 Challenges and Best Practices
Module 4: AI-Driven Project Risk Management
4.1 AI in Risk Management – An Introduction
4.2 AI for Risk Mitigation and Response
4.3 AI for Financial and Resource Risk Management
4.4 AI in Risk Management: The Future Scope
4.5 Case Study – AI-based Project Risk Management
Module 5: Planning Project Work Breakdown and Structuring and Project Scheduling by AI
5.1 Introduction to Work Breakdown Structure (WBS)
5.2 AI for WBS Creation
5.3 AI in Project Scheduling
5.4 AI for Resource-Constrained Scheduling
5.5 Case Studies: AI-based WBS and AI Algorithms for Project Scheduling
Module 6: Effective Project Budgeting Using AI
6.1 Introduction to AI in Budgeting
6.2 AI for Estimating Costs and Budget Allocation
6.3 AI for Budget Optimization
6.4 Future of AI in Project Budgeting
6.5 Case Study: AI Algorithms for Project Scheduling, AI- Based Model for Estimating Costs and Budget Allocation
Module 7: AI for Planning Human Resources
7.1 Introduction to AI in Human Resource Planning
7.2 AI for Workforce Allocation
7.3 AI in Skill Matching and Employee Performance Analysis
7.4 The Future of AI in Human Resource Planning
7.5 Case Studies: Designing AI-Based Models for HR Planning
Module 8: Stakeholder Management Using AI
8.1 Introduction to Stakeholder Management and AI
8.2 Identifying and Categorizing Stakeholders Using AI
8.3 Stakeholder Conflicts Management with AI
8.4 Ethics and Future Prospects in AI-based Stakeholder Management
8.5 Case Studies: AI Tools for Stakeholder Management
Module 9: AI-based Project Monitoring
9.1 Introduction to Project Monitoring and AI
9.2 AI-based Tools for Monitoring Project Progress
9.3 AI for Risk Monitoring
9.4 Case Studies: AI Tools for Project Monitoring
Module 10: Transformative Role of Project Management
10.1 Current State of AI in Project Management
10.2 Ethical Considerations in AI-Based Project Management
10.3 Technical Challenges in AI Integration
Additional Module: AI Agents for Project Management Practitioner
1. Understanding AI Agents
2. How Does an AI Agent Work
3. Applications and Trends of AI Agents in Project Management
4. Core Characteristics of AI Agents
5. Significance of AI Agents in Project Management
6. Types of AI Agents
7. Case Study-AI Agents for Agile Project Delivery – Atlassian in Action
8. Hands-On Activity
Tools you will explore
Python for Project Analytics
Machine Learning Libraries for Project Insights (Scikit-learn, TensorFlow)
Project Data Handling Tools (Pandas, NumPy)
Visualization Platforms for Project Dashboards (Power BI, Tableau)
Project Data Storage using SQL & NoSQL Databases
APIs for Project and Workflow Integration
Cloud Platforms for AI-Enabled Project Management (AWS & Azure Services)
OpenAI & LangChain for AI-Assisted Project Tools
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€530
E-Learning
max 999
5 dagen
AI+ Sales Practitioner™
Formerly known as AI+ Sales™ <br> <br> Boost Sales Success Through AI-Driven Insights
Sales Transformation: Harness AI to boost sales operations, CRM integration, and forecasting
Hands-on Approach: Practical workshops covering AI tools and ethical sales practices
Data-Driven Insights: Learn to analyze, optimize, and automate sales processes
Growth-Oriented: Drive ethical business growth and maximize performance
Course Overview
Course Introduction Preview Module 1: Introduction to Artificial Intelligence (AI) in Sales
1.1 Fundamentals of AI
1.2 Historical Journey and Evolution of AI in Sales
1.3 AI Tools & Technologies Transforming Sales
1.4 Benefits and Challenges in Adoption of AI in Sales
1.5 Real-world Examples and Applications of AI in Sales
1.6 Future of AI in Sales
Module 2: Understanding Data in Sales
2.1 Categories of Sales Data
2.2 Techniques for Effective Data Collection
2.3 Basics of Data Analysis and Interpretation
2.4 Data Management Methods
2.5 Data Protection Principles
2.6 Data Integration in CRM Systems
2.7 Overview of Analytical Tools
2.8 Ethical Use of Sales Data
2.9 Case Studies: Real-World Data Applications
Module 3: AI Technologies for Sales
3.1 Introduction to Machine Learning in Sales
3.2 Predictive Analytics: Forecasting Sales Trends
3.3 NLP: Enhancing Customer Interactions
3.4 Chatbots: Automating Customer Service
3.5 Segmentation: Tailoring Customer Experiences
3.6 Personalization: Customizing Sales Approaches
3.7 Recommendation Engines: Driving Product Suggestions
3.8 Sales Automation: Streamlining Sales Processes
3.9 Performance Analysis: Measuring Sales Effectiveness
Module 4: Implementation of AI in CRM Systems
4.1 Foundation of CRM Systems
4.2 AI Integration into CRM Systems
4.3 Lead Scoring
4.4 Customer Insights
4.5 Sales Automation
4.6 Personalized Communication
4.7 Chatbots in CRM
4.8 Gaining Actionable Insights from Data
4.9 Case Studies
Module 5: Sales Forecasting with AI
5.1 Introduction to Sales Forecasting
5.2 Overview of Predictive Models in Forecasting
5.3 Data Preparation for Analysis
5.4 Identifying Sales Patterns and Trends
5.5 Enhancing Forecast Reliability
5.6 Key Forecasting AI Tools in AI
5.7 Utilizing Real-time Data for Forecasts
5.8 Developing Forecasts for Different Outcomes
5.9 Measuring the Success of Sales Forecasts
Module 6: Enhancing Sales Processes with AI
6.1 Task Automation
6.2 AI-driven Email Marketing
6.3 Social Media with AI Analytics
6.4 AI-powered Lead Generation
6.5 Customer Segmentation
6.6 Optimizing Sales Visits and Calls
6.7 Tailoring Content with AI Insights
6.8 Real-time Sales Activity Monitoring
6.9 Upselling and Cross-selling with AI
Module 7: Ethical Considerations and Bias AI
7.1 Ethical Use of AI in Sales
7.2 Bias Identification in AI Systems
7.3 Bias Mitigation
7.4 Transparency in AI Decision-Making
7.5 Accountability for AI Actions
7.6 Safeguarding Customer Data
7.7 Regulatory Compliance
7.8 Building Customer Trust through Ethical AI
7.9 Anticipating Ethical Issues in AI Advancements
Module 8: Practical Workshop
8.1 Scenario-Based Exercises
8.2 Addressing Sales Challenges with AI
8.3 Collaborative AI Implementation Plans
Optional Module: AI Agents for Sales
1. What Are AI Agents
2. Types of AI Agents
3. Applications and Trend of AI Agents in Sales
Tools you will explore
Salesforce Einstein
Conversica
Uniphore
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€995
Klassikaal
max 12
1 dag
AI+ Sales Practitioner™ eLearning
Formerly known as AI+ Sales™ <br> <br> Boost Sales Success Through AI-Driven Insights
Sales Transformation: Harness AI to boost sales operations, CRM integration, and forecasting
Hands-on Approach: Practical workshops covering AI tools and ethical sales practices
Data-Driven Insights: Learn to analyze, optimize, and automate sales processes
Growth-Oriented: Drive ethical business growth and maximize performance
Course Overview
Course Introduction Preview Module 1: Introduction to Artificial Intelligence (AI) in Sales
1.1 Fundamentals of AI
1.2 Historical Journey and Evolution of AI in Sales
1.3 AI Tools & Technologies Transforming Sales
1.4 Benefits and Challenges in Adoption of AI in Sales
1.5 Real-world Examples and Applications of AI in Sales
1.6 Future of AI in Sales
Module 2: Understanding Data in Sales
2.1 Categories of Sales Data
2.2 Techniques for Effective Data Collection
2.3 Basics of Data Analysis and Interpretation
2.4 Data Management Methods
2.5 Data Protection Principles
2.6 Data Integration in CRM Systems
2.7 Overview of Analytical Tools
2.8 Ethical Use of Sales Data
2.9 Case Studies: Real-World Data Applications
Module 3: AI Technologies for Sales
3.1 Introduction to Machine Learning in Sales
3.2 Predictive Analytics: Forecasting Sales Trends
3.3 NLP: Enhancing Customer Interactions
3.4 Chatbots: Automating Customer Service
3.5 Segmentation: Tailoring Customer Experiences
3.6 Personalization: Customizing Sales Approaches
3.7 Recommendation Engines: Driving Product Suggestions
3.8 Sales Automation: Streamlining Sales Processes
3.9 Performance Analysis: Measuring Sales Effectiveness
Module 4: Implementation of AI in CRM Systems
4.1 Foundation of CRM Systems
4.2 AI Integration into CRM Systems
4.3 Lead Scoring
4.4 Customer Insights
4.5 Sales Automation
4.6 Personalized Communication
4.7 Chatbots in CRM
4.8 Gaining Actionable Insights from Data
4.9 Case Studies
Module 5: Sales Forecasting with AI
5.1 Introduction to Sales Forecasting
5.2 Overview of Predictive Models in Forecasting
5.3 Data Preparation for Analysis
5.4 Identifying Sales Patterns and Trends
5.5 Enhancing Forecast Reliability
5.6 Key Forecasting AI Tools in AI
5.7 Utilizing Real-time Data for Forecasts
5.8 Developing Forecasts for Different Outcomes
5.9 Measuring the Success of Sales Forecasts
Module 6: Enhancing Sales Processes with AI
6.1 Task Automation
6.2 AI-driven Email Marketing
6.3 Social Media with AI Analytics
6.4 AI-powered Lead Generation
6.5 Customer Segmentation
6.6 Optimizing Sales Visits and Calls
6.7 Tailoring Content with AI Insights
6.8 Real-time Sales Activity Monitoring
6.9 Upselling and Cross-selling with AI
Module 7: Ethical Considerations and Bias AI
7.1 Ethical Use of AI in Sales
7.2 Bias Identification in AI Systems
7.3 Bias Mitigation
7.4 Transparency in AI Decision-Making
7.5 Accountability for AI Actions
7.6 Safeguarding Customer Data
7.7 Regulatory Compliance
7.8 Building Customer Trust through Ethical AI
7.9 Anticipating Ethical Issues in AI Advancements
Module 8: Practical Workshop
8.1 Scenario-Based Exercises
8.2 Addressing Sales Challenges with AI
8.3 Collaborative AI Implementation Plans
Optional Module: AI Agents for Sales
1. What Are AI Agents
2. Types of AI Agents
3. Applications and Trend of AI Agents in Sales
Tools you will explore
Salesforce Einstein
Conversica
Uniphore
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€225
E-Learning
max 999
1 dag
AI+ Legal Practitioner™
Formerly known as AI+ Legal™ <br> <br>AI-Powered Legal Insights: Building Trust, Delivering Results
Future of Law: Equip legal professionals with AI tools for research, privacy, and compliance
Critical Thinking: Focus on ethical concerns, precedents, and contract management
Advanced Tools: Covers ML for legal documents, analytics, and case outcome prediction
Hands-on Projects: Learn via simulations for real-world legal AI deployment
Course Overview
Course Introduction Preview
Module 1: Introduction to Artificial Intelligence (AI) and Its Implications for Law
1.1 AI Basics
1.2 AI in the Legal Industry
1.3 Understanding AI Technologies
Module 2: Machine Learning Fundamentals for Lawyers
2.1 Foundations of Machine Learning
2.2 Machine Learning in Legal Contexts
2.3 Practical Machine Learning Tools
Module 3: Natural Language Processing in Legal Research
3.1 Basics of NLP
3.2 NLP Applications in Law
3.3 Advanced NLP Techniques
Module 4: AI in Contract Review and Management
4.1 Contract Analysis with AI
4.2 AI Tools for Contract Management
4.3 Practical Contract Management Scenarios
Module 5: Predictive Analytics in Legal Decision-Making
5.1 Introduction to Predictive Analytics
5.2 Predictive Analytics in the Legal Field
5.3 Implementing Predictive Analytics
Module 6: Ethical and Privacy Considerations of AI in Law
6.1 AI Ethics in Legal Practice
6.2 Privacy Concerns with AI
6.3 Regulatory Landscape for AI
Module 7: Legal and Regulatory Framework for AI
7.1 Current Legal Frameworks for AI
7.2 Drafting AI Regulations
7.3 AI and Legal Practice
Module 8: Implementing AI in Legal Practices
8.1 AI Integration Strategies in Legal practices
8.2 Case Studies of AI in Law Firms
8.3 Future of AI in Legal Profession
Optional Module: AI Agents for Legal
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
ChatGPT
Humata AI
Amto AI
AI Lawyer
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€995
Klassikaal
max 12
1 dag
AI+ Legal Practitioner™ eLearning
Formerly known as AI+ Legal™ <br> <br>AI-Powered Legal Insights: Building Trust, Delivering Results
Future of Law: Equip legal professionals with AI tools for research, privacy, and compliance
Critical Thinking: Focus on ethical concerns, precedents, and contract management
Advanced Tools: Covers ML for legal documents, analytics, and case outcome prediction
Hands-on Projects: Learn via simulations for real-world legal AI deployment
Course Overview
Course Introduction Preview
Module 1: Introduction to Artificial Intelligence (AI) and Its Implications for Law
1.1 AI Basics
1.2 AI in the Legal Industry
1.3 Understanding AI Technologies
Module 2: Machine Learning Fundamentals for Lawyers
2.1 Foundations of Machine Learning
2.2 Machine Learning in Legal Contexts
2.3 Practical Machine Learning Tools
Module 3: Natural Language Processing in Legal Research
3.1 Basics of NLP
3.2 NLP Applications in Law
3.3 Advanced NLP Techniques
Module 4: AI in Contract Review and Management
4.1 Contract Analysis with AI
4.2 AI Tools for Contract Management
4.3 Practical Contract Management Scenarios
Module 5: Predictive Analytics in Legal Decision-Making
5.1 Introduction to Predictive Analytics
5.2 Predictive Analytics in the Legal Field
5.3 Implementing Predictive Analytics
Module 6: Ethical and Privacy Considerations of AI in Law
6.1 AI Ethics in Legal Practice
6.2 Privacy Concerns with AI
6.3 Regulatory Landscape for AI
Module 7: Legal and Regulatory Framework for AI
7.1 Current Legal Frameworks for AI
7.2 Drafting AI Regulations
7.3 AI and Legal Practice
Module 8: Implementing AI in Legal Practices
8.1 AI Integration Strategies in Legal practices
8.2 Case Studies of AI in Law Firms
8.3 Future of AI in Legal Profession
Optional Module: AI Agents for Legal
1. Understanding AI Agents
2. Case Studies
3. Hands-On Practice with AI Agents
Tools you will explore
ChatGPT
Humata AI
Amto AI
AI Lawyer
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Instructor-led OR Self-paced course + Official exam + Digital badge
€225
E-Learning
max 999
1 dag
AI+ Legal Agent Specialty™
Formerly known as AI+ Legal Agent™<br><br>Mastering AI in Legal Systems: Your Path to Autonomous Innovation
Core Concepts Covered: Legal workflows, AI technologies, Natural Language Processing (NLP), and contract review automation
Advanced Topics: Explore Generative AI, predictive analytics in case outcomes, AI-driven legal research, and compliance monitoring
Capstone Application: Design real-world legal AI agents for tasks like contract review, legal research, and compliance tracking
Career Readiness: Build expertise to thrive in AI-powered legal roles, with mentorship and hands-on training in designing legal AI agents
Module 1: Introduction to LegalTech and AI Agents
1.1 AI Basics
1.2 What is LegalTech?
1.3 A Brief History of AI
1.4 Why AI in Law?
1.5 Emerging Trends in Legal AI Agents and the Rise of Intelligent Automation
1.6 Case Study: Revolutionizing Legal Drafting: Allen & Overy’s Integration of Harvey AI:
1.7 Case Study: AI-Powered Contract Review in a Multinational Legal Department
Module 2: What is an AI Agent?
2.1 AI Agents in the Legal Field
2.2 Defining Characteristics of an AI Agent
2.3 How AI Agents Differ from AI Tools
2.4 Types of AI Agents (High-Level Functional Overview)
2.5 Types of AI Agents (Design Architecture-Based)
2.6 Case Study
2.7 Tools and Libraries for AI Agent Development in LegalTech
2.8 Legal AI Agents in Trend
Module 3: GPT and NLP Foundation for Legal Agents
3.1 Introduction to NLP in AI Agents
3.2 Language Models
3.3 Customizing GPT for Legal Work
3.4 The Rising Role of Prompt Engineering in Legal AI
Module 4: AI Agents for eDiscovery
4.1 Introduction: What Is eDiscovery and Why Automate It?
4.2 Introduction to DISCO AI (Cecilia)
4.3 Cecilia Q&A
4.4 AI-Powered Investigations with Reveal AI
Module 5: Contract Review in Legal Workflows
5.1 What is Contract Review?
5.2 What Is an AI Contract Review Agent?
Module 6: Legal Research Agents
6.1 What is a Legal Research Agent?
6.2 Real-World Insights — AI Lawyer in Action
6.3 AI Legal Research – Use Cases in Practice
Module 7: Compliance & Risk Monitoring Agents
7.1 Compliance and Risk Monitoring
7.2 Compliance & Risk Monitoring Agents
7.3 Hands-On Activity
Module 8: Legal Chatbots & Virtual Legal Assistants
8.1 Introduction to Legal Chatbots
8.2 Key Use Cases in Legal Practice
8.3 Legal Architecture & Design Principles
Module 9: AI Agents for IP Filing and Patent Drafting
9.1 Introduction to AI in IP Filing and Patent Drafting
9.2 Core AI Agent Functionalities
9.3 Introduction to AI Tools for Patent Drafting and Management
Module 10: Case Outcome Prediction Agents
10.1 Introduction to Case Outcome Prediction?
10.2 Feature Engineering in Legal Case Outcome Prediction
10.3 The Rise of Multi-Agent Legal Workflows
Module 11: Ethics, Fairness, and Transparency in Legal AI
11.1 Managing Bias in Legal AI
11.2 Legal Accountability in Autonomous Agent Deployment
Module 12: Capstone Project – Building Your AI Legal Agent
12.1 Applying AI to Solve Real Legal Problems
12.2 Document Your Inputs and Prompts
Tools you will explore
TensorFlow
Power BI
Keras
SQL
Apache-Spark
Python
Matplotlib
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Included Instructor-led OR Self-paced course + Official exam + Digital badge
€995
Klassikaal
max 12
1 dag
AI+ Legal Agent Specialty™ eLearning
Formerly known as AI+ Legal Agent™<br><br>Mastering AI in Legal Systems: Your Path to Autonomous Innovation
Core Concepts Covered: Legal workflows, AI technologies, Natural Language Processing (NLP), and contract review automation
Advanced Topics: Explore Generative AI, predictive analytics in case outcomes, AI-driven legal research, and compliance monitoring
Capstone Application: Design real-world legal AI agents for tasks like contract review, legal research, and compliance tracking
Career Readiness: Build expertise to thrive in AI-powered legal roles, with mentorship and hands-on training in designing legal AI agents
Module 1: Introduction to LegalTech and AI Agents
1.1 AI Basics
1.2 What is LegalTech?
1.3 A Brief History of AI
1.4 Why AI in Law?
1.5 Emerging Trends in Legal AI Agents and the Rise of Intelligent Automation
1.6 Case Study: Revolutionizing Legal Drafting: Allen & Overy’s Integration of Harvey AI:
1.7 Case Study: AI-Powered Contract Review in a Multinational Legal Department
Module 2: What is an AI Agent?
2.1 AI Agents in the Legal Field
2.2 Defining Characteristics of an AI Agent
2.3 How AI Agents Differ from AI Tools
2.4 Types of AI Agents (High-Level Functional Overview)
2.5 Types of AI Agents (Design Architecture-Based)
2.6 Case Study
2.7 Tools and Libraries for AI Agent Development in LegalTech
2.8 Legal AI Agents in Trend
Module 3: GPT and NLP Foundation for Legal Agents
3.1 Introduction to NLP in AI Agents
3.2 Language Models
3.3 Customizing GPT for Legal Work
3.4 The Rising Role of Prompt Engineering in Legal AI
Module 4: AI Agents for eDiscovery
4.1 Introduction: What Is eDiscovery and Why Automate It?
4.2 Introduction to DISCO AI (Cecilia)
4.3 Cecilia Q&A
4.4 AI-Powered Investigations with Reveal AI
Module 5: Contract Review in Legal Workflows
5.1 What is Contract Review?
5.2 What Is an AI Contract Review Agent?
Module 6: Legal Research Agents
6.1 What is a Legal Research Agent?
6.2 Real-World Insights — AI Lawyer in Action
6.3 AI Legal Research – Use Cases in Practice
Module 7: Compliance & Risk Monitoring Agents
7.1 Compliance and Risk Monitoring
7.2 Compliance & Risk Monitoring Agents
7.3 Hands-On Activity
Module 8: Legal Chatbots & Virtual Legal Assistants
8.1 Introduction to Legal Chatbots
8.2 Key Use Cases in Legal Practice
8.3 Legal Architecture & Design Principles
Module 9: AI Agents for IP Filing and Patent Drafting
9.1 Introduction to AI in IP Filing and Patent Drafting
9.2 Core AI Agent Functionalities
9.3 Introduction to AI Tools for Patent Drafting and Management
Module 10: Case Outcome Prediction Agents
10.1 Introduction to Case Outcome Prediction?
10.2 Feature Engineering in Legal Case Outcome Prediction
10.3 The Rise of Multi-Agent Legal Workflows
Module 11: Ethics, Fairness, and Transparency in Legal AI
11.1 Managing Bias in Legal AI
11.2 Legal Accountability in Autonomous Agent Deployment
Module 12: Capstone Project – Building Your AI Legal Agent
12.1 Applying AI to Solve Real Legal Problems
12.2 Document Your Inputs and Prompts
Tools you will explore
TensorFlow
Power BI
Keras
SQL
Apache-Spark
Python
Matplotlib
Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Included Instructor-led OR Self-paced course + Official exam + Digital badge
€225
E-Learning
max 999
1 dag