Applied AI for Data Analysis, Automation and Decision-Making

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Applied AI for Data Analysis, Automation and Decision-Making

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Course Overview:

The Applied AI for Data Analysis, Automation and Decision-Making course is a practical corporate Artificial Intelligence Training Course designed to strengthen analytical, automation, systems-analysis, and decision-support capabilities. It is particularly relevant as AI for Computer Analysts, Artificial Intelligence for Computer Analysts, and an Applied AI Course for Computer Analysis Professionals, while remaining suitable for broader technical, data, and operational roles.

The course introduces machine-learning workflows, AI-powered data analysis, predictive analytics, generative AI, intelligent automation, human-centered systems, and responsible AI governance. Participants examine how data moves from collection and preparation through model development, evaluation, deployment, and continuous improvement. The programme also connects AI initiatives to measurable organizational outcomes through business requirements, KPIs, process improvement, and technology-driven decision-making.

The content reflects the AI and data analytics lifecycle described in the AIDA Guidebook, including machine-learning methodology, data management, KPIs, human-machine interaction, organizational change, privacy, security, and governance. It also incorporates the NIST AI Risk Management Framework approach to governing, mapping, measuring, and managing AI risks throughout the system lifecycle.

Through applied exercises and business cases, participants learn to identify viable AI opportunities, assess data readiness, redesign processes for automation, evaluate AI outputs, and recommend intelligent solutions that improve accuracy, efficiency, service quality, and managerial decision-making.

Target Audience:

Computer Analysts and Systems Analysts
Data Analysts and Business Intelligence Analysts
Business Systems Analysts
Application and Software Analysts
Process Improvement Analysts
Automation and Digital Transformation Specialists
Information Systems Specialists
Technology Project Coordinators
Database and Reporting Specialists
Technical Business Analysts
Operations Analysts
Professionals seeking Computer Analyst AI Training

Targeted Organizational Departments:

Information Technology and Information Systems
Data Analytics and Business Intelligence
Digital Transformation and Innovation
Business Process Management
Strategy and Corporate Planning
Operations and Service Delivery
Enterprise Architecture
Software Development and Applications
Risk, Compliance and Information Governance

Targeted Industries:

Banking, Financial Services and Insurance
Government and Public Administration
Telecommunications and Technology
Healthcare and Life Sciences
Manufacturing and Industrial Operations
Energy, Utilities and Oil and Gas
Transportation and Logistics
Retail and E-Commerce
Education and Research

Course Offerings:

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

Explain the practical roles of artificial intelligence, machine learning, generative AI, predictive analytics, and intelligent automation.
Translate business and systems requirements into viable AI and analytics use cases.
Apply Data Analysis with Artificial Intelligence to identify trends, patterns, anomalies, and operational insights.
Assess data availability, quality, relevance, privacy, and readiness for AI applications.
Distinguish between supervised, unsupervised, predictive, and generative AI approaches.
Design conceptual machine-learning pipelines covering data preparation, model training, validation, deployment, and monitoring.
Use AI-Powered Data Analysis to improve reporting, forecasting, classification, and decision support.
Identify processes suitable for Business Process Automation with AI.
Prioritize automation opportunities using business value, feasibility, risk, and performance criteria.
Design AI-Driven Decision Support Systems that augment rather than replace informed human judgment.
Apply human-in-the-loop principles when designing Human-Centered AI Systems.

Training Methodology:

The course uses a practical, workplace-oriented learning methodology combining instructor-led explanation, guided analysis, case studies, group work, scenario exercises, system-design discussions, and feedback sessions. Each major concept is connected to realistic organizational challenges such as inefficient reporting, repetitive workflows, inconsistent data quality, delayed decisions, unstructured documents, prediction requirements, and the need for responsible AI controls.

Participants work through an end-to-end AI use-case methodology. They begin by defining a business problem, identifying users and stakeholders, establishing measurable goals, assessing data requirements, selecting an appropriate AI approach, and evaluating possible operational risks. The human-centered machine-learning material emphasizes stakeholder involvement, measurable success criteria, responsible data collection, iterative testing, and human review throughout the system lifecycle.

For intelligent automation exercises, participants distinguish processes, procedures, and tasks before selecting automation candidates. This reflects business-process automation guidance that recommends treating automation as a business-analysis and change-management initiative rather than only a technology implementation.

Group activities include process mapping, data-readiness reviews, use-case prioritization, prompt design, model-evaluation discussions, risk identification, KPI definition, and implementation planning. Participants receive structured instructor feedback on the feasibility, value, governance, and human impact of their proposed solutions. Demonstrations may reference relevant AI tools, but the course remains platform-neutral and focuses on transferable analytical and implementation skills.

Course Toolbox:

AI use-case identification framework
AI opportunity prioritization matrix
Business problem definition template
Data-readiness assessment checklist
Data quality and suitability review guide
Machine-learning lifecycle reference model
Predictive analytics use-case canvas
Generative AI use-case assessment template
Prompt design and output-validation guide
Process, procedure and task mapping template

The course may provide insights, demonstrations, examples, and comparisons of relevant AI tools. Software licenses, commercial platforms, automation tools, generative AI subscriptions, and technical products are not provided as part of the course.

Course Agenda:

Day 1: Applied AI Foundations and Business Use Cases

Topic 1: Artificial Intelligence, Machine Learning, Analytics and Automation
Topic 2: Practical AI Applications for Computer Analysts
Topic 3: Translating Business Problems into AI Use Cases
Topic 4: AI for Systems Analysis and Requirements Definition
Topic 5: Evaluating AI Value, Feasibility and Organizational Readiness
Topic 6: Building an AI Use-Case Prioritization Matrix
Reflection & Review: Participants review the distinctions between AI, machine learning, analytics, generative AI and automation, then present one workplace problem suitable for further AI analysis.

Day 2: AI Data Analytics and Predictive Modeling

Topic 1: Data Collection, Preparation and Quality for AI
Topic 2: Data Analysis with Artificial Intelligence
Topic 3: Exploratory Analysis, Patterns, Trends and Anomalies
Topic 4: Supervised and Unsupervised Machine Learning
Topic 5: Predictive Analytics for Forecasting and Classification
Topic 6: Evaluating Models and Analytical Outputs
Reflection & Review: Participants assess a sample dataset, identify data-quality risks and select an appropriate analytical or machine-learning approach for the stated business objective.

Day 3: Generative AI and Intelligent Decision Support

Topic 1: Generative AI Capabilities, Limitations and Use Cases
Topic 2: Prompt Design for Analytical and Systems Tasks
Topic 3: AI Tools for Computer Analysts
Topic 4: Designing AI-Driven Decision Support Systems
Topic 5: Human-Centered AI Systems and Human-in-the-Loop Controls
Topic 6: Validating AI Outputs, Recommendations and Explanations
Reflection & Review: Participants compare AI-generated outputs, identify unsupported conclusions or risks, and define where human review must remain within the decision process.

Day 4: AI Automation and Process Transformation

Topic 1: Business Process Automation with AI
Topic 2: Distinguishing Processes, Procedures and Repetitive Tasks
Topic 3: RPA, OCR, NLP, Machine Learning and Workflow Automation
Topic 4: Selecting Processes for Intelligent Automation
Topic 5: Designing Human–Automation Handoffs and Exception Paths
Topic 6: Measuring Automation Benefits, Costs and Performance
Reflection & Review: Participants map an existing business process, identify eligible automation steps and define measurable performance improvements such as cycle time, accuracy, exception rates and service quality.

Day 5: Responsible AI, Risk Management and Implementation

Topic 1: Responsible AI and Data Governance
Topic 2: Privacy, Security, Fairness and Algorithmic Transparency
Topic 3: AI Risk Management using Govern, Map, Measure and Manage
Topic 4: AI System Evaluation and Continuous Monitoring
Topic 5: Building an Applied AI Implementation Roadmap
Topic 6: Presenting the AI Business Case and Final Recommendations
Reflection & Review: Participants present a complete AI or automation proposal covering the business problem, data requirements, proposed solution, expected value, human oversight, risks, KPIs and phased implementation plan.

FAQ:

What specific qualifications or prerequisites are needed for participants before enrolling in the course?

No advanced qualification in artificial intelligence, machine learning or programming is required. Participants should have a general understanding of business processes, information systems, data analysis, reporting, technology projects or operational decision-making. Familiarity with spreadsheets, databases, dashboards or process documentation is helpful but not mandatory. The course is suitable both for professionals beginning their AI learning journey and for experienced analysts seeking a structured Artificial Intelligence Training Course for Computer Analysts.

How long is each day's session, and is there a total number of hours required for the entire course?

Each day's session is generally structured to last around 4–5 hours, with breaks and interactive activities included. The total course duration spans five days, approximately 20–25 hours of instruction.

Does the course teach participants to build AI models, or to evaluate and apply them?

The course focuses primarily on applied analysis, use-case definition, data readiness, intelligent automation, model evaluation, decision support, risk management and implementation planning. Participants learn how machine-learning models and generative AI systems work at a practical level, but the programme is not designed as an advanced coding or data-science course.

This distinction is important because successful AI deployment requires more than technical model construction. It also requires clear objectives, suitable data, measurable success criteria, user involvement, privacy controls, governance, organizational readiness and continuous monitoring. The course therefore prepares participants to contribute effectively to multidisciplinary AI projects and to evaluate whether proposed solutions are useful, trustworthy and aligned with operational requirements.

How This Course is Different from Other Applied AI for Data Analysis, Automation and Decision-Making Courses:

This course differs from many introductory AI programmes because it does not focus only on tool demonstrations, general AI terminology or isolated prompt-writing exercises. It combines four connected capabilities: AI-powered data analysis, intelligent automation, decision-support design and responsible system implementation.

The programme is structured around the actual responsibilities of analysts and systems professionals. Participants learn how to define requirements, assess data, analyze workflows, identify automation candidates, interpret model outputs, evaluate risks and communicate recommendations. This makes it a Professional AI Training for Computer and Systems Analysts rather than a generic awareness programme.

Its human-centered approach also distinguishes it from purely technical Machine Learning and Automation for Computer Analysts courses. Participants examine how people provide data, feedback, validation and operational judgment across the AI lifecycle. The human-centered machine-learning reference emphasizes that users and stakeholders should be involved during conceptualization, implementation, evaluation and deployment.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Vienna	Week 30, 2026 20 - 24 Jul 2026	5 Days	Onsite	€5,700
Amman	Week 30, 2026 26 - 30 Jul 2026	5 Days	Onsite	€4,100
Abu Dhabi	Week 32, 2026 03 - 07 Aug 2026	5 Days	Onsite	€4,500
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Paris	Week 34, 2026 17 - 21 Aug 2026	5 Days	Onsite	€5,700
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Barcelona	Week 37, 2026 07 - 11 Sep 2026	5 Days	Onsite	€5,700
Zoom	Week 38, 2026 14 - 18 Sep 2026	5 Days	Online	€1,500
Manama	Week 38, 2026 20 - 24 Sep 2026	5 Days	Onsite	€4,700
Istanbul	Week 40, 2026 28 Sep - 02 Oct 2026	5 Days	Onsite	€4,500
Prague	Week 41, 2026 05 - 09 Oct 2026	5 Days	Onsite	€6,000
Casablanca	Week 42, 2026 12 - 16 Oct 2026	5 Days	Onsite	€4,100
Amsterdam	Week 43, 2026 19 - 23 Oct 2026	5 Days	Onsite	€5,700
Milan	Week 44, 2026 26 - 30 Oct 2026	5 Days	Onsite	€5,700
Abu Dhabi	Week 45, 2026 02 - 06 Nov 2026	5 Days	Onsite	€4,500
Johannesburg	Week 45, 2026 08 - 12 Nov 2026	5 Days	Onsite	€6,000
Cairo	Week 46, 2026 09 - 13 Nov 2026	5 Days	Onsite	€4,100
Athens	Week 47, 2026 16 - 20 Nov 2026	5 Days	Onsite	€6,700