Euroasia Training Euroasia Training
AR

Data Analytics, Artificial Intelligence and Decision Sciences

Business Decision-Making Enhanced by AI

12 units

Please select a city/session before registration.

About this program

Artificial intelligence revolutionizes decision-making by delivering predictive insights, revealing concealed patterns, and enhancing the efficiency of complex processes. For organizations, implementing AI-driven approaches translates raw data into actionable intelligence that supports quicker, more intelligent, and consistent decisions.
This program provides pragmatic frameworks for embedding AI into business decision-making. Participants will delve into predictive analytics, automation, risk modeling, and customer insight strategies. The course also covers ethical issues, governance, and change management essential for effective AI deployment.
EuroQuest International Training emphasizes the integration of business strategy with AI technologies, equipping leaders to confidently leverage advanced analytics in practical business scenarios.

Key outcomes

  • Comprehend AI’s impact on improving decision-making frameworks
  • Leverage predictive and prescriptive analytics to drive superior results
  • Utilize AI applications across finance, operations, and customer engagement
  • Mitigate risks through AI-based forecasting and modeling techniques
  • Promote transparency and responsibility in AI implementation
  • Develop data-centric business strategies aligned with organizational objectives
  • Strengthen collaboration between humans and AI in decision workflows
  • Incorporate AI into governance structures and compliance procedures
  • Address challenges to AI adoption within organizations
  • Assess the return on investment and effects of AI-enhanced decision-making
  • Foster a cultural shift towards data-driven approaches
  • Create a comprehensive plan for company-wide AI integration

Who should attend

  • Senior executives and business leaders
  • Strategy and innovation managers
  • Data and business analysts
  • Operations and finance managers
  • Risk and compliance professionals

Course outline

1

Unit 1: Overview of AI Applications in Decision-Making

  • Contrasting AI with conventional decision-making models
  • Progression of data-centric methodologies
  • The commercial advantages of implementing AI
  • Illustrative examples of AI influencing business decisions
2

Unit 2: Essentials of Data and Analytics

  • Methods for gathering and consolidating data
  • Organizing data to extract AI-driven insights
  • Principles of data governance and maintaining data quality
  • Strategies to break down data silos
3

Unit 3: Techniques in Predictive and Prescriptive Analytics

  • Core concepts of predictive analytics
  • Using prescriptive analytics to enhance optimization
  • Modeling scenarios to manage risk
  • Applications of analytics across various industries
4

Unit 4: Machine Learning for Enhanced Decision Support

  • Introduction to supervised and unsupervised ML techniques
  • Detecting patterns and analyzing anomalies
  • Applying ML in finance, operations, and human resources decisions
  • Practical examples of ML deployment
5

Unit 5: Leveraging AI for Customer and Market Intelligence

  • Personalization techniques and recommendation systems
  • Analyzing sentiment and boosting customer interaction
  • Role of AI in product innovation and pricing strategies
  • Forecasting changes in the market
6

Unit 6: AI Applications in Operations and Supply Chain Management

  • Optimizing processes and automating tasks
  • Implementing AI in logistics and procurement
  • Predictive maintenance and efficient resource distribution
  • Managing operational risks
7

Unit 7: Strategic Planning and Risk Prediction Using AI

  • Utilizing AI for comprehensive enterprise risk assessment
  • Forecasting scenarios with big data analytics
  • Integrating risk evaluation into decision-making processes
  • Case studies from finance and insurance sectors
8

Unit 8: AI Governance, Ethical Considerations, and Accountability

  • Addressing ethical issues in AI-powered decisions
  • Ensuring algorithm transparency and explainability
  • Mitigating bias and promoting fairness
  • Frameworks for regulatory adherence
9

Unit 9: Collaborative Decision-Making Between Humans and AI

  • Harmonizing AI outputs with leadership judgment
  • Developing human-in-the-loop decision frameworks
  • Managing organizational change during AI integration
  • Fostering confidence in AI technologies
10

Unit 10: Driving Innovation and Growth Through AI

  • Employing AI to create novel business models
  • Stimulating innovation in products and services
  • Incorporating AI into digital transformation initiatives
  • Gaining a competitive edge through AI
11

Unit 11: Evaluating the ROI and Effects of AI-Driven Decisions

  • Key indicators for assessing performance
  • Monitoring improvements in efficiency, revenue, and risk mitigation
  • Applying AI feedback loops for ongoing enhancement
  • Effectively communicating AI benefits to stakeholders
12

Unit 12: Capstone Simulation on AI-Driven Decision-Making

  • Collaborative AI strategy development exercise
  • Constructing decision frameworks utilizing AI tools
  • Delivering presentations to a simulated board
  • Formulating an action plan for enterprise-wide AI deployment