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Quest Training

Artificial Intelligence: Enterprise Strategies & Applications Training Course

This advanced training course provides a comprehensive strategic and practical framework for understanding, adopting, and scaling Artificial Intelligence across the enterprise. It focuses on moving beyond AI awareness…

AIF · AI FundamentalsAll LevelsClassroomEnglish , Arabic
Duration
5 Days
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Sectors:Artificial Intelligence & ApplicationsCategories:AI Fundamentals

Course Overview

This advanced training course provides a comprehensive strategic and practical framework for understanding, adopting, and scaling Artificial Intelligence across the enterprise. It focuses on moving beyond AI awareness toward building organizational capabilities that improve business performance, operational efficiency, innovation, decision-making, and competitive advantage. Participants will explore how to develop enterprise AI strategies aligned with corporate objectives, identify high-value AI opportunities, evaluate use cases, and prioritize initiatives based on business value, feasibility, cost, risk, and strategic importance. The course examines practical AI applications across key business functions, including operations, finance, human resources, customer experience, marketing, sales, risk management, supply chain, procurement, data analytics, intelligent automation, and executive decision-making. Participants will learn how to assess where AI can create measurable and sustainable organizational value. The programme also addresses data readiness, technology requirements, AI governance, risk management, cybersecurity, privacy, ethics, workforce transformation, and change management. Through practical exercises, case studies, and an integrated workshop, participants will develop a structured approach for applying AI across the enterprise and building a scalable AI transformation roadmap.

Learning Objectives

  • By the end of this course, participants will be able to:
  • Understand the strategic role and evolution of Artificial Intelligence in modern enterprises.
  • Analyze the impact of AI on business models, operating models, and organizational performance.
  • Assess enterprise readiness and maturity for AI adoption.
  • Identify high-value AI use cases across business functions.
  • Evaluate AI initiatives based on business value, feasibility, cost, complexity, and risk.
  • Develop an enterprise-level AI strategy aligned with organizational priorities.
  • Align AI initiatives with strategic objectives and performance indicators.
  • Assess data, technology, infrastructure, and talent requirements for AI implementation.
  • Apply intelligent automation approaches to improve business processes.
  • Strengthen decision-making through AI, analytics, and predictive capabilities.
  • Establish effective AI governance, risk management, and responsible AI practices.
  • Develop organizational change and workforce strategies to support AI adoption.
  • Measure the business impact and return on investment of AI initiatives.
  • Build an enterprise AI portfolio and prioritize strategic initiatives.
  • Develop a practical roadmap for implementing and scaling AI across the organization.

Who Should Attend

This course is designed for executives, senior managers, department heads, and professionals responsible for strategy, digital transformation, Artificial Intelligence, data, technology, innovation, operations, business development, and organizational performance. It is particularly suitable for CEOs, CIOs, Chief Digital Officers, Chief Data Officers, AI and data leaders, digital transformation managers, strategy and innovation leaders, IT managers, operations managers, project and programme managers, business unit leaders, and professionals involved in AI investment and transformation initiatives. The programme is highly relevant to government and public-sector organizations, banks and financial institutions, oil and gas and energy companies, industrial organizations, telecommunications companies, healthcare organizations, and large enterprises seeking to establish or accelerate enterprise-wide AI adoption.

Learning Outcomes

  • Upon completion of the course, participants will be able to:
  • Assess the strategic contribution of AI to enterprise objectives.
  • Analyze how AI can transform business models, processes, and organizational functions.
  • Evaluate organizational readiness and AI maturity.
  • Identify and categorize potential AI use cases across the enterprise.
  • Prioritize AI applications based on value, feasibility, and strategic alignment.
  • Develop business cases for AI initiatives and investments.
  • Define data, technology, infrastructure, and capability requirements.
  • Design intelligent automation initiatives for business process improvement.
  • Apply AI and predictive analytics to enhance decision-making.
  • Evaluate AI governance, risk, ethics, privacy, and security requirements.
  • Develop workforce transformation and change management approaches.
  • Establish KPIs for measuring AI performance and business impact.
  • Build an integrated portfolio of enterprise AI initiatives.
  • Develop an actionable AI implementation and scaling roadmap.

Course Outline

Course Outline

Day 1

Artificial Intelligence & Enterprise Transformation

  • Understanding Artificial Intelligence and its evolution
  • Key AI capabilities and enterprise applications
  • AI as a strategic organizational capability
  • The impact of AI on business and operating models
  • AI-driven productivity and operational efficiency
  • AI, innovation, and competitive advantage
  • Enterprise AI maturity and readiness assessment
  • Identifying organizational capability and skills gaps
  • Leadership responsibilities in enterprise AI transformation
  • Building an AI-enabled organization
  • Practical Exercise: Enterprise AI Readiness & Maturity Assessment
Day 2

Enterprise AI Strategy & Use-Case Development

  • Developing an enterprise AI strategy
  • Aligning AI strategy with corporate objectives
  • Identifying strategic AI opportunities
  • AI use-case discovery and development
  • Evaluating business value and strategic relevance
  • Assessing technical feasibility and implementation complexity
  • Developing AI business cases
  • Estimating costs, benefits, and return on investment
  • Building and prioritizing an enterprise AI portfolio
  • Resource allocation and AI investment decisions
  • Practical Exercise: AI Use-Case Evaluation & Strategic Prioritization
Day 3

Enterprise AI Applications & Intelligent Automation

  • AI applications in business operations
  • AI for finance, accounting, and financial planning
  • AI for human resources and talent management
  • AI for marketing, sales, and customer experience
  • AI for risk management, compliance, and internal controls
  • AI for procurement and supply chain management
  • Predictive analytics and intelligent decision support
  • Intelligent automation and process optimization
  • Generative AI applications in enterprise environments
  • Integrating AI with enterprise systems and business processes
  • Practical Exercise: Designing an AI Use Case for Business Process Improvement
Day 4

Data, Technology, Governance & AI Risk

  • The role of data in successful AI adoption
  • Data quality, accessibility, management, and readiness
  • AI infrastructure, platforms, and technology requirements
  • Integrating AI solutions with enterprise systems
  • Enterprise AI governance frameworks
  • AI policies, accountability, and decision rights
  • Responsible and ethical AI
  • AI risk identification and management
  • Privacy, data protection, and cybersecurity considerations
  • Human oversight and accountability
  • Workforce transformation and AI capability development
  • Practical Exercise: Designing an Enterprise AI Governance & Risk Framework
Day 5

AI Implementation, Value Measurement & Enterprise Scaling

  • Translating AI strategy into an execution programme
  • Developing an enterprise AI implementation roadmap
  • Defining initiatives, priorities, milestones, and dependencies
  • Managing AI programmes and transformation portfolios
  • Establishing AI performance indicators and success measures
  • Measuring business value and return on AI investment
  • Benefits realization and executive performance reporting
  • Managing implementation challenges and corrective actions
  • Scaling successful AI applications across the enterprise
  • Building sustainable enterprise AI capabilities
  • Final Workshop: Developing an Enterprise AI Strategy, Application Portfolio & Implementation Roadmap

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