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Quest Training

AI Strategy & Implementation Training Course

This advanced training course provides a practical and strategic framework for developing and implementing an effective Artificial Intelligence strategy within organizations. 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 practical and strategic framework for developing and implementing an effective Artificial Intelligence strategy within organizations. It focuses on moving beyond AI awareness toward identifying high-value opportunities, establishing strategic priorities, building organizational readiness, and translating AI investments into measurable business outcomes. Participants will explore how to align AI initiatives with organizational strategy, customer needs, operational priorities, and performance objectives. The course covers AI use-case identification, business value assessment, implementation planning, technology considerations, data readiness, governance, risk management, and responsible AI. The programme also addresses the organizational and leadership dimensions of AI implementation, including stakeholder alignment, change management, workforce transformation, capability development, and operating-model design. Participants will learn how to manage AI initiatives as strategic transformation programmes rather than isolated technology projects. Through practical exercises, case studies, and an integrated AI strategy workshop, participants will develop the ability to evaluate AI opportunities, prioritize initiatives, establish implementation roadmaps, define performance measures, and create a structured approach for scaling AI across the organization.

Learning Objectives

  • By the end of this course, participants will be able to:
  • Develop an AI strategy aligned with organizational objectives and strategic priorities.
  • Assess organizational AI maturity, readiness, capabilities, and transformation requirements.
  • Identify and evaluate high-value AI use cases across business functions and processes.
  • Assess the business value, feasibility, risks, and implementation complexity of AI initiatives.
  • Establish clear criteria for prioritizing AI investments and initiatives.
  • Design practical AI implementation roadmaps with defined milestones and priorities.
  • Evaluate data, technology, infrastructure, talent, and capability requirements for AI adoption.
  • Establish appropriate AI governance, risk management, ethics, and accountability frameworks.
  • Align executive leadership, business functions, technology teams, and other stakeholders around AI initiatives.
  • Develop change management and workforce strategies to support AI adoption.
  • Define KPIs and performance measures for evaluating AI programme outcomes.
  • Strengthen the organization’s ability to scale successful AI initiatives sustainably.

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 transformation. It is particularly suitable for Chief Executive Officers, Chief Information Officers, Chief Digital Officers, Chief Data Officers, transformation leaders, AI and data managers, strategy professionals, innovation leaders, IT managers, business unit leaders, project and programme managers, and decision makers involved in AI investment and implementation. The programme is also highly relevant to government entities, financial institutions, banks, oil and gas companies, energy organizations, healthcare organizations, telecommunications companies, industrial organizations, and large corporations seeking to establish or accelerate their AI transformation agenda.

Learning Outcomes

  • Upon completion of the course, participants will be able to:
  • Translate organizational strategy into practical AI strategic priorities.
  • Assess current AI maturity and identify capability gaps.
  • Build an AI opportunity portfolio across business functions.
  • Evaluate AI use cases based on value, feasibility, risk, and strategic alignment.
  • Develop business cases for AI investments and transformation initiatives.
  • Establish AI implementation priorities and sequencing.
  • Define the people, process, data, technology, and governance requirements for AI adoption.
  • Develop an enterprise AI implementation roadmap.
  • Establish governance structures and decision-making mechanisms for AI programmes.
  • Identify and manage AI-related operational, regulatory, ethical, cybersecurity, and reputational risks.
  • Develop workforce and change management approaches for AI adoption.
  • Define AI performance indicators, benefits realization measures, and executive dashboards.
  • Establish approaches for scaling successful AI initiatives across the organization.
  • Develop an actionable AI strategy and implementation plan for their organization.

Course Outline

Course Outline

Day 1

AI Strategy, Business Value & Organizational Readiness

  • The strategic role of AI in modern organizations
  • AI as a business transformation capability
  • Understanding AI-driven business models and operating models
  • Linking AI strategy with corporate and departmental objectives
  • AI maturity assessment and organizational readiness
  • Identifying strategic AI opportunities
  • AI value creation and competitive advantage
  • Assessing current capabilities, gaps, and constraints
  • Executive leadership and AI strategic decision-making
  • Practical Exercise: Organizational AI maturity and readiness assessment
Day 2

AI Use Cases, Business Cases & Investment Prioritization

  • Identifying high-value AI use cases
  • AI applications across functions and industries
  • Business problems versus technology-driven AI opportunities
  • Use-case evaluation frameworks
  • Assessing business value, feasibility, complexity, and risk
  • Developing AI business cases
  • Estimating costs, benefits, and expected return on investment
  • Prioritizing AI initiatives and building an AI portfolio
  • Managing AI investment decisions and resource allocation
  • Practical Exercise: AI use-case prioritization and business case development
Day 3

AI Implementation, Data, Technology & Operating Model

  • Translating AI strategy into implementation programmes
  • AI implementation lifecycle and delivery models
  • Data readiness and data quality requirements
  • AI technology and infrastructure considerations
  • Cloud, platforms, models, and AI architecture considerations
  • Build, buy, partner, and hybrid AI implementation approaches
  • Designing an AI operating model
  • Roles, responsibilities, capabilities, and talent requirements
  • Managing AI projects and transformation portfolios
  • Practical Exercise: Designing an AI implementation operating model
Day 4

AI Governance, Risk, Ethics & Change Management

  • Establishing effective AI governance frameworks
  • AI accountability, policies, and decision rights
  • Responsible and ethical AI implementation
  • AI risk identification and management
  • Data privacy, security, compliance, and regulatory considerations
  • Model risk and operational risk management
  • Human oversight and responsible decision-making
  • Leading organizational change and AI adoption
  • Workforce transformation, reskilling, and capability development
  • Stakeholder engagement and executive alignment
  • Practical Exercise: AI governance and change management framework
Day 5

AI Implementation Roadmap, Performance & Scaling

  • Building the enterprise AI implementation roadmap
  • Defining strategic initiatives, milestones, dependencies, and priorities
  • Establishing AI programme governance and execution structures
  • AI performance management and KPI design
  • Measuring business impact and benefits realization
  • Executive AI dashboards and performance reporting
  • Managing implementation risks and corrective actions
  • Scaling successful AI initiatives across the organization
  • Building a sustainable AI innovation pipeline
  • Executive leadership for long-term AI transformation
  • Final Workshop: Developing an Integrated AI Strategy & Implementation Roadmap

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