About the Course

Artificial intelligence is moving from isolated experimentation into core organizational strategy, operating models and decision-making. Senior leaders increasingly need to distinguish genuine value opportunities from technology hype, set clear priorities, allocate resources responsibly and create the conditions for safe adoption. Without strategic direction, organizations risk fragmented pilots, uncontrolled tool use, weak accountability, duplicated investment and limited measurable value.

This programme equips leaders with a practical framework for shaping an enterprise AI agenda. It covers AI capabilities and limitations, organizational readiness, use-case identification, value assessment, AI operating models, data readiness, governance, workforce implications, transformation leadership and investment prioritization. Participants examine how generative AI, analytics, automation and emerging agentic capabilities can support strategic objectives while remaining subject to human oversight and organizational controls.

The course is designed to move participants from awareness to an actionable leadership position. Through executive simulations, readiness assessments, use-case prioritization exercises and strategy workshops, participants develop an AI opportunity portfolio, governance priorities and an implementation roadmap that can be adapted to their organizations. Sponsoring institutions benefit from stronger executive alignment, clearer investment logic and a more disciplined path from experimentation to responsible scale.

Target Participants

This course is designed for chief executives, directors, senior managers, heads of department, strategy and transformation leaders, digital and technology managers, innovation leads, programme directors, public-sector executives, business unit leaders and professionals responsible for sponsoring or guiding AI adoption within their organizations.

What You Will Learn

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

• Analyse the strategic implications of artificial intelligence for organizational performance and competitiveness.
• Assess organizational readiness for AI adoption across leadership, data, technology, process, workforce and governance dimensions.
• Prioritize AI use cases using value, feasibility, risk and strategic-alignment criteria.
• Design an enterprise AI strategy aligned with institutional objectives and measurable outcomes.
• Define leadership roles, decision rights and governance mechanisms for responsible AI adoption.
• Evaluate investment options, partnerships and sourcing models for AI initiatives.
• Lead workforce transition, capability development and organizational change related to AI.
• Develop a phased AI transformation roadmap with priorities, owners, milestones and performance measures.

Course Duration

• Classroom – 5 Days
• Online – 7 Days

Course Outline

AI Strategy Foundations

• Evolution from automation to generative and agentic AI
• Strategic capabilities and limitations of modern AI
• AI value creation across functions and sectors
• Common causes of failed AI initiatives
• Leadership responsibilities in AI-enabled organizations

Enterprise Readiness

• AI maturity dimensions and readiness criteria
• Data availability, quality and accessibility
• Technology architecture and integration readiness
• Process maturity and automation potential
• Workforce capability and change readiness

Opportunity Discovery

• Business problem framing for AI
• Use-case discovery across the value chain
• Opportunity identification workshops
• Problem suitability for AI versus conventional solutions
• Building an enterprise AI opportunity inventory

Use-Case Prioritization

• Value and impact assessment
• Feasibility and data-readiness assessment
• Risk and control assessment
• Effort, cost and dependency analysis
• Prioritization matrices and portfolio sequencing

AI Operating Model

• Centralized, federated and hybrid operating models
• Roles for business, data, technology and risk teams
• AI centres of excellence and enablement functions
• Decision rights and escalation paths
• Vendor and partner ecosystem management

Data Strategy

• Data as an AI strategic asset
• Data governance responsibilities
• Enterprise knowledge access for generative AI
• Data privacy, confidentiality and intellectual property
• Data investment priorities for AI scale

Responsible Adoption

• AI governance principles
• Human oversight requirements
• Bias, transparency and explainability considerations
• AI risk appetite and acceptable-use boundaries
• Regulatory and ethical considerations for leaders

Workforce Transformation

• Human-AI collaboration models
• Role redesign and task decomposition
• AI literacy and capability building
• Workforce resistance and adoption barriers
• Leadership communication for AI-enabled change

Investment Governance

• AI business cases and value hypotheses
• Total cost of ownership considerations
• Build, buy and partner decisions
• Pilot-to-scale investment gates
• Benefits realization and portfolio review

Transformation Roadmap

• Strategic ambition and target state
• Phased implementation planning
• Governance milestones and capability dependencies
• AI performance indicators and executive dashboards
• Executive action plan and commitment review

Practical Outputs

• Enterprise AI readiness assessment
• AI opportunity inventory
• Use-case prioritization matrix
• AI operating-model outline
• AI governance priorities checklist
• AI transformation roadmap

Training Approach

The programme uses executive briefings, facilitated discussions, readiness diagnostics, strategic case analysis and decision simulations. Participants work through realistic leadership choices involving use-case selection, investment trade-offs, governance boundaries, workforce impacts and vendor decisions. Practical work is organized around a progressive enterprise AI strategy exercise so that each module contributes to a usable institutional roadmap.

Organizational Benefits

• Clearer enterprise direction for AI adoption
• Stronger alignment between AI investment and organizational priorities
• Improved executive oversight of AI opportunities and risks
• Reduced duplication from fragmented departmental AI initiatives
• More disciplined prioritization of high-value use cases
• Better workforce preparation for human-AI collaboration
• Stronger accountability for responsible AI deployment

Certification

Participants who successfully complete this course will receive a Certificate of Course Completion from Devimpact Institute, recognizing the knowledge, practical skills and competencies developed through the programme.

Tailor-Made Course

This course can be customized and delivered as an in-house programme to address the specific policies, systems, sector context and capacity-development priorities of an organization. The course duration, modules, case studies and practical exercises may be adjusted following a training needs assessment.