This course equips leaders with the knowledge and skills to guide AI adoption and transformation, with particular emphasis on Global Shared Services (GSS). Participants explore how AI can improve productivity, service quality, and customer experience while aligning initiatives with organizational priorities.

The course covers AI-focused leadership development, adoption strategy, opportunity prioritization, responsible governance, workforce readiness, and value realization. It prepares leaders to define transformation priorities, establish accountability, and develop a practical AI adoption roadmap.

Duration 3 Day – 21 hrs.

 

Objectives

• Develop AI-focused leadership capabilities to guide organizational change and informed decision-making.
• Formulate an AI adoption strategy for Global Shared Services aligned with business priorities and service delivery goals.
• Define leadership responsibilities in driving AI transformation, including sponsorship, accountability, governance, and workforce enablement.
• Identify opportunities to improve productivity, service quality, and customer experience through AI.
• Explain core AI concepts, capabilities, and limitations relevant to leadership decisions.
• Prioritize AI opportunities based on business value, feasibility, data readiness, and risk.
• Establish responsible AI principles and appropriate human oversight.
• Lead workforce readiness, stakeholder engagement, and capability development for AI adoption.
• Define measures for tracking AI adoption, operational performance, and business value.
• Develop a phased roadmap for implementing and scaling AI across shared services.

 

Target Audience 

• Senior executives and business leaders
• Global Shared Services and Global Business Services leaders
• Shared services center heads and operations managers
• Digital transformation and innovation leaders
• Functional leaders in finance, human resources, procurement, IT, and customer service
• Strategy, service delivery, and customer experience managers
• Process improvement and operational excellence leaders
• Technology, data, and AI program sponsors
• Change management and organizational development leaders

 

Prerequisites 

• Basic understanding of business operations, organizational objectives, and service delivery processes.
• Experience in leadership, management, process improvement, or transformation initiatives is beneficial.
• No programming, data science, or advanced technical knowledge is required.

Course Outline 

Day 1: AI Foundations and Leadership Readiness

Module 1: Understanding AI for Business Leaders

• Core concepts: artificial intelligence, machine learning, and generative AI
• Differences between AI, automation, and robotic process automation
• AI capabilities, limitations, and common misconceptions
• Enterprise AI applications and business implications
• The importance of data quality, context, and human judgment

 Module 2: AI-Focused Leadership Development

• Leadership competencies for an AI-enabled organization
• Strategic thinking and decision-making under uncertainty
• Building AI literacy and a culture of responsible experimentation
• Balancing innovation with operational accountability
• Collaboration between business, technology, and data teams

Module 3: Leadership Responsibilities in AI Transformation

• Executive sponsorship and transformation ownership
• Defining a shared vision and desired business outcomes
• Assigning decision rights and accountability
• Aligning stakeholders across global and regional operations
• Communicating priorities, expectations, and organizational impact

 Module 4: AI Opportunities in Global Shared Services

• AI applications across finance, HR, procurement, IT, and customer service
• Improving productivity through knowledge support and task assistance
• Enhancing service quality through consistent information and error detection
• Improving customer experience through faster, more relevant service
• Identifying process constraints and preserving human escalation paths

  

Day 2: AI Adoption Strategy and Responsible Governance

Module 5: Developing an AI Adoption Strategy for Global Shared Services

• Aligning AI adoption with enterprise and shared services priorities
• Evaluating organizational, process, technology, and data readiness
• Defining the scope and ambition of AI adoption
• Balancing global standardization with local service needs
• Establishing strategic priorities and adoption principles

Module 6: Identifying and Prioritizing AI Use Cases

• Discovering opportunities across shared services workflows
• Evaluating business value, feasibility, complexity, and risk
• Understanding data availability and integration dependencies
• Selecting suitable pilot initiatives
• Defining success criteria and identifying unsuitable AI applications

 Module 7: Building the AI Business Case

• Connecting AI investments to operational and strategic outcomes
• Estimating implementation, integration, and ongoing operating costs
• Distinguishing capacity gains, cost savings, and service improvements
• Considering build, buy, and partnership options
• Establishing ownership of expected benefits

 Module 8: Responsible AI Governance and Oversight

• Responsible AI principles and leadership accountability
• Privacy, confidentiality, security, and appropriate data use
• Managing inaccurate outputs, bias, and inconsistent performance
• Defining human review, escalation, and approval boundaries
• Vendor oversight and internal AI usage policies
• Coordinating with legal, compliance, security, and risk teams

  

Day 3: Leading Implementation and Sustaining Value

Module 9: Leading Workforce and Organizational Change

• Understanding the impact of AI on roles and responsibilities
• Addressing employee concerns and building trust
• Identifying AI literacy and capability development needs
• Engaging stakeholders and developing change champions
• Encouraging adoption while maintaining service continuity

 Module 10: Establishing an AI Operating Model

• Defining business, technology, data, and governance responsibilities
• Comparing centralized, federated, and hybrid operating models
• Integrating AI into service delivery and process ownership
• Establishing support, exception handling, and escalation arrangements
• Managing collaboration across functions and locations

 Module 11: Measuring AI Adoption and Business Value

• Establishing performance baselines and outcome measures
• Tracking productivity, turnaround time, and service quality
• Monitoring customer experience and employee adoption
• Reviewing operating costs, realized benefits, and unintended effects
• Using performance evidence to guide improvement and scaling decisions

 Module 12: Developing the AI Transformation Roadmap

• Translating strategy into a phased implementation roadmap
• Defining priorities, milestones, resources, and dependencies
• Assigning executive sponsors and accountable owners
• Planning the first 90 days of AI adoption
• Establishing criteria for scaling, adjusting, or stopping initiatives
• Sustaining leadership engagement and continuous improvement