This one-day course equips leaders with a practical understanding of how organizations can adopt artificial intelligence (AI) to improve decision-making, service delivery, and business performance. It explores AI applications across industries and their relevance to Global Shared Services (GSS), including finance, human resources, procurement, IT support, and customer operations.

Participants examine GSS use cases, identify opportunities within their functions, and evaluate AI investments against expected business value. The course also addresses responsible adoption, workforce readiness, and the leadership decisions needed to move from initial ideas to scalable business outcomes.

 

Duration 1 Days – 7 hrs.

 

Objectives

• Explain core AI concepts and their implications for organizational leadership.
• Recognize AI adoption patterns across industries and their relevance to GSS.
• Identify practical AI opportunities across shared services functions.
• Use AI to support decision-making, knowledge access, and operational improvement.
• Prioritize AI initiatives based on business value, feasibility, and risk.
• Evaluate investment requirements, expected benefits, and measures of success.
• Outline a responsible AI pilot with clear ownership and business outcomes.

 

Target Audience 

• GSS and Global Business Services executives and leaders
• Shared services center heads and functional managers
• Finance, HR, procurement, IT, and customer operations leaders
• Transformation, innovation, and operational excellence leaders
• Business leaders responsible for AI adoption and investment decisions

 

Prerequisites

• Basic understanding of business operations or shared services processes
• Familiarity with organizational goals and operational performance measures
• Awareness of current challenges within the participant’s business function
• No programming, data science, or previous AI experience required

 

Course Outline 

Day 1: AI Leadership, Business Value, and GSS Adoption

Module 1: AI Essentials for Business Leaders

• Predictive AI, generative AI, and AI-enabled automation
• Differences between AI, traditional automation, and robotic process automation
• AI capabilities, limitations, and the need for human judgment
• Leadership responsibilities in AI adoption

 Module 2: AI Adoption Across Industries

• Common adoption patterns: employee assistance, process improvement, and service innovation
• Applications in financial services, manufacturing, retail, healthcare, and professional services
• Lessons transferable to GSS environments
• Adoption enablers: process readiness, data quality, integration, and workforce capabilities

 Module 3: AI Case Studies in Global Shared Services

• Illustrative finance case: invoice exception handling and collections prioritization
• Illustrative HR case: employee query support and policy knowledge retrieval
• Illustrative procurement case: spend analysis and supplier information summarization
• Illustrative service operations case: ticket classification, routing, and agent assistance
• Comparison of business problems, AI contributions, human oversight, and outcome measures

 Module 4: AI for Leadership Decisions and Operational Effectiveness

• Summarizing operational reports and identifying issues requiring attention
• Supporting demand forecasting, capacity planning, and resource allocation
• Exploring scenarios and developing decision options
• Improving access to policies, procedures, and organizational knowledge
• Verifying AI outputs and protecting sensitive business information

 Module 5: AI Investment, Business Value, and Responsible Adoption

• Selecting opportunities based on value, feasibility, and risk
• Comparing purchased solutions, embedded AI features, and custom development
• Considering licensing, integration, data preparation, change management, and ongoing support costs
• Defining expected benefits: capacity released, service quality, cycle time, and employee experience
• Distinguishing productivity gains from realizable financial savings
• Establishing baselines, success measures, accountability, and pilot expansion criteria
• Addressing privacy, security, output reliability, and workforce adoption

 Module 6: Capstone — Develop a GSS AI Opportunity Brief

• Select a priority GSS business problem
• Describe the proposed AI use case and intended users
• Identify data needs, process dependencies, and human oversight
• Define expected benefits, cost considerations, and measurable outcomes
• Outline a pilot, accountable owner, and next steps