Five GBS Leadership Actions to Deliver High-Impact AI   

The State of AI in GBS Organizations 

Global Business Services (GBS) organizations are on the brink of using artificial intelligence (AI) in more transformative ways. According to the SSON Research and Analytics 2025 report, “GBS as the Engine of Digital Transformation”:  

• 55% of organizations are using AI in a limited, ad hoc way focused on specific tasks.
• 21% report not using AI at all.
• About 20% have moved beyond isolated pilots, integrating AI across several functions or embedding it deeply in operations. 

These results show that while most organizations are still experimenting, a select group is realizing significant value from enterprise-wide AI adoption. For GBS leaders aiming to join these top performers, there are five critical leadership actions to take. 

Clarifying AI Impact vs. Value 

Often, organizations claim that automation “frees up employees for higher-value work.” While this adds value, true impact means driving measurable improvements in business outcomes—such as revenue growth, cost optimization, speed to market, customer loyalty, and compliance. Emerging types of AI, like agentic AI and autonomous process agents, offer the potential for larger business impacts when deployed effectively. This article focuses on leadership actions that drive AI business impact, not just incremental value. 

Leadership vs. Technical Actions 

There are many technical actions to deploy AI, such as choosing the right technology stack and configuring AI models through effective prompts and explanations. However, this summary concentrates on the following five non-technical leadership actions required for impactful AI in GBS. 

1. Select Meaningful, High-ROI AI Use Cases 

Real impact comes from deploying AI at scale in ways that directly improve key business KPIs. While pilots are helpful, focus on projects that significantly affect cash flow, revenue, or margin—often in front- and middle-office rather than traditional back-office areas.  

For example, McKinsey & Company research1 shows generative AI is being deployed most in marketing, sales, product and service development, customer operations, and software engineering, as opposed to more traditional GBS finance and HR functions. When choosing use cases, conduct thorough ROI analysis with supporting KPIs, as some AI solutions can be costly or require extensive data preparation. 

2. Apply AI Across End-to-End Processes and Data Flows 

Improving business outcomes requires applying AI across entire processes, not just isolated tasks. Since AI is highly dependent on data, ensure strong data connectivity and integration throughout the process. AI requires a granular process and data understanding.  

GBS leaders must coordinate closely with those overseeing global process ownership and master data management to enable effective AI use. 

3. Invest in Strategic, Measurable Change Management 

Change management is often treated as an afterthought, limited to communications and slogans. For AI, it must be concrete and strategic. Start by understanding your company’s AI operating and organizational model:  

• Is there a centralized AI Center of Excellence, or does GBS serve that role?  
• Are external providers involved?  

Identify all stakeholders and their roles throughout the AI lifecycle. Clearly define your place in the process, build strong relationships, and maintain alignment. As AI initiatives progress, assess how changes will impact key stakeholders, users, suppliers, and employees—and develop targeted change strategies accordingly. 

4. Reorient GBS Talent and Skills for AI Transformation 

The SSON Research and Analytics report highlights essential skill sets for digital transformation: 

• Process automation and optimization
• AI and machine learning literacy (varies by AI operating model)
• Cross-functional project management
• Advanced data analytics and interpretation
• Customer experience and service design 

Even when deep technical expertise isn’t required, GBS professionals must understand how to interpret AI outputs and apply them effectively. Leaders should prioritize blended skill development, combining technical know-how with business acumen. 

5. Strengthen AI Governance, Risk, and Compliance 

While AI can accelerate processes, it can also amplify mistakes if not properly governed. Current AI systems plateau at about 92% data quality, leaving room for inaccuracies, bias, or “hallucinations”.  

GBS leaders should: 

• Implement AI quality monitoring and bias audits
• Maintain strict data security protocols
• Update contracts with AI-specific performance and compliance clauses when outsourcing 

Effective AI governance safeguards both operational integrity and brand reputation. 

Conclusion 

AI offers enormous potential for GBS organizations.  However, these technologies come with high costs and risks, particularly for more advanced categories such as agentic AI and autonomous process agents.  To maximize return on investment, GBS leaders must go further than past automation efforts—focusing on initiatives that deliver true business impact rather than incremental gains. To gain more insights from our SSO Network, please join us for our upcoming Intelligent Document Processing Virtual Summit.