AI and Organisation Design: Comparing Asia Pacific and the United States
Ken Brophy
Asia Pacific (APAC): AI Adoption with Structural Adaptation
1. AI-Augmented Shared Services
Many APAC organisations (or their Shared Services partners in the Philippines, India, etc.) are embedding AI into centralised service hubs to improve efficiency across Finance, HR, Procurement, IT, and more. Robotic Process Automation (RPA), natural language processing, and machine learning are reducing manual workloads and enabling smaller, more skilled teams. As a result, operating models are shifting towards leaner Shared Services, with a focus on analytics capability building and process design roles that oversee human–machine workflows.
2. Gradual Redesign to Respect Cultural Norms
AI-driven automation challenges traditional roles and reporting lines – but APAC organisations often approach this change incrementally. Respect for hierarchy, employment stability, and national regulatory environments shape how work is restructured. As such, rather than undertaking radical redesigns, some firms layer AI on top of existing structures, using pilots and proofs of concept prior to full rollout.
3. Bridging Capability Gaps Through Federated Models
With AI talent often in short supply – particularly outside major tech hubs – some Asia Pacific firms are adopting federated AI models. Central AI teams define frameworks, tools, and governance, while business units and local markets adapt and apply AI regionally. This model allows for consistency in AI use and ethics, while enabling responsiveness to diverse regional needs. It also supports capability transfer and gradual scaling across a wide geographic footprint.
United States: AI-Centric Operating Model Transformation
1. Embedding AI into Core Business Architecture
US firms – particularly in technology, retail, and financial services – appear to be embedding AI directly into product teams, customer journeys, and decision-making structures. This is leading to AI-native business models, where functions such as Marketing, Risk, and Operations are restructured around data and algorithmic decision-making. Entire roles (e.g. Prompt Engineers, AI Ethicists) and squads (e.g. AI/Machine Learning Product Squads) are being introduced, reshaping job architectures and flattening hierarchies for speed.
2. AI-Driven Redefinition of “Work”
With generative AI accelerating the automation of cognitive tasks, the US companies we are engaging with are pushing beyond efficiency to redefine roles and competencies. Jobs are being redesigned to focus on uniquely human skills (e.g. creativity, judgement, collaboration), while AI handles routine analysis and synthesis. This has driven structural changes, such as the introduction of dynamic talent marketplaces where internal staff are deployed to evolving AI-enabled projects based on skill signals.
3. Governance Models and Guardrails as Design Features
In response to ethical, legal, and reputational risks, many US organisations are incorporating AI governance directly into their operating models, with compliance overlays embedded alongside delivery structures. This ensures safe experimentation and responsible scaling. AI operating models are no longer purely technical – they now integrate risk, policy, and brand protection at a structural level.
In Summary
While both Asia Pacific and the United States are actively redesigning their organisations in response to AI, there are clear distinctions in how they do so (while acknowledging some shared elements). APAC’s approach tends to emphasise pragmatic integration, adapting to cultural and market realities. In contrast, the US appears to be restructuring more aggressively, embedding AI into core workflows and governance, and rethinking the nature of work itself.
Understanding these regional nuances is essential for designing operating models that are not only AI-ready but also locally resonant and scalable.
