What is AI Ops Team Structure?
AI Ops Team Structure is the organisational design that defines how roles, responsibilities, and reporting lines are arranged to manage AI systems effectively in day-to-day business operations. It encompasses the mix of technical and business-side talent, coordination models, and governance mechanisms needed to keep AI initiatives running smoothly and delivering value.
What is AI Ops Team Structure?
AI Ops Team Structure refers to how an organisation arranges the people responsible for building, deploying, maintaining, and governing AI systems. Unlike traditional IT teams that manage software and hardware, AI operations teams must blend data science expertise, engineering skills, domain knowledge, and business acumen into a cohesive unit that can handle the unique demands of AI systems.
Getting the team structure right is one of the most consequential decisions a company makes in its AI journey. A poorly organised AI team leads to duplicated effort, slow deployment cycles, misalignment between technical capabilities and business priorities, and ultimately, AI projects that fail to deliver value.
Common AI Ops Team Models
Centralised Model
In a centralised model, all AI talent sits within a single team, often called a Centre of Excellence or AI Lab. Business units submit requests to this team, which prioritises and executes projects.
Advantages:
- Consistent standards, tools, and practices across the organisation
- Efficient use of scarce AI talent
- Strong knowledge sharing between AI practitioners
Disadvantages:
- Can become a bottleneck as demand grows
- Risk of disconnection from business context and real-world user needs
- Business units may feel they lack ownership of AI initiatives
Decentralised Model
In a decentralised model, each business unit has its own AI staff embedded directly within the team. These practitioners report to business unit leaders rather than a central AI function.
Advantages:
- Deep integration with business processes and priorities
- Faster response to business-unit-specific needs
- Strong domain knowledge within each AI team
Disadvantages:
- Inconsistent standards and duplicated infrastructure
- Difficulty sharing knowledge and best practices across teams
- Risk of reinventing solutions that another team has already built
Hub-and-Spoke Model
The hub-and-spoke model combines elements of both approaches. A central AI hub sets standards, provides shared infrastructure, and manages governance, while embedded AI practitioners in each business unit handle day-to-day operations and use case development.
Advantages:
- Balances consistency with business-unit responsiveness
- Central hub maintains standards while spokes stay close to business needs
- Knowledge flows both ways, from the hub to spokes and back
Disadvantages:
- Requires clear communication channels and well-defined responsibilities
- Can create tension between central governance and local autonomy
- Needs strong leadership to prevent the model from drifting toward pure centralisation or decentralisation
Key Roles in an AI Ops Team
Regardless of the structural model, effective AI operations require several core roles:
- AI/ML Engineers: Build, train, and deploy machine learning models. They translate data science prototypes into production-ready systems.
- Data Engineers: Build and maintain the data pipelines that feed AI systems. Reliable data infrastructure is the foundation of reliable AI.
- MLOps Engineers: Manage the operational infrastructure for AI, including model monitoring, automated retraining, deployment pipelines, and version control.
- AI Product Manager: Bridges the gap between technical teams and business stakeholders. Defines requirements, prioritises the roadmap, and ensures AI projects align with business goals.
- Domain Experts: Business-side professionals who understand the processes, customers, and context that AI systems serve. Their input is essential for defining useful AI applications and validating outputs.
- AI Governance Lead: Ensures AI systems comply with regulations, ethical standards, and internal policies. This role grows in importance as AI deployments scale.
Structuring AI Ops for SMBs in Southeast Asia
Most SMBs in ASEAN do not have the resources to hire large, specialised AI teams. A practical approach involves:
- Start small: Begin with two to three versatile practitioners who can handle multiple roles, such as an ML engineer who also manages data pipelines and a product manager who doubles as a governance lead.
- Leverage external partners: Use AI consultancies and managed service providers for specialised tasks while building internal capability gradually.
- Upskill existing staff: Train current employees in AI fundamentals so they can serve as domain experts and AI champions within their teams.
- Plan for growth: Design your team structure with a clear path for adding roles as AI maturity and demand increase.
Cross-border considerations are also important. If your business operates across multiple ASEAN markets, you may need local AI liaisons who understand market-specific regulations, data residency requirements, and cultural nuances that affect how AI systems should behave.
Common Mistakes to Avoid
- Hiring only data scientists: Data scientists build models, but without engineers, product managers, and governance roles, models rarely make it to production or deliver sustained value.
- Ignoring the business side: An AI team that lacks domain expertise or business alignment will build technically impressive solutions that nobody uses.
- Over-centralising too early: Forcing all AI activity through a single team before establishing strong processes creates bottlenecks and frustration.
- Underinvesting in MLOps: Many organisations focus on model building but neglect the operational infrastructure needed to keep models running reliably in production.
AI Ops Team Structure directly determines whether your AI investments translate into operational results or stall at the proof-of-concept stage. For CEOs, the team structure decision is strategic because it shapes how quickly your organisation can deploy AI, how reliably those deployments perform, and how effectively AI efforts align with business priorities.
A well-structured AI ops team reduces time-to-value on AI projects, prevents costly duplication of effort, and builds the organisational muscle needed to scale AI across the business. Conversely, a poorly structured team leads to talent attrition, slow delivery, and AI projects that fail to move from experimentation to production.
For SMBs in Southeast Asia, where AI talent is expensive and in short supply, getting the structure right from the start is especially important. You cannot afford to waste scarce talent on duplicated work or misaligned priorities. A thoughtful team structure maximises the impact of every hire and every dollar invested in AI capabilities.
- Choose a team model that matches your current AI maturity. Start centralised for consistency, then evolve toward hub-and-spoke as demand grows.
- Ensure your team includes both technical roles and business-facing roles like AI product managers and domain experts.
- Invest in MLOps capability early. The ability to deploy, monitor, and maintain models in production is as important as the ability to build them.
- Plan for multi-market complexity if operating across ASEAN, including local regulatory knowledge and cultural understanding.
- Build career paths for AI team members to improve retention in a competitive talent market.
- Define clear interfaces between centralised and embedded team members to prevent confusion about responsibilities.
- Review and adjust your team structure at least annually as your AI portfolio and organisational needs evolve.
Frequently Asked Questions
What is the minimum AI ops team size for an SMB?
A practical minimum for an SMB starting its AI journey is two to three people: one ML or data engineer who can build and deploy models, one product or project manager who connects AI work to business priorities, and ideally one data engineer or analyst who ensures data pipelines are reliable. These roles can be supplemented by external partners for specialised tasks. As your AI portfolio grows, you can add dedicated MLOps, governance, and domain specialist roles.
Should AI ops report to the CTO or the CEO?
For most organisations, AI ops should report to the executive who is most accountable for AI delivering business results. In many cases this is the CTO or Chief Data Officer, but in organisations where AI is central to business strategy, a direct reporting line to the CEO can be appropriate. The key principle is that AI ops should not be buried several layers below the leadership team, because strategic alignment and rapid decision-making require executive-level sponsorship.
More Questions
The most effective way to prevent bottlenecks is to adopt a hub-and-spoke model as your AI demand grows. The central hub sets standards and provides shared tools, while embedded practitioners in business units handle day-to-day work. Additionally, investing in self-service AI tools and platforms allows business users to handle routine tasks independently, reserving the AI ops team for complex projects that require specialised expertise.
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