Determining organizational roles and responsibilities
The integration of artificial intelligence (AI) into businesses has led to the emergence of new responsibilities for using AI technologies effectively. Just as organizations once required new skills to navigate digital transformation, they now need clear ownership for the adoption, integration, and governance of AI. Part of an AI CoE's responsibility is to determine whether new roles are required, which responsibilities can be assigned to existing teams, and how those roles should work together.
Before listing possible roles, define the responsibility model for AI adoption. The AI CoE sets strategy, standards, governance guardrails, reusable patterns, intake and prioritization, and skilling. Platform teams provide governed foundations such as identity, networking, policy, observability, and approved services. Workload and business teams own use cases, domain data, integration, delivery, and business outcomes.
An AI CoE should then determine the appropriate scope and responsibilities for the roles or capabilities that will support successful AI adoption. Not every organization needs every title. Smaller organizations might assign these responsibilities to existing leaders or virtual teams, while larger or more mature organizations might create dedicated roles. Capabilities an AI CoE may consider include:
Executive sponsor or AI leadership archetype
AI initiatives need visible leadership and sponsorship. Some organizations assign this accountability to an existing CIO, CTO, Chief Data Officer, Chief Digital Officer, or business executive. Others create a dedicated role such as Chief Artificial Intelligence Officer (CAIO) or Head of AI. These titles are alternative leadership archetypes, not mandatory positions. The important decision is to establish clear accountability for AI strategy, funding, prioritization, responsible AI expectations, and communication with senior leadership.
AI CoE and advisory leadership
The AI CoE or advisory leadership group helps translate executive sponsorship into practical guidance. This group defines reusable patterns, reviews intake and prioritization, advises delivery teams, coordinates skilling, and helps align AI work with enterprise architecture, security, governance, and responsible AI practices.
Business, product, and domain roles
AI adoption also requires business and product ownership. Business or domain owners help identify valuable use cases, clarify domain data and processes, and validate that AI solutions support real work. An AI product or strategy owner can manage the roadmap, backlog, success measures, change management, and stakeholder alignment.
Prompt engineering should be treated as a cross-cutting capability across AI engineering, product, design, and business roles, not necessarily as a durable standalone headline role. Teams might design system instructions, examples, prompt patterns, retrieval context, and tool descriptions together. Those patterns should be validated with evaluations, safety checks, and model-specific guidance before they're reused or scaled.
Technical, data, security, operations, and evaluation capabilities
Foundational AI roles such as data scientists, ML engineers, AI architects, and software engineers remain important. Modern AI work also extends beyond NLP or LLM-centric tasks to include multimodal models, agentic patterns, retrieval-augmented generation (RAG) and grounding, reasoning workflows, evaluations, and safe orchestration. Depending on the organization's use cases, the following responsibilities may be assigned to dedicated roles or shared across teams:
- Platform and workload engineers provide the governed foundations and application integration needed for AI solutions. Platform teams manage approved services, identity, networking, policy, observability, and deployment patterns. Workload teams apply those foundations to deliver AI-enabled applications and integrate them with business systems.
- AI application and agent engineers build AI-enabled apps and agents that use models, data, tools, and orchestration safely. Their responsibilities may include RAG and grounding patterns, tool calling, workflow orchestration, multi-agent coordination, memory or state design, evaluations, and safeguards that help agents act within approved boundaries.
- Data engineers and data stewards prepare, protect, and maintain the data used by AI systems. They support data quality, metadata, lineage, access control, grounding sources, vector indexes, and lifecycle processes so models and agents use appropriate and reliable information.
- Responsible AI, governance, and risk specialists help define policies, review processes, impact assessments, controls, documentation, and exception handling. They work with delivery teams so AI systems are explainable, fair, privacy-aware, secure, and aligned with organizational requirements.
- AI security engineers apply security controls to harden AI applications, models, data flows, and agents. Their responsibilities may include defending against prompt injection and jailbreaks, conducting red-team exercises, protecting model and data boundaries, controlling agent access to tools and organizational resources, and reducing risks such as data leakage or unsafe actions.
- Evaluation and agent-optimization specialists define how AI quality, safety, reliability, and business impact are measured. They may create test sets, automated and human evaluation processes, regression checks, reasoning and retrieval assessments, and trace reviews that help teams improve prompts, tools, grounding, models, and agent behavior.
- AI Operations teams manage deployed AI systems using practices sometimes called GenAIOps, LLMOps, or AgentOps. They monitor reliability, cost, performance, safety, and recovery; maintain observability and tracing across prompts, retrieval, model calls, tool use, and agent steps; manage registries for prompts, models, agents, and evaluations; and support governance reporting over time.
Successful AI deployment requires a team or virtual team that can dedicate time to upskilling and align AI initiatives with business goals. The goal isn't to create a large role catalog, but to make sure the necessary responsibilities are understood, assigned, and governed.