Building the Right AI Team in 2025: Essential Roles Every Company Needs

By Francisco Fernández, Tech & Strategy Consultant

Estimated reading time: 6 minutes 

Why Getting the Team Right Matters

As AI continues to reshape industries, assembling the right team is no longer optional—it’s strategic. Steve Nouri recently highlighted on LinkedIn how advancing AI agents and agentic frameworks demands new roles, deeper specialization, and dynamic collaboration.

Meanwhile, according to The Financial Times, organizations are creating Chief AI Officer (CAIO) positions at a rapidly growing rate to bridge technology, ethics, governance, and business transformation. Additionally, effective AI initiatives depend on coordination between the CIO, CFO, and Strategy Officer, pointing to an executive triumvirate as essential for sustainable integration.

In this landscape, companies that hire in haste without role clarity risk siloes, misalignment, and missed impact. Here’s a refined blueprint for building an AI team today—with the right mix of roles, responsibilities, and strategy. 

7 Core Roles for a Modern Enterprise AI Team 

Role #1: Chief AI Officer (CAIO) — Strategy + Oversight + Transformation

The CAIO role—now nearly tripled globally—is foundational. Financial Times reports that CAIOs today combine technical fluency with business acumen, governance oversight, and risk mitigation. They often manage transformation programs, educate executives, and ensure that AI helps drive measurable ROI.

Executive coordination—between the CIO, CFO, and Strategy Officer—ensures AI initiatives are financially sound, strategically aligned, and operationally feasible. 

Role #2 & #3: AI/ML Engineers & Data Scientists — The Core Builders

No AI program can exist without technical expertise. Engineers manage infrastructure, model training, and deployment. Data scientists design experiments, define metrics, and build prototypes.

Steve Nouri emphasizes the rise of agentic AI—multi-agent systems that work collaboratively. These systems need engineers who can design the "Agent Core", memory modules, and agent workflows that self-reflect and adapt over time. 

Role #4: AI Ethicist / Risk Manager — Guardrails in the Age of Scale

Ethics isn’t an afterthought. As AI interventions grow, so to do concerns around bias, misinformation, security, and regulatory compliance. Steve argues that teams need dedicated roles to address these risks early—and continuously.

This role aligns with the "three lines of defense" model in risk management—anchoring internal review, governance, and executive oversight. 

Role #5: AI Product Manager / Agent Architect — From Vision to Execution

AI products must serve users, not just live in code. Leading organizations now need specialized professionals who can define use cases, oversee multi-agent workflows, and translate agentic AI into customer or process value.

Steve Nouri’s work underscores the need for this role: managers who design modular agents that talk to each other, iterate, and deliver continuously across domains.

Role #6: AI Ops / Data Engineer — Stability at Scale

Building models is one thing—running them at scale is another. Watching systems, maintaining data pipelines, optimizing for latency, and ensuring explainability falls on AI Ops and Data Engineers.

Without this role, prototypes fail to become reliable, enterprise-grade tools. 

Role #7: Transformation & Change Lead — Culture Meets Capability

AI isn’t just technology—it’s change. Adoption fails without internal evangelism, training, upskilling, and cross-functional buy-in.

The Change Lead role drives workshops, monitors cultural uptake, and aligns stakeholders—making AI not feel like an external project, but a shared future.

Sizing & Sequencing: How to Staff and Evolve

• Early stage startups: Focus on a CAIO (or senior PM), AI Engineer, and Ops role.

• Medium-sized firms: Add AI Product, Data Science, and Ethics.

• Large enterprises: Staff full teams in each role plus the executive triumvirate.

Sequence hires to balance execution (Engineers), assurance (Ethicists/Ops), and strategic oversight (CAIO + Transformation). 

Today’s AI landscape rewards speed—but punishes misalignment. AI teams need structure, not just talent. A smart team includes technical builders, risk guardians, product thinkers, and cultural evangelists.

Without it, you risk what Machiavelli warned: short-term gains without loyalty, control, or longevity.