In today’s world, artificial intelligence is no longer experimental. In fact, it’s a business imperative for many companies. While the conversation around AI transformation often centers on tools and technology, perhaps the main challenge lies in building the right team. AI isn’t plug-and-play; it requires leadership, infrastructure, compliance, communication, and a deep understanding of how humans and machines work together. With 69% of IT leaders planning to increase hiring due to generative AI, it’s clear that the future of AI is human-powered. That’s why it’s vital to have the right AI workforce in place.

Of course, not every organization needs to fill AI-driven roles immediately. Some may reskill existing staff; others may bring in external consultants or contract talent. The Doyle Group helps companies navigate this complexity — sourcing permanent hires, finding niche consultants, or offering strategic workforce guidance that aligns with their AI maturity and goals.

With that being said, what are some of the key roles necessary for organizational AI success?

Chief AI Officer or Head of AI

Every AI initiative needs executive-level accountability, and that’s where a Chief AI Officer — or Head of AI — comes in. This leader is responsible for shaping the company’s overall AI strategy and ensuring that technological investments directly support business goals. They align teams, oversee ethical and regulatory considerations, and coordinate AI development across departments. Without this role, AI efforts can become disjointed or misaligned with long-term value creation.

Change Management and Training Lead

Even the best AI tools will fail if people don’t understand how — or why — to use them. A Change Management and Training Lead guides internal communication, education, and reskilling to support adoption. Fear of change and poor communication are among the most common causes of AI project failure. This role builds buy-in by helping employees see AI not as a threat, but as a tool for doing their jobs better.

AI Program Manager or Transformation Lead

AI programs span departments, timelines, and technologies. The Program Manager or Transformation Lead is the connector who ensures these efforts stay on track. They oversee pilots, manage stakeholder communication, and coordinate execution across the organization. This role is vital to scaling AI from isolated experiments to integrated, enterprise-wide tools.

Data Engineer

No AI system can function without data — and not just any data, but high-quality, accessible, well-structured data. The Data Engineer builds the pipelines and infrastructure that feed AI models. This role prepares raw information for analysis and ensures performance, reliability, and scalability. A lack of strong data engineering often results in stalled or unreliable AI performance.

Machine Learning Engineer

While Data Engineers build the roads, Machine Learning Engineers drive the vehicles. These professionals design and train the models that make AI intelligent. They optimize algorithms, ensure scalability, and often collaborate closely with both Data Scientists and business teams. Their work transforms static data into automated, learning-driven systems.

Data Scientist

At the heart of AI’s value is its ability to deliver insights. The Data Scientist discovers those insights — identifying trends, making predictions, and guiding decision-making. This role helps organizations answer “what now?” and “what next?” with clarity. By turning data into strategy, Data Scientists make AI practical and relevant to business units.

AI Ethicist or Risk and Compliance Lead

AI isn’t just about speed or efficiency — it’s about trust. As regulations evolve and public scrutiny increases, organizations must ensure that AI is fair, unbiased, and responsible. The AI Ethicist or Risk and Compliance Lead evaluates models for bias, monitors privacy concerns, and establishes internal governance frameworks. They help protect both brand reputation and regulatory standing.

MLOps Engineer

Just as software teams rely on DevOps, AI teams need MLOps. The MLOps Engineer ensures that machine learning models remain stable, scalable, and integrated within core systems. They manage deployment workflows, monitor model performance, and automate maintenance tasks — allowing AI to function reliably in production environments.

AI UX Designer

The AI UX Designer translates complex AI capabilities into experiences that users trust and understand. Whether developing internal dashboards or customer-facing tools, this role is essential to making AI feel human, approachable, and transparent. Usability isn’t a luxury — it’s a prerequisite for adoption.

Departmental AI Champions

Successful AI adoption depends on more than just central teams — it requires internal advocates. Departmental AI Champions are business unit leaders who apply AI within their own domains, from marketing and finance to logistics and HR. They understand the specific pain points and opportunities in their field and help bridge the gap between technical teams and everyday users.

Talent Partner With Tech Fluency

Finally, companies need someone who can navigate the evolving AI talent landscape. A Talent Partner or Hiring Manager with strong tech fluency helps define roles, identify upskilling opportunities, and support long-term workforce strategy. They don’t just fill roles — they help design the structure that sustains AI growth.

Not every organization will need all of these roles today. But knowing what they are — and how they interact — is the first step toward building a resilient, scalable AI workforce. Whether you’re reskilling your current team, hiring strategically, or expanding through project-based expertise, The Doyle Group helps you build a team that’s ready for what’s next. Reach out today to begin a conversation with our team of recruitment experts.