The Essential Guide to Generative AI Team Composition

When it comes to building generative AI capabilities, the question of who should be on the team is just as critical as how the work gets structured. This isn't just another software project—generative AI introduces unique challenges around data governance, quality assurance, and aligning tools with business goals. So, assembling the right mix of skills and mindset matters.

At the core of this conversation is a balancing act: technical expertise, business context, and risk management. You need a team that can build effectively while ensuring compliance, quality, and strategic alignment.

The Core Roles You Need

A successful generative AI team blends leadership, technical depth, and subject matter expertise. Here's who you'll want in the room:

Product Leadership

Someone needs to set the direction. This person should have a solid grasp of both the strategic importance of AI and the realities of building it. They help define success metrics, align AI initiatives with broader business goals, and translate high-level objectives into something the team can actually build and test.

Legal & Compliance Expertise

AI applications, particularly in regulated industries, come with non-negotiable compliance requirements. Someone with a deep understanding of data usage policies, output compliance, and contractual considerations around AI models needs to be involved from the start—no one wants to discover governance issues after launch.

A lot of this would ideally be covered day-to-day by the product manager mentioned above, but it's essential that they have ready access to specialists who can provide input and alignment early in the process. This helps avoid late-stage blockers and speeds up iterations, ensuring what's being built can actually be deployed in production.

Software Engineers (Backend-Focused)

Generative AI development is, at its core, a software engineering challenge. The emphasis here should be on backend engineers with experience in API integration, model orchestration, and system architecture. It's less about training models from scratch and more about building reliable systems around them.

Domain Experts

Generative models need context. Domain experts bring the business knowledge required to ensure the AI's outputs make sense for your specific needs. Whether it's financial services, customer support, or healthcare, these experts help define what "good" results look like and provide the reference materials the AI will rely on.

Quality Assurance & Testing Specialists

Testing generative AI isn't just about functional correctness—it's about managing unpredictability. QA specialists (with the right tools) help ensure outputs remain consistent, compliant, and aligned with the intended use case, even as models evolve.

Data Scientists & Data Engineers (Sometimes)

Many companies over-index on data scientists and data engineers, often because they approach generative AI as a continuation of traditional machine learning projects. The reality, however, is often quite different. Most businesses today benefit from using pre-trained commercial or open-source models, making the challenge more about engineering robust applications than building custom models.

That said, including data scientists and engineers can make sense in specific cases—like when working with proprietary, non-text data or when integrating generative AI with outputs from other machine learning models as part of a larger system.

Beyond Roles: The Type of People That Matter

But here's the thing—just checking off these roles on an org chart won't get you far. What matters more than job titles is the mindset of the people you bring in.

You need people with the curiosity and self-starting energy of Pippi Longstocking: "I have never tried that before, so I think I should definitely be able to do that."

These are the experimenters, the ones who embrace rapid iteration and aren't afraid to break things (safely) in the name of learning. They're the ones who, when they hit a wall, won't wait for formal training—they'll be on GitHub digging through experimental repos or scrolling through ArXiv for the latest AI papers. They'll try things, fail fast, and try again—because that's how progress happens in a space evolving this quickly.

This willingness to explore matters more than the size of your team. A small, driven group of problem-solvers will always outpace a larger team stuck in a "let's gather requirements and wait for the perfect conditions" mindset.

We've seen companies need 15+ people and three months to build a single internal chatbot with a RAG (retrieval-augmented generation) system. And we've seen companies achieve the same with just a product manager and two engineers—in two days.

While there's a lot to be said for investing in generative AI, the better approach is often starting smaller with the right people and ensuring the right conditions for them to thrive.

The Bottom Line

The right team for generative AI isn't just a mix of skills—it's a mix of people who thrive in ambiguity. You need those who get excited about exploring the unknown and aren't intimidated by it.

Because in generative AI, the tools are evolving faster than any playbook can keep up with. What will set you apart isn't just having the right roles—it's having the right people who can adapt, learn, and deliver as the landscape shifts.