How to Build an AI Product Team: Roles You Actually Need

Key Takeaways

• An AI product team brings together product, engineering, data, design, business knowledge, and governance.

• The right AI team structure depends on the product, the team, and the stage of the project.

• Small teams can combine roles. Larger products often need more specialists.

• Clear roles help teams turn an AI idea into a product people can use.

What Does an AI Product Team Look Like?

An AI product team brings together the people needed to build and improve an AI product. The team may include people from product, engineering, data, design, security, and business areas. There is no single structure that works for every company.

A new AI product may start with a small team. One person may handle more than one role. As the product grows, the company may add more specialists.

The Institute of AI Product Management also describes AI product development as a team effort that brings together product, engineering, data, and other skills.

Before choosing roles, companies should first define the problem they want AI to solve. They should also define what a good result looks like.

The Flock's AI Discovery approach can help teams identify AI opportunities, set priorities, and define what the product needs before development starts.

1. AI Product Manager: Connecting the Product to the Business

The AI product manager decides what the team should build and why. This person defines the problem, understands user needs, and sets priorities. They also make sure the product supports business goals.

The AI product manager works with engineering, data, design, and business teams. For AI products, this role is important because the solution may change as the team tests different ideas.

A good AI product manager should be able to answer questions such as:

• What problem are we solving?

• Who will use the product?

• How should people interact with the AI?

• What should a good result look like?

• How will we measure success?

Their job is to keep the team focused on a useful product and a clear business goal.

2. AI or Machine Learning Engineer: Building the AI

The AI or machine learning engineer builds and connects the AI used by the product. Depending on the project, they may work with existing AI models or adapt them to a specific use case. They may also connect AI services, test outputs, improve performance, and add AI to the product.

The role can be different from one company to another. Some products need deep machine learning skills. Others depend more on existing models, APIs, and AI tools. This is why companies should understand the different AI roles before hiring.

The right engineer depends on what the product needs.

3. Software Engineer: Turning AI Into a Product

An AI model is only one part of the product. Software engineers build the systems that allow people to use it.

They may work on:

• Frontend interfaces

• Backend services

• APIs

• Integrations

• Authentication

• Business logic

• Databases

• Connections with other company systems

Software engineers make sure the AI works well inside the product. They also help connect AI with the rest of the company's technology. Their role is changing as AI becomes part of software development itself.

OpenAI's guide to AI-native engineering explains how coding agents can support planning, design, coding, testing, review, and deployment. Engineers still make the key decisions and remain responsible for quality.

Companies building an AI development team therefore need engineers who can use AI in two ways. They need to build AI into products and use AI in their own development workflow.

4. Data Roles: Giving AI the Right Information

Many AI products depend on company data.

This may include:

• Documents

• Customer information

• Product data

• Transactions

• Internal knowledge

• Business records

Data engineers make this information available and organized. They also make sure the data can be used by the product. Data scientists may study patterns, run tests, and measure how well the AI works.

The right machine learning team structure also depends on the data the product needs.

For example, an internal AI assistant may need access to company documents and systems.

In that case, strong data integration may be important. A simpler product that uses an external AI service may need fewer data specialists.Companies should choose data roles based on the product, not on a fixed team model.

5. Product Designer: Designing How People Use AI

AI changes how people interact with software.

Users may need to write prompts, review results, correct mistakes, give feedback, or approve a recommendation. Product designers decide how those interactions should work.

They should think about questions such as:

• What information should users provide?

• How should AI results appear?

• Can users correct mistakes easily?

• Is it clear when AI created an output?

• When should a person make the final decision?

These questions matter when AI creates text, predictions, recommendations, or other results that may change each time.

Designers work closely with product and engineering teams. Their goal is to make the AI experience clear and useful. This type of teamwork is a key part of an effective AI product team.

6. Domain Expert: Bringing Business Knowledge

An AI product also needs people who understand the real business context. A domain expert knows the industry, process, or type of work where the product will be used.

This could include areas such as:

• Finance

• Healthcare

• Logistics

• Legal services

• Retail

• Human resources

• Manufacturing

Domain experts help the team understand what users need. They can also help define rules, find edge cases, and review AI results.

For example, a legal AI product may need input from legal professionals. A financial AI product may need people who understand finance and risk.

Domain experts are especially useful at the start of a project.They help the team decide what a good result should look like in real situations.

7. Governance and Security: Defining How AI Should Be Used

Governance should have a clear owner in the artificial intelligence team structure. Depending on the company, this may be handled by security, legal, compliance, engineering leaders, or a dedicated AI governance team.

The goal is simple: define how AI can be used safely and responsibly.

The team may need to review:

• Data use

• Privacy

• Access

• Permissions

• Security

• Reliability

• Human oversight

• AI outputs

• Compliance rules

Teams should think about governance from the start. They should not wait until the product is ready to launch. As the product grows, companies may add dedicated roles for these areas.

McKinsey's research on technology teams in the AI era also highlights the need to rethink roles, skills, and how technology teams work with the business.

How to Structure an AI Product Team

There is no fixed number of people needed to build an AI product.

The right team depends on:

• The product

• The problem it solves

• The technology

• The skills already available

• The stage of the project

A small team may include:

• An AI product manager

• An AI or machine learning engineer

• A software engineer

• A product designer

• A domain expert

One person may cover more than one role.

As the product grows, the company may add people in data, security, governance, infrastructure, and software engineering.

Companies do not need to hire every role at once. They can add specialized talent as the product grows.

The Flock's Software Teams and Talent on Demand models help companies add the skills they need at each stage.

Companies can also work with AI Verified Engineers, whose ability to work effectively with AI has already been validated.

FAQs About AI Product Teams

1. What roles are needed in an AI product team?

Most AI product teams need people with skills in product, AI or machine learning, software engineering, data, design, business knowledge, and governance.

The exact roles depend on the product and the team the company already has.

2. How many people should be on an AI product team?

There is no fixed number.A small team may have only a few people who cover several roles. Larger AI products may need more specialists in engineering, data, design, security, and governance.

3. What does an AI product manager do?

An AI product manager defines what the team should build and why.They understand user needs, set priorities, connect the product to business goals, and coordinate the team.

They also help define how success will be measured.

4. How is an AI team different from a software development team?

An AI product team includes many of the same roles as a software development team. It may also need people with skills in AI models, data, AI testing, domain knowledge, user experience, and governance.

5. How can companies build an AI team faster?

Companies can add specialized talent to their existing teams instead of hiring every role internally. The Flock provides Software Teams, Talent on Demand, and AI Verified Engineers based on the skills each AI product needs.