What is an AI product team?
An AI product team is an engineering team of cross-functional specialists who have the skills to build custom software with AI functionality. Companies without AI expertise often collaborate with external AI development teams to scale engineering capacity and access specialized technical knowledge, including predictive analytics, generative AI services, and computer vision. It's a practical business solution for both covering short-term AI needs, such as implementing a new feature, and outsourcing the entire AI product development process.
Key AI Team Roles
A typical AI team structure includes a product manager, an AI architect, data scientists, machine learning experts, data engineers, software developers, QA specialists, and UI/UX designers. The composition of a dedicated development team varies based on system complexity, required integrations, data availability and quality, AI implementation domain, and, most importantly, the company's hiring needs.
Main Roles in an AI Product Team
| Role | Description | Responsibilities |
| Product manager | Shapes the product vision and coordinates the team | Sets product goals and roadmap; Prioritizes features; Aligns AI development with business value |
| AI architect | Oversees an entire AI system development | Leads AI architecture development; Coordinates the work of tech specialists; Provides tech consulting |
| Data scientist | Analyzes data | Runs Exploratory Data Analysis; Builds models and interprets results |
| Machine learning engineer | Builds and deploys ML models | Creates, implements, and monitors ML models; Builds pipelines and APIs |
| Data engineer | Creates data infrastructure | Designs ETL/ELT workflows for data processing; Manages data storage |
| Software developer | Develops non-AI components of the system | Develops the front-end and back-end of the core system; Integrates AI into the app |
| QA specialist | Tests the software product | Tests AI and other software features; Validates model outputs |
| UI/UX designers | Designs user interfaces | Analyzes target users; Creates wireframes and prototypes; Runs usability testing |
Responsibilities of an AI Product Team
Building a cross-functional AI team in-house or hiring an external one provides companies with dedicated AI product delivery support that includes the following operations:
- Identifying optimal ways to implement AI based on business goals
- Early-stage feasibility analysis and AI product strategy development.
- Defining product vision and success metrics.
- Software development planning and prioritizing tasks.
- Software prototyping and user interface design.
- Automating ML pipelines through MLOps practices.
- Preparing data for model training.
- ML model development, training, and fine-tuning.
- Developing the core system for AI implementation.
- Quality assurance through continuous testing.
How AI Product Management Teams Differ from Traditional Product Teams
AI product lifecycle requires a different set of skills and tech stack than traditional software development, as the functionality of the developed systems differs. AI product teams also considerably depend on data availability and quality, with less predictable software development output and more iterations. Here are some other characteristics of AI vs. traditional teams:
| AI Product Team | Traditional Product Team | |
| Technical knowledge | Specialized knowledge | General approaches |
| Team structure | + Data scientists, data engineers, ML engineers | Software engineers, designers, QAs |
| Flexibility | Data affects functionality and the engineering process | Features are defined at the project start |
| Success Metrics | Model performance and accuracy | Usage, revenue, user retention |
| Risks | Biased models, inaccurate predictions | Bugs, useless features |
Benefits of Engaging an AI Product Team
As the competition for AI talent intensifies due to the growing demand for AI implementation, many companies opt to hire an external team for product delivery support. It enables them to start AI product development faster and brings a range of other benefits, including:
- Enhanced product-market fit for AI features through expert analysis.
- Faster product time-to-market and competitive advantage.
- Accelerated innovation thanks to a cross-functional team covering all engineering needs.
- More flexibility in terms of staffing and scaling the team up or down.
- Ethical and legal compliance with the guidance of engineers who know how to process data securely.
Costs of AI Product Management
An engineering budget varies depending on standard factors such as project scope, expertise level and seniority required, duration of cooperation, location, and talent availability. When it comes to an AI collaboration budget, businesses must also consider ML model complexity, data quality, the need for preprocessing, existing infrastructure, compliance requirements, and post-launch model maintenance. Additionally, many companies run custom tech training workshops for teams to facilitate AI adoption and maximize the benefits of AI implementation.
Use Cases of Hiring an AI Product Team
Partnering with an AI product team is an effective solution for businesses in data-rich industries that want to enhance their software systems with AI. Here are some common cases when hiring an AI development team is feasible:
- Healthcare startups collaborate with AI product teams to build AI platforms for automated genetic map interpretation.
- R&D companies hire cross-expertise AI teams to design intuitive UI/UX for AI and ML systems, facilitating software adoption by research organizations.
- Companies using AI-powered drones for reforestation projects use the assistance of an AI product team to develop algorithms that process images and monitor tree health.
Key Takeaways
An AI product team provides companies with tech expertise to speed up AI adoption and ensure the model fits their business needs. By hiring an established team from an external vendor they can implement AI much faster, gain a competitive advantage, and minimize risks. The AI team can assist with specific tasks or cover the entire AI product lifecycle, from initial business analysis to prototyping, development, and post-launch maintenance, making innovations easily accessible to any company.
