Building a Conversational AI Team
Conversational AI teams vary based on project scope, budget, and maturity. While there’s no single blueprint, successful teams rely on a core set of roles to drive development and iteration. As projects scale or evolve, specialized roles become essential. This guide outlines key team structures and responsibilities to help you build an effective team, whether you're launching your first AI assistant or expanding an existing one.
Proof of Concept (POC) Team
When evaluating an AI assistant platform and proving value, you can start with a lean team—sometimes as small as two people.
Project Owner/Manager
In a POC, the Project Owner defines the scope, shapes the initial user experience with subject matter expertise, and secures buy-in from key stakeholders. They align business objectives with technical feasibility, facilitate collaboration, and drive the project toward actionable insights and next steps.
Developer:
The Developer is the technical backbone of the POC, turning ideas into a working AI assistant. They handle setup and installation, build the first conversational flows, and connect the assistant to key systems. As the project evolves, they fine-tune functionality, troubleshoot issues, and explore ways to optimize performance. With a mix of problem-solving, innovation, and hands-on development, they help prove feasibility and lay the foundation for future scaling.
Pilot Team
Once you decide to bring your assistant into production, expanding the team helps ensure a successful launch. Here’s what a pilot team might look like:
Project Owner/Manager
As the assistant moves into production, the Project Owner/Manager plays a larger role in keeping projects on track. They align teams, timelines, and goals while coordinating developers, designers, and stakeholders. From defining requirements to tracking milestones, they ensure the assistant ships on time, meets scope, and aligns with business objectives.
Builder
The Builder implements conversation designs, ensuring the assistant understands user inputs and manages dialogue effectively. They collaborate with Conversation Designers to create user journeys using no-code tools or custom code and conduct initial testing to verify flow functionality. Designers may also contribute to implementation, depending on their skills and project needs.
Conversation Designer
The Conversation Designer defines user interactions with the AI assistant. Bringing them in early ensures a strong foundation for success. They map out conversational flows, write dialogue, and balance user needs with system capabilities. Working closely with developers and builders, they refine experiences to drive better outcomes.
Test Manager
The Test Manager ensures the AI assistant performs reliably by writing and automating test cases, catching errors before deployment, and evaluating conversation flows. By identifying edge cases and validating responses, they help maintain a high-quality assistant.
Developer
The Developer extends the assistant’s capabilities, integrating it with backend systems, APIs, and custom responses. They customize behavior, scale deployments, and optimize performance. Developers may also set up CI/CD pipelines to streamline updates and maintenance.
Scaling/Extended Team
As the assistant expands across business units, channels, and modalities, you may need additional roles, either full-time or on a consulting basis.
Subject Matter Experts (SMEs)
As the assistant grows, SMEs help expand its knowledge base, ensuring responses remain accurate and relevant to evolving business needs.
Content Manager/Copywriter
At scale, maintaining consistent, high-quality responses across languages and channels is crucial. The Content Manager ensures accuracy, brand alignment, and clarity—whether responses are dynamically generated or template-based. As the assistant scales, they may also manage the Retrieval-Augmented Generation (RAG) knowledge base.
Analyst and/or Data Scientist
The Analyst tracks key metrics, analyzes real user interactions, and identifies areas for improvement. As the assistant scales, their insights help refine performance and enhance user experience.
Machine Learning Engineer
The ML Engineer fine-tunes models to improve accuracy, reduce latency, and optimize infrastructure costs. Whether full-time or consulting, they enhance the assistant’s intelligence and efficiency.
Solution Architect
The Solution Architect designs scalable, secure architectures and integrates backend systems. Their role is most critical during implementation, ensuring reliability and flexibility.
Front-End Developer
The Front-End Developer builds and integrates the assistant’s UI, ensuring smooth, intuitive interactions. Their work is most active during the setup phase but may continue to refine the experience.
DevOps Specialist
The DevOps Specialist ensures stability, scalability, and efficient deployment. They manage infrastructure, CI/CD pipelines, security, and monitoring, keeping the assistant running smoothly.
Scrum Master
Conversational AI teams iterate quickly, and the Scrum Master plays a key role in keeping development efficient. They ensure smooth workflows, align teams on delivering value, and remove blockers. By fostering collaboration across practitioners, developers, and product managers, they drive continuous improvement and help teams stay focused on their goals.
For questions about conversational AI teams, visit the Rasa Forum or contact your Customer Success Manager.