Building Product-Focused AI Teams

LLMs can significantly improve products and internal tools. However, many organizations are overwhelmed by the rapid growth of generative AI and the operational changes it requires. The biggest challenge is successfully putting together the right team – the AI team – to build and deploy mission-critical applications. In our experience, the recipe for success is

• Understanding the nature of an AI team and its unique composition, skill set, and role in the wider organization.
• Understanding the importance of a good team lead.

Read this blog post for a rundown of the most important points. For more details, check out our white paper on "Leading a successful AI team."

The Human Factor in AI Development

Building with AI means bridging the gaps between data science, software development, user needs, and business requirements. This challenge is more human than technical. As organizations increasingly adopt AI technologies, the need for cross-functional, product-focused teams is becoming more and more apparent. However, there is often a lack of clarity about the characteristics and leadership styles required for these teams to succeed.

What Is an AI Team?

An AI team is a diverse group collaborating to create AI-powered products or tools. This dynamic team typically includes developers, data scientists, AI/ML engineers, and non-technical domain experts. The team leader plays a crucial role in the project's success, combining vision with practical skills.

The AI Team Leader

The AI team leader is critical to managing the team and the process, driving rapid iteration and user centricity. They need to:

• Communicate effectively with technical and non-technical stakeholders.
• Set and maintain product vision.
• Make critical decisions.
• Facilitate communication between team roles.

The AI Team's Skill Set

Coming together to build products that serve end users well requires universal skills, such as a strong ability to communicate with people from other departments and different levels of technical expertise. Not everyone has these skills, so it is the responsibility of the team leader to facilitate communication across roles. They are also responsible for maintaining the product vision and making critical decisions. Here are the other top 7 skills that are critical to the success of an AI project:

Use Case Orientation

Your product must solve real user problems. You need to understand user behaviors, needs, and what generative AI can realistically do. For instance, an AI team developing a customer service chatbot needs to know common customer inquiries, pain points, and how to make conversations feel natural to create a truly helpful tool.

Iterative Product Development

Regularly testing and refining prototypes based on feedback ensures that the product meets user needs and expectations.

LLM Expertise

Teams must understand how LLMs work, their differences, and how to combine them effectively. They must also monitor and evaluate models continuously.

Subject Matter Expertise

Involving domain experts throughout the product development process ensures that the AI tools meet the specific needs of professionals like lawyers, doctors, or accountants.

UI/UX Design

Considering how the AI product fits into user workflows and ensuring it is easy to use and integrate with existing systems is critical.

Full-Stack AI Development

This involves understanding database management, cloud computing, and cybersecurity to handle data efficiently, securely, and ensure the robustness of AI applications.

Prioritize Skills Over Roles

The focus should be on skills rather than specific roles. Product-focused, learning-oriented individuals who work well in teams are more valuable than specific job titles. This makes the team more flexible and innovative. For example, a team member with a background in psychology might bring invaluable insights into user behavior, even without traditional tech credentials. By prioritizing skills, you create a more adaptable team that can tackle diverse challenges in the rapidly evolving AI landscape.

What Makes AI Teams Successful

To achieve success, AI teams must concentrate on developing products that set them apart from competitors. They need to stay focused on their core mission, avoiding distractions such as model training, system deployment, or production monitoring – tasks now categorized as the 'undifferentiated heavy lifting' of AI.

Because AI teams combine diverse talents from individuals used to different tools and terminologies, it's important to help them communicate and share information effectively.

Effective AI teams need tools that streamline repetitive tasks and that allow them to collaborate on an AI product with different levels of technical load. Enterprise AI production platforms help design and test AI pipelines, optimize models, build continuous delivery infrastructure, and monitor applications in production.

deepset Cloud: The AI Team's Development Platform

Our LLM platform deepset Cloud offers a comprehensive approach to AI development, from prototype to production. It is an AI development platform designed and built with the AI team's unique requirements in mind. The advantages of deepset Cloud at a glance:

• Iterate on your prototype in rapid development cycles and get a production-ready AI application to market faster.
• Keep your team lean: significantly reduce costs associated with full-time AI consultants and LLM Ops (data integration/system build). While exact savings can vary, organizations have reported cost reductions of up to 30-40% by streamlining these processes with deepset Cloud.
• Be confident in the quality of your product thanks to market-leading gen AI observability tools and transparent tracking of LLM groundedness, hallucinations, and many other metrics.

Taking the Next Step

Building a successful AI team requires a diverse skill set, effective collaboration, and the right tools. To get started:

1. Assess your current talent and identify skill gaps.
2. Invest in training programs to upskill existing team members.
3. Recruit strategically to fill critical roles, focusing on skills over traditional job titles.
4. Implement collaborative tools and platforms like deepset Cloud to streamline your AI development process.
5. Promote a culture of continuous learning and adaptation to keep pace with rapid AI advancements.

By understanding and implementing these strategies, your organization can build AI applications that are not only innovative but also aligned with user needs and business goals. The journey to AI excellence starts with the right team – are you ready to assemble yours?

To learn more about what an AI team actually looks like, download our free white paper, Leading a Successful AI Team.