How to Build an Efficient AI-backed Team

3 min read

Industrial and technological challenges of the present day require the knowledge of AI and ML to stay competitive. As Artificial Intelligence gains an increasing role in IT projects, building a solid team with AI expertise is becoming an intensive need for many. The ins and outs of this process are deeply familiar to us as a software development company. 

How to Pack an AI-backed Team?

For clarity of terms, an AI-backed team works on an IT project that massively involves AI and ML technologies.      

Building an in-house team of AI talents goes together with integrating data analysis and AI culture into the organization’s culture. From our experience, the creation of the AI-backed team must be preceded by AI-driven automation of internal processes and best development practices. The AI-focused environment implies the work with specific data sources, AI pipelines, and frameworks. This technical background shapes the mindset of developers and focuses them on finding data-based solutions. They also learn to combine AI tech-savviness with understanding the business challenges of AI-powered projects. Altogether it proves the critical role of the elevated in-house AI culture in building a professional AI team. 

Now that we established the importance of existing AI practices that determine the successful building of the AI-backed team. The next step will be to examine how this team is staffed, managed and how we measure its efficiency. 

Assembling teams with Artificial Intelligence expertise relies not only on specific roles but also on skills, practices, and qualifications. It’s expected that an AI-backed team will have prowess with the following set of skills:

  • Linear algebra, probability, and statistics
  • Programming
  • Algorithms and frameworks
  • Big data technologies. Data-specific AI expertise. 
  • Technical product management
  • Problem-solving and communication skills.

 

It’s a sheer challenge to staff an AI team from the ground up, so by developing AI practices in the company, you ensure the right talents will be hired with the help of current employees trained in AI and ML. By engaging AI and ML talents at the MVP building stages, you lay the foundation for further scaling of AI projects. It could be a strategy for start-ups to raise funds and hire more talent as the product grows. 

Profiles in the AI-backed team

Teams with an AI focus resemble a pool with various skills and expertise rather than an established structural unit of the company with a standard set of roles. Roles are constantly evolving to match the needs of existing AI projects and technological challenges. 

Further, we’ll describe specific roles in such a team and help you understand the team structure.

image3 development

AI-backed Team

 

Managing the AI-back team and measuring its performance 

The team coordination framework for AI projects must ensure the work is agile. So the Data-Driven Scrum framework is practical as it streamlines the processes with a focus on agility. Short iterations are followed by brainstorming related to the next steps and plans. The results of each iteration must be clear to all team members.

Another principle of the AI-backed team’s management is the gradual scaling of the solution. It is incrementally scaled instead of being deployed as a full-scale product. Data and ML engineers should closely interact with the DevOps team and ensure the code is delivered step-by-step. 

The team’s performance is assessed through analysis of its technological processes, code and documentation quality, and the quality of interaction between the team members. Other important operational KPIs entail cycle time, deployment frequency, code review metrics, developer productivity, and customer satisfaction. Some examples of operational metrics followed by us:
– Velocity/month or sprint means the volume of work produced per a specific period. In development terms, this is the number of story points. An AI-backed team exhibits 3 – 5 times greater agility than a regular team of developers. 

– Bug time to fix indicates the number of days it takes for the team to fix 50% of the bugs. Similar indicators find the time required to fix high-priority bugs during a month or the average time a team takes to fix those bugs. 

 

Infrastructure

AI technology is data-intensive and resource-consuming. You will work with tremendous volumes of unstructured data. Before you staff your project or agency with relevant specialists, ensure data-processing resources are available. It also includes managing a database. Most importantly, the hardware capacities must be enough to cover the intense needs of AI projects. 

 

Approach to staff training 

JetRuby runs extensive training programs and career enhancement within its Academy. We create learning activities through mentoring, coaching programs, and team-building activities. Upskilling existing employees and training junior staff have always been distinctive features of our approach.

Retention of specialists has been another critical factor in our team management strategy. Exciting projects and challenges prove to be an engaging and competitive process. It’s also through the diversity of projects to work on specialists are encouraged to contribute their skills and knowledge.

 

Bottom Line: Cost-efficiency of an AI-backed Team

The teams of developers focusing on AI tools are more successful in achieving cost-efficiency than regular teams. It happens due to the extensive use of automation tools that frees developers from repetitive tasks and enables them to dedicate their effort to more creative parts of work. The main advantage of this approach is the lower price of development. 

We hope our article has provided insight into how an AI-backed team builds and operates. Our following review will introduce you to the unique combination of AI-powered technologies used by our team to speed up the development process without sacrificing its high quality. 

 

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