A Simple Guide to Building an Ideal AI Team Structure in 2024

Find out the ideal AI team structure for your business by understanding the concepts, roles, responsibilities, general approaches, etc.

AI team structure

AI is the most popular buzzword these days. Everyone, from tech enthusiasts to corporate leaders, is talking about it. And we can all agree that these AI technologies are no longer the future; they are the present.

As a startup owner, investing in an AI team is a strategic move that can ensure you stay ahead of the curve. In this guide, we will walk you through how you can assemble your AI team structure to get complete leverage. 

Let’s start from the basics. 

What is AI and Why Does It Matter?

According to Wikipedia, Artificial intelligence (AI), in its broadest sense, refers to the intelligence displayed by machines, especially computer systems.

Don’t stress over all the technical jargon!!

Simply, Artificial intelligence (AI) is the field of computer science that designs machines, especially computers, that can think and act like people. 

It can do things that are considered “smart,” such as learning from experiences, analyzing information, recognizing patterns, making decisions, and solving problems.

Amazing, isn’t it? The hype of AI is now making sense.   

Recently, AI has delivered on some of the field’s long-held promises that people can see and interact with, leading to rapid growth in the AI software market in the future.

A report from Forbes shows that by 2027, the AI software market is about to reach 407 billion U.S. dollars. So, as a tech startup founder, having an AI team can be a game-changer for you and your startup. Here is why

  • Explore new opportunities, develop unique algorithms, and create products or services competitors may not have.
  • Generate insights, optimize decisions, and personalize user experiences with the startup’s data.
  • Automate tasks, optimize allocation and fuels strategic focus for a lean, efficient operation.
  • Boost customer satisfaction and retention with AI, personalize recommendations, and automate support with chatbots.
  • Migrate risks by analyzing and pinpointing issues early for proactive solutions and informed choices.
  • Build scalable solutions that adapt to rapid growth and evolve as business needs.
  • Open doors to funding, partnerships, and market dominance as AI attracts investors nowadays. 

What Does an AI Team Do?

From the above discussion, you already know why your startup needs an AI team. Now, let’s move on to what they will do for you. 

Identifies Needs

Your AI team will be responsible for understanding the needs and challenges and collaborating with stakeholders and other teams to find an AI solution.

Data Management

Providing meaningful insights and actionable recommendations by analyzing data is one of the most significant responsibilities of your AI team. For this, they need to identify and collect relevant data, ensure its quality and security, and develop and maintain databases and data warehouses.

Algorithm Development

As your AI team, they will handle the entire lifecycle of the algorithm, from development, design, and implementation, to optimizing algorithms to solve specific problems. 

Model Building and Training

Another important responsibility is developing, training, and fine-tuning machine learning models using appropriate datasets for predictive analysis, classification, clustering, etc. 

Integration and Monitoring

Integrating AI models and algorithms into the targeted systems and workflows is your AI team’s responsibility. Then, deploying AI solutions to production environments. After that, they will also ensure seamless operation and monitor performance.

Automation:

AI team automation tasks involve streamlining and enhancing various processes within your team or organization. Automation can be applied in task management, project and data management, communication, etc.  

Research

Your AI team will be responsible for researching and exploring new AI applications and staying updated with recent AI and machine learning advancements so that they can experiment with new techniques and technologies.

Finding the Right Technical Skills

So far, you have understood what AI is, why you need an AI team, and what an AI team will do for your startup. Now, it’s time for you to understand who will be on your team.

However, before you go on to team building, there’s something that startup founders like you should know that gets overlooked a lot: Find the right technology set while hiring. 

Most companies require a four-year degree in math, data science, statistics, or computer science for entry-level AI positions. Advanced degrees, such as master’s and doctorate in computer or cognitive science, are increasingly common and desired for specialized roles.

Even so, acquiring the necessary AI skills takes years of training, and becoming proficient in AI cannot be achieved in just a few months. Therefore, you and your team must rely on more than training to overcome the talent gap.

Make sure that, in addition to educational degrees, the members you are hiring have the following skills and knowledge.

  • Programming
  • Linear algebra, probability, and statistics
  • Big data technologies
  • Algorithms and frameworks
  • Communication
  • Problem-solving

The Common Roles in an AI Team

With these details in mind, you can start looking for your AI team member. Here are our offerings. We have identified the common roles in an AI team and listed them here for your convenience. Riccardo Ocleppo, the founder and CEO of the Open Institute of Technology (OPIT), seems to agree.

” The Core Roles in an AI Team Often Constitute an AI Architect, Data Scientist, Data Engineer, and AI Ethicist, Each Playing a Different Yet Crucial Role “

Riccardo Ocleppo,
Founder and CEO @ OPIT

AI Product Manager

Your AI product manager is the bridge between AI tech and your startup’s business objectives. They wear many hats, but their primary responsibility is to ensure the success of your AI product.

They oversee the whole process from the development and deployment of AI products. They also collaborate across various teams, ensuring every team is on the same page. They understand the market and competitors, research, and measure success through key metrics.

Machine Learning Engineer

The one who takes complex machine learning models and turns them into practical applications is your Machine Learning Engineer. They are critical members of the data team.

Their responsibilities include designing, implementing, and optimizing the machine learning algorithms. They also build scalable and efficient AI pipelines. The job doesn’t end there, though, as MLEs also monitor these systems, troubleshoot any problems, and keep them up-to-date.

Data Scientist

Your data scientist is one who converts raw data into actionable insights. They use their statistics, programming, and machine learning knowledge to uncover hidden patterns and trends that the AI models aim to solve.

With the data, they build and train machine learning models to extract valuable insights and even make predictions. They also create visual representations of data findings to communicate insights clearly to stakeholders.

Data Engineer

The one who designs, builds, and maintains data pipelines is your Data Engineer. This infrastructure allows data scientists and other users to access and analyze information.  

They also work on data storage and security by choosing the right technologies. They also ensure the data quality, accuracy, consistency, and scalability.

Another CEO, Eliot Vancil, shared his perspective on the responsibilities of different roles of the AI team.

“To make a good AI team, you need a lot of different kinds of experts. Data Scientists and Data Engineers are important roles, according to my experience. Data Scientists analyze and model data and Data Engineers build and manage data infrastructure. AI/ML engineers create and use AI models, while data analysts provide insights and make charts and graphs.”

Eliot Vancil
CEO @ Fuel Logic LLC

AI-Engineer

Your AI engineer is the one who turns AI concepts into reality. They incorporate data science, software engineering, and DevOps into the AI project. 

They are also responsible for designing, programming, and training AI’s complicated algorithms to work like a brain. They monitor the model’s performance post-deployment, identifying and fixing any issues that may arise.

AI-Researcher

Your AI researcher is an expert in the field of artificial intelligence. They constantly explore new methodologies, algorithms, and techniques to advance the field of AI. 

They create and perform experiments to test new ideas and assess different techniques. Then, analyze the results to refine theories and make progress.

AI Architect

Your AI architect is the designer of your organization’s AI journey. They define the overall vision and roadmap for AI and, with their expertise, design AI system architecture that connects seamlessly with IT infrastructure.

They also focus on using AI technology to change business processes and promote innovation in a way that is both successful and moral.

AI Ethicists

The one who has the moral responsibilities is your AI ethicist. They ensure that AI is developed and used ethically, responsibly, and moderately.

They integrate ethics into AI models and algorithms and promote responsible AI. AI Ethicists work to identify and mitigate bias in AI systems.

You can also include other additional roles, such as data analyst, domain expert, AI UX/UI designer, computer vision engineer, deep learning engineer, software engineer, DevOps engineer, data scientist master, data architect, etc., in your team when necessary. 

How to Build a Perfect AI Team Structure?

Once you have figured out the necessary roles, it’s time for you to structure your team, which depends on your organization’s size, complexity, location, and go-to-market strategy. 

However, a perfect one-size-fits-all team structure doesn’t exist. Therefore, I will share a common structure of a typical starter team to help you tailor it to your organization’s needs.

Flat Organization Structure

As a startup, your initial team can follow a flat AI organizational structure. Your product manager oversees the whole team, and your machine learning engineer (MLE), data scientist, data engineer, AI engineer, AI researcher, and AI ethicist report to him/her.

Functional Organization Structure

Once your team grows, you can move to a functional AI organization Structure, where you can get a functional manager, in this case, for the AI or Data department.  

In this AI structure, other team members(MLE, data scientist, data engineer, data analyst, AI engineer, AI researcher, AI architect, AI UX/UI Designer, etc.) report to the corresponding manager, and the managers report to the CTO.

Matrix Organization Structure

You can use this AI organization structure if you want to start working on multiple projects. Here, the team members will work on multiple projects at the same time. Both your project and functional managers will oversee the team members’ work.  And they will report back to your CTO. 

As I mentioned earlier, building an AI team structure needs planning and consideration of various factors. Location is one of them with a significant impact on the AI project’s cost, quality, and efficiency. The three most popular structures you can choose from are in-house, offshore, and hybrid. Here is a brief discussion of each approach.

In-house AI Team

An in-house AI team structure means your AI team will be fully dedicated to your project and collaborate within your office. 

This approach allows the company to build internal expertise while giving you a full control over your team, product quality, and flexibility in customization. 

However,  before deciding, you have to consider the cost factor. In-house development can be costly and time-consuming as it requires significant investment in staff and infrastructure.

Outsourcing or Offshore AI Team

Conversely, outsourcing your AI team can be a cost-effective way. You can save up to 60% by hiring a company or individual to create your AI product, especially for expert-level work. 

Outsourcing saves time, as the external team with an established AI organizational structure handles planning, coding, testing, and deployment. It also guarantees high-quality code and research and reduces risk.

However, this process isn’t completely easy or risk-free. It’s tough to lead a team that works from afar, and it’s also challenging to find the right partner for development.

Read Also:
A thorough comparison of In-house Development vs Outsourcing (Comparative Analysis)

Hybrid AI Team 

Hybrid teams combine a core in-house team with an outsourcing team. You can work with an outsourcing agency to do it. This approach offers special rates for development, free maintenance, and additional support if team members leave. For instance, Technext ensures availability and flexibility with backup developers for client projects.

Related Article:
A Detailed Case-study on Ed-Tech Platform with AI-powered Resume Builder

Learn more about this project’s full journey from our developer Shahriaz Kabir Polash in his blog “How We Created a Production Web App Utilizing OpenAI API

Tips to Structure AI Team

Last but not least, here are our extra tips to help you structure your AI team in 2024. 

  • Clearly articulate what your team aims to achieve with AI development.
  • Set specific time-bound (SMART) goals that are measurable, achievable, and relevant.
  • Foster a collaborative culture with cross-functional collaboration and regular meetings.
  • Invest in continuous learning  and development for your team 
  • Establish clear ethical guidelines for your AI development and deployment team
  • Design a flexible and scalable architecture to accommodate growing data.
  • Define key performance indicators (KPIs) to measure the success of your AI projects

Here is an extra tip from the founder of Kredite Schweiz, Michael Schmied

When we were forming our AI team, we decided to have separate teams for research, development, and maintenance phases. – Our research team dives into new AI tech and ideas—always pushing the limits. The development team then turns these innovations into real, practical tools that we can use. Finally, the maintenance team keeps everything running smoothly—fixing issues and optimizing performance”

Michael Schmied
Founder @ Kredite Schweiz

Get started with Your AI team 

I hope you enjoyed our guide to a successful AI team structure. You must remember that the appropriate team structure varies, and no perfect structure exists. Your structure will change as your startup grows. 

The secret is to start with a clear structure with room for growth. If you are still confused or want an expert opinion, consult an expert

Best of Luck!

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