How to Successfully Build an AI Team for Your Organization

“In many organizations, data scientists are still wearing too many hats due to a dearth of talent across other roles. AI is a team sport. On their AI team, CIOs and technology innovation leaders need to have data scientists, data engineers and complement the team with AI architects and machine learning (ML) engineers. Together they can envision, build, deploy and operationalize an end-to-end ML/AI pipeline.”

Arun Chandrasekaran, Distinguished VP Analyst, Gartner, at virtual Gartner IT Symposium/Xpo® 2020

Indeed, to effectively implement and scale AI projects, organizations must establish a well-rounded AI team capable of customizing tools and adapting them to the company’s unique needs. This team should not work in isolation but must collaborate closely with business experts, IT professionals, and other key stakeholders to drive meaningful outcomes from AI.

In this article, we’ll lend you a helping hand in learning about how to build an AI team, the key specialists involved, the significant benefits such a department can bring to your company, and much more.

What Is an AI Team?

An AI team is a diverse group of experts within a company who work together to design, implement, and oversee artificial intelligence solutions that bring value to the organization. Their collective goal is to realize the tremendous benefits of AI, whether it’s improving efficiency, automating routine tasks, boosting customer experiences, or extracting meaningful insights from data.

Effective teamwork and leveraging each member’s unique expertise are the key to a successful AI team. Once bringing together specialists from business intelligence, machine learning, natural language processing, deep learning, and generative AI, the team can address challenges and deliver solutions that support the organization’s strategic goals.

Typically, an AI team includes project managers, data scientists, AI engineers, and machine learning experts. Each member plays a crucial role in the process, from preparing data and developing and training AI models to deploying solutions and ensuring their long-term success.

Why Do You Need an AI Team?

Let’s face it: artificial intelligence is changing the game. If you want to stay ahead of the curve, having an AI team is no longer a luxury – it’s a necessity.

Not convincing enough? Here are five compelling reasons why you should form an AI team today.

Ethical AI and Risk Management

AI is powerful, but it comes with its own set of challenges. Bias, privacy concerns, and unintended consequences are all real risks. That’s where a dedicated AI team comes in. They’re experts in ethical AI, risk assessment, and responsible AI development. They’ll ensure your AI projects are built on a strong foundation of ethical principles and minimize potential risks.

Collaboration and Engagement

An AI team can supercharge collaboration and employee engagement. How? Automating repetitive tasks to free up your employees’ time for more creative and fulfilling work. They can also help identify opportunities for automation, making work more efficient and less stressful.

In fact, research from MIT Sloan shows AI can boost business productivity by as much as 40%. This means your employees can focus on higher-value tasks, leading to greater job satisfaction and overall improved performance.

Efficiency and Scalability

The world of AI is moving fast. A dedicated AI team can ensure your organization keeps pace by streamlining processes, allocating resources efficiently, and delivering solutions quickly.

While building an in-house AI team might seem costly upfront, it pays off in the long run. You’ll have direct control over projects, enjoy faster response times, and benefit from direct communication, resulting in significant time and cost savings.

Data-Driven Decision-Making for Better Outcomes

AI teams are masters of data. They can analyze huge amounts of information, uncovering patterns and trends that humans might miss. This data-driven approach helps your organization make more informed and accurate decisions, reducing bias and leading to stronger business outcomes.

Also read: How Is AI Used in Decision-Making Processes?

Continuous Learning and Improvement

AI is constantly developing, with new tools and models emerging. An AI team acts as your organization’s AI champion, staying up-to-date on these advancements and sharing their knowledge with the rest of your workforce. They’ll attend workshops and training, continuously upskilling themselves and keeping your company competitive.

When Does Your Business Need an AI Team?

An AI team can be a powerful asset. But when is the right time to build one?

Check out 5 common scenarios below where an AI team could be just what your company needs:

• Building an AI-Driven Culture

If your organization is already using AI or is in the early stages of adoption, establishing an AI team can demonstrate commitment to this technology. This team promotes AI usage, enhances employee skills, and collaborates with different departments to identify practical AI applications, fostering a culture centered around AI.

• Preparing for AI Adoption

To successfully develop AI software, your organization must have quality data and the right infrastructure in place. If these elements are lacking, an AI team can help establish the necessary frameworks and ensure a smooth transition to AI technologies.

• Automating and Optimizing Processes

AI can significantly boost efficiency by automating repetitive tasks and refining decision-making. An AI team can create tailored solutions – like genAI chatbots, predictive analytics, and robotic process automation (RPA) – that allow employees to concentrate on more complex, value-added responsibilities.

• Gaining a Competitive Advantage

With increasing AI adoption across industries, an AI team can keep your organization ahead of competitors. Continuously exploring new AI opportunities and leveraging advanced technologies will help secure a strategic edge, as noted by 84% of global executives who believe AI will enhance their competitiveness.

• Effectively Managing AI Projects

AI implementation requires careful management. A dedicated AI team provides centralized oversight to ensure all AI activities align with organizational goals, efficiently manage projects, and mitigate risks related to AI deployment.

10 Important Roles to Structure Your AI Team

The roles within an AI team will vary depending on the company’s needs, projects, and available resources. Not all teams will require every role, and some functions might be combined or outsourced. Below are 10 possible roles divided into core technical and supportive/strategic categories:

Core Technical Roles

• AI engineer: Responsible for building and maintaining AI software using frameworks like TensorFlow, PyTorch, and Azure ML. They focus on coding, testing, debugging, and deploying models while collaborating with other engineers and data scientists to integrate AI solutions with existing technologies.

• Machine learning engineer: Develop, fine-tune, and implement machine learning models, focusing on effective integration into software systems while optimizing them for scalability.

• Data scientist: Analyze and interpret data to identify patterns and insights that inform AI models. They define the business problems that AI will address and use methods like regression, classification, and clustering to train and refine models.

• Data engineer: Handle the collection, cleaning, and structuring of data that fuels AI systems. They manage data pipelines and databases, leveraging tools such as SQL, Hadoop, or Spark to maintain data quality, accessibility, and security.

• DevOps engineer: Facilitate the smooth deployment and operational support of AI systems. They collaborate with developers, data teams, and architects to implement reliable and manageable solutions in production environments.

• Research scientist: Focus on innovation to develop new AI models and approaches. They experiment with novel algorithms, analyze results, and push the boundaries of AI applications, helping keep the company competitive.

Supportive/Strategic Roles

• AI ethicist and legal advisor: These professionals guide AI projects to align with ethical standards and legal requirements, addressing fairness, privacy, transparency, etc. Their work plays an important role in minimizing risks and maintaining responsible AI practices, especially as ethical challenges grow more prominent.

• Project manager/ AI strategist: Project managers or AI strategists manage the entire AI project lifecycle, from planning to execution. They define project objectives, allocate resources, manage timelines, and coordinate between technical and business teams to keep projects on track.
• Domain expert: Domain experts bring specialized deep industry knowledge and work with AI teams to shape project goals and validate solutions. Their insights help tailor AI applications to meet the specific challenges of the organization’s sector.

• Business analyst: Acting as a liaison between business units and technical teams, business analysts interpret AI-generated insights and translate them into actionable strategies. They align AI projects with broader business objectives, such as optimizing customer engagement or improving operational efficiency.

Step-by-step Guide to Building an Effective AI Team

Now that you know the roles needed for a successful AI team, it’s time to discover how to actually form one.

Here’s a step-by-step guide to help you get started:

Step 1. Define AI Objectives

Before assembling your AI team, start by clearly identifying your organization’s goals for implementing AI.

Determine the specific problems you want to solve, the outcomes you expect, and the skills needed to meet these targets. By aligning the team’s work with your business goals, you’ll set a solid foundation for success.

Step 2. Identify Key Roles and Responsibilities

Once the goals are set, define the necessary roles and responsibilities within your AI team. Identify the skills and qualifications needed for each position and map out how these roles will work together.

Kindly refer to the potential roles mentioned earlier to get a clearer picture of the team structure.

Step 3. Decide Between In-House, Outsourcing, or Hybrid Models

You now need to decide how to build your perfect AI team. There are three primary approaches:

• In-house team: Building an internal AI department allows for greater control and involvement in the processes. You will manage both the projects and the team’s growth. However, the recruitment and maintenance cycle will take a lot of time and effort.

• Outsourcing: If cost efficiency is a priority, outsourcing specialists, especially a remote AI team, is a great option. While the external team manages the technical aspects, the hiring company handles HR matters. It’s highly recommended that you seek assistance from an AI consulting service to get invaluable advice and support to get your business up and running. They will help identify areas where your business really needs AI and develop AI solutions while optimizing cost and resources.
• Hybrid: Combining in-house and third-party experts enables you to maintain internal control while leveraging external expertise when needed.

Step 4. Recruit Top Talent

If you opt for an in-house team, focus on hiring the best talent. Look for candidates with strong technical skills, hands-on experience, and a passion for AI fields such as machine learning, natural language processing, and deep learning. Desirable skills include proficiency in programming languages like Python and R, knowledge of AI frameworks like TensorFlow or PyTorch, and a solid understanding of algorithms and statistical modeling.

Alternatively, if you want to streamline this process, you can consider staff augmentation services, where experts handle recruitment, saving you enormous time and effort.

Learn more: The comprehensive guide on hiring AI developers

Step 5. Support and Manage the Team

Once your team is in place, it’s important to nurture their development and keep them motivated. Offer training opportunities, encourage collaboration, and maintain a positive work environment.

By investing in your team’s development, you’ll create a stable, productive environment where AI initiatives adhere to predefined goals, projects stay on track, and employees remain engaged.

Assemble a Winning AI Team for Your Organization with Neurond

In conclusion, forming an AI team is essential for effectively navigating the world of innovative technologies. Our article has provided a thorough guide on the importance of AI teams, when your organization may require one, and the necessary steps for effectively assembling such a team.

If you’re inspired to build a full-fledged AI team now but need additional guidance or support, Neurond is here to assist you throughout your AI adoption journey. We not only provide expert AI consulting services from the initial planning stages but also top-notch AI development solutions tailored to your business’s specific requirements.

Ready to build your robust AI team? Contact us now!

FAQs

1. How much does it cost to build an AI team?

Building an AI team can cost anywhere from $500,000 to over $1 million annually for a small team (3-5 professionals), depending on factors like:

• Team roles: Data scientists, ML engineers, and AI developers.
• Salaries (US, 2024): Data Scientist: $127K-$206K/yr, ML Engineer: $177K-$271K/yr, AI Developer: $116K-$169K/yr.
• Location: Costs are higher in tech hubs like Silicon Valley or New York.
• Additional expenses: Ongoing training, recruitment, and retention costs.

Larger AI teams can cost significantly more, reaching millions annually.

2. Can small businesses afford to build an AI team?

3. What’s the difference between hiring full-time AI professionals vs. outsourcing? 

Hiring full-time AI professionals and outsourcing differ in many ways, representing their pros and cons:

Full-time hiring:

• Pros: Greater control over projects, knowledge retention, better integration with company culture
• Cons: Higher costs, less flexibility, longer ramp-up time

Outsourcing:

• Pros: Lower overall cost, access to specialized expertise, more flexibility
• Cons: Less control, potential data security risks, possible loss of knowledge after project completion

The choice depends on various factors such as the company’s size, budget, long-term AI strategy, project complexity, and desired level of control. Many companies opt for a hybrid approach, maintaining a small in-house AI team while outsourcing specific tasks or supplementing with external expertise as needed.