How much does it cost to build an artificial intelligence (AI) team?

First published on November 8, 2021

Last updated at February 23, 2022


4 minute read

John Patrick Hinek



For many organizations, the future of business means AI. Onboarding talent who is capable of collecting data via machine learning and artificial intelligence is essential for keeping up to date and relevant. We will break down how much some businesses can expect to pay when bringing on AI talent.


  • Intro

  • Project goal

  • Onboarding talent

  • Data

  • Closing thoughts


In an age where data has become its own form of business currency, artificial intelligence (AI) and machine learning (ML) tools have become essential for nearly all businesses. For many businesses, to organize and understand your company’s data to its fullest potential, an AI team must be deployed. According to a 2020

LinkedIn emerging careers report

, demand for AI related jobs has risen 74% annually. Cost considerations when launching an AI team can vary greatly based on a number of factors.

Project goal

AI is an umbrella term which encapsulates a great deal of tools and business solutions. While it’s easy to get caught up in the allure of launching an AI team, it’s essential your team is built on people who have the capacity to reflect the goals and needs of your business. Smaller businesses may not need a full-stack AI team, while larger companies will need to onboard a wide range of roles to cover all operational needs. Strategizing specific problem areas your team hopes to solve will make calculating and predicting total cost much more accurate.

Onboarding talent

With any hiring process, bringing on an AI team will require a great diversity of roles. It is extremely important that AI team members be team players in collaborating across all members of the organization. Here are a few positions which are often essential for AI teams:

Machine learning engineers are responsible for researching, building, and deploying artificial intelligence algorithms and systems across a business platform.

According to an


report in 2021, the average base salary for machine learning engineers in the US was $141,023/year, with the role at high performing companies reaching upwards of $400,000/year.

A senior data scientist is an expert in data analysis, developing machine learning models to pull data, and able to pull important data to solve business problems. Senior members of this role are responsible for interacting with other members of the business to get the most impact out of machine learning models.


, a 2021 report estimates that senior data scientists make an average base salary of $138,031 in the US. Careers at top companies for data scientists show a base salary consistently above $200,000.

Big data engineers are responsible for designing, developing, implementing, and constantly maintaining software systems across an organization. This could be in the form of projects such as taking data from one database, transforming it, and adding it to another.

Big data engineers can expect to make an average base salary of $118,853/year in the US according to a 2021


report. Top-performing companies can pay upwards of $250,000 for big data engineers.

An AI product manager uses all aspects of AI to enhance existing products and shape new ones. As so many companies are starting to integrate AI into their systems, having a product manager who can seamlessly do so is essential.

According to a 2021


report, product managers make an average base salary of $96,011 with high performing companies paying well into the high $100,000s.

For most organizations, an AI team is necessary to accomplish business needs. However, there are a number of emerging AI SaaS tools which are developer friendly to those without AI experience. These tools can be used for a number of business solutions: data organization, data analysis, deep learning, etc. Compared to the cost of hiring and onboarding an AI developer, these tools have the potential to be extremely cost and time effective.


AI is fueled by the data given to its algorithms. Data comes in many forms, and how that data is organized can have a great impact on how expensive initial costs an AI team can charge. Data that is structured and well organized makes it easy for AI teams to hit the ground running, as little work is required for data organization. Businesses with unstructured data may need to pay extra to have their data scraped, organized, and structured correctly for data processing.

Closing thoughts

Curating an in-house AI team can have great financial benefits to a business. With the direction of industry pushing quickly towards AI, businesses need to consider the risk of being left behind by not implementing AI teams. Future solutions like the rise of AI SaaS tools mentioned above have the potential to benefit businesses who don’t have the finances to pay for a full AI team. Until then, AI careers will continue to be in extremely high demand and offer great investments to businesses.

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