AI Analyst Job Description and Salary

Data visualization, predictive analytics, and artificial intelligence (AI), including generative AI, are reshaping how analysts work. Today, AI tools allow professionals to process massive datasets and uncover insights in minutes rather than weeks. 

This shift illustrates how integrating AI applications into everyday roles is changing the way people work and creating measurable gains in their productivity. For example, across the workforce, employees using generative AI use about 5.4% fewer hours to do their work in a week, according to the Federal Reserve Bank of St. Louis.

For those seeking to advance in their data analytics career and remain competitive in a business environment increasingly reliant on AI, earning an advanced degree, such as a master’s in applied artificial intelligence, can make a measurable difference. Programs for these types of degrees help individuals build the critical skills AI analysts need to solve real-world problems, lead transformation, and drive innovation across industries.

What Is an AI Analyst?

AI analysts—also called AI data analysts—use tools such as large language models (LLMs), natural language processing (NLP), machine learning, neural networks, and deep learning to analyze complex datasets and derive meaningful insights from the data that help organizations make informed decisions.

What Does an AI Analyst Do?

An AI analyst or AI data analyst with a data analytics background might combine traditional analytics techniques, such as manual data processing and basic statistics, with advanced algorithms to quickly process information. Since AI tools allow for faster analysis of large datasets, insights derived from the data can be used to make real-time predictions more quickly than when using traditional data analytics processes.

Because AI is applied differently across industries, AI analysts’ specific responsibilities vary. However, their common tasks include:

• Connecting business knowledge with AI capabilities—for example, by meeting with operations teams to define key performance indicators (KPIs) that an AI forecasting model will track
• Turning raw data into AI-ready information—for example, by transforming social media feedback into structured data that AI can analyze to discover trends, customer sentiment, and needed service improvements
• Converting business processes into data workflows that AI systems can use—for example, by using global shipping and inventory data to build an AI agent that optimizes supply chain operations
• Defining key questions and converting AI results into useful insights—for example, by identifying the factors driving customer churn and presenting AI findings in a way that supports business decisions

AI analysts typically work in analytics departments within organizations that are adopting AI in their daily operations. They are employed in various industries where data drives growth—such as finance, technology, healthcare, retail, and manufacturing.

How to Become an AI Analyst

AI analysts blend technical skills with business knowledge to make AI results meaningful. Success in the role is not just about having expertise in computer or data science; understanding how the business operates is also crucial. 

Every individual’s path to becoming an AI analyst or AI data analyst is different, but there are common requirements related to education and experience that they all need to meet.

Educational Requirements

AI analyst roles generally fall within the broader field of data science. Most positions require at least a bachelor’s degree in mathematics, statistics, computer science, or a related discipline. Some employers prefer or require candidates to have an advanced degree—such as a master’s or doctorate—particularly for roles involving complex modeling or research.

As organizations look to integrate AI into their core business operations, graduate programs in applied artificial intelligence can offer aspiring AI professionals a curriculum that covers an ideal combination of technical and strategic decision-making skills. These programs teach professionals how AI systems work and how to apply them to improve efficiency, innovation, and business performance in any organization.

Experience Requirements

AI analysts typically have some level of experience in programming, statistics, or data analysis—or all three. As these roles often involve working with traditional data analytics tools as well as AI tools, individuals with proficiency in languages such as Python or R, combined with knowledge of data visualization tools like Excel or Tableau, are ideally suited to fill them.

Many AI analysts begin their careers as interns or junior analysts, focusing on tasks like cleaning data, performing statistical analyses, or generating reports. Through hands-on experience, they sharpen their technical and analytical skills while keeping up with AI and machine learning trends. 

As these professionals gain more experience, they may take on more complex responsibilities, including: 

• Developing and maintaining machine learning models
• Preprocessing large datasets
• Optimizing algorithms for performance and accuracy

A key part of an AI analyst’s success is the ability to collaborate with both business and technical teams, making strong interpersonal and communication skills essential as well.

AI Analyst Salary and Job Growth Projections

Because the AI analyst role is a relatively new one, its job outlook and salary must be inferred from that of related roles such as data scientist and management analyst.

According to the U.S. Bureau of Labor Statistics (BLS), employment of data scientists is expected to grow 34% from 2024 to 2034, much faster than the average for all occupations. Employment of management analysts also is expected to grow at a higher-than-average rate, 9%, during the same period.

In terms of the AI analyst salary, as of May 2024, the BLS reports that data scientists had a median annual salary of $112,590, while for management analysts it was $101,190. 

Across industries, professionals with AI-related skills tend to earn more than their peers without these skills. PwC reports that workers with AI expertise earn a 56% wage premium, up from 25% the previous year, reflecting strong and growing demand for professionals who can integrate AI into business operations.

Become a Leader in Applied AI

The path to becoming an AI analyst is unique to each person. Some professionals already working in data analytics choose to leverage their business skills to move into AI-driven roles, which demands preparation and skill building in AI-related subjects. Others need to change careers entirely, which requires pursuing additional study and training. Individuals not yet in the workforce who want to enter this growing field also need training.

Bryant University’s Master of Science in Applied Artificial Intelligence program is a flexible, fully online program that can be completed in 12 to 24 months. Through core courses and a choice of a specialized track in either business analytics or health informatics, students develop advanced expertise in AI applications, data management, and ethical AI practices. 

For those not ready to commit to a full master’s program, Bryant University also offers an online Graduate Certificate in Applied AI program, which provides students with foundational AI knowledge and practical skills that are valuable across industries.

By enrolling in one of Bryant’s programs, you can set yourself up for success in the rapidly evolving field of AI analytics and help your organization translate data into strategy and innovation.