AI Career Salaries & Pay: What AI Careers Really Pay

AI-related careers are often described as some of the highest-paying opportunities in technology. But salary figures can be surprisingly difficult to interpret.

A job title such as Machine Learning Engineer, AI Solutions Architect, or AI Product Manager may appear on thousands of job postings without corresponding to a single government occupational category. Salary estimates may also combine different experience levels, locations, industries, bonuses, stock compensation, and employer types.

That can make a simple question—“How much does this AI career pay?”—much harder to answer accurately.

This guide takes a more careful approach. Instead of presenting impressive salary numbers without context, we will look at reliable occupational benchmarks, explain what those numbers actually measure, and show you how to evaluate the earning potential of an AI-related career for yourself.

You will also see why two people working in similar AI-related roles can earn very different amounts—and why the highest-paying career is not necessarily the best career choice for you.

The goal is not to chase the largest salary number. The goal is to understand what a career may realistically offer and whether the time, skills, education, and career transition required to pursue it make sense for you.

How Much Do AI Careers Pay?

AI-related careers can offer strong earning potential, but there is no single salary that represents an “AI career.” AI work spans occupations including data science, software development, computer research, cybersecurity, systems analysis, technology architecture, and technology management.

The U.S. Bureau of Labor Statistics provides national wage information for established occupations that overlap with many AI-related careers. These figures give us useful benchmarks, but they should not be interpreted as guaranteed salaries for every job carrying an AI-related title.

The table below uses official occupational categories as reference points. The median wage represents the point at which half of workers in the occupation earned more and half earned less.

AI Career Salary Benchmarks

These benchmarks provide a starting point for comparing several career directions associated with AI. Some are direct occupational categories, while others are useful reference points for newer AI job titles that do not yet have their own government classification.

AI-Related Career Direction	BLS Occupational Benchmark	2025 Median Pay	2025–35 Growth
Data Science & Applied AI	Data Scientists	$120,230	35%
AI / ML Software Development	Software Developers	$135,980	10%*
AI Research	Computer & Information Research Scientists	$140,300	22%
AI & Cybersecurity	Information Security Analysts	$129,180	21%

The software development growth projection applies to the broader BLS occupational group that includes software developers, quality assurance analysts, and testers.

Source: U.S. Bureau of Labor Statistics, Occupational Outlook Handbook. Median pay figures are for May 2025; employment projections cover 2025–2035. Salary figures are national medians, not expected starting salaries or guaranteed earnings.

These occupations are benchmarks—not exact salary equivalents for every AI job title. For example, the Bureau of Labor Statistics does not publish a separate Occupational Outlook Handbook category for “Machine Learning Engineer.” Depending on the work involved, a machine learning position may overlap with software development, data science, computer research, or another occupation.

That distinction matters. A salary attached to a related occupation can help you understand the market, but it should not be presented as though every person with a similar AI job title earns that amount.

Why AI Salary Numbers Can Be Misleading

Salary figures can look precise even when they are measuring very different things. Before comparing two AI-related careers—or deciding whether a career change is financially worthwhile—it helps to understand what is actually behind the number.

Median Pay Is Not Starting Pay

A median wage is the midpoint of earnings for workers in an occupation. Half earn more and half earn less.

It does not mean that someone entering the occupation should expect to earn the median immediately. Experience, education, specialized skills, location, industry, employer, and level of responsibility can all influence compensation.

The lower end of an occupational wage range should not automatically be labeled “entry-level” either. Workers can earn below the median for many reasons, and government wage percentiles do not identify years of experience.

Base Salary and Total Compensation Are Different

A salary estimate may refer only to base pay, while another compensation figure may include bonuses, commissions, profit sharing, or equity such as company stock.

This distinction can be especially important in technology companies, where total compensation for some positions can be substantially different from base salary alone.

When comparing opportunities, make sure you know whether the number represents salary or total compensation.

Job Titles Are Not Standardized

Companies can use different titles for very similar work—or the same title for substantially different responsibilities.

A Machine Learning Engineer at one company might primarily build production software. At another, the role could emphasize model development, data science, experimentation, or research.

The same problem can occur with titles such as AI Engineer, AI Solutions Architect, AI Product Manager, Generative AI Engineer, and AI Consultant.

That is why the actual responsibilities and required skills are often more useful than the job title when comparing salary information.

Location Can Change the Number

National salary figures are useful benchmarks, but labor markets differ substantially by location. Compensation can vary between states, metropolitan areas, and regions, while the cost of living can vary just as dramatically.

A higher salary in one location therefore does not automatically mean greater purchasing power or a better financial outcome.

Industry Matters Too

The industry employing you can have a major effect on compensation even when the underlying occupation is similar.

For example, BLS data show substantial differences in median wages for computer and information research scientists across major employing industries. That illustrates why asking “What does this occupation pay in this industry?” can sometimes be more useful than asking only “What does this job title pay?”

A salary number is most useful when you know what occupation, experience level, location, industry, and type of compensation it actually represents.

What About Specific AI Careers?

Many of the job titles people associate with artificial intelligence do not correspond neatly to a single government occupational category. That does not make salary research impossible, but it means the closest occupational benchmark should be treated as a reference point rather than an exact salary for the job title.

Here is how to think about several common AI-related careers.

Machine Learning Engineer

Machine Learning Engineers often work at the intersection of software development, data science, and machine learning. They may build models, develop production systems, create data pipelines, evaluate model performance, or integrate machine learning capabilities into products.

There is no standalone BLS Occupational Outlook Handbook category for Machine Learning Engineer. Depending on the position, useful benchmarks can include:

• Software Developers — $135,980 median pay
• Data Scientists — $120,230 median pay
• Computer and Information Research Scientists — $140,300 median pay for some research-intensive work

The best benchmark depends on what the position actually requires. A production-focused machine learning engineering job may resemble software development more closely than an experimental research position does.

Data Scientist

Data Scientist is considerably easier to benchmark because BLS maintains a specific occupational category for data scientists.

The 2025 national median wage was $120,230, and BLS projects employment in the occupation to grow 35% from 2025 to 2035.

Data science roles can still vary considerably. Some emphasize statistical analysis and business decision-making, while others involve programming, machine learning, experimentation, or advanced modeling.

AI Research Scientist

Research-oriented AI work can often be compared with the BLS occupation Computer and Information Research Scientists.

The 2025 national median wage was $140,300, with employment projected to grow 22% from 2025 to 2035.

Some research positions require advanced education. BLS identifies a master’s degree as the typical entry-level education for computer and information research scientists, although requirements vary by employer and position.

AI Software Engineer

AI Software Engineer is another title without one universal occupational definition. Many positions, however, involve substantial software development: building applications, integrating models and APIs, developing AI-enabled product features, testing systems, and maintaining production software.

For those positions, Software Developers—$135,980 median pay in 2025—can provide a useful benchmark.

A position focused more heavily on research, data science, or specialized machine learning may warrant a different comparison.

AI Solutions Architect

AI Solutions Architect can involve designing how AI models, cloud services, data systems, applications, security controls, and existing business technology work together.

There is no direct BLS salary category for AI Solutions Architect. Depending on the actual responsibilities, related occupational information may come from software development, computer network architecture, systems analysis, technology management, or other technical occupations.

For this career especially, do not treat a salary estimate for a related occupation as though it were an official salary for AI Solutions Architects.

AI Product Manager

AI Product Managers typically combine product strategy, customer needs, business priorities, technical understanding, and coordination among engineering, design, data, and other teams.

BLS does not maintain a dedicated AI Product Manager occupational category. Product-management compensation can also vary substantially according to employer, industry, responsibility, experience, and whether bonuses or equity are included.

For that reason, someone researching an AI Product Manager opportunity should rely heavily on current compensation information for comparable positions in the relevant industry and location, rather than assuming a national AI salary estimate applies.

AI & Cybersecurity Roles

AI is creating opportunities within cybersecurity while also changing the threats security professionals must address. Relevant work can include protecting AI systems, securing data and infrastructure, evaluating AI-related risks, and using AI-enabled security tools.

For many security-focused roles, Information Security Analysts provide a useful benchmark: $129,180 median pay in 2025, with 21% projected employment growth from 2025 to 2035.

More specialized AI-security positions may differ substantially in responsibilities and compensation.

The closer the benchmark matches the work actually being performed, the more useful the salary comparison becomes. Start with the responsibilities—not simply the words “AI” in the job title.

Entry-Level Pay vs. Experienced Pay

One of the easiest mistakes to make when researching AI career salaries is to treat the bottom of a wage range as “entry-level pay” and the top as “senior-level pay.”

That is not what the data tell us.

The Bureau of Labor Statistics reports how earnings are distributed across workers in an occupation. For example, its May 2025 data show:

• Data Scientists: lowest 10% earned less than $67,240; median $120,230; highest 10% earned more than $199,130
• Software Developers: lowest 10% earned less than $82,460; median $135,980; highest 10% earned more than $214,670
• Computer and Information Research Scientists: lowest 10% earned less than $82,200; median $140,300; highest 10% earned more than $230,630
• Information Security Analysts: lowest 10% earned less than $75,090; median $129,180; highest 10% earned more than $199,850

These ranges demonstrate how widely compensation can vary within the same occupation. But they do not tell us that workers in the lowest 10% are beginners or that workers in the highest 10% are senior professionals.

A worker’s position within the wage distribution can reflect many factors, including experience, location, industry, employer, education, specialization, responsibility, and the particular work being performed.

What Can Someone Entering an AI Career Expect?

There is no reliable national number that every new AI professional should expect to earn.

Someone moving into AI-related work may also not be starting from zero. A healthcare professional, engineer, financial analyst, marketer, teacher, or experienced manager who develops relevant AI capabilities may bring years of valuable domain knowledge into a new role.

That is why career changers should ask a better question than simply:

“What is the entry-level salary?”

They should also ask:

“How much of my existing experience will an employer value in this role?”

Someone making a career transition may be new to a particular AI skill while still bringing substantial professional expertise, industry knowledge, leadership ability, customer understanding, or technical experience.

Experience Matters—but So Does the Kind of Experience

More years in the workforce do not automatically produce higher AI-related pay. What matters is how relevant your experience is to the problems an employer needs you to solve.

Experience may become particularly valuable when it combines:

• knowledge of an industry or profession
• relevant AI and technical skills
• evidence that you can apply those skills to real problems
• communication and collaboration ability
• judgment about when AI should—and should not—be used

This is one reason AiCareerTrack encourages people to evaluate what they already know before assuming that preparing for AI requires starting an entirely new career.

Your existing career experience may be part of your value in an AI-enabled role—not something you have to discard before you can begin.

Why Industry and Location Can Change AI Pay

Two people performing similar technical work can earn substantially different amounts depending on where they work and the industry employing them.

That is one reason a national median should be treated as a benchmark rather than a prediction of what a particular employer will offer.

Why Industry Matters

The same occupation can have very different compensation levels across industries.

Computer and information research scientists provide a striking example. According to the U.S. Bureau of Labor Statistics, the May 2025 median wage for this occupation was $140,300 nationally.

But among major industries employing these workers, the median was $211,270 for software publishers and $85,460 for state colleges, universities, and professional schools.

That does not mean every research scientist at a software company earns $211,270 or that every university researcher earns $85,460. These are industry medians. But the difference demonstrates why the employer’s industry can matter enormously when evaluating earning potential.

Similar differences can occur throughout AI-related work. Healthcare, financial services, technology companies, government, education, manufacturing, consulting, and other industries may value—and compensate—the same underlying skills differently.

Why Location Matters

Where a job is located can also affect compensation.

National wage statistics combine workers from many different labor markets. Salaries in one metropolitan area or state may be substantially different from salaries for similar work elsewhere.

Remote work adds another consideration. Some employers use compensation bands based partly on where the employee lives, while others use different approaches.

But salary alone does not tell you whether one location provides a better financial opportunity.

A higher-paying job may also come with higher housing costs, taxes, transportation expenses, childcare costs, or other living expenses. Conversely, a somewhat lower salary in another location could potentially provide greater purchasing power.

When researching a particular career, compare local compensation and local living costs, not just a national salary figure.

Compare Opportunity, Not Just Salary

Compensation is important, but it is only one part of a career decision.

Consider:

• salary and other compensation
• cost of living
• job availability
• expected employment growth
• advancement opportunities
• education or training required
• time needed to become competitive
• stability and benefits
• work you actually want to perform

A $150,000 career is not automatically a better opportunity than a $120,000 career if reaching the higher-paying position requires years of additional education, substantial debt, relocation, or work that does not fit your interests and strengths.

The useful question is not simply “Which AI career pays the most?” It is “Which opportunity offers the right combination of compensation, accessibility, growth, and fit for me?”

What Actually Moves You Toward Higher Pay?

High salaries usually are not awarded simply because a job title contains the words “AI,” “machine learning,” or “data.”

Employers generally pay for capabilities they need, problems they need solved, and responsibilities they need someone to handle. AI skills can increase your value, but their value depends partly on what you are able to accomplish with them.

Build Skills That Solve Valuable Problems

Learning how an AI tool works can be useful. Being able to apply it to a meaningful problem is more valuable.

Depending on the career, that might mean being able to:

• analyze complex data and communicate useful conclusions
• build reliable software that incorporates AI capabilities
• improve an inefficient business process
• evaluate the quality and limitations of AI-generated information
• protect AI systems and sensitive data
• design solutions that integrate AI with existing technology
• translate business or customer needs into practical technical requirements

The more directly your skills contribute to useful outcomes, the easier it becomes for an employer to understand their value.

Combine AI Skills With Domain Expertise

AI knowledge becomes particularly powerful when combined with knowledge of an industry or profession.

Someone who understands healthcare workflows, financial analysis, manufacturing operations, marketing, education, aerospace, logistics, or another field may recognize problems and constraints that someone with technical knowledge alone could miss.

This creates an important career-development formula:

Existing expertise + relevant AI capability + practical application

For many professionals, strengthening that combination may be more realistic—and potentially more valuable—than abandoning years of experience to compete for an entirely unrelated entry-level technology position.

Demonstrate What You Can Do

Completing a course can show that you studied a subject. Employers may still want evidence that you can apply what you learned.

Depending on the career, useful evidence might include:

• a portfolio project
• a working application or prototype
• a data analysis with clearly explained conclusions
• a documented workflow improvement
• a case study
• contributions to relevant projects
• measurable results from work you performed

The goal is not to accumulate projects simply to make a portfolio larger. A few credible examples that demonstrate judgment and useful capability can be more persuasive than many superficial exercises.

Take On Greater Responsibility

Higher compensation often accompanies greater responsibility.

That responsibility may involve designing systems, making technical decisions, leading projects, managing risk, communicating with senior stakeholders, mentoring others, owning important business outcomes, or coordinating work across multiple teams.

Technical skill therefore is not the only path to greater career value. Communication, leadership, judgment, reliability, and understanding the consequences of decisions can become increasingly important as responsibility grows.

Keep Learning Without Chasing Every New Tool

AI tools and platforms are changing rapidly. Staying current matters, but trying to learn every new product is neither practical nor necessary.

Focus first on durable capabilities: understanding AI fundamentals, evaluating outputs, working effectively with data, solving problems, communicating clearly, and learning how AI applies within your field.

Then add tools and specialized skills when they support a real objective.

A new tool may become obsolete. The ability to understand a problem, evaluate possible solutions, learn what is necessary, and apply technology responsibly is much more durable.

Higher earning potential usually comes from becoming more useful, capable, trusted, and difficult to replace—not simply from collecting more AI terminology for your résumé.

Is a Higher-Paying AI Career Worth the Transition?

A career with a higher salary can look attractive on paper. But the salary at the destination is only part of the decision.

Before pursuing a major career change, consider what it may cost—in money, time, lost income, and effort—to become competitive for the new role.

A $30,000 increase in annual salary could be a meaningful improvement. But the calculation looks different if reaching that salary requires two years of education, substantial tuition, leaving a stable position, relocating, or accepting lower earnings while gaining experience.

Calculate the Cost of Getting There

Consider the full investment required to pursue the career.

That may include:

• tuition and course fees
• certification or examination costs
• books, software, equipment, or cloud-computing expenses
• time spent studying
• reduced working hours
• income you may give up during training
• relocation expenses
• time required to build practical experience

Not every career transition requires all of these. In some cases, a relatively inexpensive course, project, or employer-supported training opportunity may be enough to begin moving in a new direction.

The important point is to estimate the cost of the transition, not merely the salary you hope to earn afterward.

Consider What You Already Earn

The financial case for changing careers depends partly on your starting point.

Someone earning $45,000 who has a realistic path toward a $100,000 occupation faces a different decision from someone already earning $110,000 who is considering an expensive transition toward an occupation with a $130,000 median wage.

That does not mean the second transition is necessarily a poor choice. Career satisfaction, advancement potential, stability, interest, flexibility, and long-term opportunity may justify it.

But comparing the new career only with your current job title—and not with your current compensation and trajectory—can make the financial benefit appear larger than it really is.

Think About Probability, Not Just Possibility

A high salary is possible in many AI-related careers. That does not mean every person who completes training will obtain one of those positions.

Ask:

• How many relevant jobs are available?
• What qualifications do employers actually request?
• How competitive are those positions?
• Do I have relevant experience I can carry into the role?
• Can I demonstrate the skills employers need?
• Would I need to relocate?
• Am I willing to begin at a different level than I originally expected?

A career plan should be based on a realistic path to employment, not merely the highest salary advertised for the occupation.

Consider the Working Life Behind the Salary

Salary tells you what a job may pay. It does not tell you what living with that job will be like.

Consider the work itself:

• What would you spend your days doing?
• Does the work fit your strengths?
• Do you enjoy solving the kinds of problems the career involves?
• What level of pressure or responsibility comes with the role?
• Are working hours and flexibility compatible with the life you want?
• Does the career offer room to grow in directions that interest you?

A higher salary has value. So do time, health, flexibility, stability, meaningful work, family responsibilities, and the ability to build a sustainable working life.

A Career Transition Can Still Be Worthwhile

None of this is an argument against pursuing a higher-paying AI career.

A transition can make excellent sense when the new direction offers stronger compensation, better long-term prospects, more interesting work, greater flexibility, or a better use of your abilities.

The purpose of evaluating the transition carefully is not to discourage change. It is to help you make the change with a clearer understanding of what you are investing and what you reasonably expect to gain.

Sometimes the best path may not require a complete career change at all. Adding relevant AI capabilities to work you already understand can potentially increase your value while allowing you to retain the experience, relationships, and professional knowledge you have spent years developing.

Do not compare your current salary with the highest salary you can find for an AI job. Compare your current path with a realistic new path—including the time, cost, probability, work, and opportunities involved in getting there.

How to Research an AI Salary Before Accepting an Offer

When you are evaluating a real job opportunity, a national salary estimate should be the beginning of your research—not the end.

Use several sources and compare what they are actually measuring.

1. Start With the Actual Responsibilities

Read the job description carefully before researching the salary.

Ask what the person will actually be expected to do. Does the position primarily involve software development, data analysis, machine learning, research, cybersecurity, product management, cloud architecture, consulting, or something else?

Ignore the prestige of the title for a moment.

A position called “AI Engineer” may involve substantially different work from another position carrying exactly the same title. Understanding the responsibilities helps you determine which salary comparisons are genuinely relevant.

2. Find the Closest Reliable Occupational Benchmark

For U.S. careers, the Bureau of Labor Statistics can provide a useful starting point for established occupations.

Look for:

• national median wages
• wage distributions
• projected employment growth
• typical education requirements
• major employing industries
• state or metropolitan-area wage information when available

If the AI job title does not have a direct occupational match, identify the occupation or occupations that most closely resemble the actual work.

Do not force a modern AI title into an official category simply to produce a precise-looking salary number.

3. Check Your Local Market

National data can tell you whether an occupation generally has strong earning potential, but an employer hires within a particular labor market.

Research compensation in the state or metropolitan area where you would work. If the position is remote, determine whether the employer adjusts compensation according to employee location.

Also consider local living costs. A higher nominal salary does not necessarily produce a higher standard of living.

4. Compare Similar Job Postings

Current job postings can help you understand what employers are offering for positions resembling the one you are considering.

Look for several positions rather than relying on one unusually high or low example.

Compare:

• responsibilities
• required experience
• education requirements
• technical skills
• industry
• location
• stated salary range
• bonuses or commissions
• equity or stock compensation

Pay transparency laws in some jurisdictions make posted salary ranges increasingly useful, but a posted range still does not guarantee that a particular candidate will receive its midpoint or upper end.

5. Separate Base Salary From Total Compensation

When comparing offers or salary reports, determine exactly what each number includes.

Total compensation may include:

• base salary
• annual or performance bonuses
• commissions
• stock or equity
• retirement contributions
• health benefits
• paid leave
• other employer benefits

Two positions with identical base salaries can therefore have meaningfully different overall financial value.

Stock compensation deserves particular care. Its future value may change and should not automatically be treated as equivalent to guaranteed cash compensation.

6. Evaluate the Entire Offer

Compensation matters, but so do the conditions under which you earn it.

Consider:

• working hours
• remote or hybrid flexibility
• commuting requirements
• paid time off
• health and retirement benefits
• job stability
• travel expectations
• professional development
• workload and responsibility
• organizational culture
• opportunities to learn

An offer that pays somewhat less could still be the better financial and career decision if it provides stronger benefits, lower expenses, greater stability, better training, or substantially better working conditions.

7. Ask What the Position Can Lead To

Your first salary in a new field is only one point in a longer career.

Ask what skills and experience the position will help you develop and what opportunities could become available afterward.

A role that provides meaningful experience with valuable AI systems, strong mentorship, industry knowledge, or increasing responsibility may improve your future opportunities even if its initial salary is not the highest offer available.

Conversely, a high-paying position that provides little opportunity to learn or advance may be less attractive over the long term.

Research the job, not just the salary. The strongest compensation decision considers what you will earn now, what you will learn, what the work requires, and where the opportunity could take you next.

How AiCareerTrack Reports AI Salary Data

Salary information can influence major decisions about education, training, career changes, and where someone chooses to work. That makes accuracy and context especially important.

AiCareerTrack uses the following principles when reporting compensation information.

Use Authoritative Sources When Available

For U.S. occupational wage and employment information, we prefer authoritative sources such as the U.S. Bureau of Labor Statistics when an appropriate occupational category exists.

Other sources may be useful for newer job titles or current employer salary ranges, but we distinguish those estimates from official occupational statistics.

Distinguish Official Occupations From Modern Job Titles

Not every AI-related job title has its own government occupational classification.

When a title such as Machine Learning Engineer, AI Solutions Architect, or AI Product Manager does not correspond directly to an official occupational category, we identify related occupations as benchmarks rather than exact matches.

We do not want an approximate comparison to look more precise than the underlying evidence allows.

Identify What the Number Measures

Whenever practical, salary information should make clear whether the figure represents:

• a median or average
• a wage percentile
• a national, state, or local estimate
• a particular industry
• a posted employer salary range
• base pay or broader compensation

Those distinctions can materially change what a salary number means.

Keep the Data Dated

Salary information changes.

When AiCareerTrack uses occupational wage statistics, we identify the period represented by the data whenever practical so readers can judge how current the information is.

A page updated in 2026, for example, may legitimately use May 2025 wage data if that is the latest official dataset available. The important thing is to tell the reader what period the number actually represents.

Salary Estimates Are Not Guaranteed Earnings

No published salary figure can guarantee what a particular person will earn.

Compensation can depend on experience, skills, education, industry, location, employer, responsibilities, performance, labor-market conditions, and other factors.

Salary information is therefore most useful as evidence for making a decision, not as a promise about the outcome of that decision.

AiCareerTrack does not present salary estimates as guaranteed earnings. Whenever possible, we identify the source, data period, occupation represented, and what the figure measures. When a modern AI job title does not correspond directly to an official occupational classification, we say so rather than presenting an approximate figure as an exact match.

What Should You Do Next?

Salary information is most useful when it helps you make a better decision.

You do not need to identify the highest-paying AI job and make it your goal. Instead, use compensation as one part of a broader comparison involving your experience, interests, skills, education, financial situation, and the kind of working life you want.

If You Are Comparing AI Careers

Start by exploring several career directions rather than choosing one based on salary alone.

Compare what the work involves, what skills employers expect, how much preparation you would need, employment prospects, and realistic compensation.

AiCareerTrack’s AI Careers resources can help you investigate individual career paths before committing to one.

If You Are Considering a Career Change

Evaluate what you already bring with you.

Your current career may have given you industry knowledge, professional judgment, communication ability, technical experience, leadership skills, customer understanding, or other strengths that could transfer into AI-enabled work.

Use Explore a New Career to compare possible directions before deciding whether a major transition makes sense.

If You Need More AI Skills First

Do not assume that the most expensive training will produce the best career outcome.

First determine which skills the career actually requires and how much of the gap you need to close.

The AI Skills Path can help you identify relevant skills, while Where Should I Learn AI? can help you decide whether free learning, a course, a certificate, or a certification makes sense for your objective.

If You Are Still Unsure

You do not need to make a permanent career decision today.

The AI Readiness Self-Assessment can help you think about your current capabilities and identify areas where additional learning or experience may be useful.

You can also use Find Your AI Path to consider whether your best next step is strengthening your current work, exploring another career, learning new skills, starting or growing a business, freelancing or consulting, or exploring additional income.

The best AI career is not necessarily the one with the highest advertised salary. It is the one that offers a realistic path toward work that fits your abilities, goals, financial needs, and the life you want to build.

Use salary data to inform the decision—not to make the decision for you.

Last reviewed: September 2026