Skills vs. Degrees: What AI Employers Actually Want from New Graduates

Key Infor­ma­tion:

• While demand for AI pro­fes­sion­als surged 25.2% in Q1 2025 and salaries reached a medi­an of $156,998, employ­ers increas­ing­ly val­ue prac­ti­cal expe­ri­ence over degrees, espe­cial­ly in fast-mov­ing sec­tors like IT and software.
• Tech­ni­cal skills like Python, Ten­sor­Flow, and sta­tis­tics are essen­tial for AI roles. Soft skills, such as com­mu­ni­ca­tion, eth­i­cal rea­son­ing, and team­work, are what set top can­di­dates apart.
• Real-world expe­ri­ence through GitHub projects, intern­ships, hackathons, and AI com­pe­ti­tions is now more crit­i­cal to land­ing AI jobs than hav­ing a tra­di­tion­al col­lege degree alone.
• AI employ­ers are shift­ing toward hir­ing based on demon­strat­ed abil­i­ty, with many pre­fer­ring port­fo­lios and cer­ti­fi­ca­tions over GPAs, and wel­com­ing can­di­dates from Boot­camps and online programs.

The AI Talent Gap and the Degree Dilemma

There’s a grow­ing demand for AI pro­fes­sion­als across near­ly every indus­try. Name an indus­try — it’s like­ly plan­ning on adopt­ing, already adopt­ing, or improv­ing on their AI sys­tems. There are even names for it:

• fin­tech
• edtech
• healthtech
• gov­tech
• legal­tech

Indeed, AI/ML spe­cial­ists are among the most in-demand pro­fes­sion­als in the IT sec­tor, third, in fact. Con­trary to pop­u­lar opin­ion, “AI can make peo­ple more valu­able, not less”.

But get­ting into the AI indus­try can be con­fus­ing for stu­dents. Com­mon ques­tions include:

• Is a bach­e­lor’s degree enough to apply for entry-lev­el jobs?
• Are tech­ni­cal skills from prac­ti­cal expe­ri­ences more impor­tant than a degree?
• What are the hard and soft skills need­ed for AI jobs?

In short, what do employ­ers actu­al­ly look for in AI job can­di­dates? So, in this arti­cle, we’ll answer this core ques­tion by cov­er­ing these topics:

• Tech­ni­cal skills expect­ed by employ­ers among AI job applicants
• Soft skills that make AI job appli­cants stand out from the competition
• Real-world expe­ri­ence and its value
• Col­lege degrees and their val­ue and short­com­ings in AI careers

When you’ve fin­ished read­ing, you’ll have a clear­er under­stand­ing of the skills need­ed for AI jobs. That, and how a col­lege degree and prac­ti­cal expe­ri­ence make these skills possible.

Relat­ed:

• Best Online Cer­ti­fi­ca­tions for a Career Tran­si­tion to AI
• Tech-Focused Online Degrees for Future-Proof Careers
• Beyond Com­put­er Sci­ence: The Sur­pris­ing Majors That Launch AI Careers
• Self-Paced Online Com­put­er Sci­ence Degrees
• Top Online Cod­ing Boot­camps vs. Com­put­er Sci­ence Degrees
• Emerg­ing Online Degree Programs

What the AI Job Market Looks Like in 2025

Here are a few of the notable AI job mar­ket trends 2025 has brought so far.

• In Q1 2025, the demand for AI-cen­tric roles increased 25.2% from Q1 2024. This was an 8.8% increase from the pre­vi­ous quar­ter, too. 
• In the same peri­od, the medi­an salary for AI jobs also increased to $156,998/year. This was a 0.8% quar­ter-over-quar­ter increase.

Indeed, AI roles aren’t only in high demand, but their com­pen­sa­tion is on the rise, too. Fur­ther­more, near­ly every indus­try is on the look­out for AI pro­fes­sion­als. But the strongest AI hir­ing is in the com­put­er soft­ware, IT, and human resources sectors.

The most in-demand job titles are:

• Machine learn­ing engineers
• Data sci­en­tists
• AI prod­uct managers

Prompt engi­neers, con­ver­sa­tion­al AI devel­op­ers, and NLP engi­neers are also in demand.

But do you need a degree for AI jobs in the first place? Well, it depends on the hir­ing orga­ni­za­tion itself.

Many orga­ni­za­tions still require a rel­e­vant bach­e­lor’s degree for entry-lev­el AI jobs. This is par­tic­u­lar­ly true for the finance, gov­ern­ment, and health­care sec­tors. Plus, research-heavy roles usu­al­ly demand a mas­ter’s degree or even a doc­tor­al degree (e.g., PhD).

But more employ­ers are shift­ing their focus from for­mal edu­ca­tion to demon­strat­ed capa­bil­i­ty. AI job post­ings requir­ing a col­lege degree have decreased by 15%. This is increas­ing­ly true for both star­tups and Big Tech com­pa­nies, such as Google, IBM, and Meta.

In these orga­ni­za­tions, project expe­ri­ence, port­fo­lios, and cer­ti­fi­ca­tions have more val­ue. Appli­cants with a strong Kag­gle or GitHub pres­ence have a com­pet­i­tive edge, too. Many employ­ers also pro­vide appren­tice­ship and upskilling pro­grams for AI roles.

The bot­tom line: Prac­ti­cal knowl­edge and adapt­abil­i­ty mat­ter, too. The AI indus­try, after all, is in con­stant flux, so these skills often mat­ter more than a col­lege degree.

Technical Skills AI Employers Expect

Tech­ni­cal skills are cru­cial in the AI indus­try because they enable prac­ti­cal imple­men­ta­tion. Such is their impor­tance that these are the top­most among AI employ­er expec­ta­tions. Remem­ber that AI is an applied field — the­o­ry is good, but it demands prac­ti­cal applications.

With that said, here are the AI tech­ni­cal skills for grad­u­ates you must develop:

Programming Languages and Tools

Being pro­fi­cient in Python is the indus­try stan­dard. Employ­ers also expect famil­iar­i­ty with R, Java, and/or C++ among AI applicants.

Can­di­dates with clean, mod­u­lar code can have a com­pet­i­tive edge. Be sure that you show­case it in your GitHub port­fo­lio and dur­ing the tech­ni­cal screening.

Machine Learning & AI Frameworks

Hands-on expe­ri­ence with AI and ML frame­works is a must, too. Again, the­o­ret­i­cal knowl­edge is the foun­da­tion, but prac­ti­cal skills are the build­ing itself. Devel­op your skills in Ten­sor­Flow, PyTorch, Scik­it-learn, and Keras for this reason.

Also, build your knowl­edge of mod­el deploy­ment tools to increase your hir­ing poten­tial. Think Flask, Dock­er, or AWS.

Math and Statistics Foundations

Employ­ers val­ue prob­lem-solv­ing skills with data over rote mem­o­riza­tion, too. Build robust skills in lin­ear alge­bra, cal­cu­lus, prob­a­bil­i­ty, and sta­tis­tics. These are essen­tial skills in the AI sys­tems cycle, from design to evaluation.

Soft Skills That Set Candidates Apart

Tech­ni­cal skills are not the only AI job require­ments for new grads, either. Trans­fer­able skills are list­ed on every AI job descrip­tion, too.

How impor­tant are soft skills in arti­fi­cial intel­li­gence jobs? These can make or break your appli­ca­tion! You and a dozen oth­er appli­cants can have the same tech­ni­cal skills. But your trans­fer­able skills can make you stand out from them.

Problem-Solving and Critical Thinking Skills

Employ­ers want thinkers and inno­va­tors who can ana­lyze, accept, and over­come chal­lenges. Yes, coders can be excel­lent AI pro­fes­sion­als, too. How­ev­er, AI isn’t only about cod­ing — more impor­tant­ly, it’s also about find­ing solu­tions for real-world issues.

Communication Skills

AI pro­fes­sion­als must be able to explain com­plex AI con­cepts to non-tech­ni­cal teams. AI, after all, does­n’t exist in a bub­ble — it exists in human soci­ety and serves its interests.

Team Collaboration Skills

Many AI roles are inter­dis­ci­pli­nary. Being able to work with a team is then essen­tial for success.

Ethical Reasoning Skills

AI pro­fes­sion­als must also under­stand the soci­etal impact of AI sys­tems. Their work also demands min­i­miz­ing bias and spot­ting poten­tial harms.

As a hir­ing man­ag­er says, “Yes, tech­ni­cal skills are the base­line. But how you think, com­mu­ni­cate, and work with oth­ers are stand­out traits.”

Real-World Experience: More Than a Degree

AI is such a fast-chang­ing indus­try that it’s almost a blink-and-you’ll-miss-it indus­try. Rapid mod­el advance­ments and fre­quent reg­u­la­tion updates are part of it.

The result: Employ­ers val­ue real-world AI and machine learn­ing skills vs degree cre­den­tials. This is true even for recent col­lege grad­u­ates apply­ing for entry-lev­el jobs.

So, what can you do to gain real-world expe­ri­ence before you graduate?

• Engage in intern­ships and/or research assistantships.
• Com­plete cap­stone projects as part of your studies.
• Make open-source con­tri­bu­tions (e.g., Hug­ging Face Trans­form­ers on GitHub).
• Com­pete — and win — in AI com­pe­ti­tions, such as on Kag­gle and GitHub. Hackathons are great, too.
• Build per­son­al ML projects (e.g., cus­tom image clas­si­fi­er) to build your credibility.

Remem­ber that employ­ers want sol­id proof that you can apply knowl­edge. Yes, being able to absorb knowl­edge has its mer­its. But apply­ing knowl­edge is at the heart of AI.

Here’s what James S. says, “I did­n’t have a CS degree. But it did­n’t stop me from build­ing my GitHub port­fo­lio. Then, I lever­aged it dur­ing job appli­ca­tions.” He’s now work­ing as an ML engineer.

So, here’s effec­tive AI career advice for stu­dents: Earn a col­lege degree, if you must. But be sure to boost your the­o­ret­i­cal knowl­edge with prac­ti­cal skills.

How Degree Programs Help—and Where They Fall Short

Earn­ing a col­lege degree is still an advan­tage, after all. You will gain the foun­da­tion­al the­o­ry, struc­ture, and research oppor­tu­ni­ties. Plus, you’ll ben­e­fit from the aca­d­e­m­ic net­work and men­tor­ship opportunities.

Some AI degree pro­grams also pro­vide hands-on train­ing with real-world datasets. You may also com­plete projects, cap­stones, and work with indus­try-stan­dard tools. As such, you’ll gain prac­ti­cal expe­ri­ence in AI careers.

How­ev­er, there are down­sides to many degree pro­grams, including:

• Out­dat­ed cur­ric­u­la with a strong focus on the­o­ry and less on applied work
• Lack of agili­ty that does­n’t cor­re­spond to the rapid changes in the AI industry
• Insuf­fi­cient cov­er­age of tools used in the field

With this in mind, you must sup­ple­ment the­o­ret­i­cal knowl­edge with prac­ti­cal skills. Start with online skill-build­ing cours­es for your self-taught journey.

• Explore Cours­era, Udac­i­ty, and oth­er MOOCs for hands-on exercises.
• Join cod­ing com­pe­ti­tions and chal­lenges on Kaggle.
• Show­case your projects on GitHub.

For­mal learn­ing lays the foun­da­tion for an AI career. Infor­mal learn­ing builds on it so you can enjoy sus­tain­able success.

What Employers Actually Say They Want

From inter­views and sur­veys with AI employ­ers, we can draw the fol­low­ing conclusions:

• Employ­ers hire for what you can do, not only for what you studied.
• Cer­ti­fi­ca­tions, project port­fo­lios, and com­mu­ni­ca­tion abil­i­ty often weigh more than GPA or alma mater.
• Adapt­abil­i­ty, curios­i­ty, and ini­tia­tive are sought-after skills among AI job applicants.

Also, more employ­ers are open to can­di­dates from boot­camps and online pro­grams. Can­di­dates with alter­na­tive cre­den­tials are also more welcome.

Conclusion: Building a Balanced Path to AI Career Success

In con­clu­sion, degrees pro­vide a valu­able start­ing point for an AI career. But these aren’t enough on their own. You must do more to stand out in the AI job market.

Skills, expe­ri­ence, and mind­set mat­ter just as much — if not more. You must then take con­trol of your learn­ing jour­ney through real-world applications.

The best way to do so is by adopt­ing a bal­anced approach. You should com­bine your degree with con­tin­u­ous skill devel­op­ment and mean­ing­ful projects. Doing so is your best shot at AI career success.