Artificial intelligence (AI) is everywhere, from chatbots to self-driving cars, and companies are racing to find talent. That’s great news if you’re considering a career change to AI.
The numbers speak for themselves. According to Stanford University's 2024 AI Index Report, the number of newly funded AI companies skyrocketed to 1,812 in 2023—a 40.6% jump from the previous year.
Generative AI attracted $25.2 billion in 2023, nearly nine times the funding of 2022 and 30 times more than in 2019. In fact, over a quarter of all AI-related private investments last year went into generative AI alone.
With this kind of growth, perhaps the question isn’t “Should I pivot to AI?” but rather “How can I transition my career to AI successfully?” Whether you're a software engineer making the shift, a complete beginner wondering how to get a job in AI without a degree, or even considering an AI career change at age 40 or beyond, there’s a path for you.
Why AI is a smart career move
AI is the technology that allows machines to learn, reason, and make decisions, often in ways that mimic human intelligence. Whether you’ve noticed it or not, it’s already woven into nearly every industry.
It powers everything from voice assistants like Siri and Alexa to fraud detection in banking, self-driving cars, and even personalized healthcare treatments. And the best thing about AI growth is that no matter your background, there’s a place for you.
“AI as a career path reminds me of the early days of the dot-com era,” says Marline Paul, business coach, AI implementation strategist, and CEO of Enilram Creative. “It's an emergent technology with tremendous growth potential, creating several advantages over more established tech fields.”
So, why is now the right time to pivot to AI?
AI professionals are in high demand
AI expertise isn’t confined to just one industry. “We're seeing unprecedented demand across industries,” Paul says. “Unlike specialized tech roles that might be limited to certain sectors, AI expertise is being sought in healthcare, finance, education, government, and virtually every other field. This cross-industry demand creates natural stability—if one sector faces challenges, opportunities remain abundant elsewhere.”
In the U.S., California, Texas, and Virginia were the states with the highest number of AI job postings in 2023, according to the Stanford report—a total that surpasses 131.000 jobs. Overall, the number of AI-related job postings represented 1.60% of the country’s total job postings.
AI jobs offer competitive salaries
“AI specialists typically command premium salaries compared to many other tech roles, with entry-level positions frequently starting at competitive rates in major markets,” Paul says.
The financial rewards for companies involved in AI are immense, driving them to invest heavily in top talent. With AI investments reaching $67 billion in the U.S. in 2023, 59% of the companies reported revenue increases after implementing AI. These businesses understand that skilled AI professionals can deliver substantial value, making the investment well worth it.
It’s just the beginning
If you’ve ever wished you got into tech before it exploded, this is your chance for a do-over. “We're still in the early adoption phase of AI, with new applications emerging daily,” Paul says. “This creates a unique opportunity for professionals entering the field now to shape its development and potentially define entirely new career paths that don't yet exist.” That means AI isn’t just a safe bet—it’s a career path where you can innovate, create, and even lead the way.
Read this next: Understanding the AI Impact on Jobs and What’s Next
Are you a fit? Assess your current skill set
If you're wondering how to transition your career to AI but aren't sure if you have the right skills, the good news is you probably do—even without a tech background. One of the biggest myths about AI careers is that you need to be a hardcore coder or have a Ph.D. in machine learning to break in. But that’s not true.
“This is perhaps the most compelling aspect of the current AI revolution—the accessibility for non-technical professionals,” Paul says. “Unlike previous tech shifts that required extensive coding knowledge, today's AI tools are increasingly designed for human interaction rather than technical programming.”
This means that many of your existing skills—whether from marketing, finance, healthcare, or education—can help you succeed in AI. For example:
- Data analysis: If you've worked with spreadsheets, reports, or market trends, you already understand the basics of data-driven decision-making.
- Problem-solving and critical thinking: AI is all about optimizing solutions. If you're good at troubleshooting, strategic thinking, or process improvement, you’ll fit right in.
- Domain expertise: AI needs experts from every field. If you understand finance, law, healthcare, or any industry, you can help apply AI in those areas.
- Communication and business strategy: AI isn’t just about technology—it’s about solving business problems. If you can explain concepts clearly and bridge the gap between technical teams and business goals, you're a valuable asset.
You don't need to become a programmer or data scientist, Paul says, “focus, instead, on becoming an effective AI collaborator who can bridge the gap between business needs and AI capabilities.”
Read this next: AI Skills: What You Need to Learn to Stay Ahead
Identify your skill gaps—and close them
When you’re changing careers to a different field, it’s common (and expected) to have some gaps to fill.
Familiarize yourself with AI tools
“Don't be intimidated by the technology,” Paul says. “Your previous professional experience is a significant advantage because you bring domain expertise that purely technical people often lack.”
Play around with ChatGPT, Midjourney, Gemini, or AI-powered analytics software to see how they work in real-world scenarios—you’ll learn more by using them than by reading about them.
Stay updated
Keep up with YouTube tutorials, LinkedIn posts, and AI-focused blogs. Also, stay informed about how AI is being implemented in your industry (or the industry you’re pivoting to).
For more technical roles, take a look at must-have AI skills
AI careers require different levels of technical expertise, depending on the role. If you're aiming for a more hands-on position, here are some essential skills to focus on:
Programming languages
The most common languages used in AI include:
- Python: Beginner-friendly, widely used, and has a ton of libraries (like TensorFlow and PyTorch)
- R: Popular for statistical computing and data analysis, especially in AI-driven research
- Java: Used in AI-powered enterprise applications, especially for large-scale systems
- SQL: Essential for working with databases, as AI models often rely on structured data
Machine learning and AI concepts
Understanding how AI works (even at a high level) can make a huge difference—especially if you're aiming for AI strategy, consulting, or implementation roles.
Some key concepts to explore:
- Machine learning: The foundation of AI, where models “learn” from data to make predictions
- Neural networks and deep learning: The driving force behind speech recognition, image processing, and generative AI tools like ChatGPT
- Natural Language Processing (NLP): Essential for AI that understands human language (think: chatbots, voice assistants, and automated translations)
- AI ethics and bias: Important for understanding the ethical implications of AI, especially in leadership roles
You don’t need to master these overnight. Start by taking a beginner-friendly AI course (many are free!) and learning how these technologies apply to your industry.
6 AI careers to consider
AI isn’t just one job—it’s an entire ecosystem of careers that range from technical roles to AI strategy and implementation positions that don’t require coding. Here are some of the most in-demand artificial intelligence career paths, along with what they involve.
1. Data scientist
A data scientist is responsible for analyzing large datasets to uncover insights, build predictive models, and support decision-making within a company. They use machine learning, statistical techniques, and data visualization tools to extract meaningful patterns from structured and unstructured data.
Data scientists often work with programming languages like Python or R and use AI-driven analytics to optimize business processes, detect fraud, or personalize customer experiences. If you have a background in data analysis, mathematics, or business intelligence, transitioning to a data science role can be a natural step into the AI field.
2. Machine learning engineer
Machine learning engineers design and develop the algorithms that allow AI systems to learn from data and improve over time. They work on everything from recommendation systems (like Netflix suggesting movies) to fraud detection in banking and self-driving cars.
This role requires strong programming skills, particularly in Python, Java, or C++, and expertise in machine learning frameworks like TensorFlow or PyTorch. If you’re a software developer or engineer looking to pivot to AI, becoming a machine learning engineer is one of the most direct transitions.
3. AI researcher
AI researchers work on the cutting edge of artificial intelligence, developing new algorithms, refining deep learning models, and exploring innovative AI applications. They often work in academic institutions, research labs, or AI divisions within large tech companies.
This role requires a deep understanding of mathematics, statistics, and theoretical computer science. Many AI researchers hold PhDs in fields like artificial intelligence, machine learning, or applied mathematics. If you enjoy problem-solving and have a passion for innovation, AI research offers the opportunity to shape the future of the industry.
4. AI developer
AI developers focus on integrating AI capabilities into software applications. They build chatbots, virtual assistants, AI-powered search engines, and recommendation systems.
Unlike machine learning engineers, AI developers don’t necessarily need to create new models—they frequently work with existing AI frameworks and APIs like OpenAI’s GPT or Google’s TensorFlow. This role is ideal for software developers who want to transition into AI without diving deep into advanced machine learning algorithms.
5. AI product manager
AI product managers bridge the gap between AI technology and business needs. They define the vision for AI-powered products, work closely with engineers and data scientists, and ensure AI solutions align with a company’s goals.
They don’t need to code, but they should understand AI concepts and be able to translate technical jargon into actionable business strategies. If you have experience in product management, marketing, or business strategy, becoming an AI product manager is a great way to enter the field without a technical background.
6. AI consultant or strategist
AI consultants help businesses adopt and implement AI solutions, ensuring companies use AI effectively to improve operations, reduce costs, or enhance customer experiences. They assess business needs, recommend AI tools, and sometimes train teams on AI adoption.
Unlike AI researchers or engineers, AI consultants don’t build AI models—they focus on strategy, integration, and business applications. This role is perfect for professionals with backgrounds in business, operations, or consulting who want to pivot to AI without needing extensive technical expertise.
Tips on how to switch to an AI career
Breaking into AI can seem daunting, especially if you don’t have a traditional tech background. But the demand for AI professionals is high, and many companies are open to hiring candidates with transferable skills and hands-on experience—even without a formal degree in AI or computer science.
Highlight your transferable skills
Leverage your previous experience—whether in marketing, finance, healthcare, or any other industry. Many AI roles require problem-solving, critical thinking, and domain expertise. The key is to connect your existing skills to AI applications.
Being able to manage projects effectively is another valuable skill. Since many AI initiatives require collaboration, if you’ve worked on cross-functional teams or led projects, highlight your ability to bridge the gap between business and AI.
Showcase your AI projects
If you’re new to AI, your portfolio is your biggest selling point. Even small, self-initiated AI projects can show your skills and ability to apply AI concepts in real-world scenarios. If you have a machine learning project, if you used AI to automate tasks, or even if you implemented AI to solve a business problem or optimize workflow, make sure to include it in your resume.
Build your network
Join learning communities and engage in AI-focused forums like r/MachineLearning (Reddit), Towards AI (Medium), and Discord or Slack groups where AI learners share their experiments and insights. Attend virtual and in-person events like NeurIPS, AI Summit, and local AI meetups to connect with industry professionals. Be generously curious—share what you’re learning, ask thoughtful questions, and contribute your perspective to AI discussions.
Embrace the “beta mindset”
AI is constantly evolving, so adopt an experimental approach. Stay open to learning, iterate on your skills, and don’t be afraid to make mistakes—each challenge is an opportunity to grow in this fast-changing field.
Looking for that perfect role? One of these open jobs on The Muse might just be the one »
FAQs
“How do I start my career in AI with no technical background?”
Switching to AI without a technical background is absolutely possible, especially with the rise of no-code AI tools and business applications of AI. Start by understanding AI fundamentals through free online courses, experimenting with AI-powered platforms, and working on small projects that apply AI to your industry. Networking with AI professionals and joining AI-focused communities can help you break into the field.
“Can I start a career in AI at 40?”
Absolutely. AI is a rapidly evolving field that values experience, domain expertise, and problem-solving skills over age. Many professionals transition into AI later in their careers, leveraging their industry knowledge to bridge the gap between business needs and AI solutions. Rather than trying to compete with fresh computer science graduates, focus on learning AI tools relevant to your current field and positioning yourself as an AI specialist within your domain.
Read this next: 10 Steps for Making a Successful Career Change After 40
“What skills should I learn to start working in AI?”
The skills you need depend on the AI role you're targeting, but essential AI skills include data analysis, machine learning fundamentals, and familiarity with AI tools. If you're aiming for a technical role, learning Python, SQL, and TensorFlow is a great start. For non-technical roles, focus on AI ethics, automation, prompt engineering, and AI-driven decision-making. Strong communication skills and an ability to translate AI concepts into business solutions will also make you a standout candidate.
How to get a job in AI without a degree?
Many AI roles prioritize skills and experience over formal education. Companies care more about your ability to apply AI to real-world problems than whether you have a degree. You can gain credibility by taking online AI courses, working on AI projects and contributing to open-source communities. Certifications from platforms like Google AI, DeepLearning.AI, and IBM can also help you demonstrate expertise without a traditional degree.