Is getting an AI certification worth it? Factors to consider

The value of certifications

Certifications confirm that someone has completed a course successfully and demonstrated basic competence in a specific subject, usually through an exam. Compared with traditional degrees, certifications are typically more focused and quicker to obtain. Whereas a technical professional might earn only one or two degrees, they can earn many certifications throughout their career.

The tech industry has long been rife with certifications. Industry and government groups, such as CompTIA or NIST, drive some, while others are vendor-specific, and designed to show expertise in specific products or systems.

However, the value of certifications is often debated. Traditional degrees are accredited -- meaning that they are validated against strict academic standards -- but no comparable established format or industry-wide content requirements exist for certifications. Because any vendor or group can create a certification, certifications can vary dramatically in difficulty, educational effectiveness, quality and value to both employee and employer.

Even so, certifications are useful for establishing a baseline of competence, especially for technical fields where knowledge and skills evolve rapidly. For employers, certifications help weed out candidates without the requisite skills, while job candidates can use them to prove their expertise. And for existing employees, a certification might be important for career advancement and new opportunities, such as involvement in future AI projects.

Choosing the right AI certifications

Selecting the right AI certification requires a strategic approach, as the best choice varies for each person. The following are some key factors to consider:

• Career alignment. Choose a certification that supports long-term career goals. For example, a data scientist might get the most value from a course focused on techniques for ensuring data quality and managing bias in machine learning training data sets.
• Course content. Review the course outline thoroughly to determine the certification program's scope and coverage. If the course does not cover important areas for the learner or the business, it might be worth considering alternative certifications.
• Real-world applicability. Certificate earners should be able to apply course learnings to real-world situations. Look for courses that focus on practical and real-world examples that can be used for the AI projects that the business intends to pursue while avoiding overly complex, theoretical or impractical certifications.
• Instructor expertise. Instructor-led courses tend to be more valuable than self-study options. Look for an instructor team with expertise and industry leadership to deliver quality education and answer challenging questions.
• Organizational reputation. A certificate is often only as good as its provider's reputation. Certifications from well-recognized organizations with a history of strong course design, detailed coverage and objective testing are typically more respected by employers and peers.

The potential downsides of AI certification

With the explosive growth of AI, certifications seem like an obvious choice for professional development. However, AI's rapidly evolving nature introduces risks for employees and employers seeking certification.

AI certifications carry value proposition risks, and the fast pace of change brings heightened volatility. Before investing in an AI certification, consider the following:

• Incomplete knowledge. The broad field of "AI" can encompass many areas of study, from prompt engineering and generative AI to advanced mathematics and data science techniques. No standards bodies define what needs to be covered in an AI certification, so it's easy to find certifications that lack scope and depth.
• No standard techniques or concepts. Certifications often teach specific methodologies, but there's no guarantee that those approaches are -- or will ever become -- the generally accepted industry standard.
• Limited recognition. A certification's value depends largely on how widely it's accepted. Some AI certifications -- especially niche or vendor-specific certifications -- might lack the credibility or legitimacy needed to impress potential employers or lead to opportunities for career advancement.
• Rapid obsolescence. As noted above, AI technologies advance quickly, with new platforms, features and capabilities appearing regularly. The ideas, approaches and methodologies taught in a certification course can become obsolete quickly, putting the time and money invested in the certification at risk.
• Need for ongoing maintenance. To keep certification status current, many certifications require periodic renewal through regular retesting or updated coursework. This is a mixed blessing: While it ensures that learners keep their skills current, it also demands ongoing effort and, often, financial investment.

Other types of AI education

Although AI certifications help establish expertise and experience, they are not the only way to learn about AI. Technical professionals can enhance their AI education in several other ways:

• Technical books. Readers can refer to countless books from major publishers on AI topics, including computer programming, data science, math and the operational aspects of AI systems.
• Hands-on practice. Practical experience with AI tools can deepen understanding. For example, improving prompt engineering skills is often best done through hands-on practice with AI tools such as ChatGPT and Dall-E. Many platforms also offer extensive documentation, including operational references, tutorials and examples.
• On-the-job experience. Technology professionals can get involved in AI projects within their organizations. For example, software developers can contribute to coding and data tasks, or join operations teams for AI training, deployment and ongoing operations. This direct experience is an ideal way for newer technologists to gain mentorship and guidance from more experienced staff.

Although these alternative learning approaches might not be sufficient to ensure full competence, they can be a worthwhile addition to formal certifications.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 20 years of technical writing experience in the PC and technology industry.