AI 認證在 2026 年是否仍值得考取？價值轉向職位匹配與實作成果

Same question, a different answer

Earlier this year, in Are AI certifications worth the investment?, I argued that AI certifications ranked among the highest-return investments an IT professional could make. Much of that case still holds. But the ground has shifted since I wrote that piece in February, and a responsible update has to say so plainly.

Two of the six flagship credentials I recommended can no longer be earned. Cloud vendors retired them in favor of exams built around generative AI and agents. Model makers and governance bodies have launched entirely new credential families. And employers, buried under AI-polished resumes, have grown more skeptical about what any single credential proves.

My updated verdict: AI certifications are still worth it, but for narrower reasons, for more specific roles and with a shorter shelf life than I suggested in the spring. The value has moved away from the badge itself and toward what the preparation forces you to build. For CIOs, that shift should change how training dollars get spent.

The great certification reset

The biggest change since spring is not a new credential. It is the disappearance of old ones.

Amazon Web Services gave its last Machine Learning Specialty exam on March 31, 2026. In its place, AWS built a role-based ladder that runs from the AI Practitioner through the Machine Learning Engineer Associate to the new Generative AI Developer Professional, which came out of beta this year. AWS also refreshed its Security Specialty exam with dedicated coverage of generative AI and machine learning security. Existing ML Specialty holders keep the credential through its original expiration date, but no one new can earn it.

Microsoft moved even more aggressively. On March 6, 2026, it announced the retirement of seven certifications and the launch of nine AI-focused replacements. The Azure AI Engineer Associate (AI-102), which I featured in the spring, retired on June 30. Its successor, AI-103, leads to the Azure AI App and Agent Developer Associate and centers on Microsoft Foundry, retrieval-augmented generation and agent orchestration. AI-900 gave way to AI-901, the Azure Data Scientist exam was replaced by a new MLOps credential and the venerable Azure Developer Associate became an AI cloud developer exam.

The pattern matters more than the exam codes. Both vendors concluded that credentials built around training models from scratch no longer reflect how enterprises actually work. Most teams now consume foundation models through managed services and spend their effort on integration, retrieval, evaluation, security and agents. The new exams test exactly that.

There are two practical lessons. First, the shelf life of an AI credential is now measured in quarters, not years. Anyone who began preparing for AI-102 or the ML Specialty after reading articles like mine had to rush or start over. Second, many job descriptions and HR requirement lists still name retired exams. A posting that asks for AI-102 is describing a skill set, not a credential anyone can still obtain.

Two new categories worth watching

Model vendor certifications

In the spring, the major AI credentials came from cloud platforms, academic programs and professional bodies. That changed on March 12, 2026, when Anthropic launched its Claude Certified Architect, Foundations exam alongside a Claude Partner Network backed by a $100 million investment. In July the company expanded the program to four role-based credentials covering everyday users, developers and two levels of architects, and reported more than 36,000 consultants certified across more than 1,300 organizations. List prices run from $99 to $175.

Model vendor credentials carry real weight in one specific setting: consulting and partner firms, where certified headcount determines partner tier and client credibility. For an enterprise practitioner, the calculus is different. Model leadership changes quickly, and a credential tied to one model family is a bet on that vendor remaining central to your stack. If your organization has standardized on a model, these exams are inexpensive and tightly scoped. If it runs a multimodel portfolio, as most do, a platform or governance credential will travel further.

The governance stack

The gap my spring article underplayed was governance. As regulation tightens and boards ask harder questions, organizations need people who can audit, secure and manage AI risk, not just build with it. IAPP research from 2026 found that only 1.5 percent of organizations feel fully satisfied with their AI governance staffing.

Three credential families now address that need:

• IAPP Artificial Intelligence Governance Professional (AIGP). Launched in 2024 and refreshed in February 2026 with expanded coverage of agentic AI and ISO 42005, it costs $649 for IAPP members and $799 for nonmembers. It suits privacy, legal and compliance leaders.
• ISACA’s advanced AI credentials. AAIA targets auditors, AAISM targets security managers and AAIR, launched in April 2026, targets enterprise risk owners. Each requires an existing credential such as CISA, CISM or CISSP, which makes them extensions of established careers rather than entry points.
• ISO/IEC 42001 Lead Auditor and Lead Implementer. These are the certifiable credentials for organizations building formal AI management systems.

For security, audit and risk professionals, these programs offer something engineering certifications cannot: a direct link between AI and the accountability frameworks executives already trust.

What the money says now

The headline compensation data has only grown stronger. PwC’s 2026 Global AI Jobs Barometer, which analyzed more than one billion job advertisements across 27 countries, found that the average wage premium for AI skills rose to 62 percent, up from 57 percent a year earlier. Jobs requiring specific AI skills grew 69 percent, compared with 9 percent for the overall job market. The premium varies widely by sector, from as high as 118 percent in consumer markets to 16 percent in government.

Read those numbers carefully, though. PwC measures a premium for AI skills demonstrated in the job market, not for AI certifications. That distinction is the most important refinement I would make to my spring article. Several of the certification-specific salary figures I cited then, including premiums of 23 to 47 percent, came from training and exam-prep sites that have a commercial interest in the answer. They are directionally consistent with the broader data, but I would no longer lean on them. Payroll-based benchmarks such as Ravio’s, which showed a 12 percent premium for individual contributors in AI roles and 3 percent for managers, remain the soberer anchor.

The PwC data also reveals where the jobs are. In the UK alone, almost 66,000 new AI roles went to “AI user” positions inside existing business functions, while roles focused on AI development added just 2,600. The bulk of the opportunity sits with people who apply AI in finance, operations, security and customer work, not with those who build models. That argues for broad literacy and governance credentials reaching far more of the workforce than engineering certifications ever will.

PwC’s leaders framed the winners as companies using AI to amplify human expertise, and the report found employers placing more weight on judgment, creativity and leadership. A certification can document AI fluency. Only experience supplies the expertise and judgment it is meant to amplify.

What hiring managers actually check

Employer surveys released this year paint a more nuanced picture than the salary headlines.

Western Governors University’s Workforce Decoded 2026 report found that 60 percent of hiring professionals say AI is making it harder to evaluate candidates’ real skills. When they assess AI skills specifically, 41 percent look at hands-on, real-world experience with AI tools, while 34 percent look for certifications. Certifications matter, but they rank second.

Resume Genius’s 2026 survey of 1,000 U.S. hiring managers points the same way. Eighty-two percent said certifications can be as valuable as a bachelor’s degree. Yet when asked how they prefer candidates to demonstrate AI skills, only 15 percent chose certifications or courses, and AI and machine learning ranked sixth among priority skills, behind communication, critical thinking, domain knowledge, project management and data analysis.

Put those findings together and a clear role emerges for certifications in 2026. They help a candidate clear the filter and earn the interview. Verification happens afterward, through work samples, technical conversations and references.

There is a counterintuitive upside here. When AI can polish any resume and draft any portfolio narrative, a proctored exam is one of the few signals a language model cannot fake on a candidate’s behalf. Rigorous, scenario-based, proctored credentials may gain credibility precisely because so much else on a resume has lost it. Unproctored completion certificates will likely move the other way.

An updated decision framework

Before committing 80 or more hours to any AI credential, I now suggest four tests:

1. Does your stack or target employer name it? A credential matched to the platform you run every day beats a prestigious one for a platform you never touch.
2. Is it proctored and scenario-based? Exams that test architecture decisions and trade-offs signal more than recall-based quizzes.
3. How current is it, and what is the vendor’s retirement track record? Check when the exam was last revised and whether holders get a renewal or upgrade path when it changes.
4. Will the preparation leave you with something to show? The best programs end with a working project you can demo in an interview. That artifact often outlasts the credential.

Applied to today’s market, those tests point to different choices by role:

The largest group, the many employees who use AI inside their daily work, may not need a paid certification at all. Free vendor academies from Microsoft, AWS, Google and Anthropic now cover practical AI fluency well.

What CIOs should do with training budgets

For IT leaders, the 2026 churn carries a budget lesson. A certification program designed in 2025 may already be funding exams that no longer exist. Here is how I would reshape it:

• Fund ladders, not badges. Tie certifications to the platforms and models your organization actually runs, and map a progression from literacy to practitioner to professional level.
• Budget for churn. Assume a 12- to 24-month useful life for technical AI credentials. Favor vendors that offer free renewal assessments or clear upgrade paths, and reserve funds for re-certification.
• Require a deliverable. Pair every funded certification with a project that solves a real internal problem. You get a working prototype, and the employee gets the portfolio evidence hiring managers now look for first.
• Audit your job descriptions. Remove retired credentials such as AI-102 and the AWS ML Specialty from postings, promotion criteria and vendor contracts, and describe the underlying skills instead.
• Invest in governance talent now. The staffing gap in AI governance, audit and risk is wider than the gap in engineering. Sponsoring existing security, audit and privacy staff into AIGP or ISACA credentials is often faster and cheaper than hiring.
• Scale literacy for free. Use vendor academies for the broad workforce, and reserve paid exams for roles where the credential carries market or contractual weight.

Treated this way, certification spending becomes a structured upskilling program with measurable outputs rather than a reimbursement line item.

The bottom line

In the spring I wrote that the real question was not whether AI certification is worth it, but which credential best positions you for the role you want. That still holds. Six months of retirements, launches and hiring data add two more questions: when, and for how long.

The market rewards AI skills more than ever, with a 62 percent average wage premium and AI-skill jobs growing nearly eight times faster than the overall market. Certifications remain one of the clearest ways to signal those skills and to structure the learning behind them. But they now expire faster, matter most when matched to a specific stack or accountability, and carry the most weight when backed by work a hiring manager can see.

Choose credentials that fit the work you do today and the platform your organization runs. Treat the preparation as the real product. And expect to do it again sooner than you think.