en.Wedoany.com Reported - June 5 – A report titled "2026 IT Certification Value Employer Report" released by Pearson, a global lifelong learning company, shows that high-impact technology fields such as AI, machine learning, cybersecurity, and cloud computing are becoming the most prominent areas of IT skill gaps for global enterprises. The report also indicates that among surveyed Chinese business managers, 42% believe their IT teams have not yet fully acquired the latest skills needed to address current technological developments, with cloud computing and artificial intelligence/machine learning identified as the most significant shortcomings.
The value of this report lies not only in listing the skills that enterprises lack but also in revealing that the construction of digital capabilities in the AI era is shifting from "buying technology" to "supplementing talent." In recent years, enterprises have rapidly increased investments in cloud platforms, large models, data systems, cybersecurity, and automation tools. However, whether these technology systems can truly yield returns depends on whether the IT team possesses the capabilities to deploy, maintain, govern, and continuously optimize these systems. Cloud computing serves as the foundation of enterprise digital infrastructure, directly impacting system migration, elastic resources, cost optimization, data architecture, and multi-cloud management. Artificial intelligence and machine learning further require teams to understand model invocation, data quality, algorithm evaluation, security boundaries, and business scenario adaptation. The report shows that the most prominent skill gaps globally are concentrated in AI and machine learning, cybersecurity, cloud computing, data science, IT project management, and software development, indicating that enterprises are not facing a shortage of individual positions but rather a composite capability gap as digital transformation enters deeper waters. For Chinese enterprises, 78% of surveyed managers listed cloud computing as a prominent gap, and 74% cited artificial intelligence/machine learning. This means that while enterprises are accelerating cloud adoption and the introduction of AI tools, their internal teams still need systematic catch-up in cloud-native architecture, AI application implementation, model toolchains, and security compliance.
Certification is becoming an important lever for enterprises to address skill gaps.
The Pearson report shows that employers estimate each certified IT employee can bring approximately $17,646 in additional annual value to the enterprise. If the entire IT team possesses the latest skills, this figure can rise to nearly $20,000. Behind this number is not the formal value of the certificate itself, but the enterprise's desire to reduce project risks, improve team delivery quality, and shorten the adoption cycle of new technologies through a verifiable skills system. Especially in AI and cloud computing scenarios, enterprises cannot rely solely on a few experts or external suppliers for long-term support. Frontline IT teams need to master a complete set of capabilities, from infrastructure management, data pipeline construction, model deployment, system monitoring, to security governance. The report also shows that 78% of surveyed Chinese enterprises bear the cost of IT certifications for their employees, the highest proportion among all surveyed countries. This phenomenon indicates that Chinese enterprises have a strong willingness to invest in internal talent retraining and also reflects that as the pace of technological role updates accelerates, enterprises prefer to enhance existing teams through training and certification rather than relying entirely on external recruitment to fill gaps.
From an industry perspective, skill gaps in cloud computing and AI will directly impact the speed of enterprise intelligent upgrades. Insufficient cloud computing capabilities can lead to bottlenecks in system migration, resource scheduling, cost control, and data governance. Insufficient AI capabilities can keep large models, intelligent agents, automated analysis, and machine learning projects at the pilot stage, making it difficult to integrate into core business processes. As enterprises begin to apply AI to customer service, R&D, marketing, supply chain, finance, risk control, and industrial sites, IT teams need to simultaneously understand business requirements, technical architecture, data security, and model effectiveness evaluation. The role of the certification system is to transform these capabilities from scattered experiences into clearer learning paths and job standards, helping enterprises assess whether their teams have the foundational conditions to undertake new technology projects. For training institutions, cloud service providers, certification bodies, and corporate universities, such reports also send clearer market signals: future IT training demand will be more concentrated on AI engineering, cloud-native, security governance, data science, and cross-departmental digital collaboration capabilities.
Pearson's report also reminds enterprises that the release of AI value does not rely solely on purchasing models or deploying platforms. Whether technology investments can be transformed into productivity ultimately depends on whether employees understand the technology, can use it correctly, and embed it into real business processes. Chinese enterprises' willingness to bear IT certification costs provides a foundation for internal skill upgrades, but the more critical follow-up is to connect certification with job capabilities, project delivery, promotion systems, and business outcomes, ensuring that cloud computing and AI training truly serve enterprise digital transformation.
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