The rise of artificial intelligence (AI) during the last decade in what experts have titled the Dot-AI Bubble has set forth an uncomfortable binary: a jobless future versus the leveraging of emerging technologies to enrich human productivity and work experience.
Prior to the 2022 rise of OpenAI’s ChatGPT, the National Bureau of Economic Research had detailed the pressing importance of AI in the American workforce, signaling that its early adoption was characterized by the takeoff of AI vacancy postings, an alteration in the types of skills demanded by companies, and lower overall hiring.
From 2010 to 2020, hirers thus responded to emerging technologies by opening AI-specific jobs, expecting employees to possess previously undesirable or unknown skills, and establishing the automation of some tasks formerly performed by human labor. As such, while AI posed an increased demand for non-automated tasks, it also reduced hiring due to AI-powered automation processes.
Studies have since focused on AI’s impact on specific labor market outcomes and technology applications, including machine and deep learning, robotics, and employment. However, there has been no scholarly consensus on its overall impact, for which it might be prudent to follow the recommendations of some researchers, who stress that “innovation can have good and bad effects, and those positive and negative outcomes are typically unevenly distributed.”
Regardless, the World Economic Forum’s (WEF) Future of Jobs 2025 report found that 59% of the global workforce will need reskilling and upskilling in the next half decade. Consequently, the most important core skills employers will be looking for include analytical thinking, resilience, leadership, technological literacy, and creative thinking, although those on the rise include AI, big data, networks, and cybersecurity.
Irrespective of academic and sectoral uncertainty, the pace and scale of AI integration are uneven across industries and geographies. As per Kryterion’s most recent book by Dr. Leslie Thomas, advanced economies- those possessing infrastructure, capital, and skilled workforces- and those with lenient AI regulations, like the United States, will integrate AI more rapidly. Those with developing economies, such as Mexico, or more comprehensive AI frameworks, like the European Union, will integrate AI more slowly.
“Disruption occurs unevenly across industries, markets, and individuals. We don’t all experience the future simultaneously,” Andrew Ng, co-founder of Google Brain and former chief scientist at Baidu, has stated.
Other factors that contribute to the irregular application of AI are industry and job variability. In this, industries like technology and retail, which have historically had integrated digital processes, will adopt AI capabilities more quickly, in contrast to those with complex ethical considerations, like healthcare or legal services. Similarly, white-collar jobs, and especially those relating to data processing, scheduling, or basic analysis, will be the first affected by automation, as opposed to high-risk and highly-regulated professions, from surgeons to airline pilots.
Regardless, the truth remains that all industries will inevitably see some level of employee dynamism because of AI. For instance, although healthcare will experience delayed impact, it could still face AI task automation in diagnostic imaging analysis, personalized medicine pathways, and drug development, to name a few. In parallel, agriculture could also be impacted through precision AI-powered technologies, crop health monitoring, and yield prediction.
“The real disruption of AI isn’t in the jobs it eliminates but in the new roles it creates. Now more than ever, certification programs must focus on training employees to become leaders of the machines, not victims of them,” noted expert Roberto Peñacastro, CEO at Leadsales.
“The focus shouldn’t be on replacing human labor with AI but on equipping workers to leverage AI as a strategic partner in every industry,” Peñacastro added.
Jobs will therefore be reconfigured, and companies will move away from static job roles in favor of deploying skill-based talent flexibly, identifying opportunities for automation, and optimizing their reskilling and upskilling strategies. Some jobs will remain humanly performed, especially those requiring emotional intelligence and ethical judgement, although others will be enhanced by AI or fully automated.
Here is where the role of secure and accessible certifications comes in; although there is much room for debate as to the detailed impact AI will have- and is currently having- on the workforce, certifications will be essential in not just closing skills gaps, but also building credibility, streamlining hiring, and democratizing access to advancement.
Workforce in transition
Although historically certifications and licensure programs have been developed with single and monolithic structures, such a model has become less practical on the eve of AI implementation, which calls for a modular approach, such as using micro-credential models with greater flexibility.
Now, however, credentialing organizations face a pivotal moment as companies restructure work towards task-based and skills-driven approaches for the effective leveraging of AI. Both employees-to-be and companies seek enhanced adaptability, incremental recognition and pathways, and the possibility for customization.
That is not to say that credentialing will lose its validity, reliability, or fairness. As much as AI implementation and application are broad across industries, so must be licensure programs that certify workers to comply with job demands. In this, although customization and flexibility might not be called for in all professions, a more modular approach guided by constant conversation with companies is imperative.
A 2023 study, for instance, found that micro-credentials offer both companies and employees the flexibility they need within the current shifting workforce, but have also become increasingly relevant when considering the needed training and reskilling emergent after the COVID-19 pandemic, as well as in scenarios following natural disasters.
A pressing example of such measures is Greece, which, although a member of the comprehensively legislated European Union, has responded to changing labor demands in skills and a paradigm shift in training with its 2021 launch of the National Skills Strategy, under the context of the Recovery and Resilience Facility. Through it, the country has promoted micro-credentials in both public and private sectors and demonstrated that such dynamism is not only possible but pressing in all modern contexts.
In a private sector context, however, a 2024 article found that the energy industry has also resorted to micro-credentials in order to target and upgrade their teams’ knowledge base, thus addressing the skills gap that some workers may experience on the eve of decarbonization and energy transition. Programs of micro-credentialing and refreshed modern certification supported mid-to-senior level energy professionals, and allowed them to acquire the foundational knowledge and skills that their dynamic sector now requires.
“While artificial intelligence has been a buzzword across industries, companies worldwide have yet to harness the full potential,” noted Dr. Ranjit Tinaikar, CEO of Ness Digital Engineering, while in conversation with The Hindu in early 2025.
“Leaders must first grasp the concept of AI to effectively guide their organisations through the transformative journey it entails. This journey is about re-skilling people,” Tinaikar further added.
The forthcoming half decade will thus be essential not only for companies in their implementation of AI but also for the certifications industry, which will also be forced to reckon with emerging employer trends. In this, certifications can serve as both buffers and bridges for workers across all sectors, a cornerstone that underlines new skills-based hiring processes.
Current workers across all sectors will be looking to respond to companies’ call for upskilling and reskilling, as AI has made evident. But certifications, licensure, and testing issuers must refer to this skills-based paradigm
By 2030, as Dr. Thomas highlights, the share of human tasks in the workforce will decrease from 47% in 2025 to 33%, while tasks handled by technology will rise from its current figure of 22% to 34%. However, 78 million net new jobs will be created in this timeframe, too.
“Many of these emerging jobs are ones we haven’t even fully imagined yet. After all, few of us had heard of a ‘prompt engineer’ before 2023, yet it rapidly became a much sought-after job role,” Dr. Thomas noted.
“We must ensure our credentials mirror the evolving job landscape, enabling certified individuals to continue to differentiate themselves both today and tomorrow,” she concluded.
Article Co-Authored by Salomé Beyer Vélez
