Omar Chihane explains why the future of skills demands that AI literacy and communication evolve together
Across regions, sectors, and economies, the labor market is sending a remarkably consistent signal about the future of skills and learning. The fastest-growing jobs today are not defined solely by technical expertise. They are increasingly shaped by the ability to apply technology in real-world contexts, collaborate across boundaries, and communicate value clearly to others.
Recent labor market data makes this shift visible. In LinkedIn’s 2026 Jobs on the Rise rankings, roles related to artificial intelligence dominate the list of fastest-growing occupations in the US. Positions such as AI engineer, AI consultant, data annotator, and machine learning researcher have seen rapid hiring growth over the past several years. These rankings are based on millions of job transitions and hiring patterns observed on the LinkedIn platform.
At first glance, this may appear to reinforce the idea that the future belongs primarily to highly technical specialists. A closer look tells a more complex story.
While demand for AI engineers and technical experts remains strong, growth is accelerating in roles that sit between technology, strategy, and execution. Titles such as AI consultant, product manager, business analyst, and digital transformation lead are rising quickly. These roles do not focus on building models in isolation. Instead, they center on understanding how AI can be applied responsibly, integrated into workflows, and communicated effectively across organizations.
This pattern matters deeply for educators, policymakers, and language professionals. It suggests that workforce readiness in the AI era depends not only on technical literacy but on the ability to interpret, explain, and collaborate. Language is central to that process.
What Labor Market Data Is Really Telling Us
LinkedIn’s broader labor market analysis shows that jobs requiring AI literacy have grown by roughly 70% year over year in the US. At the same time, many of the fastest-growing roles emphasize cross-functional collaboration, stakeholder communication, and strategic decision-making.
Consider the role of AI consultant, one of the fastest-growing positions in recent LinkedIn rankings. These professionals help organizations identify where AI adds value, assess risks, and guide adoption across teams. Their effectiveness depends as much on communication and judgment as it does on technical understanding. They must explain complex ideas to nontechnical audiences, align teams around shared goals, and navigate ethical and organizational considerations.
Similarly, roles such as data annotator, which play a critical role in training AI systems, require attention to linguistic nuance, cultural context, and clarity of interpretation. These jobs sit at the intersection of technology and language, underscoring that human communication remains deeply embedded in AI systems themselves.
Across regions and industries, the same signal appears. Growth is strongest in roles that combine technical exposure with the ability to translate insight into action. The challenge for education is not simply to teach more technical skills but to prepare learners to use those skills in communicative, collaborative environments.
AI Literacy Is Becoming Foundational, but Language Makes It Usable
Artificial intelligence is increasingly embedded in everyday work. Professionals interact with AI through language by prompting systems, reviewing outputs, explaining insights, and documenting decisions. In this sense, AI literacy and language proficiency are inseparable.
Yet education systems have historically treated them as separate domains. Technical programs emphasize coding, systems, and analytics. Language programs focus on fluency, comprehension, and expression. While both approaches are valuable, they no longer reflect how work actually happens.
In practice, the ability to apply AI effectively depends on communication. Professionals must articulate use cases, question outputs, collaborate with diverse teams, and make decisions that others can understand and trust. These are fundamentally language-mediated skills.
This reality reframes the role of language education. Communication is not an add-on to technical expertise. It is the mechanism through which technical knowledge becomes meaningful and impactful.
Implications for Higher Education and Language Learning
For higher education institutions, these labor market signals call for a more integrated approach to curriculum design. AI literacy should not be confined to computer science programs, nor should language learning be isolated from real-world application. Students across disciplines need opportunities to engage with AI tools while practicing communication, critical thinking, and ethical reasoning.
Project-based learning offers one promising path forward. When students are asked to analyze data, use AI-enabled tools, and present findings to diverse audiences, they develop both technical fluency and communicative competence. They learn not only how systems work but how to explain, question, and apply them responsibly.
Language education, including English language instruction, plays a critical role in this process. Real-world language use involves far more than grammatical accuracy. It includes synthesizing information, participating in discussions, writing analytically, and collaborating across cultures. These are precisely the skills demanded by AI-enabled workplaces.
For English language learners, the stakes are particularly high. English continues to function as a global language of collaboration in higher education, research, and multinational organizations. Proficiency in English enables access to learning resources, professional networks, and cross-border opportunities. As AI reshapes work, the ability to communicate effectively in English becomes even more closely tied to mobility and participation.
Workforce Readiness and Equity in the AI Era
As AI and communication skills become increasingly intertwined, there is a risk of widening existing inequities. Learners without access to integrated instruction, technology, or assessment may find themselves excluded from emerging opportunities, not because of lack of ability but because of structural gaps in preparation.
Addressing this risk requires intentional design. Educators need support and professional development to confidently integrate applied AI literacy into language-rich learning environments. Assessment systems need to measure applied proficiency, the ability to use language and tools together in authentic contexts, rather than isolated skills.
Policymakers and education leaders also have a role to play. Standards and funding models should encourage cross-disciplinary learning that reflects real-world demands. Preparing learners for the future of work means recognizing that technical skills and communication skills develop most powerfully when taught together.
Listening to the Signal
The fastest-growing jobs are not calling for less humanity in work. They are calling for more judgment, more collaboration, and more communication alongside technological fluency.
Labor market data makes clear that AI literacy is becoming foundational across sectors. At the same time, the roles growing most rapidly are those that rely on the ability to translate insight into action through language. Communication remains central to how value is created, shared, and sustained.
For those working in language, literacy, and education, this moment offers clarity. Language education is not peripheral to the future of work. It is essential to it.
If we listen carefully to what the labor market is telling us, we can design learning pathways that prepare individuals not just to adapt to change, but to shape it thoughtfully and responsibly.
Omar Chihane is global general manager, TOEFL, at Educational Testing Service (ETS).