Artificial intelligence is changing how businesses work, but it is also changing what employers look for when hiring and developing talent.
Today, having strong technical expertise is still important. At the same time, professionals increasingly need to understand how AI tools work, where they can add value, and how to use them responsibly. This raises an important question: Do employers want technical expertise or AI fluency?
The answer is increasingly less about choosing one over the other.
As AI becomes part of everyday business operations, employers are looking for people who can combine technical knowledge, AI fluency, problem-solving ability, and human skills.
The shift is already visible in global workforce research. The World Economic Forum's Future of Jobs Report based on more than 1,000 employers representing over 14 million workers, identifies AI and big data, networks and cybersecurity, and technological literacy among the fastest-growing skills through 2030. At the same time, analytical thinking, creative thinking, resilience, flexibility, and lifelong learning remain important.
What Is AI Fluency?
AI fluency does not necessarily mean becoming an AI engineer.
Instead, AI fluency means having enough knowledge and practical ability to understand, use, evaluate, and work effectively with AI systems.
For many professionals, this could include:
- Understanding what AI can and cannot do
- Using generative AI tools for appropriate tasks
- Writing effective prompts
- Reviewing and improving AI-generated outputs
- Recognizing inaccurate or unreliable information
- Understanding basic AI risks and limitations
- Protecting sensitive information when using AI tools
- Knowing when human judgment should take priority
The OECD describes growing demand for both specialized AI professionals and workers who have a more general understanding of AI. It also highlights the importance of expanding AI literacy across the workforce.
Therefore, AI fluency is becoming relevant far beyond traditional technology jobs.
Why Technical Expertise Still Matters
The growing importance of AI does not make technical expertise irrelevant.
In fact, AI can make domain knowledge even more valuable.
Consider a few examples.
A graphic designer who understands design principles can use AI image tools more effectively than someone who only knows how to generate images.
A software developer with strong programming fundamentals can better evaluate AI-generated code and identify errors.
An accountant who understands financial rules can review AI-generated analysis more effectively.
A technician who understands machinery can use AI-assisted diagnostic systems while recognizing when a recommendation does not make practical sense.
In each case, AI can support the professional, but domain expertise provides the context needed to use AI correctly.
This distinction matters because AI-generated output is not automatically accurate simply because it looks convincing.
The Rise of the Hybrid Professional
The workplace is increasingly creating demand for professionals who can combine different types of skills.
Think of this as a hybrid skill set:
Technical expertise + AI fluency + human skills
Technical expertise provides the foundation.
AI fluency helps professionals use emerging tools.
Human skills support communication, judgment, collaboration, creativity, and decision-making.
The World Economic Forum's 2025 research reflects this combination. AI and big data are identified as the fastest-growing skills, while analytical thinking remains the leading core skill reported by employers. Creative thinking, resilience, flexibility, agility, and curiosity are also expected to increase in importance.
This suggests that the future workforce will not simply be divided into "technical people" and "AI people."
Instead, many roles may require professionals to understand both their field and the technology changing that field.
AI Fluency Is Not the Same as AI Expertise
It is also important to distinguish AI fluency from advanced AI expertise.
An AI researcher may need advanced knowledge of machine learning, statistics, model development, and computing.
A marketing professional may only need to understand how to use AI for research, content ideation, customer analysis, and workflow automation.
A human resources professional may need AI literacy to evaluate recruitment tools and understand their limitations.
A customer support employee may use AI-assisted response systems without needing to understand how the underlying model was trained.
Therefore, AI skills should match the responsibilities of the role.
Not everyone needs to become an AI specialist. However, more professionals may need enough AI literacy to work effectively in an AI-enabled workplace.
Why Employers Are Paying Attention to AI Skills
Businesses are adopting AI to improve productivity, automate repetitive work, analyze information, support decision-making, and develop new products and services.
The World Economic Forum reports that 86% of surveyed employers expect AI and information-processing technologies to transform their businesses by 2030.
This transformation creates a practical challenge.
Businesses do not simply need AI tools. They need people who can use those tools effectively.
That means employers may increasingly value candidates who can demonstrate how they use technology to solve real problems.
For example, instead of simply writing "AI knowledge" on a resume, a candidate could demonstrate experience using AI to:
- Analyze information
- Automate repetitive tasks
- Improve customer communication
- Support research
- Generate and refine content
- Assist with coding
- Organize data
- Improve workflows
The important point is not simply knowing the name of an AI tool. It is understanding how that tool can contribute to useful work.
Technical Skills Give AI Context
AI can process information quickly, but professionals still need to understand the context behind that information.
Imagine an electrical technician using an AI-assisted diagnostic system.
The system might identify a possible fault. However, the technician still needs practical knowledge to inspect the equipment, verify the diagnosis, consider safety requirements, and decide what action to take.
The same principle applies across industries.
In healthcare, professionals need domain knowledge to interpret AI-supported information.
In construction, workers need practical expertise alongside digital tools.
In manufacturing, technicians need knowledge of equipment, processes, and safety while working with automation.
In finance, professionals need financial knowledge to evaluate AI-generated analysis.
Therefore, AI works best when it complements professional expertise rather than replacing the need for it.
Human Skills Are Still Important
Technology may be changing how people work, but it does not eliminate the need for human capabilities.
The World Economic Forum identifies analytical thinking as the leading core skill among employers surveyed for its 2025 report. Resilience, flexibility and agility, leadership and social influence, creative thinking, and motivation and self-awareness also rank highly.
These skills matter because workplaces involve more than technical tasks.
Professionals need to communicate with colleagues, understand customers, make decisions, resolve problems, manage uncertainty, and collaborate across teams.
AI can support some of these activities, but people remain responsible for many of the decisions and relationships surrounding them.
As a result, developing human skills alongside technical and AI capabilities can create a more balanced professional profile.
AI Is Changing Technical Jobs Too
AI fluency is not only relevant to office workers.
Technical and vocational occupations are also experiencing technological change.
Modern technicians, electricians, mechanics, manufacturing professionals, construction workers, and other skilled workers increasingly encounter digital equipment, automation, sensors, connected systems, and software-based tools.
The World Economic Forum notes that growing roles requiring manual skills increasingly combine those abilities with technological literacy and problem-solving skills.
This creates an important opportunity for vocational education.
A modern vocational program can teach the practical skills required for a profession while also introducing learners to relevant digital technologies.
That combination can help learners prepare for workplaces where physical and digital systems increasingly work together.
What This Means for Students and Job Seekers
For students and early-career professionals, the changing skills landscape provides a useful direction.
Instead of asking, "Should I learn technical skills or AI?", a better question may be:
"How can I use AI to strengthen the skills I already have?"
A practical approach could involve four steps.
1. Build Strong Fundamentals
Start with the core skills required for your chosen profession.
Learn the tools, processes, concepts, and practical techniques that employers expect.
2. Develop AI Literacy
Learn how AI tools work at a practical level and understand their strengths and limitations.
You do not need to master advanced machine learning unless your career requires it.
3. Apply AI to Real Tasks
Use AI to solve problems related to your field.
For example, a digital marketer can use AI for research and content planning, while a developer can use AI coding assistants for development and debugging support.
4. Keep Learning
AI technology is changing quickly.
Continuous learning can help professionals adapt as tools, workflows, and employer expectations evolve.
The World Economic Forum estimates that 59 out of every 100 workers would need training by 2030 under its workforce scenario, reinforcing the importance of ongoing upskilling and reskilling.
Vocational Training Has a Role to Play
The shift toward AI-enabled work also changes what effective vocational training can look like.
Training programs can increasingly combine:
- Practical technical skills
- Digital literacy
- AI awareness
- Industry-specific technology
- Problem-solving
- Communication
- Workplace collaboration
- Continuous learning
This approach helps learners prepare not only for the job they are entering today, but also for the technologies that may influence that job tomorrow.
Skill development organizations such as Meritude can support this transition by connecting practical learning with changing workplace requirements and helping learners build skills that remain relevant as industries evolve.
The goal is not to turn every learner into an AI specialist.
Instead, the goal is to help people become capable professionals who know how to work effectively with modern technology.
What Employers May Look for in the AI Era
The answer to the question in this article's title is becoming clearer.
Employers do not necessarily have to choose between technical expertise and AI fluency.
Increasingly, they need combinations of both.
A strong candidate may bring:
Technical expertise
Knowledge and practical ability in a specific profession.
AI fluency
The ability to use and evaluate AI tools appropriately.
Analytical thinking
The ability to understand information and solve problems.
Adaptability
The willingness to learn as technology and roles change.
Human skills
Communication, collaboration, creativity, leadership, and judgment.
This combination can help professionals contribute effectively in workplaces where humans and AI increasingly work together.
The Future Belongs to Adaptable Skills
AI is changing the workplace, but it is not creating a simple choice between humans and machines.
Instead, it is changing the combination of skills people need.
Technical expertise provides depth. AI fluency provides technological awareness. Human skills provide judgment, creativity, communication, and adaptability.
The International Labour Organization's 2025 analysis also finds that most jobs exposed to generative AI are more likely to be transformed than made redundant because human involvement remains necessary across many occupations.
For workers, this means that learning should not stop with one qualification or one technology.
For employers, it means that building an adaptable workforce may require investment in both technical development and AI literacy.
And for training providers, it creates an opportunity to prepare learners for a workplace where practical expertise and technology increasingly work together.
The future of work may not belong to people who choose technical expertise over AI fluency, or AI fluency over technical expertise.
It may increasingly belong to professionals who can combine both and keep learning as the workplace evolves.