Home » Forget Coding – Employers Want AI-Literate Talent

Forget Coding – Employers Want AI-Literate Talent

by Stella Lee | Feb 16, 2025

Remember the coding hype? Not long ago, everyone was told to learn how to code. Schools rushed to add programming courses, bootcamps promised six-figure salaries, and “learn to code” became the career advice du jour. But here’s the thing: while coding is still valuable, it’s no longer the golden ticket to career success. In fact, as AI programs become more sophisticated, coding skills are becoming less desirable in many roles. Low-code and no-code AI platforms now allow professionals to build applications, analyze data, and automate workflows—without writing a single line of code. Employers today aren’t just looking for programmers—they’re looking for AI-literate professionals who can work alongside AI, critically analyze its outputs, and make informed decisions.

What Employers Are Looking For

AI literacy is more than just understanding what AI is; it involves an intentional development of competencies that enable us to critically comprehend, utilize, evaluate, and collaborate with AI technologies. It’s about gaining a nuanced view of AI’s potential while being acutely aware of its limitations and risks. This understanding helps dispel the common yet mistaken belief that AI is an omnipotent, infallible entity and instead positions it as a powerful but imperfect tool that requires human oversight and critical thinking.

Yes, AI skills are becoming indispensable in today’s labor market, but there is a disconnect between the demand for AI-literate talent and the availability of structured upskilling opportunities.  While many organizations prioritize hiring employees with AI knowledge, far fewer are investing in training their current workforce to adapt to AI-driven changes. In fact, according to one report, only 38% of US employers offer AI literacy training —a significant gap that leaves many professionals unprepared for the evolving workplace.

This raises an important question: What exactly are employers looking for in AI-literate talent? And how do we better prepare ourselves and others to meet these expectations? 

Let’s explore how different areas of AI literacy equip us to navigate the modern workplace, enhance our professional value, and stay competitive in the current job market:

1. The Foundational Understanding of AI

Employers don’t expect everyone to be AI developers, but they do expect professionals to have a working knowledge of AI concepts and their real-world implications. These include the abilities to:

  • Differentiate different types of AI. Not all AI systems function the same way—while traditional AI models rely on structured decision-making and predefined rules, Generative AI (GenAI) creates new content by identifying and replicating patterns in data used to train AI models. Understanding these differences helps staff determine the right AI tools for specific tasks and apply AI solutions more effectively in their workflows.  

  • Recognize AI’s history and evolution. AI didn’t just appear in 2023 – its foundations date back decades, from early expert systems to modern deep learning breakthroughs. Knowing AI’s trajectory helps professionals contextualize hype cycles and set realistic expectations for its impact on their industry.    

  • Understand AI’s capabilities and limitations. People’s reactions to AI range from excessive enthusiasm to deep fear. Employers need staff who can realistically assess what AI can do well and where it still struggles.  AI-literate professionals should be able to leverage AI’s strengths while identifying areas where human oversight is necessary.

  • Recognize how AI models reply on data. AI is only as good as the data it’s trained on. Professionals need to know why data quality matters, how training data impact AI models.

2. Human-AI Collaboration

As more organizations implement AI tools at scale, employers will need staff who can actively participate alongside AI. The concept of Human-in-the-Loop – where humans oversee, refine, and collaborate with AI – is rapidly becoming a workplace requirement. To effectively collaborate with AI, professionals should be able to:

  • Effectively use AI tools and understand their roles in the workflow. AI is a tool, not a decision-maker. Knowing when and how to integrate AI ensures it complements, rather than complicates, workplace processes.

  • Critically evaluate AI tools. It is a challenge to navigate an overwhelming array of tools with many that are overhyped or outright ineffective. Knowing how to evaluate AI tools critically prevent us from blindly trusting marketing claims and falling for AI snake oil.

  • Assess AI-generated outputs. AI models can produce inaccurate, biased, or misleading information if not carefully monitored. Professionals need the skills to verify the models’ accuracy, assess their relevance in context, and identify errors before they impact decision-making. 

  • Recognize when AI is not the right solution.  AI isn’t always the answer. AI-literate professionals should know when to trust AI and when to pivot to a human-driven approach. Consider Japan’s experiment with robotic hotel staff—after realizing the robots weren’t improving efficiency and often created more problems than they solved, the hotel laid off its AI-powered employees.

3. Ethics and Responsible Use of AI

The ethical use of AI is one of the most pressing challenges organizations face today. While AI offers immense benefits, it also introduces risks related to bias, misinformation, and unintended consequences. As AI systems become more integrated into business and learning environments, responsible AI use isn’t just a technical issue—it’s a professional responsibility.

AI-literate professionals should be able to:

  • Spot misinformation, bias, and hallucinations. AI models are not infallible. They can generate false information with high confidence, reinforce biases present in training data, and even fabricate content (known as “hallucinations”). Being able to spot these errors needs to be integrated into our daily practices.

  • Contribute to AI policies, guidelines, and best practices. AI ethics isn’t just the responsibility of tech teams—every professional interacting with AI should have a say in how it’s used responsibly.

  • Mitigate risks when things go wrong. AI errors are inevitable, but a solution-oriented mindset is key to addressing them effectively. Instead of simply blaming the technology, AI-literate professionals should focus on understanding what went wrong, why it happened, and how to prevent similar issues in the future.

  • Draw from insights from other industries. Many AI challenges are not unique to any one field—learning from case studies across different sectors helps organizations anticipate and avoid potential pitfalls. Consider the example of Fable, a US-based reading platform, faced backlash after its Reader App produced offensive and questionable comments. The controversy highlighted the risks of unchecked AI outputs and unchecked training data can quickly become liabilities rather than assets.


What This Means for Higher Education & Workforce Development

If schools and workforce development programs focus only on teaching technical AI skills, they’ll miss the bigger picture. AI is impacting every industry, and we need to prepare talent—regardless of job title—to work with AI responsibly.

  • AI education must go beyond STEM. AI is not just a technical field—it has ethical, economic, and social implications. A cross-disciplinary approach that integrates AI literacy into business, healthcare, the humanities, and the arts will equip professionals to apply AI in a holistic way.

  • Technology is secondary. Critical thinking comes first. AI tools will continue to evolve, but the ability to think critically, evaluate AI outputs, and apply sound judgment will always be essential. Workforce development programs must teach people how to think with AI, not just how to use it.

  • Integrate 21st-century skills. AI proficiency should be paired with skills like problem-solving, adaptability, communication, and ethical reasoning. These human-centric capabilities ensure that AI is used as an enabler, not a decision-maker.

  • Hands-on experience matters. Employers prioritize practical knowledge over theoretical learning. AI literacy programs must include real-world applications, internships, and project-based learning that allow learners to work with AI tools, interpret AI-driven insights, and navigate AI-powered workflows.

  • Ethics and responsible AI use must be a priority, not an afterthought.  AI systems can reinforce bias, generate misinformation, and have unintended consequences. Every AI literacy program must emphasize ethical considerations—ensuring that professionals understand how to mitigate AI risks, advocate for transparency, and use AI in ways that align with organizational and societal values.

Final Thought: Are You AI-Ready?

The workforce of the future isn’t just made up of coders—it’s made up of AI-literate, adaptable professionals who can leverage AI without losing their critical thinking skills.

The real question isn’t “Can you code?” but “Can you work with AI?”

Are you ready?