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Imagine a world where marketing managers oversee AI copywriters, sales leaders direct AI-powered CRM systems, and engineers supervise code-generating agents. This is already starting to happen.
By 2030, AI is projected to displace 92 million jobs while creating 170 million new ones, according to the World Economic Forum. Rather than replacing humans, AI is redefining their roles. In the near future, individual contributors will transition into AI managers who orchestrate workflows between human creativity and machine efficiency. Instead of coding or other technical skills, the most sought-after skill of tomorrow will be the ability to manage AI systems and teams of AI agents effectively. The key to surviving this shift? AI literacy.
The AI literacy divide: Beyond hype to practical mastery
A recent study from Deloitte found that only 20% of leaders feel the talent at their organizations is prepared to deploy AI successfully. Many assume AI integration requires hiring armies of machine learning engineers. This misconception is as outdated as believing every company needs a team of electrical engineers to use lightbulbs.
AI is infrastructure, not magic. You don’t need to understand AI transformer architectures any more than you need to grasp TCP/IP protocols to send an email. The problem for most organizations is simply that employees don’t know how to leverage AI tools effectively. The challenge for leaders today, then, lies in bridging the gap between awareness and applied proficiency.
The three pillars of AI literacy
The good news is that anyone can learn AI literacy. Today’s business leaders can start by building their team’s, and their own skills in three core areas of AI literacy: understanding what AI can do, improving prompting skills, and managing AI’s limitations.
1. Generative AI awareness
AI evolves faster than human intuition. Six months ago, ChatGPT couldn’t generate realistic images (remember the people with tiny teeth?). Today, tools like Midjourney v6 produce photorealistic outputs indistinguishable from human-created content.
2. AI prompting proficiency
Effective AI use requires structured prompts. For example, I use a 5C prompting framework: clarity, contextualization, command, chaining, and continuous refinement.
- Clarity: Start with a clear and specific task for the AI agent. A marketer might start with, “generate 10 search ads for an online learning solution.”
- Context: Then, share relevant context. Continuing the example above, the marketer might add details about their company, define the audience for the ad, and state the goals of the ad campaign.
- Command: Here, I specify what the output should look like. The marketer might include, “format the 10 search ads in a table, including relevant SEO keywords as an additional column. Limit each ad to 100 characters.”
- Chaining: This is also known as “chain of thought” prompting. Spell out the specific tasks you’d like the AI agent to complete and in what order. The marketer might say, “Start by reviewing recent ads from X, Y, and Z learning companies.”
- Continuous refinement: The final step will depend on the output from the AI agent. It’s up to you, the human, to review the output and ask for revisions from your AI agent as needed.
3. Manage limitations
AI hallucination rates hover around 3% for top models—a small percentage, but one that can have huge impacts. Human oversight of AI outputs is critical. When an AI-generated legal brief cited non-existent cases a few years ago, it wasn’t the tool that failed; it was the human who skipped verification.
Build an AI-literate organization
For hiring managers and business leaders, an easy place to start is by embedding AI literacy into job descriptions. For example: In the 1990s, just about every job that involved computers asked for Excel proficiency. Soon, AI workflow design will define many of tomorrow’s roles. To get your teams ready for this shift, prioritize hands-on AI training at your company. Reading about AI is like learning to swim from a book. Eventually, you need to dive in to gain mastery of the tools.
This doesn’t mean that soft skills no longer matter—in fact it’s quite the opposite. Leaders should be helping their teams refine the human skills that will matter most in the near future. A few of these include strategic decision making, to ensure AI agents are carrying out the right tasks to meet business goals, and empathetic communication to lead, inspire, and collaborate effectively with other humans.
AI as an amplifier of human potential
History shows that rather than eliminating the need for human skills, technological revolutions amplify it. While the printing press reduced demand for scribes, it also created publishers, journalists, and educators—and ultimately, led to increased rates of literacy across the globe. Similarly, rather than replace humans, AI will empower those who master it to achieve new levels of productivity and innovation.
The most successful organizations will be those that view AI not as a threat, but as a force multiplier. By investing in AI literacy today, business leaders are doing more than future-proofing their workforce; they’re unlocking human potential to solve problems once deemed impossible.
The question isn’t whether AI will change people’s jobs. It’s whether you’ll be the one wielding it—or watching from the sidelines.
Tigran Sloyan is cofounder and CEO of CodeSignal.