What AI Literacy Actually Means

AI literacy is not about becoming a machine learning engineer. It's not about understanding neural networks or training models. It's about using AI tools effectively to get work done - the same way people learned to use spreadsheets, word processors, and email.

Think about Microsoft Office. In the 1990s, companies didn't outsource Office usage to consultants. They trained their employees. They gave people computers, showed them how to use Excel and Word, and let them practice. Over time, Office literacy became universal. Today, no one says "our people aren't technical enough to use Excel." It's expected.

AI literacy is the same. It's a foundational skill that every employee should have, at every level of the organization - from the C-suite to customer-facing teams. And just like Office, the best way to build AI literacy is to give people access to tools, teach them how to use those tools, and let them apply AI to real work.

Why Your Domain Experts Are the Key

Here's what makes AI different from previous technology shifts: the interface is natural language. Plain English is the programming language. Prompting, context engineering, and agent design are skills that domain experts can learn faster than AI specialists can learn your domain.

This flips the traditional equation. A marketing professional who understands customer behavior and learns to prompt effectively will create more value than an AI engineer trying to learn marketing. A supply chain manager who understands logistics and learns to build agents will solve real problems faster than a consultant who understands neither. A finance analyst with AI literacy can surface insights that would take weeks manually.

The key insight: AI enables domain experts to do their jobs better. It doesn't replace domain expertise with AI expertise. That's why internal capability matters. The people who know your business best are the ones who should be using AI - not external consultants who don't.

What This Looks Like in Practice

At the individual level, AI literacy means employees can:

• Use AI to improve how they currently do their jobs (faster research, better writing, smarter analysis)
• Leverage AI agents to automate repetitive tasks, speed up processes, and reduce errors
• Think creatively about how AI could enable entirely new ways of working

At the organizational level, AI literacy means:

• Employees across all functions understand how AI can support decision-making
• Teams experiment with AI tools and share what works
• Leadership understands AI well enough to make strategic choices about where to invest and where to build

When AI literacy becomes widespread in your organization, employees don't just use AI to answer questions - they build AI agents that handle entire workflows. This is where AI literacy transforms from individual productivity gains into organizational leverage. Three categories of agents matter most:

Agents that take over "keep-the-lights-on" work so employees can focus on high-value tasks. The routine processing, the data entry, the status updates that consume hours but create little value.

Agents that reduce mistakes and speed up critical processes. The quality checks, the compliance reviews, the analysis that needs to happen quickly and accurately.

Agents that democratize organizational knowledge. The strategy, the vision, the context that typically stays locked in leadership meetings and slide decks.

Building these capabilities requires understanding your specific work, your specific bottlenecks, your specific opportunities. A consultant can build agents for you. But they can't transfer the judgment about which agents matter, or the intuition about how work actually flows, or the relationships that determine whether tools get adopted. That knowledge lives in your organization. The question is whether your organization will learn to act on it.

The Organizational Transformation

AI will also flatten organizations. When everyone has access to the same information, the same tools, and the same decision-making support, the traditional hierarchy breaks down. The distance between C-suite and front line shrinks. Strategy documents don't need to be simplified and cascaded when AI agents can make them accessible to everyone. Knowledge doesn't need to be gatekept when AI can democratize it.

This isn't about AI replacing people. It's about people with domain expertise - people who understand your customers, your products, your processes - using AI as a tool to deliver more value.

But none of this happens if you outsource AI. It only happens if you build capability internally.