Computer literacy is the ability to use computers to perform tasks.

Digital literacy is the ability to use digital technologies to perform tasks.

Data literacy is the ability to collect data and analyze insights.

AI literacy is the ability to use AI to automate workflows.

A workflow or process is a set of steps or tasks to achieve a business goal. It can be anything from creating a marketing campaign, reviewing legal documents, or responding to customer support._

Each literacy precedes after one another.

With generative AI, it has given non-technical users the ability to automate the extraction of answers and creation of assets with nothing more than a chat interface.

Of course, generative AI is just the tip of the iceberg of AI. There’s more to AI and AI literacy than generative AI.

However it has given an entry point for people to work with AI at a basic level. Like knowing how to use a text editor on a computer allowed users to be computer literate.

Thus the creation of “citizen AI practitioner” as its equivalent counterpart for data literacy, “citizen data scientist”.

A citizen data scientist is a knowledge worker without formal data and analytics training, capable of working with data to extract business insights.

A citizen AI practitioner is a knowledge worker without formal AI training, capable of automating workflows using basic AI technologies such as generative AI.

The difference is the minimum threshold to become literate in working with data vs AI.

The minimum skills threshold to become data literate:

• Organizing data into the right structure

• Cleaning dirty data

• Analyzing data

• Choosing the right visualization options

• Display visualizations in an analytics dashboard

• Storytelling insights from data

The minimum skills threshold to become AI literate:

• Make a command through a prompt

On a side note, high computer literacy is required to this day. No one is expected to be at the master level of computer literacy except for the IT guys.

A citizen AI practitioner does not need formal training in AI to work with generative AI, since the barrier to entry in using it is very low. The minimum skills threshold to become AI literate is to be able to use generative AI to automate workflows.

Since all knowledge workers are digital literate, they have been accustomed to chat interface that are ubiquitous among chat apps

Never before has a transition towards a form of literacy been as easy as generative AI because it brought over existing behavior from chat interfaces.

New technology + existing behavior = technology adoption

Generative essentially bypassed the need to be able to work with data.

You don’t need to know how to clean dirty data in order to use AI.

You don’t need to know how to extract insights for a bunch of data in order to use AI.

You don’t need to know how to storytelling with data in order to use AI.

Although data literacy skills will help to improve AI literacy skills, it’s not a pre-requisite since it’s intuitive to use generative AI.

You don’t need to be data literate to be AI literate. The reverse is also true. You don’t need to be AI literate to be data literate. Both are interdependent with each other.

Welcome to the new future of work where knowledge workers are being augmented with AI to become hyper-productive.

With this comes a new set of risks, but I’m more positive than negative about the future of work with AI.