Building AI Literacy: A Critical, New Skill for Data Leaders

The use of artificial intelligence (AI) has become ubiquitous in everyday life. From Netflix’s personalized recommendations on what series to binge to virtual assistants such as Siri and Alexa providing weather, traffic, and news updates, AI has been embedded in our day-to-day routines. 

It’s just as present in the workplace, too. With AI tools like Claude and ChatGPT streamlining tasks and Otter.ai and Microsoft Co-Pilot transcribing and summarizing meetings and more, the modern workforce interacts with AI on a daily basis. And as new integration standards such as Model Context Protocol (MCP) take hold, AI is poised to play an even bigger role in our daily tasks.

In reality, we’ve only begun to scratch the surface of AI’s true potential. As AI—and generative AI (GenAI) in particular—continue to reshape work, one skill is quickly emerging as essential: AI literacy.

What is AI Literacy?

According to IBM, AI literacy is “the ability to comprehend various aspects of artificial intelligence—including its capabilities, limitations, and ethical considerations—and to use it for practical purposes.” 

It’s complex and involves much more than simply understanding how AI technologies work. Data leaders, in particular, need to also understand the business implications for AI, including both its potential and risks. 

Components of AI Literacy

AI literacy goes beyond simply knowing what artificial intelligence is. It also involves understanding how it works, where to apply it, and how to engage with it responsibly. As AI becomes increasingly integrated into tools, workflows, and decision-making processes, it’s imperative that data leaders develop a strong foundation of skills that enable them to confidently navigate, question, and collaborate with AI systems.

DataCamp, an online learning platform for data and AI skills, identifies three core components to AI literacy:

1. Technical understanding 

Understanding how AI works is a key element of AI literacy. For example, to evaluate and select the right AI tools for their business, data leaders must understand how AI systems collect and process data, the types of insights they generate, and the security and privacy protections in place to ensure data remains safe and secure. They should also understand which AI solutions are AI-native and which ones simply bolt on AI capabilities to a traditional platform and what the implications are.  

In addition, data leaders must also understand that effective AI requires high-quality, trustworthy data. Without it, AI can deliver faulty, misleading, or incorrect insights that negatively impact decision-making.   

2. Practical understanding

AI literacy also involves knowing the role AI will play in the business, including how it will automate workflows, generate insights, and foster better decision-making. Further, as GenAI and AI agents take hold, it’s important to understand the different ways in which business users will interact with AI and AI-generated insights. 

3. Ethical understanding

AI raises many questions—and concerns—when it comes to the ethical use of data. That’s why data leaders must clearly understand how the AI they deploy uses enterprise data. It’s also important to consider potential biases and discrimination the AI could introduce, and how the organization will mitigate concerns and address potential misuse. 

6 Steps to Build AI Literacy

Building AI literacy—and GenAI literacy—requires data leaders to delve deep into the capabilities that AI technologies provide, as well as the data, processes, and practices that support it. Below, we’ve outlined six steps to help you get started.

1. Understand what AI is—and what it is not

The best place to start is by understanding the fundamentals: how AI works, where you can use it, and how it can drive business impact. It’s also important to distinguish between the different types of AI such as machine learning, GenAI, computer vision, AI agents, etc., as well as the specific use cases for each.

You’ll also want to keep in mind that while AI holds great potential to advance modern business, it’s not infallible. For AI to be effective, it requires high-quality data, as well as human oversight and feedback.  

‍2. Learn AI concepts and terminology

Next, you will want to familiarize yourself with basic AI concepts such as algorithms, models, training data, and agentification (AI-based automation). You should also explore more advanced capabilities such as prompt engineering, semantic search, APIs, and MCP. 

Another important element of AI literacy is developing a clear understanding of how to train AI systems to deliver trustworthy insights while avoiding the introduction of hallucinations or bias. 

3. Recognize the ethical implications of AI

Ethical AI asks the question: “Are we doing the right things to ensure our AI is unbiased and free from risk?” It’s what helps to ensure that the AI you build or implement—and the data used to power the models—is fit for the task. Ethical AI isn’t just about compliance. It’s about understanding how to build trust and accountability into every stage of the AI lifecycle.

4. Understand the role of data

AI is only as good as the data that powers it. That’s why the importance of clean, accurate, trustworthy data cannot be overstated. However, many organizations lead with technology, not data. This is a mistake. Instead of leading with technology, you must follow the MDM journey, which starts with assessing and improving your data. Then, you’ll thoughtfully review that data with your users so they can use it effectively for analytical and operational purposes. Once you’ve taken these steps, then you’re ready to operationalize your data in AI technologies by connecting it to your essential business systems. 

5. Explore real-world applications

Understanding how your business can use AI in data management is critical. For example, many organizations use AI in master data management (MDM). Unlike traditional solutions that rely on rules, AI-native MDM helps organizations to quickly and efficiently deliver the clean, unified, trustworthy data AI applications need to deliver better insights and drive more confident decisions. AI-native MDM also supports capabilities, such as real-time entity resolution, data enrichment, and semantic search, which further ensure enterprise data remains ready for use in AI applications.  

AI also has broader, industry applications as well, such as for real-time personalization in retail and healthcare provider management.     

‍6. Take a test drive

Experimenting with AI tools is one of the best ways to experience how they work. Using Claude or ChatGPT, for example, helps you learn how to engineer prompts and evaluate insights for accuracy. You can also schedule a demo with Tamr to experience how its AI-native MDM solution can help you deliver the trustworthy data needed to power AI applications. 

Laying the Groundwork for Smarter AI Strategies

As AI continues to transform the ways we work, it’s essential for data leaders to develop a clear understanding of how these systems operate, recognize their limitations, and learn how to apply them responsibly. Investing in AI literacy today lays the groundwork for effective, future-ready AI strategies. With a strong foundation in AI literacy, data leaders are well-positioned to promote AI adoption, champion ethical practices, and drive meaningful business outcomes.