The AI Readiness Checklist: Start with Data Literacy

Why data literacy is the real foundation for AI success

Many organisations today are striving to become AI-ready. However, the focus often skews toward implementing new technologies, launching pilot projects, or experimenting with automation. While these efforts are important, they miss a crucial foundation. AI readiness is not primarily a technology question. It is a human capability challenge.

Specifically, it is about whether your people have the skills to read, write, and comprehend data.

This foundational capability, data literacy, enables individuals and teams to understand AI outputs, question AI-driven decisions, and make informed choices in a rapidly changing digital environment. Without it, AI investment risks becoming a cost with limited return. With it, AI becomes a force multiplier for innovation, efficiency, and impact.

If your organisation is building a roadmap for AI readiness, data literacy must be the first checkpoint.

The risk of skipping data literacy

AI is only as effective as the people interpreting its outputs. According to Forrester, many organisations overlook a simple but critical truth: "your employees aren't ready for AI" because they lack the necessary literacy to engage with and evaluate AI-driven tools.

This gap creates real-world risks. When employees don't have the confidence or capability to verify or challenge AI insights, they may either blindly rely on them or reject them entirely. In both cases, the potential value of AI is lost. Decisions become overly automated or completely disconnected from the humans we're supposedly 'enabling' with these technologies.

Gartner reinforces this concern by highlighting that "AI literacy is a critical component of digital dexterity," and encourages leaders to embed it into workforce development strategies.

These are not theoretical problems. They translate into misaligned priorities, underperformance in transformation efforts, and reduced confidence in technology. Teaching people how to use AI tools is not enough. They must understand how those tools work, what assumptions underpin them, and how to evaluate their output in context.

Relying on AI without data literacy is like relying on a car's navigation system without having ever travelled in a car or having a grasp of how to read the road - possible, but ill-advised.

The data literacy first approach

Building AI capability starts with developing data literacy across the organisation. This does not mean that everyone needs to become a data analyst. Instead, it means that all employees, from frontline staff to senior executives, can:

• Understand how data is collected, structured, and used

• Interpret performance metrics and analytics dashboards with confidence

• Identify potential bias in data or AI outputs

• Ask relevant questions about insights and recommendations

• Communicate findings clearly and apply them to decision-making

In sectors where the stakes are high, such as healthcare, finance, public policy, or education, the consequences of low data literacy are even more significant. Leaders and teams must be able to work with AI in a way that is transparent, ethical, and grounded in evidence.

The Databilities® framework provides a structured approach to building this foundation. It defines the specific knowledge, skills, and behaviours that different roles require to be data-literate, and therefore AI-literate.

Once organisational capabilities are assessed using Databilities®, leaders can compare results to the Global Data Literacy Benchmark. This provides a clear view of where your workforce stands compared to global trends and identifies priority areas for development.

Build literacy, then build AI

Harvard’s AI Literacy Club captures this point effectively. Being AI-literate is not just about technical skills. It is about civic, ethical, and social understanding. People must grasp what AI is, what it is not, and how it affects their work and responsibilities.

This is where a strong data literacy foundation delivers its greatest value. It enables your people to:

• Collaborate effectively with AI systems

• Identify use cases where AI can improve outcomes

• Escalate concerns when AI performance or output is questionable

• Advocate for responsible AI use

• Lead innovation with greater confidence and purpose

As AI tools continue to evolve, your people need to evolve with them. Data literacy should not be seen as a one-time training module but as a continuous capability development initiative. This is particularly important as scrutiny of AI grows across regulatory, ethical, and public domains.

Think of data literacy as the fuel for your AI engine. Without it, the engine stalls. With it, your organisation moves forward with speed and direction.

Let's start with your people

AI readiness is not simply a matter of adopting the latest technology, it's about enabling your people to use that technology wisely and effectively.

Begin by using the Databilities® framework to assess your current organisational capability. Then compare your performance to the Global Data Literacy Benchmark to understand your relative strengths and gaps.

Use these insights to shape targeted development programs, inform leadership expectations, and embed data literacy into your strategy.

Because when your people are confident with data, they will have the building blocks to better understand and be confident with AI. And that is where meaningful readiness begins.