Note: a version of this article was first published for Forbes Business Council on March 5, 2025.

Organizations that are racing to become data-informed and AI-enabled often invest heavily in technical training. It makes sense that they’d do this: they need their employees to be able to access, prepare, and analyze data to make sense of it all. Carrying out these tasks requires proficiency in a variety of data-working tools.

That’s why I’ve spent much of my own career leading workshops centered around specific analytical applications. But my work with organizations has taught me that while teaching employees to use tools and systems is important, technical skills alone aren’t enough to create teams that are highly literate in data and AI.

The combination of data literacy and AI literacy is the ability to read, understand, create, and communicate with data, while using and critically assessing AI. But true literacy requires a set of traits that’s much broader than what’s taught by the vast majority of data and AI training programs. 

Here are essential non-technical traits that transform technical skills into genuine data and AI literacy:

1. Ethical Awareness and Responsibility

We don’t let kids use the stove until they understand how dangerous it can be, and we don’t let someone drive a car until they’ve learned the basic safety rules of the road. Similarly, we shouldn’t give a team advanced technical skills until they’ve learned how much harm can be done with them.

Yet that’s exactly what organizations have been doing for decades, and it’s why many data initiatives and AI projects go awry – teams are equipped with powerful tools before they fully grasp the ethical ramifications of their work. The result has been everything from privacy breaches to misinterpreted analyses that cause harm and lead to costly business losses. Data ethics and responsible AI are two cornerstones of the foundation of data and AI literacy, and laying a firm foundation comes first.

To begin developing this trait, open a discussion with your team about the potential negative impacts of data misuse and AI failures in your specific industry and context.

2. A Critical Yet Open-Minded Attitude

One of the most overlooked aspects of data and AI literacy is the ability to remain open to new possibilities while maintaining a balanced skepticism about technology. There’s a tendency to put too much trust in data, and to erroneously believe that AI gives us objective and flawless inputs.

The truth is that data can be dirty, inaccurate, and biased. AI that has been trained on imperfect data can reflect those imperfections. For this reason, we want our team members to critically evaluate their outputs. But we want them to do so without closing themselves off to the insights they can provide. There’s a world of difference between skepticism and cynicism; we want our teams closer to the former than the latter.

To begin developing this trait, encourage your teams to question and validate their assumptions while remaining receptive to unexpected insights that emerge from their analysis.

3. A Mindset of Continuous Improvement

I’ve spent a significant part of my career working in continuous improvement, leading teams and training project leaders to reduce costly errors and waste in business processes. I began to see the world through a process-focused lens; it opened my eyes to valuable opportunities I had previously been missing.

Our present obsession with technology has distracted us from looking closely at our processes. Teams that are highly literate in data and AI understand that value is delivered via processes, and that efficient and reliable processes are the key to successful organizations. If data is one of our most valuable assets, then we don’t just want our teams to use it, we want them to improve it, in a never-ending virtuous cycle.

To begin developing this trait, start mapping your processes end-to-end and identify the biggest opportunities to use data to improve performance.

4. A Spirit of Collaboration

The word “inclusive” has taken on a political overtone lately, so some embrace it while others reject it out of hand. But let’s be clear: we want our teams to include and invite people with different perspectives and experiences to the table whenever we use data and AI.

If a team is lacking diversity of thought, then it can be easy for them to miss deficiencies in their data or critical blind spots in their analysis that will negatively impact groups not represented on the team. Overlooking important use-cases is harmful to business outcomes and stakeholder value alike, and should be avoided at all costs. Data is a team sport. 

To begin developing this trait, identify areas of uniformity, and seek out different perspectives when planning data initiatives and AI implementations.     

5. Effective Communication and Advocacy

The “last mile” of data and AI involves human-to-human interactions: an analyst communicating insights to an audience, a “human-in-the-loop” of an AI system sharing recommendations to coworkers, a team implementing changes from data-informed decisions.

To travel this last mile well, data professionals need to switch modes of operation from the technical to the human. Affecting people in our world is a different job than transforming and analyzing data. It requires empathy, patience, and advocacy. But if we don’t embrace this messy set of human tasks, then we’ll fail to finish that last mile. To realize the value of our digital assets, we need to walk them into the real world.

To begin developing this trait, practice presenting data to different audiences, and emphasize the human element of change management.

Moving Forward

Now that you’ve considered non-technical keys to success in data and AI, identify which are most lacking or in need of further development.

Recent frameworks like the “21 Key Traits of Data & AI Literacy” offer comprehensive approaches to developing these capabilities, including both technical and non-technical elements. Such resources, along with associated courses and assessments, can help organizations take a more holistic approach to building these critical competencies.

The organizations that thrive in the coming decades will be those that can master both the technical and the non-technical, and thereby realize the value of true data and AI literacy.