What is AI literacy?
At Brilliant Noise, our day-to-day lives as consultants and capability-building experts often revolve about the how of AI literacy, so we need a solid foundation of what it is.
We’ve developed a definition of AI literacy that works for our world: business, specifically non-technical professionals and knowledge workers, usually connected with work that includes strategy, decision-making, creativity, marketing, innovation and communications.
At the moment for us, “AI” usually means generative AI, like the large language models used via chatbots like ChatGPT and Claude. Other forms of AI are important, but you will develop a deeper understanding of them if you need to, or access experts who will help integrate them.
So for our context, here’s a working definition of what AI literacy is:
AI literacy is the ability to understand, evaluate and use artificial intelligence systems and tools in a responsible, ethical and effective way. It is an evolving set of skills, including critical thinking, knowledge of the limitations of AI systems, and the ability to evaluate their output in relation to work in a field and where they can be applied. For example, in decision making and data analysis, knowing when systems are accurate or prone to error, or in creative processes, how systems can complement human cognition.
And here it is broken into three more parseable sentences.
AI literacy is the ability to understand, evaluate and use artificial intelligence systems and tools in a responsible, ethical and effective way.
It is an evolving set of skills, including critical thinking, knowledge of the limitations of AI systems, and the ability to evaluate their output in relation to work in a field and where they can be applied.
For example, in decision making and data analysis, knowing when systems are accurate or prone to error, or in creative processes, how systems can complement human cognition.
Other descriptions of AI literacy are available and useful, but often they are specific to fields like scientific research or learning itself (a.k.a. pedagogy). We needed a working definition that can be used more generally and the above is it, for now.
In a recent Nature article, “The Quest For AI Literacy”, the director of the US National Science Foundation, Sethuraman Panchanathan, said that teaching oneself about AI continues a theme he always been passionate about:
“I’ve always looked at this as literacy,” he says. Earlier in his career, at Arizona State University, he remembers saying how every student at the university needs informatics literacy. “And now that matured to data science literacy, now AI literacy,” he says. With AI advances come a need for upskilling and reskilling at all educational levels and throughout one’s career. “The only constant is change,” he says. “And lifelong learning is an important imperative.”
We talk a lot about AI literacy, but for now, let’s take a step back and look at what came before it to understand where we are now a little bit better.
A potted history of literacy at Brilliant Noise
Where informatics and data analytics were the Panchanathan’s precursors to AI literacy, digital literacy was ours at Brilliant Noise.
Back in 2011, when our founders were developing the plan for what would become Brilliant Noise, Antony our CEO used the opportunity of a TEDX talk to articulate some nascent ideas about digital literacy and put forward some ideas abut the future, and what kind of “super skills” we might gain or use in order to take advantage of this powerful new technology. These were:
Networks: The importance of understanding how networks work, both online and offline. Recognising the power of personal and social networks is crucial, as the web allowed people to access larger networks, connect with others, and share information more effectively.
Sharing as a core digital activity. The ability to share knowledge, resources, and information freely and easily was seen as key to creating value within a network. He argued that instead of deciding what to share, the focus should have been on deciding what not to share, as sharing had minimal cost and greatly strengthened connections.
Focus and Flow: the importance of managing attention and workflow to maximise productivity, including using techniques like the Pomodoro Technique to focus on tasks in short, intense bursts, and understanding when to engage with the network for information and when to concentrate on deep work without distractions.
A lot of what Antony says in this video from 14 years ago about digital literacy can be applied to how we think about AI today. For example, there’s a lot of value locked within our existence in networks and how we can tap into the enormous potential of living in a world where we're connected billions of other people and the sum of human knowledge, but it does take some effort, learning and development of literacy to reap the rewards.
In 2013 and 2014, we worked with Microsoft where these ideas about digital literacy and the principles underpinning this new way of working revolved into a short book for what became Microsoft’s mobile devices division’s (FKA Nokia) principle handbook called Design Your Day.
The book brought together new insights from neuroscience, with the practice design thinking and observations about how we use digital tools. Part I introduced foundational concepts like time and energy management, habit formation, and insights from neuroscience, providing a toolkit of ideas for optimising daily routines. Part II guided the reader in applying these concepts through design thinking, encouraging experimentation with daily schedules, prioritisation, and defending against distractions.
It contains things like:
Question Your Routine: examining and observing your habits and how you go about your day, and asking yourself about whether they serve you well. This self-awareness can lead to small changes that enhance your energy, focus, and productivity.
Create an Environment for Success: Design and control your surroundings and schedule to support moments of deep work, inspiration and productivity.
Prototypes over Perfection: Rather than expecting perfection, treat each day as a prototype. Leave room for trial and error and earn from each day’s successes and setbacks, adjusting and refining as you go to build better days over time.
Together, these parts aimed to help readers create and continuously refine a personalised, effective daily structure for a new kind of work that now incorporated digital tools.
One promotional event for the book included a talk from Caroline Webb, who was then working on her book How To Have a Good Day – one of the first books to use behavioural economics, psychology and neuroscience to suggest ways to transform the quality of our everyday lives. Caroline’s thinking helped us learn a lot about how new insights from neuroscience could be applied to our everyday work and lives.
Much of our thinking and work in this area became learning materials used to run programmes for firms like the Financial Times, Barilla and adidas. We also developed a Test–Learn–Lead™ innovation pipeline process to manage ideas as experiments underpinned by these core principles. And now the rise of generative AI has made many of the lessons from those experiences even more pertinent.
Just like digital literacy before it, AI literacy isn't a “nice-to-have”. It's foundational to staying competitive. As we discussed, businesses that build AI fluency within their teams can unlock game-changing productivity gains, while those that hesitate risk falling behind. Our team has seen firsthand how integrating AI into workflows – from content creation to data analysis – drives both efficiency and creativity.
For a deep dive into AI Literacy, download our paper, Prepared Minds now!
