AI Literacy & Why Leadership Learning Needs to Change
Mastering AI isn’t just about courses or certifications - it’s about thinking, deciding, and leading everything differently.
Corporate learning is stuck in the past and ill equipped to prepare leaders for AI-driven change.
Despite all the talk of digital transformation, most organisations still treat learning as a static, one-time event rather than the continuous, adaptive process it needs to be. AI is evolving at breakneck speed, yet corporate learning remains trapped in an obsolete model: classroom-based training, compliance-driven e-learning, and generic leadership programmes that haven’t changed in years.
This approach might work for fixed, known skills, but when it comes to AI, automation, and digital-era fluency, it fails - fast. AI isn’t a fixed competency; it’s an ever-evolving landscape where today’s best practices could be obsolete tomorrow. Yet most organisations act as if AI knowledge can be packaged into a training module and remain relevant for years.
This mirrors the first wave of enterprise AI adoption, where companies tried to plug AI into old processes rather than rethinking how they work. Now, they risk making the same mistake with AI learning - rolling out outdated corporate training approaches for an entirely new skillset that demands experimentation, iteration, and strategic fluency.
The outcome is a workforce that may know how to use AI tools, but doesn’t know how to think with or about AI, nor what questions to ask.
One-Size-Fits-All Learning Doesn’t Work for AI
AI skills aren’t universal - they depend on context, role, and strategic intent. For example:
A finance executive needs to understand how AI enhances forecasting and risk modelling.
A supply chain leader needs to grasp AI-powered logistics and automation.
A marketing director needs to explore AI-driven customer insights and personalisation.
Yet, most corporate AI training assumes everyone needs the same generic introduction to large language models, automation tools, and ethical considerations. This one-size-fits-all approach fails to help individuals develop meaningful AI fluency in their own domain.
Instead of learning how to use AI to enhance decision-making and strategy, leaders are stuck completing the AI equivalent of a corporate PowerPoint skills course - useful at a surface level, but ultimately missing the point. Research suggests that when AI is used without structured guidance and reflection, it can actually reduce critical thinking rather than enhance it:
“Generative AI tools like ChatGPT improve task-specific outcomes but have limited impact on deeper learning, such as critical thinking and analysis.”
Even at the highest levels, executive education remains performance-driven rather than transformational. The experience is polished, engaging, and often led by world-class facilitators - but does it truly change how leaders think and operate? Too often, these programs optimise for ‘butts in seats’ and Net Promoter Scores, rather than intellectual engagement and difficult, mind-shifting learning.
Executives don’t need more well-produced learning experiences, they need something that challenges them, forces them to rewire how they work, and equips them to navigate complexity.
And nowhere is this more urgent than in AI. Passive AI awareness is useless. Real AI fluency demands a radical shift in how leaders learn, process information, and apply new knowledge.
AI Fluency is a New Literacy
Corporate learning today treats AI like just another software rollout: train employees on which buttons to press, certify them on best practices, and move on. But AI is fundamentally different.
AI requires a new kind of literacy, much like programming or data fluency.
Success isn’t about ‘using AI’, it’s about knowing what to ask, how to structure prompts, and how to integrate AI into workflows.
AI learning isn’t linear, it’s iterative, requiring real-world experimentation, adaptation, and contextual learning.
This is why static corporate training fails. You can’t learn AI by sitting in a classroom and taking notes. AI doesn’t stand still long enough for this model to be effective.
“More companies should incorporate training into daily workflows so that instead of sending employees elsewhere to get training, they can get training and certifications as part of their jobs.”
This shift - from separating learning from work to embedding it within workflows - is fundamental to AI fluency. If we expect AI to continuously update its models, why do we expect human learning to be delivered in one-off, pre-packaged classroom sessions?
An AI-Driven Organisation Means Rethinking Everything
Corporate leaders like to believe they are learning AI. They read articles, attend workshops, and sit through webinars that promise to demystify the technology. But in most cases, they aren’t developing AI fluency, just basic technical know-how.
Leaders also need to think about the bigger picture: how do their organisations operate in an AI-powered future? So many aspects of our legacy bureaucratic structures and systems will need to change if we are to really take advantage of AI and automation. We cannot just ‘learn’ a new set of AI skills and apply them to the old system as we did with previous waves of technology-led transformation.
Knowing how AI works is useful. But knowing how to think, work, and make decisions alongside AI - and understanding how to re-engineer workflows, structures and the value chain to take advantage of what AI and automation can do - is what will separate those who will thrive from those who will be left behind.
Three Pillars of AI Fluency
Take two executives, both leading large teams inside an organisation adopting AI. One approaches AI as just another software tool - a productivity booster, an efficiency driver. The other sees AI as a thinking partner, a collaborator, and a catalyst for reimagining how work gets done.
Only one of them will thrive in the AI era.
The difference? The second executive has AI fluency, built on three critical competencies:
1. Thinking in Partnership with AI
Many leaders assume AI is an answer machine, but the reality is far messier. AI can generate, predict, synthesise, but it can’t yet truly understand.
This is more than just ‘prompt engineering’. It’s about:
Developing a mental model of how AI processes and generates information.
Knowing when to trust AI outputs and when to challenge them.
Learning how to ask better questions, because AI is only as good as the inputs it receives.
2. Automating and Orchestrating Workflows
In the early days of automation, companies used robotic process automation (RPA) to speed up inefficient workflows rather than rethink them. The same mistake is being made with AI.
Executives fluent in AI don’t just automate individual tasks, they rethink entire workflows:
What decisions need to be automated, and which need human oversight?
How do AI systems and human teams collaborate in real time?
What work can be redesigned from the ground up, rather than just optimised?
3. Decision-Making in an AI-Saturated World
With AI, we are moving from data-driven decisions to AI-assisted decision-making. But this requires a fundamental shift in how leaders interpret information.
AI doesn’t provide truth - at best, it provides probabilistic outputs.
The best decisions aren’t made by blindly trusting AI, but by understanding its limitations and biases.
Leaders must develop critical thinking habits to spot when AI-generated insights are flawed or misleading.
The Danger of Surface-Level AI Knowledge
Right now, many executives are in the danger zone. They think they are learning AI, but they are only scratching the surface.
They have read the AI books and listened to the AI podcasts.
They can explain the basics of large language models at a dinner party.
They believe that AI is transformational, but their own workflows and organisational systems are not changing to take advantage of its affordances.
This illusion of knowledge is a bigger threat than ignorance. Executives who think they understand AI - but don’t - will make overconfident, misinformed, and risky decisions.
In the next section, we’ll explore how individual leaders can take a radically different approach to learning in the AI era.
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