For years our team has worked with a model of three literacies that determine whether a team thrives or just survives: technology literacy, health literacy, and financial literacy. AI has added a fourth. Or perhaps it has revealed that what we were calling technology literacy was too narrow. Either way, the model needs updating, and the psychological implications of that update run deeper than the framework itself.
The Original Three Literacies
The model started as a practical observation. Teams that had all three literacies consistently outperformed teams that had one or two.
Technology literacy: Understanding how the tools you use actually work. Not coding, necessarily, but understanding systems well enough to make good decisions about them. Knowing why a particular architecture choice matters. Understanding the tradeoffs in a vendor selection.
Health literacy: Understanding what your body and mind need to sustain high performance. Sleep, exercise, stress management, knowing when to stop. The tech industry's romanticisation of burnout is one of the most destructive patterns in modern work culture.
Financial literacy: Understanding the economics of the business you are in. Not just reading a P&L, but understanding how your decisions affect the financial health of the company, your team, and your own career.
Teams that have all three make better decisions, sustain performance longer, and navigate change more effectively. The research supports this, and so does what we observe in practice.
Why AI Changes the Model
AI introduces a new dimension that does not fit neatly into "technology literacy." Understanding AI requires something beyond understanding technology:
Judgement under uncertainty. Traditional technology is deterministic. You learn how it works and you know what it will do. AI is probabilistic. Working effectively with AI requires comfort with uncertainty, the ability to evaluate probabilistic outputs, and the judgement to know when the AI is probably right and when it probably is not.
Critical evaluation. AI generates confident-sounding outputs that may be wrong. Working with AI requires a specific kind of critical thinking: the ability to evaluate generated content against your own knowledge, identify errors, and know when to trust and when to verify.
Adaptive workflow design. AI changes how work is done, not just what tools you use. Integrating AI into your workflow requires redesigning how you approach tasks, not just learning a new interface.
These skills are adjacent to technology literacy but distinct from it. Knowing how a database works does not help you evaluate whether an AI-generated analysis is trustworthy. Different cognitive skill.
The Four Literacies
The updated model:
Technology literacy remains foundational. Understanding systems, architectures, and tradeoffs. This now includes understanding the basics of how AI systems work: what models are, what training data means, why outputs vary.
AI literacy is the new addition. The ability to work effectively with AI tools. Evaluating outputs. Designing prompts and workflows. Knowing when AI adds value and when it does not. Understanding the limitations. This is distinct from technology literacy because it requires comfort with uncertainty and probabilistic thinking.
Health literacy becomes more important, not less. AI increases cognitive load in new ways. Evaluating AI outputs, maintaining vigilance for errors, adapting to constantly changing tools. The mental overhead of the AI transition is significant, and teams that do not manage their cognitive and emotional health will burn out faster.
Financial literacy extends to understanding AI economics. What does AI cost? Where does the ROI come from? How do you evaluate whether an AI investment is worthwhile? Understanding the unit economics of AI (cost per inference, cost per capability, cost per outcome) is a new financial literacy requirement.
The healthiest teams I work with are the ones that treat AI as a tool to manage, not a force to endure. They are deliberate about what they use it for, honest about what it costs them cognitively, and disciplined about switching it off.
Dr Gerson Tuazon
AI Strategy & Health Innovation
The Wellbeing Dimension
What the research did not fully prepare us for was how much the AI transition would affect team wellbeing. Not the "robots are taking our jobs" anxiety, though that is real. Something more subtle.
Cognitive fatigue. Working with AI requires constant evaluation. Is this output correct? Did it miss something? Should I accept this suggestion or override it? This is mentally exhausting in a way that using deterministic tools is not. Teams report feeling more tired at the end of a day working with AI tools, even when they are technically more productive.
Identity disruption. When AI can do 70% of what you spent years learning to do, it challenges your sense of professional identity. This is not rational, but it is real, and it affects motivation, engagement, and wellbeing.
Pace anxiety. AI capabilities are improving rapidly. The tools you learn this quarter may be different next quarter. The constant pace of change creates a low-grade anxiety that accumulates over time.
Decision fatigue. More AI tools mean more decisions about when to use AI, which AI to use, and how to integrate AI outputs. The proliferation of AI options creates decision fatigue even when each individual decision is small.
What Teams Need
Boundaries. Explicit agreements about when AI is used and when it is not. Not everything needs to be AI-assisted. Protecting spaces for unassisted thinking, creative work, and human interaction is not resistance to AI. It is health management.
Recovery time. Transitioning to AI-augmented workflows is cognitively demanding. Teams need time to absorb changes, build new habits, and recover from the adaptation effort. Continuous AI deployment without recovery time leads to burnout.
Honest conversation. Teams need space to discuss how AI makes them feel. The anxiety, the identity disruption, the fatigue. These are not weaknesses. They are normal responses to significant change, and naming them makes them manageable.
Skills investment. The best antidote to pace anxiety is competence. Organisations that invest in ongoing AI skill development (not one-off training, but continuous learning) see lower anxiety and higher adoption.
The Integration
The four literacies work together. Technology literacy gives you the foundation to understand AI. AI literacy gives you the ability to use it effectively. Health literacy gives you the self-awareness to manage the cognitive and emotional load. Financial literacy gives you the perspective to evaluate whether it is all worth it.
Teams that develop all four are not just more productive. They are more resilient. They adapt faster, burn out less, and make better decisions about when and how to use AI.
The AI age demands more from people, not less. The organisations that acknowledge this and invest in the full spectrum of literacy, not just the technical skills, will build teams that thrive through the transition rather than merely surviving it.