Beyond AI Literacy: Why Strategic Foresight Is the Missing Capability for the AI Age

Learning to use AI is important. Learning to anticipate what AI could change, disrupt, and create may be even more important.

Artificial intelligence is moving from novelty to infrastructure.
Across universities, workplaces, governments, and professional communities, the response has been understandable: teach people how to use AI.
We are building AI literacy programs, prompt libraries, toolkits, workshops, policies, and short courses. These efforts matter. People do need to understand what AI can do, where it can fail, how to use it critically, and how to work with it responsibly.
But I increasingly believe that AI literacy, by itself, is not enough.
Why?
Because AI literacy largely helps us understand and use the systems that exist now.
The deeper challenge is preparing people for systems, roles, risks, opportunities, and institutions that may not yet exist.
That requires another capability:
Strategic foresight.
The AI Literacy Paradox
The faster AI changes, the shorter the shelf life of purely tool-based knowledge becomes.
A model that feels transformative today may be replaced tomorrow. A workflow people spend months mastering may soon be automated. A role that looks secure may be reconfigured by capabilities that are still emerging.
This creates a paradox.
The more urgently we teach people to use today’s AI tools, the more easily we can mistake tool familiarity for future readiness.
Someone may know how to prompt a language model, generate an image, automate a workflow, or use an AI assistant effectively and still be unprepared for the larger questions:

• What happens when AI changes the structure of my profession?
• Which parts of my work should remain human?
• What new dependencies are we creating?
• What happens when AI systems become more autonomous?
• How will regulation, culture, economics, and public trust shape adoption?
• Which capabilities will become more valuable precisely because machines become more capable? AI literacy gives us an essential foundation. But future readiness demands more than knowing how to operate the present. From AI Literacy to Futures Literacy Futures literacy begins from a different assumption: The future is not a single destination waiting to be predicted. There are multiple plausible futures. Different choices, technologies, regulations, cultural shifts, economic pressures, environmental constraints, and social responses can produce very different outcomes. That means the objective is not to predict the future perfectly. It is to become better at using the future to think differently about the present. For individuals, this means asking what different futures of work, education, leadership, entrepreneurship, and human-machine collaboration could look like. For institutions, it means challenging the assumption that today’s operating model will simply continue with better technology added to it. For societies, it means asking whose futures are being imagined, whose interests are being prioritized, and who gets to participate in shaping change. AI literacy asks: How do I use this technology?

Futures literacy adds:
What might this technology change, and what alternatives should we prepare for?

That second question becomes increasingly important when technological change is rapid, uncertain, and uneven.
Strategic Foresight Is Not Prediction
Strategic foresight is sometimes misunderstood as forecasting the future.
That is not how I see it.
Foresight is about exploring multiple plausible futures in order to make better decisions today.
It helps us scan for signals of change, question assumptions, identify critical uncertainties, construct alternative scenarios, and stress-test current decisions against different possibilities.
For example, instead of asking:
What will higher education look like in 2035?

A foresight approach asks:

• What different forms could higher education take by 2035?
• Which forces could drive those changes?
• Which assumptions about degrees, assessment, faculty roles, employability, and knowledge may no longer hold?
• What signals today suggest that some futures are becoming more or less plausible?
• Which capabilities should we build now that remain valuable across several different futures? This is particularly useful in the AI age because uncertainty is not a temporary inconvenience. It is part of the environment. A Capability Stack for the AI Age I increasingly think future readiness requires several capabilities working together rather than a single form of literacy.
• AI Literacy People need a practical understanding of AI systems. They should know how to use them, assess their outputs, understand their limitations, and recognize where human verification is necessary.
• Critical and Responsible AI Literacy Using AI effectively is not enough. People need to examine bias, privacy, authorship, transparency, accountability, reliability, environmental costs, and the consequences of delegating decisions to automated systems.
• Futures Literacy People need to become comfortable with plurality and uncertainty. They should be able to imagine alternative futures, challenge inherited assumptions, and understand that the future is shaped rather than simply received.
• Strategic Foresight Institutions and leaders need structured ways to translate uncertainty into present-day decisions. This includes horizon scanning, weak-signal detection, scenario planning, assumption testing, systems thinking, and strategic options. Together, these capabilities move us from simply asking people to become AI users toward helping them become future-ready decision-makers. Why Universities Matter Universities have an especially important role in this transition. If higher education responds to AI only by teaching tools, it risks preparing students for the current interface rather than the future environment. The question cannot simply be: Which AI tools should students know before graduation?

A more durable question is:
What capabilities will help students remain adaptive when the tools, occupations, and rules change again?

That shifts attention toward critical thinking, judgment, systems thinking, ethical reasoning, creativity, interdisciplinary collaboration, entrepreneurship, adaptability, and foresight.
It also changes how we think about curriculum.
Instead of adding a single AI course and declaring the institution “AI-ready,” universities can ask how uncertainty, technological change, human agency, and responsible innovation should be integrated across disciplines.
An accounting student, a psychologist, a business student, an engineer, and a journalist will encounter AI differently.
But all of them will need to make decisions in environments that AI is reshaping.
Responsible AI Also Requires Anticipation
Responsible AI discussions often focus on principles such as fairness, privacy, transparency, accountability, and safety.
These are essential.
But responsibility also has a temporal dimension.
A system may appear harmless at small scale and become problematic at large scale.
A useful automation may gradually create dependency.
A productivity improvement may change entry-level career pathways.
A seemingly neutral recommendation system may reshape incentives, visibility, and power.
This is why strategic foresight complements responsible AI.
It asks us to look beyond immediate performance and explore second-order consequences.
Not only:
Does this system work?

But also:
What happens if it becomes widely adopted?

What happens if the surrounding conditions change?

What happens to people, institutions, and capabilities after five or ten years of dependence on systems like this?

Responsibility requires us to think ahead.
Pakistan and the Global South Cannot Be Passive Consumers of AI Futures
This discussion matters particularly for Pakistan and the wider Global South.
Many dominant narratives about AI futures are produced in environments with very different infrastructure, labour markets, educational systems, regulatory capacity, languages, and economic conditions.
Simply importing those narratives is not enough.
Our futures will be shaped by local realities:

• uneven digital access
• affordability
• language and cultural context
• demographic pressures
• educational inequality
• labour-market informality
• institutional capacity
• energy and infrastructure constraints
• global technology dependence The Global South therefore needs more than access to AI tools. It needs the capability to ask its own questions about AI. Which futures of work are desirable for our societies? Which forms of automation strengthen opportunity, and which deepen dependency? How can AI support local knowledge rather than marginalize it? What does responsible AI look like in multilingual, unequal, rapidly urbanizing societies? How can young people become creators and shapers of technological futures rather than only consumers of technologies designed elsewhere? These are foresight questions. And they matter as much as technical capability. From Future-Takers to Future-Makers There is a deeper reason I connect AI literacy with strategic foresight. It is about agency. If people understand only how to use the systems placed in front of them, they remain largely reactive. They adapt to technological futures created elsewhere. Foresight creates space for another posture. It encourages people to examine assumptions, imagine alternatives, identify preferable futures, and ask what actions today could move us toward them. This does not mean everyone becomes a futurist. It means more people become aware that the future is not simply something that happens to them. The same principle applies to institutions. A university can wait for AI to disrupt assessment and then react. Or it can explore several plausible futures of learning and redesign assessment before the pressure becomes unavoidable. A company can wait until an occupation is transformed. Or it can anticipate changes in tasks and begin building new capabilities earlier. A policymaker can respond after disruption becomes visible. Or use foresight to examine emerging risks and opportunities before they become crises. The goal is not prediction. The goal is agency. Beyond AI Literacy AI literacy is essential. But it should be the beginning of the conversation, not the end. We need people who can use AI. We also need people who can question it. We need people who can work with intelligent systems. We also need people who can anticipate how those systems might reshape work, education, institutions, relationships, power, and human agency. We need technical capability. We need ethical judgment. And we need the capacity to imagine and navigate multiple futures. The real challenge of the AI age may therefore not be teaching everyone the latest tools fast enough. It may be developing people and institutions that can remain thoughtful, adaptive, responsible, and agentic even when the tools keep changing. That is why I believe strategic foresight is becoming a missing capability for the AI age. Not because it tells us what the future will be. But because it helps us decide what we should do when the future is uncertain. And, perhaps most importantly, it reminds us that we are not only preparing for the future. We are participating in its creation. About the Author Dr. Salman Ahmed Khatani is a Futurist, Associate Professor, author, and Founder of Fiker Futures Academy. His work focuses on strategic foresight, futures literacy, AI literacy, responsible AI, the future of work, and future-ready education. He is the author of Designing Tomorrow.