DEV Community: Dr Salman Ahmed Khatani

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

Dr Salman Ahmed Khatani — Mon, 05 Oct 2026 00:47:33 +0000

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.

The AI Skills Race Is Missing the Real Problem: How Do We Prepare for Technologies That Keep Changing?

Dr Salman Ahmed Khatani — Fri, 25 Sep 2026 23:37:26 +0000

The conversation around AI skills is moving faster than most education and training systems can keep up with.

A new model appears.

A new interface changes how people work.

A new agent can complete tasks that previously required several separate tools.

A new workflow becomes normal.

Then another generation of technology arrives.

This creates an uncomfortable question:

What happens when the skills we are teaching people have a shorter lifespan than the programmes designed to teach them?

The problem is not that AI literacy is unimportant.

It is that AI literacy, by itself, may not be enough.

If the technology keeps changing, then the deeper capability is learning how to adapt when the tools, workflows and even professional roles keep changing with it.

The AI Skills Race

Organizations everywhere are trying to become “AI-ready.”

Employees are learning prompting.

Teams are experimenting with copilots.

Universities are introducing AI courses.

Professionals are learning automation, data analysis and generative AI workflows.

All of this makes sense.

But much of this training still assumes that the important question is:

Which AI tools should people learn?

That question has value, but it has a short shelf life.

The stronger question is:

What capabilities will continue to matter when the tools themselves change?

Tool Knowledge Is Not the Same as Adaptability

There is a difference between knowing how to use a tool and knowing how to respond when the environment around that tool changes.

A professional might become highly skilled in one AI platform.

But what happens when another platform replaces it?

What happens when AI agents begin completing entire workflows rather than individual tasks?

What happens when organizations redesign jobs around automation?

What happens when a role changes so much that the original job description no longer makes sense?

Tool proficiency helps people operate within a system.

Adaptability helps people navigate when the system itself changes.

The Half-Life of AI Skills Is Shrinking

In fast-changing technological environments, some skills become outdated quickly.

This does not mean they are useless.

It means they should not be treated as permanent.

The faster AI evolves, the more organizations need to distinguish between:

skills that help people use today’s technology
capabilities that help people adapt to tomorrow’s technology

The first category is still necessary.

The second may become more valuable.

From AI Literacy to Future Readiness

AI literacy usually focuses on understanding and using artificial intelligence effectively.

That includes knowing how to interact with AI systems, evaluate outputs, recognize limitations and use AI responsibly.

But future readiness requires another layer.

People also need to ask:

What happens if this technology becomes cheaper?
What happens if it becomes more autonomous?
What happens if regulation slows adoption?
What happens if trust becomes a bigger issue than capability?
What happens if AI changes entry-level jobs faster than universities redesign degrees?
What happens if entire workflows are automated?

These are not questions about tools.

They are questions about possible futures.

Why Futures Literacy Matters

Futures Literacy is not about predicting the future correctly.

It is about becoming more aware of the assumptions we make about the future and exploring alternative possibilities.

UNESCO’s work on Futures Literacy advances the idea that imagined futures influence the decisions we make in the present.

This matters because people make decisions based on assumptions about tomorrow all the time.

Students choose degrees because they imagine certain careers will remain valuable.

Companies invest because they expect particular markets to grow.

Universities design curricula based on expectations about future skills.

Governments build policies around assumptions about future economies and societies.

The problem begins when these assumptions become invisible.

We start treating one imagined future as inevitable.

Futures Literacy helps us question that.

Strategic Foresight Turns Uncertainty Into Action

Strategic Foresight takes this further.

It helps individuals and organizations explore multiple plausible futures and use them to make better decisions today.

Instead of asking:

What will happen?

Strategic Foresight asks:

What could happen?
What would cause different futures to emerge?
What signals should we watch?
What assumptions might fail?
Which capabilities would remain valuable across several scenarios?

That changes the role of AI strategy.

Instead of simply adopting tools, organizations begin preparing for different technological environments.

The Rise of Agentic AI Makes This More Important

The shift from conversational AI toward increasingly agentic systems makes this issue more urgent.

The difference is significant.

A chatbot helps with a task.

An agent may coordinate multiple tasks.

A more autonomous system may plan, execute, monitor and revise work with limited human intervention.

This means the future of AI is not only about better interfaces.

It may also involve the redesign of workflows, decision rights, management structures and professional responsibilities.

A person who knows how to prompt a chatbot may still be unprepared for an organization in which AI agents participate in complex work processes.

That is why future readiness must go beyond interface-level skills.

The Real Question for the Future of Work

The future-of-work debate often becomes too narrow.

People ask:

Which jobs will disappear?

Which jobs will survive?

Which professions are safe?

But work rarely changes in such a simple way.

Jobs are bundles of tasks.

AI may automate some tasks, augment others and create entirely new ones.

The deeper question is:

How will work be reorganized?

That requires us to look at technology together with:

organizational design
regulation
education
labour markets
culture
infrastructure
trust
demographics
human behaviour

Technology does not operate in isolation.

Different societies may therefore experience very different AI futures.

There Will Not Be One Future of Work

The future experienced by a software engineer in San Francisco may not be the same as the future experienced by a teacher in Karachi.

The future of a healthcare worker in Nairobi may differ from the future of an accountant in London.

The future of a small business owner in Lahore may differ from that of a multinational executive in Singapore.

This is why global AI narratives need local interpretation.

Every region needs to ask:

What does AI mean in our own institutional, economic, educational and social context?

A More Durable Capability Stack

Instead of treating AI literacy as the final goal, I believe we need a broader capability stack.

1. AI Literacy

Understand what AI systems can do and how to use them effectively.

2. Critical AI Literacy

Question outputs, assumptions, biases, limitations and claims.

3. Responsible AI Literacy

Understand ethics, accountability, governance, privacy and social consequences.

4. Human Capabilities

Develop creativity, empathy, judgement, communication and collaboration.

5. Learning Agility

Build the ability to learn, unlearn and relearn repeatedly.

6. Futures Literacy

Recognize assumptions about the future and explore alternatives.

7. Strategic Foresight

Translate uncertainty into scenarios, choices, experiments and action.

The objective is not to make everyone a professional futurist.

The objective is to make future-conscious thinking a normal part of professional life.

What This Means for Universities

Universities face a structural challenge.

Curricula move slowly.

Technology moves quickly.

If universities respond to every technological change by adding a course on the latest tool, they may always remain one step behind.

A better approach is to combine AI education with future-oriented learning.

Students can learn how to use AI tools.

But they can also learn how to scan for emerging signals.

They can explore alternative scenarios for their professions.

They can question assumptions about careers.

They can examine how technology interacts with ethics, institutions and society.

They can learn how to make decisions under uncertainty.

This prepares them not only for today’s tools, but for tomorrow’s changes.

What This Means for Organizations

Organizations face the same problem.

Many are currently asking:

How do we adopt AI?

A stronger question is:

What different operating environments could AI create for us?

Imagine three futures.

In the first, AI mainly augments employees.

In the second, autonomous systems perform large parts of knowledge work.

In the third, regulation, trust concerns or institutional resistance significantly slow adoption.

Would your organization succeed in all three?

Which capabilities would remain useful?

Which investments are robust?

Which assumptions are most fragile?

What experiments should you run now?

This is where foresight becomes practical rather than theoretical.

AI-Ready Is Not the Same as Future-Ready

An AI-ready organization can use today’s technology.

A future-ready organization can adapt when today’s technology becomes outdated.

That distinction matters.

Because the competitive advantage may not come from adopting every new tool first.

It may come from building an organization capable of learning faster than its environment changes.

The Skill We Are Not Teaching Enough

Much of AI education focuses on capability.

How do I use this system?

How do I automate this task?

How do I write a better prompt?

Those are useful questions.

But another question deserves equal attention:

What happens when this changes?

That question opens the door to foresight.

It turns technical training into strategic learning.

The Future Will Keep Moving

AI will keep evolving.

The current models will not be the final models.

The current interfaces will not be the final interfaces.

The current workflows will not be permanent.

And many current job descriptions may not remain intact.

This does not make AI literacy less important.

It makes it incomplete.

The people and institutions best prepared for the AI age will not simply be those who master the latest tools.

They will be those who can keep learning, question assumptions, explore alternative futures and make intelligent decisions under uncertainty.

In a world where the tools keep changing, the most durable skill may be the ability to navigate what comes next.

About the Author

Dr Salman Ahmed Khatani is an Associate Professor, futurist and researcher working at the intersection of Strategic Foresight, Futures Literacy, Responsible AI, the Future of Work and future-ready education.

He is the Founder of Fiker Futures Academy and author of Designing Tomorrow.

Further reading

Pakistan 2035: Why We Need Futures Literacy Before the Future Arrives

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

Originally published on Futures, AI & Strategic Foresight.