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AI literacy helps people understand and evaluate AI systems; strategic foresight helps organizations consider how change could unfold and prepare for more than one possible future. Neither replaces the other. The case for treating foresight as a crucial AI-era capability is strong, but current evidence does not establish that it is uniquely “the missing capability” or that AI-assisted foresight reliably improves decisions.
What is strategic foresight?
Strategic foresight is a structured way to explore multiple plausible futures, anticipate change, and prepare for possible opportunities and challenges. It is not an attempt to name one future in advance. The OECD puts it plainly: “Strategic foresight is not about predicting a single future.”
Common methods include:
- Horizon scanning: looking for early signals of change that may affect a decision or organization.
- Megatrends analysis: considering broad, long-term forces and how they may interact.
- Scenario planning: developing plausible alternative futures to examine what they could mean for choices made now.
- Visioning and back-casting: describing a desired future, then reasoning backward about steps that could help move toward it.
The OECD describes the purpose as building the capacity to explore and prepare for multiple plausible futures and their associated opportunities and challenges. That definition comes from a public-policy context; the methods can also inform business decisions, but their effectiveness across sectors should not be assumed without separate evidence.
How is strategic foresight different from AI literacy?
They address different questions. AI literacy is about understanding and assessing AI; foresight is about reasoning through alternative futures and deciding what choices could prepare for them. The OECD and European Commission’s 2026 AI-literacy framework defines relevant knowledge, skills, and attitudes for learners to understand AI systems, evaluate outputs critically, and use AI ethically and creatively. Its stated scope is primary and secondary education, so it is a useful grounding for the term, not a complete framework for adult professional competencies.
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| Dimension | AI literacy | Strategic foresight |
|---|---|---|
| Primary question | How does this AI system work, and should I trust or use its output? | What plausible futures could unfold, and how should we prepare? |
| Time orientation | Understanding and using AI in present interactions and tasks. | Emerging signals, longer horizons, and alternative futures. |
| Typical methods | Understanding systems, critically evaluating outputs, and considering ethical use. | Horizon scanning, megatrends analysis, scenario planning, and back-casting. |
| Decision contribution | Better-informed use of AI. | Strategies and options considered against more than one plausible future. |
| Possible failure mode | Relying on an AI output without adequately assessing it. | As an implication of the method, treating one scenario as a forecast or failing to connect scenarios to decisions. |
The distinction is not a choice between technical understanding and future-oriented thinking. Foresight without enough AI literacy can leave people poorly equipped to question AI-generated analysis. AI literacy without foresight can help people use current systems while leaving them unprepared for how the technology and its consequences may change.
Why the AI age makes foresight relevant
AI systems are already in use, but their long-term implications remain uncertain. The OECD notes that “the long-term implications of rapidly advancing AI systems remain largely unknown.” Under those conditions, a single forecast can create false confidence. Exploring alternatives instead helps decision-makers ask what assumptions they are making, what signals might challenge those assumptions, and which choices could remain useful across different outcomes.
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This is the article’s argument for foresight as a significant capability—not a proven claim that every organization lacks it or that foresight is the one capability that matters most. The evidence supports preparation under uncertainty, not a universal ranking of skills.
How AI is being used in strategic foresight
A joint OECD and World Economic Forum report, AI in Strategic Foresight: Reshaping Anticipatory Governance, published on 19 November 2025, draws on a global survey of 167 practitioners from government, business, academia, and civil society. The report says most surveyed foresight professionals already use AI to scan for signals of change, analyse large datasets, and support scenario development. Many reported time savings and expanded analytical capacity; a smaller, growing group was experimenting with AI as a creative or analytical partner in systems mapping and scenario design.
Those are qualitative descriptions: the report abstract does not give percentages for “most” or “many,” or a quantified amount of time saved. The 167 respondents are a practitioner-survey sample, not a representative estimate of how many organizations use AI for foresight.
AI can therefore expand the capacity to gather and examine information, but these reported uses do not establish that AI-supported foresight produces better decisions or outcomes. The OECD and WEF report also identifies concerns about reliability, transparency, and bias; gaps in technical skills; and uneven access. Public-sector use may be constrained by data-security concerns and unclear guidelines.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to combine AI literacy and foresight in practice
A useful way to connect the capabilities is to treat AI as an aid to inquiry, not an authority on what the future will be. For a strategic decision involving AI, a team can use foresight methods to structure the questions and AI tools to assist selected research tasks, while people remain responsible for interpretation and decisions.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- Define the decision. State what choice needs to be made and the time horizon that matters. A broad question such as “What will AI do?” is less actionable than identifying a particular policy, service, or investment decision.
- Scan for change signals. Gather relevant developments and use AI, where appropriate, to help search or analyse large bodies of information. Check important claims against their underlying sources rather than treating generated summaries as verification.
- Build multiple plausible scenarios. Use scenario planning to examine different ways relevant uncertainties could develop. Do not present one scenario as the expected future simply because it seems most familiar or because an AI system produced it.
- Test options against the scenarios. Ask what each possible future would mean for the decision, and which actions remain sensible across more than one scenario. This makes the work relevant to a choice rather than an exercise in speculation.
- Review assumptions and safeguards. Examine the evidence, possible bias, data sensitivity, and limits of the analysis. Keep responsibility for consequential interpretations and decisions with accountable people.
- Revisit as signals change. Foresight is preparation amid change, not a one-time declaration of what will happen. Reconsider the scenarios and options when relevant assumptions or signals shift.
This approach is a practical synthesis of the OECD’s foresight methods and the reported opportunities and risks of AI use; it should not be read as a tested recipe with guaranteed results.
What this means for people and organizations
For individuals, AI literacy supports more informed engagement with AI systems, while foresight adds a habit of asking how a technology-driven change could affect future choices. For organizations, foresight can help make uncertainty part of planning rather than an afterthought. The two capabilities work best together: understand the tool and its limits, then consider the range of futures in which decisions about it may matter.
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