AI’s Role in Strategic Foresight: A Hybrid Future for Thinking
The fluorescent glow of the situation room glinted off Anya’s glasses as she scanned another dense report.
She was a strategic futurist, tasked with peering into tomorrow for a major government agency, yet most of her days were consumed by sifting through today’s avalanche of data.
Global trends, weak signals, emerging technologies—each demanded meticulous analysis, often stretching her team to its limits.
She remembered a recent scenario planning session, where hours were lost just synthesizing raw information before any truly imaginative work could begin.
It was not a lack of human brilliance, but a sheer bandwidth problem.
The question gnawed at her: how could they truly prepare for unpredictable futures when they were so bogged down in the present?
This, she knew, was where artificial intelligence promised a different path, a chance to augment their vision, not diminish it.
In short: Artificial intelligence is becoming deeply integrated into strategic foresight, assisting governments, businesses, and researchers in planning for the future by scanning horizons, generating scenarios, and interpreting weak signals.
The Unsettling Question: Human Futurists in an AI World
Artificial intelligence is no longer a distant tool in the strategic foresight profession; it is now embedded in the process of shaping how we imagine the future.
Across governments, corporations, and research institutions, AI is helping humans scan horizons, generate scenarios, and interpret weak signals at speeds once unimaginable (World Economic Forum, 2025).
This rapid transformation, however, raises an unsettling question: If machines can predict, simulate, and speculate faster than humans, what is left for the human futurist (World Economic Forum, 2025)?
Strategic foresight itself is a profession focused on navigating complexity through human judgment and planning for the future (World Economic Forum, 2025).
The introduction of AI marks a significant shift.
In mid-2025, a comprehensive survey by the Organisation for Economic Co-operation and Development (OECD) and the World Economic Forum gathered insights from 167 foresight experts across 55 countries, spanning public, private, academic, and civil society institutions.
Their findings revealed a field in undeniable transition: two-thirds of these practitioners already use AI in some aspect of their strategic foresight work (OECD & World Economic Forum, 2025).
This widespread adoption confirms AI’s critical role in future planning, compelling us to redefine the collaboration between human intuition and machine intelligence.
Three Levels of AI Integration in Foresight Work
The OECD and World Economic Forum research clearly delineates three distinct levels of maturity in how AI is being integrated into strategic foresight work (OECD & World Economic Forum, 2025).
These levels illustrate a progression from simple assistance to deep, systemic embedding, revealing how professionals are experimenting with new forms of cognition.
At Level 1, AI functions primarily as a digital research assistant.
This is where most foresight experts currently operate.
Here, AI is deployed to accelerate the early research phase, performing tasks such as synthesizing data, conducting horizon scans, and clustering signals.
Between 60% and 69% of survey participants utilize AI in this foundational manner.
Experts noted that at this initial level, AI excels at gathering and summarizing information but remains incapable of true judgment (OECD & World Economic Forum, 2025).
Level 2 involves a more advanced application, where AI serves as an idea generator and sparring partner.
Although less common than Level 1, the research suggests practitioners should invest time in this stage.
At this level, AI tools can help systematize and summarize signals, offer innovative ideas for study structure, and suggest future scenarios based on uploaded data.
Furthermore, AI can compare collected signals with other factual data and significantly speed up the search for relevant information (OECD & World Economic Forum, 2025).
This stage moves beyond mere data collection to active co-creation.
Level 3 represents the rarest and most frontier application, embedding AI throughout the entire foresight process.
In these advanced cases, foresight teams are not just using off-the-shelf tools; they are building tailored solutions and deploying sophisticated AI agents that continuously collect, cluster, and analyze information streams.
This complete integration marks a profound shift, indicating a profession actively experimenting with deep human-AI collaboration (OECD & World Economic Forum, 2025).
Benefits and Perils: AI’s Dual Impact on Future Planning
When foresight experts were asked about the benefits of AI, their responses highlighted both immense opportunities and significant risks.
The productivity revolution brought by AI is undeniable, yet it introduces new complexities that demand careful navigation.
The most frequently cited advantage of AI, named by 39% of respondents, is time efficiency (OECD & World Economic Forum, 2025).
AI handles repetitive, labor-intensive tasks like scanning and synthesis, freeing practitioners to focus on higher-order work such as interpretation and narrative building.
This frees human foresight experts for more creative and judgmental tasks.
Other significant benefits include improved data processing and analysis, cited by 17% of respondents, allowing for the uncovering of hidden trends in vast datasets.
Idea generation and creativity were cited by 12%, who use AI to produce first drafts or gain new perspectives.
For another 10%, scenario development is a key benefit, aiding in generating and refining multiple possible futures.
Additionally, 7% noted improved quality and scope, and 4% pointed to increased accessibility for non-experts, indicating that AI is lowering barriers to entry in a field once limited to specialists (OECD & World Economic Forum, 2025).
Overall, practitioners confirm that AI accelerates their ability to conduct foresight (OECD & World Economic Forum, 2025).
For example, 43% of civil society organizations with AI experience find it highly useful, and 47% of private sector participants deem it moderately useful (OECD & World Economic Forum, 2025).
However, this same survey underscores profound unease with using AI, as its advantages arrive with new risks (OECD & World Economic Forum, 2025).
The most prominent concern is output quality and trustworthiness.
Many users observe that AI-generated material can feel shallow or derivative—more remix than revelation—leaving them uncertain about what can be relied upon and what requires reworking (OECD & World Economic Forum, 2025).
Bias is another major issue.
Respondents highlighted the dominance of English-language and Western data sources, which can distort global perspectives and obscure weak signals emerging from other regions or cultural contexts (OECD & World Economic Forum, 2025).
Ethical and governance gaps also weigh heavily on practitioners.
Many organizations still lack clear guidelines for responsible use, and public sector teams, in particular, remain cautious due to data security and confidentiality constraints.
Combined with the opacity of AI’s reasoning, this can transform foresight work into an audit exercise, where practitioners spend more time validating than imagining (OECD & World Economic Forum, 2025).
Even AI’s strengths create new dependencies.
As AI automates more foresight functions, practitioners risk losing touch with the very intuition and pattern recognition that define the discipline.
Several foresight practitioners warn of a creeping deskilling effect—the temptation to outsource judgment to algorithms trained on yesterday’s knowledge (OECD & World Economic Forum, 2025).
This ethical reflection emphasizes the crucial need for human-AI collaboration.
Forging an Ethical Path: Imperatives for Strategic Foresight Practitioners
The OECD and World Economic Forum survey illuminates a field standing on the edge of transformation.
To integrate AI ethically and effectively into strategic foresight practice, practitioners face several imperatives.
These steps are crucial for ensuring that strategic planning AI enhances, rather than diminishes, human capabilities and ethical standards.
- First, build AI literacy, especially in the public sector, where confidence in using AI lags industry.
This ensures that all users understand AI’s capabilities and limitations, fostering informed decision-making.
- Second, develop robust ethical frameworks to ensure transparency, accountability, and data integrity.
Clear guidelines are essential to address concerns about bias and trustworthiness, promoting responsible AI use in government and enterprise foresight.
- Third, encourage experimentation through small pilots and sandboxing to explore new AI-enabled methodologies.
This allows organizations to test and adapt AI tools in controlled environments, fostering innovation while managing risks.
- Fourth, preserve human creativity by treating AI as a collaborator, not an oracle.
Recognize that machines can map the past and model probabilities, but the courage to imagine alternatives and choose among futures remains profoundly human (World Economic Forum, 2025).
This human-AI collaboration is essential for developing nuanced future scenarios.
By adhering to these imperatives, organizations can navigate the complexities of AI in foresight, ensuring that technology serves humanity’s highest aspirations.
Tools, Metrics, and Cadence for AI in Strategic Foresight
To effectively implement and manage AI in strategic foresight, a robust framework of tools, metrics, and review cadence is essential.
This approach will ensure AI supports strategic planning AI efficiently and ethically.
Tools for AI Strategic Foresight include:
- Horizon Scanning Platforms: Automated tools for continuous monitoring of trends, weak signals, and emerging issues across vast datasets.
- Scenario Generation Software: AI-powered platforms that can generate multiple plausible future scenarios based on input data, aiding in strategic planning.
- Data Visualization Tools: Software to interpret complex AI outputs, making insights accessible and actionable for human decision-makers.
- Collaboration Platforms: Secure environments for human foresight teams to work alongside AI tools, share insights, and debate interpretations.
- Ethical AI Governance Systems: Tools for monitoring AI model bias, data provenance, and adherence to established ethical frameworks.
Key Metrics for AI Integration in Foresight:
- Time Efficiency Gains: Measure the reduction in time spent on data collection, synthesis, and initial analysis.
- Quality of Insights: Assess the novelty, depth, and actionable nature of AI-assisted foresight outputs.
- Bias Detection Rate: Track the identification and mitigation of biases in AI-generated data or scenarios.
- Human-AI Trust Score: Survey practitioners’ confidence in AI outputs and their comfort level with collaboration.
- Scenario Diversity: Evaluate the breadth and originality of future scenarios AI generates or helps refine.
- AI Literacy Levels: Measure improvements in practitioner understanding and capability in using foresight tools.
Review Cadence: A structured review cadence is essential.
- Weekly: Focus on tactical implementation; review AI-generated reports for immediate relevance and flag any unusual outputs.
- Monthly: Assess the performance of AI models against specific foresight tasks and adjust parameters or data inputs.
Evaluate team AI literacy.
- Quarterly: Conduct strategic evaluations of AI’s overall contribution to foresight projects.
Review ethical framework adherence and address any emergent bias concerns.
- Annually: Perform a comprehensive audit of the AI strategy in foresight, re-evaluating its alignment with organizational goals, technological advancements, and the evolving landscape of technology ethics.
Glossary
- Strategic Foresight: A systematic process for anticipating and preparing for alternative futures, involving scanning, scenario development, and strategic choice.
- Horizon Scanning: The process of systematically exploring potential threats, opportunities, and changes in the external environment.
- Weak Signals: Early, often ambiguous, indicators of potentially significant future developments.
- Scenario Development: The process of creating multiple plausible stories about how the future might unfold.
- AI Literacy: The understanding of artificial intelligence concepts, capabilities, and ethical implications.
- Deskilling Effect: The reduction in human skill or expertise due to over-reliance on automated systems.
- Human-AI Collaboration: A partnership where humans and AI work together, each leveraging their unique strengths, to achieve common goals.
Conclusion
Anya leaned back, a small smile playing on her lips.
The initial unease about machines predicting the future had given way to a profound appreciation for their partnership.
Her team, once bogged down in data, now debated richer, more diverse future scenarios, their human creativity amplified, not replaced.
The insights from the OECD and World Economic Forum survey confirmed her lived experience: AI is indeed transforming how we anticipate change, but foresight’s fundamental purpose remains unchanged.
It is about helping societies navigate uncertainty with wisdom, ethics, and imagination (World Economic Forum, 2025).
The future of foresight is truly hybrid (World Economic Forum, 2025), a symbiotic dance between human ingenuity and artificial intelligence.
Machines can map the past and model probabilities, but the courage to imagine alternatives, to choose among futures, remains profoundly human (World Economic Forum, 2025).
This is the true frontier: smarter collaboration.
Embrace it, and redefine what thinking ahead truly means for your enterprise.
References
OECD & World Economic Forum. (2025-06-01). What AI’s role in strategic foresight tells us about the future of thinking.