Redefining Strategic Foresight in the Age of AI and Algorithms

" Strategic foresight " refers to the creation of forward-thinking perspectives for an organization based on sufficient information. Traditional strategic foresight relied on analysis, human intuition, experience, and creativity to identify signals of change, develop scenarios, and build strategies. This process incorporates diverse perspectives on possible future developments and unique viewpoints on the world. Today, technological advances, particularly in the field of artificial intelligence (AI), are adding new dimensions to these diverse perspectives.

The new advantage brought by AI's emergence is its potential to augment human capabilities and fundamentally transform how organizations approach future thinking. This article outlines three ways to leverage AI to broaden future thinking and key points to consider.

<Table of Contents>
▼ Streamline Complex Strategic Foresight Processes with AI

▼ Three Ways to Leverage AI in Strategic Foresight Processes (and What to Avoid)

▼ Finding the Right Balance is Key in Human-AI Collaboration
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Streamlining Complex Strategic Foresight Processes with AI

frog recently partnered with an AI-powered future intelligence platform to pioneer an innovative strategic foresight initiative for major infrastructure operators. This initiative leveraged AI computational power to streamline the multi-layered strategic foresight process, integrating and analyzing thousands of change signals to uncover transformative insights.

This collaboration represents a major leap forward, transforming the traditional scenario planning process into a data-driven, dynamic approach. This unique methodology enables clients not only to anticipate future changes but also to align innovation strategies with emerging business opportunities. It will set a new industry standard for informed decision-making.

Fundamentally, the strategic foresight process consists of three stages. AI can be leveraged for various tasks across all stages, significantly expanding the scope of foresight initiatives and boosting efficiency.

1. Scenario Development &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; We lay the groundwork for analysis and ultimately identify various future possibilities related to the target theme.

• Reading the signals of change
• Identifying causal relationships among key variables
• Generating scenarios using AI
• Painting future scenarios with vivid storytelling
• Create materials that visually represent the future

2.Scenario Planning
Assess the impact your company's business, as depicted in scenario planning, will have on the future, and formulate flexible conditional strategies for each future scenario.

• Assess and simulate scenario impacts
• Evaluate the robustness of various strategic responses
• Examine the opportunity costs of each strategic path

3.Scenario Monitoring
Continuously monitor to ensure each strategy maintains its validity and adapts to changes as conditions evolve.

• Identify early signs of potential scenarios
• Conduct sentiment analysis (※1)
• Track pattern changes
• Analyzing the effectiveness of responses

When expanding strategic foresight initiatives, AI acts as a force that significantly multiplies their effectiveness. It can process millions of possibilities in an instant, uncovering often-overlooked signals of change, patterns, and correlations.

However, AI's benefits extend beyond computational power. By training AI to apply advanced analytical tools like sentiment analysis and cross-impact analysis (※2), raw data can be transformed into actionable intelligence. This broadens the scope of strategic foresight activities, enhances accuracy, and improves lead times.

Furthermore, AI can help identify "weak signals"—early indicators that are still faint or infrequent, often unnoticed by humans, yet suggest the potential for significant change. Spotting such signals allows organizations to get an early start on adapting to change. This is an invaluable benefit for any organization.

※1 Sentiment analysis: A method of analyzing human emotions based on text or audio data.

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※2 Cross-impact analysis: An analytical method for predicting the probability of specific events and forecasting how various factors and variables will influence future decision-making.

Three Ways to Leverage AI in Strategic Foresight (and What to Avoid)

1.Question Assumptions
AI can help overcome cognitive biases that often cloud human judgment. AI treats all available information impartially, reducing the potential for "confirmation bias" (overemphasizing information that affirms existing beliefs) and "availability bias" (overemphasizing information that is easily recalled). By incorporating diverse perspectives and objective analysis, AI systems help organizations make well-informed decisions free from the preconceptions inherent in human judgment.

However, the following points warrant attention:
The capabilities of AI models are determined by the data they learn from. This means that while careful use can overcome bias, conversely, it can inadvertently perpetuate existing biases or make incorrect inferences based on the learned data. To maintain the integrity of strategic foresight activities, continuous human guidance and oversight are essential not only for decision-making but also for AI training, validation, and monitoring.

2.Enhancing Creativity
AI can support the creative aspects of scenario planning. By systematically altering input parameters, AI can broadly explore possibilities—including those that may seem unconventional or implausible at first glance but could actually be strategically significant.

AI can also help construct text using targeted prompts, conveying scenarios in a conversational tone that resonates with the audience. After scenario planning is complete, AI can be leveraged to anticipate the potential impacts and outcomes of dozens, if not hundreds, of scenarios, generating the opportunities and risks associated with each. Furthermore, AI can be trained to propose strategic pathways to prepare for various unforeseen events, making strategic planning more robust and enabling it to proceed more swiftly.

Key considerations here are as follows:
Current AI capabilities cannot yet replicate emotions, empathy, or the complex dynamics of human relationships, nor can it decipher the deep-seated assumptions that shape our worldviews. To energize strategic foresight efforts and translate strategic insights into actionable plans, it requires not only human input but also the ability to question and redefine existing mental models.

For instance, convincing a team to adopt a new strategic direction requires more than just presenting compelling data. It demands the empathy to understand the team's concerns, the communication skills to clearly explain the benefits of change, and the leadership to guide the team through the transition period.

What matters here is evoking trust and cultivating a shared vision for the future. AI can present the "what" and "when," but the "why" and "how" must be presented by human leaders. Understanding the dynamics within the organization, stakeholder interests, and emotional nuances, the leader's role is to add context, validity, and urgency to data-driven insights.

※3 Mental Model: Unconscious fixed ideas or assumptions held by everyone.

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3.Finding Patterns
AI bots continuously gather real-time insights throughout the foresight process. They search through countless research papers, news articles, social media, and more according to predefined criteria. They identify consistent patterns within large datasets to paint the full picture. Continuously learning and improving its performance, the AI evolves its search algorithms over time, enhancing its ability to identify patterns and pinpoint relevant signals. AI-powered horizontal scanning is like a tireless, highly capable spy that learns from past performance to boost future work efficiency.

Key considerations here are as follows:
Simply finding patterns is insufficient. Integrating AI into strategic foresight inevitably confronts us with the "black box"—the opaque core of AI decision-making. The logical basis for AI-generated outputs isn't always clear, presenting decision-makers with a challenge. The pursuit of rational explanation is not confined to academia. It is essential to verify whether strategic choices derived from AI's complex neural networks are understandable, trustworthy, and actionable for those responsible for the outcomes— .

Finding the right balance is crucial in human-AI collaboration.

Strategic foresight that leads to success balances imagination and reflection with evidence and analysis. This stems from the understanding that human experience is inherently complex, multifaceted, and constantly evolving. The process of defining challenges must be human-led. AI systems can analyze, predict, and provide information, but they lack the ability to understand the nuances of human values, emotions, and social contexts. While AI is evolving and impacting various aspects of our lives, it remains the human role to define the challenges when applying this technology and establish ethical boundaries for its operation.
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To achieve the right balance between human and AI collaboration within an organization, the following operational principles are essential:

• Prioritize culture: Focus on learning and adapting to new ways of working to steer organizational awareness in the right direction. This means deploying the right tools for the job, incorporating diverse perspectives, and enabling rapid adaptation to change.
• Start with insights: Extract insights from large datasets and use them to drive decision-making processes. Successful data interpretation enables understanding new developments and applying foresight methodologies.
• Leverage technology: Build (or establish access to) a technological infrastructure capable of processing and interpreting complex information for strategic foresight and development.

At frog, we believe the inherent limitations of AI clearly demonstrate the importance of human-AI collaboration. Therefore, we exercise caution when utilizing AI in client work. Our goal is a synergistic relationship where AI's computational power and objective analysis are leveraged, while humans contribute context-specific understanding and ethical judgment. This is particularly crucial when tackling "wicked problems"—complex, multifaceted challenges like climate change that involve high uncertainty and are deeply intertwined with social and cultural contexts.

AI holds the potential to transform strategic foresight, but the path to realizing this lies in balanced collaboration between AI and humans, each contributing their unique strengths to the process. The immediate challenge will be to integrate AI technology not as a replacement for human strategic insight, but as an extension of it, ensuring the path to success in tomorrow's world is both data-driven and human-centered.

This article is also published in the web magazine "AXIS".