The rapid advancement of artificial intelligence (AI) is revolutionizing various sectors, offering unprecedented opportunities for prediction and optimization. However, this technological leap is creating a significant chasm: the "foresight gap." This gap isn't a technological failure; it's a systemic issue stemming from a misalignment of institutional structures and resource distribution. This essay will explore the widening disparity between those who can anticipate and leverage AI-driven insights and those who lack the capacity to respond effectively, focusing on the need for a more equitable and inclusive approach to AI development and deployment.
The Unequal Distribution of Foresight
In regions like East Africa, AI tools are increasingly used to predict rainfall patterns, crop yields, and soil degradation. Agritech startups and multinational agribusinesses utilize satellite imagery and machine learning models to optimize planting schedules and mitigate pest risks. This advanced foresight provides significant resilience, allowing corporations to adjust sourcing strategies and hedge against potential losses. However, smallholder farmers, who constitute a substantial portion of the global food supply (approximately one-third), often lack the resources to utilize these same insights. Access to irrigation, credit, and supportive institutional frameworks is severely limited. The constraint isn't the lack of foresight itself; it's the absence of capacity to act upon it.
This disparity highlights a critical challenge: the growing difference between those equipped to anticipate disruptions and those with the means to adapt. AI is transforming risk assessment, resource management, and supply chain navigation, but it simultaneously amplifies existing inequalities. This isn't necessarily due to direct harm inflicted upon low-capacity actors but rather the accelerated adaptive advantage enjoyed by those already well-positioned. As foresight becomes paramount in sustainability strategies, the focus shifts from identifying risks to effectively addressing them. The question becomes: who possesses the agency to act, and why are some better equipped than others?
At the core of this issue lies the "foresight gap"—the increasing distance between insight and the ability to act upon it. The problem isn't the scarcity of data. Many actors, including municipalities, farming cooperatives, and suppliers, access forecasts, dashboards, and predictive models. The crucial issue is that insight, devoid of sufficient financing, technical tools, or enabling institutions, often leaves actors aware of risks but powerless to mitigate them. The result is uneven resilience, where some thrive while others struggle to keep pace.
AI: Exacerbating Existing Inequalities
AI could inadvertently reinforce this unequal distribution of resilience. Corporations with sophisticated modeling capabilities can reconfigure procurement, redirect investment, and proactively manage operations. Meanwhile, suppliers in more vulnerable environments bear the brunt of the consequences. Risk may shift, but it doesn't diminish. This selective adaptation allows resourceful entities to strengthen their positions while others absorb the shocks. Over time, this dynamic undermines both equity and systemic stability. A transition that merely reallocates risk without fostering shared capacity creates overall fragility.
This pattern extends beyond agriculture. Cities with the financial means to invest in AI-integrated infrastructure planning improve energy efficiency and emergency response capabilities. In contrast, many cities, especially in the Global South, operate with outdated systems and limited technical capacity. In the insurance sector, AI is altering climate risk pricing, leading to higher premiums or the withdrawal of coverage in high-risk areas. Similarly, in supply chains, predictive analytics allow some firms to reroute around disruptions, leaving those at the forefront vulnerable.
These shifts introduce a significant, often underestimated, systemic risk. When adaptation is selective, the costs of disruption ripple across sectors and geographies. Fragility at the margins—among smallholders, subcontractors, or overstretched public agencies—can trigger cascading effects. The 2022 floods in Pakistan serve as a stark example: extreme weather forced global retailers to adjust orders and logistics, but smaller suppliers endured months of operational paralysis and significant income loss. Without sufficient capacity to absorb shocks at all levels, the entire system becomes increasingly brittle. Concentrated resilience cannot guarantee collective stability.
The Need for a Just Transition
This situation highlights a fundamental tension. AI is often presented as a force for inclusivity, but without appropriate governance, it risks doing the opposite. The foresight gap is not a mere technical glitch; it reflects underlying disparities in capital, capabilities, and institutional design. Without concerted efforts to equitably distribute foresight and enable action, the gap will only widen.
A just transition requires a shift in focus. The conventional framework emphasizes costs, benefits, and protections—particularly for workers and communities—which remain crucial. However, in the context of AI, justice must also encompass access to adaptive capacity. The transition cannot rely solely on those already possessing the necessary tools; it must actively support others in acquiring them. This is not merely a matter of fairness; it's essential for managing shared risks in an interconnected world.
Institutional Reforms for Equitable AI Deployment
This perspective necessitates significant institutional reforms:
1. Investing in Public Foresight Infrastructure: Investment in public infrastructure is crucial. Predictive tools must be designed and deployed with broad usability in mind, including open-access climate models, data collaboratives, and analytics suitable for under-resourced settings. National adaptation plans and resilience strategies should be informed by intelligence that accurately reflects real-world constraints. This infrastructure should not be confined to ministries or multilateral organizations; it must be accessible to frontline actors—local governments, cooperatives, and civic organizations—equipped to act and mobilize effectively.
2. Fostering AI-to-Action Partnerships: Companies leveraging AI to manage their risk exposure should contribute to the adaptive capabilities of their suppliers, contractors, and local communities. This isn't mere philanthropy; it's a pragmatic approach to reducing risk concentration across the value chain. Some firms are exploring shared data platforms with suppliers or financing adaptation initiatives as part of broader ESG-linked targets. However, these efforts remain isolated. A shift in mindset is necessary—from risk extraction to risk co-management.
3. Redefining Fiduciary Responsibility: Boards and investors must assess whether AI-enabled strategies enhance system-level resilience or simply reinforce firm-level protection. Are companies merely redistributing risk to weaker links, or are they investing in broader capacity building? These are strategic questions, not mere compliance matters. Fiduciary duty should encompass how foresight tools influence risk allocation and whether they contribute to long-term value creation that is stable, inclusive, and credible.
The Evolving Landscape of AI and Sustainability
It's important to acknowledge that AI is more than a predictive tool. Generative models and large language systems are shaping knowledge access, decision-making, and strategy refinement. These technologies can potentially expand adaptive capacity, particularly when designed for public benefit or integrated into frontline decision-making. However, this potential doesn't negate the foresight gap; it simply alters its contours. As AI becomes more deeply embedded in corporate strategies and public infrastructure, the fundamental question remains: who can meaningfully utilize these tools, and under what conditions?
The trajectory of AI in sustainability isn't predetermined. It will be shaped by decisions concerning governance, design, and accountability. The foresight gap is not a technological failure; it's a challenge of institutional alignment. AI is often evaluated based on its predictive, automation, and optimization capabilities. However, in the context of sustainability, a different question is paramount: does it support a transition that fosters system-wide resilience? Concentrated foresight without distributed capacity creates brittleness. Resilience must extend beyond individual firms, sectors, or regions.
Those shaping AI's role in the transition—corporate leaders, investors, regulators—are defining more than just tools; they are shaping trajectories. The critical issue is no longer whether AI can enhance foresight (that question has been answered). The real question is whether we align that foresight with the capacity to act—broadly, deliberately, and urgently. Leadership will be measured not by who identified the risk first but by who ensured others were prepared when it arrived. The future of a sustainable and equitable world hinges on bridging the foresight gap and ensuring that the benefits of AI are shared broadly, not concentrated in the hands of the already privileged.