AI disruption zones identified: Study flags education, energy and fintech as high-risk sectors

The study identifies three key sectors, namely digital education, renewable energy, and financial markets, as the most critical domains facing both high transformative potential and profound uncertainty. Using a six-step scenario-building process beginning with targeted queries in the Scopus academic database, the authors analyzed academic literature to extract and classify trends using topic modeling tools such as TF-IDF vectorization and Latent Dirichlet Allocation.

Researchers at the Universidade de Lisboa have proposed a new methodology for forecasting artificial intelligence’s societal and economic impact, offering structured scenarios to guide global policymakers and industry leaders through uncertain terrain.

The study, published under the title "Exploring the Societal and Economic Impacts of Artificial Intelligence: A Scenario Generation Methodology" on the arXiv preprint server, applies a novel combination of literature analysis, topic modeling, and simulation to identify high-impact, high-uncertainty sectors most vulnerable to AI-driven transformation.

Which sectors will AI disrupt most, and why are they considered critical?

The study identifies three key sectors, namely digital education, renewable energy, and financial markets, as the most critical domains facing both high transformative potential and profound uncertainty. Using a six-step scenario-building process beginning with targeted queries in the Scopus academic database, the authors analyzed academic literature to extract and classify trends using topic modeling tools such as TF-IDF vectorization and Latent Dirichlet Allocation.

This analysis revealed six overarching impact domains: AI in education, molecular medicine, machine learning, sustainability, fintech, and healthcare. Of these, AI-powered education, energy sustainability, and fintech were mapped in the highest-impact/highest-uncertainty quadrant of an Impact-Uncertainty Matrix - a strategic planning tool that helps decision-makers prioritize actions under conditions of volatility.

Education, for example, is expected to undergo seismic shifts as AI enables hyper-personalized, adaptive learning systems. But the digital divide, ethical ambiguities, and disparities in infrastructure raise serious concerns about equitable access. Renewable energy, while essential for meeting global climate targets, is constrained by unpredictable breakthroughs and market adoption rates. Fintech, meanwhile, may revolutionize financial services but comes with risks of cyber instability, speculative bubbles, and unequal inclusion.

These three domains were selected for future scenario modeling because they sit at the crossroads of maximum potential and maximum unpredictability - factors that demand urgent strategic attention from governments and private actors alike.

What possible futures are forecast, and how do they diverge?

The authors constructed four scenarios to reflect different future trajectories: Optimistic Future, Technological Stagnation, Sustainability Focus, and Economic Downturn. Each scenario explores how AI’s influence across the critical domains could evolve under shifting economic, technological, and political conditions.

In the optimistic scenario, AI is rapidly integrated across education systems, enabling personalized learning experiences. Renewable energy expands through sweeping grid modernization, while fintech becomes a stable, secure ecosystem powered by inclusive innovation.

Conversely, the technological stagnation scenario assumes sluggish integration of AI in education, persistent reliance on fossil fuel infrastructure, and a fintech sector that fails to deliver promised efficiencies, leading to market volatility and public distrust.

The sustainability focus scenario envisions targeted investments that emphasize digital inclusion in education, high policy-driven funding in green infrastructure, and fintech geared toward environmental financing tools. In contrast, the economic downturn scenario predicts severe underinvestment in digital and green technologies, leading to fragmented education systems, stagnating renewable adoption, and a sharp decline in fintech activity.

Each scenario is not merely theoretical - it incorporates mathematical modeling to simulate the evolution of economic growth, social well-being, and technological advancement over time. The model applies logistic growth and Gompertz functions, drawing from macroeconomic forecasting techniques to simulate complex feedback loops. These simulations are designed to help policymakers visualize plausible futures and calibrate interventions accordingly.

How can this methodology inform policy, and what challenges remain?

The researchers stress that scenario planning must be dynamic, continuously updated with empirical data, and supported by interdisciplinary governance. The methodology proposed goes beyond traditional trend analysis by integrating machine learning tools for scenario generation, an innovation the authors argue could be widely adopted in strategic foresight and regulatory planning.

The study also warns that while AI holds promise across all three identified sectors, without robust regulatory frameworks, ethical safeguards, and proactive investment strategies, the technology could exacerbate global inequalities. Challenges such as algorithmic bias, fragmented governance structures, and data privacy concerns remain unaddressed in many jurisdictions.

In education, the researchers call for ethical AI guidelines, equitable access policies, and significant upgrades to digital infrastructure. For renewable energy, strategies include boosting R&D for clean technologies, modernizing energy systems, and offering policy incentives for green adoption. In fintech, key measures include digital literacy programs, robust cybersecurity protocols, and governance frameworks that balance innovation with stability.

The methodology also proposes quantifying uncertainties and outcomes through simulated indicators like economic productivity, well-being indices, and innovation diffusion. The inclusion of noise variables in the model introduces stochastic realism, accounting for unpredictable events such as geopolitical conflict or financial crises that may disrupt otherwise linear progressions.