By Engr. Dr. Muhammad Tamoor | December 27, 2025
Energy poverty is not merely a technical failure; it is a governance failure. In countries like Pakistan, decades of short-term planning, fossil-fuel dependency, and institutional inertia have produced an energy system that is expensive, unreliable, and socially regressive. Despite possessing vast renewable resources, Pakistan continues to oscillate between shortages and surplus capacity, all while passing inefficiencies onto consumers through ever-rising tariffs. As climate pressures intensify and fiscal space shrinks, it is increasingly evident that incremental reforms will not suffice. The integration of renewable energy with artificial intelligence (AI) offers a rare opportunity to correct deep structural flaws—but only if policymakers are willing to rethink how energy systems are planned and governed.
Renewable energy has often been treated as a symbolic commitment rather than a systemic solution. Solar and wind capacity has grown, but largely in isolation from grid reform, storage planning, and demand management. The result is predictable: clean energy exists on paper, while consumers face load-shedding and unaffordable bills. Renewables, by their nature, demand intelligence. Without advanced forecasting, flexible grids, and responsive demand systems, they remain underutilized and, at times, disruptive. AI provides precisely the tools required to turn renewables from a political slogan into a functional backbone of the energy economy.
At a basic level, AI allows energy systems to anticipate rather than react. By analysing weather data, consumption patterns, and grid behaviour, AI-driven models can forecast supply and demand with far greater accuracy than traditional methods. For Pakistan, where poor forecasting has contributed to both capacity overhang and acute shortages, this capability alone could save billions of rupees annually. Yet such tools remain largely absent from energy planning, which continues to rely on outdated assumptions and fragmented data.
The dysfunction of Pakistan’s power sector is perhaps most visible at the distribution level. Transmission and distribution losses, theft, delayed fault detection, and weak enforcement have long undermined system efficiency. While political discourse frequently focuses on tariff hikes, far less attention is paid to how power is lost before it reaches the consumer. AI-enabled grid management can identify anomalies in real time, predict equipment failure, and pinpoint theft patterns with remarkable precision. These are not experimental technologies; they are already in use globally. That they have not been deployed at scale in Pakistan speaks less to technical barriers and more to institutional reluctance.
More controversially, AI challenges the prevailing obsession with large, centralised power projects. Energy-poor nations have repeatedly been encouraged to pursue mega-plants and long transmission lines, often financed through expensive debt. The results have been mixed at best. AI-managed microgrids and decentralised renewable systems offer a more resilient alternative, particularly for rural and underserved areas. These systems, capable of balancing local generation and consumption autonomously, reduce dependence on fragile national grids. In a country where rural electrification remains uneven, decentralisation should not be viewed as a compromise, but as a strategic choice.
Energy storage further underscores the importance of intelligence in the renewable transition. Batteries are frequently described as prohibitively expensive, yet inefficiency is the real cost driver. Poorly managed storage systems degrade rapidly and deliver suboptimal performance. AI can optimise charging cycles, extend battery life, and ensure energy availability during peak demand. For a cash-strapped energy sector, extracting maximum value from storage assets is not optional—it is essential.
The economic implications of intelligent renewable systems are equally significant. Energy insecurity has long constrained industrial productivity, discouraged investment, and eroded export competitiveness in Pakistan. Reliable and affordable electricity is not a luxury; it is a prerequisite for economic stability. By reducing outages, stabilising costs, and improving efficiency, AI-driven renewable systems can restore confidence among businesses and investors. Moreover, they create new opportunities for skilled employment in data science, engineering, and system management—areas where Pakistan must build capacity if it hopes to compete in a digital global economy.
There is also a broader social argument that cannot be ignored. Energy poverty exacerbates inequality, particularly in access to education, healthcare, and clean water. Intelligent energy systems can prioritise essential services, allocate resources more equitably, and reduce the urban-rural divide. However, this outcome is not automatic. Without deliberate policy design, AI risks reinforcing existing inequalities, concentrating control in the hands of a few large players while marginalising smaller consumers.
This raises uncomfortable but necessary questions about governance. Who controls the data that powers AI systems? How are decisions made, and in whose interest? Energy policy in Pakistan has historically suffered from opacity and weak accountability. Introducing AI without robust regulatory oversight would merely digitise existing flaws. Policymakers must therefore treat data governance, transparency, and public accountability as central pillars of the energy transition, not afterthoughts.
Critics often highlight the energy consumption of AI itself, arguing that data centres and computational demands undermine sustainability goals. While this concern is valid in abstract terms, it rings hollow in systems plagued by massive inefficiency. In energy-poor nations, the marginal energy cost of AI is outweighed many times over by the savings it enables. The greater risk lies not in using AI, but in continuing to mismanage energy systems under the guise of caution.
International actors have a role to play, but their involvement must evolve. Too often, developing countries are offered financing for hardware without the accompanying digital and institutional capacity needed to manage it effectively. Technology transfer, training, and knowledge-sharing must take precedence over headline-grabbing capacity targets. Global debates on AI governance should also account for the realities of energy-poor nations, whose priorities differ markedly from those of advanced economies.
Ultimately, the convergence of renewable energy and artificial intelligence presents a choice. It can be treated as another pilot project—well-publicised, under-implemented, and quickly forgotten. Or it can become the foundation of a serious effort to reform energy systems that have failed both economically and socially. For Pakistan, which faces mounting climate risks and persistent fiscal constraints, the cost of inaction is far greater than the risks of reform.
The future of energy will undoubtedly be green. But for energy-poor nations, it must also be intelligent. Without AI, renewable energy risks becoming yet another missed opportunity. With it, there is a chance—perhaps the last—to build an energy system that is affordable, resilient, and just. The question is no longer whether the technology exists, but whether the political will does.