The Ministry of Statistics and Programme Implementation has released a new framework to assess how ready government ministries and departments are to adopt Artificial Intelligence.
New Delhi : The Ministry of Statistics and Programme Implementation (MoSPI) has unveiled a working paper that introduces a detailed framework to evaluate the readiness of government ministries and departments to adopt Artificial Intelligence (AI), Machine Learning (ML), and Big Data in the field of official statistics. The initiative comes at a time when traditional statistical systems are struggling to cope with the unprecedented explosion of digital information and the complexities of large-scale data.
New Delhi : The Ministry of Statistics and Programme Implementation (MoSPI) has unveiled a working paper that introduces a detailed framework to evaluate the readiness of government ministries and departments to adopt Artificial Intelligence (AI), Machine Learning (ML), and Big Data in the field of official statistics. The initiative comes at a time when traditional statistical systems are struggling to cope with the unprecedented explosion of digital information and the complexities of large-scale data.
According to the Ministry, the existing statistical processes that rely on conventional data collection methods are increasingly inadequate in a world where Big Data, defined by its massive volume, speed, and variety, dominates the ecosystem. The working paper highlights that official statistics in India need modernization to meet the growing demand for granular, real-time, and reliable data. It underscores that the adoption of AI and ML tools, coupled with Big Data techniques, will be critical to bridging this gap and ensuring that policy decisions are backed by evidence-based insights.
The paper lays out a self-assessment framework designed for ministries and departments to measure their institutional preparedness for integrating AI into statistical systems. The tool is not meant to rank or rate government entities but to provide them with a clear picture of their current capabilities and areas that need strengthening. The assessment is structured around six major themes—Generic Information, Strategic Coordination, Data Quality and Readiness, Policy Framework, IT Infrastructure, and Human Resources. Each theme is evaluated through a set of questions, scored on a scale of 0 to 3, which reflect the extent of preparedness ranging from the absence of basic systems to the full-scale implementation of AI and Big Data strategies.
Departments are graded into four categories—Pre-Foundation, Foundation, Practitioner, and Expert. At the lowest level, an institution is only beginning to explore AI and Big Data strategies, while the highest level, Expert, indicates that data science is deeply embedded, with skilled professionals leading AI projects, supported by secure cloud infrastructure, risk management mechanisms, and clearly defined data governance frameworks. The framework, as MoSPI explains, aims to help government institutions transition gradually into adopting AI-led processes while ensuring data quality, security, and accountability.
The working paper draws extensively from global best practices, including the “Global Assessment of Institutional Readiness for the Use of Big Data in Official Statistics” prepared by the United Nations Global Working Group. MoSPI stressed that the goal of the framework is to ensure that Indian ministries are equipped with the right policies, infrastructure, and human resources to mainstream AI and Big Data in the production of official statistics.
Significantly, the Ministry has clarified that this initiative is distinct from the work of the Ministry of Electronics and Information Technology (MeitY). While MeitY focuses on providing IT infrastructure and technical guidelines, MoSPI’s responsibility is to harness these tools to generate better statistical outputs for policymakers. This distinction, the Ministry underlined, is essential to ensure a coordinated approach without duplication of efforts.
The report concludes that the integration of AI, ML, and Big Data into official statistics is no longer optional but essential. It argues that the framework will help ministries identify gaps, adopt international best practices, and modernize their operations. By doing so, the government aims to enhance transparency, build greater public trust in official statistics, and empower evidence-based policymaking. Ultimately, this initiative is seen as a crucial step toward creating a modern, AI-driven statistical ecosystem that can respond swiftly to the evolving needs of governance and society.
The paper lays out a self-assessment framework designed for ministries and departments to measure their institutional preparedness for integrating AI into statistical systems. The tool is not meant to rank or rate government entities but to provide them with a clear picture of their current capabilities and areas that need strengthening. The assessment is structured around six major themes—Generic Information, Strategic Coordination, Data Quality and Readiness, Policy Framework, IT Infrastructure, and Human Resources. Each theme is evaluated through a set of questions, scored on a scale of 0 to 3, which reflect the extent of preparedness ranging from the absence of basic systems to the full-scale implementation of AI and Big Data strategies.
Departments are graded into four categories—Pre-Foundation, Foundation, Practitioner, and Expert. At the lowest level, an institution is only beginning to explore AI and Big Data strategies, while the highest level, Expert, indicates that data science is deeply embedded, with skilled professionals leading AI projects, supported by secure cloud infrastructure, risk management mechanisms, and clearly defined data governance frameworks. The framework, as MoSPI explains, aims to help government institutions transition gradually into adopting AI-led processes while ensuring data quality, security, and accountability.
The working paper draws extensively from global best practices, including the “Global Assessment of Institutional Readiness for the Use of Big Data in Official Statistics” prepared by the United Nations Global Working Group. MoSPI stressed that the goal of the framework is to ensure that Indian ministries are equipped with the right policies, infrastructure, and human resources to mainstream AI and Big Data in the production of official statistics.
Significantly, the Ministry has clarified that this initiative is distinct from the work of the Ministry of Electronics and Information Technology (MeitY). While MeitY focuses on providing IT infrastructure and technical guidelines, MoSPI’s responsibility is to harness these tools to generate better statistical outputs for policymakers. This distinction, the Ministry underlined, is essential to ensure a coordinated approach without duplication of efforts.
The report concludes that the integration of AI, ML, and Big Data into official statistics is no longer optional but essential. It argues that the framework will help ministries identify gaps, adopt international best practices, and modernize their operations. By doing so, the government aims to enhance transparency, build greater public trust in official statistics, and empower evidence-based policymaking. Ultimately, this initiative is seen as a crucial step toward creating a modern, AI-driven statistical ecosystem that can respond swiftly to the evolving needs of governance and society.
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