The 3A2I
framework
A systems approach for scalable, equitable, responsible AI.
Embedding intelligence consistently across every phase of the use-case lifecycle creates a connected “central nervous system” for AI, one that learns continuously, adapts in real time, and remains aligned with human values and societal priorities.
3A2I framework does precisely this. Our 3A2I framework is guided by a simple principle: AI must be exposed to diverse environments and datasets and embedded with an architecture that integrates AI across all touchpoints.
Dimensions of the 3A2I framework
Dimensions of the 3A2I framework
Foundation
Access, Acceptance, and Assimilation, which represent the 3As, create the base for AI systems to learn continuously and responsibly, earn societal trust, and absorb insights and experiences across sectors.
Pathways to success
Implementation and Institutionalisation (2I) enable practical, scalable deployment and long-term integration of AI-driven initiatives into enduring systems.
3A2I framework
Access
Providing all sectors and stakeholders with the essential resources needed to harness AI effectively. These include the availability of quality data, advanced technology, robust digital infrastructure, and a skilled workforce.
Acceptance
Cultivating trust and encouraging positive engagement with AI innovations within communities, organisations, and among end-users. It involves addressing concerns related to data privacy, ethical use, and potential biases in AI systems while promoting transparency and inclusivity.
Assimilation
Integrating knowledge, expertise, and collaborative partnerships to develop and pilot AI applications that are relevant and effective.
Implementation
The practical deployment of AI solutions with minimal friction. It requires reducing regulatory hurdles, streamlining approval processes, and enabling agile methodologies that promote continuous testing, learning, and iteration.
Institutionalisation
Embedding AI-driven initiatives sustainably within organisational structures, policies, and cultures. This includes establishing governance frameworks, ethical standards, and accountability mechanisms that ensure AI applications remain reliable, responsible, and aligned with long-term goals.
Key stakeholders
The government serves primarily as an enabler, exerting its greatest influence on ensuring access and fostering acceptance.
This includes making strategic investments in digital infrastructure such as broadband networks, AI-enabled cloud platforms, and open data ecosystems, accessible across regions and communities, thereby expanding the environment for AI systems to learn and evolve.
Civil society, comprising NGOs, multilaterals, research institutions, academia, and think tanks, acts as a facilitator supporting the government.
This diverse coalition plays a pivotal role at the intersection of acceptance and assimilation.
Business/private sector assumes the role of monetiser/commercialiser, driving efforts in implementation and institutionalisation.
Building on the access and trust established by the government and civil society, businesses develop scalable AI solutions tailored to market and user needs.
Synergistic ecosystem: These elements cumulatively form the building blocks of a comprehensive, systemic model designed to build diverse learning environments and experiences. However, these blocks do not follow a rigid linear sequence. Rather, these stakeholders engage dynamically across all five building blocks, continuously providing feedback and adapting as needed.
How the 3A2I
architecture works
India's AI journey has so far faced the universal challenge of translating fragmented successes into systemic transformation. The 3A2I architecture offers a structured framework to align capabilities, governance, and incentives, integrate AI learnings and expertise, and embed AI into the heart of sectors in a systemic manner.
The following section demonstrates how this model addresses key challenges across five priority sectors, offering illustrative examples of how AI can help tackle major obstacles and drive sectoral improvement while ensuring equity.
Agriculture production decisions suffer from the problem of remaining disconnected from real-time market demand. This disconnect leads to unpredictable crop surpluses in some areas while shortages persist elsewhere, creating a cycle of excess waste, volatile prices, and suppressed incomes for farmers.
A strategic shift towards a demand-led agricultural model is essential for optimising India's agricultural productivity and economic outcomes.
Phased roadmap with a 3A2I framework
Phase 1
Building the foundation
This phase will involve building unified digital platforms that consolidate unique farmer identifiers, geo-referenced plot data, crop details, and market information. Contract farming and direct market linkage platforms along with digital agri-services hubs managed by farmer producer organisations and cooperatives will be integrated onto this platform. The existing Electronic National Agriculture Market should be made interoperable across various mandis and direct market channels, facilitating transparent, real-time price discovery.
Phase 2
Piloting the solution
During this phase, the unified digital platform will be piloted and tested. Quality-linked market infrastructure and traceability protocols will also be piloted in collaboration with farmers and buyer groups including businesses. Intelligent demand-forecasting and logistics solutions, based on data from the integrated digital platform, would be tested across different conditions.
Phase 3
Scaling and institutionalisation
Integrating lessons from the AI-pilots and tests conducted with the integrated platform, and demand-supply infrastructure, this phase will focus on implementing these across different regions, crops, farmer groups. In this stage, conscious efforts should be taken to start monetising the system created towards getting a return on investments made.
AI-driven analysis will be employed to differentiate correlation from true impact, refining which features to prioritise for scaling. AI models will be developed to automatically customise roll-out plans by region and crop, considering agro-climatic conditions, infrastructure, and digital readiness. Additionally, AI-based predictive models will estimate revenue potential across various user segments and dynamically adjust business models such as subscriptions, transaction fees, and data products in real time to optimise monetisation and sustainable growth.
3A2I at work
The 3A2I framework roadmap promotes AI integration in agriculture by ensuring broad data access, building trust and acceptance among farmers, embedding AI into existing practices, implementing scalable AI solutions with supportive policies, and establishing strong governance for sustainable use. This approach fosters continuous improvement aligned with societal needs.
India's skilling ecosystem is at the brink of an opportunity to transform the nation’s workforce into a future-ready one, prepared for a technology-driven economy. In its current form, despite multiple initiatives, improvements in access, and increased enrolment, foundational skill gaps persist, and pathways remain misaligned with labour market needs. Continuous upskilling is often inaccessible, awareness of career pathways is limited, and vocational education lacks aspirational value⁹. Consequently, employability is low, productivity suffers, and firms bear high retraining costs.
What India needs to do now is move to a skilling system that embraces quality over quantity, focusses on outcomes over enrolment, aligning curricula with market demand, ensures seamless movement from skilling pathways to meaningful employment, and enables lifelong learning.
Phased roadmap with a 3A2I framework
Phase 1
Building the foundation
This phase would focus on laying the foundations of the digital and data-heavy backbone required to support competency-based, outcome-oriented skilling systems in the country. Central to this aim is the creation of an AI-enabled database that houses data around skilling and learner profiles from across geographies and the creation of an AI-enabled platform that integrates student learning data, personalised learning modules, labour market intelligence, and credentials frameworks.
AI can be used to gather data from across regions and sources and clean it, creating consistent and comparable skill profiles.
Phase 2
Piloting the solution
In this phase, the integrated digital platform and AI models could be piloted in real-world settings, across selected regions and states. The AI-driven matching systems developed in Phase 1 could be tested to assess whether learners are being matched to the right skills, training programmes, and job roles. Models could be retrained based on outcomes and feedback from all sources.
Credentials would be stored securely, using blockchain technology in conjunction with AI, allowing learners in pilot states to access the database at anytime, anywhere.
Phase 3
Scaling and institutionalisation
This phase would focus on scaling successfully piloted models nationally and integrating them into education and labour market systems. Skilling modules could be enhanced at this stage, integrating AI into delivery and testing of skills.
Commercial deployment of the core infrastructure would include AI-driven job-matching platforms and AI models for accelerating data collection, supported by intelligent feedback mechanisms that enable continuous system learning. Education and skilling pathways would be continuously updated to align with transitions including AI adoption, green growth, and manufacturing among others.
3A2I at work
This roadmap operationalises the 3A2I framework by systematically addressing each pillar of successful adoption. Access is enabled through digital education infrastructure and skills platforms that democratise data and learning opportunities. Acceptance is fostered through trusted intermediaries and transparent, outcome-linked processes that build confidence among learners, employers, and institutions. Assimilation is achieved by embedding AI-enabled learning, assessment, and career guidance into education and workforce systems. The phased pilots and feedback-driven scaling drive effective Implementation, ensuring solutions remain responsive to real-world conditions. Finally, by scaling these systems, unlocking effective fundings, and achieving full integration of these mechanisms and outcome-based accountabilities, the roadmap secures Institutionalisation.
India's power sector is characterised by transmission and distribution infrastructure that drives high technical losses and operational inefficiencies. Transmission capacity additions lag targets due to equipment shortages and regulatory hurdles, exacerbating grid congestion. Storage capacity remains insufficient to manage renewable variability effectively. Financially stressed DISCOMs are accumulating losses to the tune of ₹6.5-6.8 lakh crore and enduring AT&C losses of around 16%¹⁰. With peak demand projected to rise from 250 GW in 2024 to 708 GW by 2047 and renewable capacity targets set at 500 GW by 2030, the lack of modern grid infrastructure and system flexibility threaten grid stability, growth, and universal energy access.
Real-time monitoring and advanced data analytics through AI are effective tools to optimise grid operations, enhance transmission and distribution efficiency, and reduce technical and commercial losses. Moreover, AI-powered predictive maintenance and automated anomaly detection will minimise outages and infrastructure failures.
Phased roadmap with a 3A2I framework
Phase 1
Building the digital and AI foundations
This phase would focus on enhancing the existing smart metering infrastructure deployed under the Revamped Distribution Sector Scheme (RDSS), which has sanctioned over 224 million smart consumer meters nationwide11.
Phase 2
Scaling AI-driven operational efficiency and renewable integration
Building on the digital foundation, this phase would focus on scaling AI deployments to address key operational challenges. AI-powered grid congestion management and real-time monitoring could be expanded to reduce renewable curtailment and streamline dispatch. Complementary regulatory reforms will facilitate faster renewable evacuation. Efforts would accelerate storage commissioning aligned with tender processes and operational goals. This would be complemented with AI-driven optimisation of battery and pumped storage dispatch integration to enhance grid flexibility amid renewable variability.
Phase 3
Institutionalising AI-enabled grid resilience and sustainable growth
Building on the successful pilots and scaled deployments from earlier stages, this phase would focus on expanding AI-driven solutions across the power sector. AI tools proven effective in optimising dispatch, predictive maintenance, and managing renewable, thermal, and storage assets would be scaled to ensure firm and reliable grid operations nationwide.
A robust financing framework is critical to driving the transformation of India’s power sector into an AI-enabled, modern, and resilient energy ecosystem. The public sector could provide foundational support through flagship programmes, focusing on smart metering, grid modernisation, and renewable integration.
3A2I at work
The roadmap applies the 3A2I framework to India's power sector by ensuring access through interoperable digital infrastructure like SMNP and IPDS, making data and AI technologies widely available. It builds acceptance via transparent operations, consumer engagement, and ethical data practices involving utilities and communities. assimilation is achieved by integrating AI tools such as forecasting and autonomous grid management into existing DISCOM workflows. Implementation involves phased AI pilots, demand-side management, and renewable integration supported by policies for efficient grid management. Institutionalisation is secured through governance platforms like UDIT, regulatory oversight, and financial incentives, ensuring sustainable and accountable AI adoption in the power sector.
Over 65% of India's population resides in rural areas, yet these regions have access to just about 30% of hospital beds and diagnostic facilities¹⁴, resulting in significant delays in care and poor health outcomes. A lack of integration between public and private providers further limits access to care providers, especially in rural and underserved areas. A wide urban-rural diagnostic divide limits access to essential imaging, pathology, and screening services, hindering early disease detection and continuity of care.
Deploying AI-powered telemedicine platforms and AI-enabled remote diagnostic units could bridge geographic barriers, optimise limited healthcare resources, and improve access to timely and quality care in remote areas.
Phased roadmap with a 3A2I framework
Phase 1
Building the foundation
This phase would focus on addressing scarcity of services and fragmentation by integrating AI-enabled telemedicine platforms and mobile remote diagnostic units into rural healthcare systems. AI-powered telemedicine solutions could be embedded within existing national digital health platforms such as the national telemedicine platform, eSanjeevani, and leveraging the digital capabilities offered by the Ayushman Bharat Digital Mission (ABDM). These models could support automated symptom assessment, AI-driven triage and personalised care recommendations, reduce physician workload, and extend last mile care.
Phase 2
Piloting the solution
In this phase, AI-enabled tools would be piloted across diverse rural contexts to assess effectiveness, accuracy, and health outcomes. This phase would address fragmentation between public and private healthcare providers by integrating data and coordination mechanisms within the AI-enabled platforms to enhance continuity and quality of care.
Large-scale training programmes for healthcare providers on AI diagnostic and decision-support tools would be conducted. Businesses would develop and provide multilingual AI-powered chatbots and voice assistants integrated into government-led platforms like e-Sanjeevani, collaborating closely with government agencies during pilot implementations to ensure effectiveness and usability, especially for low-literacy and remote populations.
Phase 3
Scaling and institutionalisation
Building on pilot learnings, this phase would focus on scaling AI-enabled care delivery and embedding it within real world healthcare operations, while testing models at scale. Businesses would scale AI-driven enhancements in telemedicine platforms, supported by continuous learning improvements of AI models based on real-world usage data and patient outcomes.
Embedding AI-powered systems that analyse real-time data on user demographics, health needs, and economic status would enable personalised plans and targeted subsidies. This would supports affordability while sustaining service delivery at scale.
3A2I at work
This roadmap operationalises the 3A2I framework by systematically addressing each pillar of successful adoption. Access is enabled by building AI-powered health data infrastructure and digital care platforms that expand reach through automated triage, multilingual interfaces, and low-bandwidth deployment. Acceptance is fostered through real-world pilots, transparent monitoring, and the integration of patient and provider feedback, building trust in AI-enabled care delivery. Assimilation is achieved by embedding AI tools directly into existing healthcare workflows. Phased pilots, continuous learning, and feedback-driven refinement ensure effective Implementation, allowing systems to adapt to real-world usage and outcomes. Finally, by institutionalising governance, monitoring mechanisms, and user-facing digital dashboards, the roadmap secures Institutionalisation.
MSMEs form the backbone of India’s economy, contributing significantly to employment, production, and exports. However, they face immense challenges in accessing formal credit due to high-risk perceptions, lack of collateral, limited financial literacy, and complex regulatory frameworks. Banks often hesitate to lend to MSMEs owing to issues like high transaction costs, inadequate credit information, and perceived commercial unviability. Despite a gradual increase in the credit share for micro and small enterprises from 14% in September 2020 to 20% by September 2024, a vast majority of MSMEs continue to struggle with inadequate formal credit access, limiting their growth potential and overall economic contribution¹².
A shift towards an AI-driven credit ecosystem could be a gamechanger for transforming credit access and financial inclusion for India's MSMEs.
Phased roadmap with a 3A2I framework
Phase 1
Building the foundation
This phase focuses on developing interoperable digital infrastructure that consolidates diverse data sources such as UDYAM and Udyam Assist registration portals, GST records, payment histories, and transactional data to establish comprehensive MSME credit profiles. Artificial intelligence enhances data reliability and validation by efficiently identifying inconsistencies, correcting errors, and reconciling fragmented information through pattern learning and anomaly detection at scale. Co-creation of AI-powered credit platforms as digital public infrastructure will facilitate inclusive credit assessments.
Phase 2
Piloting the solution
During this stage, AI-powered credit scoring models leveraging diverse data (including supply chain insights and order volumes) will need to be deployed and piloted across banks and NBFCs. The Credit Guarantee Fund Trust for Micro and Small Enterprises (CGTMSE)¹³ will need enhancement with AI-enabled automation of risk evaluation to improve efficiency and transparency.
Phase 3
Scaling and institutionalisation
This phase emphasises embedding AI credit assessment fully into lending systems, enabling sustainable and scalable MSME financing. Advanced AI-powered advisory services, such as chatbots and virtual assistants, will be deployed to guide MSMEs on financial management, compliance, and risk mitigation. AI models will continually train and mature by analysing transaction patterns to detect fraud, identify early defaults, and flag high-risk borrowers ("lemons") enhancing the accuracy and reliability of credit decisions. AI and IoT-enabled insurance and risk solutions will be integrated to improve supply chain resilience and creditworthiness.
3A2I at work
The roadmap applies the 3A2I framework to improve MSME credit access using AI. It ensures access through interoperable digital credit platforms integrating diverse data. Acceptance is built via ethical AI governance, transparent processes, and literacy programs. Assimilation embeds AI credit solutions into MSMEs’ workflows through shared platforms and local training. Implementation involves phased AI credit scoring and risk management with real-time feedback. Institutionalisation integrates AI into mainstream lending, supported by governance and incentives for sustainable, inclusive financing. This creates adaptive ecosystems driving MSME growth and financial resilience.
