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The Report  is Launched. :)

The next phase of India's AI growth will be shaped as much by states as by national policy. The UP.AIACT.IN Report 2026 provides a rigorous assessment of Uttar Pradesh's AI-adjacent economy and offers actionable pathways for accelerating innovation, competitiveness, and inclusive development.

Dr Shobhit Mathur
Vice-Chancellor
Rishihood University, Sonipat

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As an Academic leader, it is my responsibility to ensure that promising academic research in Central India does not halt before reaching the market. The UP.AIACT.IN Report 2026 provides such a highly pragmatic and necessary roadmap for bridging this gap, offering structural solutions. It is an essential blueprint which is well drafted and reasonably researched to provide or achieve genuine industry-academia fusion and finally turning campus innovation into a viable and dynamic enterprise with thinking incubation.

Dr Brijendra Singh Yadav
Director
School of Legal Studies
Chandigarh University Uttar Pradesh

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In the race to lead in AI adoption following the recent summit in Delhi, Indian states are rapidly gaining traction. The UP.AIACT.IN Report 2026 is a timely and much-needed contribution, offering a clear and actionable roadmap for Uttar Pradesh—one of the country’s largest and most significant states—to seize this opportunity. Its grounded, implementation-focused approach makes it especially relevant for shaping the next phase of AI-driven growth. Congratulations to the team and especially Abhivardhan - the resident ambassador who understands tech and the State.

Sanjay Notani
Partner
Economic Laws Practice, Mumbai

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In a sea of abstract AI frameworks, we rarely see the level of granular accountability found here. By answering the 'who, where, and when' of AI development, this report moves beyond observations into actionable foresight. The UP.AIACT.IN Report is the new benchmark for AI policy.

Roger Spitz
President & CEO
Disruptive Futures Institute

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The UP.AIACT.IN Report 2026 provides a comprehensive, forward-looking analysis of Uttar Pradesh’s AI ecosystem and practical sectoral insights that can inform AI policy, governance, and regulation in India’s evolving digital economy.

Abhinav Gupta
Faculty Coordinator
Chair on Consumer Research and Policy
National University of Study and Research in Law, Ranchi

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The UP.AIACT.IN Report 2026 stands out as an "implementation-first playbook" prioritizing "operational specificity" over policy theatre. Its unique "Shippable formula" delivers "city-rooted, testable outcomes" that leverage Uttar Pradesh’s massive talent scale for sovereign AI transformation.

Ankur Gupta
Legal & Policy Professional, Singapore

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This is not another AI vision document, it is a shippable blueprint. A first-of-its-kind report for Central India’s AI and IT landscape, it fuses ambition with execution, showing how Uttar Pradesh can leap from policy theatre to population-scale impact, grounded in talent, sovereignty, and real institutional capacity.

Sohom Banerjee
Senior Research Associate
CUTS International
Research Scholar
Jaipuria Institute of Management, Noida

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This report presents a distinctive and pragmatic vision for UP by integrating AI governance with manufacturing potential, resource optimization and employment generation. Its clear distinction from Bengaluru-centric IT service model and emphasis on utilising UP's Industrial and demographic strengths make the recommendations both practical and regionally relevant in India Law Plus AI governance regime.

Dr Taruna Jakhar
Assistant Professor (Law)
School of Law, Forensic Justice & Policy Studies
National Forensic Sciences University, Gandhinagar

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The report provides a comprehensive take on pitching UP as the the next probable destination for hosting a growing interest in India's AI plans. The report provides a comprehensive forecasting based on contemporary reality in India's tech space, learning lessons and bringing out merits about 'Why UP, Why Now' as the next frontier of AI development in the country.

Vignesh Ram, PhD
Assistant Professor
Department of Geopolitics and International Relations
Manipal Academy of Higher Education, Manipal

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The UP.AIACT.IN 2026 report is a refreshing departure from the usual high-level "policy-speak." As someone who builds AI that works—minus the hype—I find its skepticism regarding the "copy-paste" model of innovation particularly sharp. Blindly obsessing over the Bengaluru or Silicon Valley blueprint is a strategic blunder; we cannot simply transpose elite-tier frameworks onto diverse, non-elite geographies and expect results. For states to move from abstract mandates to shipping tangible code, we need the operational specificity ISAIL champions: industry-led technical stewardship and a pivot from generic bootcamps to sector-specific rigour. It's time we stop treating execution as a "post-strategy" problem.

Dr Vivek Manoharan, PhD
Coimbatore-based Scientist and Entrepreneur, working in the AI & Computer Vision space

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Uttar Pradesh combines scale and complexity in MSMEs and talent, requiring tailored AI strategies. This first state AI execution playbook is a critical resource to federating India’s AI future and supporting subnational policymaking.

Sakshi Abrol
Senior Manager
UK India Business Council (UKIBC)

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Abhivardhan and his team are emerging voices for original thinking grounded in the significance of Uttar Pradesh as a governing jurisdiction. Their UP.AIACT.IN Report 2026, especially the playbook on Data Governance, reflects a thoroughness that only comes from minds that comprehend the friction between what law can enforce and what policy can only aspire to. Essential reading for policymakers, legal professionals, and anyone serious about building regulatory frameworks that hold.

Suresh Kumar
Founder & Managing Attorney
Unimarks Legal Solutions

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The work on UP.AIACT.IN by Abhivardhan and his colleagues could be a game-changer for Uttar Pradesh’s road map for AI integration and implementation. Their report caters to regional macro & micro-level issues, is insightful, well-structured, and offers the clarity and depth needed for informed state policy considerations.

Animesh A. Bordoloi
Senior International Case Counsel
Asian International Arbitration Centre

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UP.AIACT.IN is an AI Industry Diffusion Report & Matrix as an extension of the AIACT.IN Project, launched by Indic Pacific in 2023.

This report + matrix addresses a simple question.

What should be some operationally specific ways to transform Uttar Pradesh's AI economy? 

This report & matrix should be viewed from the perspective of how one can transform an ambitious industrial & intellectual hub like UP, with its local and unique realities and how talent, resource, and sector-specific aspects can be aligned to build UP's AI and manufacturing economies.

UP.AIACT.IN does not impose models from other places, but retrofits certain measures, which help the talent and start-up economy prefer UP as the next innovation hub of Central India. 

UP.AIACT.IN also fills a crucial gap in documenting the history and patterns of Uttar Pradesh's information technology industry in collaboration with the Indian Society of Artificial Intelligence and Law.

About UP.AIACT.IN

The AI Transformation Matrix

Powered by AIACT.IN, the matrix offers a consolidated version of 77 distinct recommendations in the form of "Sectoral Playbooks" from the Indic Pacific team, the Contributing Authors of the UP.AIACT.IN Report, and the individual and Alliance members of the Indian Society of Artificial Intelligence and Law. It specifically defines what solutions are plausibly achievable in 18–24 months, and identifies the exact measurable impact that indicates success.

​

We credit Sankarshan Mukhopadhyay, one of the contributing authors of this report, for suggesting this matrix's idea.

AI Decision Infrastructure

Problem Statement

AI deployments frequently become mere technology showcases that ultimately turn into line items in audit objections.

Our Recommendations

Citizen Facing AI Services

Problem Statement

Systems obscuring AI usage within documentation, treating human escalation merely as a supplemental feature, and utilizing generic rejection language that blocks citizen recourse.

Our Recommendations

Administrative Capacity

Problem Statement

Administrative bandwidth limitations and misaligned incentive structures render traditional classroom style training incompatible with the mission mode responsibilities of nodal officers.

Our Recommendations

Official Training and Competency

Problem Statement

Traditional deployment training creates a risk of algorithmic dependency, inability to parse statistical reliability, and vulnerability to unverified commercial marketing claims.

Our Recommendations

Technical Education Access

Problem Statement

A significant supply side gap exists in accessible, high quality technical education, while existing online postgraduate offerings do not constitute an adequate substitute due to poor pedagogical quality, high pricing relative to cohort size, and limited reach.

Our Recommendations

Technology Education and Workforce Strategy

Problem Statement

Over indexing on narrow coding skills and treating technology careers as strictly synonymous with software engineering creates critical workforce gaps as AI assisted tools reduce the effort needed for routine programming tasks.

Our Recommendations

School Level Foundation Layer

Problem Statement

Lack of early-stage structural awareness regarding advanced technological careers, leading to narrow professional expectations and a baseline deficit in design-led problem solving.

Our Recommendations

Undergraduate Level Interdisciplinary Layer

Problem Statement

Procedural-heavy curricula produce graduates who can execute known solution patterns efficiently but structurally struggle with novel problem formulation, mathematical abstraction, and first-principles reasoning.

Our Recommendations

Postgraduate Level Socio-technical Strategy Layer

Problem Statement

Academic isolation of technical development from commercial application layers, platform regulation constraints, and digital public infrastructure management.

Our Recommendations

UP Specific Higher Education Rebalancing

Problem Statement

State university enrolment composition lacks technical scale, leaving an operational gap between academic theory and real world application.

Our Recommendations

State Funded AI Incubation Centres

Problem Statement

A well documented pattern of institutional resource misallocation persists in publicly funded incubation programmes.

Our Recommendations

Language Model Incentivization

Problem Statement

Deficit in domain-specific and regionally contextualized language models for local language corpora.

Our Recommendations

Entrepreneurial Mentorship

Problem Statement

Complete absence of high-tier, industry-grade mentorship for local tech founders within academic institutions.

Our Recommendations

Government Process Automation

Problem Statement

Delays in automating low-complexity government processes coupled with a lack of real-world execution tracks for engineering students.

Our Recommendations

Technical Retraining and Skilling

Problem Statement

Massive cohorts of diploma and tier-2/3 engineering graduates remain structurally underutilized due to a lack of productive, industry-aligned training beyond basic digital literacy.

Our Recommendations

Executive Supervision of Emerging Talent

Problem Statement

Department level automation is bottlenecked by the traditional capacity building framework for administrative officers, which fails to leverage fresh graduates and interns as operational force multipliers.

Our Recommendations

Industry Led Technical Stewardship

Problem Statement

Junior developer outputs in public projects frequently lack security, scalability, and standardization due to a severe gap between abstract academic theory and industry grade deployment practices.

Our Recommendations

AI Talent Discovery and Infrastructure Incentives

Problem Statement

Traditional competitive frameworks focus heavily on cash prizes that do not address the foundational infrastructure costs blocking early stage ventures from moving past raw prototypes into public utility.

Our Recommendations

Industry Led Skilling and Curriculum Co Development

Problem Statement

Training programs frequently fail to guarantee deployment roles for tier 2 and tier 3 engineering graduates, while enterprise Centres of Excellence tend to drift into vague multi sector umbrellas managed by repurposed startup ecosystem personnel rather than verified domain experts.

Our Recommendations

AI Tutors via Feature Phones and IVR

Problem Statement

Existing digital learning programs assume device ownership and data connectivity, structurally excluding rural children in districts like Shravasti or Bahraich where household smartphone access remains highly uneven and foundational literacy gaps are severe.

Our Recommendations

Shiksha Mitra UP Teacher AI Co Pilot

Problem Statement

The state employs the largest government teacher workforce in India, yet these teachers manage complex multi grade classrooms without localized, automated administrative and curriculum support.

Our Recommendations

AI PRAGYA Dropout Track

Problem Statement

The current AI PRAGYA architecture is structurally inclined toward young people already in formal institutions, thereby completely missing out of school youth in the rural informal workforce who desperately require digital skilling.

Our Recommendations

Predictive FLN Dropout

Problem Statement

Teachers managing 40 to 80 students across multiple grades identify at risk children reactively after attendance has already collapsed, rendering manual tracking of early warning disengagement signals humanly impossible.

Our Recommendations

Peer Collaboration Infrastructure

Problem Statement

Younger technical talent and researcher cohorts who are not yet formalized into registered enterprises lack collaborative, resource equipped environments outside the boundaries of rigid, formal incubation setups.

Our Recommendations

NRI and Diaspora Capital Mobilization

Problem Statement

The regional diaspora represents a heavily underutilized resource of both high tier expertise and international venture networks, typically dropped into superficial interactions.

Our Recommendations

International Student and Professional Mobility

Problem Statement

Non affluent students face immense financial barriers blocking access to international technical exchange programs, while traditional mobility models focus entirely on exporting talent without drawing global intellectual traffic back.

Our Recommendations

Diaspora Capital Incentivization

Problem Statement

Returning technology and professional retirees represent a massive, unactivated source of early stage angel investment and corporate mentorship, while their capital remains parked outside the local economy.

Our Recommendations

Institutional Attraction and Expansion

Problem Statement

Advanced academic and technical infrastructure is overly concentrated in specific pockets like Noida, leaving major regional economic zones completely excluded from elite research pipelines.

Our Recommendations

Assets Classification and Financier First Approach

Problem Statement

Unutilized storage of GPU hardware results in a compounding deficit to the sovereign Artificial Intelligence capabilities of the State due to accelerated depreciation rates of computational capacity compared to traditional physical infrastructure.

Our Recommendations

Governance Structure and SPV Establishment

Problem Statement

Standard departmental asset disposal protocols and the rigidities of standard General Financial Rules delay commercial processes and technical refresh cycles.

Our Recommendations

Active Compute Aggregation and SLA Triggers

Problem Statement

Systemic bottlenecks in the validation-to-provisioning pipeline where wait times exceeding six months can transform cost-saving measures into barriers to innovation and allow administrative latency to nullify technological competitive advantages.

Our Recommendations

Compute Portfolio Optimisation and Liquidity Models

Problem Statement

Initiatives risk being permanently tethered to rapidly depreciating silicon and legacy infrastructure liabilities if compute portfolios remain static and unutilized allocations cannot be recycled.

Our Recommendations

Cloud Infrastructure Policy and Consortium Access

Problem Statement

High risks of hardware obsolescence and severe operational cost overruns including power, cooling, and security, alongside single institutions monopolizing computational allocations.

Our Recommendations

Government Co Investment Model

Problem Statement

The current posture of government entities acting as Limited Partners (LPs) in venture capital funds is structurally disadvantageous, forcing the state to bear early-stage risk while paying steep management fees (typically 2% management, 20% carry) to private VCs who harvest returns the state helped create.

Our Recommendations

Anchor Customer and Market Catalyst

Problem Statement

Nascent ventures face extreme structural constraints at their most capital-scarce stage due to a lack of demand-side interventions, first-customer validation, and commercial access.

Our Recommendations

Procurement Quota and Architecture

Problem Statement

Traditional public procurement relies on large, winner-takes-all mega contracts that structurally exclude startups and smaller tech vendors from participating on merit.

Our Recommendations

Procurement Safe Protocols

Problem Statement

Capable startups routinely decline government contracts due to severe compliance uncertainty, exhausting 6–12 month payment cycles, and highly ambiguous intellectual property clauses.

Our Recommendations

Diaspora Engagement and Reputational Incentives

Problem Statement

Traditional diaspora engagement frameworks fail to leverage the reputational and emotional pull of formal state recognition, missing a low-cost opportunity to activate massive inbound investment.

Our Recommendations

Global Branding and Innovation Networks

Problem Statement

Sister city alliances are traditionally treated as empty, ceremonial designations based on historical diplomatic convention rather than functioning as active, data-driven network instruments.

Our Recommendations

AI Dedicated Power Infrastructure Strategy

Problem Statement

AI infrastructure is power intensive in ways that general industrial policy does not adequately address.

Our Recommendations

Sustainable Infrastructure and Resource Constraints

Problem Statement

Traditional data centre planning treats water and cooling infrastructure as an afterthought, exposing inland states to severe structural failure since landlocked geographies cannot replicate coastal mitigation strategies like municipal desalination plants.

Our Recommendations

Cooling Architecture and Industrial Water Supply Chains

Problem Statement

Reliance on evaporative cooling architecture threatens regional groundwater resources and triggers severe environmental friction in water stressed districts.

Our Recommendations

Infrastructure Transparency and Open Data Disclosure

Problem Statement

Standard clearance based oversight mechanisms create severe approval dependencies that delay vital infrastructure buildout while obscuring critical resource consumption from local communities.

Our Recommendations

Private Capital and Early Stage Financing

Problem Statement

Domestic family offices, HNIs from the UP diaspora, and corporate venture arms represent a significantly underleveraged source of early stage capital, leaving regional startups isolated from active deal flow networks.

Our Recommendations

Last Mile Diagnostic Deployment

Problem Statement

Disease surveillance and screening frameworks rely on centralized pattern detection or state level aggregation, preventing genuinely localized outbreak response and stalling high scale frontline deployment.

Our Recommendations

Life Sciences Regulatory Reform

Problem Statement

Delays in fast tracking clinical trial clearances and fragmented Institutional Review Board (IRB) approvals across state universities stymie trial initiation.

Our Recommendations

Clinical Infrastructure and Patient Cohort Matching

Problem Statement

State capital expenditure is routinely diluted into general hospital IT, leaving the state without physically separate infrastructure for advanced clinical trials and creating massive bottlenecks in identifying eligible patient cohorts.

Our Recommendations

Funding Translational Research

Problem Statement

Traditional public spending simply funds university labs and hopes for commercialization, leaving high-potential healthcare and bio-engineering research stuck at Technology Readiness Level 3 (TRL 3).

Our Recommendations

Biological Computation Infrastructure

Problem Statement

Individual researchers in state universities face bottlenecks in accessing specialized bio-informatics environments and structured clinical data because they cannot afford commercial platforms out of standard lab grants.

Our Recommendations

Biotech Incubation Infrastructure

Problem Statement

Biotech and MedTech startups cannot incubate in standard co-working spaces because they face prohibitive capital expenditure on physical lab infrastructure and exhaust their financial runway waiting for lengthy ethical approvals.

Our Recommendations

Interdisciplinary Curriculum Reform

Problem Statement

Biotechnology education over-indexes on isolated physical synthesis, leaving graduates unequipped to formulate computational biological hypotheses and unaware of complex regulatory data compliance barriers.

Our Recommendations

Academic Translational Policy

Problem Statement

Severe career friction and the lack of structural tenure protection deter tenured life science faculty from commercializing their biological research into active market ventures.

Our Recommendations

Deep Tech Capital Syndicates

Problem Statement

Private capital in the health sector historically over-indexes on generic, low-margin healthcare IT platforms like appointment booking or basic EHRs, leaving structural deep-tech biology underfunded.

Our Recommendations

Commercialisation Valley of Death Mitigation

Problem Statement

Academic research frequently collapses when university grant funding terminates before the technology becomes sufficiently proven to qualify for traditional Series A venture capital.

Our Recommendations

Domain Specific Ecosystem Aggregation

Problem Statement

Generic AI summits fail to address life sciences requirements, leading to speculative startup solutions built in search of a non-existent market and keeping software development purely theoretical.

Our Recommendations

Embedded Industry Mentorship

Problem Statement

Mentorship remains limited to passive, one-off guest lectures that fail to steer advanced student research toward solving immediate, market-validated bottlenecks.

Our Recommendations

Agricultural Advisory and Environmental Corridor Monitoring

Problem Statement

Small producers suffer from weak negotiating power, a lack of alignment with export standards, and a severe environmental monitoring deficit across critical regional ecological corridors.

Our Recommendations

Civic Innovation and Local Prototyping

Problem Statement

Grassroots civic innovators, individuals, and student teams lack structured avenues, funding, and administrative support to test localized public utility applications.

Our Recommendations

Sandbox Operational Conditions and Compliance

Problem Statement

Publicly funded tech pilots frequently decay into temporary, non-actionable demonstrations because they lack post-deployment budgets, map to vague non-enforceable metrics, and fail to identify a permanent operational owner.

Our Recommendations

High Fidelity Data Collection Standards

Problem Statement

Poor data quality and restricted data access create data monopolies that damage the regional AI ecosystem, while unmonitored data curation pipelines leak or corrupt vital metrics.

Our Recommendations

Contractual Data Localization

Problem Statement

Public administration bodies lack operational frameworks to isolate regional data streams from foreign hosting jurisdictions, creating sovereign compliance liabilities.

Our Recommendations

Open Data Policy Infrastructure

Problem Statement

Core development datasets remain trapped behind ad hoc departmental discretion, triggering legal gray zones for startups and blocking access for qualified academic institutions.

Our Recommendations

Anti Lock In Procurement Clauses

Problem Statement

Public technology deployments face extreme vendor lock-in risks, black-box architectures, and high terminal costs because standard tenders lack strict architectural and accountability safeguards.

Our Recommendations

Dashboard Architecture and Predictive Intelligence

Problem Statement

Aggregated composite dashboards spanning 20 or more indicators fail consistently because no single authority holds the operational mandate to act on every indicator, meaning when everyone is notified, no one is accountable. Furthermore, predictive AI outputs produce absolute zero value if the receiving department cannot act on them without convening an inter-departmental committee.

Our Recommendations

Social Welfare AI Governance Baseline

Problem Statement

Unvalidated deployment of AI models outside their demonstrated technical capabilities, lack of human fallback loops at critical decision steps, and superficial community integration inside social welfare operations.

Our Recommendations

Performance Reporting and Language Localisation

Problem Statement

Administrative blind spots caused by unmonitored model drift in health or agricultural workflows, hidden demographic performance variances, and the inability of local district officers to decode black-box algorithmic determinations.

Our Recommendations

Data Custody and Asset Property Rights

Problem Statement

Technology vendors claiming exclusive custody over generated operational data, inference logs, and error metrics, which builds data monopolies and locks the state out of its own public information assets.

Our Recommendations

AI Enabled Sanitation and IoT Monitoring

Problem Statement

Sanitation at major high footfall spiritual nodes relies on arbitrary, time scheduled cleaning cycles irrespective of actual conditions, driving visible degradation that directly limits international tourist retention and state sanitation rankings.

Our Recommendations

Predictive Crowd Management and Analytics

Problem Statement

Mass pilgrimage events suffer from fragmented crowd tracking and reactive security protocols, treating surging demographic volumes as an unpredicted crisis rather than a core infrastructure variable.

Our Recommendations

Dynamic Urban Transit and Smart Parking

Problem Statement

Static transit timetables and unmanaged parking clusters generate severe congestion and degrade air quality within high density, historic heritage zones.

Our Recommendations

Algorithmic Heritage Commerce

Problem Statement

Traditional artisanal crafts and handlooms remain lost within undifferentiated general marketplaces, starving local creators of direct commercial access to the high density tourist footprint.

Our Recommendations

Open Tourism Data Systems

Problem Statement

Indian smart city initiatives suffer from fragmented, siloed data architectures that make data operationally unworkable, while visitor traffic remains unsustainably concentrated in a few saturated circuits like Agra, Varanasi, and Ayodhya.

Our Recommendations

AI-Integrated Timed Entry and Zone-Based Visitor Circulation

Problem Statement

Unregulated crowd concentration causes micro-vibration damage, humidity spikes, and surface wear to historic surfaces. Furthermore, high-footfall spiritual sites are incorrectly treated as single-point destinations instead of multi-functional campuses spanning significant acreage.

Our Recommendations

AVGC-XR State Policy Framework

Problem Statement

India's share of the global AVGC-XR market is currently less than 1%, while traditional models fail to leverage regional linguistic and cultural asset bases, leaving massive creative content markets structurally untapped.

Our Recommendations

National Institutional Infrastructure Co-Investment

Problem Statement

Advanced creative technology infrastructure and apex institutional training centers are completely absent in the state, forcing local creative talent to migrate externally and stalling original domestic intellectual property (IP) creation.

Our Recommendations

Flagship Orange Economy Events and Creator Networks

Problem Statement

Creative economy events are frequently reduced to generic, one-off promotional campaigns that lack sustained political capital, failing to construct a permanent global distribution network for regional creators.

Our Recommendations

Creative AI Financing and Talent Discovery

Problem Statement

Non-English background creators face structural exclusion from global distribution networks due to a lack of affordable localization tools, while creative AI talent is systematically undervalued compared to traditional technical talent.

Our Recommendations

UP.AIACT.IN

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28 Stakeholders. 4 Editors. 10 Sectors. 1 Uttar Pradesh.

UP.AIACT.IN white logo.png

India's first AI Transformation Report and Matrix on the State of Uttar Pradesh, India

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