India AI Impact Summit 2026

regional-ministerial-dialogue-on-ai-ready-digital-infrastructure

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TRACK:Democratizing AI Resources
SESSION OVERVIEW

Add to Calendar Watch Live 20 Feb 2026 12:30 PM - 1:30 PM Bharat Mandapam L1 Meeting Room No. 18 Knowledge Partners Asian Development Bank

VIDEO RECORDING

Regional Ministerial Dialogue on AI-Ready Digital Infrastructure

Abstract

The dialogue gathered senior officials from the WTO, Uzbekistan, Indonesia, and the Asian Development Bank to examine what “AI‑ready” digital infrastructure means for the Global South. After an opening briefing on the four pillars of AI‑ready data—discoverability, trustworthiness, interoperability, and usability—the moderator launched a fireside‑chat that explored gaps and opportunities in data, compute, skills, and standards. Panelists highlighted national strategies (Uzbekistan’s $200 m data‑center plan, Indonesia’s sovereign‑AI roadmap, WTO’s trade‑growth projections, and ADB’s financing model), debated priority‑setting, financing, and regional cooperation, and surfaced concrete recommendations for policy, investment, and capacity‑building. The session closed with a reminder that AI is a tool, not a universal solution, and that its impact hinges on coordinated infrastructure, talent, and regulatory frameworks.

Detailed Summary

The session opened with an Opening Speaker who framed the discussion around AI‑ready data. Four inter‑linked elements were presented:

  1. Discoverability – the need for well‑defined, cross‑domain metadata so datasets can be found and catalogued.
  2. Trustworthiness – a quality‑assessment framework to certify data credibility.
  3. Interoperability – unique identifiers and common schema that let disparate datasets “talk” to each other.
  4. Usability across systems – shared standards and classifications to prevent semantic mismatches.

The speaker stressed that these pillars form the bedrock for any AI‑driven system and that they are being piloted with ministries and state governments across India.

Key Insight: A robust data foundation is as critical as compute power; without discoverable, trustworthy, interoperable, and usable data, AI models cannot deliver reliable outcomes.

The speaker also raised a resource‑efficiency question: most generative AI models require massive compute cycles for each query, consuming gigawatts of power. By contrast, a human body consumes only ≈100 W (2,000 kcal). This contrast prompted a call to explore model‑efficiency and alternative inference architectures.

2. Transition to Fireside‑Chat

The moderator thanked the opening speaker, introduced the fireside‑chat format, and explained the broader scope of “digital infrastructure” as defined by the ITU’s “3 S”: Solutions, Standards, Skills. The moderator announced the panel’s guiding question:

“From your vantage point, what is the most critical gap or the most exciting opportunity for the Global South to generate positive impact through AI?”

Each panelist was asked to give a brief self‑introduction before responding.

3. Panelist Introductions & First Round of Perspectives

3.1 Johanna Hill – WTO

  • Opportunity: AI can boost global trade by up to 40 % by 2040 (the “40 by 40” effect).
  • Prerequisites: Digital infrastructure, skilled workforce, and policy readiness.
  • Sectoral insights: AI is already reshaping agriculture, health, and services; it can unlock new tradable goods and services.
  • SME focus: While larger firms in advanced economies adopt AI, SMEs in developing economies still face an adoption gap, especially in integrating AI into core business processes.

Key Insight: AI‑enabled trade growth hinges on inclusive digital ecosystems that bring small firms online.

3.2 Uzbekistan Representative

  • Core gaps:
    1. Unequal access to compute
    2. Advanced AI digital skills
  • Strategic actions:
    • Launched a 5 million‑AI‑leaders program with the UAE, targeting students, professionals, and public servants.
    • Allocated USD 200 m to build a government data‑center equipped with NVIDIA GPUs.
    • Partnering with Saudi firm Data Vault to build a 5‑billion‑USD renewable‑energy data‑center (target operation in 2–3 years).
    • Adopted an AI Strategy 2030, targeting USD 1.5 bn in AI‑product exports by 2030.

Key Insight: State‑driven investments combine human‑capital programmes with large‑scale, renewable‑energy‑backed compute to avoid being merely AI consumers.

3.3 Prof. Hamam Riza – Indonesia

  • Context: Co‑chair of Indonesia’s National AI Roadmap 2030 and president of a national AI research consortium.
  • Triple deficit:
    1. Data & compute infrastructure – inadequate connectivity and network capacity.
    2. Localized high‑quality datasets – scarcity of AI‑ready Indonesian data.
    3. Talent shortage – insufficient AI‑skilled workforce.
  • Policy focus: Address the digital/AI divide; even though 92 % of knowledge workers use basic AI tools, awareness of risk & responsible AI remains low.

Key Insight: Closing the “triple deficit” is essential for public‑service AI (health, agriculture) and for unlocking the projected US $1 tn AI‑driven economy.

3.4 Mio – ADB (India Country Director)

  • Foundational needs: Reliable power, affordable devices, broadband connectivity.
  • Service‑level focus: Rather than only building foundations, apply AI to sectoral services (agriculture, water, irrigation).
  • Financing model: ADB offers sub‑commercial loans to fund public‑goods digital infrastructure, mobilising private capital through public‑private partnerships (e.g., linking roads/water projects with digital facilities).
  • Knowledge transfer: Supports master‑planning, strategy development, and capacity‑building across municipalities and regions, often bringing in international experts.

Key Insight: ADB’s comparative advantage lies in leveraging private‑sector finance while ensuring cross‑sectoral integration of AI solutions.

4. Deep‑Dive Topics

4.1 Prioritisation & Funding (Uzbekistan)

  • Budget: USD 300 m earmarked for AI projects across government services (health, education, cybersecurity, etc.).
  • Infrastructure roll‑out: Building data lakes and government data‑centers, offering data free or at low cost to SMEs and startups.
  • International collaboration: UAE partnership for the AI‑leaders programme (over 1 m registrants).
  • Incentives for foreign investors: Tax breaks, cheap electricity, customs exemptions for data‑center projects (> USD 100 m).

Open Question: How sustainable are incentive regimes when scaling across multiple sectors?

4.2 Cloud‑Hyperscaler Landscape (Indonesia)

  • Current state: Multiple cloud regions established by global hyperscalers; however, capacity must be amplified to meet AI’s compute and data‑storage demands.
  • Sovereign AI push: Regulations being drafted to encourage local AI model development aligned with cultural contexts.
  • Physical infrastructure: Construction of GPU‑rich data centers, edge‑computing nodes, and special economic zones to attract hyperscalers.
  • Policy alignment: Coordination with the Vice Minister of Communication and Digital Affairs to embed AI in national strategy.

Key Insight: Indonesia seeks to federate global cloud services with domestic sovereign AI capabilities, blending external scale with internal relevance.

4.3 Trade Competitiveness (WTO)

  • AI can lower trade costs and create AI‑enabled products/services that cross borders.
  • AI Trade Policy Openness Index (published in the WTO’s World Trade Report) measures openness; lower‑income economies may appear “open” due to absence of regulation, which can be a competitiveness risk.
  • Regional cooperation: Sharing infrastructure, harmonising standards, and joint research to achieve economies of scale.

Recommendation: Develop responsible‑AI standards and policy harmonisation to avoid “regulatory fragmentation” that could hinder trade.

4.4 ADB’s Democratisation of Compute Initiative

  • A working group (co‑led by ADB) is exploring cross‑border compute sharing to reduce duplication of massive data‑center investments.
  • Still in early stages, but aims to create regional compute pools that can be accessed by multiple member countries under transparent governance.

Open Question: What governance model will ensure fair access, data sovereignty, and cost‑recovery across participating nations?

4.5 Private‑Sector Capital Mobilisation (Uzbekistan)

  • Targeting USD 1 bn of private investment for AI‑related infrastructure by 2030.
  • Partnerships with Huawei for 5.5 G/6G rollout and AI ecosystem development.
  • Creation of venture‑fund‑of‑funds and seed‑stage funds; USD 50 m currently allocated to AI startups.
  • Emphasis on upskilling public servants to facilitate adoption of AI‑centric data‑centers.

Key Insight: A blended financing model (public funds + venture capital + foreign strategic partners) is essential for scaling AI ecosystems in emerging markets.

4.6 Regional Cooperation & Standards (WTO & ADB)

  • WTO highlighted experiences from Digital Trade in Africa and Latin America: regional protocols (e.g., ACFTA digital protocol) can standardise practices and lower entry barriers.
  • ADB stressed that regional cooperation must avoid over‑emphasis on AI as a silver bullet; social considerations (employment impacts) remain critical.

Recommendation: Establish regional AI standards bodies that include government, industry, and civil‑society to co‑design interoperability and data‑sovereignty frameworks.

5. Closing Remarks

The moderator wrapped up with a caveat: AI is not a universal remedy; its effectiveness depends on problem identification, skill development, infrastructure investment, and appropriate regulation. He thanked the panel, the audience, and ADB for hosting the summit, and called for continued collaboration across the Global South to translate AI potential into tangible societal benefits.

Key Takeaways

  • Data foundations matter: Discoverability, trustworthiness, interoperability, and usability are the four pillars of AI‑ready data.
  • Compute‑energy paradox: Current AI models consume gigawatts of power; sustainable, efficient model designs are urgently needed.
  • Strategic national roadmaps: Uzbekistan, Indonesia, and India (via ADB) each pair skill‑building programmes with large‑scale compute investments (e.g., renewable‑powered data centres).
  • SME inclusion is critical: The WTO warns that AI‑driven trade growth will be limited unless small firms can adopt AI tools effectively.
  • Financing blend: Public funds, concessional loans (ADB), tax incentives, and venture‑capital pools together mobilise the capital required for AI infrastructure.
  • Regional cooperation: Shared standards, joint research, and cross‑border compute pools can achieve economies of scale and reduce duplication.
  • Policy openness vs. regulation: A lack of regulation can appear as openness but may hinder trust‑based AI trade; balanced, responsible AI policies are essential.
  • Human‑centred AI: All participants stressed that AI must serve human welfare, not just technological advancement.
  • Infrastructure beyond data centres: Skills development, regulatory frameworks, and sector‑specific AI applications (agriculture, health, climate‑smart initiatives) are equally vital.
  • Cautious optimism: While AI presents massive opportunities, policymakers must match solutions to real problems, ensuring that AI complements—not replaces—human labour and social objectives.

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