From Evidence to Scale: Testing, Financing and Operationalizing Technology and AI for Development and Humanitarian Action.

Abstract

The panel explored how artificial‑intelligence (AI) and digital technologies that have been proven in pilots can be moved to sustainable, large‑scale deployment for agriculture, food security, rural development, and humanitarian action. Panelists examined real‑world evidence from their agencies, discussed policy and regulatory implications for governments, highlighted financing models for digital public infrastructure, described operational successes on the ground, and reflected on how to evaluate AI‑enabled programmes. The session concluded with an audience Q&A that raised practical concerns about public‑sector policy, gender‑inclusive access, data governance, and AI infrastructure in tier‑2/3 regions of India.

Detailed Summary

Moderator (Vaishnavi Pavithran) welcomed the audience, introduced the panel, and explained the session’s purpose: to shift the AI India Summit from “dialogue” to “impact”. She highlighted the three Rome‑based agencies—World Food Programme (WFP), FAO, and IFAD—as custodians of SDG 2 (Zero Hunger) and described the agenda as a journey from evidence → financing → policy → operationalisation.

2. Rapid‑Fire Personal Introductions

A 30‑second rapid‑fire round gave the audience a glimpse of each panelist’s perspective:

Panelist	Key point shared in rapid‑fire
Dr. V. Ananta Nageshwaran – AI in the National Economic Survey (NES) and the value of human expertise: “Garbage‑in, garbage‑out – richer prompts produce better AI outputs; AI does not replace humans but augments them.”
Dr. Vincent Martin (FAO) – AI as a catalyst for speed, precision, and foresight in scientific discovery (vaccines, early‑warning systems).
Brenda Mulele‑Gunde – Personal use of ChatGPT for creative image generation; expressed enthusiasm for rapid AI‑driven prototyping.
Magan Naidu (WFP) – Myth that AI is “magical”; emphasized data quality, human expertise, and time‑intensive training as essential.
Dr. Indran Naidu (IFAD) – AI’s three‑tier benefits: (1) democratizing high‑quality information for individuals, (2) enabling customized, evidence‑based interventions for governments/agency, (3) strengthening evaluation through deeper, more granular data.

3. Policy Implications for Government Regulation

Question (Moderator → Dr. Nageshwaran): “Given AI’s rapid evolution, what are the policy implications for regulating AI and the role of government in the public sector?”

Dr. Nageshwaran’s response (summarised and organised into seven policy pillars):

Sequencing Over Speed – Adopt a phased, step‑wise regulatory approach rather than rushing to legislate.
Risk‑Based, Proportionate Regulation – Tailor rules to the specific risk profile of AI applications, avoiding blanket restrictions.
Techno‑Feudal Challenge – Guard against excessive market concentration; maintain democratic oversight over AI firms.
Labor‑Market Sensitivity – Design AI policy to avoid social instability; ensure productivity gains are broadly shared.
AI Economic Council – Create an inter‑ministerial body (the “AI Economic Council”) to coordinate socio‑economic, data‑privacy, and security considerations.
Data Governance – Embed robust data‑ownership, privacy, and security standards across all AI initiatives.
Foundational Skills & Education – Prioritise AI‑literacy in curricula (critical thinking, prompt engineering) rather than solely technical training.

Key Insight: The Indian government’s recent NES chapter on AI underscores a “bottom‑up” regulatory philosophy, complementing market‑led and heavy‑regulation models.

4. AI as an Enabler of Innovation (FAO Perspective)

Question (Moderator → Dr. Martin): “How is AI streamlining innovation and being scaled across FAO?”

Dr. Martin’s overview:

Challenge Identified: Solutions are abundant; the bottleneck is connectivity—linking stakeholders to the right information.
Two Main FAO AI Initiatives:
1. Agri‑Food System Technology & Innovation Outlook (AFSTIO) – the first global database of agri‑food technologies; AI crawls and consolidates data from heterogeneous, reliable sources, making the knowledge “accessible and actionable”.
2. Portfolio Sense‑Making & Management – AI parses massive numbers of project reports to map FAO’s innovation portfolio across themes (livestock, forestry, health, etc.). This yields a clear, data‑driven view of which solutions are most promising for scaling, enabling targeted investment and policy support.
Human‑Centred Emphasis: While AI accelerates discovery, FAO stresses the need for inclusive, value‑driven design, warning against “AI‑only” solutions and reinforcing the importance of soft skills (networking, behavioural design).

Key Insight: AI can transform a “data‑rich but insight‑poor” environment into a rapid‑learning ecosystem, but only when paired with human judgement and inclusive governance.

5. Financing Digital Public Infrastructure (IFAD Perspective)

Question (Moderator → Brenda Mulele‑Gunde): “What financing mechanisms enable scaling AI from pilots to sustainable national programmes?”

Brenda’s analogy: Roads vs. cars – governments build digital public infrastructure (the “roads”), while private actors supply the “cars” (AI applications).

Components of Digital Public Infrastructure (DPI):

Farmer Registries – Centralised data on who farmers are, their locations, crops, and market linkages.
Interoperability – Standardised data exchange protocols enabling multiple systems to “speak the same language”.
Data Governance & Policy – Legal frameworks ensuring privacy, security, and equitable use.
Digital Payments & Identity – Seamless, bank‑free transaction mechanisms for subsidies, market payments, etc.
Extension Delivery Platforms – Digital channels for advisory services, weather alerts, etc.

Financing Logic:

Sovereign Instruments – Government budget allocations for DPI (roads).
Non‑Sovereign Instruments – Private‑sector capital (cars) mobilised through blended catalytic investments that de‑risk private participation.
Shift from Grants to Scale: Grants fuel pilots; DPI financed by sovereign funds unlocks private investment for scale.

Key Insight: Successful AI scaling hinges on a “public‑private partnership” model where the state funds foundational infrastructure and the private sector delivers innovative services.

6. Operationalising AI on the Ground (WFP Perspective)

Question (Moderator → Magan Naidu): “How is AI being operationalised in field operations, and what tangible benefits are observed?”

Magan’s three‑fold impact narrative:

Supply‑Chain Optimisation – AI‑driven routing reduces delivery time and costs, ensuring food reaches the right people at the right moment.
Food‑Resilience Building – AI analyses soil, climate, and market data to advise smallholders on optimal cropping, enhancing self‑sufficiency.
Emergency Response & Early Warning – AI predicts flood/drought risks, enabling proactive pre‑positioning of supplies; satellite/drone imagery assists in rapid search‑and‑rescue.

Cost‑to‑Serve Reduction: Agentic AI (auto‑generated solutions) lowers development costs, enabling broader reach.
Human‑Centred Design: AI is framed as an assistive technology, not a replacement; it augments staff capacity and improves decision‑making.

Key Insight: AI’s value is highest when it integrates directly into operational workflows, reducing latency and improving predictive capacities in both routine and crisis contexts.

7. Evaluating AI‑Enabled Programs (IFAD Evaluation Office)

Question (Moderator → Dr. Indran Naidu): “How does the evaluation function assess AI programmes, and are you using AI in your own evaluation work?”

Indran’s response:

AI‑Enhanced Evidence Collection:
- Scale & Depth: AI expands sample sizes beyond traditional surveys, capturing richer, more granular data.
- Cost Efficiency: AI reduces research costs, freeing resources for deeper analysis.
Evaluation Criteria for AI Interventions: Relevance, efficiency, transparency, impact, and scalability – applied to specific AI‑driven projects (e.g., IFAD’s India Country Programme).
Bias Guardrails: Emphasised that AI must not amplify existing biases; human oversight remains essential.
Knowledge Sharing: Findings are disseminated via the Evaluation Corporation Group (heads of evaluation across MDBs), fostering cross‑institution learning.

Key Insight: AI can magnify the reach and precision of evaluation, but its use must be carefully governed to maintain independence and avoid bias.

8. Audience Q&A – Core Themes

Questioner (Affiliation)	Main Question	Panelist(s) Responding	Summary of Response
Subhash (Energy sector, Indian public sector)	“Is there a specific AI policy for India’s public‑sector enterprises?”	Dr. Nageshwaran (follow‑up by moderator)	No single omnibus policy; METI (Ministry of Electronics & Information Technology) issues a broad framework, while each sector (finance, energy, etc.) tailors its own guidelines.
Manjula Pradeep (Civil Society)	“How can AI contribute to achieving zero‑poverty/zero‑hunger (SDG 2)?”	Magan Naidu (WFP)	AI can accelerate food‑resilience, enable proactive crisis response, and empower communities to become self‑reliant, but must be coupled with inclusive design.
Megha Desai (SEWA – women’s farmers)	“Women smallholders face high AI‑tool costs and subscription fees. What solutions exist?”	Brenda Mulele‑Gunde	Emphasised backward compatibility (making AI services usable on feature phones), collective learning through women’s farmer groups, and ensuring market & finance access to close the digital divide.
Shelley (Founder, Shellcopter Academy – North‑East India)	“AI may optimise ultra‑processed food supply chains, deepening dietary inequities. How to prevent this?”	Dr. Nageshwaran & Dr. Martin (FAO)	Recognise the risk of algorithmic reinforcement of existing inequities; policy must embed ethical safeguards, promote traceability for organic produce, and ensure AI‑driven market incentives do not marginalise smallholders.
Unnamed farmer (via audience)	“We receive many AI‑generated advisory messages (pest, sowing, etc.). How to know which to trust?”	Dr. Martin (FAO)	Data quality and localized data governance are critical. FAO prioritises national datasets and contextualised models to reduce bias and provide a “single source of truth.”
Piyush Kanel (IFAD staff)	“With rising AI compute needs, are there plans to locate data‑centres in tier‑2/3 Indian states?”	Dr. Nageshwaran	India’s AI “centres of excellence” are geographically dispersed; while data‑centres require specific physical conditions, the broader AI ecosystem (design, testing, talent) can be distributed across regions.

Additional observations from Q&A:

Sector‑specific policy: Financial regulators (RBI, SEBI) are already issuing AI‑focused guidance; similar sectoral adaptations are anticipated elsewhere.
Women’s digital inclusion: Emphasis on feature‑phone compatibility, community‑based training, and market linkage to mitigate cost barriers.
Data governance: Repeated call for high‑quality, locally‑sourced datasets to avoid misinformation and algorithmic bias.
Infrastructure: “Road‑vs‑car” analogy reinforced: building digital public infrastructure is a prerequisite for private AI solutions, but the physical location of compute resources can be strategic rather than uniformly centralised.

9. Closing Remarks

Vaishnavi Pavithran wrapped up the session with three succinct take‑aways:

Inter‑system linkage: AI must be interoperable across data, policy, and service platforms.
Human element: AI is an enabler, not a replacement; human expertise, ethics, and governance remain central.
Grounded relevance: The panel’s focus on women, smallholder farmers, and vulnerable populations highlighted the need for tangible, affordable AI solutions rather than abstract “cloud” visions.

The audience was thanked, mementos were presented to each panelist, and a formal group photograph concluded the event.

Key Takeaways

Policy must be phased, risk‑based, and sector‑specific. India’s approach combines a broad AI framework from METI with tailored guidelines from ministries and regulators (e.g., RBI, SEBI).
Digital public infrastructure (DPI) is the “road” for AI services. Sovereign funding for farmer registries, interoperability standards, digital payments, and extension platforms unlocks private‑sector “cars”.
AI accelerates innovation but cannot substitute human judgment. FAO’s technology outlook and portfolio‑sense‑making platforms illustrate how AI makes vast knowledge actionable while requiring expert curation.
Financing models should shift from grant‑driven pilots to blended catalytic investments. Public‑sector DPI de‑risks private AI‑service providers, enabling scale.
Operational impacts are measurable: WFP reports AI‑enhanced supply‑chain routing, climate‑aware crop advisory, and AI‑driven early‑warning for emergencies, all lowering cost‑to‑serve and improving timeliness.
Evaluation of AI projects benefits from AI‑augmented evidence collection (larger samples, lower cost) but must retain independent human oversight to guard against bias.
Gender‑inclusive design is essential. Feature‑phone compatibility, community learning models, and market‑access mechanisms are needed to ensure women smallholders can adopt AI tools.
Data quality and localized governance are paramount. Reliance on national datasets and “single source of truth” reduces misinformation in farmer advisories.
AI infrastructure can be geographically dispersed. While data centres need specific physical conditions, AI talent, design, and testing can flourish in tier‑2/3 hubs, supporting inclusive national AI ecosystems.
AI must be governed ethically to avoid reinforcing inequities. Policies should address algorithmic bias, ensure transparency in food‑system optimisation, and protect vulnerable groups from becoming further marginalised.

See Also:

India AI Impact Summit 2026

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