Transforming Agriculture_ AI for Resilient and Inclusive Food Systems

20 Feb 2026 18:00h - 19:00h

Session at a glance

Summary

This discussion focused on how artificial intelligence can support the transition toward more transparent, responsible, and inclusive food systems, bringing together leaders from government, industry, academia, and international organizations. The session was co-hosted by the Netherlands and the OECD, with participants from Indonesia, India, FAO, and Wageningen University sharing diverse perspectives on AI’s role in agriculture and food security.

Ambassador Harry Verweij of the Netherlands emphasized how AI and digitalization offer enormous opportunities to increase productivity and sustainability in food production while enhancing climate resilience. He highlighted Dutch innovations like precision farming that can achieve up to 90% water savings through smart irrigation and predictive models for disease control. The OECD’s Sara Rendtorff Smith presented promising evidence from real-world AI deployments, including AI-enabled precision spraying that reduces pesticide use by up to 30% without compromising yields, and computer vision systems that cut herbicide use in half by targeting only weeds.

However, speakers acknowledged significant challenges in AI adoption across different regions. While 96% of Australian farmers use digital tools, only 12% of Chilean farmers do, highlighting a concerning digital divide. Professor Arwin Sumari from Indonesia outlined his country’s unique challenges, including 17,000 islands separated by oceans and unequal distribution of AI talent, while describing Indonesia’s seven-pillar AI roadmap focusing on regulation, ethics, investment, data, innovation, talent development, and use cases.

Debjani Ghosh from India’s NITI Aayog emphasized the need for problem-driven AI solutions rather than applying AI broadly to every challenge. She identified food wastage as a critical area where AI could make significant impact, noting the paradox that while the world produces enough food for 8 billion people, millions remain hungry due to distribution and access issues. Dr. Arun Pratihast from Wageningen University stressed the importance of making AI solutions work in low-tech farming environments, citing three main challenges: data scarcity, lack of trust from farmers, and scalability issues.

The discussion concluded that while AI offers vast potential for transforming food systems, successful implementation requires problem-driven approaches, local context consideration, farmer engagement, and building trust through transparency and responsible data collection practices.

Keypoints

Major Discussion Points:

– AI’s potential to enhance food system resilience and anticipatory action: Speakers emphasized how AI can help predict and respond to agricultural shocks, climate challenges, and supply chain disruptions before they escalate, with examples including early warning systems for pests, diseases, and weather events.

– The digital divide and inclusivity challenges in AI adoption: A central concern was ensuring AI benefits reach smallholder farmers and developing countries, addressing gaps in digital infrastructure, connectivity, and access to technology that could deepen existing inequalities.

– Data governance, trust, and transparency issues: Multiple speakers highlighted the need for responsible data collection and sharing, farmer trust in AI systems, and the importance of explainable AI that farmers can understand and rely upon for decision-making.

– Problem-driven vs. technology-driven approaches: Panelists stressed the importance of identifying specific agricultural problems first (like food waste reduction) rather than applying AI broadly, and ensuring solutions work in low-tech farming environments with local context.

– International cooperation and ecosystem building: Discussion of collaborative frameworks between governments, industry, academia, and international organizations to scale AI solutions responsibly, with examples from Netherlands-Indonesia partnerships and OECD initiatives.

Overall Purpose:

The discussion aimed to explore how artificial intelligence can support the transition toward more transparent, responsible, and inclusive food systems, bringing together leaders from government, industry, academia, and international organizations to examine both opportunities and practical challenges in AI deployment for agriculture.

Overall Tone:

The tone was consistently optimistic yet pragmatic throughout the conversation. Speakers maintained an encouraging outlook about AI’s transformative potential while acknowledging significant challenges. The discussion was collaborative and solution-oriented, with participants building on each other’s points and sharing concrete examples. There was a notable emphasis on urgency given global food security challenges, but the tone remained constructive and focused on actionable partnerships and policy frameworks.

Speakers

Speakers from the provided list:

– Sara Rendtorff Smith: Session moderator, representing the OECD

– Harry Verweij: Ambassador-at-Large and Special Envoy for AI of the Kingdom of the Netherlands, co-chair of the sixth working group on economic growth and social good

– Dejan Jakovljevic: Chief Information Officer and Director of Digital FAO and Agroinformatics Division at FAO of the United Nations, based in Rome

– Arwin Datumaya Wahyudi Sumari: Indonesian Air Force officer and professor at the State Polytechnic of Malang, co-inventor of the Knowledge Growing System (cognitive artificial intelligence framework)

– Debjani Ghosh: Distinguished Fellow and Chief Architect of NITI Frontier Tech Hub

– Arun Pratihast: Senior Researcher at Wageningen University Environmental Research

– Speaker 5: Role/title not mentioned

Additional speakers:

– Ambassador Fawai: Ambassador-at-Large and Special Envoy for AI of the Kingdom of the Netherlands (Note: This appears to be the same person as Harry Verweij, as Sara introduces “Ambassador Fawai” but Harry Verweij responds)

Full session report

This comprehensive discussion on artificial intelligence’s role in transforming global food systems brought together diverse perspectives from government, industry, academia, and international organisations to examine opportunities and practical challenges in AI deployment. Co-hosted by the Netherlands and the OECD, the session featured participants from Indonesia, India, FAO, and Wageningen University, each contributing unique insights into both the transformative potential and implementation challenges of AI in agriculture.

Setting the Strategic Context

Ambassador Harry Verweij of the Netherlands opened the discussion by positioning AI and digitalisation as powerful tools for addressing interconnected challenges in global food systems. He acknowledged working with colleagues from Indonesia as co-chairs of the sixth working group and expressed Netherlands’ support for Indonesia’s ambition to join the OECD. The Ambassador emphasised that strengthening global food security represents a strategic priority for the Netherlands, particularly given the country’s role as both a major agricultural trader and innovation hub despite its relatively small size.

The Ambassador highlighted concrete achievements in Dutch precision farming, including AI-enabled smart irrigation systems achieving up to 90% water savings and predictive models for disease control that optimise crop yields whilst minimising inputs. The Netherlands’ approach demonstrates how advanced AI ecosystems emerge from the intersection of strong technical universities, innovative companies like ASML, NXP, and Philips, and collaborative partnerships between science, government, and industry. Crucially, he stressed that every country faces unique local challenges requiring tailored solutions, emphasising the importance of international cooperation and knowledge sharing.

Evidence-Based Potential and Current Applications

Sara Rendtorff Smith from the OECD provided compelling evidence of AI’s real-world impact in agriculture, drawing from studies across the EU and Southeast Asia. The data reveals significant environmental benefits without compromising productivity: AI-enabled precision spraying has reduced pesticide use by up to 30% whilst maintaining yields, and computer vision systems that distinguish between crops and weeds can cut herbicide use by half.

Beyond immediate farm applications, AI is revolutionising agricultural innovation itself. Researchers have identified drought-tolerant traits in crops, whilst AI platforms in Asia are shortening breeding cycles by predicting optimal combinations for enhanced resilience in vital staple crops. The OECD’s work also highlights AI’s role in strengthening entire food supply chains through enhanced traceability, market transparency, and smart logistics systems that can reduce losses, improve compliance, and strengthen food safety.

Indonesia’s Comprehensive National Approach

Professor Arwin Datumaya Wahyudi Sumari provided detailed insights into Indonesia’s unique challenges and comprehensive response strategy. As he explained, Indonesia faces extraordinary logistical challenges: “17,000 islands separated by ocean. We only have 36% of land, 64% of water, and 100% of air.” The country operates across three time zones, has unequal distribution of AI talent, and faces significant price variations where rice costs can multiply several times in remote eastern regions compared to western areas.

Indonesia’s response involves a sophisticated seven-pillar national AI roadmap encompassing regulation, ethics, investment, data governance, innovation, talent development, and specific use cases. This framework explicitly embraces multi-stakeholder collaboration through what Sumari described as a “helix” model involving government, industry, academia, media, and communities. The approach includes AI systems for predicting soil conditions before opening new agricultural land—part of the president’s program to develop almost 1 million hectares of new rice fields—and optimising fertiliser content for different crop types.

Significantly, Professor Sumari distinguished between “smart farming” and “intelligent farming,” noting: “We don’t say smart farming. Smart is not really intelligent. Intelligent is different.” This distinction represents a more sophisticated understanding of AI’s potential to create adaptive, learning systems through what he calls the “Knowledge Growing System”—his co-invention that evolves with local conditions and farmer needs rather than simply automating existing processes.

Reframing the Food Security Challenge

A critical insight emerged regarding the fundamental nature of global food security challenges. Both Debjani Ghosh from India’s NITI Aayog and Dejan Jakovljevic from FAO highlighted a crucial paradox: whilst the world produces sufficient food to feed 8 billion people, approximately 700 million people remain hungry. This suggests that the primary challenge is not production capacity but rather distribution, access, and waste reduction.

Ghosh argued that the biggest problem to address through AI is food wastage throughout supply chains, requiring focus on logistics, cold chain infrastructure, and transportation optimisation rather than simply increasing agricultural output. This perspective represents a fundamental shift from traditional agricultural AI applications that focus primarily on farm-level productivity improvements to addressing systemic inefficiencies in food distribution networks.

The Digital Divide and Trust Challenges

However, the discussion revealed a stark digital divide that threatens to deepen existing inequalities. Whilst 96% of farmers are using digital tools in Australia, only 12% do so in Chile, illustrating how technological advancement can exacerbate global disparities. As Jakovljevic observed, this divide has become an existential issue where exclusion from digital ecosystems increasingly means exclusion from economic and social systems entirely.

The challenge is particularly acute because, unlike previous technological transitions, it is no longer possible to operate effectively outside digital ecosystems. This reality means that farmers and communities without access to AI technologies face not just reduced opportunities but potential complete marginalisation from modern agricultural systems.

Trust represents perhaps the most critical factor in successful AI adoption. Multiple speakers emphasised that farmers must have control over how their data is collected, shared, and used, and that AI systems must be explainable and transparent rather than operating as “black boxes.” This requirement extends beyond technical explainability to encompass genuine farmer participation in AI system development.

The Problem-Driven Approach Imperative

A critical theme throughout the discussion was the need for problem-driven rather than technology-driven approaches. Ghosh articulated this challenge directly: “the biggest problem with AI today is that we throw AI at every problem that exists and expect that something will happen out of it.”

Dr. Arun Pratihast from Wageningen University reinforced this perspective through concrete examples from field research across Asia, Africa, and Latin America, including work with the World Cereal Project with the European Space Agency. He identified three fundamental challenges preventing effective AI implementation: data scarcity at local levels, lack of trust between farmers and AI systems, and scalability issues that prevent successful pilots from expanding to broader applications.

Pratihast’s research demonstrates that whilst AI models may work effectively at global scales, they often fail when applied to local contexts where farmers operate. This failure occurs because farmers’ expectations and needs differ significantly from what AI developers anticipate, leading to advisory systems that farmers don’t trust or follow. The solution requires engaging farmers in the development process and ensuring that AI systems work effectively in low-tech environments with limited connectivity.

International Cooperation and Practical Solutions

The discussion highlighted the essential role of international cooperation in scaling AI benefits globally. The OECD’s work on developing AI policy toolkits and maintaining policy navigators covering over 2,000 policies across 80 jurisdictions demonstrates the complexity of creating interoperable systems that can support cross-border applications.

Promising solutions are emerging to address implementation challenges. The development of multilingual AI advisory services accessible through basic phone calls rather than smartphones demonstrates how AI can be made more inclusive. Similarly, the focus on developing AI solutions that work in low-connectivity environments whilst gradually building digital infrastructure represents a pragmatic approach to bridging the digital divide.

The emphasis on creating centres of excellence focused on specific problems rather than generic AI applications offers another pathway forward. Rather than establishing broad AI centres, the focus should be on problem-specific centres addressing challenges such as cold chain optimisation, climate-resilient crop development, or supply chain waste reduction.

Unresolved Challenges and Future Directions

Despite promising developments, several critical challenges remain. The lack of adequate data sharing mechanisms, particularly in developing countries, continues to limit AI effectiveness. Scaling successful AI pilot projects beyond initial implementations remains a persistent challenge, suggesting that current approaches to technology transfer and capacity building require fundamental revision.

The balance between horizontal AI governance and sector-specific agricultural regulations across different jurisdictions also requires further development to ensure coherent and effective policy frameworks. Fragmented data governance frameworks present particular challenges for AI tools that support trade, traceability, and resilient food supply chains across borders.

Conclusion: Towards Inclusive and Resilient Food Systems

The discussion concluded with recognition that whilst AI offers vast potential for transforming food systems, realising this potential requires fundamental shifts in approach. Success depends on moving from technology-driven to problem-driven approaches, ensuring that solutions are developed with rather than for farmers, and creating governance frameworks that promote both innovation and inclusion.

The path forward requires sustained international cooperation, significant investment in digital infrastructure and capacity building, and continued focus on ensuring that AI benefits are broadly shared rather than concentrated among those who already have access to advanced technologies. Most importantly, it requires recognition that AI is not a panacea but rather a powerful tool that must be deployed thoughtfully and responsibly to address specific, well-defined challenges in global food systems.

The convergence of perspectives from government, industry, academia, and international organisations around these principles suggests growing maturity in understanding AI’s role in agriculture, providing a foundation for coordinated action to ensure that AI contributes to building food systems that are more productive, sustainable, transparent, responsible, and inclusive for all stakeholders.

Session transcript

Sara Rendtorff Smith

Session started. Thank you. the Netherlands, and Indonesia, as you’ll see reflected on the panel. And together with our distinguished panelists, we’ll explore how artificial intelligence can support the transition towards food systems that are more transparent, responsible, and inclusive. So this session is bringing together leaders from government, industry, academia, and international organizations to examine both opportunities and the practical challenges ahead from data sharing and infrastructure to governance frameworks and the partnerships needed to ensure that AI benefits are broadly shared. And before we begin the panel discussion, it’s my honor to invite His Excellency, Ambassador Fawai, Ambassador -at -Large and Special Envoy for AI of the Kingdom of the Netherlands, who will deliver welcome remarks. Welcome, Ambassador.

Harry Verweij

Thank you, Sarah. Is this working? Yeah. Thank you all for sharing this wonderful moment for me because we’re here with Madam Gorshan and Admiral Samari from Indonesia. Together we formed the chair and co -chair of the sixth working group on economic growth and social good in preparation for the summit. And I just wanted to say how much I was impressed with you, Madam Gorshan, how you managed the working group and how the outcomes were drafted and delivered, especially also delivered in the plenary. It’s not up to me, but I say well done. Really great. But thank you very much. It was really a wonderful journey with you. So, ladies and gentlemen, the use of digitalization and artificial intelligence in agriculture is developing rapidly.

It offers enormous opportunities to increase the productivity and sustainability of local food production. It offers opportunities to improve nature conservation and to foster a sustainable foster climate resilience in an inclusive and sustainable way. When this is all – when this – it also contributes to the autonomy and stability of countries. For the Netherlands, strengthening global food security is a strategic priority. Reliable, sustainable, and affordable food systems are essential for societal stability, economic development, and particularly in vulnerable regions. The ambitions in our digitalization agenda for agriculture, nature conservation, and food are to connect digitalization to the transition of agriculture needed for more food security, reduction of environmental impact, and climate resilience via public and private investments.

Our primary focus on increasing productivity with lower environmental impact and improving climate adaptation, strengthening the resilience of food systems through response. use of AI and digital technologies. Concerning today’s topic, the Dutch ambition is to enhance food security by making food systems more resilient and sustainable for all stakeholders. In my vision, digitalization and AI are powerful tools for that. They have already proven that they can significantly increase food productivity and reduce food losses. In addition, AI solutions can enhance the efficiency and resilience of food systems by supporting farmers to respond to sustainability requirements, make risk assessments, implement sustainable farming practices, and enable them to provide trustworthy and quality data sets about those efforts to be shared throughout the supply chain.

The Netherlands has a strong AI ecosystem. Thanks to our technical universities and partners, we have a strong ecosystem of AI and companies like ASML, NXP, and Philips. Despite its relatively small size, the Netherlands is not only a huge trader in agricultural produce, but also a global key player in agro -innovation and technology development due to the interaction between plant and animal science and technological knowledge systems in the Netherlands. Companies, science and government invest mutually in solutions for societal challenges. Examples include precision farming with AI, such as water savings of up to 90 % through smart irrigation, optimal crop yields with minimal input, and predictive models for disease control. To support digitalization in the agricultural sector in low – and middle -income countries, the Netherlands facilitates Dutch ICT agribusinesses to collaborate with businesses and startups there.

And as you are… We are aware in the Netherlands that strong ICT ecosystems and highly innovative agricultural ecosystems come together. ICT agricultural solutions combine the in -depth agricultural knowledge and advanced technology development in my country. Examples are applications for early warning of pests and diseases, optimization of water use and optimized plant breeding processes. Dutch companies and knowledge institutions are open to co -work on tailor -made solutions. Every country has its own typical local challenges and requires tailor -made solutions. Today special attention will be drawn to AI -powered solutions for small farmers and SMEs in producing countries in order to enhance their access to global agricultural supply chains while protecting their data. Our goal is to improve the ICT ecosystem and improve the ICT ecosystem in our country.

We are committed to work together on this through knowledge sharing, co -operation and collaboration. creation and capacity building so that AI solutions are locally relevant, inclusive and accessible to farmers. The need for an inclusive AI has also been central to our discussions in the working group of the Economic Growth and Social Group leading up to the summit. It fits well the summit motto, people, planet and progress. So I would like to thank India for its leadership in focusing on an inclusive AI future and underline that the Netherlands stands ready to contribute by forging concrete partnerships, sharing knowledge and technology while striving for measurable results in order to ensure that AI serves all of humanity. And I recall the Honourable Prime Minister’s speech in Flendry to which he alluded as well.

Ladies and gentlemen, we are honored to organize this important event together with the OECD, the go -to organization when it comes to AI governance, and to discuss the opportunities for international knowledge sharing and cooperation with FAO, the Wageningen University in the Netherlands, and the distinguished co -chairs of the Working Group on Economic Growth and Social Growth, India and Indonesia. We warmly thank India for hosting this summit and look forward to continuing and strengthening our cooperation in the field of AI and agriculture, both bilaterally and within the global partnership on AI. We also thank our co -chair Indonesia for continuing cooperation and we would like to highlight our appreciation and firm support of Indonesia’s ambition to join the OECD and its commitment to global standards and evidence -based policymaking.

International knowledge sharing and cooperation is needed to accelerate the development and application of new technologies. With the help of trustworthy AI. Having AI. And agricultural ecosystems on the agenda in this important AI summit is extremely valuable and a. forward in order to make a positive impact for all stakeholders. I wish you a fruitful meeting and look forward to our conclusions, and thank you for this opportunity to listen. So the floor is now Sarah.

Sara Rendtorff Smith

Thank you, Ambassador. And on behalf of the OECD, I just want to thank once again the Netherlands for the leadership in convening this timely discussion. And as was just reflected in the Ambassador’s remarks, the Netherlands is obviously a pioneer in advancing food and agriculture innovation, and we are so delighted to have them as co -chairs as well of the OECD FAO Advisory Group on Responsible Agricultural Supply Chains. From the OECD’s perspective, we clearly see this dynamic of agriculture and food systems today operating in an increasingly volatile environment, and farmers face a wide variety of shocks, from droughts, floods, pests, to conflicts and economic crises. With growing frequency and severe… and so therefore strengthening resilience while also ensuring inclusion, as was also stressed by Ambassador Federe, is really an urgent global priority that I hope we can talk about today.

AI in this regard offers significant potential. We’re seeing AI systems and tools being applied to optimize the use of critical resources, as was already mentioned, such as water, fertilizer, and pesticides, and also to reduce environmental pressure while enhancing productivity. The OECD and JPEI, which also met today in a ministerial session, have been examining AI use cases in agriculture with a focus on the EU and on Southeast Asia, and we continue these dialogues. And what we’re seeing there is that the evidence from real -world deployment is really, really promising. So, for example, AI -enabled precision spraying has reduced pesticide use by up to 30 percent, and this is actually without compromising yield. while computer vision green on brown systems can cut herbicide used by up to half by targeting only the weeds that require the treatment and thus not the crops.

And in addition, we’re seeing how forecasting, monitoring, and early detection of climatic and biological threads means that AI systems can strengthen our capacity to respond to crises before they even escalate, so some degree of preemption. AI is also revolutionizing agricultural innovation itself and supporting more efficient plant breeding that can develop climate -adaptive variety in a fraction of the traditional time. And here we also have some interesting data seeing in Central Europe that researchers have identified drought -tolerant traits in crops such as sorghum and chickpea that boost yields by up to 25 % during end -season drought. And in Asia, meanwhile, we’re also seeing global AI hybrid rice platform demonstrating how AI can shorten breeding cycles by predicting optimal parent combinations and enhancing resilience in one of the world’s most vital staple crops.

Beyond the farm gate, AI is also reinforcing the resilience of our entire food supply chains. And AI -enabled traceability, market transparency, and smart logistics can reduce losses, improve compliance, and strengthen food safety systems. Evidence from these digital traceability initiatives across the OECD members demonstrates a growing maturity of exactly these systems, so something really to look out for. But technology alone, as we know, does not ensure impact, and so adoption is where we’re really looking now, and that remains quite uneven still. And this is obviously why we’re all here in Delhi. So while we’re seeing in Australia that 96 % of farmers are using digital tools, the same number for Chile is just 12%. And this is highlighting a digital divide that could deepen existing inequalities if we don’t look to address it.

There’s also important challenges in the use of AI, and this goes back to sort of the core work of the OECD, looking not just at the benefits but also the challenges associated with AI. Farmers and regulators need transparency in how AI systems make their decisions, but at the same time fragmented data governance frameworks introduce complexity to the use of AI tools that support the trade, traceability, and resilient food supply chains across the border. And this highlights the need for greater interoperability, which is also a theme at this summit. So structural barriers including high cost, limited digital skills, and lack of trust. These are some of the things that continue to slow the uptake of AI.

So bridging these gaps, which should be a priority for all of us, requires investment in connectivity and other digital infrastructure, in skills and affordable solutions. So smallholders, women, farmers in remote areas who play a critical role in enhancing global food security, they’re able to also benefit from AI’s potential. And farmers must be able that their data is collected, shared, and used responsibly. So in this area, the OECD is working to help countries put in place policies that promote these objectives through an AI policy toolkit. And this toolkit will provide practical, context -specific guidance to countries. The toolkit builds on our policy navigator. If you haven’t already visited it, it’s on osd .ai. And it so far covers more than 2 ,000 policies across 80 jurisdictions.

So this is where you can find examples. Examples of national AI strategies, but also in specific sectors. And we continue to update this, and for anyone in this room representing a country not represented, we encourage you to visit and to also contribute your policies. We’re also advancing work on digital governance in agriculture. This is within GPAY that I mentioned earlier, a priority there, where we examine governance models across countries and their applications for responsible digital transformation more broadly. We also see strong complementarities with the global AI impact comments, which is a key deliverable of this summit, and which shares concrete use cases of AI with known impact and scaling potential. So for the OECD advancing trustworthy AI consistent with our OECD AI principles requires a strong enabling ecosystem alongside technological progress.

And what we’re seeing is that if we succeed, we’re really in a position to raise productivity. sustainably and also strengthen resilience in agricultural supply chains, including by ensuring that the benefits of innovation are widely shared and existing divides are not deepened in the process. So I really look forward to this panel’s insights to help us take this conversation forward, looking at practical pathways to achieve this vision. And with this, it’s my pleasure to introduce our esteemed panel. Many have traveled far to be here. So first, I would like to introduce Professor Arvind Sumari, who is an Indonesian Air Force officer and professor at the State Polytechnic of Malang. Welcome. And also we have with us, next to Professor Sumari, we have Mr. Dayan Jakoblevich.

He’s Chief Information Officer and Director of Digital FAO and Agroinformatics Division at FAO of the United Nations, based in Rome. We also have with us… We have with us today the pleasure of having Debjani Ghosh, Ms. Debjani Ghosh. Distinguished Fellow and Chief Architect of NITI Frontier Tech Hub. And finally, it’s my pleasure to introduce Dr. Arun Pratihast, Senior Researcher at Wageningen University Environmental Research. So welcome to this session. And what we will see today is each of our speakers bringing a unique perspective on how AI can help build food systems that are resilient and inclusive, which is the topic of the session. And after the panel discussion, I will also be giving the floor to anyone in the room who might have questions.

So now let’s begin. I’ll hand the floor over to Dan, who will set the scene for the conversation. Dan, you have the floor.

Dejan Jakovljevic

Thank you very much. And I would like to welcome everyone on behalf of the Food and Agriculture Organization. I thank you to our hosts here. The summit from India, but also ECD and. government of the Netherlands ambassador thank you when we look at agri -food I heard in the interventions before about the agriculture and the food we look at agri -food systems from the FAO perspective why because the food itself as if we look at the agriculture food is one product but not only one so there is a whole ecosystem behind agriculture of products that are not necessarily food and they are equally important when we make considerations when we look at for example at the water use transport and many others so in from agri -food systems perspective AI brings us fantastic opportunities and if we look at our topic today in terms of inclusiveness and resilience and inclusion and inclusion and inclusion and resilience and inclusion and resilience and inclusion and inclusion and inclusion and inclusiveness and resilience and resilience and resilience inclusiveness is still a big issue if we just think back back maybe two, three years before the, let’s say, chat GPT came out, the inclusiveness and the digital divide was still strong and present.

And the key issue is that it used to be possible to exist outside of the digital ecosystem. We all know we could maybe go to the bank, but nowadays it’s not. So if a farmer or communities are outside of the digital ecosystem, they suddenly are outside of any ecosystem almost. And now with the AI, it makes it even worse. So this is something we need to continue to press on and jointly in making sure that everybody has equal opportunity within the digital ecosystems. And on the positive, let’s say, note, on the positive, let’s say, note, on the AI when it comes to inclusiveness. We see very encouraging opportunities with AI. What I mean by that is we can, in fact, lower the entry barrier to knowledge.

Just two days ago, I’ve seen here actually this opportunity at the event, great advancements, the new tool that was produced by government of India where farmers can, with a phone call, as not everybody has a smartphone, can get advisory in the area of agriculture, from shrimp cultivation to pest diseases and similar. So this is great. The service can be in many languages. So this is a fantastic opportunity example where AI can help us actually lower the entry point to the AI. In the same time, for governments, it’s even more so difficult. to have the capacity to build the AI infrastructure to provide such services. So this is, again, I think one area, and forums like this help us consider what it takes to build it.

When we look at the resilience specifically, I was very happy to hear in the previous openings you mentioned resilience in terms of, Jeff and from Ambassador, we heard on anticipation. So I would say this is the key word. The key word is anticipation. So anticipate the shocks to the agri -food systems that impacts the food security. We know we have natural disasters. We know we have also conflicts. We have many different factors that impact agri -food systems. So building the systems that are capable of absorbing the shocks of these situations and anticipating. Anticipatory actions to when the shocks happen, what can be done to kind of. go over these shocks. So this is where AI can be a great enabler, where we can then, with new capabilities, anticipate these shocks, and with the help of data and our joint work, really, put together decision -making tools, anticipatory tools, situation rooms, to be able to quickly not only anticipate, but when something happens, we don’t really improvise, but we have tools in hand to address these situations.

We still have about 700 million people without food on the table today. So from this perspective at FAO, and I’m sure we shared the same sense of urgency to actually do something. So I wanted to say from this perspective, we are very grateful to be part of this conversation and thank you for your time. And we can work together in finding the new solution. So I thank you for that. and I’m looking forward to our panel. Thank you.

Sara Rendtorff Smith

intelligence research group and are the co -inventor of the Knowledge Growing System, a cognitive artificial intelligence framework designed to enable adaptive and evolving decision making. So from Indonesia’s vantage point, we’d be interested to hear where you see the most significant AI capability gaps across the agricultural system and where you see the greatest opportunities at the same time for AI to make food supply chains more efficient and resilient, something we also heard as a priority. And we also know that Indonesia is one of the countries advancing an ambitious AI agenda. So if you could briefly outline also the key pillars of Indonesia’s AI roadmap, this is of interest and to explain how you are balancing horizontal AI governance with more sector -specific regulation in agriculture.

Over to you. Thank you.

Arwin Datumaya Wahyudi Sumari

Thank you, Sarah. First, I would like to deliver my appreciation and congratulations to the host, India, and also my chair, Ms. Goss, and also my dear colleagues from the land ambassador harry first letter for coaching our working group together and also other speakers and Sarah thank you and our audience regarding your question about the artificial intelligence for Indonesia as we already know together that Indonesia is not only the agriculture but also maritime nation we we were self -sufficient in in rice about 20 30 years ago and then it wasn’t a I for making our country had sufficient in in rice but nobody I is something that that can make our program to be to become a self -sufficient country in right can be achieved.

We are much aware that the ideology is developing very fast, not only in America or Europe, but also in Asia, especially in Indonesia. This rapid and democratic application across all agricultural potential areas presents significant challenges, especially given the potential location which are separated by ocean. And you already know that Indonesia has 17 ,000 islands separated by ocean. We only have 36 % of land, 64 % of water, and 100 % of air. And this is a challenge for us. If you don’t believe me, you can count the numbers of our islands. And this is a challenge for us. And we also have another challenge. We are living above the ring of fire. There are also other challenges for our people of Indonesia.

And as I mentioned previously, this gap is further widened by lack of democratically supporting AI infrastructure, such as telecommunication. We have three different times region, the west region, center region, and eastern region. And each one has different one hour, one to another. And also, there is a problem with unequal distribution of AI talent. I think the problem is not only in Indonesia, but also all over the world. In terms of the biggest opportunities for utilization. AI in the food supply chain, especially in agriculture country like Indonesia. efforts to do such as like we can use AI for prediction of soil condition and nutrition before opening new land for agriculture. Our president has a program to open almost 1 million hectares of new rice files.

1 ,000 hectares in some big island of Indonesia in order to get the safe efficiency in the next five years. And then we also use the AI for prediction of the most appropriate food crops given the soil condition and nutrition of existing agricultural land. We have seven dozen islands and each island has different soil condition, different soil nutrition. And you can use AI to predict what kind of nutrition, what kind of soil condition, what kind of vitamin that belongs to that soil. So we can predict the proper crops, the proper plants that have to be planted in that area. The second one about optimizing the most optimal fertilizer content to produce the best harvest result as well as optimizing the volume of water required according to the type of fertilizer given.

Some of my students, they did some experiment how to predict the percentage of fertilizer combined together to get the most optimum production of any kind of crops. Even if it is corn, rice, or sweet potato. And then we also can use AI for intelligent farming. We don’t say smart farming. Smart is not really intelligent. Intelligent is different. There is knowledge that has to be grown in the system. So intelligent farming is just like a human. They grow their knowledge within their brain. By optimizing the seed planting in the land so that plants can grow and develop healthily to produce the best products to optimization of the harvest process until delivery to logistic warehouses. So it’s just like end -to -end mechanism.

And then we also can predict the weather dynamics just as a short step of the flood and something like that. So we can predict the weather dynamics to obtain the right conditions. So that’s the vision for planting seed and reducing the level of crop failures. The crop failures that… This often happens if the farmer, they fail to predict what kind of pest, what kind of, what type of the soil and everything. And then the last one, optimizing the logistic transportation route to reduce the operational and other unnecessary costs. You can count how much operational costs to deliver the crop production from one island to another island in Indonesia. The price in the eastern area can be double or triple times in eastern area.

So if we buy rice in eastern area only $1, it can be $3. $5, $6 in. eastern area. So that’s why we need AI to optimize the transportation and logistic transportation routes. Whether it is from water, from the ocean or sea, and also from the air. Regarding the policy and regulation, you asked about the air roadmap, right? And then about how to balance the horizontal AI government with sector -specific agriculture, right? Yeah, we are proud. UNESA is proud to be a leader in our region, exploring how AI policy and regulation can be powerful tools for promoting trustworthy AI, especially in critical verticals like the agricultural sector. This one. Agriculture is very important to UNESA because most of the people in Indonesia, they are farmers not only in Java Island but also in other big islands in Indonesia if you see, there are five big islands in Indonesia from western area like Sumatra and then Java and the southern area we have Borneo in the central, also Sulawesi, or Celebes and the biggest one in the eastern area is Papua Island still have so much area that can be explored to become a rice field our national AI roadmap is not merely a technological blueprint it is a strategic framework designed to create an ecosystem that harnesses AI for inclusive and resilient system, including food system, so there are two keywords in here inclusive and resilient inclusive means it must be transparent AI must be transparent, AI must be explainable.

We’ve been having problems with the neural network -based system that the black box cannot be explained in plain. And then the second was Sicilian. This is very important for agricultural -based nation. So the implementation of AI needs a strong and sustainable national ecosystem, like my dear colleague, Ambassador, first of all mentioned about ecosystem. The AI cannot be implemented, cannot be applied without a strong and sustainable ecosystem that collaborate all stakeholders, not only government, but also business, industries, communities, media, and also academia. so we have a concept of helix maybe you ever heard about quad helix, five helix, six helix that’s very important so when we are developing the ANS roadmap the government in this case Ministry of Digital Information and Communication and Digital Affairs is open a voluntary contribution from all stakeholders not only the government but also from industry, academia media and communities so our roadmap has seven pillars that include AI regulation AI ethics, that’s important the third one is investment like it was mentioned before about financing when I was working the attending the US forum in AI export they mentioned about financing financing is very important, without that there is no AI ecosystem financing and investment and then the third AI data, the fifth one AI innovation and then the next one AI talent development the last one is AI use case so because we embrace all stakeholders so we assure there is no one left behind.

Thank you.

Sara Rendtorff Smith

Thank you very much professor and we can come back to those in more detail later perhaps in the Q &A but I really want to thank you for sharing the promising use cases from Indonesia, very instructive I think for this discussion and now I would like to turn over you talked about the helix and how we work together to have the industry perspective from Ms. Ghosh India as we mentioned also co -chairs the summit working group and so I’d be interested to hear now that we’re seeing AI as quickly becoming foundational to agricultural productivity and food security but the big question now is whether as we mentioned it will deepen inequalities or indeed democratize the opportunity so from your vantage point Ms. Ghosh what practical steps are needed to broaden access to AI capabilities so that emerging economies and smallholder farmers can also benefit and fully participate and as adoption accelerates hopefully broadly how should public -private partnerships evolve to scale responsible AI deployment and prevent the AI divide?

Thank you.

Debjani Ghosh

It’s a very long answer question. I’ll try and keep my answer very short. But before I do that I have to acknowledge the presence of Yeah, okay. But before I do that I have to acknowledge the presence of I think one of the The biggest experts in this field of agriculture in this room, Professor Ramesh Chand, who’s also a very esteemed member of NITI Aayog. And I requested him not to come for this session. I’m going to be too nervous if you’re going to be sitting right in front of me. But yeah, let’s see if we live up to his expectations or not. You know, the biggest problem with AI today is that we throw AI at every problem that exists.

And we expect that something will happen out of it. Right. And as a result, we generalize the technology a bit too much. See, the thing with AI is if you really want to unlock the technology, you have to know what exactly are you solving for? What problems? And then you have to go deep because there are so much that has to come together for AI to work. For example, is the data in place? How good is the quality? Is the ecosystem in place? Are capabilities in place? So AI requires investments. And AI is a pretty deep investment overall. Right. So it’s very important to understand what problems do you want to solve with AI. And I think that’s one of the biggest issues today because we are not taking the time to think through it.

We keep saying AI is the magical world for everything. Right. So now let’s look at the food system. And I hope I’m correct. Professor Chan, I’ve learned this a bit from you also. But I think the biggest issue today is while the world is producing enough food to feed, I think, 8 billion people. But there are still millions and millions. Who are hungry. So there’s a paradox. And I think when you start breaking it down further to understand the exact problems as to why this exists. distribution? The entire access to food, do you have access to food so there is surplus and there is deficiency and then you don’t have a bridge to ensure that there is distribution happening at real time that is needed.

And what this results in is tremendous amount of food shortage, food wastage. And some of the culprits when we think of it, of course geopolitical wars are a big culprit, conflicts are a big culprit but climate is another big culprit. So this is how you sort of at least how I, because I look at everything from a tech lens, I’m by no means an expert in the domain but when I look at it from a technology lens and I say how do I best apply the technology to this problem, this is the domain that we have to play with. So now when you look at it if I have to say where do I want to go deep the problem to solve for at least when I look at all of this is the biggest problem to suffer in the food supply chain according to me right now just purely looking at it from a tech lens is the wastage.

How do I bring down food wastage? What role can AI play to bring down food wastage? So then you start looking at logistics, you start looking at supply, the cold chains that exist globally or not. You start looking at trade, you start looking at geopolitical agreements because all of that will come into mind. Now in terms of industry coming together to solve for AI again if you want the best out of industry you have to ensure that there is alignment on the problem statement you want to solve. Otherwise everyone will come and everyone will do the same pilot everywhere. That’s what’s happening today. When you look at AI executions around India and around the world, and because of the AI commons that we have built, every country is trying out the same thing, farmer advisory, right?

Every country is trying it out, but why is it not scaling? Why are we not solving for other problems? So again, it’s very important to identify what is the problem statement? How do you ensure that when industry gets involved, there is a route to market? And there is a route to commercialization because that becomes very important for industry. And one of the things that we advocate is coming up with maybe a center of excellence, a center of innovation that is identified to solve specific problems. I think one of the problems today we have with COEs are you have AI COEs, you have blockchain COEs. I really don’t understand what that means. But what if we had a COE to say that how do we ensure that the cold chain problem is solved across the country?

How do we have a COE that ensures that climate resilient crops in XYZ areas can be grown, right? And then bringing the industry together to say that how do we collaborate to create, I think gives you the right kind of outcomes. Thank you

Sara Rendtorff Smith

very much, Mishkosh. And this is a perfect segue, I think, to our next speaker, turning to the research community and how to really bridge research into advanced AI to more practical tools. So, Dr. Pratihast, I would like to turn to you now for, you know, some examples of, you know, how these advanced AI tools can really be made to good use in more low -tech farming environments. And maybe you can give us some concrete examples, what distinguishes those who succeed from those who don’t. Maybe speaking also to some of the points that Mishkosh raised. Thank you. Thank

Arun Pratihast

you. Thank you for invitation. It’s very timely discussion. And of course, always when we talk about AI, we often talk about the technology, how fast the model are, how big the data set they can handle, what are the parameters. That we always talk. But if you think about the food system, and of course, Terry mentioned that, you know, food system have different layers. And bottom of this layer is basically a smallholder farmers. And that farmer operate in a different environment. If you look at it last year, there’s billions of euro investment has been done in the tech industry to build more models. Is the same thing happen to the smallholder farmers? No. So there is often there is a problem that what we want to solve in the server room or computer, it doesn’t work in the field.

Right. So we. Really need to think how. the AI or model which we are really developing that is applicable to the grassroots level. And so within the Wakeningen and personally, I have been working in Asia, Africa and Latin America. And one of the problems, basically, there is three problems we are facing in this whole AI domain nowadays. First is really data scarcity. Still, there is not enough data. The data is not shared. As you mentioned, there is no ecosystem. There is no fair infrastructure where data can be shared. And that hinders the model. The model works on a global scale, but when you want to work on the local scale, it doesn’t work. It doesn’t provide the input that is expected for the smallholder farmers.

Second is the trust. Often, the farmers don’t own, and then, of course, the… the model and the farmer’s expectation is different and then there’s often not much trust how to apply this in the local level. That’s why most of this advisory is failed. Farmer doesn’t follow the advisory because it doesn’t make sense. And then third thing is scalability. Often we think that scale is not only the technical scale. Like you process something fast doesn’t mean that it can apply the same way. So we need to really think differently. And that’s why like we started I give a couple of three concrete examples. One example about food security. We need to understand what is the map.

Where are the crops? There is no global map that is accurate enough. So with the help of European Space Agency four years ago we started the World Cereal Project where we try to map the global crop length. So we started the World Cereal Project the World Cereal Project and we started still the maps are not perfect because India, China, many countries they don’t share their data so there is no data and if there is no data we have fantastic model we have built very nice geo -embedding with NASA harvest but applicability of this model is still very low in this country second thing is about high tech solution in low tech environment for example chocolate industry cocoa, agroforestry is really suffering from the climate change and we have established many advisory services but not from the researcher or tech perspective but engaging farmer perspective and that works we build basically chatbot with their language that really understand what they need and how we can translate their problem they know which disease are coming so we are using computer vision from their lens and then we are training and that works So there are a couple of things which we really see that if you really want to make these things working, you need to make sure that these solutions should work in low -tech environment.

Most of the things, connectivity has gone up. People are on social media, but still data is not there. Data infrastructure is not there. And always tech industry or like we as a modeler, whatever we call. So we see always data as the input and output. Data should be as the infrastructure. We should engage farmers in that infrastructure. And then only we can achieve the

Sara Rendtorff Smith

Thank you very much. And I think with this, unfortunately, we’re coming to a close on time. I think maybe the speakers can be kind enough to stay a little bit after if there are questions. We won’t have much time for Q &A. but just to thank you all for really providing a diverse set of perspectives for the timely discussions to the ambassador of the Netherlands for framing this important discussion and I think some of the key takeaways perhaps is that there is vast potential and we saw the Indonesian perspective of all these very concrete examples also Dejan talking about potential for anticipatory action and we heard about this global and even domestic paradox of food insecurity when there really is enough food but it may not be distributed enough or properly and also I think importantly that to have impact with AI we need to make sure that it is problem driven that it is driven by the local context and the farmers who need to use it and maybe lastly a very important point which is exactly core to the work we do at the OECD that to drive this adoption we also need to ensure that there is trust in what is produced.

And this requires, obviously, a number of factors, such as explainability and transparency and so on, and also responsible data collection. But just with that, let me thank the panelists for their rich inputs. Please do stick around a little bit for some questions, maybe in the margin. And thanks again to the Kingdom of the Netherlands for co -hosting this event with the OECD. Thank you.

Speaker 5

Thank you. Thank you.

Sara Rendtorff Smith

Speech speed

94 words per minute

Speech length

International cooperation and partnership models

Topics

Artificial intelligence | Information and communication technologies for development | The enabling environment for digital development

Harry Verweij

Speech speed

143 words per minute

Speech length

International cooperation and partnership models

Topics

Artificial intelligence | International cooperation and partnership models (mapped to Information and communication technologies for development) | The enabling environment for digital development

Dejan Jakovljevic

Speech speed

140 words per minute

Speech length

738 words

Speech time

316 seconds

Inclusivity, equity and the digital divide

Explanation

Dejan stresses that the digital divide remains strong and that phone‑based advisory services can lower entry barriers for smallholders.

Evidence

“…inclusiveness and the digital divide was still strong and present.” [29]. “Just two days ago, I’ve seen here actually this opportunity at the event, great advancements, the new tool that was produced by government of India where farmers can, with a phone call, as not everybody has a smartphone, can get advisory in the area of agriculture…” [59]. “What I mean by that is we can, in fact, lower the entry barrier to knowledge.” [60].

Major discussion point

Inclusivity, equity, and the digital divide

Topics

Closing all digital divides | Capacity development | Artificial intelligence

International cooperation and partnership models

Explanation

He links AI to anticipatory action and joint capacity building, highlighting FAO’s role in building resilient food systems.

Evidence

“So this is where AI can be a great enabler, where we can then, with new capabilities, anticipate these shocks, and with the help of data and our joint work, really, put together decision -making tools, anticipatory tools, situation rooms…” [40]. “So anticipate the shocks to the agri -food systems that impacts the food security.” [111]. “So building the systems that are capable of absorbing the shocks of these situations and anticipating.” [112].

Major discussion point

International cooperation and partnership models

Topics

Artificial intelligence | Social and economic development | Capacity development

Arwin Datumaya Wahyudi Sumari

Speech speed

109 words per minute

Speech length

International cooperation and partnership models

Topics

Artificial intelligence | Information and communication technologies for development | The enabling environment for digital development

Debjani Ghosh

Speech speed

156 words per minute

Speech length

International cooperation and partnership models

Topics

Artificial intelligence | Financial mechanisms | The enabling environment for digital development

Arun Pratihast

Speech speed

152 words per minute

Speech length

Governance, trust, and responsible data use

Topics

Data governance | Artificial intelligence | Human rights and the ethical dimensions of the information society

Speaker 5

Speech speed

9 words per minute

Speech length

4 words

Speech time

26 seconds

Expression of gratitude for collaborative AI efforts

Explanation

The speaker concluded by thanking the participants, signalling endorsement of the summit’s collaborative approach to AI for resilient and inclusive food systems.

Evidence

“Thank you.” [1]

Major discussion point

Collaboration and partnership endorsement

Topics

The enabling environment for digital development

Agreements

Closing all digital divides | Artificial intelligence | Social and economic development

Data governance | Human rights and the ethical dimensions of the information society | Artificial intelligence

Unexpected consensus

Problem-focused rather than technology-focused approach to AI

Speakers

– Debjani Ghosh
– Arun Pratihast
– Sara Rendtorff Smith

Arguments

AI is often applied generically without identifying specific problems to solve, leading to failed pilots and lack of scaling

Solutions must work in low-tech environments and engage farmers in data infrastructure rather than treating them as passive recipients

Need for interoperable governance frameworks to support cross-border trade, traceability, and resilient food supply chains

Explanation

Unexpectedly, speakers from industry, academia, and international organizations all converged on criticizing technology-first approaches, instead advocating for problem-driven AI development that prioritizes user needs and practical implementation challenges

Topics

Artificial intelligence | The enabling environment for digital development

Food distribution rather than production as the primary challenge

Speakers

– Debjani Ghosh
– Dejan Jakovljevic

Arguments

Focus should be on solving food wastage in supply chains rather than production, requiring alignment on specific problem statements for effective industry collaboration

Global paradox exists where enough food is produced for 8 billion people yet 700 million remain hungry due to distribution failures

Explanation

Both industry and UN perspectives unexpectedly aligned on identifying distribution and logistics as the core challenge rather than agricultural production capacity, suggesting a shift in how food security problems are conceptualized

Topics

The digital economy | Social and economic development

Overall assessment

Summary

Strong consensus emerged around AI’s technical potential for agriculture, the need for inclusive and collaborative approaches, and the importance of addressing distribution rather than production challenges. Speakers consistently emphasized problem-driven rather than technology-driven solutions.

Consensus level

High level of consensus with significant alignment across different stakeholder perspectives (government, industry, academia, international organizations). This suggests a maturing understanding of AI’s role in agriculture that prioritizes practical implementation and inclusive access over technological capabilities alone. The implications are positive for coordinated policy development and implementation strategies.

Differences

Different viewpoints

Primary focus for AI applications in food systems

Speakers

– Debjani Ghosh
– Harry Verweij
– Arwin Datumaya Wahyudi Sumari

Arguments

Focus should be on solving food wastage in supply chains rather than production, requiring alignment on specific problem statements for effective industry collaboration

AI can increase productivity and sustainability while reducing environmental impact through precision farming, smart irrigation (90% water savings), and predictive disease control models

Summary

Ghosh argues for prioritizing food wastage and supply chain issues over production, while Verweij emphasizes productivity and sustainability improvements, and Sumari focuses on optimizing production through soil prediction and logistics

Topics

Artificial intelligence | Social and economic development | The digital economy

Approach to AI implementation and problem definition

Speakers

– Debjani Ghosh
– Arun Pratihast

Arguments

AI is often applied generically without identifying specific problems to solve, leading to failed pilots and lack of scaling

Solutions must work in low-tech environments and engage farmers in data infrastructure rather than treating them as passive recipients

Summary

Ghosh emphasizes the need for specific problem definition before AI application, while Pratihast focuses on adapting AI solutions to work in low-tech environments and engaging farmers as active participants

Topics

Artificial intelligence | Closing all digital divides | The enabling environment for digital development

Unexpected differences

Scale and scope of AI intervention priorities

Speakers

– Debjani Ghosh
– Multiple other speakers

Arguments

AI is often applied generically without identifying specific problems to solve, leading to failed pilots and lack of scaling

Various speakers promoting broad AI applications across multiple agricultural domains

Explanation

Unexpectedly, Ghosh took a contrarian stance against the general enthusiasm for broad AI applications, arguing for more focused problem-solving approaches while other speakers promoted comprehensive AI adoption across various agricultural sectors

Topics

Artificial intelligence | The enabling environment for digital development

Overall assessment

Summary

The main areas of disagreement centered on prioritization of AI applications (production vs. supply chain focus), implementation approaches (problem-specific vs. comprehensive), and the balance between technological advancement and practical farmer needs

Disagreement level

Topics

Artificial intelligence | The enabling environment for digital development | Capacity development

Data governance | Human rights and the ethical dimensions of the information society | Artificial intelligence

Takeaways

Key takeaways

AI offers significant potential to transform agriculture through precision farming, predictive analytics, and supply chain optimization, with proven results like 30% pesticide reduction and 90% water savings

The main challenge is not food production but distribution – enough food exists to feed 8 billion people yet 700 million remain hungry due to supply chain inefficiencies and wastage

A major digital divide exists in AI adoption, ranging from 96% farmer adoption in Australia to only 12% in Chile, which could deepen existing inequalities if not addressed

Successful AI implementation requires problem-driven approaches rather than generic technology deployment, with focus on specific issues like food wastage reduction rather than broad applications

Trust and transparency are critical for farmer adoption – AI systems must be explainable and developed with farmer input rather than imposed from external tech perspectives

International cooperation and knowledge sharing are essential, requiring interoperable governance frameworks and multi-stakeholder collaboration including government, industry, academia, and communities

AI solutions must be designed to work in low-tech environments and engage farmers as active participants in data infrastructure rather than passive recipients

Resolutions and action items

OECD to continue developing AI policy toolkit with practical, context-specific guidance for countries

OECD to maintain and expand policy navigator covering AI policies across jurisdictions, encouraging more countries to contribute

Netherlands committed to forging concrete partnerships and sharing knowledge/technology for inclusive AI solutions

Indonesia to implement national AI roadmap with seven pillars: regulation, ethics, investment, data, innovation, talent development, and use cases

FAO to continue building anticipatory tools and situation rooms for food system shock response

Focus on establishing centers of excellence around specific problem statements (e.g., cold chain solutions, climate-resilient crops) rather than generic AI centers

Unresolved issues

How to effectively bridge the digital divide and ensure equitable access to AI technologies across different economic contexts

Lack of adequate data sharing mechanisms and infrastructure, particularly in developing countries where farmers don’t share agricultural data

No global accurate crop mapping system exists due to data sharing restrictions by major countries like India and China

Fragmented data governance frameworks that complicate cross-border AI applications in food supply chains

High costs, limited digital skills, and infrastructure gaps that continue to slow AI uptake among smallholder farmers

How to scale successful AI pilot projects beyond initial implementations

Balancing horizontal AI governance with sector-specific agricultural regulations across different jurisdictions

Suggested compromises

Develop AI solutions that work in low-connectivity environments while gradually building digital infrastructure

Create multilingual, accessible AI advisory services (like phone-based systems) that don’t require smartphones or high-tech devices

Focus on specific, measurable problem-solving (like food wastage reduction) rather than attempting to solve all agricultural challenges simultaneously

Establish public-private partnerships with clear routes to market and commercialization to ensure industry engagement while serving public good

This distinction between ‘smart’ and ‘intelligent’ systems introduces a more sophisticated understanding of AI capabilities, suggesting systems that learn and adapt rather than just automate existing processes.

Impact

This conceptual distinction elevated the technical discussion and introduced the idea of adaptive, learning systems. It influenced how other speakers thought about AI not just as a tool but as an evolving capability that could grow with farmer needs and local conditions.

Overall assessment

Explanation

Data should be treated as infrastructure rather than just input/output, requiring new frameworks for farmer engagement and data sharing

Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.