Unlocking the Potential of AI in Agricultural Research: Insights from ICRISAT’s Sensitization Workshop and Survey Findings
ICRISAT organized an AI sensitization training session on September 2, 2025, to build awareness and strengthen institutional capacity in Artificial Intelligence, aligning with its broader mission of advancing digital agriculture. The training introduced staff, researchers, and students to AI fundame...
| Main Authors: | , , , , , , |
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| Format: | Informe técnico |
| Language: | Inglés |
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2025
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| Online Access: | https://hdl.handle.net/10568/180217 |
| _version_ | 1855533580973768704 |
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| author | Rupavatharam, Srikanth Patil, Mukund Gelaye, Kidia Reddy, Nagarjuna Mitnala, Sreevani Dhungel, Rajeev Ravula, Padmaja |
| author_browse | Dhungel, Rajeev Gelaye, Kidia Mitnala, Sreevani Patil, Mukund Ravula, Padmaja Reddy, Nagarjuna Rupavatharam, Srikanth |
| author_facet | Rupavatharam, Srikanth Patil, Mukund Gelaye, Kidia Reddy, Nagarjuna Mitnala, Sreevani Dhungel, Rajeev Ravula, Padmaja |
| author_sort | Rupavatharam, Srikanth |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | ICRISAT organized an AI sensitization training session on September 2, 2025, to build awareness and strengthen institutional capacity in Artificial Intelligence, aligning with its broader mission of advancing digital agriculture. The training introduced staff, researchers, and students to AI fundamentals, practical research tools, generative AI applications, and responsible use, followed by a post-session survey to assess adoption levels, challenges, and future needs.
The survey of 65 participants revealed high levels of early adoption, with 66% already using AI, primarily for writing, reviewing, and data processing. A smaller but significant group is applying AI for model training, dashboard development, and advanced data management. Despite this proactive uptake, key institutional challenges remain, including inadequate data readiness, limited access to high-performance compute resources, and critical concerns around data privacy, bias, and authorship accountability.
Participants expressed overwhelming enthusiasm for additional training, with 86% indicating interest in future AI skill-building programs. Areas of demand include predictive modelling and data analysis, AI/ML application development, domain-specific applications in genomics and climate science, and formal training on ethical AI practices. Notably, over half of the respondents proposed innovative AI-driven ideas ranging from research acceleration tools to farmer-facing applications for disease detection, soil health monitoring, and market linkages, highlighting strong internal potential for innovation.
Based on these insights, the report recommends the development of an institutional AI strategy, investment in data governance and compute infrastructure, a structured multi-tiered training program, establishment of an AI ethics and governance committee, and mechanisms to nurture staff-led innovation.
Overall, the session and survey confirm both the readiness and the ambition of ICRISAT’s community to adopt AI, while also charting a clear path forward. With strategic investments and responsible practices, ICRISAT has the opportunity to lead globally in embedding AI into agricultural research-for-development, fostering innovation that benefits farmers, partners, and food systems worldwide. |
| format | Informe técnico |
| id | CGSpace180217 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | CGSpace1802172026-01-21T02:11:28Z Unlocking the Potential of AI in Agricultural Research: Insights from ICRISAT’s Sensitization Workshop and Survey Findings Rupavatharam, Srikanth Patil, Mukund Gelaye, Kidia Reddy, Nagarjuna Mitnala, Sreevani Dhungel, Rajeev Ravula, Padmaja generative ai machine learning data management artificial intelligence ICRISAT organized an AI sensitization training session on September 2, 2025, to build awareness and strengthen institutional capacity in Artificial Intelligence, aligning with its broader mission of advancing digital agriculture. The training introduced staff, researchers, and students to AI fundamentals, practical research tools, generative AI applications, and responsible use, followed by a post-session survey to assess adoption levels, challenges, and future needs. The survey of 65 participants revealed high levels of early adoption, with 66% already using AI, primarily for writing, reviewing, and data processing. A smaller but significant group is applying AI for model training, dashboard development, and advanced data management. Despite this proactive uptake, key institutional challenges remain, including inadequate data readiness, limited access to high-performance compute resources, and critical concerns around data privacy, bias, and authorship accountability. Participants expressed overwhelming enthusiasm for additional training, with 86% indicating interest in future AI skill-building programs. Areas of demand include predictive modelling and data analysis, AI/ML application development, domain-specific applications in genomics and climate science, and formal training on ethical AI practices. Notably, over half of the respondents proposed innovative AI-driven ideas ranging from research acceleration tools to farmer-facing applications for disease detection, soil health monitoring, and market linkages, highlighting strong internal potential for innovation. Based on these insights, the report recommends the development of an institutional AI strategy, investment in data governance and compute infrastructure, a structured multi-tiered training program, establishment of an AI ethics and governance committee, and mechanisms to nurture staff-led innovation. Overall, the session and survey confirm both the readiness and the ambition of ICRISAT’s community to adopt AI, while also charting a clear path forward. With strategic investments and responsible practices, ICRISAT has the opportunity to lead globally in embedding AI into agricultural research-for-development, fostering innovation that benefits farmers, partners, and food systems worldwide. 2025-12-31 2026-01-20T12:28:20Z 2026-01-20T12:28:20Z Report https://hdl.handle.net/10568/180217 en Open Access application/pdf Rupavatharam, Srikanth; Patil, Mukund; Gelaye, Kidia; Reddy, Nagarjuna; Mitnala, Sreevani; Dhungel, Rajeev; & Ravula, Padmaja. 2025. Unlocking the Potential of AI in Agricultural Research: Insights from ICRISAT’s Sensitization Workshop and Survey Findings. Patancheru, India: ICRISAT. |
| spellingShingle | generative ai machine learning data management artificial intelligence Rupavatharam, Srikanth Patil, Mukund Gelaye, Kidia Reddy, Nagarjuna Mitnala, Sreevani Dhungel, Rajeev Ravula, Padmaja Unlocking the Potential of AI in Agricultural Research: Insights from ICRISAT’s Sensitization Workshop and Survey Findings |
| title | Unlocking the Potential of AI in Agricultural Research: Insights from ICRISAT’s Sensitization Workshop and Survey Findings |
| title_full | Unlocking the Potential of AI in Agricultural Research: Insights from ICRISAT’s Sensitization Workshop and Survey Findings |
| title_fullStr | Unlocking the Potential of AI in Agricultural Research: Insights from ICRISAT’s Sensitization Workshop and Survey Findings |
| title_full_unstemmed | Unlocking the Potential of AI in Agricultural Research: Insights from ICRISAT’s Sensitization Workshop and Survey Findings |
| title_short | Unlocking the Potential of AI in Agricultural Research: Insights from ICRISAT’s Sensitization Workshop and Survey Findings |
| title_sort | unlocking the potential of ai in agricultural research insights from icrisat s sensitization workshop and survey findings |
| topic | generative ai machine learning data management artificial intelligence |
| url | https://hdl.handle.net/10568/180217 |
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