AI and Agriculture: Method Before Technology

AI and Agriculture: A Necessary Methodological Transformation

The recent report from the General Council for Food, Agriculture, and Rural Areas (CGAAER) highlights the urgency for France to accelerate the integration of artificial intelligence (AI) in the agricultural sector. However, the main challenge no longer lies in demonstrating its potential, which is already recognized by cooperatives, industrial players, and agricultural organizations. These stakeholders are aware that agent-based AI is poised to radically transform their professions. The real question now is how to successfully achieve this transformation. Unlike a simple software or a traditional digital project, AI fundamentally alters work methods, knowledge transfer, decision-making, and support for professionals in the field. This change is crucial for a strategic sector in France, which sits at the intersection of food sovereignty, economic competitiveness, and ecological transition. Effectively adopting AI could enhance the resilience, performance, and attractiveness of French agriculture for decades to come, provided this tool is not viewed solely from a technological perspective.

The Importance of Adapting to Agricultural Professions

For AI to be truly useful, it must be designed with the specific challenges faced by agricultural professionals in mind and the expected benefits. One of the major issues is the transfer of skills, as more than a third of French farms will see their operators retire by 2030. New farmers and advisors are entering an increasingly complex environment. In this context, AI can become a valuable tool for support, training, and knowledge transfer.

Historically, the value of the advisory profession relied on possessing knowledge. In the future, the challenge will be to quickly mobilize relevant information, analyze specific situations, and propose tailored solutions for each farm and its pedoclimatic environment. AI will not replace human expertise, but it will make it more contextual, responsive, and relevant.

Moreover, a significant portion of agricultural professionals' time is still spent on administrative tasks such as invoicing, administrative procedures, traceability, and reporting. By automating some of these activities, AI frees up time for teams to focus on their core business and value creation.

No Time to Waste, But Move Methodically

The question is no longer whether AI will transform agriculture, but under what conditions this will happen. France has a rich agronomic heritage, as well as considerable data and expertise. Failing to utilize these resources to design the tools of tomorrow would be a strategic and economic forfeiture. If the intelligent assistants that will guide agricultural decisions are developed abroad, what data will they use? What vision of agriculture will they convey? What recommendations will they produce? These questions underscore the importance of sovereignty in this field.

International competition is already underway, with several countries investing heavily to get ahead in these technologies. France cannot afford to lag behind. We have the history and the skills necessary to seize the future. Waiting would mean allowing other countries to define the standards of tomorrow.

However, accelerating does not mean replicating the methods of past digital transformations. AI represents both a technological and methodological break, and traditional approaches are already showing their limits. This is why major players in the AI ecosystem are investing heavily in dedicated teams for its deployment and integration within organizations.

From Experimentation to Effective Transformation

The transformation through AI is not out of reach. The mistake would be to multiply projects without follow-up or to settle for theoretical roadmaps. The organizations that will succeed are those that accept learning through experience, provide tools to teams, experiment with concrete use cases, measure results, and continuously adjust their practices.

This approach is all the more realistic given that the conditions are right. A new generation of farmers is ready to adopt these technologies. Many engineers and digital experts wish to apply their skills to the agricultural world. The human potential is present. Deployment methods already exist and have proven effective in other industries.

The issue is no longer whether AI will find its place in agriculture. It is already transforming professions. The real challenge now is to create the conditions for its large-scale adoption, starting from the ground up, with the actual uses and needs of professionals.

Agriculture will not achieve its transformation through the best technological roadmap. It will succeed by putting AI at the service of those who bring it to life every day.

Guillaume Roger, Chief Business Officer of Ekumen