"AI is a powerful tool for
sustainable productivity growth"
DLG President Hubertus Paetow on AI as a productivity accelerator on farms
Interview from DLG’s Winter Conference 2026
The DLG Winter Conference 2026 places Artificial Intelligence (AI) at the centre under the theme “AI – a productivity accelerator for the farm.” On 24 and 25 February, experts from agriculture, agricultural engineering, the food sector and research gathered in Hanover, Germany, to discuss how AI is sustainably transforming efficiency, precision and decision‑making processes. A particular focus of the 2026 DLG Winter Conference lies on strengthening the connection between agriculture and the food industry.
DLG President Hubertus Paetow opened the plenary session with a technical introduction on Wednesday, 25 February. In this interview for the DLG Member Newsletter, Paetow encourages the agricultural and food sectors to integrate artificial intelligence into everyday operations.
DLG Newsroom: Mr Paetow, at the DLG Winter Conference your technical introduction to the topic “AI – a productivity accelerator for the farm” was well received. How does this title align with the DLG mission of sustainable productivity growth??
Hubertus Paetow: “Productivity accelerator” does not mean “more output at any cost,” but rather exactly what is anchored in the DLG mission of sustainable productivity growth: more value creation per unit of resource used — in a way that is economically viable, environmentally responsible and socially accepted over the long term.
Artificial intelligence can be a powerful lever for this. It enables a significantly more precise use of resources because decisions on soil cultivation, sowing dates, fertilisation or crop protection can be made more reliably and efficiently based on interconnected data. This leads to lower losses and more output per unit of fertiliser, diesel or working time.
At the same time, AI helps to make the increasing complexity of agricultural decisions more manageable. Farm managers today are navigating market demands, environmental regulations, technical diversity and weather risks; AI can process data from the farm, machinery and the market so that decisions can be made quickly, soundly and proactively — and that is a central prerequisite for sustainable productivity.
Furthermore, AI makes work on the farm more productive and more attractive, because many routine tasks such as documentation, planning or analysis can be simplified or even automated. This frees up more time for leadership, strategy, crop and animal monitoring — and thus for improving the quality of operational decisions.
In short: if we use AI consciously to conserve resources, improve decisions and strengthen people on the farm, then it is not a contradiction to the DLG mission — it is a powerful tool for sustainable productivity growth.
What concrete applications of AI do you already see in agriculture today – Where do you see untapped potential??
We already see a whole range of very concrete applications today: from image recognition in crop and animal monitoring to assistance systems for machine settings, and through to automated documentation and field record support.
In my view, three areas are underestimated and therefore still underused:
First, relieving the burden of office work and administration — everything from cross-compliance and quality assurance documentation to communication with advisers and authorities.
Second, learning systems that derive increasingly better recommendations step by step from a farm’s own data.
Third, the linking of multiple data sources — weather, markets, sensors, machinery — into truly integrated decision‑support systems for the farm.
How will AI change collaboration along the value chain in the long term – from upstream suppliers through trade and processing to the end consumer??
AI will make the value chain more transparent and more connected. Upstream suppliers, traders and processors will evaluate data from production, logistics and markets in real time — for example, to manage variety recommendations, supply chains or quality requirements more dynamically. For farms, this can mean: more specific requirements, but also more targeted support from technology and advisory partners.
At the same time, there is the opportunity to make the performance of primary production more visible and more financially recognised: climate impact, biodiversity, animal welfare, quality. AI‑supported documentation and evaluation systems can help bring trustworthy information to end consumers. What will be crucial is that farms are not just data providers but actively co‑shape which data is used and under what conditions.
Many farm managers ask themselves: How much AI makes sense for my farm – and where does technological overload begin??
I would say: What makes sense is always what solves a concrete problem on the farm – and does so with a reasonable amount of effort. That means: start small, with clearly defined use cases. For example: “How do I save time on documentation?”, “How do I make better decisions in crop protection?” or “How do I improve the utilization of my machinery?”. If an AI solution provides a convincing answer to one of these questions, it is a candidate.
Technological overload begins where more technology creates more complexity than benefit: when systems are incompatible, when management spends its time on updates and interfaces, or when employees mentally check out because they no longer understand the tools. In those cases, you need the courage to say “no” and use fewer, well‑integrated AI tools rather than trying to follow every trend. AI should relieve the farm, not become an end in itself.
