Agriculture is ready for AI, but its data isn’t

农业已为人工智能做好准备，但数据尚未就绪

Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork. The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop yield by 26%, reduce water use by 41%, and cut chemical usage by 33%. However, what AI vendors usually won’t tell you is that these solutions are only effective if you have a clean, solid data foundation.

人工智能正在改变农业的可能性，但行业领导者在投资人工智能之前，应警惕是否已打好基础。其应用前景广阔，尤其对于那些正面临化肥成本波动、天气不可预测以及利润空间极小等挑战的行业而言。研究表明，人工智能驱动的预测模型可将作物产量提高 26%，减少 41% 的用水量，并降低 33% 的化学品使用量。然而，人工智能供应商通常不会告诉你的是，只有当你拥有干净、坚实的数据基础时，这些解决方案才能发挥作用。

However, at Reltio, we have experience in this area, including leading technology strategy at a major agricultural distributor and building a data platform used by enterprises worldwide–we’ve seen it first hand.

然而，Reltio 在这一领域拥有丰富的经验，包括曾为一家大型农业分销商主导技术战略，并构建了被全球企业使用的数据平台——我们对此有着切身的体会。

What AI vendors won’t tell you

人工智能供应商不会告诉你的事

Vendor conversations in agriculture tend to follow a familiar pattern. The pitch leads with grand promises around using AI to monitor crop health in real time, optimize irrigation, and squeeze more yield from every acre. The promise is compelling, but what rarely comes up is the question of whether the data foundation underneath those promises is accurate and complete. If not, there is a real and significant risk that AI will generate misleading outputs that seem authoritative but inspire action that is, at best, counterproductive.

农业领域的供应商对话往往遵循一种熟悉的模式。推销的重点通常是关于利用人工智能实时监测作物健康、优化灌溉以及从每英亩土地中榨取更多产量的宏伟承诺。这些承诺固然诱人，但很少有人会问：支撑这些承诺的数据基础是否准确且完整？如果不是，那么人工智能极有可能产生看似权威但实际上适得其反的误导性输出。

For instance, a yield prediction model fed inconsistent historical data will generate imprecise forecasts. Similarly, a precision irrigation system drawing on fragmented sensor data will make watering decisions that waste resources instead of saving them. In each case, the AI is failing because the data it was trained on was not sufficient to produce trustworthy outputs. In agriculture, every AI hallucination is a liability, and the likelihood of error is high.

例如，如果产量预测模型输入的是不一致的历史数据，它就会生成不精确的预测。同样，一个依赖碎片化传感器数据的精准灌溉系统，其做出的灌溉决策反而会浪费资源而非节约资源。在这些案例中，人工智能的失败是因为其训练数据不足以产生可信的输出。在农业领域，每一次人工智能的“幻觉”都是一种负担，且出错的可能性很高。

Why agriculture is a uniquely challenging test case

为什么农业是一个极具挑战性的测试案例

The data landscape across a modern agricultural operation or a large distributor serving thousands of growers is extraordinarily complex. Modern farming environments make extensive use of IoT devices and machinery. Irrigation systems are automated, tractors navigate fields autonomously, and drones capture field imagery at scale. However, machine data is disparate by nature. Add in external sources, including weather feeds, U.S. Department of Agriculture data, and third-party market information, and the question of how you bring all of it together into something coherent becomes a significant undertaking.

现代农业运营或服务于数千名种植者的大型分销商，其数据环境极其复杂。现代农业环境广泛使用物联网设备和机械。灌溉系统实现了自动化，拖拉机可以自主导航，无人机则大规模地捕捉田间影像。然而，机器数据本质上是分散的。再加上天气预报、美国农业部数据和第三方市场信息等外部来源，如何将所有这些数据整合为一个连贯的整体，就成了一项艰巨的任务。

Agricultural AI also needs to understand more than just customer attributes; it needs to understand the land: GPS coordinates, farm boundaries, field blocks, and soil variation across a single property. Where do you apply fertilizer, and at what rate, and in which specific area of the farm? Not all parts of a field are the same, and an AI system that treats them as if they are will produce recommendations that are at best imprecise and at worst damaging. There is also a compliance dimension due to the chemicals and the responsibility involved. Operational AI in agriculture needs significantly more checks and governance than it might in a lower-stakes environment. When a flawed recommendation gets acted upon in the field, the consequences can be severe.

农业人工智能不仅需要了解客户属性，还需要了解土地：GPS 坐标、农场边界、地块以及同一地块内的土壤差异。你在哪里施肥？施肥比例是多少？在农场的哪个特定区域施肥？田地的不同部分情况各异，如果人工智能系统将它们一视同仁，其产生的建议往好里说是“不精确”，往坏里说则是“具有破坏性”。此外，由于涉及化学品和相关责任，还存在合规性维度。农业中的运营型人工智能比低风险环境下的系统需要更多的检查和治理。当错误的建议在田间被付诸实施时，后果可能是严重的。

What data readiness means in practice

数据就绪在实践中意味着什么

Data readiness is the difference between AI delivering on its promise vs. a “garbage in, garbage out” scenario. Fundamentally, being ready for AI means having a data model that accurately reflects how the business operates. For a company like Wilbur-Ellis, a 104-year-old, family-owned agricultural distributor, that means understanding who your customers are, which fields they farm, which inputs they need, which suppliers those inputs come from, what they paid last season, and how all of that connects to margin. That information needs to be current, consistent, and accessible across the organization, rather than locked in separate systems that were never designed to talk to each other.

数据就绪是人工智能实现其承诺与陷入“垃圾进，垃圾出”困境之间的分水岭。从根本上讲，为人工智能做好准备意味着拥有一个能准确反映业务运作方式的数据模型。对于像 Wilbur-Ellis 这样拥有 104 年历史的家族农业分销商来说，这意味着要了解客户是谁、他们耕种哪些田地、需要哪些投入品、这些投入品来自哪些供应商、他们上一季支付了多少费用，以及所有这些因素如何与利润挂钩。这些信息必须是实时的、一致的，并且在整个组织内可访问，而不是被锁定在从未设计过互通的独立系统中。

Similarly, for farming operations themselves, data readiness means having a reliable, connected picture of what is happening across every field: soil health records, input application histories, yield data from previous seasons, equipment performance, and real-time sensor readings from irrigation systems. Governance matters just as much as structure. Prices change, relationships evolve, and suppliers come and go. An AI system drawing on data that was accurate six months ago but has not been maintained will make recommendations based on a version of the business that no longer exists.

同样，对于农业运营本身而言，数据就绪意味着要对每一块田地的情况有一个可靠、互联的了解：土壤健康记录、投入品施用历史、往季产量数据、设备性能以及来自灌溉系统的实时传感器读数。治理与结构同样重要。价格在变，关系在演变，供应商也在更替。如果人工智能系统所依据的数据是六个月前准确但未进行维护的数据，那么它所做出的建议将基于一个已不复存在的业务版本。

Building the foundation that makes AI trustworthy

构建让人工智能值得信赖的基础

The good news is that the path to data readiness is feasible. It starts with a strong data model: a single, governed source of truth that connects customers, suppliers, products, pricing, orders, and margins in a way that reflects how the organization operates. From there, it requires data pipelines fast enough to deliver insights when decisions need to be made, governance frameworks that keep that data trustworthy over time, and security controls that ensure sensitive commercial information is accessible to the right people under the right conditions.

好消息是，实现数据就绪的路径是可行的。它始于一个强大的数据模型：一个单一的、受治理的“事实来源”，它以反映组织运作方式的方式连接客户、供应商、产品、定价、订单和利润。在此基础上，它还需要足够快的数据管道，以便在需要决策时提供洞察；需要能够长期保持数据可信度的治理框架；以及确保敏感商业信息在适当条件下仅由合适人员访问的安全控制措施。

This is precisely the challenge that Reltio, an SAP company, was built to solve. Reltio enables companies to unify their fragmented data so AI agents and systems can operate from a complete picture of the business. Reltio builds a trusted system of context, known as the context intelligence layer, that brings all entities, relationships, rules together under one roof and makes business data easy to access and interpret. For Wilbur-Ellis, building that trustworthy data foundation has meant being able to ask more complex questions and trust the answers, which is the precondition for any AI system to be genuinely useful.

这正是 SAP 旗下公司 Reltio 旨在解决的挑战。Reltio 使企业能够统一其碎片化数据，从而使人工智能代理和系统能够基于完整的业务图景进行运作。Reltio 构建了一个被称为“上下文智能层”的可信上下文系统，将所有实体、关系和规则汇集在一起，使业务数据易于访问和解读。对于 Wilbur-Ellis 而言，构建这一可信数据基础意味着能够提出更复杂的问题并信任答案，这是任何人工智能系统真正发挥作用的前提。

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