Why “AI-Powered” is no longer a product strategy in agriculture

Sandra Eneremadu

For years, “AI-powered” has been the headline feature of agricultural technology. Investor decks, product pages, and conference demos have leaned heavily on machine learning, computer vision, and predictive analytics as shorthand for innovation.

Recently, I removed “AI-powered” from a product description I was writing. Not because the platform no longer uses AI; it does extensively, but because leading with AI obscures what actually matters: whether the product helps farmers make better decisions.

When AI Becomes the Product

A pattern repeats across agricultural technology. Teams build genuinely impressive models, achieve high accuracy rates, perform sophisticated feature engineering, and use elegant algorithms. The technology works. The product is then designed to showcase the AI rather than solve a real-world decision problem.
I’ve seen a precision agriculture platform with a computer-vision model that identified cassava diseases with 94% accuracy. The app displayed confidence scores, detection heat maps, and model explanations. Farmers stopped using it after two weeks.

Why? Because disease identification wasn’t the decision they needed help with. Experienced farmers already recognise most diseases visually. What they struggled with was deciding what to do next: whether treatment was available locally, affordable, and economically sensible given current market prices.
The AI answered a question farmers weren’t asking and ignored the ones they were.
The Honesty Test

I now apply a simple test to product descriptions:
If I remove every mention of AI, machine learning, and algorithms, does the product still clearly explain what problem it solves and why a farmer should care?

If the answer is no, there’s a product problem that no amount of AI will fix.
Farmers don’t care whether recommendations come from deep learning models, expert systems, or simple rules. They care whether recommendations are relevant to their specific context, actionable within their constraints, trustworthy enough to act on and delivered when timing matters.
I have seen rule-based systems with lower accuracy outperform advanced ML models because they explained their reasoning in ways farmers could trust and act on.

What Actually Drives Adoption
Leading with “AI-powered” often signals that a product team is optimising for technical impressiveness rather than user value. It prioritises how something works over why it matters.

Real adoption depends on product discipline, deep understanding of user decisions, design that fits existing workflows, honest trade-offs between sophistication and usability, and measurement of sustained behaviour change, not downloads.
These are product problems, not algorithmic ones.

Writing for Decisions, Not Demos
When I write product descriptions now, I start with the decision, not the technology.
Not: “AI-powered crop disease detection.”
But: “Know whether treating diseased plants is worth the cost before you spend.”
Not: “Machine learning-based soil nutrient analysis.”
But: “Get fertiliser recommendations you can afford and access locally.”
Not: “Intelligent planting calendar optimisation.”
But: “Plan planting around when you’ll have labour and cash, not just ideal weather.”
The AI is still there, doing important work in the background. It’s just no longer the headline.

Why This Shift Matters Now

Agricultural technology is reaching maturity. Early-stage funding was often driven by impressive demos and technical novelty. Sustained success, however, depends on whether farmers continue to use the products after the pilot incentives end.

That transition from demo to dependency doesn’t happen because of AI sophistication. It happens because products solve real problems in ways that respect real constraints.
I stopped using “AI-powered” because I want to build products that farmers value, not ones that simply impress investors. Increasingly, those are not the same thing