"The greenhouse industry is at a crossroads where people face a pivotal choice: embrace the potential of artificial intelligence (AI) to stand out in a crowded market or risk being commodified by sticking to outdated AI methods", says Adam Greenberg with IUNU. "Before implementing new technology, one must ask themselves: How will AI give me a competitive advantage or am I buying sand in the desert?"
To thrive in this environment, he explains we must embrace what is being called "The High and Hard Problem." "This is the challenge of improving both the types of data we collect and the way we analyze it. Without both of these, growers risk becoming average, missing out on opportunities to achieve excellence."
Right now, there's a spike of AI tools claiming to transform greenhouse operations. "But many of these solutions miss the mark because they're not approaching the problem the right way."
The four quadrants of greenhouse AI
Adam explains the breakdown of AI tools with the image of two simple axes. " Old vs. new data and old vs. new analysis", he says, explaining how they break down:
Old data, old analysis:
Using traditional metrics (temperature, humidity) processed with basic tools like Excel. This has its place but isn't enough anymore.
Old data, new analysis:
Applying AI and machine learning to the same old data. This is where most companies operate, but it doesn't unlock new opportunities.
New data, old analysis:
Collecting innovative data (e.g., automated crop growth measurements) analyzed with outdated tools, leaving its potential untapped.
New data, new analysis:
The sweet spot—combining fresh data with advanced analytics to deliver transformative insights.
Why following the average isn't enough
According to Adam, one of the biggest challenges growers face today is working with technology companies that are stuck in the "Old Data, New Analysis" quadrant. "We call this the commoditized quadrant. These companies commoditize AI, taking your data, running generic algorithms, and selling it back to you. This approach forces growers to follow the average, making it harder to stand out and succeed long-term."
"As AI becomes more common, it's not enough to just have it. Anyone can apply AI to temperature or humidity data—it's no longer groundbreaking. Without new types of data, these systems provide less value over time, leaving growers paying for solutions that only maintain the minimum performance."
Supporting growers, not controlling them
To truly meet the needs of the industry, Adam says AI providers must aim higher. "They should focus on introducing new types of data (like automated crop registration metrics) alongside new analytical techniques. Providers should avoid hoarding data or dictating how it's used. Instead, they should empower growers with tools that allow them to innovate, excel, and rise above the average."
Why it matters
"This isn't just about technology—it's about how we approach growth and collaboration", he concludes. Solving the High and Hard Problem means:
● Encouraging teamwork across the industry.
● Avoiding "closed source" ecosystems that limit access to data.
● Empowering growers with tools that bring real, measurable improvements.
"To remain competitive in a rapidly evolving industry, we must tackle the High and Hard Problem head-on. That means going beyond AI that keeps things average and instead creating real value with new data and new analysis. Anything less is like selling sand in the desert. The future of greenhouse innovation depends on setting and meeting these expectations."
For more information:
IUNU
[email protected]
iunu.com