What About Plants? Artificial Intelligence (Part I)

I’m writing this article because among all the AI talk out there, I haven’t seen very approachable explanations for what AI will mean for the average business. It definitely doesn’t exist in the garden center or landscape industries. Although I’m not an expert, I think I can shed some light on the topic and simplify it by steering away from the big, confusing words and sticking to the need-to-know!

What is it?

You’ve undoubtedly heard about AI or Artificial Intelligence before whether it be IBM’s Watson beating the best chess players in the world or Amazon Alexa’s role within the modern home. Business owners and operators that aren’t among the elite tech companies probably don’t think AI is relevant for their businesses. Indeed, the popular examples do come from Silicon Valley today. However, beneath the jarring headlines predicting AI’s takeover of the world, there are real, approachable companies building AI strategies that will set them up for strong leadership in their industries for the next generation.

Fundamentally, what AI technology is doing is creating a data model of your customer, product or business itself. Simply stated, that means you have your data organized in such a way that it’s able to describe what’s happening in real life in your business. Having the data organized in that way is a huge advantage in itself. Where the AI advantage comes in is when that data can ‘learn’ from the daily interactions in your business. Naturally, there are questions your business needs to find answers to. Slowly but surely, you learn those answers. By capturing your learnings into the model, your model improves itself. In other words, it is better able to describe the world your business lives in. Once it can do that, it can start to give you answers to your next questions since it’s had practice answering those questions before. The key is that these answers don’t come without asking the right questions. The successful businesses of the future will have individuals in their company who can have these eloquent ‘conversations’ with the data model.

Two phases of AI adoption:

AI as a Service

Many modern software applications are starting to build AI tools into its subscription offering. The easiest example is chat bots. No doubt most of you have interacted with a chatbot on a website of your favorite retailer, clothing brand or airline. Zendesk is a leading platform that offers this within its customer service suite. Chalet uses Zendesk and is currently building up the necessary data (customer questions and staff answers). Zendesk has a data model that specifically is organized around written conversations between customers and staff. Many industries have similar questions that get asked of their client businesses. The businesses on Zendesk get the advantage of that robust data model when building their own chat bot. They simply have to pay a fee for that service – thus, ‘AI as a Service’. It leaves a little spot in its model for data from our specific business – the questions and answers. The AI learns the answers to questions and can then answer similar questions in the future, thus the ability to basically talk to a robot!

AI as a strategy

Each industry has unique challenges and opportunities. The Zendesks of the world create generalized AI models that can be used by any company willing to pay the subscription price. However, good long-term strategies serve to create sustainable competitive advantages within an industry. The AI as a Service described above can create short-term competitive advantages until the rest of the industry jumps on the bandwagon and the playing field is equal. A long-term competitive advantage can be created when building a strategy around an AI model that’s a unique fit for an industry. For better or worse, the garden center and landscape industries are about as unique as they come!

I stress the long-term nature because it takes quite a bit of time to build an AI model. Due to the cold winters, many Chicagoland green industry companies like Chalet end up with an “off-season”, for lack of a better term, in Q1 where business-improvement projects are taken up. This is not one of those projects. This takes commitment over at least 3-5 years before seeing real ROI. But the benefits it creates can last a generation or beyond. In fact, AI has been seen as the next industrial revolution. The question is who are those farmers that will take advantage of the machines while their counterparts are using oxen?

What does it mean for the green industry?

At the end of the day, what makes our businesses great is our ability to serve customers. Most AI models will revolve around the customers and their interactions with products and the businesses. Naturally, let’s take plants as the product. Where the customer uses those plant products is in the garden or landscape. Remember how important asking questions are in the world of AI? What questions do we as landscape professionals have about those landscapes? What do we want to learn? While tech companies want to know who your parents are, where you went to school and what you ate for breakfast, we want to know things like the size of the customer’s yard, how much sun it gets, and what soil type they have. Mostly through observation, these questions are answered. But these questions only inform the next questions like what plant we should put where. Due to hard won experience, we recommend plants based on some rules of thumb of what plants work in which landscape situations. That has worked well enough over the years.

The AI generation we’re stepping into will turn those rough rules of thumbs into super precise plant and place fit suggestions that will lead to increasingly high success rates as the model continues to learn. Plant replacement rates will plummet, customer happiness will skyrocket and the business that takes advantage of this opportunity will profit.

Who will take advantage?

The green industry is also unique in how it’s structured. Many smaller, often family-owned businesses service relatively confined geographies compared to industries with multi-national companies. This is due to how capital intensive it is to expand into new geographies and how unique the plant palette is in these areas. While larger companies like BrightView exist, the industry hasn’t necessarily been kind to the larger players due to those underlying challenges. Certainly the capital at the higher end of the scale will be able to afford the engineers to build these AI models. But, the advantage that the smaller business has is its knowledge of the local landscapes and rich history with the customers there. Low employee turnover and family ownership are also boons to the patience and fortitude required to see through a long-term strategy of this magnitude.

Over the next 5-10+ years, I look forward to watching this play out. I welcome any thoughts from anyone who has read this far!

I recently gave a presentation to the Illinois Landscape Contractors Association (ILCA) Young Professionals Committee. This gave more of a granular example of how data on plants can be used in a landscape setting. Stay tuned and I will follow up with a Part 2 related to that topic.