Are we thinking about AI the right way ?

AI has dominated conversations across multiple industries and AgTech is no different. Today I want to highlight four themes to consider when thinking about AI and how they apply to the AgTech sector. 

Who is RHISHI, Our Technologies Harvester ?

Rhishi has had leadership roles at Mineral (an Alphabet company), The Climate Corporation (Bayer), Amazon, and other technology companies. He has led two startups through exits, and one through a spin out. He has extensive experience in artificial intelligence, supply chain and logistics, product, data & technology strategy, robotics & computer vision, sustainability, and data interoperability.

Sustaining or Disruptive Innovation?

Is the AI powered application or product going to be sustaining within the context or is it going to be disruptive?

A sustaining innovation will strengthen the competitive position of incumbent players, especially in industries where distribution and access to data is controlled by the incumbents. For example, seed companies like Syngenta, and Bayer are sitting on huge amounts of data about agronomic practices on the farm for commodity row crop farmers in the US, Canada, EU, Brazil etc.

Many of the input companies and agriculture retailers and cooperatives, also control complex workflows around agronomic advice and associated sales of input products, and services to the farmer / grower.

In situations where incumbents have a lot of data or are in the center of a complex workflow, it is much harder to have a disruptive innovation compared to a sustaining innovation with AI. Due to this many of the AI led innovations which have to rely on existing data streams or involve working with existing complex workflows, will end up strengthening the competitive position of incumbent players.

For example, AI workflows around agronomic advice, creating prescriptions based on agronomic data will be sustaining innovations which do not fundamentally change the industry structure, but will strengthen the position of existing incumbents. Any AI products and workflows which use LLMs to present insights from existing data in an easy manner will be sustaining innovations, and strengthen the position of incumbents.

Image generated by ChatGPT with Prompt “Image of a scientist thinking about AI in a farm with robots, drones, and sensors around”

To create a non-sustaining AI innovation, one will have to play with a different set of rules than someone like a Bayer or Deere. Entrepreneurs who want to leverage the latest advances in AI, will have to find data poor and simple workflow environments as they will be more amenable to application of AI. For example, the current specialty crop buying and selling workflow is not digitized. It is a manual process. If one can digitize the process,  they can apply AI to a newly lit part of the supply chain to drive better results.

Autopilot or Copilot?

Will your AI solution augment human capabilities to provide insights, suggestions, and tools for decision making (co-pilot) or as an autopilot (fully automating tasks with minimal human intervention)?

Complex high value workflows will typically involve a “human-in-the-loop” and so are more amenable to a co-pilot approach. The co-pilot approach is great to build trust with the co-pilot, train staff quickly and efficiently, improve customer service, and drastically reduce issues related to training and churn.

If there are processes which are repetitive, have easier workflows, and are for lower value decisions, an auto-pilot approach can free up time from employees, and help them focus on the high value use case.

The question is not whether a particular process will completely replace humans or not. It is important to break down tasks based on their complexity, repeatability, and the value of each task. For tasks, which are complex, taking a co-pilot approach is better. It also helps protect you from liability. Co-pilot tools create long term value due to enhanced trust from users, consistent performance, and lower operational costs.

For tasks, which are simple, repetitive, and relatively low value, it should be easy (relatively) to completely automate the task using AI.

Proprietary of Shared?

Much of field agriculture for commodity row crops is characterized by proprietary data format of different field level operations like planting, harvest, application etc. It is difficult for startups to break in, when much of the data is either locked up or is in proprietary formats.

If you are a startup, you are better off if you can create or tap into new data. For example, what are the data poor environments in the world today? Smallholder farming data is poorly covered. The challenge here is less about building AI models, but more about getting data at scale and efficiently. If you can do that, then you can create a stronger AI based company as you will have access to this data easily. If we think about commodity row crops, companies like Deere and Climate have access to huge amounts of proprietary data (though it belongs to the grower).

Some of the freely available satellite imagery like Sentinel 2 is non-proprietary data, though newer satellite data could be proprietary in nature. It is easier for startups to work with open data sources compared to proprietary.

Another approach is for organizations to create proprietary data. For example in biological products, there is dearth of data to support different analysis, and prove the efficacy of the biological product. If a startup can create new data sets, to feed new AI innovations, they can be disruptive, and valuable. Certain markets like smallholder markets (not enough data) would be good targets for some enterprising startups to go ahead and collect a large corpus of data to run AI models.

Is the early-mover advantage linear or compounding?

With rapid advances in AI, it is a valid question to consider if an early mover-advantage is linear or compounding.

In winner-takes-all models, early mover advantage compounds, if network effects are present. It makes sense to go for an early mover advantage. If your models continue to get better with more and more data, an early lead will be unassailable.

But many of the recent advances in AI have rendered some of the old models obsolete. Does it make sense to wait for newer more powerful models to come out, and use them to build their use cases? For example, when I worked at Mineral, we spent significant time and money in building ML and AI models using vision systems from scratch. When new vision models were released for object detection, and segmentation, it felt like all the investment we had done, had gone down the drain, as these new models could do many things out of the box, compared to our homegrown ML models.

So should you engage with AI as soon as possible, or wait for the right time to build the right product?

I am a firm believer in continuous engagement. The AI models are important but they are just one piece of the puzzle. To build AI models, you need to set up infrastructure capabilities and invest in the management of clean, and actionable data sets, which can feed your AI model.

Even if you do not want to engage with AI right now, one should definitely invest in building technical, business, and human capabilities to work with AI. Being prepared will give you an edge over organizations which are not prepared and are not engaging with AI.

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