Jan Bosch is a research center director, professor, consultant and angel investor in startups. You can contact him at jan@janbosch.com.


Bringing AI to the edge

Leestijd: 3 minuten

It seems that much of my work these days is concerned with bringing AI to the embedded-systems domain, understanding what the implications are and how companies should deal with it. In the discussions with technical experts and business leaders, however, I constantly run into several misconceptions.

First, there are still several people out there who think about ML/DL models as single-point solutions for a specific problem where we can build the model, integrate it, deploy the resulting software and be done. Although one could operate in this way, it’s basically ignoring the vast majority of benefits that you could deliver. Instead, it’s about continuous everything. It’s about a constant flow of software updates to the field, a constant flow of data coming back from your systems, constant retraining and updating of models. We all read about DevOps, DataOps and AIOps, but that really is what it’s all about. The shorter you can make the cycles, the more value you can deliver.

Second, many view AI as a technical challenge to be solved by R&D but unrelated to the rest of the company. The fact is that it will change your business model. If you’re currently using a transactional business model where you get a one-time payment when you sell the product but need to provide software updates throughout the economic life of the product, you’re in trouble as your cost is continuous while your revenue is not. The only way to get around this is to align your revenue model with your cost model. For most companies that I work with, it means combining the upfront product sales with a continuous service revenue model. This is actually a great way to generate more revenue from your customers.

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