A very comprehensive blog post about Ikea’s business application of AI! The blog post focused more on the front-end AI applications and touched upon the back-stage digital operations as well. I wonder how interconnected the front and back ends are and what Ikea is thinking about as the next steps in the AI application!
Very interesting and dense blog post! Correct me if I’m wrong, but I understand that C3 AI provides apply-to-all horizontal AI infrastructures to customers regardless of what industries or applications the customers are in. But horizontal AI tech stacks (AI libraries, cloud, etc.) are being commoditized and most of the time incumbents are still in need of vertical industry customization and integration and consulting services even if it’s low-to-no code. What then do you think is C3 AI’s unique competitive advantage in this context?
The blog post covers interesting perspectives on the application of technology in agriculture – seeing and spraying and the win-win-win interactions among consumers, crops, and farmers. I wonder where you see as the next frontier of Agri tech in addition to seeing and spraying?
I like your inputs on the model! I guess labeling photos with abstract concepts and interpreting the cultural contexts is difficult so the photos come out rather simplified or unclear. But the results are indeed encouraging!
Creative inputs Feifei! I wonder what caused this issue – is it because Craiyon doesn’t have all the data inputs about these artists? Also, I would suggest you try the Dall-E 2 model again, I believe it should have better results!
So interesting! I also did a comparison between Dall-E 2 and Craiyon but our observations were slightly different. The data inclusiveness angle is very relevant and thought-provoking
Interesting post! The chart is really helpful and illustrative. I’m curious about the sustainability and scalability of Instacart – it seems that the capabilities required by Instacart and Uber Eats are pretty similar, do you see a potential competition or collaboration between the two? How can Instacart differentiate (in addition to scaling to benefit from network effect?)
The value creation triangle is really interesting! And the challenges and competition faced by Coursera especially under COVID 19 is real. I wonder how sustainable online learning platforms can be (given people are relatively disengaged with online learning than the in-person model. E.g., I was never able to finish one course on mooc/coursera), would it be different for different categories of education content?
Very thoughtful comment, Feifei! At its core, I guess any industry with a relatively concentrated upstream and a fragmented, non-standard downstream could use a platform like Win-win to act as a unique connector for both ends. The key here is that it should be relatively difficult or costly for upstream large players to reach the end customers and that the transactions themselves should not be of too low volume, otherwise, not economically viable enough for the platform. Some immediate examples I can think of are local service providers such as nail salons, spa, or barber shops.
Interesting post, Jonathan! The blog post perfectly illustrated what we learned in this module about platform businesses, such as cross-side network effect, disintermediation, scalability, etc. And the exhibits are very helpful for readers to understand the essence and evolution of Zillow’s business model. In the graph, it mentions the adjacent services in the loop – what kind of adjacent services do you think can be relevant for Zillow if it wants to maintain the network effect in the future?
Very interesting, Daniel! I didn’t know Netflix would even change the display pictures for the same movies recommended, how detailed! I wonder what KPIs they use to measure the effectiveness of recommendations and reinforce/reward the recommendation mechanism (in addition to churn?). Also, would they design anything in the recommendation system to prevent user addiction/too long watch time?
Super interesting blog post, Lina! I love how Sushiro uses tags (almost like sensors?) to track the sushi and collect live data and how these data are being used to help with inventory mgmt, service delivery, marketing & sales, etc. I wonder how they’re able to collect the customer’s data, is there a membership loyalty program or anything equivalent? It’d be super interesting to see how they segment customers and predict customer behavior.
Very interesting blog post, Nitya! I’m always amazed by how traditional media transformed themselves and embraced the digital world. I wonder if there’s anything that they do that applies the insights of readers’ preferences/behavior to the physical book stores (e.g., bookshelf display, supply chain, etc.) and other partners in the value chain.