A rather comprehensive read on Nestle’s effort in conducting innovation. I was surprised to see how advanced they are in terms of extending their tentacle to all potential innovators: students, researchers, average consumers etc. What I would be curious is: has any of their initiative failed? and if so, why they fail? I think it’s very easy to praise open innovation because it’s based on an inclusive methodology, but are there any drawbacks? Happy to discuss more!
Fascinating read. It’s amazing how NGOs utilize technology to create impact unforeseen before. One question I have in mind is the replicability of the business model ( which you’ve touched upon). Especially in developing countries where employment is key for local labor, addictive manufacturing can be devastating to many who have relied on construction for living. Is there a way to upskill these labour using the technology so they continue to be employable?
A great article that not only addresses the 4 questions posed by the Challenge but also adds additional in-depth of why the megatrend matters to the industry and GE in particular. I especially appreciate the flow of you article as for most layman, it’s not clear what exactly the technology does and why it has become a hot potato in recent years. Your bullet points in the beginning sets a solid foundation for the later parts of the article where you elaborate on how GE has adopted the technology in a chronological order. Lastly, by introducing MNF, the article brought in business complexity arisen from the technology. At the end of the day, technology should only be assisting business. It’s how the management leverage AM to make profit that justify the usage of it. Well done.
Your article made a good point on the importance of personalization in the beauty industry, and how technology plays a vital role in facilitating such services. However, it is not clear which megatrend ( AI, Augmented Reality or Additive manufacturing ) your want to focus. For example, I would be very curious to learn how exactly Modiface uses data to predict customers’ preference over how he/she would look like: what type of foundation, what type of brushes, and what lipstick color. These are the fundamental question we need to address when talking about AI, because it is the process of how analyzing data helps an organization serve its customer more effectively and thus make a profit that matters the most. What I do appreciate from your article is that you brought the perspective of healthcare in the scene as it’s an important factor in personalizing one’s make up product purchases.
The deployment of AI among CRM products has been a hot topic lately. Many have boasted their capability of identify, defect and surface hidden patten that are unobserved by humans. From your article, you’ve done a terrific job detailing the functionality of Einstein and how it adds value to Salesforce’s CRM products compared to its competitors. I found the idea that “Einstein surfaces relevant insights about an account based on sources such as external news websites ” to be a particularly interesting feature. It’s not necessarily AI-oriented, but definitely bringing value to customer through untapped resources. What I wish you could have included is the scoring mechanism that powers the predictive analytics, the engine of Einstein. I think by giving a specific example of how a deal is scored would be helpful to readers who are not familiar with the product and technology engine beind it.
It’s not clear to me how bAIcis drives drug production discovery decision from your article. To your layman readers, it might help to give an example of how AI powers specific use case. Also, it’s opaque what ” identify target patients, biomarkers and other cell-based phenotypic data” means. It would be great to illustrate more how the software interact with patients as I assume it introduces more complex variable to the AI algorithm. What I like about the article is that you also talk about the company other than the AI software. You emphasize on its product marketing and commercialization strategy which are as important as the technology that is advancing the company itself.
Kapsula’s value proposition reminded me that of Gap when it planned to use data/AI to replace traditional designers. It is clear data reveals trend based on an increasing amount of data on the internet. However, when it comes to personalized design, I still think there’s a lot of challenge to be trendy, creative yet individualistic. In the era of fast fashion, one can easily obtain information on what is trendy, but the key is to be trendy yet true to oneself. The key to strike such a balance could be quantity of data, but probably more on the quality of data, e.g data one might get in a brick-and-mortar retailer where salesperson talks to customer and get a sense of what he/she really wants. How to get such data online will be crucial to Kapsula’s success.
The idea of crowdsourcing human stories is compelling and innovative at its core, simply because there is no one correct version of human history, but a collection of all those who have lived and who are living. Hence, I find the value proposition of StoryCorps to be powerful. Moreover, the way it has continuously reinvented itself through digital platform ( the mobil application ) exemplifies how technological tools helps solicit more feedback from crowd through careful orchestration ( e.g. thanksgiving listen). One concern I have is not on the outreach potential of StoryCorps but how humans make sense of the recording. In another words, the company has collected a vast amount of unstructured data, but questions remains as: what insights can we draw from the data, how the data helps human understand ourselves, and what can we do with such understanding. By answering these questions, StoryCorps can fundamentally bring open innovation to the benefit of humanity.