Interesting post! I think crowdsourcing is a great example of how Pepsi has taken strides to stay relevant in a landscape of changing consumer preferences. With regard to R&D complacency, I actually think there is potential for the “competition” posed by open innovation to push internal R&D teams to be even more innovative and to take greater risks. There’s also huge upside if Pepsi can find ways to foster collaboration between internal R&D and the consumers who are providing innovative ideas, and I think an interesting avenue for them to explore is how to leverage its vast capabilities to push these crowdsourced ideas even further. One potential idea is a incubator of sorts.
Such a fascinating topic – and a great article! This technology has the potential to truly disrupt the pharmaceutical industry. But I think the problem of how to scale this is very real. While the actual production of the drugs may be cheaper (in some cases, like that of Daraprim, drastically so), distributing 3D printers to local hospitals in rural areas could prove a mammoth task in and of itself. This also raises the question of how companies can monetize this technology and ensure that they are adequately compensated, while also keeping in mind the vast social good that comes from providing a cheap supply of life-saving drugs in rural areas.
Having myself worked for Walmart, I can’t help but comment on this great post! With regard to your first question, I can say from firsthand experience that Walmart is investing VERY heavily in recruiting the necessary data science and engineering talent to grow their machine learning capabilities, and part of this is evidenced by their strategic acquisitions of more “millennial,” tech-focused companies (Jet, Bonobos, Flipkart). Their many investments in Store No. 8 as an incubator also speak to this commitment – the launch of Jet Black, a personal shopping assistant powered by AI, is a perfect example of this.
As for your second question, my personal opinion is that, while Walmart certainly wants to maintain its deep connection with its core customer base, its aspirations are more far-reaching than just this income bracket. Walmart is trying to compete squarely with Amazon, and that means it needs to find ways to be everything to everyone.
Great article on a very interesting topic! I find Olay’s use of both deep learning and a questionnaire to be particularly interesting, and for me it begs the questions – to what degree are Olay’s recommendations driven by it photographic analysis vs. the responses inputted by users? Depending on the questions asked, both methods could yield similar results, and I’m curious to know the degree to which Olay is currently leveraging one vs. the other to determine product recommendations. Especially since the potential set of recommended products is quite small, I’d be interested to know how much of the technology is truly driven by AI vs. predetermined adjacencies between responses and products (e.g., if the user indicates “aging” as a concern, recommend Olay Regenerist cream).
Great post! You raise a particularly interesting question around consumer readiness to embrace these innovations. A large-scale proof of concept (e.g., TU Eindhoven’s bridge) is one thing, but wide adoption of 3D printing in construction and infrastructure will require quite a step change in the mindset of both the industry and the consumer. Until the industry commits to proving out the concept on a massive scale, I think we’ll continue to see user hesitation.