Loved this piece! Share your interest in applications of open innovation to the public sector. I think you raise an important question in your concluding thoughts, considering the tradeoff between holding one “flashy” competition and a more dispersed network of open innovation challenges. For me, the decision comes down to the variety of problems HHS is trying to solve: if they’re looking for a more holistic solution to the opioid epidemic, then a single challenge is the way to go. But to me, it feels like there are some clear “silos” in which they could hold a series of different innovation challenges to tackle different opioid epidemic-related problems (e.g. illicit drug use, prescription drug use, overdose treatment).
Thanks for sharing this insightful piece about GM’s additive manufacturing investments. While I agree this could be an important long-term investment for the company, I’m curious what the short-term applications would be for the company. The wind tunnel and smaller-scale car model testing seems interesting, but still feels relatively insignificant compared to other strides companies are making in additive manufacturing. The key distinction seems to be between companies embracing additive manufacturing as a “fad” that can look like a good investment to investors, and companies actually using this concept effectively.
Thank you sharing this super compelling piece – love to see more applications of open innovation in the public sector! I share your belief that open innovation can be a truly powerful (and cheap) approach for government agencies to innovate more efficiently. I also really like your recommendation of better centralizing the existing innovation challenges – how do you think this breadth/depth could be best managed? I could imagine a world in which one centralized innovation conference would garner the media attention and attract enough talent that you wouldn’t need a dispersed network of challenges.
Thanks for sharing this interesting piece Carlos! Sounds like GE Aviation is on the cusp of new cutting edge technology – an investment that could pay off in the long run. I share your concerns about the economics, especially if much of the product development currently looks like the 100x cost you provide in your example. However I agree that if they’re able to bring these costs down it could have significant positive implications. One other concern is the safety implications of additive manufactured products (vs. traditional manufacturing processes). Given the significant safety requirements that aviation equipment must satisfy, I worry that additive manufacturing may have a long road ahead before it is considered “safe enough” for the aviation industry.
Thank you for sharing this well-researched piece – very interested in how machine learning can be applied to issues in the public sector. I share your concerns about the inherent biases tied to these types of machine learning processes. Given the reliance on historical data, I worry these “heat map” predictive analytics will only perpetuate current racist policing practices among NYPD. Additionally, the placement of more acoustic sensors in predominantly low-income minority neighborhoods in New York City only serves to magnify the existing “over-policing” of these areas that result in more unjust arrests.
Super interesting piece linking both machine learning and the “inbound concept” we learned in marketing. I share your concerns about the massive amounts of data challenging Glossier’s ability to “mine” the data effectively and derive important insights. It seems plausible that they could inaccurately glean trends from the data without understanding the full picture given the significant size and scope. Interested to see what happens with the social-selling app!