I think this is an exemplary piece that thoroughly explores the unique reasons the auto industry stands to benefit from additive manufacturing- shorter design cycles, price pressures, and even shrinking markets due to ride sharing. I found it particularly interesting to consider decentralized production as potential solutions for high shipping costs, local design preferences and ultimately, complete user customization! I also appreciated the way the author highlights a central tension for all manufacturers- do you use the additive manufacturing as a competitive edge in product design or as a way to optimize manufacturing? I also agree that while large companies may make meaningful use of additive manufacturing, there is a fundamental way in which they are wedded to their pre-existing investments in traditional manufacturing. It may indeed require a new market entrant with no legacy capital sunk in traditional manufacturing methods; they will have the freedom to design a production process that can make full use of additive manufacturing.
This is a fantastic piece exploring the ways familiar brands can incorporate open innovation into their business strategy. I particularly liked the way the author explained how open innovation can be used to facilitate Starbucks’ efforts in global expansion, by offering locals the opportunity and agency to shape the menu and customer experience to their own geography. One question I had was whether this sort of product-based open innovation was narrowly targeting consumers as opposed to other entities in the business environment- such as consultants, suppliers or providers of other services? I would think that those most motivated to introduce innovative suggestions would be those who stand to benefit from potential partnerships or supply deal. Could Starbucks create a separate channel for those looking to reform other aspects of their business model- such as catered events, grocery products or equipment sales?
To address your questions, I do not think that the development of Espresso machines is a new development or one that poses a meaningful threat to Starbucks business model. I would also not recommend the adoption of self-service machines in Starbucks stores as it undermines the experience of those that choose to wait on line for in-person service. However, if Starbucks wanted to add vending-machine like coffee dispensers in places that were otherwise unserved, I think that could potentially be an interesting opportunity.
This is a fantastic summary of how an innovative company is leveraging machine learning in all aspects of its business. I actually worked at Lemonade for 5 months prior to starting work at HBS, and frankly, I could not have done a better job summarizing this company’s approach to AI and the many ways it can be used to reduce fraud, quantify risk and cut administrative costs. The author also does a fantastic job exploring the potential synergies between insurance data and the IoT. I would actually encourage the author to view those synergies from two sides:
1. The insurance company acquires data on the behaviors and risk profile of an individual and their home (Pricing Risk)
2. The insurance company could take steps to change behaviors and mitigate risks- informing and encouraging the use of better alarm, flood or electrical systems (Mitigating Risk)
I do want to raise one potential area of concern. While Lemonade collects a ‘wide’ range of customer data, how much flexibility do they have in terms of pricing their customer? Are they allowed to charge customers more based on any data points they may have collected from social media? Where do privacy concerns come into play? The answer to this question is, of course, that it is highly complex. Insurance is regulated in the United States on a state level, with each state possessing its own rules about what are and are not appropriate criteria for underwriting and pricing. But the author is certainly right that Lemonade is looking to innovate in the space and that the potential payoff is tremendous!
This is an absolutely fascinating piece on how AI could help transform legal research. As a JD/MBA, I know that this topic is one that gets considerable attention at the law school. I wanted to raise a couple of interesting points from a seminar on this very topic:
1. To my mind, the value of machine learning in improving the legal research process is simply that its current state is so time-consuming, expensive and cumbersome. You, however, raised the very crucial point that in addition to allowing you to find relevant sources, machine learning could also be used to help craft strategies with the highest probabilities of success. This is a crucial difference between legal research and other research as there is often a clear ruling in a case- a binary yes or no, that can be used by machine learning to construct different legal approaches based on probable outcomes.
2. You explained that Lexis Answers allows for improved search capabilities. Namely, that it doesn’t simply search a database for matches, but should use natural language processing to parse the question and then deliver the most appropriate results. While important in the context of legal research, developing these capabilities is of much much broader interest to the world at large. It would revolutionize all search engines. My question is whether it is realistic to hope that Lexis Nexis will achieve great results in this area when governments and much larger companies like Google and IBM are dedicating billions of dollars to these problems.
3. One potential problem with the adoption of Lexis’ search products is precisely the amount it reduces legal research time. As this is a service being marketed to law firms primarily, it would drastically reduce the number of billable hours they are able to charge for their services. My question is how do you foresee compensation incentives, price competition and reduced complexity impacting the adoption of this technology? How do you see this playing out in different channels and different industries?
Great examination of Coca Cola’s ability to integrate machine learning into many levels of its business- product development, distribution, promotion and supply chain management. I did, however, want to raise a couple concerns with the author’s recommendations:
1. I’m not entirely sure that I agree that machine learning can help Coca Cola with product innovation. Certainly, data science and analysis is crucial for Coca Cola to understand consumer preferences and trends. However, to say that it can help with product innovation means that it can identify different tastes that consumers find appealing and correctly assert that they could be combined together. While that is possible, that would rely on a complex understanding of biology, taste science or at least data from other culinary sources or databases. I think, instead, this author is merely asserting that investing in a strong data science platform and team is crucial to Coca Cola’s ability to continue to develop products consumers love. That is something I wholeheartedly agree with.
2. I certainly agree with the author that using machine learning to predict customer behavior and downstream demand for its product could help Coca Cola distribute its product more efficiently, place its vending machines more selectively and even incorporate location-specific advertising in its vending machines. One question I have is what infrastructure is necessary for this to happen. My understanding is that the vast majority of vending machines are not owned by beverage or snack companies, but by vending machine businesses. Would simply adding a few of their own vending machines provide Coca Cola with the scale to achieve their ambitions?
3. Lastly, Coca Cola is an enormous company with many different products in its portfolio. Do you view these innovative machine learning solutions to be something that Coca Cola should implement for all of its products, or simply those with huge scale, like Coke and Sprite?
Fascinating read! I love the way the author frames Boeing’s recent success and Airbus’ recent struggles around its adoption of additive manufacturing.
I wanted to share one question and one thought:
1. How does Boeing think about which parts to 3D manufacture and which parts to source from traditional suppliers? Is it parts that have many small pieces that can be consolidated into a single piece? Is it parts that their suppliers aren’t able to deliver reliably? Is it parts that have a lot of variation between their different plane models?
2. I love the idea of Boeing using their expertise in 3D manufacturing as a springboard to innovate the traditional airplane design. In addition to the all the cool amenities and comforts one could imagine them incorporating, I was wondering if it could allow them more flexibility in creating planes of different sizes. One could imagine, for example, a situation where engines were so complex and involved so many parts, that it was too expensive to manufacture small ones for commercial use. However, with 3D printing, perhaps 30 – 40 seat planes become economically feasible. This could increase seat utilization, and help increase their suite of product offerings to different airlines that might better suit their needs. It could also enter the market for private jets.
This piece provides a fascinating look into machine learning’s potential impact on pharmaceutical development. I think you did an excellent job situating Pfizer’s approach as an intermediate investment in the technology and forced us to consider if it was sufficient.
I wanted to raise 2 thoughts for you to consider:
1. You mentioned that it might be for pharmaceutical companies to partner with start-up AI companies out of fear of them becoming future competitors in the space. You concluded that it would be better to develop AI capabilities in-house or else acquire a start-up with expertise in the area. I would argue that’s one way in which Pfizer’s current strategy, of partnering with IBM, makes perfect sense. No matter how much data Watson accumulates, or how adept it becomes at developing drugs, there is very little chance that IBM will enter the pharmaceutical development space! So perhaps one solution is simply partnering with larger organizations.
2. You finished your article by posing the question of integration. How can these platforms aggregate data from clinical trials and scientific reports when the conditions, the methods of collection, and the numbers of participants lack consistency? One suggestion might be a system that uses weights for each data-set depending on the question you’re considering. For example, if I’m looking to develop a drug that treats sleep apnea in 15-25 year olds, I can weight the results of studies differently depending on their number of participants, the age range and the credibility of the methodology. The major problem with this, of course, is that it would still require a human to input these weighting factors based on their discretion.
Stellar piece, no pun intended. The author argues persuasively that NASA is uniquely suited to benefit from open innovation. It is an area of high public interest, promises fame and credibility, and, of course, is in desperate need of funding. The author encourages the reader to not only consider the impact of open innovation on NASA as a company, but to grapple with what it means for NASA’s employees. In fact, the question becomes, what does it mean to be an employee when contributions to a company can come from the public? On the one hand, I would argue that employees roles move higher up the decision ladder in response to open innovation. Rather than being tasked with being individual contributors, an employee’s job becomes coordinating, assessing, and integrating the suggestions they receive from the public. Traditionally, winning a design competition would come with promises of lucrative partnerships, supply deals or consulting contracts. In the case of NASA, whose budget, projects and ambitions are so uncertain, how can you motivate corporations to undertake serious research when the prospect of financial gain is so tenuous?