This was fascinating. Thanks for sharing! I want to touch on your last point, “…technology is not a fad; it is a mindset” and the talent challenge it could prevent for a place like GM. You’ve stressed the need for GM to develop this capability internally, and I agree, in the long term, this is the most sustainable option for staying relevant in a changing industry. However, I question how effective GM will be in attracting that talent in the short to medium term. As you mentioned, in the short term, GM has partnered to expand its capability in this area because its the most efficient and effective way to utilize this capability quickly. While GM is trying to shift its perception, it takes time for potential employees to view the company differently and to view their opportunities for advancement differently. GM needs to shift the perception of their products and of the opportunities for innovation at their company if they want bright minds with the ability to bring long term additive manufacturing successes to life. GM is right to invest now, but I struggle with how long it will actually take for industry talent to catch on.
This was such an interesting read! Thank you for sharing. Your question around measurement resonated deeply. Navient is both a profit-earning enterprise and a entity providing a social benefit for one of the most challenging problems of our generation – student debt. Identifying the right metrics is how a company like Navient will be able to develop a business case for a change in the status quo. I appreciate Navient’s open innovation approach to improving the entire process as there are potentially applicable solutions the entire industry hasn’t considered yet. If they can develop the right metrics to quickly assess ideas, they’ll be more able to make step-change improvements.
Carlos, I really enjoyed reading your post. I love the question you post at the end around how competitors will drive and influence future innovation in the industry. With new technology competition creates the opportunity to learn and create an all-together better product for the customer. I am left wondering how our regulatory environment will change in response to these new innovations. Can the end consumer trust these products as much as an engine made the “normal” way? You mention the stage gate process GE has put in place, but has there been an response from the FAA or other regulatory bodies? Are there risks we’re not yet aware of? Thanks for sharing information on a fascinating and relevant topic!
This is a great follow up to the question surrounding the value of POS data during the beer simulation game! In many ways, by directly understanding their consumers revenue sources, Square is best positioned to evaluate the viability of a potential borrower. My one concern with Square dipping its toes in this water is their lack of knowledge of activities outside of transaction data. If company X has an outstanding mortgage of $X, it won’t show up in its transactions data, but it does impact the long term viability of the company and potentially its ability to repay. Transaction data can tell you a lot about a business, but I’m hesitant to say it can stand on its own. If Square is serious about drastically scale its model, I think the company should utilize a few more non-transaction related data points in its process.
Your question regarding monetizing their inventory management machine learning algorithm is fascinating, because, in some ways, it would move Stitch Fix from a retailer to a service provider. In a sub-sector of retail that’s quite competitive, you’re able to add an additional revenue stream and potentially gain access to more data to improve your offering as well. I do worry about the overall sustainability of this business as barriers to entry are so low and customer service and quality of recommendations matter a lot. Stitch Fix needs to find way to beat out its competitors and became the “Bandaid” of the subscription service and personal shopping market.
Is it an art or is it a science? This is a question has plagued professional sports since moneyball introduced advanced analytics. Your post raises many potential benefits for developing a better understanding of the scientific impact of different decisions throughout the game. It does make me wonder where we stop though. Does the game actually get better if we introduce machine learning, or, as you suggest, do players and coaches become either more complacent or too targeted in their preparation and decision making? Will we still sit on the edge of our seats watching the “Hail Mary” pass or will the game become so prescribed that it loses its authenticity? Personally, I think this poses great risks for recruiting as the metrics players are recruited on might not align with what it takes to be successful in the big leagues.
Thanks for sharing!
I found your anecdote on how humans can play a role in machine learning fascinating. So often when topics like these are discussed by the public there is great fear that machines will replace humans. Your post highlights the many benefits of moving to a more automated, smarter system that will be less prone to errors, but also doubles down on the necessity of human involvement in the development – beyond writing the initial code. The post does leave me wondering about data security and privacy concerns. How can a company like Jumio ensure this sensitive information doesn’t end up in the wrong hands? Anyone who has ever traveled outside his or her home country has been taught to keep his or her passport close. When your passport has been “read” by Jumio, what stops a malicious third party from hacking that information and using it inappropriately?
I agree that the heart of the struggle for innovation in the public sector is a talent issue. Some of our generations brightest minds are working on problems that provide great commercial benefits, but unfortunately do not always directly align with the public good. It’s hard to convince a 23-year-old engineer with offers from Google and Microsoft to accept a lower salary and a job outside the Valley. In regards to your first question, I’d assert that civic duty is not enough and that the public sector – particularly state and local government – is not set up to drive innovation through public dollars. To me, this is an opportunity for public-private partnerships, where innovative, well-funded private organizations or philanthropic foundations with the prestige and access to diverse talent can complement the commitment to serving the needs of community. To tie in our Marketing class, social good can actually align with both the mission and competencies of private companies and public-private partnerships to drive open innovation seems like a great place to start.
Great response! I loved reading this particularly in light of the conversations we’ve had in both TOM and LEAD. I agree with the risks and limitations you mentioned, but I wonder how a program like this would assess the positive elements of culture. Bias is definitely a risk in the current evaluative process, but as we’ve seen in LEAD, there are real performance benefits to achieving some degree of certainty that your candidate “fits” with the culture of your organization. Can a machine be trained to assess that as well or will that always be left up to a biased human observer? Are there industries where this wouldn’t prove useful based on the types of job roles required? Is there a ceiling for its usefulness (e.g. could a company use it to find its next CEO)? I agree that innovations like these offer value as companies become more aware of potential biases in their own hiring processes, but I share your concern that they will never be a fool-proof substitute for human assessment – as biased as it can be.