Jane Smith's Profile
In response to your question, I believe Ford should fully embrace the self-driving trend and invest in R&D to become a thought leader in the emerging, disruptive trend. They should partner with the industry groups that have formed on self-driving and be proactive instead of reactive. Ford should leverage 3-D printing by making it a cultural norm within the organization to engage with the technology. The adoption of the technology within the organization needs to be fostered if the fullest potential is to be realized. Ford has a competitive advantage to embrace the self driving technology and should use this as a synergy to their existing core business.
Great article on the use of open innovation! In response to your questions, I believe the implementation phase will require a large scaling effort that will require a unique and diversified skillset. If VHO can source the right talent, from change management, technical, to leading effective teams, they will be better positioned to have a successful launch. The biggest hurdle I see, or the bottleneck, will be talent. VHO should devote a considerable amount of resources to the next phase of implementation and supplement the winning teams by filling the talent gaps.
Awesome article! Baseball is one of the richest data sources available and can leverage machine learning to its fullest potential. One of the key concerns you mentioned is spot on: manager buy-in. It is one thing for a manger to believe in and attempt to implement analytics, but a manger that understands and interprets analytics is immensely powerful and unique in baseball. In response to your second question, I believe accountability from an organizational perspective is critical to evaluate the effectiveness of analytics vs. luck as a manager. Once the analytics department relays the “playbook”, if they see the manager implement or make decisions based on this they can evaluate the usefulness of the data analysis.
Excellent article! I wonder how blockchain will, if in fact at all, fit into the ecosystem MassMutual has built. I think one of the biggest areas of opportunity is with the gig economy. With an estimated size of 68 million people  the gig economy is one of the largest opportunities in the insurance industry. In response to your first question, I believe the insurance industry will be one of the biggest resistors to entirely phasing out human advisors. Insurance products can be difficult to understand and the process requires a lot of specialization, which lends people to build personal relationships with a trusted advisor. It will take a lot of time and convincing for people to abandon their advisor.
One of the biggest business risks around open innovation is government regulation. This is particularly important for the pharmaceutical industry as it is heavily regulated. As an organizational strategy, Takeda should take a proactive, instead of reactive, approach to managing regulations. Partnering with other pharmaceutical companies and working closely with the industry governing bodies should allow Takeda to stay ahead of the curve. If government regulators were to impose stricter regulations it could render some of the open innovation technologies as illegal or unethical. There is also the opportunity to stumble upon ethical concerns in the eyes of consumer. I believe a strategic, pragmatic, and collaborative approach to these issues will increase the likelihood that Takeda can avoid these concerns.
In response to your first question, I believe this should be leveraged to transform the in-person shopping experience. I see this technology as the entry point for the customer and as a way to customize the shopping experience without burdening the organization with high employee costs. The problem I see with this is if all humans were eliminated from the in-person shopping experience. Although we could use this technology, I do not believe we should entirely phase out humans. Particularly with the clothing industry, consumers find customer service associates to be a differentiation in the marketplace.
I find your second question to be extremely thought provoking. If the stock market were to entirely be transitioned to machines and machine learning/AI, it would in theory leave very little room for “seeking alpha”. One possibility that could lend to differences in machine learning results, is the bias that is encoded in the machine learning process. Each algorithm could be coded differently and would render differing results. The problem with this is that once one person discovers the code that was entered, it can easily be replicated.
In response to your second question, I do not think Hinge should be worried. They may experience a slight “seasonality” in their user base, but overall the trend should be consistent. I believe this to be true for 2 reasons. 1. As users find success, this success should be leveraged and publicized by Hinge, thus attracting more users to the platform. 2. As users find matches and leave the platform, younger generations will age and begin to use the platform. Hinge should also be able to capitalize on the increasing population size.