I find that your question regarding the long-term consequences of growing adoption of OI across industries and what impact this will have on brand differentiation in the future is very relevant. You also ask what the right equilibrium between internal vs. external innovation efforts is. I think that as more brands adopt OI, it is true that the majority will have “the world as its lab” – however, the beauty is that the world is incredibly large and diverse and it will likely always be possible for a company to capture innovation and ideas from different populations or in different manners than their competitors do. Thus if done more effectively than others, gaining a competitive advantage in theory is possible. External efforts might determine the product outcomes, but internal efforts will still be crucial as these are the ones that innovate in regards to how to most effectively apply OI to reach a diverse set of differentiated product outcomes.
I do believe that crowdsourcing has been and will continue to be beneficial to Lego’s past, present and future strategy. Lego has struggled to stay relevant in the past, and thus has had limited success in its own creativity – therefore they have little to lose leveraging upon the people. However, I believe the problem with Lego’s crowdsourcing is that it will source ideas from those that are already interested in Lego rather than from a new customer base, and thus they will not discover how to reach into new target audiences on a larger scale.
I am open to the fact that, as mentioned in the article, the Lego Forma pilot can grow the customer base slightly to include creative adult hobbyists – if it markets itself appropriately. However, this is likely a fairly limited group, which makes me question whether it is enough for Lego to continue to stay relevant? Further, even if such group is sufficiently large, they have the benefit that the adult hobbyist today is from a generation which has a high awareness of Lego from childhood and is thus more prone to buying Lego. In the future, with such limited awareness among the younger generations, will Lego be successful in targeting these adult hobbyists they are trying to reach or will they look to other brands that they already recognize?
I found particularly interesting your question whether rapid prototyping capabilities could dilute the quality of ideas developed into prototypes, particularly within healthcare players, as new inventions can be game-changers in peoples’ lives, quite literally. This applies not the least to medical device manufacturers such as Medtronic. I am quite convinced that -at least today- innovative ideas that can be revolutionary, particularly within healthcare, come from human minds. However, such ideas do not appear suddenly and turn into a finished and successful product. Rather, it is the process through which one discovers and develops ideas – it is there where the creativity flows. If relying too much upon rapid prototyping one may risk losing that human element of creativity and innovation that comes in experimenting, failing and learning, and thus as we move forward we must be very conscious of these risks and hedge against them.
I do agree that GE “convincing itself” of the benefits of AM capabilities is incredibly important to gain credibility from outside actors. However, I am still unconvinced that “convincing itself” must necessarily involve applying the technology across its own businesses. Being an early adopter of AM within the Aviation business with successful outcomes could be conviction enough for GE, and in combination with announcing a (vague) intention to roll AM out across product lines, it could be sufficient for outside buy-in as well. The key is for GE to create a value proposition that is compelling enough. If GE clearly conveys that AM has been used successfully but is still in its early age, its value proposition would be that companies that buy the technology will embark upon a new era where they will gain strong competitive advantage (before widespread adoption) if they engage now. More risk-prone companies will possibly even see it as a positive signal that GE has not yet rolled it out, because it means that they are catching the wave before others when the proposition is still discrete.
Interesting article! I agree that encouraging platform-wide data sharing will be extremely tricky to accomplish given many companies finding significant value and privacy in their transaction data. However, I think a larger question that is not to be overlooked is whether we, the customer, would want such platform-wide data sharing? In theory, such data sharing could become so large that it would track a majority of steps we take (traveling or not) such that we are guided by the coalition partners to make certain choices through advertising and similar. In considering whether to encourage or allow such platforms we must first understand the limitations this puts on our privacy and our decision making processes, as the latter would be less free or independent than we would like to think they are.
What I found really interesting to consider in your article is your question regarding how Netflix can approach and predict new trends where the algorithm has no existing data to learn from. The very question as to how machine learning can introduce true innovation with no precedent data overall is an open question to me. From various discussions I have come to the belief/understanding though that advanced machine learning could possibly “solve for” innovation by looking at a vast majority of previous trends within an industry, e.g. film (or more specifically films at Netflix), and using this data to understand how new trends have emerged over time. Through such analysis, it could potentially come up with an algorithm that can predict what the next trend might be, even though it will seem to us very unexpected.
For now, it may be necessary for Netflix to use human judgment on top of machine learning if it is not sophisticated enough to distinguish “noise” from “signal”. However, if one were to use machine learning to predict innovation, one should be very careful to use human judgment on top of such findings. Humans tend to think of the future in terms of what they have already experienced and thus generally be bad at predicting trends that have not yet existed, hence us being “surprised” at “unexpected” trends. Applying our limited thinking to alter the findings of the algorithms can in such case have detrimental effects in predicting new trends. Thus, I agree with you that using human judgment on top of machine learning can be useful, but “not trusting data” because it does not seem “expected” could potentially fail to predict innovation.