Deep Learning, Disruption and the Platformization of Business
The business world has evolved into a much more difficult and competitive environment. This situation has been exacerbated because of disruptive changes in the global economy. The potential of more nimble competitors to disrupt the businesses of incumbents have never been more likely. Peter Diamandis describes the Six D’s of Exponentials [DIA] as consisting of the following:
Digitization — Anything that can be digitized can lead to the same exponential growth we find in computation. Anything that is digitized, or alternatively virtualized, is unencumbered by physical law and thus cost less to mass produce and moves faster in dissemination.
Deception — Once digitized or virtualized, initial growth deceptively appears linear however given time exponential growth becomes obvious. For many, it is too late to react once growth of a competitor hits this transition.
Disruption- New markets are created that are more effective and less costly. Existing markets that are tied to the physical world become extinct. We’ve seen this in music, photography and many other areas.
Demonetization- As costs head towards zero, so does the ability to solicit a payment for it. Thus a business has to reinvent its revenue model or come up with new ways of monetization.
Dematerialization — Physical products disappear and are replaced by a more convenient and accessible alternative. This is the stuff that used to be on your desk that have been replaced entirely by your smartphone:
Democratization — More people now have access to technology at a lower cost. The means of production have become more accessible to everyone. This access is no longer confined to the big corporation or the wealthy. We see this fragmentation everywhere where producers are publishing their own books, music and videos. This thus feeds back into itself, where smaller players are able to come into competition.
Obviously, there is an ever pressing need for enterprises to take drastic action by re-engineering how they run their businesses to survive this disruption. John Hagel proposes four kinds of platforms [HAG]that leverage networking effects as an organizational mechanism to combat disruptive businesses. Here is a video of John Hagel explaining the platforms:
The four platforms that John Hagel proposes are as follows:
Aggregation Platforms — These are essentially marketplaces that facilitate transactions among participants. Think eBay as an example or Kaggle in the ML space.
Social Platforms — These platforms encourage long relationships among participants and also lead to the formation of cliques of like minds rather that a hub and spoke model. Facebook and Twitter are examples of these.
Mobilization Platforms — These are platforms that facilitate the coordinated action of a group of people in a task that takes considerable time to complete. Hagel uses the term ‘process networks’ where these kinds of platforms goes beyond a single transactions or conversations. There are platforms that coordinate supply chains or distribution operations. Hagel proposes Open source is an example of this kind of platform where many participants contribute together in complex ways to build and maintain a product.
Learning Platforms- These are a more dynamic and adaptive environment where a group of people comes together to collectively learn how to address a more complex problem. This is a place where participants can connect to each other ask for a question, share experiences and provide advice. Open source projects that are actively managed with distributed source control, test driven development, issue tracking, and continuous integration is a good example of a learning platform. The key ingredient here is that there is a learning mechanism that gets codified continuously. The reason we find this in software development should not come as a surprise since software development is intrinsically a learning process.
Effective Business Process Re-engineering requires that we go beyond just seeking out optimization opportunities through automation. The days of corporations that are structured like machines are numbered. One should instead seek out agile processes. Leading companies today have adaptive and nimble processes, and it is from this vantage point strive to discover opportunities that can lead to networking effects. So instead of optimizing processes, reimagine them as platforms [SCH]:
For legacy industries and companies, the surest way to make a key process more robust, resilient and less vulnerable to disruption is to platformize it. The future of process innovation is platformization; the future of platforms belongs to the processes that make platform users more valuable.
The most disruptive technology that is emerging today is called Deep Learning. It is an Artificial Intelligence technology. Unfortunately, most companies are blind to the existence of Deep Learning. Even the companies that do have an awareness, there is very little understanding as to how to take advantage of this technology. Businesses need a guide, a playbook that gives details about the methodology and strategy to move forward.
The effective way to think about Deep Learning adoption is to see how it where it can enhance a platform strategy. This is because we want to leverage the networking effects as best described by this picture:
That is, more users lead to more data. This leads to smarter Deep Learning algorithms and therefore better products. The cycle then feeds into itself. In a world of constant disruption, networking effects are essential for any defensible business. However, it takes more than understanding why this is important. It requires an understanding of platforms that enable it as well as the kinds of Deep Learning algorithms that enhance these platforms.
One of the most intriguing of platforms is the Learning platform. John Hagel says it best:
What if we change the assumption, though? What if each fax machine acquired more features and functions as it connected with more fax machines? What if its features multiplied at a faster rate as more fax machines joined the network? Now, we’d have a second level of network effect — we’d still have the network effects that come by simply increasing the number of fax machines, but now there’s an additional network effect that accrues as each fax machine adds more and more features as a result of interacting with other fax machines.
What Hagel is saying is that the participants of the network adaptively become more effective and capable as a participant in the learning network. In other words, not only is there the conventional networking effect, but another one that kicks it into overdrive.
So how does Deep Learning play into enhancing a Learning platform? The idea at its most simple incarnation is that Deep Learning technology can be employed to augment many tasks. One task is to speed up digesting of information by a worker. In today’s information-rich environments, we are constantly inundated by more and more information. Deep Learning technology can help parse, digest, curate and present that information such that we can focus on the most value-added activity. The more information we can digest, the quicker we learn. This can be further improved by tightening the feedback loop through the augmentation of agile processes.
One concrete example of this is in the context of the mining industry. One of the big problems with mining is that the sequence of equipment are daisy chained like Christmas lights. If in the event of failure of one piece, the entire production grinds to a very expensive halt. We can certainly place Deep Learning monitoring devices on the equipment to be able to predict future failure, however, to do so, requires data of different kinds of failures across different kinds of devices. This problem of lack of data can be addressed by having a learning platform where multiple mining companies come together to share their data from the field. As a result, companies that aren’t sharing their data and aren’t sharing their learning experience are at a disadvantage.
There has in fact been some research that studies the mathematics of a learning organization and how it relates to innovation. Technology Review describes this research [EmTech]:
As more data is shared, a language is developed (i.e. the collective vocabulary) and expands and as a consequence new vocabulary, a new way of expression is created and this leads to greater innovation. This is based on the obvious realization that almost all of human knowledge is captured in language. In the grand scheme of things, intelligence is ultimately all about language. This encompasses languages that humans use today, complex mathematical language and all the way to machine designed languages. Deep Learning is all about using machine assisted language creation.
I end with a quite wild prediction from the World Economic Forum that predicts “The Largest Internet Company in 2030”[WEL]:
“I’ve been predicting that by 2030 the largest company on the internet is going to be an education-based company that we haven’t heard of yet,” Frey, the senior futurist at the DaVinci Institute think tank, tells Business Insider [FREY].
Machine learning will accelerate in a similar fashion in the education space, Frey says. Online bots will pick up on a student’s strengths and weaknesses and use a series of algorithms to tailor the lessons accordingly. Research suggests[RAND] this personalized method is among the most effective at raising kids’ overall achievement.
If you want to discuss more about this subject, let us know at “Intuition Machine”.
References
[DIA] Six D’s of Exponentials
[HAG]http://www.marketingjournal.org/john-hagel-harnessing-the-full-potential-of-platforms/
[SCH]https://hbr.org/2016/12/instead-of-optimizing-processes-reimagine-them-as-platforms
